The news: Software called TextFooler can trick natural-language processing (NLP) systems into misunderstanding text just by replacing certain words in a sentence with synonyms. In tests, it was able to drop the accuracy of three state-of-the-art NLP systems dramatically. For example, Googles powerful BERT neural net was worse by a factor of five to seven at identifying whether reviews on Yelp were positive or negative.

How it works: The software, developed by a team at MIT, looks for the words in a sentence that are most important to an NLP classifier and replaces them with a synonym that a human would find natural. For example, changing the sentence The characters, cast in impossibly contrived situations, are totally estranged from reality to The characters, cast in impossibly engineered circumstances, are fully estranged from reality makes no real difference to how we read it. But the tweaks made an AI interpret the sentences completely differently.

Why it matters: We have seen many examples of such adversarial attacks, most often with image recognition systems, where tiny alterations to the input can flummox an AI and make it misclassify what it sees. TextFooler shows that this style of attack also breaks NLP, the AI behind virtual assistantssuch as Siri, Alexa and Google Homeas well as other language classifiers like spam filters and hate-speech detectors. The researchers say that tools like TextFooler can help make NLP systems more robust, by revealing their weaknesses.

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Software that swaps out words can now fool the AI behind Alexa and Siri - MIT Technology Review

TLDR: With the KarenApp Scheduling Software, you can let customers book appointments, receive reminders and even make payments, all automatically.

If youre a busy entrepreneur or a freelance professional, booking appointments can turn into a major headache. Thats because it isnt just about scheduling an appointment. Its about making sure youve included everyone that needs to be included. Its about everyone knowing where a virtual or in-person meeting is happening. And if your hours are billable, its definitely about making sure youre getting paid for your time.

The KarenApp Scheduling Software isnt just a scheduling app. Its an AI-driven personal booking assistant that can help you or your entire organization all but fully automate your calendars without the needless flurry of back-and-forth emails, miscommunications and other logistical nightmares.

KarenApp connects to your Google calendar and gets to work, letting you talk to your calendar in natural language. If you want to schedule a meeting next Thursday, the Karen AI understands when next Thursday is and will get it done. And the more you use Karen, the better shell come to understand your preferences.

Once you enter information about your particular business, KarenApp generates an engaging landing page so potential customers can book appointments easily. With pricing included on your page, anyone who wants to see you will not only know when youre available so they can book a time that works for everyone, but also what your services will cost.

Once an appointment is set, KarenApp will automatically send reminders about your meeting to all parties; as well as allow you to access information about that client or alert and include other team members that should be involved.

If youre meeting virtually, KarenApp lets you automatically add a Zoom link to the appointment. And when you want to get paid for an appointment upfront, customers can connect their Stripe account and automatically pay for their meeting through KarenApps secured interface. Users can also expect an option for PayPal payments coming soon.

With the $49.99 KarenApp Nest Plan, three team members can schedule up to 100 meetings per month on this lifetime subscription thats regularly a $490 value. For larger teams, you can also upgrade with similar savings to a 6-member, 500-meeting Hive Plan ($99.99) or a full 12-member, 1,000-meeting Woods Plan ($149.99).

Prices are subject to change.

Read next: This VPN and password manager from Nord protects your web connection and passwords at over 70% off

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This AI-driven appointment booker takes annoying scheduling out of your hands - The Next Web

Executive Summary

Recent advances in AI are best thought of as a drop in the cost of prediction.Prediction is useful because it helps improve decisions. But it isnt the only input into decision-making; the other key input is judgment. Judgmentis the process of determining what the reward to a particular action is in a particular environment.In many cases, especially in the near term, humans will be required to exercise this sort of judgment. Theyll specialize in weighing the costs and benefits of different decisions, and then that judgment will be combined with machine-generated predictions to make decisions. But couldnt AI calculate costs and benefits itself? Yes, but someone would have had to program the AI as to what the appropriate profit measure is. This highlights a particular form of human judgment that we believe will become both more common and more valuable.

With the recent explosion in AI, there has been the understandable concern about its potential impact on human work. Plenty of people have tried to predict which industries and jobs will be most affected, and which skills will be most in demand. (Should you learn to code? Or will AI replace coders too?)

Rather than trying to predict specifics, we suggest an alternative approach. Economic theory suggests that AI will substantially raise the value of human judgment. People who display good judgment will become more valuable, not less. But to understand what good judgment entails and why it will become more valuable, we have to be precise about what we mean.

Recent advances in AI are best thought of as a drop in the cost of prediction. By prediction, we dont just mean the futureprediction is about using data that you have to generate data that you dont have, often by translating large amounts of data into small, manageable amounts. For example, using images divided into parts to detect whether or not the image contains a human face is a classic prediction problem. Economic theory tells us that as the cost of machine prediction falls, machines will do more and more prediction.

Prediction is useful because it helps improve decisions. But it isnt the only input into decision-making; the other key input is judgment. Consider the example of a credit card network deciding whether or not to approve each attempted transaction. They want to allow legitimate transactions and decline fraud. They use AI to predict whether each attempted transaction is fraudulent. If such predictions were perfect, the networks decision process is easy. Decline if and only if fraud exists.

However, even the best AIs make mistakes, and that is unlikely to change anytime soon. The people who have run the credit card networks know from experience that there is a trade-off between detecting every case of fraud and inconveniencing the user. (Have you ever had a card declined when you tried to use it while traveling?) And since convenience is the whole credit card business, that trade-off is not something to ignore.

This means that to decide whether to approve a transaction, the credit card network has to know the cost of mistakes. How bad would it be to decline a legitimate transaction? How bad would it be to allow a fraudulent transaction?

Someone at the credit card association needs to assess how the entire organization is affected when a legitimate transaction is denied. They need to trade that off against the effects of allowing a transaction that is fraudulent. And that trade-off may be different for high net worth individuals than for casual card users. No AI can make that call. Humans need to do so.This decision is what we call judgment.

Judgment is the process of determining what the reward to a particular action is in a particular environment. Judgment is howwe work out the benefits and costs of different decisions in different situations.

Credit card fraud is an easy decision to explain in this regard. Judgment involves determining how much money is lost in a fraudulent transaction, how unhappy a legitimate customer will be when a transaction is declined, as well as the reward for doing the right thing and allowing good transactions and declining bad ones. In many other situations, the trade-offs are more complex, and the payoffs are not straightforward. Humans learn the payoffs to different outcomes by experience, making choices and observing their mistakes.

Getting the payoffs right is hard. It requires an understanding of what your organization cares about most, what it benefits from, and what could go wrong.

In many cases, especially in the near term, humans will be required to exercise this sort of judgment. Theyll specialize in weighing the costs and benefits of different decisions, and then that judgment will be combined with machine-generated predictions to make decisions.

