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.

Here is the original post:

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

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

Please contact our Customer Support Center to get the complete Table of ContentsRead the full report: https://www.reportlinker.com/p05478480/?utm_source=GNW

About ReportlinkerReportLinker is an award-winning market research solution. Reportlinker finds and organizes the latest industry data so you get all the market research you need - instantly, in one place.

__________________________

Story continues

Continued here:

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

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.

More here:

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

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."

Story continues

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.

View source version on businesswire.com: https://www.businesswire.com/news/home/20220511006052/en/

Contacts

Paloma OchiEmail: press@glean.com Website: http://www.glean.com

The rest is here:

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

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.)

See the rest here:

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

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."

Read this article:

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

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.

The rest is here:

Predicting Others Behavior on the Road With Artificial Intelligence - SciTechDaily

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.

View original post here:

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

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.

See the article here:

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

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."

Excerpt from:

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

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.

Read more from the original source:

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

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.

Subscribe to Financial Express SME newsletter now: Your weekly dose of news, views, and updates from the world of micro, small, and medium enterprises

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.

Continue reading here:

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

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)

To receive extensive list of important regions, ask for TOC here @https://www.databridgemarketresearch.com/toc/?dbmr=global-artificial-intelligence-in-medical-imaging-market&AS

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:

What are the modes and strategic moves considered suitable for entering the Artificial Intelligence in Medical Imaging Industry?

Whats the competitive strategic window for openings in the Artificial Intelligence in Medical Imaging Market?

Which are the products/ parts/ operations/ areas to invest in over the cast period in the Artificial Intelligence in Medical Imaging Industry?

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

What are the technology trends and nonsupervisory fabrics in the Artificial Intelligence in Medical Imaging Market?

Whats the request size and Forecast of the Artificial Intelligence in Medical Imaging Market?

Enquire for customization Report @https://www.databridgemarketresearch.com/toc/?dbmr=global-artificial-intelligence-in-medical-imaging-market&AS

The report has been prepared using the latest methods and tools for primary and secondary research. Our analysts rely on government documents, white papers, press releases, reliable investor information, financial and quarterly reports, and public and private interviews to gather data and information about the market in which they operate.

About Data Bridge Market Research:

An absolute way to forecast what future holds is to comprehend the trend today!

Data Bridge Market Research set forth itself as an unconventional and neoteric Market research and consulting firm with unparalleled level of resilience and integrated approaches. We are determined to unearth the best market opportunities and foster efficient information for your business to thrive in the market. Data Bridge endeavours to provide appropriate solutions to the complex business challenges and initiates an effortless decision-making process. Data bridge is an aftermath of sheer wisdom and experience which was formulated and framed in the year 2015 in Pune.

Data Bridge Market Research has over 500 analysts working in different industries. We have catered more than 40% of the fortune 500 companies have a network of more than 5000+ clientele around the globe. Data Bridge adepts in creating satisfied clients who reckon upon our services and rely on our hard work with certitude.We are content with our glorious 99.9 % client satisfying rate.

Contact Us:-

Data Bridge Market Research

US: +1 888 387 2818

UK: +44 208 089 1725

Hong Kong: +852 8192 7475

Email:-corporatesales@databridgemarketresearch.com

See the original post:

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

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.

Download Sample PDF Copy of this Report to understand structure of the complete report (Including Full TOC, Table & Figures) @ https://www.databridgemarketresearch.com/request-a-sample/?dbmr=global-artificial-intelligence-in-healthcare-market&aZ

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.

Browse Full Report Along With Facts and Figures @ https://www.databridgemarketresearch.com/reports/global-artificial-intelligence-in-healthcare-market?AZ

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.

