Whyalla Salvos look to help community with issues hindering the town – Whyalla News

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For 56 years the Salvation Army has continually helped Australian communities and right now, they are more relevant than ever in the Eyre Peninsula.

Every May, generations of Australians have donated to the thousands of Salvation Army volunteers knocking on doors, collecting in shopping centres and other public places.

With the community still dealing with the global pandemic, Doorway's Manager, Antoinette Day and her team deal with daily service provision to those Eyre Peninsula community members who are looking for help.

"Homelessness is definitely a major issue, as there is not enough interim accommodation and with the cold weather the risk to health and safety is increased," Antoinette said.

"There are very limited rental properties available. There is also an increase in people presenting with Addictions and Mental health issues.

"The increase in the cost of living has had a huge impact, as people with low incomes struggle to keep on top of bills and feed their families."

The COVID-19 pandemic has made social, employment and mental health problems within the Whyalla region rise.

"Many people lost their jobs and income," Antoinette said.

"The lockdowns caused many people to become isolated, especially those with no family here. There have been family breakdowns due to financial troubles and mental health issues.

"The price of food has increased significantly and many people have said they can't afford meat, fruit and vegetables, they get basics and school lunches.

"Government payments need to reflect the rise in the cost of living. More accommodation or shelters for the homeless, even tents and sleeping bags would help."

This year The Salvation Army will be collecting at static points and require volunteers to collect at different locations.

This year the locations are at Westlands by Woolworths, Whyalla Norrie Woolworths and Foodland IGA Whyalla.

For anyone needing help in the Whyalla area, there will be Salvation Army events that can lend a helping hand in the near future.

"The Salvation Whyalla Corps (Church) through our on-site Caf are looking to start an evening Connect group that will include dinner. Planning is still in its infancy at this stage," Antoinette said.

"Our Corps seek to be inclusive of people wanting to connect and engage for various reasons, our small Thrift Shop, Cafe, and Prayer Lounge are spaces we as Christians are intentional about connecting and engaging in the day to day lives of our volunteers, customers and community."

For anyone looking to contribute or lend their time to help out others through the Salvation Army, you can call Antoinette on 0439 204 161.

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Whyalla Salvos look to help community with issues hindering the town - Whyalla News

Golden State Warriors at Memphis Grizzlies Game 5 odds, picks and predictions – USA TODAY Sportsbook Wire

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The No. 3 seed Golden State Warriors (3-1) visit the FedExForum Wednesday to play the No. 2 seed Memphis Grizzlies (1-3) in Game 5 of the Western Conference semifinals at 9:30 p.m. ET (TNT). Below, we look at the Warriors vs. Grizzlies odds and lines, and make our expert NBA picks, predictions and bets.

Golden State rallied from a dismal first-half offensive performance to beat Memphis 101-98 in Mondays Game 4. The Warriors scored just 38 first-half points and finished 9 of 37 from behind the arc.

The Grizzlies struggled in the half-court without All-Star PG Ja Morant, who will most likely miss the rest of this series with a knee injury. Memphis guards Dillon BrooksandDesmond Banescored a combined 20 points on 29.6% shooting (8 of 27).

Odds provided by Tipico Sportsbook; access USA TODAY Sports Scores and Sports Betting Odds hub for a full list. Lines last updated at 12:16 p.m. ET.

Warriors

Grizzlies

Warriors 115, Grizzlies 103

PASSbecause the Warriors (-175) is a little too expensive for a close-out game on the road vs. a Grizzlies (+140) team thats shown it can play without their best player.

According to VegasInsider.com, roughly 90% of the action is on Golden States ML and its typically not profitable to follow such lopsided markets in sports betting.

But I dont like Memphiss chances of extending this series because the Warriors are outperforming the Grizzlies in 3 of the 4 factors and Morant put Memphis on his back in the first 3 games of this series.

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LEAN WARRIORS -4.5 (-105) only because we are getting to the party a little late since Golden State opened as 3.5-point favorites (per VegasInsider.com), but have been steamed up to the current number by the market.

However, the Warriors couldnt have played any worse in Game 4 and the Grizzlies +4.5 (-120) could only muster 98 points. Steph Curry,Klay Thompson andJordan Poole all shot terribly in Game 4 and that wont happen again.

Also, Golden State is outrebounding Memphis and the Grizzlies were the top rebounding team during the NBAs regular season. The Warriors shooters will start cashing in on the extra possessions gained through rebounding.

Most importantly, Memphis is scoring 13.7 fewer points per 100 possessions in non-garbage time this postseason with Morant off the floor (according to CleaningTheGlass.com) and he was the best player on the floor through the first 3 games of this series.

LEAN WARRIORS -4.5 (-105) since we are getting the worst of the number.

PASSsince my prediction aligns too closely with the markets projected score.

If anything, I lean to the Under 218.5 (-110) because the Under has cashed in 10 of the last 14 Warriors-Grizzlies meetings and Memphiss offense is greatly diminished with Morant out of the lineup.

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Golden State Warriors at Memphis Grizzlies Game 5 odds, picks and predictions - USA TODAY Sportsbook Wire

The future of golf betting? Heres what the PGA Tour thinks it will look like – Golf.com

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By: Evan Rothman May 11, 2022

A rendering of the forthcoming DraftKings sportsbook at TPC Scottsdale.

PGA Tour

Has wagering on golf taken off in the four years since a Supreme Court decision helped bring about legalized sports betting in many U.S. states? You bet your Nassau it has.

The amount of money wagered with PGA Tour official betting operators aka the handle rose almost 50 percent from 2020 to 21, and total bets increased almost 40 percent. Further growth is pretty much a given; among the more interesting questions is what kind of golf betting the future might bring, and what role it will play in the Tours business more generally. To find out, we spoke to Scott Warfield, the Tours vice president of gaming.

GOLF: The PGA Tour is bullish on in-play betting wagers made during an event, not before. Why?

Scott Warfield: Whos going to shoot the lowest round? This player versus that player. You get your bet in Monday, Tuesday, Wednesday; you come back on Sunday to see if you won. Its very stale, very stagnant.

Where were moving is focusing more on the live. Theres three holes left, and a threesome is coming through. Whos going to have the low score on this hole? Jon Rahm has 250 yards to the pin from the fairway on a par-5. Here are the odds he can get it inside 20 feet, 10 feet, five feet.

In more mature markets, like Europe, where theyve had legalized sports betting for decades instead of years, in-play betting represents somewhere in the 70-80 percent range of all bets. Theyre less likely to bet on things like over-under, or a money line, or a spread. They bet more on the next tennis point, or whos going to have the next foul or score the next basketball point. Its much more microtransactions.

In the U.S., that in-play number is about 30 percent growing, but significantly smaller. Its been an illegal territory, and the easiest thing to bet on with a friend or a bookie or whatnot is that money line or spread or whos going to win the game. Over the next three, five, seven years, we see that in-game number continuing to increase to 50, 60, 70 percent. If the American bettor follows that trajectory, its my belief there are two sports that stand the best chance to capitalize baseball and golf.

GOLF: Why those two?

SW: Because of the amount of content and the pace of play, which is leisurely enough to work perfectly with in-play betting. With IMG Arena, our exclusive data provider in the space, were able to offer different opportunities around every hole, every player, every shot. Because of our investment in ShotLink data and, again, the pace of play, it sets itself up great for the growth of in-play betting here in the States.

GOLF: Explain the role of IMG Arena in in-play betting.

SW: You really cant do in-play betting in golf without the official league data. And if you want that data, i.e. ShotLink, thats where the IMG Arenas Golf Event Centre product comes in. Consider squatters people who come in and can watch an event and basically price and model different bet types without official league data. Its nearly impossible in golf, right? To know whether the shot is at 129, or is that 911? Unless you have the Golf Event Centre.

A ShotLink tablet in action on the PGA Tour.

GOLF: The Tour has five official betting operators. Whats their role, beyond just taking bets?

SW: FanDuel, DraftKings, BetMGM, PointsBet, Parx those operators are the mouthpiece to sports fans, to sports bettors. Having partnerships with these market leaders helps because since theyre in business with us, theyre helping promote our events.

On days when theres an NFL game on, youre still getting promotion of PGA Tour events. Or in the spring when the Tour is up against NBA playoff games, theyre still promoting the Tour and talking about it.

Were trying almost to create a market. Traditionally, this has just been try and predict the outright winner of a golf tournament. We want to get to a place where its much more in-play and live and microtransactions. Part of that evolution is educating sports fans that if you didnt bet on the outright winner by Thursday morning, its OK. That market remains open, and it moves throughout the event as players bogey or par or birdie. Its education and entertainment and thats where the operators play a disproportionately important role for us, because theyre doing a lot of that work.

GOLF: Whats the story with the upcoming sports book project at TPC Scottsdale?

SW: DraftKings is operating in the state of Arizona as our designee, and theyre building a retail sportsbook at TPC Scottsdale that will be one of the best sportsbooks in the world, a global destination for golf fans. Ive shown it to a few of my golf buddies. Kiawah, Pinehurst and Bandon Dunes will still be in the rotation for our annual golf trips but now youll have a chance to go to TPC Scottsdale, play 36 holes of golf, sit in a world-class DraftKings sportsbook and watch your favorite events. Itll be a big one for us in the fall of 23.

GOLF: Beyond the financial considerations, why is it so important?

SW: The WM Phoenix Open is our largest crowd, and its a very young crowd. The scene is a scene. Given the brand of that event, having a sportsbook there just adds to the allure of one of the years most popular tournaments.

On top of that, the other 51 weeks a year have us really excited. Again, youre going to be able to play a couple world-class courses and drive your cart over in between those two courses to an open-air sportsbook with fire pits. Its going to be a unique thing.

We get asked a lot if this is something well see a lot of, and the answers no. It has to be the right course, the right tournament, and the right brand fit. This checked all those boxes for us. I wont say there wont be another one, but this will be unique. I think for DraftKings it will be almost a West Coast headquarters. Thats how theyre thinking about the uniqueness of this facility.

GOLF: How would you sum up the PGA Tours relationship with sports betting where it is, where its heading?

SW: For us, this whole space for us is about engagement. The Supreme Court made a decision in 2018 that allowed the states to regulate sports betting, if they so choose. Weve been trying to operate within that framework, continue to maintain and ensure absolute integrity with our product, and leverage the opportunity to engage the core fans and grow our audience.

Yes, there will be commercial benefit to all the stakeholders in all the sports. But first and foremost, we look at it as a way in this fragmented media landscape to get a fan to watch an extra three holes each weekend or attend two more events every year, to get a 25-year-old whos never thought about PGA Tour golf and viewing it or attending it as something they should consider.

Theres not a lot of new ways into sports fandom. Social media, e-sports, sports betting, the metaverse. Those are all areas where we can engage that key 21- to 35-year-old fanbase and do it responsibly and appropriately. For us, its continuing to find those partners who do it the right way, and creatively and look at what this might look like 7-10 years from now, not necessarily what it looks like today.

A former executive editor ofGOLF Magazine, Rothman is now a remote contract freelancer. His primary role centers around custom publishing, which entails writing, editing and procuring client approval on travel advertorial sections. Since 2016, he has also written, pseudonymously, the popular Rules Guy monthly column, and often pens the recurring How It Works page. Rothmans freelance work for both GOLFandGOLF.com runs the gamut from equipment, instruction, travel and feature-writing, to editing major-championship previews and service packages.

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The future of golf betting? Heres what the PGA Tour thinks it will look like - Golf.com

Lockheed testing artificial intelligence to fight wildfires – FOX 31 Denver

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JEFFERSON COUNTY, Colo. (KDVR) A global defense company shared details with FOX31 about the use of cutting-edge technology used in battlegrounds around the world to help fight wildfires.

Recognizing patterns, learning from experience, drawing conclusions, making predictions, or taking action, said Dan Lordan, senior manager for AI integration at Lockheed Martin Artificial Intelligence Center.

All those words describe artificial intelligence. Lockheed Martin, whose space division is based out of Jefferson County, wants to use AI to help gather critical details during a wildland fire.

Lordan says it starts with mapping out a wildfire. It can take hours to determine the size, shape, location and areas emitting the most heat.

With AI, the promise is we can cut that down to minutes, said Lordan.

Lockheed has teamed up with tech company NVIDIA to help create maps and models. Together they use variables like wind, humidity, vegetation and topography to not only determine what the fire is doing but also what it will do next.

Currently, Lordan said predicting a fires rate of spread and direction can take up to a day.

The promise is you can break that down to hours, said Lordan.

This means the ability to give command teams critical information and recommendations will decrease response time and make better decisions about fire suppression. This can range from digging trenches to performing back burns and using aerial suppression activity.

The same time reducing promises will apply to updating data and maps on the areas most prone to fires down to days instead of years.

The application of this technology to wildfires is already taking place.

Currently, we are flight testing prototype software with the Colorado Division of Fire Prevention and Control, said Lordan. We are very excited about the progress we are making there.

Lockheed is also working to build a wildfire research lab where private and government groups can work to collaborate on bringing new technologies to help prevent and fight wildfires.

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Lockheed testing artificial intelligence to fight wildfires - FOX 31 Denver

Artificial Intelligence/Machine Learning and the Future of National Security – smallwarsjournal

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Artificial Intelligence/Machine Learning and the Future of National Security

AI is a once-in-a lifetime commercial and defense game changer

By Steve Blank

Hundreds of billions in public and private capital is being invested in AI and Machine Learning companies. The number of patents filed in 2021 is more than 30 times higher than in 2015 as companies and countries across the world have realized that AI and Machine Learning will be a major disruptor and potentially change the balance of military power.

Until recently, the hype exceeded reality. Today, however, advances in AI in several important areas (here, here, here, here and here) equal and even surpass human capabilities.

If you havent paid attention, nows the time.

AI and the DoD

The Department of Defense has thought that AI is such a foundational set of technologies that they started a dedicated organization -- the JAIC -- to enable and implement artificial intelligence across the Department. They provide the infrastructure, tools, and technical expertise for DoD users to successfully build and deploy their AI-accelerated projects.

Some specific defense-related AI applications are listed later in this document.

Were in the Middle of a Revolution

Imagine its 1950, and youre a visitor who traveled back in time from today. Your job is to explain the impact computers will have on business, defense and society to people who are using manual calculators and slide rules. You succeed in convincing one company and a government to adopt computers and learn to code much faster than their competitors /adversaries. And they figure out how they could digitally enable their business supply chain, customer interactions, etc. Think about the competitive edge theyd have by today in business or as a nation. Theyd steamroll everyone.

Thats where we are today with Artificial Intelligence and Machine Learning. These technologies will transform businesses and government agencies. Today, 100s of billions of dollars in private capital have been invested in 1,000s of AI startups. The U.S. Department of Defense has created a dedicated organization to ensure its deployment.

But What Is It?