But couldnt AI calculate costs and benefits itself? In the credit card example, couldnt AI use customer data to consider the trade-off and optimize for profit? Yes, but someone would have had to program the AI as to what the appropriate profit measure is. This highlights a particular form of human judgment that we believe will become both more common and more valuable.

Like people, AIs can also learn from experience. One important technique in AI is reinforcement learning whereby a computer is trained to take actions that maximize a certain reward function. For instance, DeepMinds AlphaGo was trained this way to maximize its chances of winning the game of Go. Games are often easy to apply this method of learning because the reward can be easily described and programmed shutting out a human from the loop.

But games can be cheated. As Wired reports, when AI researchers trained an AI to play the boat racing game, CoastRunners, the AI figured out how to maximize its score by going around in circles rather than completing the course as was intended. One might consider this ingenuity of a type, but when it comes to applications beyond games this sort of ingenuity can lead to perverse outcomes.

The key point from the CoastRunners example is that in most applications, the goal given to the AI differs from the true and difficult-to-measure objective of the organization. As long as that is the case, humans will play a central role in judgment, and therefore in organizational decision-making.

In fact, even if an organization is enabling AI to make certain decisions, getting the payoffs right for the organization as a whole requires an understanding of how the machines make those decisions. What types of prediction mistakes are likely? How might a machine learn the wrong message?

Enter Reward Function Engineering. As AIs serve up better and cheaper predictions, there is a need to think clearly and work out how to best use those predictions. Reward Function Engineering is the job of determining the rewards to various actions, given the predictions made by the AI. Being great at itrequires having an understanding of the needs of the organization and the capabilities of the machine. (And it is not the same as putting a human in the loop to help train the AI.)

Sometimes Reward Function Engineering involves programming the rewards in advance of the predictions so that actions can be automated. Self-driving vehicles are an example of such hard-coded rewards. Once the prediction is made, the action is instant. But as the CoastRunners example illustrates, getting the reward right isnt trivial. Reward Function Engineering has to consider the possibility that the AI will over-optimize on one metric of success, and in doing so act in a way thats inconsistent with the organizations broader goals.

At other times, such hard-coding of the rewards is too difficult. There may so be many possible predictions that it is too costly for anyone to judge all the possible payoffs in advance. Instead, some human needs to wait for the prediction to arrive, and then assess the payoff. This is closer to how most decision-making works today, whether or not it includes machine-generated predictions. Most of us already do some Reward Function Engineering, but for humans not machines. Parents teach their children values. Mentors teach new workers how the system operates. Managers give objectives to their staff, and then tweak them to get better performance. Every day, we make decisions and judge the rewards. But when we do this for humans, prediction and judgment are grouped together, and the distinct role of Reward Function Engineering has not needed to be explicitly separate.

As machines get better at prediction, the distinct value of Reward Function Engineering will increase as the application of human judgment becomes central.

Overall, will machine prediction decrease or increase the amount of work available for humans in decision-making? It is too early to tell. On the one hand, machine prediction will substitute for human prediction in decision-making. On the other hand, machine prediction is a complement to human judgment. And cheaper prediction will generate more demand for decision-making, so there will be more opportunities to exercise human judgment. So, although it is too early to speculate on the overall impact on jobs, there is little doubt that we will soon be witness to a great flourishing of demand for human judgment in the form of Reward Function Engineering.

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How AI Will Change the Way We Make Decisions - Harvard Business Review

As connected devices proliferate, new ways of processing have come to the fore to accommodate device and data explosion.

For years, organizations have moved toward centralized, off-site processing architecture in the cloud and away from on-premises data centers. Cloud computing enabled startups to innovate and expand their businesses without requiring huge capital outlays on data center infrastructure or ongoing costs for IT management. It enabled large organizations to scale quickly and stay agile by using on-demand resources.

But as enterprises move toward more remote models, video-intensive communications and other processes, they need an edge computing architecture to accommodate data-hogging tasks.

These data-intensive processes need to happen within fractions of a second: Think self-driving cars, video streaming or tracking shipping trucks in real time on their route. Sending data on a round trip to the cloud and back to the device takes too much time. It can also add cost and compromise data in transit.

Customers realize they dont want to pass a lot of processing up to the cloud, so theyre thinking the edge is the real target, according to Markus Levy, head of AI technologies at NXP Semiconductors, in a piece on therise of embedded AI.

In recent years, edge computing architecture has moved to the fore, to accommodate the proliferation of data and devices as well as the velocity at which this data is moving.

To read the complete article, visit IoT World Today.

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IoT trends continue to push processing to the edge for artificial intelligence (AI) - Urgent Communications

Berlin-based Ada Health, which has developed a doctor-in-your-pocket style app that uses artificial intelligence to try to diagnose symptoms, has been backed by investment arms of South Korea's Samsung and German pharmaceutical giant Bayer.

Ada Health announced Thursday it has raised a $90 million funding round at an undisclosed valuation that brings total investment in the company up to around $150 million.

Bayer led the round through its Leaps by Bayer investment arm, while Samsung invested through the Samsung Catalyst Fund, a U.S.-based venture capital fund that Samsung Electronics uses to back companies worldwide. Samsung Electronics' former chief strategy officer and corporate president, Young Sohn, has joined the board of Ada Health.

Founded in 2011 by entrepreneurs Dr. Claire Novorol, Martin Hirsch and Daniel Nathrath, Ada Health says its app has been downloaded over 11 million times.

"The app basically works like a WhatsApp chat with your trusted family doctor, but 24/7," CEO Nathrath told CNBC.

The patient starts by entering their symptoms, and an AI chat bot will ask a series of questions to try to determine the issue. After that, the app will present the patient with the conditions that are most likely to be the cause and offers some suggestions on what to do next to address the issue.

The iOS and Android apps give generic advice such as to see a GP in the next three days. But when patients interact with Ada Health through a health system that uses the app, they can go straight into booking an appointment and sharing the outcome of their pre-assessment with a real doctor, Nathrath said.

He said the company has signed deals with several health systems, health insurers and life sciences companies. Axa OneHealth, Novartis, Pfizer and SutterHealth are listed as partners on Ada Health's website.

While the app is free for patients to download, Ada Health charges partners for access to its software.

The company said the new funding will be used to help it expand deeper into the U.S., which is already its biggest market with 2 million users. Elsewhere, Ada Health has roughly 4 million users across the U.K., Germany, Brazil and India, with roughly 1 million in each.

The funding will also be used to improve the company's algorithms, add to the medical knowledge base and go beyond 10 languages, Nathrath said.

He also also wants to feed the Ada Health app with more information beyond symptom data provided by the patient. That could include lab data, genetic testing and sensor data, Nathrath said.

"Smartwatches and other sensors have really made a big leap forward," Nathrath said. "Nowadays you can measure your blood pressure, you can do an ECG, measure heart rate variability and blood oxygen levels."