Some Trending Reports of Healthcare Industry:

Hom/Self-Testing Market to Garner Brimming Revenues by 2027 l PRIMA Lab SA, bioLytical Laboratories Inc., OraSure Technologies, Inc., NecLife

Healthcare Data Informatics Software Market Estimated to Record Highest CAGR by 2027

Medical Social Working Services Market Breakdown, Recent Developments and Global Opportunities by 2027

Healthcare Enterprise Software Market New Opportunities, Segmentation Details with Financial Facts by 2027

Ethical Pharmaceuticals Market Rising Trends, Latest Technologies Research and Future Scope by 2029

Telepathology Service Market Share, Demand, Top Players, Industry Size, Future Growth by 2028

Home Rehabilitation Products and Services Market Size, Industry Growth, Competitive Analysis, Future Prospects and Forecast to 2028

Healthcare Electronic Data Interchange (EDI) Market Size Outlook, Types, Application & Global Forecast to 2028

Facial Injectable Market Trends, Market Share, Industry Size, Growth, Opportunities and Forecast to 2028

Phototherapy Market Business Growth, Demand, Trends, Key Players and Future Outlook to 2027

Holographic Imaging Market Focuses on Key players, Drivers, Size, Share, Growth and Opportunities by 2028

Needle-Free Diabetes Care Market Share, Outlook, Trends, Size, Demand, Forecast to 2029 and Growth Estimations by Experts

About Us:

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.

Contact us:

United States: +1 888 387 2818

UK: +44 208 089 1725

Hong-Kong : +852 8192 7475

Email corporatesales@databridgemarketresearch.com

Read the original post:

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

Slide: 1 / of 1. Caption: Merjin Hos

Last September, a Google offshoot called Jigsaw declared war on trolls, launching a project to defeatonline harassment using machine learning. Now, the team is opening up thattroll-fighting system to the world.

On Thursday, Jigsaw and its partners on Googles Counter Abuse Technology Team releaseda new piece of code called Perspective, an API that gives any developer access to the anti-harassment tools that Jigsaw has worked on for over a year. Part of the teams broader Conversation AI initiative, Perspective uses machine learning to automatically detect insults, harassment, and abusivespeech online. Enter a sentence into its interface, and Jigsaw says its AI can immediately spit out anassessment of the phrases toxicity more accurately than any keyword blacklist, and faster than any human moderator.

The Perspectivereleasebrings Conversation AI a step closer to its goal of helping to foster troll-free discussion online, and filtering out the abusive comments that silence vulnerable voicesor, as the projects critics have less generously put it, to sanitize public discussions based on algorithmic decisions.

Conversation AI has always been an open source project. But by opening up that system further with an API, Jigsaw and Google can offer developers the ability to tap into that machine-learning-trained speech toxicity detector running on Googles servers, whether for identifying harassment and abuse on social media or more efficiently filtering invective from the comments on a news website.

We hope this is a moment where Conversation AI goes from being this is interesting to a place where everyone can start engaging and leveraging these models to improve discussion, says Conversation AI product manager CJ Adams. For anyone trying to rein in the comments on a news site or social media, Adams says, the options have been upvotes, downvotes, turning off comments altogether or manually moderating. This gives them a new option: Take a bunch of collective intelligencethat will keep getting better over timeabout what toxic comments people have said would make them leave, and use that information to help your communitys discussions.

On a demonstration website launched today, Conversation AI will now let anyone type a phrase into Perspectives interface to instantaneously see how it rates on the toxicity scale. Google and Jigsaw developed that measurement tool by taking millions of comments from Wikipedia editorial discussions, the New York Times and other unnamed partnersfive times as much data, Jigsaw says, as when it debuted Conversation AI in Septemberand then showing every one of those comments to panels of ten people Jigsaw recruited online to state whether they found the comment toxic.

The resulting judgements gave Jigsaw and Google a massive set of training examples with which to teach their machine learning model, just as human children are largely taught by example what constitutes abusive language or harassment in the offline world. Type you are not a nice person into its text field, and Perspective will tell you it has an 8 percent similarity to phrases people consider toxic. Write you are a nasty woman, by contrast, and Perspective will rate it 92 percent toxic, and you are a bad hombre gets a 78 percent rating. If one of its ratings seems wrong, the interface offers an option to report a correction, too, which will eventually be used to retrain the machine learning model.

The Perspective API will allow developers to access that test with automated code, providing answers quickly enough that publishers can integrate it into their website to show toxicity ratings to commenters even as theyre typing. And Jigsaw has already partnered with online communities and publishers to implement that toxicity measurement system. Wikipedia used it to perform a study of its editorial discussion pages. The New York Times is planning to use it as a first pass of all its comments, automatically flagging abusive ones for its team of human moderators. And the Guardian and the Economist are now both experimenting with the system to see how they might use it to improve their comment sections, too. Ultimately we want the AI to surface the toxic stuff to us faster, says Denise Law, the Economists community editor. If we can remove that, what wed have left is all the really nice comments. Wed create a safe space where everyone can have intelligent debates.