Compared to the classic computing weve had for the last 75 years, AI has led to new types of applications, e.g. facial recognition; new types of algorithms, e.g. machine learning; new types of computer architectures, e.g. neural nets; new hardware, e.g. GPUs; new types of software developers, e.g. data scientists; all under the overarching theme of artificial intelligence. The sum of these feels like buzzword bingo. But they herald a sea change in what computers are capable of doing, how they do it, and what hardware and software is needed to do it.

This brief will attempt to describe all of it.

New Words to Define Old Things

One of the reasons the world of AI/ML is confusing is that its created its own language and vocabulary. It uses new words to define programming steps, job descriptions, development tools, etc. But once you understand how the new world maps onto the classic computing world, it starts to make sense. So first a short list of some key definitions.

AI/ML - a shorthand for Artificial Intelligence/Machine Learning

Artificial Intelligence (AI) - a catchall term used to describe Intelligent machines which can solve problems, make/suggest decisions and perform tasks that have traditionally required humans to do. AI is not a single thing, but a constellation of different technologies.

Machine Learning (ML) - a subfield of artificial intelligence. Humans combine data with algorithms (see here for a list) to train a model using that data. This trained model can then make predications on new data (is this picture a cat, a dog or a person?) or decision-making processes (like understanding text and images) without being explicitly programmed to do so.

Machine learning algorithms - computer programs that adjust themselves to perform better as they are exposed to more data.

The learning part of machine learning means these programs change how they process data over time. In other words, a machine-learning algorithm can adjust its own settings, given feedback on its previous performance in making predictions about a collection of data (images, text, etc.).

Deep Learning/Neural Nets a subfield of machine learning. Neural networks make up the backbone of deep learning. (The deep in deep learning refers to the depth of layers in a neural network.) Neural nets are effective at a variety of tasks (e.g., image classification, speech recognition). A deep learning neural net algorithm is given massive volumes of data, and a task to perform - such as classification. The resulting model is capable of solving complex tasks such as recognizing objects within an image and translating speech in real time. In reality, the neural net is a logical concept that gets mapped onto a physical set of specialized processors. See here.)

Data Science a new field of computer science. Broadly it encompasses data systems and processes aimed at maintaining data sets and deriving meaning out of them. In the context of AI, its the practice of people who are doing machine learning.

Data Scientists - responsible for extracting insights that help businesses make decisions. They explore and analyze data using machine learning platforms to create models about customers, processes, risks, or whatever theyre trying to predict.

Whats Different? Why is Machine Learning Possible Now?

To understand why AI/Machine Learning can do these things, lets compare them to computers before AI came on the scene. (Warning simplified examples below.)

Classic Computers

For the last 75 years computers (well call these classic computers) have both shrunk to pocket size (iPhones) and grown to the size of warehouses (cloud data centers), yet they all continued to operate essentially the same way.

Classic Computers - Programming

Classic computers are designed to do anything a human explicitly tells them to do. People (programmers) write software code (programming) to develop applications, thinking a priori about all the rules, logic and knowledge that need to be built in to an application so that it can deliver a specific result. These rules are explicitly coded into a program using a software language (Python, JavaScript, C#, Rust, ).

Classic Computers - Compiling

The code is then compiled using software to translate the programmers source code into a version that can be run on a target computer/browser/phone. For most of todays programs, the computer used to develop and compile the code does not have to be that much faster than the one that will run it.

Classic Computers - Running/Executing Programs

Once a program is coded and compiled, it can be deployed and run (executed) on a desktop computer, phone, in a browser window, a data center cluster, in special hardware, etc. Programs/applications can be games, social media, office applications, missile guidance systems, bitcoin mining, or even operating systems e.g. Linux, Windows, IOS. These programs run on the same type of classic computer architectures they were programmed in.

Classic Computers Software Updates, New Features

For programs written for classic computers, software developers receive bug reports, monitor for security breaches, and send out regular software updates that fix bugs, increase performance and at times add new features.

Classic Computers- Hardware

The CPUs (Central Processing Units) that write and run these Classic Computer applications all have the same basic design (architecture). The CPUs are designed to handle a wide range oftasks quickly in a serial fashion. These CPUs range from Intel X86 chips, and the ARM cores on Apple M1 SoC, to thez15 in IBM mainframes.

Machine Learning

In contrast to programming on classic computing with fixed rules, machine learning is just like it sounds we can train/teach a computer to learn by example by feeding it lots and lots of examples. (For images a rule of thumb is that a machine learning algorithm needs at least 5,000 labeled examples of each category in order to produce an AI model with decent performance.) Once it is trained, the computer runs on its own and can make predictions and/or complex decisions.

Just as traditional programming has three steps - first coding a program, next compiling it and then running it - machine learning also has three steps: training (teaching), pruning and inference (predicting by itself.)

Machine Learning - Training

Unlike programing classic computers with explicit rules, training is the process of teaching a computer to perform a task e.g. recognize faces, signals, understand text, etc. (Now you know why you're asked to click on images of traffic lights, cross walks, stop signs, and buses or type the text of scanned image in ReCaptcha.) Humans provide massive volumes of training data (the more data, the better the models performance) and select the appropriate algorithm to find the best optimized outcome.

(See the detailed machine learning pipeline later in this section for the gory details.)

By running an algorithm selected by a data scientist on a set of training data, the Machine Learning system generates the rules embedded in a trained model. The system learns from examples (training data), rather than being explicitly programmed. (See the Types of Machine Learning section for more detail.) This self-correction is pretty cool. An input to a neural net results in a guess about what that input is. The neural net then takes its guess and compares it to a ground-truth about the data, effectively asking an expert Did I get this right? The difference between the networks guess and the ground truth is itserror. The network measures that error, and walks the error back over its model, adjusting weights to the extent that they contributed to the error.)

Just to make the point again: The algorithms combined with the training data - not external human computer programmers - create the rules that the AI uses. The resulting model is capable of solving complex tasks such as recognizing objects its never seen before, translating text or speech, or controlling a drone swarm.

(Instead of building a model from scratch you can now buy, for common machine learning tasks, pretrained models from others and here, much like chip designers buying IP Cores.)

Machine Learning Training - Hardware

Training a machine learning model is a very computationally intensive task. AI hardware must be able to perform thousands of multiplications and additions in a mathematical process called matrix multiplication. It requires specialized chips to run fast. (See the AI hardware section for details.)

Machine Learning - Simplification via pruning, quantization, distillation

Just like classic computer code needs to be compiled and optimized before it is deployed on its target hardware, the machine learning models are simplified and modified(pruned) touse less computingpower, energy, and memory before theyre deployed to run on their hardware.

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Artificial Intelligence/Machine Learning and the Future of National Security - smallwarsjournal

Global Artificial Intelligence (AI) Market to Reach US$341.4 Billion by the Year 2027 – Yahoo Finance

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ReportLinker

Abstract: Whats New for 2022? - Global competitiveness and key competitor percentage market shares. - Market presence across multiple geographies - Strong/Active/Niche/Trivial.

New York, May 11, 2022 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Global Artificial Intelligence (AI) Industry" - https://www.reportlinker.com/p05478480/?utm_source=GNW - Online interactive peer-to-peer collaborative bespoke updates - Access to our digital archives and MarketGlass Research Platform - Complimentary updates for one yearGlobal Artificial Intelligence (AI) Market to Reach US$341.4 Billion by the Year 2027

- Amid the COVID-19 crisis, the global market for Artificial Intelligence (AI) estimated at US$46.9 Billion in the year 2020, is projected to reach a revised size of US$341.4 Billion by 2027, growing at a CAGR of 32.8% over the analysis period 2020-2027.Services, one of the segments analyzed in the report, is projected to grow at a 32.6% CAGR to reach US$142.7 Billion by the end of the analysis period.After an early analysis of the business implications of the pandemic and its induced economic crisis, growth in the Software segment is readjusted to a revised 30.4% CAGR for the next 7-year period. This segment currently accounts for a 37.9% share of the global Artificial Intelligence (AI) market.

- The U.S. Accounts for Over 41.2% of Global Market Size in 2020, While China is Forecast to Grow at a 39.1% CAGR for the Period of 2020-2027

- The Artificial Intelligence (AI) market in the U.S. is estimated at US$19.3 Billion in the year 2020. The country currently accounts for a 41.22% share in the global market. China, the world second largest economy, is forecast to reach an estimated market size of US$64.7 Billion in the year 2027 trailing a CAGR of 39.1% through 2027. Among the other noteworthy geographic markets are Japan and Canada, each forecast to grow at 27.6% and 29% respectively over the 2020-2027 period. Within Europe, Germany is forecast to grow at approximately 31.2% CAGR while Rest of European market (as defined in the study) will reach US$64.7 Billion by the year 2027.

- Hardware Segment Corners a 19.9% Share in 2020

- In the global Hardware segment, USA, Canada, Japan, China and Europe will drive the 36.6% CAGR estimated for this segment. These regional markets accounting for a combined market size of US$7.7 Billion in the year 2020 will reach a projected size of US$68.5 Billion by the close of the analysis period. China will remain among the fastest growing in this cluster of regional markets. Led by countries such as Australia, India, and South Korea, the market in Asia-Pacific is forecast to reach US$46.7 Billion by the year 2027.

- Select Competitors (Total 865 Featured) AIBrain, Inc. Advanced Micro Devices, Inc. Amazon Web Services Baidu, Inc. Cisco Systems, Inc. eGain Corporation General Electric Company Google, Inc. Intel Corporation International Business Machines Corporation (IBM) Meta (Facebook company is now Meta) Micron Technology, Inc. Microsoft Corporation Nippon Telegraph and Telephone Corporation Nuance Communications, Inc. NVIDIA Corporation Omron Corporation Oracle Corporation Rockwell Automation, Inc. Salesforce.com, inc. Samsung Electronics Co., Ltd. SAP SE SAS Institute Inc. Siemens AG

Read the full report: https://www.reportlinker.com/p05478480/?utm_source=GNW

I. METHODOLOGY

II. EXECUTIVE SUMMARY

1. MARKET OVERVIEW Impact of Covid-19 and a Looming Global Recession With IMF Making an Upward Revision of Global GDP for 2022, Companies Remain Bullish About an Economic Comeback EXHIBIT 1: World Economic Growth Projections (Real GDP, Annual % Change) for 2020 through 2022 Artificial Intelligence Gains Significant Interest as Industries Expedite Digital Transformation Strategies A Peek into Application of AI in War Against the Pandemic Machine Learning Benefits Healthcare Organizations COVID-19-Led Budgetary Reticence Dampens Spending, but AI Enjoys Resilient Interest in Banking Sector Retailers Rely on AI to Stay Afloat & Embrace New Normal Emphasis on Technology Adoption Elicits AI Implementation in Manufacturing Industry Competition AI Marketplace Characterized by Intense Competition EXHIBIT 2: Artificial Intelligence (AI) - Global Key Competitors Percentage Market Share in 2022 (E) Growing Focus on AI by Leading Tech Companies with Huge Financial Resources AI Presents Compelling Opportunities for Established & Startup Companies Competitive Market Presence - Strong/Active/Niche/Trivial for 300 Players Worldwide in 2022 (E) Funding Landscape Remains Vibrant in the AI Technology Space EXHIBIT 3: Global AI Investment (in US$ Billion) for the Years 2015 through 2021 EXHIBIT 4: Distribution of Global Investment in AI by Region/ Country: 2021 EXHIBIT 5: Number of AI Startups with $1 Billion Valuations for the Years 2014-2020 EXHIBIT 6: AI Cumulative Funding (in US$ Billion) by Category (As of 2020) AI Applications and Major Startups Artificial Intelligence (AI): A Prelude Technologies Enabling AI Market Outlook Prominent Factors with Implications for Evolution & Future of Artificial Intelligence Advances in Real World AI Applications Bolster Growth Inherent Advantages of AI Technology to Accelerate Adoption in Varied Applications Banking Sector Shows Unwavering Interest in AI AI Reshapes the Future of Manufacturing Industry AI-based Services Segment Captures Major Share of Global AI Market Developed Markets Dominate, Asia-Pacific to Spearhead Future Growth Deep Learning and Digital Assistant Technologies Present Significant Growth Potential Major Challenges Faced in AI Implementation World Brands Recent Market Activity