"Our ambition is really to build what we call a personal operating system for health where you wouldn't just have a symptom check, but you would be able to integrate all relevant sources of health information in a way where ideally Ada becomes this companion that can alert you before the 100 problem becomes a 100,000 a year problem."

Ada Health has received less funding than other "doctor" apps like Babylon and Kry.

Unlike Babylon and Kry, Ada Health doesn't allow patients to hold a video call with a GP.

Ada briefly ran a service called Doctor Chat that allowed users to consult with a registered GP through an on-demand chat portal. However, it was deactivated in March 2018 after being live for around a year.

"We were expecting a lot more people to actually use this than they did," Nathrath said, adding that people prefer the automated chat experience to video calls with GPs.

"When you look at telehealth, you can't scale it as well as you can an AI solution because you still need to hire a lot of doctors in different countries," Nathrath said.

The investment in Ada Health comes just over two weeks after British health start-up Huma raised $130 million from the venture arms of Bayer, Samsung and Hitachi.

Other investors in Ada Health's latest round include Vitruvian Ventures, Inteligo Bank, F4 and Mutschler Ventures.

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Samsung and Bayer invest in A.I. doctor app Ada Health - CNBC

Forresters Srividya Sridharan looks at how AI is changing and what we can expect in 2021.

AI is transformational. AI is exciting. AI is mysterious. AI is scary. AI is omnipresent.

Weve heard this oscillating narrative over the last few years and will continue to in the future, but in this unprecedented year, one thing became clear enterprises need to find a way to safely, creatively and boldly apply AI to emerge stronger both in the short term and in the long term.

2020 gave leaders the impetus, born out of necessity and confidence, to embrace AI with all its blemishes. The kinks in AI still remain: lack of trust, poor data quality, data paucity for some and a dearth of the right type of tools and talent.

2021 will see companies and C-level leaders tackle some of these challenges head on, not because they want to but because they have to. Heres why its time for AI to shine.

In 2021, the grittiest of companies will push AI to new frontiers, such as holographic meetings for remote work and on-demand, personalised manufacturing. They will gamify strategic planning, build simulations in the boardroom and move into intelligent edge experiences.

Coupled with this, lucky laggards will use no-code automated machine learning to implement five, 50, or 500 AI use cases faster, leapfrogging their competitors with capable, entrenched data science teams that take a traditional, code-first approach to machine learning.

In 2021, more than a third of companies in adaptive and growth mode will look to AI to help with workplace disruption for both location-based, physical or human-touch workers and knowledge workers working from home.

This will include applying AI for intelligent document extraction, customer service agent augmentation, return-to-work health tracking or semiautonomous robots for social separation.

2021 will showcase the good, the bad and the ugly of artificial data, which comes in two forms: synthetic data that allows users to create datasets for training AI, and fake data that does the opposite; it perturbs training data to deliberately throw off AI.

Companies are also facing increasing pressure from consumer interest groups and regulators to prove datas lineage for AI, including data audit trails to ensure compliance and ethical use.

In 2021, blockchain and AI will start joining forces more seriously to support data provenance, integrity and usage tracking.

BySrividya Sridharan

Srividya Sridharan is a vice-president and research director at Forrester.A version ofthis articleoriginally appeared onthe Forrester blog.

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3 reasons why 2021 will be AI's time to shine - Siliconrepublic.com

Artificial Intelligence is having a drastic impact on the way companies interact with their customers.

Most people are familiar with artificial intelligence because of movies like iRobot or Star Wars. Over the years, technology proved that artificial intelligence wont always be a science fiction myth. In the year 2014 alone, a total of $300 million was invested in AI startup companies, as reported by Bloomberg. AI has been making things much simplerfor a lot of businesses which inevitably makes customers happy.

In fact, AI is becoming so big that according to Gartner, 85% of total customer interactions will not be managed by humans as of the year 2020. Forrester is even predicting that AI will take over a total of 16% of American jobs at the end of the decade.

Because of the development in technology, it is actually possible to communicate with computers the same way that we also communicate with people. The great thing about AI is that it is able to store tons of information in their memory banks and to pull them out any time. This type of function is extremely helpful for many companies in improving customer experience as it gives the customers what they exactly wanted. This adds to the overall customer satisfaction of the public. Remember that customer service is an integral ingredient of customer satisfaction; so the whole fact that AI can strengthen it will immediately ensure a higher customer satisfaction rate.

Over time, many technology companies have been delving into AI and have come up with a lot of interesting results. Siri happens to be one of the most famous apps of them all that aids in the iPhones customer satisfaction. For example, if you ask her to search something in Google for you, she will respond and bring you to the Google page with the search results presented.

Another one would be Watson, which is an even smarter AI app. Watson is known to be able to understand and respond to customers through cognition and not just memory banks from a database. In a nutshell, created by IBM, Watson is a problem solving robot thats been around since 2004.

Of course, Ive already mentioned how Apple made use of Siri to further help iPhone users get the most out of their phones. Just like Siri, Cortana is also an artificial intelligence assistant that also helps phone users, only Cortana can be found in Windows devices instead of Apple.

Weve also got Cogito which happens to be a very intelligent customer support robot that improves customer service of customer service representatives.

The travel industry also vastly benefits from AI apps. Take Baarb for example, a platform that uses AI technology to intelligently find the best travel spots for customers. All recommendations made by the platform are personalized and suited for each customers wants. These are only some of the companies that make use of AI for customer experience.

One of the most wonderful things about AI is how AI can actually make customer experience more personalized through the collection of data and also execution of humanlike traits. AIs work by first collecting data of their customers and storing them into their memory banks. They then use the information to interact with the customers. The more data that they store, the more intelligently they can interact. In a way, they are almost humanlike. They learn, they remember, then they apply.

By taking a look at some of the examples given above, we can see how the AIs use customer data to enhance experience. Siri, for example, stores information that will allow her to suggest tasks to be carried out for your needs. Baarb also does the same thing, but focuses on your travel preferences to come up with the best trips for your next vacation.

What makes AIs amazing are their ability to use data stored in their memory and use it to aid customers -- just like a customer service representative would.

AI is slowly becoming an integral part of our lives. With the use of this type of technology, creating good customer experiences for your consumers will be so much easier. With their sharp efficiency and human like traits, AI will definitely take over many tasks that were once done by humans. We just have to be ready for it.

Nathan Resnick

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How Artificial Intelligence is Improving Customer Experience - Business.com

AI is now able to synthesize new sounds from old ones, and even compose original music

"Can machines be creative?" This question is the target of a recent Google undertaking, dubbed Project Magenta,focused on bringing artificial intelligence into the art world.