Despite that impulse to create an increasingly necessary safe space for online discussions, critics of Conversation AI have argued that it could itself represent a form of censorship, enabling an automated system to delete comments that are either false positives (the insult nasty woman, for instance, took on a positive connotation for some, after then-candidate Donald Trump used the phrase to describe Hillary Clinton) or in a gray area between freewheeling conversation and abuse. People need to be able to talk in whatever register they talk, feminist writer Sady Doyle, herself a victim of online harassment, told WIRED last summer when Conversation AI launched. Imagine what the internet would be like if you couldnt say Donald Trump is a moron.

Jigsaw has argued that its tool isnt meant to have final say as to whether a comment is published. But short-staffed social media startup or newspaper moderators might still use it that way, says Emma Llans, director of the Free Expression Project at the nonprofit Center for Democracy and Technology. An automated detection system can open the door to the delete-it-all option, rather than spending the time and resources to identify false positives, she says.

Were not claiming to have created a panacea for the toxicity problem. Jigsaw founder Jared Cohen

But Jared Cohen, Jigsaws founder and president, counters that the alternative for many media sites has been to censor clumsy blacklists of offensive words or to shut off comments altogether. The default position right now is actually censorship, says Cohen. Were hoping publishers will look at this and say we now have a better way to facilitate conversations, and we want you to come back.'

Jigsaw also suggests that the Perspective API can offer a new tool to not only moderators, but to readers. Their online demo offers a sliding scale that changes which comments about topics like climate change and the 2016 election appear for different tolerances of toxicity, showing how readers themselves could be allowed to filter comments. And Cohen suggests that the tool is still just one step toward better online conversations; he hopes it can eventually be recreated in other languages like Russian, to counter the state-sponsored use of abusive trolling as a censorship tactic. Its a milestone, not a solution, says Cohen. Were not claiming to have created a panacea for the toxicity problem.

In an era when online discussion is more partisan and polarized than everand the president himself lobs insults from his Twitter feedJigsaw argues that a software tool for pruning comments may actually help to bring a more open atmosphere of discussion back to the internet. Were in a situation where online conversations are becoming so toxic that we end up just talking to people we agree with, says Jigsaws Adams. Thats made us all the more interested in creating technology to help people continue talking and continue listening to each other, even when they disagree.

Read more from the original source:

Now Anyone Can Deploy Google's Troll-Fighting AI - WIRED

Humans have often sought the fantasy of having someone who understands them. Be it a fellow companion, a pet or even a machine. No doubt man is a social animal. Yet, this may not be the exact case in case of a man engineered machine or system. Although, machines are now equipped with AI that helps them beat us by sifting through scores of data and analyze them, provide a logical solution when it comes to emotional IQ this is where man and the machine draw the line. Before you get excited or feel low, AI is now in a race to integrate the emotional aspect of intelligence in its system. Now the question is, Is it worth the hype?

We are aware of the fact that facial expressions need not be the same as what one feels inside. There is always a possibility of disconnect by a huge margin. Assuming that AI can recognize these cues by observing and comparing it with existing data input is a grave simplification of a process that is subjective, intricate, and defies quantification. For example, a smile is different from a smug, smirk.

A smile can mean genuine happiness, enthusiasm, trying to put a brave face even when hurt or an assassin plotting his next murder. This confusion exists even in gestures too. Fingers continuously folding inwards the palm can mean Come here at some places while at other places it means Go away. This brings another major issue in light: cross-cultural and ethnic references. An expression can hold a different meaning in different countries. Like thumbs-up gesture is typically considered as well done or to wish Good Luck or to show agreement. In Germany and Hungary, the upright thumb means the number 1. But, it represents the number 5 in Japan. Whereas in places like the Middle East, thumbs-up is a highly offensive thumbs-down. The horn fingers gestures can mean to rock and roll at an Elvis Presley themed or heavy metal concert. But in Spain, it means el cornudo which means translates as your spouse is cheating on you. Not only that pop culture symbols like the Vulcan salute from Star Trek may not be known to people who have not seen the series.