2. FOCUS ON SELECT PLAYERS

3. MARKET TRENDS & DRIVERS Accelerating Pace of Digital Transformation to Benefit Demand for AI EXHIBIT 7: Digital Transformation by Industry: 2020 EXHIBIT 8: Industry Adoption of Artificial Intelligence (AI) by Function: 2020 Noteworthy Technological Trends to Watch-for in Artificial Intelligence Space Machine Learning and AI-Assisted Platforms Personalize Customer Experiences in Marketing Applications EXHIBIT 9: Ranking of Business Outcomes Realized through AI Application in Marketing Businesses to Gain from Application of AI in Predictive Marketing Analytics and Demand Forecasting Growing Role of AI in the Metaverse AI Hosting at Edge to Drive Growth EXHIBIT 10: Global Edge Computing Market in US$ Billion: 2020, 2024, and 2026 AI-enabled Analysis and Forecasts Aid Organizations Make Profitable Decisions AI-Powered Biometric Security Solutions Gain Momentum EXHIBIT 11: Global Biometrics Market in US$ Billion: 2016, 2020, and 2025 New and Improved Concepts in ML and AI take Stage IIoT & AI Convergence Brings in Improved Efficiencies EXHIBIT 12: Global Breakdown of Investments in Manufacturing IoT (in US$ Billion) for the Years 2016, 2018, 2020 and 2025 EXHIBIT 13: Industry 4.0 Technologies with Strongest Impact on Organizations: 2020 Increasing Adoption of AI Technology to Boost AI Chipsets Market Combination of Robotics and AI Set to Cause Significant Disruption in Various Industries AI Innovations Widen Prospects Blockchain & Artificial Intelligence (AI): A Powerful Combination Big Data Trends to Shape Future of Artificial Intelligence AI in Retail Market: Multi-Channel Retailing and e-Commerce Favor Segment Growth EXHIBIT 14: Digital Transformation in Retail Industry Promises Lucrative Growth Opportunities: Global Retail IT Spending (In US$ Billion) for the Years 2018, 2020, 2022 & 2024 AI for a Competitive Edge for Retail Organizations Online Retailers Eye on Artificial Intelligence to Boost Business in Post-COVID-19 Era AI & Analytics Help Retailers Survive Economic & Operational Implications of COVID-19 AI for Fashion Retail and Beauty AI for Grocery, Electronics, and Home & Furniture Ecommerce Attracts Strong Growth Detailed Insight into How e-commerce Makes use of AI EXHIBIT 15: Global B2C e-Commerce Market Reset & Trajectory - Growth Outlook (In %) For Years 2019 Through 2025 EXHIBIT 16: Retail M-Commerce Sales as % of Retail E-commerce Sales Worldwide for the Years 2016, 2018, 2020 & 2022 Financial Sector: AI and Machine Learning Offer Numerous Gains Fintech Deploys AI to Target Millennials AI in Media & Advertising: Targeting Customers with Right Marketing Content Possibilities Galore for AI in Digital Marketing AI-Enabled CRM Market: Promising Growth Opportunities in Store Artificial Intelligence Set to Transform Delivery of Healthcare Services AI to Play a Significant Role in Automation and Improving Clinical Outcomes EXHIBIT 17: Global Healthcare AI Market - Percentage Breakdown by Application for 2020 AI in Pharmaceutical Sector COVID-19 Spurs New Developments and Expedites AI Adoption in Healthcare Industry Artificial Intelligence Holds Potential to Accelerate Detection & Treatment of COVID-19 Rising Prevalence of Diabetes to Drive AI Adoption in Diabetes Management Market EXHIBIT 18: World Diabetes Prevalence (2000-2045P) Barriers Restraining AI Adoption in Healthcare Sector Automotive AI Market: Need to Enhance Customer Experience Propels Growth EXHIBIT 19: Automotive AI Market By Segment Demand Recovery in Automobile Sector Steers Growth Opportunities EXHIBIT 20: World Automobile Production in Million Units: 2008- 2022 Increasing Focus on Electric Vehicles and Autonomous Vehicles Provide the Perfect Platform to Shape Future Growth EXHIBIT 21: Global Autonomous Vehicle Sales (In Million) for Years 2020, 2025 & 2030 Automakers Focus on Integrating AI-Powered Driver Assist Features in Vehicles AI to Enhance Connectivity, Provide Infotainment and Enhance Safety in Vehicles AI for Smart Insurance Risk Assessment of Vehicles Artificial Intelligence Steps into Manufacturing Space to Transform Diverse Aspects Industrial AI to Influence Manufacturing in a Major Way Industrial IoT, Robotics and Big Data to Stimulate AI Implementations EXHIBIT 22: Global Investments on Industry 4.0 Technologies (in US$ Billion) for the Years 2017, 2020, & 2023 EXHIBIT 23: Global Predictive Maintenance by Market in US$ Billion for Years 2020, 2022, 2024, and 2026 AI as a Service Market: Obviating the Need to Make Huge Initial Investments AI in Education Market to Exhibit Strong Growth EXHIBIT 24: Global Market for AI in Healthcare Sector (2019): Percentage Breakdown of Revenues by End-Use - Higher Education and K-12 Sectors Focus on ITS, IAL and Chatbots Favors Market Growth Agriculture Sector: A Promising Market for AI Implementations AI Technologies Used in Agricultural Activities - A Review AI Poised to Create Smarter Agriculture Practices in Post- COVID-19 Period Food & Beverage Industry to Leverage AI Capabilities to Resolve Production Issues and Match Up to Customer Expectations AI Adoption Gains Acceptance in Modern Warfare Systems in the Defense Sector Energy & Utilities: Complex Landscape and High Risk of Malfunctions Enhances Need for AI-based Systems COVID-19 Raises Demand for AI Technologies in Oil & Gas Sector EXHIBIT 25: Top Technology Investments in Oil and Gas Sector: 2020 AI in Construction Sector: Need for Cost Reduction and Safety at Construction Sites Drive Focus onto the Use of AI-based Solutions

4. GLOBAL MARKET PERSPECTIVE Table 1: World Recent Past, Current & Future Analysis for Artificial Intelligence (AI) by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2020 through 2027 and % CAGR

Table 2: World Historic Review for Artificial Intelligence (AI) by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 3: World 12-Year Perspective for Artificial Intelligence (AI) by Geographic Region - Percentage Breakdown of Value Revenues for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets for Years 2015, 2021 & 2027

Table 4: World Recent Past, Current & Future Analysis for Services by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2020 through 2027 and % CAGR

Table 5: World Historic Review for Services by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 6: World 12-Year Perspective for Services by Geographic Region - Percentage Breakdown of Value Revenues for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World for Years 2015, 2021 & 2027

Table 7: World Recent Past, Current & Future Analysis for Software by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2020 through 2027 and % CAGR

Table 8: World Historic Review for Software by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 9: World 12-Year Perspective for Software by Geographic Region - Percentage Breakdown of Value Revenues for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World for Years 2015, 2021 & 2027

Table 10: World Recent Past, Current & Future Analysis for Hardware by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2020 through 2027 and % CAGR

Table 11: World Historic Review for Hardware by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 12: World 12-Year Perspective for Hardware by Geographic Region - Percentage Breakdown of Value Revenues for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World for Years 2015, 2021 & 2027

Table 13: World Recent Past, Current & Future Analysis for Computer Vision by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2020 through 2027 and % CAGR

Table 14: World Historic Review for Computer Vision by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 15: World 12-Year Perspective for Computer Vision by Geographic Region - Percentage Breakdown of Value Revenues for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World for Years 2015, 2021 & 2027

Table 16: World Recent Past, Current & Future Analysis for Machine Learning by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2020 through 2027 and % CAGR

Table 17: World Historic Review for Machine Learning by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 18: World 12-Year Perspective for Machine Learning by Geographic Region - Percentage Breakdown of Value Revenues for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World for Years 2015, 2021 & 2027

Table 19: World Recent Past, Current & Future Analysis for Context Aware Computing by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2020 through 2027 and % CAGR

Table 20: World Historic Review for Context Aware Computing by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 21: World 12-Year Perspective for Context Aware Computing by Geographic Region - Percentage Breakdown of Value Revenues for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World for Years 2015, 2021 & 2027

Table 22: World Recent Past, Current & Future Analysis for Natural Language Processing by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2020 through 2027 and % CAGR

Table 23: World Historic Review for Natural Language Processing by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 24: World 12-Year Perspective for Natural Language Processing by Geographic Region - Percentage Breakdown of Value Revenues for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World for Years 2015, 2021 & 2027

Table 25: World Recent Past, Current & Future Analysis for Advertising & Media by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2020 through 2027 and % CAGR

Table 26: World Historic Review for Advertising & Media by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 27: World 12-Year Perspective for Advertising & Media by Geographic Region - Percentage Breakdown of Value Revenues for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World for Years 2015, 2021 & 2027

Table 28: World Recent Past, Current & Future Analysis for BFSI by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2020 through 2027 and % CAGR

Table 29: World Historic Review for BFSI by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 30: World 12-Year Perspective for BFSI by Geographic Region - Percentage Breakdown of Value Revenues for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World for Years 2015, 2021 & 2027

Table 31: World Recent Past, Current & Future Analysis for Healthcare by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2020 through 2027 and % CAGR

Table 32: World Historic Review for Healthcare by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 33: World 12-Year Perspective for Healthcare by Geographic Region - Percentage Breakdown of Value Revenues for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World for Years 2015, 2021 & 2027

Table 34: World Recent Past, Current & Future Analysis for Retail by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2020 through 2027 and % CAGR

Table 35: World Historic Review for Retail by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 36: World 12-Year Perspective for Retail by Geographic Region - Percentage Breakdown of Value Revenues for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World for Years 2015, 2021 & 2027

Table 37: World Recent Past, Current & Future Analysis for Automotive & Transportation by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2020 through 2027 and % CAGR

Table 38: World Historic Review for Automotive & Transportation by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 39: World 12-Year Perspective for Automotive & Transportation by Geographic Region - Percentage Breakdown of Value Revenues for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World for Years 2015, 2021 & 2027

Table 40: World Recent Past, Current & Future Analysis for Manufacturing by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2020 through 2027 and % CAGR

Table 41: World Historic Review for Manufacturing by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 42: World 12-Year Perspective for Manufacturing by Geographic Region - Percentage Breakdown of Value Revenues for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World for Years 2015, 2021 & 2027

Table 43: World Recent Past, Current & Future Analysis for Agriculture by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2020 through 2027 and % CAGR

Table 44: World Historic Review for Agriculture by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 45: World 12-Year Perspective for Agriculture by Geographic Region - Percentage Breakdown of Value Revenues for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World for Years 2015, 2021 & 2027

Table 46: World Recent Past, Current & Future Analysis for Other End-Uses by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2020 through 2027 and % CAGR

Table 47: World Historic Review for Other End-Uses by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 48: World 12-Year Perspective for Other End-Uses by Geographic Region - Percentage Breakdown of Value Revenues for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World for Years 2015, 2021 & 2027

III. MARKET ANALYSIS

UNITED STATES Artificial Intelligence (AI) Market Presence - Strong/Active/ Niche/Trivial - Key Competitors in the United States for 2022 (E) Artificial Intelligence Market: An Overview Healthcare: A Promising Application Market for AI Technology Funding for AI Startups Continues to Grow EXHIBIT 26: Top Funded AI Startups in the US: 2021 Market Analytics Table 49: USA Recent Past, Current & Future Analysis for Artificial Intelligence (AI) by Component - Services, Software and Hardware - Independent Analysis of Annual Revenues in US$ Million for the Years 2020 through 2027 and % CAGR

Table 50: USA Historic Review for Artificial Intelligence (AI) by Component - Services, Software and Hardware Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 51: USA 12-Year Perspective for Artificial Intelligence (AI) by Component - Percentage Breakdown of Value Revenues for Services, Software and Hardware for the Years 2015, 2021 & 2027

Table 52: USA Recent Past, Current & Future Analysis for Artificial Intelligence (AI) by Technology - Computer Vision, Machine Learning, Context Aware Computing and Natural Language Processing - Independent Analysis of Annual Revenues in US$ Million for the Years 2020 through 2027 and % CAGR

Table 53: USA Historic Review for Artificial Intelligence (AI) by Technology - Computer Vision, Machine Learning, Context Aware Computing and Natural Language Processing Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 54: USA 12-Year Perspective for Artificial Intelligence (AI) by Technology - Percentage Breakdown of Value Revenues for Computer Vision, Machine Learning, Context Aware Computing and Natural Language Processing for the Years 2015, 2021 & 2027

Table 55: USA Recent Past, Current & Future Analysis for Artificial Intelligence (AI) by End-Use - Advertising & Media, BFSI, Healthcare, Retail, Automotive & Transportation, Manufacturing, Agriculture and Other End-Uses - Independent Analysis of Annual Revenues in US$ Million for the Years 2020 through 2027 and % CAGR

Table 56: USA Historic Review for Artificial Intelligence (AI) by End-Use - Advertising & Media, BFSI, Healthcare, Retail, Automotive & Transportation, Manufacturing, Agriculture and Other End-Uses Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 57: USA 12-Year Perspective for Artificial Intelligence (AI) by End-Use - Percentage Breakdown of Value Revenues for Advertising & Media, BFSI, Healthcare, Retail, Automotive & Transportation, Manufacturing, Agriculture and Other End-Uses for the Years 2015, 2021 & 2027

CANADA Market Overview Top-Tier Canadian Cities Primed for AI Growth Market Analytics Table 58: Canada Recent Past, Current & Future Analysis for Artificial Intelligence (AI) by Component - Services, Software and Hardware - Independent Analysis of Annual Revenues in US$ Million for the Years 2020 through 2027 and % CAGR

Table 59: Canada Historic Review for Artificial Intelligence (AI) by Component - Services, Software and Hardware Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 60: Canada 12-Year Perspective for Artificial Intelligence (AI) by Component - Percentage Breakdown of Value Revenues for Services, Software and Hardware for the Years 2015, 2021 & 2027

Table 61: Canada Recent Past, Current & Future Analysis for Artificial Intelligence (AI) by Technology - Computer Vision, Machine Learning, Context Aware Computing and Natural Language Processing - Independent Analysis of Annual Revenues in US$ Million for the Years 2020 through 2027 and % CAGR

Table 62: Canada Historic Review for Artificial Intelligence (AI) by Technology - Computer Vision, Machine Learning, Context Aware Computing and Natural Language Processing Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 63: Canada 12-Year Perspective for Artificial Intelligence (AI) by Technology - Percentage Breakdown of Value Revenues for Computer Vision, Machine Learning, Context Aware Computing and Natural Language Processing for the Years 2015, 2021 & 2027

Table 64: Canada Recent Past, Current & Future Analysis for Artificial Intelligence (AI) by End-Use - Advertising & Media, BFSI, Healthcare, Retail, Automotive & Transportation, Manufacturing, Agriculture and Other End-Uses - Independent Analysis of Annual Revenues in US$ Million for the Years 2020 through 2027 and % CAGR

Table 65: Canada Historic Review for Artificial Intelligence (AI) by End-Use - Advertising & Media, BFSI, Healthcare, Retail, Automotive & Transportation, Manufacturing, Agriculture and Other End-Uses Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 66: Canada 12-Year Perspective for Artificial Intelligence (AI) by End-Use - Percentage Breakdown of Value Revenues for Advertising & Media, BFSI, Healthcare, Retail, Automotive & Transportation, Manufacturing, Agriculture and Other End-Uses for the Years 2015, 2021 & 2027

JAPAN Artificial Intelligence (AI) Market Presence - Strong/Active/ Niche/Trivial - Key Competitors in Japan for 2022 (E) Market Analytics Table 67: Japan Recent Past, Current & Future Analysis for Artificial Intelligence (AI) by Component - Services, Software and Hardware - Independent Analysis of Annual Revenues in US$ Million for the Years 2020 through 2027 and % CAGR

Table 68: Japan Historic Review for Artificial Intelligence (AI) by Component - Services, Software and Hardware Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 69: Japan 12-Year Perspective for Artificial Intelligence (AI) by Component - Percentage Breakdown of Value Revenues for Services, Software and Hardware for the Years 2015, 2021 & 2027

Table 70: Japan Recent Past, Current & Future Analysis for Artificial Intelligence (AI) by Technology - Computer Vision, Machine Learning, Context Aware Computing and Natural Language Processing - Independent Analysis of Annual Revenues in US$ Million for the Years 2020 through 2027 and % CAGR

Table 71: Japan Historic Review for Artificial Intelligence (AI) by Technology - Computer Vision, Machine Learning, Context Aware Computing and Natural Language Processing Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 72: Japan 12-Year Perspective for Artificial Intelligence (AI) by Technology - Percentage Breakdown of Value Revenues for Computer Vision, Machine Learning, Context Aware Computing and Natural Language Processing for the Years 2015, 2021 & 2027

Table 73: Japan Recent Past, Current & Future Analysis for Artificial Intelligence (AI) by End-Use - Advertising & Media, BFSI, Healthcare, Retail, Automotive & Transportation, Manufacturing, Agriculture and Other End-Uses - Independent Analysis of Annual Revenues in US$ Million for the Years 2020 through 2027 and % CAGR

Table 74: Japan Historic Review for Artificial Intelligence (AI) by End-Use - Advertising & Media, BFSI, Healthcare, Retail, Automotive & Transportation, Manufacturing, Agriculture and Other End-Uses Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 75: Japan 12-Year Perspective for Artificial Intelligence (AI) by End-Use - Percentage Breakdown of Value Revenues for Advertising & Media, BFSI, Healthcare, Retail, Automotive & Transportation, Manufacturing, Agriculture and Other End-Uses for the Years 2015, 2021 & 2027

CHINA Artificial Intelligence (AI) Market Presence - Strong/Active/ Niche/Trivial - Key Competitors in China for 2022 (E) Market Overview China Continues Investments in AI Startups EXHIBIT 27: Chinese AI Market: Funding for AI Startups (in $ Billion): 2016-2020

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Global Artificial Intelligence (AI) Market to Reach US$341.4 Billion by the Year 2027 - Yahoo Finance

Why artificial intelligence is vital in the race to meet the SDGs – World Economic Forum

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Seven years have passed since world leaders met in New York and agreed 17 Sustainable Development Goals (SDGs) to resolve major challenges including poverty, hunger, inequality, climate change and health.