Magenta and other creative AI endeavors draw on the power of deep neural networks, systems that allow computers to sort through large amounts of data, recognizing patterns, and eventually generating their own pictures, music and more. These networks had previously been put to artistic use by Google for its "DeepDream" project, which was designed to visualize how neural networks think. Researchers could feed the tool images, which it thenreinterpretedinto often abstract, and oftentrippy, works.

Last year, Google started Project Magenta to apply what it learned from these AI-created masterpieces to further push the limits of computer creativity in art, music, videos and more. Now,TheNew York Times'Cade Metz tuned into the software giant's recent projects to see (and hear) what's come of the endeavor.

Along with the announcement of Project Magenta last summer, Google released the neural network's first song. The Google team gave its algorithm four notes (C, C, G, G) to work with, and then let the machine compose a roughly 90-second song with a piano sound.The littleditty is upbeat, starting slow but picking up with a drum beat added behind it as it explores patterns using those four notes.

But now, Google programmers are using those networks to not only create new pieces of music, but new instruments. For example, a tool calledNSynth, has analyzed hundreds of notes played by a variety of modern instruments, mapping out the features that makea guitar sound like a guitar, or a trumpet sound like a trumpet. Using these maps, users can then combine instrument characteristics to create brandnew sound makers.

A more recent project from Google trained an algorithm with examples of classical piano music to create a tool that can compose its own music within the framework of classical piano techniques, reports MatthewHutsonforScience. While you won't findPerformanceRNN, as the algorithm is called, composing a symphony any time soon, it can create short original music phrasings that are "quite expressive," as programmersIan Simon andSageevOorewrote last month on the Project Magenta blog. And another algorithm has been trained from Magenta's code to be able to respond to notes that people play with its own original snippets of music, in effect creating a "duet" with an AI.

Other Google algorithms have worked on edging more into the visual art world, reportsHutson. For example, the algorithmSketchRNNhas analyzed thousands of examples of human drawings to teach a computer to create basic sketches of common shapes, such aschairs, cats and trucks.

Once these models have been "trained," writes Google researcher David Ha, the computercan analyze and recreate previously submitted drawings in original ways. It can even correctmistakes researchers added in to make the images appear more accurate, such as drawing a pig with four legs instead of five.Similar to the blended instruments ofNSynth, artists can game these models by doing things like submitting drawings of chairs to a program that draws cats, creating blended sketches that lie somewhere between the shapes.

Some other projects haven't worked out just yet,Hutsonreports, such as a tool to create new jokes. (They just weren't funny.)

Google aren't the only ones interested in artsy AI. As Metz notes, last year, researchers at Sony trained an neural network tocompose new songs in the styles of existing artistseven creatinganpop songthat resemblesa composition from the Beatles. Another neural networkcomposed its ownChristmas songwhen shown a picture of a Christmas tree.

Though some people are concerned that AI could replace us all, developers don't see these tools as ever supplanting human creativity,Hutsonreports. But rather, these algorithms are tools that can helpinspire and channel imagination into new creations.

Maybe one day, your muse could be a computer.

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AI Is Edging Into the Art World in Psychedelic Ways - Smithsonian

TechCrunch

Astro raises an $8 million Series A for its AI-powered email solution for teams TechCrunch On the surface, Astro, launching its public beta today, is a nifty but not completely necessary email client that combines machine intelligence and a bot interface to improve workflows and increase the signal to noise ratio of mail for power users. But ... Astro aims to fix your email mess with an AI chatbot - The VergeThe Verge Astro is an AI-powered email client with big dreams | PCWorldPCWorld Astro raises $8.3 million for its email app with AI assistantVentureBeat all 5 news articles »

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Astro raises an $8 million Series A for its AI-powered email solution for teams - TechCrunch

NITI Aayogs AI workshop called Artificial Intelligence The India Imperative took place recently in New Delhi.

The AI workshop- The India Imperative took place on 19th Dec20 at NITI Aayog Bhawan where all the relevant stakeholders from across the country were present. From ministers from states to representatives from the IT industry to professors from IITs were part of the workshop.

The event highlighted Indias leading think tank continuous work in ensuring continuous activity in the AI field. NITI Aayog has also been publishing Approach Papers to create execution plans and showcase essential suggestions along with stakeholders.

The CEO of NITI Aayog started the workshop with Indias AI aspirations and the importance of AI for all the sectors. He also recommended AI Superpower by Kai-Fu. He also added that India is expected to reach $15 trillion which will be more than the US and China together.

In his keynote address, CEO Amitabh Kant kickstarted the deliberations by emphasising on the importance of AI for All in realising Indias artificial intelligence aspirations. The main 5 sectors that the workshop focused on were- healthcare, agriculture, education, infrastructure and transportation that would benefit the most from AI.

Arnab Kumar, the Programme Director gave a presentation on AI For All and discussed the 4 fundamental themes:

1.Data Rich to Data Intelligent

2.Research & Development

3. AI-specific Computing

4.Large scale AI adoption

The inaugural session was followed by the breakout sessions which included the following topics- structured data infrastructure for AI, research ecosystem for AI, Moonshots for India and Adoption- Focus on healthcare, education and agriculture.

In the breakout session at the workshop on Artificial Intelligence The India Imperative, scalable approach to building solutions for a billion citizens, by leveraging technologies like AI/ML was discussed by the participants.

In 2018 2019, the government-mandated NITI Aayog to create the National Program on AI, with the aim of guiding the research and development in new innovation in artificial intelligence for India. NITI Aayog came out with the National Strategy for Artificial Intelligence (NSAI) discourse paper in June of 2018 to highlight Indian governments importance and role in boosting AI.

NITI Aayog has taken a three-part approach here undertaking exploratory proof-of-concept AI projects in different areas of the country, creating a national strategy for a vibrant AI ecosystem in India and collaborating with experts and stakeholders in the field. The recent workshop was a part of the think tanks engagement with stakeholders, including multiple startups.

One such startup had been Silversparro, an AI-powered video analytics firm invited by Niti Aayog for a day-long session on realising Indias AI aspirations. The startup presented its views on how AI can help India leapfrog in sectors like manufacturing, heavy industry and giving a boost to SMEs.

We are heartened by Niti Aayogs focus on making India an AI Superpower. We are also proud to be contributing directly by leveraging AI for making Indian Manufacturing more productive with our latest offering Sparrosense AI Supervisor said Abhinav Kumar Gupta, Founder & CEO at Silversparro.

At the workshop, there were several speakers talking about leadership and vision in the AI workshop Artificial Intelligence The India Imperative event in Delhi by NITI Aayog, including those from state governments such as Telangana. In fact, Telangana presented Year of AI at NITI Aayogs workshop.

Also Read: India Lags Behind In AI Research, But Will 7,000 Crore Boost Change Things?