Not only that, but it is also found that AI tends to assign negative emotions to people of color even when they are smiling. This racial bias can cause severe consequences in the workplace hampering their career progression. In recruitments where AI is trained on analyzing male behavior patterns and features is prone to make faulty decisions and flawed role allocation in female employees. Furthermore, people show different emotional range as they grow up. A child may be more emotionally engaging than an adult who is reserved about expressing them. This can be a major glitch in automatic driving cars or AI which specifically studies the drowsiness of the driver. Elderly and sick people may give the impression of being tired and sick in comparison to a standardized healthy guy.

If we must opt for upgrading AI with emotional intelligence and unassailable, we must consider the exclusivity of the focus groups who are used to train the system. AI has to understand rather than be superficially emotional. Hence the AI has to be consumer adaptive just like humans. We need to bring out the heterogeneous interpretation in the way humans express their emotions. At the office, we have to understand how emotionally engaged the employees are. Whether it is the subjective nature of emotions or discrepancies in emotions, it is clear that detecting emotions is no easy task. Some technologies are better than others at tracking certain emotions, so combining these technologies could help to mitigate bias. Only then it can become immune to unforgiving criticisms.

Share This ArticleDo the sharing thingy

About AuthorMore info about author

More here:

Can Emotional AI Supersede Humans or Is It Another Urban Hype? - Analytics Insight

As brands increasingly build internal statistical models and algorithms to tailor experiences, marketing cloud vendors are responding by building AI capabilities into their offerings.

The latest in line is Adobe which last week announced it is opening up its data science and algorithmic optimisation capabilities in Adobe Target, the personalisation engine of Adobe Marketing Cloud.

The company said brands will be able to insert their own data models and algorithms into Adobe Target to deliver the best experience to customers.

It will also add new capabilities in Adobe Target by integrating with Adobe Sensei, its AI and machine learning framework, to further enhance customer recommendations and targeting precision, optimise experiences and automate the delivery of personalised offers.

Among the key aspects of the announcement;

Consumer expectations have sky-rocketed to the point that hyper personalisation is no longer optional for brands, its imperative, said Aseem Chandra, VP, Adobe Experience Manager and Adobe Target.

Progressive brands are already developing proprietary algorithms. When integrated into Adobe Target, brands can combine their own expertise with the power of Adobes AI and machine learning tools to predict what customers want and deliver it before they ask, driving strong business value and brand loyalty.

The ability to bring in proprietary algorithms into a leading marketing platform is a first for the industry. Brands benefit from the ability to blend their industry expertise with Adobe Senseis powerful machine learning and AI capabilities in Adobe Target to deliver individualised customer experiences at massive scale.

For example, a financial services company that created its own algorithm to predict which customers are most likely to respond to an offer can insert that algorithm into Adobe Target to test live traffic against the model to deliver the best possible offer to each customer.

Adobe Target, part of Adobe Marketing Cloud, has leveraged AI and machine learning algorithms for over a decade and is used by major brands worldwide like AT&T, Lenovo, Marriott and Sprint. Highly personalised experiences are leveraged across online channels, including web, mobile, email and more.

With Adobe Experience Manager and Adobe Campaign marketers can seamlessly manage and deliver personalised content. Integration with Adobe Analytics Cloud and Adobe Advertising Cloud ensures that every interaction with customers is hyper personalised.

Adobe was recently named the only leader in The Forrester Wave: Digital Intelligence Platforms, Q2 2017 report, and received the highest scores possible in nine criteria, including behavioral targeting and online testing.

Previous post

Next post

Read the original post:

Adobe Extends AI Offering - Which-50 (blog)

Most economists agree that advances in robotics and AI over the next few decades are likely to lead to significant job losses. But whats less often considered is how these changes could also impact social mobility. A new report from UK charity Sutton Trust explains the danger, noting that unless governments take action, the next wave of automation will dramatically increase inequality within societies, further entrenching the divide between rich and poor.

The are a number of reasons for this, say the reports authors, including the ability of richer individuals to re-train for new jobs; the rising importance of soft skills like communication and confidence; and the reduction in the number of jobs used as stepping stones into professional industries.

Traditionally, jobs like these have been a vehicle for social mobility.