The pandemic undoubtedly diverted attention from some of these issues in the past couple of years. But even before COVID-19, the UN was warning that progress to meet the SDGs was not advancing at the speed or on the scale needed. Meeting them by 2030 will be tough.

Yet I remain optimistic. The pandemic demonstrated like nothing else the power of working collaboratively, across borders, for the benefit of society. It concentrated minds, funding and policy to accelerate research into virus detection, disease treatments, vaccines and manufacturing platforms.

It was a truly remarkable effort from the global community to develop effective vaccines within a year of the virus first being detected, and these and other treatments have dramatically reduced the viruss fatality rate. This can be attributed to the brilliance, perseverance and creativity of scientists across the world. But they were not working alone: Artificial intelligence (AI) also played a key part.

The US company Moderna was among the first to release an effective COVID-19 vaccine. One reason it was able to make this breakthrough so quickly was the use of AI to speed up development. Modernas Chief Data and Artificial Intelligence Officer Dave Johnson explains that AI algorithms and robotic automation helped them move from manually producing around 30 mRNAs (a molecule fundamental to the vaccine) each month, to being able to produce around 1,000 a month.

Moderna is also using AI to help their mRNA sequence design. Its co-founder Noubar Afeyan recently predicted during a visit to Imperial College London that immune medicine will see large advances in the coming years, and we can look forward to a future where medicine is more pre-emptive than reactionary.

If we can catch disease early and delay it, at a minimum, we could have a lot more impact at a lot less cost, he said. This is a great example of how AI can free up time for scientists to accelerate discovery and dedicate efforts to solving big challenges.

We are also seeing examples of AI technology driving improvements in other areas of healthcare, such as disease screening for cancer and malaria. Researchers from Google Health, DeepMind, the NHS, Northwestern University and colleagues at Imperial have designed and trained an AI model to spot breast cancer from X-ray images.

The computer algorithm, which was trained using mammography images from almost 29,000 women, was shown to be as effective as human radiologists in spotting cancer. At a time when health services around the world are stretched as they deal with long backlogs of patients following the pandemic, this sort of technology can help ease bottlenecks and improve treatment.

For malaria, a handheld lab-on-a-chip molecular diagnostics systems developed with AI could revolutionize how the disease is detected in remote parts of Africa. The project, which is led by the Digital Diagnostics for Africa Network, brings together collaborators such as MinoHealth AI Labs in Ghana and Imperial College Londons Global Development Hub. This technology could help pave the way for universal health coverage and push us towards achieving SDG3.

There are numerous other examples of how advances in AI could support our understanding of climate change (SDG13), enable our transition to sustainable transport systems (SDG11), and accelerate agri-tech to help farmers end food poverty and malnutrition (SDG2) among many benefits to the other SDGs too.

For example, the Alan Turing Institute, the UKs national centre for data science and artificial intelligence, are using machine learning to better understand the complex interactions between climate and Arctic sea ice.

With an expanding global population, we face challenges around food demand and production not only how to reduce malnourishment but the impact on the planet too, such as deforestation, emissions and biodiversity loss. To meet these needs, the use of AI in agriculture is growing rapidly and is enabling farmers to enhance crop production, direct machinery to carry out tasks autonomously, and identify pest infestations before they occur.

Smart sensing technology is also helping farmers use fertilizer more effectively and reduce environmental damage. An exciting research project, funded by the EPSRC, Innovate UK and Cytiva, will help growers optimize timing and amount of fertilizer to use on their crops, taking into account factors like the weather and soil condition. This will reduce the expense and damaging effects of over-fertilizing soil.

Developing sustainable and smart transport systems will also be vital as cities and countries look to reduce the impact of air pollution and improve infrastructure. In the last decade, AI has powered a revolution in transport and mobility, from autonomous vehicles to ride-sharing apps and route-planners. AI is also being used to make public transport systems more efficient, reduce traffic congestion and pollution, and improve safety.

Despite its benefits to research and medicine, integrating AI into society and innovation is not always smooth sailing. Recent controversies on facial recognition, automated decision-making and COVID-related tracking, have led to some caution and suspicion. We need to ensure that AI is employed in ways that are trusted, transparent and inclusive. We need to make sure that there is an internationally coordinated, collaborative approach, just as there was in the pandemic.

The World Economic Forums Global AI Action Alliance brings together more than 100 leading companies, governments, international organizations, non-profits and academics united in a commitment to maximize AI's societal benefits while minimizing its risks.

The World Economic Forums Centre for the Fourth Industrial Revolution, in partnership with the UK government, has developed guidelines for more ethical and efficient government procurement of artificial intelligence (AI) technology. Governments across Europe, Latin America and the Middle East are piloting these guidelines to improve their AI procurement processes.

Our guidelines not only serve as a handy reference tool for governments looking to adopt AI technology, but also set baseline standards for effective, responsible public procurement and deployment of AI standards that can be eventually adopted by industries.

We invite organizations that are interested in the future of AI and machine learning to get involved in this initiative. Read more about our impact.

It is imperative that we put good processes and practices in place to ensure AI is developed in a positive and ethical way to see it adopted and used to its fullest by citizens and governments.

We must now work together to ensure that artificial intelligence can accelerate progress of the Sustainable Development Goals and help us get back on track to reaching them by 2030.

Written by

Alice Gast, President, Imperial College London

The views expressed in this article are those of the author alone and not the World Economic Forum.

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Why artificial intelligence is vital in the race to meet the SDGs - World Economic Forum

Glean Named to Forbes AI 50 List of Top Artificial Intelligence Companies of 2022 – Yahoo Finance

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Inclusion on the list marks the first entry for the work assistant that helps employees search across all their companys apps to find exactly what they need

PALO ALTO, Calif., May 11, 2022--(BUSINESS WIRE)--Glean, the work assistant with intuition, announces that the company has been named to the Forbes AI 50 awards list. The list recognizes standouts in privately-held North American companies making the most interesting and effective use of artificial intelligence technology. This accomplishment is the first inclusion for Glean, which came out of stealth just over half a year ago. The accolade is based on Gleans ongoing innovative use of AI to help employees search for exactly what they need and discover the things they should know at work.

In todays increasingly complex workplacewhere distributed work is on the rise and the SaaS explosion has acceleratedemployees are desperate for an easier way to access the information and people needed to do their work. A recent study revealed that employed Americans on average spend 25% of their work week looking for the documents, information, or people they need to do their jobs. This is so draining that 43% say theyd consider leaving their jobs if their employer didnt provide them with an easy way to access the information and people they need to get their jobs done.

Glean brings all of a companys knowledge together, so employees can search for exactly what they need and discover the things they should know. At its core, Glean delivers powerful unified search across all applications used at a company, using a deep understanding of who individuals are, what theyre working on, and who theyre working with, to instantly deliver highly personalized results. Since launching in September 2021, the company has seen incredible customer engagement.

"The growth of remote and hybrid work has given rise not only to productivity problems, but also employee experience problems. With company information scattered across potentially hundreds of applications, finding the right information has become a difficult and time-consuming process. And employees can feel left out of the loop when they cant find what they need to get their jobs done," said Arvind Jain, CEO and founder of Glean. "Glean combines an intuitive work assistant with powerful and flexible search that leverages AI and deep learning to scan across a companys entire collection of workplace apps to quickly locate the desired information. The ability to instantly deliver the knowledge and information workers need is resulting in new levels of efficiency and employee delight. This benefits the workers and the organizations alike."

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Gleans ongoing commitment to reducing time spent looking for information and duplicating work has seen tangible impact among their customers. Customers report that their employees on average save 2-3 hours per employee per week by using Glean. Earlier this year Glean also released new features including Go Links and Collections to help companies organize their knowledge.

About Glean

Glean is the work assistant with intuition. It brings all your companys knowledge together, so you can search for exactly what you need and discover the things you should know. Glean searches across all your companys apps, understanding context, language, behavior, and relationships with others, to find personalized answers to your questions. It surfaces knowledge and makes connections with the people who can helpmaking it easier for you and your team to get big things done. Glean is led by Arvind Jain (Google, Co-Founder of Rubrik), T.R. Vishwanath (Microsoft, Facebook), Tony Gentilcore (Google), and Piyush Prahladka (Google, Uber), with funding from General Catalyst, Lightspeed Venture Partners, Kleiner Perkins, and The Slack Fund. Learn more at http://www.glean.com and follow us on LinkedIn and Twitter.

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Glean Named to Forbes AI 50 List of Top Artificial Intelligence Companies of 2022 - Yahoo Finance

Can Artificial Intelligence remove unintended bias from health care? Clinicians optimistic, but wary – Medical University of South Carolina

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During one of the many live collaboration panels of MUSCs 2022 Innovation Week, an interesting discussion ensued, mirroring a common debate in health care and that is: How does artificial intelligence (AI) fit in?

Last week, as several clinicians and key members of the Clemson-MUSC AI Hub which was formed in 2021 were on hand at the Gazes Cardiac Research Institute, it became quickly evident that AI is gaining traction throughout the world of heath care. But equally evident was the fact that theres still some skepticism from the mainstream when it comes to the best ways to use it.

For congenital cardiologist G. Hamilton Baker, M.D., associate professor of pediatrics, AI remains a tremendous untapped resource.

AI is such a blanket term, he said in an interview right after the formation of the Clemson-MUSC AI Hub last year. Were leveraging data science and wrangling those giant databases with appropriately applied machine learning methods.

Baker has been utilizing AI in his work for several years now, working on a number of different AI+Biomedical projects ranging from congenital heart disease to diabetic eye disease.

I feel very strongly about education on AI. The goal is to teach clinicians how to understand and utilize AI. We arent asking people to learn how to code, we simply want them to learn how AI can work for them, Baker said.

At the Gazes, the topic quickly centered on AI and bias. Some clinicians believe the most elegant aspect of AI is that it removes unintended biases by letting the computers which are inherently without bias because theyre metal and silicone do the data crunching and leaving the treatment to the physicians.

When two clinicians might disagree on something, AI can help uncover unknown biases and dispel others, said MUSC Public Health Sciences assistant professor Paul Heider, Ph.D. AI just looks at the data and makes decisions that are based on that alone.

However, others argued that those AI programs were written by humans, and those inadvertent biases almost certainly were sprinkled in.

Trustworthiness is a key word that we need to be focusing on here, said Brian Dean, Ph.D., chairman of the Division of Computer Science at Clemson University. Because the AI system is becoming less of a smart sensor that provides input to the medical decision-making process and more of a teammate. So we have to be super careful because, after all, AI was trained based on human expert opinion, which is biased.

Dean agreed that AI is an extremely valuable tool for the medical field, cautioning all to simply be judicious with its use.

Jihad Obeid, M.D., co-director of the Biomedical Informatics Center at MUSC, agreed. If you use it as a decision aid, rather than a decision-maker, he said, AI can be a real asset.

Regardless of the differences of opinion in the room, panel members agreed that AI has unlimited potential for researchers and clinicians alike.

When it comes to AI in health care, its so tempting to talk about the hype, all the big stuff it can do, Baker said. But the truth of the matter is there are plenty of easy, smart projects where AI could really make a significant difference, and we just need more people on board.

According to MUSC provost Lisa K. Saladin, PT, Ph.D., MUSC is already using AI to develop techniques that can help to diagnose and treat a range of ills, including cancer, Alzheimers disease, substance abuse, child abuse, epilepsy, aphasia, inflammatory skin conditions and cardiac issues.

Baker said that clinicians who are interested in implementing AI into their research or practice should look into the AI Hub, as it offers a host of resources, including funding for AI. During this years Innovation Week, the Clemson-MUSC AI Hub gave out $100,000 worth of grants to five worthy projects.

We want people to know about this, he said. I know there are lots of people out there who could really use our help. We want to accelerate the adoption of AI for those who are interested."

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Can Artificial Intelligence remove unintended bias from health care? Clinicians optimistic, but wary - Medical University of South Carolina

Living in the age of conscious-technology An interview with artificial intelligence innovator, Matthew James Bailey – Crestone Eagle

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The Crestone Eagle May, 2022

by Jennifer Eytcheson

JE: Hi Matthew, thanks for joining me today. As I look out the window of the Eagle office toward the mountains, artificial intelligence is not the first thing that comes to mind. Tell me why you chose Crestone as your base camp to help change the future of artificial intelligence, how we think about it and how we will use it.

MJB: Good morning Jennifer. Thank you very much for the opportunity to be here. As with many people, I didnt choose Crestone, Crestone chose me. My journey here started about six years ago as part of my own spiritual journey into consciousness. Ive been living here 2 years continuing in that journey, whilst also discovering new strategies and inventions to tap into the world of technology to make this world a better place to inhabit. Being in Crestone helps remind me that consciousness is the most valued and precious treasure within this universal experience. Every single person holds this treasure. New systems must hold this in the forefront of innovation and I am here to shepherd that.

Speaking of huge undertakings for humankind, I understand you will be collaborating with a world-leading space agency? Whats going on there?

Yes, thats right. They are an impeccably credible organization. They just launched the James Webb Telescope to explore new truths in our universe. They are dedicated to new truths for humanity. Recently I was invited by NASA as a visiting scholar to speak to their executives and leaders on how to build a new ethical intelligence using my formula. The conversation was rich in stimulating and philosophical dialogue. Imagine emotional intelligence on earth and in space!

Sounds intriguing. I look forward to hearing more about that in the future. What are some other things youve been working on lately?

What Im doing is building a platform to support the innovation of new conscious-centric, ethical-enabled support for human progression. Education and empowerment are key for the leaders of a new tomorrow, so we are creating a university full of master classes where people can be trained and equipped on how to become extraordinary leaders in the world of humans and machines.

I also do talks around the world in business and government and on social media platforms. For example, we had a discussion on Clubhouse last week with one of Crestones global leaders, John P. Milton, and Foster Gamble who co-created one of the most viewed documentaries in human history, ThriveWhat on Earth Will It Take?. Were building a group of global leaders that bring together different pieces of the jigsaw puzzle to build this new global platform to enable ethical-centered futures for humanity and to support the health and well-being of consciousness on our planet.