India has rich publicly available data, and across government departments, the various processes have been digitised for reporting and analytics insights, which are feeding into information systems and visualisation dashboards. According to NITI Aayog, this data is being utilised to track and visualise processes and make iterative enhancements.

National Data and Analytics Platform (NDAP), an initiative aimed to aid Indias progress by promoting data-driven discourse and decision-making, NDAP also aimed to standardise data across multiple government sources, provide flexible analytics and make it easily accessible in formats conducive for research, innovation, policy-making and public consumption. As part of it, multiple data sets have been presented using a standardised schema, by using common geographical and temporal identifiers.

However, the data landscape can be further improved as the entire public government data can be smoothly accessible to all stakeholders in a user-friendly manner. Further, data across different government assets should be interlinked to enable analytics and insights, such websites of ministries and departments of the central and state governments.

Also Learn: AIRAWAT: NITI Aayog Describes How Indias AI Infrastructure Will Look Like

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How A Recent Workshop Of NITI Aayog Gave Boost To India's AI Ambitions - Analytics India Magazine

Accenture, AIB, ESB, National Transport Authority, INFANT Centre amongst those recognised for innovation in Artificial Intelligence.

Entries from Trinity College, UCC, Waterford Institute of Technology and DCU reflect increase in academic research in AI.

Nominations demonstrate innovation across health, customer service, and science.

Dublin October 1st, 2019: Today, AI Ireland received a record 50% increase in entries in its second year, showcasing innovation in Artificial Intelligence across healthcare, finance, customer service, communications, and academia.

The second AI Awards 2019 will be presented at the Gibson Hotel on Wednesday 20th November. These Awards, which are part of the not-for-profit organisation AI Ireland, support the growth and development of Data Science, Machine learning, and Artificial Intelligence in Ireland.

This year also saw the introduction of a new category for Intelligent Automation-Best Use of RPA (Robotic Process Automation) to reflect the rapidly growing sector within AI in the Irish market.

The increase in nominations shows the Irish AI sector is innovating with potentially game changing outcomes for organisations and society, said Mark Kelly, Chief Customer Officer at Alldus & Founder of AI Ireland.

The sheer breadth of AI and Machine Learning, research, development and implementation across private, public and academic organisations is exciting and shows how we are embracing the potentially massive opportunities and benefits that AI can bring.

Cathriona Hallahan, Managing Director, Microsoft Ireland, At Microsoft, we are infusing AI into everything we deliver, across our computing platforms and experiences, as we believe democratising access to intelligence will help solve the worlds most pressing challenges. We are committed to driving AI adoption and innovation in Ireland which will support the ambition of Ireland being a digital leader in Europe. The AI awards are key to fostering and recognising homegrown talent and entrepreneurship and we are delighted to support the awards for a second year and to see to the quality of the applications increase. We look forward to the exciting innovations that will be showcased at the ceremony in November in the hope they will enable more people and organisations to do more into the future.

The 2019 AI Awards Shortlists.

Best Application of AI in a Large Enterprise

Accenture for their Job Matching solution.

Allied Irish Bank (AIB) for their AIB Services Insights Project.

Johnson Controls for their first-of-its-kind AI-powered security product, known as Converged Cyber-Physical Security (CCS).

Mastercard Labs for Duka Connect, a Mobile Point of Sale (mPoS) solution for small merchants in emerging markets.

Best Use of AI in a Consumer/Customer Service Application

Accenture for their Knowledge Exchange (KX) AI integration.

Idiro Analytics for their work with Digicel in integrating AI into customer services to reduce churn.

SAP for their Business Operations and Self-Healing (BoSh), an out of the box AI platform to support business automation.

Webio for their Conversational Middleware Service enabling organisations to connect applications, digital assets across different communications platforms (SMS, WhatsApp to Alexa).

Best Application of AI in a Student Project

Meredith Telford, Ulster University for her work on using AI to accelerate production of 3D printed cardiac models using machine learning, improving diagnosis and enabling surgeons to practice virtually before surgery.

Cian Vaughan, National College of Ireland for his work integrating movement and gesture recognition for Irish Sign Language to help deaf people fluently interact with devices in the future.

Ciaran O'Mara, University of Limerick for his work on Machine Learning Based Traffic Network Analysis Tool, using AI for traffic management.

Rory Boyle, Trinity College Dublin for his work on brain predicted age difference score (brainPAD), a way of representing brain health outside of a patients chronological age.

Best Use of AI in Sector

Liopa for their LipRead technology that uses AI in Visual Speech Recognition (VSR) or automated lip reading.

National Transport Authority - for their use of AI to better analyse data to provide high-quality accessible and sustainable transport solutions nationwide.

Soapbox Labs for their work on developing speech recognition technology for children.

TVadSync for its Smart TV based Automatic Content Recognition (ACR) that provides brands and marketers insight into the ad effectiveness of their media campaigns as well as deep behavioural analysis of their customer base.

NEW AWARD! Intelligent Automation - Best Use of RPA & Cognitive

Doosan Bobcat for their work it UiPath to use Robotic Process Automation to automate tasks for employees, delivering savings of 400 hours per month.

ESB for using AI to optimise its rollout of smart meter technology.

HealthBeacon for their digitally connected Smart Sharp Bin service that supports patients who self-inject medications at home to ensure adherence to medical treatment schedule.

McKesson for their work on integrating AI tools like RPA to standardise approach to incorporate the various sources of data and systems that combine to allow the day-to- day activity to occur across Europe.

Best Application of AI in an Academic Research Body

Connect Centre, Dublin City University for their work using AI to optimise superfast internet speeds by tackling non-linear distortions in optical fibre networks.

Connect Centre, Waterford Institute of Technology for their work on SmartHerd, an IOT based system to predict lameness in dairy cattle.

INFANT Centre, University College Cork for their work using deep learning to detect neonatal seizure detection.

Sigmedia, Trinity College Dublin for their work in combining visual and speech cues in a speech recognition system.

Best Application of AI in a Startup

Getvisibility for their use of Machine Learning and Natural Language Processing to discover and categorise unstructured data sets.

Rinocloud for their work in using AI to reduce time and risk in research and development and enhancement of skin disease remedies affecting 3% of the global population and 20% of children under 10 years of age.

Telenostic for their work on deep learning or convolution neural networks (CNNs) to accurately model and predict Parasitic Infections (PI) within animals.

Truata for their work in providing a new standard in data hosting and anonymisation. Using proprietary processes, methodologies and intellectual property, the solution makes it possible for organizations to analyse their data while complying with privacy and data protection regulations.

Microsoft Ireland is the principal sponsor of the 2019 AI Awards. The awards will also be further supported by the IDA Ireland, Alldus, ISG, McKesson, Mazars, Mason Hayes Curran, the ADAPT Centre and GeoDirectory.