For example, the demand for paralegals and similar professions is likely to be reduced over the coming years as artificial intelligence is trained to handle more administrative tasks. In the UK more than 350,000 paralegals, payroll managers, and bookkeepers could lose their jobs if automated systems can do the same work.

Traditionally, jobs like these have been a vehicle for social mobility, Sutton Trust research manager Carl Cullinane tells The Verge. Cullinane says that for individuals who werent able to attend university or get particular qualifications, semi-administrative jobs are often a way in to professional industries. But because they dont require more advanced skills theyre likely to be vulnerable to automation, he says.

Similarly, as automation reduces the need for administrative skills, other attributes will become more sought after in the workplace. These include so-called soft skills like confidence, motivation, communication, and resilience. Its long established that private schools put a lot of effort into making sure their pupils have those sorts of skills, says Cullinane. And these will become even more important in a crowded labor market.

Re-training for new jobs will also become a crucial skill, and its individuals from wealthier backgrounds that are more able to do so, says the report. This can already be seen in the disparity in terms of post-graduate education, with individuals in the UK with working class or poorer backgrounds far less likely to re-train after university.

The report, which was carried out by the Boston Consulting Group and published this Wednesday, looks specifically at the UK, where it says some 15 million jobs are at risk of automation. But the Sutton Trust says its findings are also relevant to other developed nations, particularly the US, where social mobility is a major problem.

Social mobility is already a big problem in America

One study in 2016 found that America has become significantly less conducive to social mobility over the past few decades. It is increasingly the case that no matter what your educational background is, where you start has become increasingly important for where you end, one of the studys authors, Michael D. Carr, told The Atlantic last year. Another report found that around half of 30-year-olds in the US earn less than their parents at the same age, compared to the 1970s, when almost 90 percent earned more.

Its important to note, though, that there is disagreement about how bad the impact of automation on the job market will be. Some reports have suggested that up to 50 percent of jobs in developed countries are at risk, while others point out that only specific tasks will be automated rather than whole professions. Economists also note that new categories of jobs are likely to be created, although exactly what, and how many, is impossible to accurately predict.

The Sutton Trust report also says that there is some reason to be optimistic about the coming wave of automation, particularly if governments can encourage people to train for STEM professions (those involving science, technology, engineering, and mathematics).

From a social mobility perspective there are two important things about the STEM sector, says Cullinane of the UK job market. Firstly, there doesnt seem to be a substantial gap in the income background of people taking STEM related subjects, and secondly, there isnt a resulting pay gap for those who come from different backgrounds. If the STEM sector is going to be the main source of growth over the medium to long term, thats a real opportunity to leverage social mobility there.

More here:

Robots and AI are going to make social inequality even worse, says new report - The Verge

While we have all been focused on facial recognition as the poster child for AI ethics, another concerning form of AI has quietly emerged and rapidly advanced during COVID-19: AI-enabled employee surveillance at home. Though we are justifiably worried about being watched while out in public, we are now increasingly being observed in our homes.

Surveillance of employees is hardly new. This started in earnest with scientific management of workers led by Frederick Taylor near the beginning of the 20th century, with time and motion studies to determinetheoptimal way to perform a job. Through this, business management focused on maximizing control over how people performed work. Application of this theory extends to the current day. A 2019reportfrom the U.C. Berkeley Labor Center states that algorithmic management introduces new forms of workplace control, where the technological regulation of workers performance is granular, scalable, and relentless.There is no slacking off while you are being watched.

Implementation of such surveillance had existed primarily in factory or warehouse settings, such as at Amazon. Recently, the Chinese Academy of Sciences reported that AI is being used on construction sites. These AI-based systems can offer benefits to employees by using computer vision to check whether employees are wearing appropriate safety gear, such as goggles and gloves, before giving them access to a danger area. However, there is also a more nefarious use case. The report said the AI system with facial recognition was hooked up to CCTV cameras and able to tell whether an employee was doing their job or loitering, smoking or using a smartphone.

Last year, Gartner surveyed 239 large corporations and found that more than 50% were using some type of nontraditional monitoring techniques of their workforce. These included analyzing the text of emails and social-media messages, scrutinizing who is meeting with whom, and gathering of biometric data.A subsequent Accenture surveyof C-suite executives reported that 62% of their organizations were leveraging new tools to collect data on their employees. One monitoring software vendor has noted that every aspect of business is becoming more data-driven, including the people side. Perhaps its true, as former Intel CEO Andy Grove famously stated, that only the paranoid survive.