Wow! With all that, youve had time to write a book. Tell me about it.

The book is called Inventing World 3.0Evolutionary Ethics for Artificial Intelligence. Its how to build a new world vision to nourish the health of human consciousness. I waited ten years to write this book, patiently biding my time for the digital world and the world itself to be ready to create a new destiny with its partnership with artificial intelligence. I wrote the book here in Crestone, inspired by nature and by the unified field. I can say many of its predictions are coming true and many of its inventions are about to be used. Its available at the Crestone Mercantile and on Amazonhttps://aiethics.world/the-book

Is there another one coming?

Yes, its in the planning stage. This book will emphasize how each culture, each philosophy and each spiritual tradition expresses its ethics, and how to create a constitution to protect each tradition going into the age of artificial intelligence. We are looking for people to support the book. We want to travel the world and speak to spiritual and political leaders to get their participation in creating the artificial intelligence manual for the future.

Lets imagine our world in 10 years. How will AI make that world different from now?

The wheels of artificial intelligence are going to take us beyond what our ancestors could have ever imagined. So let me give you an example. Imagine a digital-buddy, your guardian, your personalized guru supporting you in every facet of life. Like having a doctor, psychiatrist, attorney with you 24/7. This will be an AI that will nourish and nurture based on your free will and sovereign choice to live a self-actualized lifestyle. This is bigger than the mobile phone or computer. After all, we want everybody on spaceship earth to thrive. Spiritual leaders like SadhGuru agree with my vision.

The challenge that artificial intelligence has invited us into is this: Will it be shepherded benevolently and do well for our human species today and tomorrow? As such we are being tested on the quality of our values and our mindset for creating better plans for humankind.

Do you see world leaders doing anything to make sure the challenges are minimized?

The world is starting to understand that we must care for the future of our species intelligence mindfully. UNESCO made an announcement late last year that their member states were adopting the first ever global agreement on the ethics of AI. This will not only protect but also promote human rights and human dignity, and will be an ethical guiding compass and a global normative bedrock allowing us to build a strong respect for the rule of law in the digital world. That is a good start. But they must go much further if they are truly to be trusted in shaping the future of artificial intelligence. The challenge is how to take the next step. This is why I wrote the book Inventing World 3.0, explaining how we can forge ahead in the world of artificial intelligence.

What else do you want people to know about your work?

Crestone is such a special place with pioneers who will lead us in different aspects of consciousness to benefit the human species. For example, John P. Milton and I are collaborating to expand The Way of Nature Empowerment Leadership training for leaders in artificial intelligence around the world. Check out https://aiethics. world to see more.

A challenge we are working with is that in order to build and progress these new systems, ethical intelligence (organic and artificial) must be the foundationthe outwards expression of consciousness itself. Consciousness has evolved and expressed itself through the human species and we must ensure that systems support the continual nourishment of our species. Some have no concept of this precious universal treasure and that is where theyre going wrong. Its important to remember the future of AI is in our hands. What will we choose?

(Authors note: Matthew taught me how to use Google Docs, Speak to Text for this interview. It wasnt perfect, I had to make correctionsespecially with his English accentbut it was a form of AI that made life a little easier by allowing me to really listen to what Matthew had to say, helping our human connection.)

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Living in the age of conscious-technology An interview with artificial intelligence innovator, Matthew James Bailey - Crestone Eagle

Artificial intelligence drives the way to net-zero emissions – Sustainability Magazine

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Op-ed: Aaron Yeardley, Carbon Reduction Engineer, Tunley Engineering

The fourth industrial revolution (Industry 4.0) is already happening, and its transforming the way manufacturing operations are carried out. Industry 4.0 is a product of the digital era as automation and data exchange in manufacturing technologies shift the central industrial control system to a smart setup that bridges the physical and digital world, addressed via the Internet of Things (IoT).

Industry 4.0 is creating cyber-physical systems that can network a production process enabling value creation and real-time optimisation. The main factor driving the revolution is the advances in artificial intelligence (AI) and machine learning. The complex algorithms involved in AI use the data collected from cyber-physical systems, resulting in smart manufacturing.

The impact that Industry 4.0 will have on manufacturing will be astronomical as operations can be automatically optimised to produce increased profit margins. However, the use of AI and smart manufacturing can also benefit the environment. The technologies used to optimise profits can also be used to produce insights into a companys carbon footprint and accelerate its sustainability. Some of these methods are available to help companies reduce their GHG emissions now. Other methods have the potential to reduce global GHG emissions in the future.

Scope 3 emissions are the emissions from a companys supply chain, both upstream and downstream activities. This means scope 3 covers all of a companys GHG emission sources except those that are directly created by the company and those created from using electricity. It comes as no surprise that on average Scope 3 emissions are 5.5 times greater than the combined amount from Scope 1 and Scope 2. Therefore, companies should ensure all three scopes are quantitated in their GHG emissions baseline.

However, in comparison to Scope 1 and Scope 2 emissions, Scope 3 emissions are difficult to measure and calculate. This is because of a lack of transparency in supply chains, lack of connections with suppliers, and complex industrial standards that provide misleading information. The major issues concerning Scope 3 emissions are as follows:

AI-based tools can help establish baseline Scope 3 emissions for companies as they are used to model an entire supply chain. The tools can quickly and efficiently sort through large volumes of data collected from sensors. If a company deploys enough sensors across the whole area of operations, it can identify sources of emissions and even detect methane plumes.

A digital twin is an AI model that works as a digital representation of a physical piece of equipment or an entire system. A digital twin can help the industry optimise energy management by using the AI surrogate models to better monitor and distribute energy resources and provide forecasts to allow for better preparation. A digital twin will optimise many sources of data and bring them onto a dashboard so that users can visualise it in real-time. For example, a case study in the Nanyang Technological University used digital twins across 200 campus buildings over five years and managed to save 31% in energy and 9,600 tCO2e. The research used IESs ICL technology to plan, operate, and manage campus facilities to minimise energy consumption.

Digital twins can be used as virtual replicas of building systems, industrial processes, vehicles, and many other opportunities. The virtual environment enables more testing and iterations so that everything can be optimised to its best performance. This means digital twins can be used to optimise building management making smart strategies that are based on carbon reduction.

Predictive maintenance of machines and equipment used in industry is now becoming common practice because it saves companies costs in performing scheduled maintenance, or costs in fixing broken equipment. The AI-based tool uses machine learning to learn how historical sensor data maps to historical maintenance records. Once a machine learning algorithm is trained using the historical data, it can successfully predict when maintenance is required based on live sensor readings in a plant. Predictive maintenance accurately models the wear and tear of machinery that is currently in use.

The best part of predictive maintenance is that it does not require additional costs for extra monitoring. Algorithms have been created that provide accurate predictions based on operational telemetry data that is already available. Predictive maintenance combined with other AI-based methods such as maintenance time estimation and maintenance task scheduling can be used to create an optimal maintenance workflow for industrial processes. Conversely, improving current maintenance regimes which often contribute to unplanned downtime, quality defects and accidents is appealing for everybody.

An optimal maintenance schedule produced from predictive maintenance prevents work that often is not required. Carbon savings will be made via the controlled deployment of spare parts, less travel for people to come to the site, and less hot shooting of spare parts. Intervening with maintenance only when required and not a moment too late will save on the use of electricity, efficiency (by preventing declining performance) and human labour. Additionally, systems can employ predictive maintenance on pipes that are liable to spring leaks, to minimise the direct release of GHGs such as HFCs and natural gas. Thus, it has huge potential for carbon savings.

Research has shown that underpinning the scheduling of maintenance activities on predictive maintenance and maintenance time estimation can produce an optimal maintenance scheduling (Yeardley, Ejeh, Allen, Brown, & Cordiner, 2021). The work optimised the scheduling by minimising costs based on plant layout, downtime, and labour constraints. However, scheduling can also be planned by optimising the schedule concerning carbon emissions. In this situation, maintenance activities can be performed so that fewer journeys are made and GHG emissions are saved.

The internet of things (IoT) is the digital industrial control system, a network of physical objects that are connected over the internet by sensors, software and other technologies that exchange data with each thing. In time, the implementation of the IoT will be worldwide and every single production process and supply chain will be available as a virtual image.

Open access to a worldwide implementation of the IoT has the potential to provide a truly circular economy. Product designers can use the information available from the IoT and create value from other peoples waste. Theoretically, we could establish a work where manufacturing processes are all linked so that there is zero extracted raw materials, zero waste disposed and net-zero emissions.

Currently, the world has developed manufacturing processes one at a time, not interconnected value chains across industries. It may be a long time until the IoT creates the worldwide virtual image required, but once it has the technology is powerful enough to address losses from each process and exchange material between connected companies. Both materials and energy consumption can be shared to lower CO2 emissions drastically. It may take decades, but the IoT provides the technology to create a circular economy.

ConclusionAI has enormous potential to benefit the environment and drive the world to net-zero. The current portfolio of research being conducted at the Alan Turning Institute (UKs national centre for data science) includes projects that explore how machine learning can be part of the solution to climate change. For example, an electricity control room algorithm is being developed to provide decision support and ensure energy security for a decarbonised system. The national grids electricity planning is improved by forecasting the electricity demand and optimising the schedule. Further, Industry 4.0 can plan for the impact that global warming and decarbonisation strategies have on our lives.

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Artificial intelligence drives the way to net-zero emissions - Sustainability Magazine

Googles I/O Conference Offers Modest Vision of the Future – The New York Times

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SAN FRANCISCO There was a time when Google offered a wondrous vision of the future, with driverless cars, augmented-reality eyewear, unlimited storage of emails and photos, and predictive texts to complete sentences in progress.

A more modest Google was on display on Wednesday as the company kicked off its annual developers conference. The Google of 2022 is more pragmatic and sensible a bit more like its business-focused competitors at Microsoft than a fantasy play land for tech enthusiasts.

And that, by all appearances, is by design. The bold vision is still out there but its a ways away. The professional executives who now run Google are increasingly focused on wringing money out of those years of spending on research and development.

The companys biggest bet in artificial intelligence does not, at least for now, mean science fiction come to life. It means more subtle changes to existing products.

A.I. is improving our products, making them more helpful, more accessible, and delivering innovative new features for everyone, Sundar Pichai, Googles chief executive, said on Wednesday.

In a presentation short of wow moments, Google stressed that its products were helpful. In fact, Google executives used the words help, helping, or helpful more than 50 times during two hours of keynote speeches, including a marketing campaign for its new hardware products with the line: When it comes to helping, we cant help but help.

It introduced a cheaper version of its Pixel smartphone, a smartwatch with a round screen and a new tablet coming next year. (The most helpful tablet in the world.)

The biggest applause came from a new Google Docs feature in which the companys artificial-intelligence algorithms automatically summarize a long document into a single paragraph.

At the same time, it was not immediately clear how some of the other groundbreaking work, like language models that better understand natural conversation or that can break down a task into logical smaller steps, will ultimately lead to the next generation of computing that Google has touted.

Certainly some of the new ideas do appear helpful. In one demonstration about how Google continues to improve its search technology, the company showed a feature called multisearch, in which a user can snap a photo of a shelf full of chocolates and then find the best-reviewed dark chocolate bar without nuts from the picture.

In another example, Google showed how you can find a picture of a specific dish, like Korean stir-fried noodles, and then search for nearby restaurants serving that dish.

Much of those capabilities are powered by the deep technological work Google has done for years using so-called machine learning, image recognition and natural language understanding. Its a sign of an evolution rather than revolution for Google and other large tech giants.

Many companies can build digital services easier and faster than in the past because of shared technologies such as cloud computing and storage, but building the underlying infrastructure such as artificial intelligence language models is so costly and time-consuming that only the richest companies can invest in them.

As is often the case at Google events, the company didnt spend a little of time explaining how it makes money. Google brought up the topic of advertising which still accounts for 80 percent of the companys revenue after an hour of other announcements, highlighting a new feature called My Ad Center. It will allow users to request fewer ads from certain brands or to highlight topics they would like to see more ads about.

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Googles I/O Conference Offers Modest Vision of the Future - The New York Times

Predicting Others Behavior on the Road With Artificial Intelligence – SciTechDaily

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Researchers have created a machine-learning system that efficiently predicts the future trajectories of multiple road users, like drivers, cyclists, and pedestrians, which could enable an autonomous vehicle to more safely navigate city streets. If a robot is going to navigate a vehicle safely through downtown Boston, it must be able to predict what nearby drivers, cyclists, and pedestrians are going to do next. Credit: MIT

A new machine-learning system may someday help driverless cars predict the next moves of nearby drivers, pedestrians, and cyclists in real-time.

Humans may be one of the biggest roadblocks to fully autonomous vehicles operating on city streets.

If a robot is going to navigate a vehicle safely through downtown Boston, it must be able to predict what nearby drivers, pedestrians, and cyclists are going to do next.

Behavior prediction is a tough problem, however, and current artificial intelligence solutions are either too simplistic (they may assume pedestrians always walk in a straight line), too conservative (to avoid pedestrians, the robot just leaves the car in park), or can only forecast the next moves of one agent (roads typically carry many users at once.)

MIT researchers have devised a deceptively simple solution to this complicated challenge. They break a multiagent behavior prediction problem into smaller pieces and tackle each one individually, so a computer can solve this complex task in real-time.

These simulations show how the system the researchers developed can predict the future trajectories (shown using red lines) of the blue vehicles in complex traffic situations involving other cars, bicyclists, and pedestrians. Credit: MIT

Their behavior-prediction framework first guesses the relationships between two road users which car, cyclist, or pedestrian has the right of way, and which agent will yield and uses those relationships to predict future trajectories for multiple agents.

These estimated trajectories were more accurate than those from other machine-learning models, compared to real traffic flow in an enormous dataset compiled by autonomous driving company Waymo. The MIT technique even outperformed Waymos recently published model. And because the researchers broke the problem into simpler pieces, their technique used less memory.

This is a very intuitive idea, but no one has fully explored it before, and it works quite well. The simplicity is definitely a plus. We are comparing our model with other state-of-the-art models in the field, including the one from Waymo, the leading company in this area, and our model achieves top performance on this challenging benchmark. This has a lot of potential for the future, says co-lead author Xin Cyrus Huang, a graduate student in the Department of Aeronautics and Astronautics and a research assistant in the lab of Brian Williams, professor of aeronautics and astronautics and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL).

Joining Huang and Williams on the paper are three researchers from Tsinghua University in China: co-lead author Qiao Sun, a research assistant; Junru Gu, a graduate student; and senior author Hang Zhao PhD 19, an assistant professor. The research will be presented at the Conference on Computer Vision and Pattern Recognition.

The researchers machine-learning method, called M2I, takes two inputs: past trajectories of the cars, cyclists, and pedestrians interacting in a traffic setting such as a four-way intersection, and a map with street locations, lane configurations, etc.