For more information, visit http://www.aiawards.ie

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Second AI Awards See A 50% Increase in Entries from A Diverse Range of Organisations - socPub

Contact tracing has quickly emerged as the go-to method of tracking the spread of the coronavirus among the general population, but there have been crucial questions around the most effective, ethical, and legal ways of doing so. New legislation introduced this week, the COVID-19 Consumer Data Protection Act (CDPA), seeks to enact legal guardrails around the collection and use of peoples data.

Its a sign of progress that legislation is emerging around this issue, but it also highlights that theres a way to go yet. The CDPA has some issues that privacy experts are concerned about, and the lack of any Democrat co-sponsors indicates a lack of bipartisan support. The Dems actually have their own version of this type of legislation, called the Consumer Online Privacy Rights Act (COPRA), which was introduced in December. Both bills emerged from the same committee the Senate Committee on Commerce, Science, and Transportation and so the lack of bipartisanship is especially notable.

The CDPA was introduced by Senators Roger Wicker (R-MS), John Thune (R-SD), Deb Fischer (R-NE), Jerry Moran (R-S), and Marsha Blackburn (R-TN). COPRA is sponsored by Senators Maria Cantwell (D-WA), along with Brian Schatz (D-HI), Amy Klobuchar (D-MN), and Ed Markey (D-MA).

Despite the partisanship, the CDPA includes much that all sides can agree on. And in an announcement about the bill, the Republican Senators said all the right things. For example, Senator Wickers statement reads, As the coronavirus continues to take a heavy toll on our economy and American life, government officials and health-care professionals have rightly turned to data to help fight this global pandemic. This data has great potential to help us contain the virus and limit future outbreaks, but we need to ensure that individuals personal information is safe from misuse.

Per the announcement, the CDPA includes the following:

In a statement to VentureBeat, Liz OSullivan, cofounder of ArthurAI and technology director of STOP (Surveillance Technology Oversight Project), said that the CDPA is a step in the right direction, but shes concerned that it doesnt go far enough. Theres nothing stopping companies from using this data to profit after the crisis, and it wont protect people in the event that ICE or other law enforcement agencies subpoena identifiable information while the crisis is ongoing, she said.

In a way, the issues here are business as usual for data privacy. All the usual concerns apply: This data is a great source of power in any hands, to be politicized or used for personal gain. If companies are left with a choice to delete or de-identify, its pretty clear which one they will choose, she said, adding that Its telling, in fact, that Palantir, a company typically associated with national security, has already won contracts to handle this data.

She emphasized that the danger with any bill that fails to keep a divide between public and private data is the creation of the illusion of privacy while handing governments, and state-adjacent corporate entities, expanded surveillance capabilities.

Andrew Burt, chief legal officer at Immuta and managing partner at bnh.ai, said in a statement to VentureBeat that the CDPA does serve to reinforce how important data and data analytics are to combatting the pandemic. Theres a reason, for example, that the most thorough plans to get Americans back to work pre-vaccine start with contact tracing and monitoring knowing who might be a carrier of the virus, and where theyve gone and who theyve been in close proximity to, is the first step to getting us to a state of reasonable safety, he said. Data collection and data analytics will form the backbone of those efforts. So I see the CDPA as a very clear acknowledgement of that fact.

But Burt also noted that there is much more that needs to be discussed around data protection laws, such as what a bill like this says about the broader state of data protection laws, the current and future role of the FTC around privacy, what counts as health data in a world of ubiquitous data generation and collection, applying time limits to new surveillance mechanisms for COVID-19, and more.

The fact that legislators are moving forward with data privacy laws is a welcome sign of progress. But Republicans and Democrats will need to do more to come to consensus lest the U.S. ends up with data laws that fail to strike the best balance between protecting people from the coronavirus and protecting people from future abuses.

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AI Weekly: CDPA bill shows progress on coronavirus-tracking data privacy, but theres still a ways to go - VentureBeat

When manufacturers strategically deploy artificial intelligence (AI) into their operating environments, the results can be game changing. Setting the stage for constant innovation. Avoiding down production lines. Anticipating and taking action on dramatic market changes before the competition. The reality for most manufacturers? AI deployments can be challenging. Getting it right takes determining and dedication to optimizing the process.

I recently connected with Landing AI's Quinn Killough to discuss what happens, and how manufacturers can move past the obstacles.

IW: When it comes to adopting AI, where do most manufacturers run into problems?

Killough: One of the major points of failure in AI projects for manufacturers is the jump from a working model in the lab to a working model in production. Many manufacturers have high hopes for AI improving automation on their plant floors, and once theyve honed-in on a valuable problem/project, they go to work in the lab and start training AI models. After just a few iterations (depending on the complexity of the problem) they may find their model is performing at the accuracy level desired.

They think, great! AI is magic and its going to solve all of our problems, and decide to take the solution to production so they can start reaping the benefits. The issue that we have seen with countless manufacturers across a number of industries is that the models performance in production is worse than it was in the lab and is not yet suitable for true deployment.

They end up needing to go back to the lab and the drawing board to iterate on the model further and in the meantime revert back to their old way of doing things on the floor. As we see it, this jump from a PoC in the lab to a fully functional, value adding, deployed model is much harder than expected. Not only is this an issue for the project, i.e. longer development times, more money spent, opportunity cost of the old system running longer, but it is also an issue of perception for AI - people can lose faith after seeing this process unfold.Landing AI's Quinn Killough

IW: Why do manufacturers struggle to get past that point?

Killough: Frequently, the number one contributor to this struggle is data quality. A machine learning model is only as good as the data you are putting into it. Machine learning engineers spend up to 80% of their time prepping their data, and there is a lot that can go wrong in this phase. Issues with data quality can come in many flavors, but the two we see the most often are poorly labeled data, and bad dataset distribution.

A model will struggle in production if the manufacturer fails to accurately label the data. When you dont have millions of datasets to wash out the impact of labeling mistakes, just one or two improperly labeled items can lead to a model that does not perform well. In the case of visual defect detection in manufacturing, a common contributor to bad labels is inadequate defect definitions. It is not uncommon for two subject matter experts on a plant floor to disagree on whether a defect is a scratch or a crack, which in turn makes it difficult for the people doing labeling to label images correctly.

When machine learning engineers are collecting data to train their model, the data collected often is not diverse enough to represent the variety of edge cases that would actually be seen in the production environment. Because of this, edge cases that are not seen frequently may be underrepresented when training and testing so the model ends up not being great at finding these. Lets consider a visual inspection project where a manufacturer is looking for several categories of defects on a phone screen - dust, dead pixels and cracks. Dust and dead pixels are super common, so they have a ton of data on these categories. Cracks, on the other hand, are a very infrequent defect and therefore there is less data and the model is not great at recognizing them. A big issue here is that oftentimes the infrequent defects are some of the most critical ones to catch. If this manufacturer were to test on the same distribution as they trained they would get good results. But when going to production they would quickly find out they cant perform on their most critical defects and be forced to pull the system.