With the onset of COVID-19 and many people working remotely, some employers have turned to productivity management software to keep track of what employees are doing while they work from home. These systems have purportedly seen a sharp increase in adoption since the pandemic began.

A rising tide of employer worry appears to be lifting all the ships. InterGuard, a leader in employee monitoring software claims three to four times growth in the companys customer base since COVID-19s spread in the U.S. Similarly, Hubstaff and Time Doctor claim interest has tripled. Teramind said 40% percent ofits current customers have addedmore user licenses to their plans. Another firm, aptly named Sneek, said sign-ups surged tenfold at the onset of the pandemic.

The software from these firms operates by tracking activities, whether it is time spent on the phone, the number of emails read and sent, or even the amount of time in front of the computer as determined by screen shot captures, webcam access, and number of keystrokes. Some algorithmically produce a productivity score for each employee that is shared with management.

Enaibleclaims its remote employee monitoring Trigger-Task-Time algorithm is a breakthrough at the intersection of leadership science and artificial intelligence. In an op-ed, the vendor said its software empowers leaders to lead more effectively by providing them with necessary information. In this respect, it appears we have advanced from Taylorism mostly in sophistication of the technology. A university research fellow shared a blunt assessment, saying these are technologies of discipline and domination they are ways of exerting power over employees.

While the ever-present push for productivity is understandable on one level managers have a right to make reasonable requests of workers about their productivity and to minimize cyber-loafing such intense observation opens yet another front in the AI-ethics conversation, especially concerns regarding the amount of information collected by monitoring software, how it might be used, and the potential for inherent bias in the algorithms that would influence results.

Monitoring of employees is legal in the U.S. down to the keystroke. based on the Electronic Communications Privacy Act of 1986. But were now living in an age where monitoring those employees means monitoring them at home which is supposed to be a private environment.

In the 1921 dystopian Russian novelWe that may have influenced the later1984, all of the citizens live in apartments made entirely of glass to enable perfect surveillance by the authorities. Today we already have AI-powered digital assistants such as Google Home and Amazon Alexa that can monitor what is said at home, though allegedly only after they hear the wake word. Nevertheless, there are numerous examples of these devices listening and recording other conversations and images, prompting privacy concerns. With home monitoring of employees, we have effectively turned our work computers into another device with eyes and ears without requiring a wake word adding to home surveillance. These tools can track not only our work interactions but what we say and do on or near our devices. Our at-home lifestyles and non-work conversations could be observed and translated into data that risk managers such as insurers or credit issuers might find illuminating, should employers share this content.

Perhaps work-from-home surveillance is now a fait accompli, an intrinsic part of the modern Information Age that risks the right to privacy of employees within their homes, as well as the office. Already there are employee surveillance product reviews in mainstream media, normalizing the monitoring practice. Nevertheless, in a world where boundaries between work and home have already blurred, the ethics of using AI technologies to monitor employees every move in the guise of productivity enhancement could be a step too far and another topic for potential regulation. The constant AI-powered surveillance risks turning the human workforce into a robotic one.

Gary Grossman is the Senior VP of Technology Practice atEdelmanand Global Lead of the Edelman AI Center of Excellence.

More:

Work-at-home AI surveillance is a move in the wrong direction - VentureBeat

The news: The White House is pumping hundreds of millions more dollars into artificial-intelligence research. In budget plans announced on Monday, the administration bumped funding for AI research at the Defense Advanced Research Projects Agency (DARPA) from $50 million to $249 million and at the National Science Foundation from $500 million to $850 million. Other departments, including the Department of Energy and the Department of Agriculture, are also getting a boost to their funding for AI.

Why it matters: Many believe that AI is crucial for national security. Worried that the US risks falling behind China in the race to build next-gen technologies, security experts have pushed the Trump administration to increase its funding.

Public spending: For now the money will mostly flow to DARPA and the NSF. But $50 million of the NSFs budget has been allocated to education and job training, especially in community colleges, historically black colleges and universities, and minority-serving institutions. The White House says it also plans to double funding of AI research for purposes other than defense by 2022.

See the article here:

The White House wants to spend hundreds of millions more on AI research - MIT Technology Review