Using this information, a relation predictor infers which of two agents has the right of way first, classifying one as a passer and one as a yielder. Then a prediction model, known as a marginal predictor, guesses the trajectory for the passing agent, since this agent behaves independently.

A second prediction model, known as a conditional predictor, then guesses what the yielding agent will do based on the actions of the passing agent. The system predicts a number of different trajectories for the yielder and passer, computes the probability of each one individually, and then selects the six joint results with the highest likelihood of occurring.

M2I outputs a prediction of how these agents will move through traffic for the next eight seconds. In one example, their method caused a vehicle to slow down so a pedestrian could cross the street, then speed up when they cleared the intersection. In another example, the vehicle waited until several cars had passed before turning from a side street onto a busy, main road.

While this initial research focuses on interactions between two agents, M2I could infer relationships among many agents and then guess their trajectories by linking multiple marginal and conditional predictors.

The researchers trained the models using the Waymo Open Motion Dataset, which contains millions of real traffic scenes involving vehicles, pedestrians, and cyclists recorded by lidar (light detection and ranging) sensors and cameras mounted on the companys autonomous vehicles. They focused specifically on cases with multiple agents.

To determine accuracy, they compared each methods six prediction samples, weighted by their confidence levels, to the actual trajectories followed by the cars, cyclists, and pedestrians in a scene. Their method was the most accurate. It also outperformed the baseline models on a metric known as overlap rate; if two trajectories overlap, that indicates a collision. M2I had the lowest overlap rate.

Rather than just building a more complex model to solve this problem, we took an approach that is more like how a human thinks when they reason about interactions with others. A human does not reason about all hundreds of combinations of future behaviors. We make decisions quite fast, Huang says.

Another advantage of M2I is that, because it breaks the problem down into smaller pieces, it is easier for a user to understand the models decision-making. In the long run, that could help users put more trust in autonomous vehicles, says Huang.

But the framework cant account for cases where two agents are mutually influencing each other, like when two vehicles each nudge forward at a four-way stop because the drivers arent sure who should be yielding.

They plan to address this limitation in future work. They also want to use their method to simulate realistic interactions between road users, which could be used to verify planning algorithms for self-driving cars or create huge amounts of synthetic driving data to improve model performance.

Predicting future trajectories of multiple, interacting agents is under-explored and extremely challenging for enabling full autonomy in complex scenes. M2I provides a highly promising prediction method with the relation predictor to discriminate agents predicted marginally or conditionally which significantly simplifies the problem, wrote Masayoshi Tomizuka, the Cheryl and John Neerhout, Jr. Distinguished Professor of Mechanical Engineering at University of California at Berkeley and Wei Zhan, an assistant professional researcher, in an email. The prediction model can capture the inherent relation and interactions of the agents to achieve the state-of-the-art performance. The two colleagues were not involved in the research.

Reference: M2I: From Factored Marginal Trajectory Prediction to Interactive Prediction by Qiao Sun, Xin Huang, Junru Gu, Brian C. Williams and Hang Zhao. 28 March 2022, Computer Science > Robotics.arXiv:2202.11884

This research is supported, in part, by the Qualcomm Innovation Fellowship. Toyota Research Institute also provided funds to support this work.

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Predicting Others Behavior on the Road With Artificial Intelligence - SciTechDaily

The 1997 chess game that thrust AI into the spotlight – ETTelecom

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Paris: With his hand pushed firmly into his cheek and his eyes fixed on the table, Garry Kasparov shot a final dark glance at the chessboard before storming out of the room: the king of chess had just been beaten by a computer.

May 11, 1997 was a watershed for the relationship between man and machine, when the artificial intelligence (AI) supercomputer Deep Blue finally achieved what developers had been promising for decades.

It was an "incredible" moment, AI expert Philippe Rolet told AFP, even if the enduring technological impact was not so huge.

Developers at IBM, the US firm that made Deep Blue, were ecstatic with the victory but quickly refocused on the wider significance.

"This is not about man versus machine. This is really about how we, humans, use technology to solve difficult problems," said Deep Blue team chief Chung-Jen Tan after the match, listing possible benefits from financial analysis to weather forecasting.

Even Chung would have struggled to comprehend how central AI has now become -- finding applications in almost every field of human existence.

"AI has exploded over the last 10 years or so," UCLA computer science professor Richard Korf told AFP.

"We're now doing things that used to be impossible."

'One man cracked'

After his defeat, Kasparov, who is still widely regarded as the greatest chess player of all time, was furious.

He hinted there had been unfair practices, denied he had really lost and concluded that nothing at all had been proved about the power of computers.

He explained that the match could be seen as "one man, the best player in the world, (who) has cracked under pressure".

The computer was beatable, he argued, because it had too many weak points.

AI-powered machines have mastered every game going and now have much bigger worlds to conquer.

Korf cites notable advances in facial recognition that have helped make self-driving cars a reality.

Yann LeCun, head of AI research at Meta/Facebook, told AFP there had been "absolutely incredible progress" in recent years.

LeCun, one of the founding fathers of modern AI, lists among the achievements of today's computers an ability "to translate any language into any language in a set of 200 languages" or "to have a single neural network that understands 100 languages".

It is a far cry from 1997, when Facebook didn't even exist.

Machines 'not the danger'

Experts agree that the Kasparov match was important as a symbol but left little in the way of a technical legacy.

"There was nothing revolutionary in the design of Deep Blue," said Korf, describing it as an evolution of methods that had been around since the 1950s.

"It was also a piece of dedicated hardware designed just to play chess."

Facebook, Google and other tech firms have pushed AI in all sorts of other directions.

They have fuelled increasingly powerful AI machines with unimaginable amounts of data from their users, serving up remorselessly targeted content and advertising and forging trillion-dollar companies in the process.

AI technology now helps to decide anything from the temperature of a room to the price of vehicle insurance.

Devices from vacuum cleaners to doorbells come with arrays of sensors to furnish AI systems with data to better target consumers.

While critics bemoan a loss of privacy, enthusiasts believe AI products just make everyone's lives easier.

Despite his painful history with machines, Kasparov is largely unfazed by AI's increasingly dominant position.

"There is simply no evidence that machines are threatening us," he told AFP last year.

"The real danger comes not from killer robots but from people -- because people still have a monopoly on evil."

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The 1997 chess game that thrust AI into the spotlight - ETTelecom

National Technology Day: How artificial intelligence is helping MSMEs to optimize processes, accelerate growth – The Financial Express

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Technology for MSME: The shift towards more efficient technology solutions from the good old websites and emails, in the name of digital adoption, is apparent among MSMEs that have shied away from evolving technologies for a long time. The shift has largely been visible because of better affordability due to growing on-demand, or pay as you go or what is called the software-as-a-service (SaaS) ecosystem in India liberating small businesses from the cost conundrum to some extent.

As India observes the National Technology Day on Wednesday to commemorate its entry into the elite club of countries having nuclear weapons with the Pokhran nuclear tests in 1998, it is also the day to remember the countrys achievement in science and innovation. While a large number of MSMEs are yet to fully benefit from the technology revolution, some of them have certainly been warming up to the new age solutions such as artificial intelligence (AI) and using it also for better growth.

The implementation of AI is across multiple use cases. For instance, Delhi-based long-haul logistics services provider JCCI Logistics has deployed AI and internet of things (IoT) solutions to manage its fleet of around 150 trucks. The company, launched in 2004, uses on-demand fleet management software for GPS tracking of vehicles, fuel management, driver analytics, and route planning.

Vehicles need to run as much as possible and thats what matters. Before deploying this solution in 2020, our monthly cumulative running was around 8,000 to 10,000 kilometres. It has increased by around 20 per cent now. The jump, I think, is primarily because of the on-board diagnostics (OBD) device that you can fit in a vehicle to get data related to fuel consumption, drivers driving behaviour, whether there is unnecessary hard acceleration or not, etc., Sachin Jain, Founder, JCCI Logistics told Financial Express Online.

OBD is essentially a machine learning (ML) and internet of things (IoT) based device that gets signals from different sensors in a vehicle and conveys them to the users dashboard with the help of the software.

JCCI Logistics have been among post-Covid adopters of deep technology solutions as the pandemic perhaps necessitated the use of software and digital for sustenance.

Covid might have caused a faster switch to some AI/ML applications since the labor force was locked up. AI/ML provides a significant opportunity for reduction in input costs, particularly those of human capital. The advent of edge AI/ML will further hasten adoption, particularly as it gets married to IoT on small devices and sensors that are available at scale and used routinely by businesses of all sizes, Utkarsh Sinha, Managing Director at advisory firm Bexley Advisors told Financial Express Online.

Among the top sectors where the use of AI accelerated during the pandemic was restaurant as the pandemic precipitated the eateries into looking at ways that could help them optimize their processes right from sales to inventory management and more.

Kabir Suri who runs Azure Hospitality, which owns restaurant chains like Mamagoto, Dhaba, Speedy Chow, etc., has been using AI in the companys operations for the past five years while Covid only reinforced his commitment to AI for efficiency and growth. We have had a direct saving of 30 per cent in past five years along with getting customer insights due to AI that has led to an uptake in revenue as well. Five years back we had around 10 outlets and now have 60 across India, Suri told Financial Express Online.

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The company has an in-house AI solution that shows live sales, total transactions, menus, items sold, total consumption per restaurant, etc. The solution captures data from every restaurant throughout the day on a real time basis and consolidates it to show up for analysis on its dashboard. This becomes important for restaurants with chains to understand the consumer-behaviour pattern, the impact of different occasions on business like festivals such as Navratras in North particularly, Christmas in Goa, and some other festivals in South, said Suri.

Moreover, the AI solution at Azure Hospitality helps Suri control the HR module as well. You can look at your salary component, leaves, attendance, holidays, payslips, etc., through a single system every day whenever you want. Basically, AI helps you make better decisions as you grow bigger by minimizing the impact of any uncertainty, Suri added.

Another sector that depends heavily on technology and AI particularly is tourism for purposes ranging from travel booking via chatbots, flight forecasting in terms of the current best price and future prices, recommendations for hotel and cab booking based on travel-related searches, etc.

There is AI at every stage in tourism and aviation, Subhas Goyal, Founder and Chairman at B2B travel company STIC Travel told Financial Express Online. The company is the exclusive General Sales Agent (GSA) a sales representative of a company in a specific region or country for 11 international airlines in India including United Airlines, Air China, Croatia Airlines etc.

STIC has been using for the past five years AI-based Microsoft Dynamics CRM to manage customer relationships, track sales leads, marketing, etc., and streamline administrative processes in sales and marketing. The company is now also implementing a chatbot assistant to answer customer queries on its platform. Goyal noted the standard queries around bookings, holiday searches can be answered by the AI bot while for further details and feedback, there would be manual intervention.

Post-Covid, more MSMEs had started to use primary technology tools at least such as social media, online service aggregators, company websites etc. According to a Crisil survey of around 540 micro and small units released in April this year, over 65 per cent respondents adopted or upgraded their use of online aggregators, social media platforms, and company websites. Among sectors, manufacturing reported higher adoption with 71 per cent respondents adopting or upgrading their use of digital platforms in comparison to 66 per cent respondents in the services sector.

Good technology is invisible. AI/ML will soon form a fundamental layer in all operations and interactions for small businesses. As technology offerings scale, it will soon be easier to get good AI to do certain tasks than to get a human to do it. The impact of this on labor force utilization will be significant, added Sinha.

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National Technology Day: How artificial intelligence is helping MSMEs to optimize processes, accelerate growth - The Financial Express

AI, philosophy and religion: what machine learning can tell us about the Bhagavad Gita – The Conversation

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Machine learning and other artificial intelligence (AI) methods have had immense success with scientific and technical tasks such as predicting how protein molecules fold and recognising faces in a crowd. However, the application of these methods to the humanities are yet to be fully explored.

What can AI tell us about philosophy and religion, for example? As a starting point for such an exploration, we used deep learning AI methods to analyse English translations of the Bhagavad Gita, an ancient Hindu text written originally in Sanskrit.

Using a deep learning-based language model called BERT, we studied sentiment (emotions) and semantics (meanings) in the translations. Despite huge variations in vocabulary and sentence structure, we found that the patterns of emotion and meaning were broadly similar in all three.

This research opens a path to the use of AI-based technologies for comparing translations and reviewing sentiments in a wide range of texts.

The Bhagavad Gita is one of the central Hindu sacred and philosophical texts. Written more than 2,000 years ago, it has been translated into more than 100 languages and has been of interest to western philosophers since the 18th century.

The 700-verse poem is a part of the larger Mahabharata epic, which recounts the events of an ancient war believed to have occurred at Kurukshetra near modern-day Delhi in India.

Read more: Indian philosophy helps us see clearly, act wisely in an interconnected world

The text of the Bhagavad Gita relates a conversation between the Hindu deity Lord Krishna and a prince called Arjuna. They discuss whether a soldier should go to war for ethics and duty (or dharma) if they have close friends or family on the opposing side.

The text has been instrumental in laying the foundations of Hinduism. Among many other things, it is where the philosophy of karma (a spiritual principle of cause and effect) originates.

Scholars have also regarded the Bhagavad Gita as a book of psychology, management, leadership and conflict resolution.

There have been countless English translations of the Bhagavad Gita, but there is not much work that validates their quality. Translations of songs and poems not only break rhythm and rhyming patterns, but can also result in the loss of semantic information.

In our research, we used deep learning language models to analyse three selected translations of the Bhagavad Gita (from Sanskrit to English) with semantic and sentiment analyses which help in the evaluation of translation quality.

We used a pre-trained language model known as BERT, developed by Google. We further tuned the model using a human-labelled training dataset based on Twitter posts, which captures 10 different sentiments.

These sentiments (optimistic, thankful, empathetic, pessimistic, anxious, sad, annoyed, denial, surprise, and joking) were adopted from our previous research into social media sentiment during the onset of the COVID-19 pandemic.

The three translations we studied used very different vocabulary and syntax, but the language model recognised similar sentiments in the different chapters of the respective translations. According to our model, optimistic, annoyed and surprised sentiments are the most expressed.

Moreover, the model showed how the overall sentiment polarity changes (from negative to positive) over the course of the conversation between Arjuna and Lord Krishna.

Arjuna is pessimistic towards the beginning and becomes optimistic as Lord Krisha imparts knowledge of Hindu philosophy to him. The sentiments expressed by Krishna show that with philosophical knowledge of dharma and mentorship, a troubled mind can get clarity for making the right decisions in times of conflict.

One limitation of our model is that it was trained on data from Twitter, so it recognises joking as a common sentiment. It applies this label inappropriately to some parts of the Bhagavad Gita. Humour is complicated and strongly culturally constrained, and understanding it is too much to ask of our model at this stage.

Due to the nature of the Sanskrit language, the fact that the Bhagavad Gita is a song with rhythm and rhyme, and the varied dates of the translations, different translators used different vocabulary to describe the same concepts.

The table below shows some of the most semantically similar verses from the three translations.