IW: What are the keys to getting past the barriers?

Killough: There are a couple of things that can be done to help get past these barriers and achieve a successful deployed model.

First, create an airtight labeling process, and second, move the project out of the lab sooner. This starts with data collection and making sure your dataset is as representative of the production process as possible. After a well-balanced dataset is established, a systematic method for driving agreement on what the definitions of the label categories are needs to be incorporated. Consensus tasks have proven to be a great way to achieve this - i.e. create a system where multiple experts review that same data and compare labels, and after this, discrepancies can be resolved in order to make label definitions less ambiguous for labelers. Now that labelers have the clearest instructions possible, they have a good chance at labeling the data accurately, but they are still human and will likely make mistakes - this is why we would recommend having a tight review process. This means that every single piece of labeled data is reviewed. It may seem like overkill, but a single mistake can be costly on model performance. Establishing this airtight data preparation process will inherently improve model performance and help project leaders avoid costly pitfalls and mistakes.

Outside of having an airtight data preparation process, we also recommend utilizing approaches that allow you to get from the lab bench to the production floor faster than most would think is intuitive. Often teams want to achieve their ultimate goal for model performance - lets say 95% accuracy is the goal - in the lab before moving out to production, but this can be flawed. There will inevitably be unforeseen changes as well as the edge cases mentioned earlier that pop up when deployed to production that hurts performance. Instead, if we spend less time in the lab to get to a lower bar of 80%, we can then deploy that model in production and start uncovering those production edge cases and issues much sooner. To do this, AI teams can use two approaches - shadow mode or putting a human in the loop. Shadow mode means running a model on actual production data without the predictions of the models impacting production - meaning your existing process is not impacted while at the same time you can iterate and improve your model.

Putting a human in the loop is another option if youd still like to benefit from an underperforming model while it is being improved. In this situation the model would run as though it was actually deployed, and a human would review each low confidence prediction. Now a low performing model can be deployed earlier, 100% of parts can still be inspected, and the model can be iterated upon to continuously do more and more of the work until fully autonomous.

IW: Once manufacturers are able to adopt AI, what steps can they take to optimize its use?

Killough: Continuous learning is the most important aspect to optimizing a deployed solution. This entails a few different aspects including data collection, inference monitoring, and a method for retraining.

Model deployment is just the beginning. It takes a solid system just to maintain current performance of a model, let alone further performance optimization. The first step here is having a method for continuous data collection. As the model is making predictions its important to be able to collect data where it may have struggled. With an inference monitoring system where operators can view real time and past predictions this process can be fairly simple.

While collecting and labeling data, a periodic retraining of the model is also very important. This periodic retraining on new data helps to optimize performance by catching and training on those infrequent edge cases. Also, by continuously retraining on the latest state of the production system you can account for naturally occurring changes in production. For example, in optical inspections, if a material changes in your production it could impact model performance due to a new appearance of the product, but not enough to set off any alarms or flag anyones attention. Having a quick and easy way to retrain and roll out a new model is important when trying to optimize or maintain an AI system - without the help of automation this task can be rather time consuming.

Original post:

AI Adoption Hitting Barriers? Here's How to Get to the Next Level - IndustryWeek

Feb. 4, 2021 BSC participates in the AI-SPRINTproject contributing its experience on the programming and parallelization of applications on distributed infrastructures. The work will be organized in two main contributions, the deployment ofCOMPSson the edge devices considered by the use cases and the implementation of the AI applications to be executed across distributed heterogeneous infrastructures (on premise, edge and public clouds). In particular,AI-SPRINTwill benefit from the recent developments on the adaptation ofCOMPSsfor Fog to Cloud platforms and will extend it to support the execution of serverless functions as a service.

On the other side,COMPSswill be adopted to develop AI and big data applications in support of the use cases also leveraging the recent enhancements to develop workflows that combine HPC compute engines with High Performance Data Analytics (HPDA) and machine learning methods. These ML implementations are available through thedislib librarythat is also part of theFujitsu-BSCcollaboration.

The technology developed within the project will be put to test by BSC on a personalized healthcare use case that will focus on privacy and security, much needed in healthcare scenarios since the information to be exchanged and processed involves medical data about patients.

More specifically, an automated system for personalized stroke risk assessment and prevention will be developed by using continuous, non-invasive monitoring of heart activity. The process will gather heart parameters collected from a wearable device, patients lifestyle information and biochemical blood indicators from a mobile application. All data will be anonymized, processed and used to train AI models cooperatively by local edge servers and cloud. At the same time, it will provide personalized notifications, alerts, and recommendations for stroke prevention.

AI-SPRINTdefines a novel framework for the design and operation of AI applications in computing continua leveraging theCOMPSsprogramming framework and supporting AI applications development by enabling the seamless design and partition of AI applications among the plethora of cloud-based solutions and AI-based sensor devices. Moreover it will generate impacts bringing together different European industrial end-users while and making available the software tools through a marketplace for AI start-ups, SMEs, system integrators, and European cloud providers statesDaniele Lezzi, Senior Researcher in theComputer Sciences department Workflows and Distributed Computingat BSC.

About COMPSs:

COMPSsis a task-based programming model known for notably improving the performance of large-scale applications by automatically parallelizing their execution. TheCOMPSsruntime has been recently extended within BSC projects:CLASSandELASTICto manage distribution, parallelism and heterogeneity in the edge resources transparently to the application programmer and to handle data regardless of persistency by supporting a single and unified data model.COMPSsis the base of the Design Tools of the project and it will support developers to easily compose AI/ML applications also leveraging the dislib library, helping end users to deal with big datasets on distributed resources and providing automatic parallelization of the code.

About Personalized Healthcare:

AI-SPRINTapplications will pave the way for an effective framework for personalized AI models preventing risks coupled with a lifestyle modificationmodification programmebenefiting people aged between 40 and 80, improving and extending human lives. The project addresses theUnited Nations strategic development goalsSDG3 (Good Health and Well Being) through the personalized healthcare pilot.

About AI-SPRINT

AI-SPRINTwill tackle the skill shortage and considerably reduce steep learning curves in the development of AI software on edge ecosystems through OSS (Operations Support System). The project addresses the followingUnited Nations strategic development goals(SDGs): SDG8 (Decent Work and Economic Growth) enabling novel AI applications running in computing continua, SDG9 (Industry, Innovation and Infrastructure) by fostering innovation in the maintenance and inspection use case and contributing OSS and SDG12 (Ensure sustainable consumption and production patterns) through farming 4.0 pilot.