Our research points the way to the use of AI-based technologies for comparing translations and reviewing sentiments in a wide range of texts.

This technology can also be extended to review sentiments expressed in entertainment media. Another potential application is analysing movies and songs to provide insights to parents and authorities about the suitability of content for children.

The author would like to acknowledge the invaluable contribution of Venkatesh Kulkarni to this research.

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AI, philosophy and religion: what machine learning can tell us about the Bhagavad Gita - The Conversation

Artificial Intelligence in Healthcare Market Global Analysis, Opportunities and Forecast to 2029 Queen Anne and Mangolia News – Queen Anne and…

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Artificial Intelligence in Healthcare market analysis report highlights the idea of high level analysis of major market segments and recognition of opportunities. Market analysis and market segmentation has been reviewed here in terms of markets, geographic scope, years considered for the study, currency and pricing, research methodology, primary interviews with key opinion leaders, DBMR market position grid, DBMR market challenge matrix, secondary sources, and assumptions. This market report carries out comprehensive analysis of company profiles of key market players that offers a competitive landscape. Artificial Intelligence in Healthcare market document exhibits important product developments and tracks recent acquisitions, mergers and research in the healthcare industry by the top market players.

The market study and analysis of the large scale Artificial Intelligence in Healthcare report lends a hand to figure out types of consumers, their views about the product, their buying intentions and their ideas for advancement of a product. To attain knowledge of all the above factors, this transparent, extensive and supreme market report is generated. And for the same, the report also describes all the major topics of the market research analysis that includes market definition, market segmentation, competitive analysis, major developments in the market, and excellent research methodology. This Artificial Intelligence in Healthcare market research report has been formed with the vigilant efforts of innovative, enthusiastic, knowledgeable and experienced team of analysts, researchers, industry experts, and forecasters.

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Leading Key Players Operating in the Artificial Intelligence in Healthcare Market Includes:NVIDIA Corporation (US), Intel Corporation (US), IBM (US), Google LLC (US), Microsoft (US), General Vision Inc. (US), Johnson & Johnson Services, Inc. (US), Siemens Healthcare GmbH (Germany), Medtronic (Ireland), CloudMedx Inc. (US)

Market Analysis and Insights: Global Artificial Intelligence in Healthcare Market:

This Artificial Intelligence in Healthcare market report provides details of new recent developments, trade regulations, import export analysis, production analysis, value chain optimization, market share, impact of domestic and localised market players, analyses opportunities in terms of emerging revenue pockets, changes in market regulations, strategic market growth analysis, market size, category market growths, application niches and dominance, product approvals, product launches, geographic expansions, technological innovations in the market. To gain more info on Data Bridge Market Research Artificial Intelligence in Healthcare market contact us for an Analyst Brief, our team will help you take an informed market decision to achieve market growth.

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Market Analysis and Insights: Global Artificial Intelligence in Healthcare Market:

This Artificial Intelligence in Healthcare market report provides details of new recent developments, trade regulations, import export analysis, production analysis, value chain optimization, market share, impact of domestic and localised market players, analyses opportunities in terms of emerging revenue pockets, changes in market regulations, strategic market growth analysis, market size, category market growths, application niches and dominance, product approvals, product launches, geographic expansions, technological innovations in the market. To gain more info on Data Bridge Market Research Artificial Intelligence in Healthcare market contact us for an Analyst Brief, our team will help you take an informed market decision to achieve market growth.

Artificial Intelligence in Healthcare Market, By Region:

Global Artificial Intelligence in Healthcare market is analyzed and market size insights and trends are provided by country, product as referenced above.

The countries covered in the Artificial Intelligence in Healthcare market report are the U.S., Canada and Mexico in North America, Germany, France, U.K., Netherlands, Switzerland, Belgium, Russia, Italy, Spain, Turkey, Rest of Europe in Europe, China, Japan, India, South Korea, Singapore, Malaysia, Australia, Thailand, Indonesia, Philippines, Rest of Asia-Pacific (APAC) in the Asia-Pacific (APAC), Saudi Arabia, U.A.E, South Africa, Egypt, Israel, Rest of Middle East and Africa (MEA) as a part of Middle East and Africa (MEA), Brazil, Argentina and Rest of South America as part of South America.

North America dominates the Artificial Intelligence in Healthcare market because of the rise in the cases of arrhythmic diseases, favorable reimbursement policies for patients, high demand for advanced treatment methods and developed healthcare infrastructure in the region. Asia-Pacific is estimated to grow in the forecast period due to the high prevalence of cardiovascular diseases, increase in adoption of advanced digital devices, large population and launch of new innovative products.

Table of Contents

Global Artificial Intelligence in Healthcare Market Size, status and Forecast to 2029

1 Market summary2 Manufacturers Profile3 Global Artificial Intelligence in Healthcare Sales, Overall Revenue, Market Share and Competition by Manufacturer4 Global Artificial Intelligence in Healthcare market analysis by numerous Regions5 North America Artificial Intelligence in Healthcare by Countries6 Europe Artificial Intelligence in Healthcare by Countries7 Asia-Pacific Artificial Intelligence in Healthcare by Countries8 South America Artificial Intelligence in Healthcare by Countries9 Middle east and Africas Artificial Intelligence in Healthcare by Countries10 Global Artificial Intelligence in Healthcare Market phase by varieties11 Global Artificial Intelligence in Healthcare Market phase by Applications12 Artificial Intelligence in Healthcare Market Forecast13 Sales Channel, Distributors, Traders and Dealers14 Analysis Findings and Conclusion15 Appendix

Check Complete Table of Contents with List of Table and Figures @ https://www.databridgemarketresearch.com/toc/?dbmr=global-artificial-intelligence-in-healthcare-market&AZ

What are the market opportunities, market risks, and market overviews of the Artificial Intelligence in Healthcare Market?

Who are the distributors, traders, and merchants in the Artificial Intelligence in Healthcare Market?

What is the analysis of sales, income, and prices of the leading manufacturers in the Artificial Intelligence in Healthcare Market?

What are the Artificial Intelligence in Healthcare market opportunities and threats faced by the global Artificial Intelligence in Healthcare Market vendors?

What are the main factors driving the worldwide Artificial Intelligence in Healthcare Industry?

What are the Top Players in Artificial Intelligence in Healthcare industry?

What is the analysis of sales, income, and prices by type, application of the Artificial Intelligence in Healthcare market?

What is regional sales, income, and price analysis for Artificial Intelligence in Healthcare Market?

Research Methodology: GlobalLiquid Chromatography Devices Market

Data collection and base year analysis is done using data collection modules with large sample sizes. The market data is analyzed and estimated using market statistical and coherent models. Also market share analysis and key trend analysis are the major success factors in the market report. To know more please request an analyst call or can drop down your enquiry.

The key research methodology used by DBMR research team is data triangulation which involves data mining, analysis of the impact of data variables on the market, and primary (industry expert) validation. Apart from this, data models include Vendor Positioning Grid, Market Time Line Analysis, Market Overview and Guide, Company Positioning Grid, Company Market Share Analysis, Standards of Measurement, Global versus Regional and Vendor Share Analysis.

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Data Bridge Market Researchhas presented itself as an unconventional, neoteric market research and consulting company with an unprecedented level of resilience and integrated approaches.We are committed to finding the best market opportunities and promoting effective information for your business to thrive in the market.Data Bridge Market Research provides appropriate solutions to complex business challenges and initiates an effortless decision-making process.

Data Bridge strives to create satisfied customers who rely on our services and rely on our hard work with certainty.Getpersonalizationanddiscounton the report by emailingsopan.gedam@databridgemarketresearch.com.Were happy with our glorious 99.9% customer satisfaction rating.

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Artificial Intelligence in Healthcare Market Global Analysis, Opportunities and Forecast to 2029 Queen Anne and Mangolia News - Queen Anne and...

Artificial Intelligence in Medical Imaging Market In Depth Analysis Along with Statistics and Forecast 2022-2029 Queen Anne and Mangolia News – Queen…

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Artificial Intelligence in Medical Imaging Market gives a detailed study of the industry, replete with a spec sheet, market segmentation, and other information, in Data Bridge Market Research. The study analyses the potential and present market scenario for the projected period of 2022-2028, offering data and updates on the worldwide Artificial Intelligence in Medical Imaging markets major segments. An influential Artificial Intelligence in Medical Imaging market report is a window to the industry which explains what market definition, classifications, applications, engagements and market trends are. By keeping end users at the centre point, a team of researchers, forecasters, analysts and industry experts work exhaustively to formulate this market research report. The market is supposed to grow during the forecast period due to growing demand at the end user level. According to this market report, new highs will take place in the Artificial Intelligence in Medical Imaging market. Artificial Intelligence in Medical Imaging market survey report offers the best professional in-depth study on the current state for the industry.

Artificial intelligence in medical imaging market is expected to gain market growth in the forecast period of 2021 to 2028. Data Bridge Market Research analyses the market to reach at an estimated value of USD 1,579.33 million and grow at a CAGR of 4.11% in the above-mentioned forecast period. Increased numbers of diagnostic procedures drives the artificial intelligence in medical imaging market.

Download Exclusive Sample (350 Pages PDF) Report: https://www.databridgemarketresearch.com/request-a-sample/?dbmr=global-artificial-intelligence-in-medical-imaging-market&AS

Top Key Players Included in This Report:

BenevolentAI, OrCam, Babylon, Freenome Inc., Clarify Health Solutions, BioXcel Therapeutics, Ada Health GmbH, GNS Healthcare, Zebra Medical Vision Inc., Qventus Inc, IDx Technologies Inc., K Health, Prognos, Medopad Ltd., Viz.ai Inc., Voxel Technology, Renalytix AI plc, Beijing Pushing Technology Co. Ltd., PAIGE, mPulse Mobile, Suki AI Inc., BERG LLC, Zealth Inc., OWKIN INC., and Your.MD Ltd. UK

The Study Is Segmented By Following:

By Technology (Deep Learning, Computer Vision, NLP, Others), Offering (Hardware, Software, Services), Deployment Type (On-Premise, Cloud), Application (X-Ray, CT, MRI, Ultrasound, Molecular Imaging), Clinical Applications (Breast, Lung, Neurology, Cardiovascular, Liver, Prostate, Colon, Musculoskeletal, Others), End-User (Hospitals, Clinics, Research Laboratories, Others)

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Geographic Segment Covered in the Report:

The Artificial Intelligence in Medical Imaging report provides information on the market area, which is divided into sub-regions and countries / regions. In addition to the market share in each country and sub-region, this chapter in this report also contains information on profit opportunities. This chapter of the report mentions the market share and growth rate for each region, country and sub-region during the estimated period.

The study has covered and anatomized the eventuality of Worldwide Artificial Intelligence in Medical Imaging Industry and provides statistics and information on market dynamics, growth factors, crucial challenges, major motorists & conditions, openings and Forecast 2028. Businesses can confidently rely on the information mentioned in the reliable Artificial Intelligence in Medical Imaging Market analysis report as it is derived only from the valuable and genuine resources. This market research report delivers comprehensive analysis of the market structure along with the estimations of the various segments and sub-segments of the market. The transformation in market landscape is analyzed in the finest Artificial Intelligence in Medical Imaging marketing report which is mainly observed due to the moves of key players or brands which include developments, product launches, joint ventures, mergers and acquisitions that in turn change the view of the global face of the industry.

The Report Answers Questions Similar as:

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What are the inhibiting factors and impact of COVID-19 shaping the Artificial Intelligence in Medical Imaging Market during the cast period?

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Artificial Intelligence in Medical Imaging Market In Depth Analysis Along with Statistics and Forecast 2022-2029 Queen Anne and Mangolia News - Queen...

5 Things All Beginners Should Know When Playing French Roulette – TheNationRoar

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Roulette is one of the most famous games in the gambling world, and even if you dont necessarily go to casinos, youve probably played it at least once in your life. It is a fun activity where you rely on luck, but you also need to put some thought into creating the right strategy that could lead you to maximize your winnings. If you love this game, and if you are interested in learning the different types of it, youve come to the right place. In this article, we are going to list some of the things that all beginners should know when playing French roulette.

The first thing that you need to know about this game is that is preferred by users all over the world because it is said that it is one of the most profitable types that you can play. When we see the odds that you have to be victorious, you have the least chance of getting the numbers right when you play the American version. The European type is in between the two, and when it comes to the French variant, you have the highest odds of getting the numbers correct and getting the winnings.

There are two rules that will help you achieve what you wanted and they are En Prison and La Partage. Keep on reading to find out how they can help you keep the cash youve invested and get much more profits than youve hoped you would.

Now lets see why the rules we mentioned earlier are going to help you be victorious. With the La Partage rule, if you make an outside bet, and the ball lands on the zero, you are able to get half of your bet back. This rule has helped a lot of players to get some of their investment back, and instead of losing it all and betting again, they can just choose to change their strategy and try again.

With the second rule, En Prison, once something like this happens, you make an outside bet, and the ball lands on the zero-field, you are presented with two options. You can choose to just take half of your investment back and change your strategy, or you can refuse this and make the same wager once again. This helps players who strongly believe that their strategy will work, and they dont want to change anything about the wager that they have made.

Keep in mind that these are not the only rules that are available for players and that differ from the standard ones that we already know, and they are available only when you enjoy this type of roulette. Check the advanced strategy paragraph to find out what they are.

Many players think that this game is much more complicated than anything they have explored before, and they want to steer away from giving it a chance. The main thing that you should know is that it is not much different than the famous European type.

There are 37 digits that you can put your wager on, including zero. The house edge is pretty low, and that is why it is extremely friendly play for beginners, and you wont have to invest too much and lose your cash to the house. The difference in this play is that there are a lot of strategies that you can explore to make sure that you get the best out of it.

Note that if you want to explore this game, you need to look for a site, like UFABET, that will let you play this version, with the special rules and strategies that you can use to your advantage.

Keep in mind that this play is made to help the player be victorious, and because of that, not all casinos offer the game. If you are new to the gambling world, and if you are not sure how to make your strategy, and which game you should start with, then you should definitely explore the French roulette.

When looking for the right place to make your wagers, you should always start by practicing and you should do your research and see if you can get accustomed to it. Dont put your real money in right away, and test out different strategies.

Know that not every casino, no matter if it is online or a land-based one will offer this type, so you should look for a place where you can play this game with its original rules.

Now lets finish by explaining what is available in this type of roulette that you cannot experience in any other type of game.

There are a lot of advanced wagers that you can place, and the most popular one is Neighbors of Zero. Here you will wager on the digits on the wheel that are between 22 and 25. You will need to place 9 chips that are going to be spread in the same portion. The Thirds of Wheel is another popular strategy that players opt for, and here you will wager on all the digits that are placed on the other side of the zero. You will need to play with 6 chips and they are spread between 27 and 33. The Zero Game is available for players who want to make a bet on that number along with the other six digits that are placed on either side of the lowest number. Finally, the Orphans game is for those who dont want to take their chances with the other strategies that we mentioned until now, and they want to spread their chips on all the digits that are not included in some of the other strategies. There are eight numbers that are left, so you can explore different options and see which one you want to follow to maximize your odds of getting the cash.

These are some of the things that you should know about the game, and keep in mind that you need to sit down and play it to understand it better. Do your research, read as much as you can about all of the strategies you can choose, and see if this type is the right one for you.

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5 Things All Beginners Should Know When Playing French Roulette - TheNationRoar

The Covid Roulette: The Hard Truth And Lessons To Be Learned About The WHCA Dinner – The Pavlovic Today

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After learning what went down at the Gridiron Club dinner superspreader event, renowned environmental and occupational health physician Dr. Donald Milton, a pioneer of the modern science of airborne transmission of respiratory viruses, who had advised the White House since the beginning of the pandemic, approached the White House Correspondents Association with a proposal to install UV lights at WHCA dinner for no charge to help prevent another superspreader event from happening.

We thought it would be nice to try and prevent that [Gridiron superspreading experience] especially given that some high profile attendees like president Joe Biden would be at the dinner, said Dr. Milton.

On August 6, 2020, world-renowned scientist Dr. Kimberly Prather, elected into the National Academy of Engineering (2019) and the National Academy of Sciences (2020), was the first to break the news to Dr. Anthony Fauci that particles containing SARS-CoV-2, the virus that causes COVID-19, are airborne.

Oh, my God, Dr. Anthony Fauci said to her. You mean it goes further than six feet? Wow, why did we get it wrong for so long?Dr. Fauci was shocked, Dr. Kimberly Prather warmly recalled their conversation.For one hundred years, the belief in the scientific community has been that a droplet falls within six feet. I convinced him that aerosols up to 100 microns can go further than six feet. That whole belief system that things dont go further than six feet was completely wrong. I showed Fauci how far things go. Its much further.

Dr. Prather is also the one who has been pushing the N95 masks in the White House. Ive also been pushing for filtration in the White House, she said.

Given that the risk of transmission of the airborne virus is much higher in inadequately ventilated and crowded indoor settings, Dr. Kimberly Prather believed that the WHCA dinner should be organized in a safe way and that it was a good idea that Dr. Miltons offered to install the UV technology at the WH Correspondents dinner.

It was a perfect place to do it. The highest risk places are indoors, low ventilation, lots of people, and no masks. At that point, when you take off the masks, theres gonna be a really high viral load in the air. Washington DC had really high case numbers, so the chances of people being there that were infected was very high even with testing. Putting UV light was the way to go.

Dr. Eric Feigl-Ding, an epidemiologist and Chief of COVID Task Force at the New England Complex Systems Institute and co-founder of the World Health Network was worried about WHCA dinner as well. In his mind, the Gridiron dinner had already proved itself to be a super spreader as an already much smaller event than WHCA dinner.

In a room of 2600 people, theres a 7.4% chance that nobody gets Covid-19.

Theres more COVID than a couple of weeks ago, WHCA dinner is so much bigger, and the density of people at this Correspondents Dinner is much higher. Altogether, its the worst of three different situations, much worse than Gridiron in every single way.

Doing the math calculation on the spot, Dr. Feigl-Ding quickly came up with the risk level of getting COVID at the WHCA indoor event, hosting 2600 people in the basement of the Washington Hilton.Lets say, hypothetically, theres only a 0.1% chance someone might carry the virus and 99.9% would not. But then, in a superspreading environment where you have 2600 people, that number [99.9% chance of not getting Covid-19] falls very fast. said Dr. Eric Feigl-Ding. The math was straightforward: in a room of 2600 people,Theres a 7.4% chance that nobody has it.

The sheer number of people at the WHCA dinner, including all the catering staff indicated the high-risk environment for covid transmission. If one person has it [Covid-19] in a super spreading environment with poor ventilation, then anyone who isnt very recently boosted or hasnt had COVID just two weeks ago would have a high chance of getting it, Dr. Eric Feigl-Ding explained. At the WHCA dinner, 7.4 % chance that nobody gets it is a serious problem. And no one wants to admit this.

UV light technology can cut down transmission by 80% and is a very standard method by now.

Health scientists were clear-eyed that something had to be done about WHCA dinner covid safety. Ventilation and filtration, according to Dr. Milton, were not enough to produce the required changes in an hour in a room of 2600 people. One of the advantages of UV light technology, according to Dr. Milton is that you know we have this infection happening by invisible particles floating in the air and we have this invisible light that is killing them.

In terms of its impact on transmission, UV light technology can cut down transmission by 80% and is a very standard method by now, explained Dr. Donald Milton. Germicidal ultraviolet light has been around since the late 1930s as a means of preventing airborne infection and has been proven effective in preventing measles transmission in schools.

The old technology required that the UV light be restricted to the upper part of the room, because they can hurt eyes; it doesnt cause permanent damage, but its painful.

The newer technology, however, doesnt penetrate the eye and it does not cause eye injury. It also doesnt cause skin cancer. You can just shine it directly into the room without having to worry about air movement so much, said Milton.

Ultraviolet light is invisible, it doesnt change the colors of what you see.

Its super spreading events that have been critical to keeping this pandemic going.

The way we can stop super spreading events, is by doing something that can remove enough virus from the air fast enough that people dont infect a lot of other people. Ventilation alone just cant do that, Dr.Milton explained.

After weeks of Dr.Miltons attempts to convince the organizers to take him up on his offer to install UV lights quickly and free of charge, the WHCA rejected the proposal.

According to Dr. Milton, dinner organizers were worried about eye irritation as well as whether the UV lights will get in the way. Because, we initially were proposing that we use them on tripods and they suggested that maybe doing that would make the President look blue on TV. But this concern was ill-founded. Ultraviolet light is invisible, it doesnt change the colors of what you see, explained Dr. Milton.

We talked about putting them around the perimeter. And so they were mainly worried about them being in the way of people moving around and wait staff moving around, said Dr. Milton. That version, where the UV lights are mounted on tripods was actually implemented in the Pentagon because it was easier to set them up quickly and get them installed. Theyre used in the US Army headquarters.

Dr. Kimberly Prather said that UV light has to be installed professionally. Don Milton offered them a company that does it professionally and makes it work.That would have been perfect for that venue. Absolutely perfect.

UV light has to be installed professionally. Don Milton offered them a company that does it professionally and makes it work.

While Dr. Milton and his scientific team answered questions and explained the science behind the UV light technology, trying to offer reassurance, it is Dr.Miltons understanding that the WHCA is a small organization and did not have the bandwidth to deal with this as it was focused on trying very hard to implement their testing and vaccine requirements, which Gridiron dinner did not have.

The additional factor for the free of charge UV light installation proposal to go down the wire was all pre and after dinner parties hosted by the major TV networks. We were not going to be able to implement this UV technology for all those other side eventsI think it would have had marginal effects but only really protected people who were just at the dinner, said Dr. Milton. Obviously, Biden didnt go to any of them, he added.

The White House Correspondents Association claimed that Miltons offer came too late. Well, it was clearly too late for them to get comfortable with the idea, was Dr. Miltons reaction to their statement.

On April 27, 2022, the three days ahead of the WHCA dinner, the District of Columbia run by Mayor Muriel Bowser stopped daily reporting of Covid cases to CDC. The nations capital, the hub of business, politics and diplomacy was about to host the dinner. Was that a good decision in the face of a new pandemic? How can the scientists know theres an outbreak if DC is not reporting the numbers to CDC? The media elite was on their way to celebrate at the basement of the Washington Hilton hotel without UV light added and CO2 sensors placed on the tables to measure the air quality and safety. The 79-year-old President Biden was also in attendance.

You remember the Trump White House, they relied on single testing, rapid testing. Its like we did not learn a lesson.

The White House Correspondents Association said that their event implemented all protocols and went beyond any guidance or regulation issued by CDC, or the DC health department.

I think what hes saying is that requiring people to test before they come in and for those people who were in direct contact with Biden. By requiring that they have a medically supervised test shortly before the event is more than is required, recommended by the CDC, said Dr Milton.

Testing is good, but its not perfect, said Dr. Eric Feigl-Ding.You remember the Trump White House, they relied on single testing, rapid testing. Its like we did not learn a lesson, he shared his frustration. He added, In the Trump White House in the October 2020 outbreak, they relied on one rapid test. If youre negative, you get to meet the President. And theyre using the same logic here, at the WHCA dinner. If you have a negative test, you get to hang out with the President at the WH Correspondents dinner. We obviously have boosters now but also, the virus is much more infectious now than ever before. Altogether, its still very risky.

Putting people in a room where they are re-breathing over two thousand other peoples breath, in the high carbon dioxide (CO2) high environment, according to Dr. Eric Feigl-Ding was a risky situation.

Indoor CO2 concentrations do not provide an overall indication of indoor air quality, but they can be a useful tool in assessing ventilation rates ( i.e. outdoor air). In the absence of other mitigation measures such as filtration and UV lights, indoor CO2 concentrations could be used as a marker for risk of respiratory virus transmission. CO2 can actually calculate the re-breathing exactly, explained Dr. Feigl-Ding.

A volunteer from the Naltic Industrials came to the WHCA dinnerwith one CO2 sensor. At 8:40 PM the CO? levels were at 2063 parts per million (PPM). Those indicators, according to Dr. Eric Feigl-Ding, were for sure a sign of poor ventilation at the WHCA dinner.

The average was 1800 PPM, 1900 PPM. Then they had some higher peaks, but still pretty bad.

Even if the space was compliant, ASHRAE ventilation standard is a minimum standard and does not cover pandemic mode.

According to Dr. Marwa Zaatari, Voting member of ASHRAE Standard 62.1 and a member of the ASHRAE Epidemic Task Force The CO2 levels measured in the space are higher than levels expected if the space were to be compliant to ASHRAE Ventilation Standard. Even if the space was compliant, ASHRAE ventilation standard is a minimum standard and does not cover pandemic mode.

I think the public should be more aware, said Dr. Prather. I would have hoped, you know, for the White House Correspondents dinner, that Dr. Fauci potentially could have been involved. My dream would have been that they made that a statement. Not Okay, we are ready to get our lives back, rip off our masks and well all catch it. That should not have been the message. The message should have been: There is a way to have dinner in a safe way. And we could put CO2 sensors out. There are things we could have done, we could have cleaned the air, we could have put filters. I have written to the White House and said put these filtrations out, the Corsi-Rosenthal Box , or HEPAs, whatever you want to put. Put those around. I asked them to put them on Joe Bidens desk, she said.

There are ways that you can filter the virus from the air and have clean air and then people can remove their masks. Pushing for removal of masks, without cleaning indoor air is not okay. Thats not the order you should get it, Dr. Prather explained.

I have written to the White House and said put these filtrations out, the Corsi-Rosenthal Box, or HEPAs, whatever you want to put. Put those around. I asked them to put them on Joe Bidens desk.

Everybody wants to take their masks off. Everybody wants their lives back, but with clean indoor air, using the ways that you can do it. You filter out the air, you clean the air, you ventilate, you bring in the fresh air. There are so many ways to protect people. And, you know, its not out there. Its not out there in the way that it could or should be. And that is, you know, incredibly frustrating after, what, two and a half years.

The ultraviolet light is a technology where if you cant get the fresh air through the air you get fresh air equivalent, said Dr. Eric Feigl-Ding. If you cannot deliver fresh air, you must deliver at least disinfected air. And so between the two solutions, you should have to meet the ASHRAE equivalence guidelines, Dr. Eric Feigl-Ding explained.

While one of the arguments for partygoers was that the dinner was voluntary, some news network employees who spoke on condition of anonymity said that they felt peer pressure to attend.

But you know who didnt volunteer to be there?, said Dr. Eric Feigl-Ding. The waiters and waitresses.

According to the Hilton PR with whom The Pavlovic Today spoke, catering staff were required by WHCA to be masked, but further specification as to mask type, whether staff should wear a cloth mask, now deprecated by the CDC,or the superior N95, was absent. Most people in the room were unmasked. So that would make a minimal difference, said Dr. Eric Feigl-Ding.

While WHCA did focus on rigorous same-day testing and vaccine requirements, people still got infected with COVID. Secretary Blinken, VOAs Steve Herman, ABCs Jonathan Karl, and many others partygoers who did not come forward.

ABCs Jonathan Karl, went through same-day medically-supervised testing. Throughout the dinner he was sitting in the audience at the ABC table next to Kim Kardashian and Pete Davidson. Announced as the winner of Excellence in Presidential Coverage Under Deadline Pressure award, ABCs veteran reporter left the table and went up on the stage to receive the prize. Before addressing the audience from the podium, he shook hands with President Biden.

I dont even know that many reporters who went to WHCA dinner and I can already count enough for two hands.

On Monday, two days after the dinner, Jonathan Karl publicly announced he tested positive for COVID-19. What was the risk of infecting the President of the United States?

Jonathan Karl may not have given it to Biden, because Jonathan Karl may have gotten it from someone at his table, so he could have just been affected. So, if Jonathan Karl was just infected, he wouldnt be infectious to Biden immediately. So it doesnt imply that Biden is immediately infected, said Eric Feigl-Ding. But its so risky, Dr. Feigl-Ding explained, because hes close contact with someone. This room is very risky. Biden was more elevated on stage and separated from other people. But still, its too incredibly risky.

George Cheek, the president and chief executive of CBS who sat next to Biden on stage, also tested positive days after the dinner.

From top TV network executives to the White House correspondents to many other guests of the guests, the COVID virus did not discriminate. Every news network has been hit with COVID, so many reporters across the networks got infected, why is no one tabulating? If journalism is the Fourth Estate overseeing political power, who watches the watchers?

I dont even know that many reporters who went to WHCA dinner and I can already count enough for two hands, said Dr. Eric Feigl-Ding.

If the WHCA dinner, including all the pre and post partygoers were to help science, so humanity could really learn from this, the networks should provide the data and research could have been done to inform the fight against the pandemic.

Dr. Milton invited WHCA goers who tested positive to contribute to science by coming to the University of Maryland School of Public Health to breathe into the Gesundheit-II so they can measure virus in breath with and without N95 mask. Gesundheit-II is a device Dr.Milton developed with colleagues at Harvard School of Public Health about 15 years ago to study viruses and peoples breath. I invited people who might have gotten infected at dinner to come out to the University of Maryland, College Park, and contribute to science by letting us measure how much viruses are in there, said Dr. Milton.

So how many people who attended WHCA dinner responded?, I asked.

Not very many, Dr. Milton was honest. We can test several people, two or three people a day. We did not exceed our testing capacity.

In science, we trust, says the media. People inside the Washington media fishbowl and political in-crowd should let the fresh air and the new ideas in.

Continued here:

The Covid Roulette: The Hard Truth And Lessons To Be Learned About The WHCA Dinner - The Pavlovic Today