For further information, visit AI-SPRINT website:https://www.ai-sprint-project.eu/

The AI-SPRINT project has received funding from the European Union Horizon 2020 research and innovation programme under Grant Agreement No. 101016577

Source: BSC

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BSC Working Towards Adoption of AI Applications to Capture Insights on Personalised Healthcare - HPCwire

Several weeks ago, artist and coder Janelle Shane tried to train a neural network to name paint colors. The results were...not good. "Stanky Bean" was a kind of dull pink, and "Stoner Blue" was gray. Then there were the three shades of brown known as "Dope," "Burble Simp," and "Turdly."

First, Shane realized that part of the initial problem was that she'd cranked up the neural net's "temperature" variable, which meant that it was picking less likely (or "more creative") possibilities as it generated paint names letter-by-letter. So she turned the temperature variable down, and found that the names were still pretty silly but they at least matched the colors most of the time. Plus, the colors themselves seemed more varied:

Next, Shane incorporated some suggestions from experts about how to tweak her dataset. Mostly, what people wanted to know was whether she'd get better results if she changed the way she represented colors. For her original dataset, she used RGB colors, which assign a numeric value to a number based on its blend of red, green, and blue. The AI would randomly generate a number within the RGB space, and slap a name on it.

The problem is, according to Shane, that RGB doesn't do a good job representing color the way human eyes perceive it. Plus, the AI kept coming up with muddy, boring colors that seemed like variations on gray and brown.

She tried two other color schemes: HSV and LAB. On her blog, Shane writes:

In HSV representation, a single color is represented by three numbers like in RGB, but this time they stand for Hue, Saturation, and Value. You can think of the Hue number as representing the color, Saturation as representing how intense (vs gray) the color is, and Value as representing the brightness. Other than the way of representing the color, everything else about the dataset and the neural network are the same...In [the LAB] color space, the first number stands for lightness, the second number stands for the amount of green vs red, and the third number stands for the the amount of blue vs. yellow.

Sadly, HSV and LAB colors didn't really produce results that made more sense than the RGB ones. Shane wound up deciding that RBG was her best option. "Maybe its more resistant to disruption when the temperature setting introduces randomness," Shane mused. The color names, however, were still "pretty bad."

But then one person sent Shane this cleaned-up dataset, which had no capital letters (so there wouldn't be two versions of each letter). It combined paint names from Behr and Benjamin Moore, as well as ones from a user-generated list created by readers of XKCD. Shane called the results "surprisingly good," and noted that the lesson here is that better data generally produces better results. And here they are:

Shane also included a "hall of fame" from her experiments with all the color schemes, which truly represent the promise of machine learning in our modern world:

Listing image by Janelle Shane

Read more from the original source:

Latest experiments reveal AI is still terrible at naming paint colors - Ars Technica

A child reacts after a big wave on a waterfront as Typhoon Hato hits Hong Kong

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Soldiers march during a changing of the Guard at the Mamayev Kurgan World War Two memorial complex and Mother Homeland statue (back) in Volgograd, Russia

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Italian emergency workers rescue a baby (C) after an earthquake hit the popular Italian tourist island of Ischia, off the coast of Naples, causing several buildings to collapse overnight. A magnitude-4.0 earthquake struck the Italian holiday island of Ischia, causing destruction that left two people dead at peak tourist season, authorities said, as rescue workers struggled to free two children from the rubble

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South Korea protesters hold placards with an illustration of U.S. President Donald Trump during a during a 72nd Liberation Day rally in Seoul, South Korea. Korea was liberated from Japan's 35-year colonial rule on August 15, 1945 at the end of World War II.

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The Chattrapathi Shivaji Terminus railway station is lit in the colours of India's flag ahead of the country's Independence Day in Mumbai. Indian Independence Day is celebrated annually on 15 August, and this year marks 70 years since British India split into two nations Hindu-majority India and Muslim-majority Pakistan and millions were uprooted in one of the largest mass migrations in history

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Jessica Mink (R) embraces Nicole Jones (L) during a vigil for those who were killed and injured when a car plowed into a crowd of anti-fascist counter-demonstrators marching near a downtown shopping area Charlottesville, Virginia

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White nationalists, neo-Nazis and members of the alt-right clash with counter-protesters as they enter Lee Park during the Unite the Right in Charlottesville, Virginia. After clashes with anti-fascist protesters and police the rally was declared an unlawful gathering and people were forced out of Lee Park

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A North Korean flag is seen on top of a tower at the propaganda village of Gijungdong in North Korea, as a South Korean flag flutters in the wind in this picture taken near the border area near the demilitarised zone separating the two Koreas in Paju, South Korea

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A rally in support of North Korea's stance against the US, on Kim Il-Sung square in Pyongyang.

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Rocks from the collapsed wall of a hotel building cover a car after an earthquake outside Jiuzhaigou, Sichuan province

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People in Seoul, South Korea walk by a local news program with an image of US President Donald Trump on Wednesday 9 August. North Korea and the United States traded escalating threats, with Mr Trump threatening Pyongyang with fire and fury like the world has never seen

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A Maasai woman waits in line to vote in Lele, 130 km (80 miles) south of Nairobi, Kenya. Kenyans are going to the polls today to vote in a general election after a tightly-fought presidential race between incumbent President Uhuru Kenyatta and main opposition leader Raila Odinga

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Pro-government supporters march in Caracas, Venezuela on 7 August

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Children pray after releasing paper lanterns on the Motoyasu river facing the Atomic Bomb Dome in remembrance of atomic bomb victims on the 72nd anniversary of the bombing of Hiroshima, western Japan.

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Russian President Vladimir Putin (L), accompanied by defence minister Sergei Shoigu, gestures as he fishes in the remote Tuva region in southern Siberia.

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A family claiming to be from Haiti drag their luggage over the US-Canada border into Canada from Champlain, New York, U.S. August 3, 2017.

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ATTENTION EDITORS -People carry the body of Yawar Nissar, a suspected militant, who according to local media was killed during a gun battle with Indian security forces at Herpora village, during his funeral in south Kashmir's Anantnag district August 4, 2017.

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A general view shows a flooded area in Sakon Nakhon province, Thailand August 4, 2017.

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A plane landed in Sao Joao Beach, killing two people, in Costa da Caparica, Portugal August 2, 2017

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Hermitage Capital CEO William Browder waits to testify before a continuation of Senate Judiciary Committee hearing on alleged Russian meddling in the 2016 presidential election on Capitol Hill in Washington, U.S., July 27, 2017

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TOPSHOT - Moto taxi driver hold flags of the governing Rwanda Patriotic Front's at the beginning of a parade in Kigali, on August 02, 2017. Incumbent Rwandan President Paul Kagame will close his electoral campaigning ahead of the August 4, presidential elections which he is widely expected to win giving him a third term in office

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TOPSHOT - Migrants wait to be rescued by the Aquarius rescue ship run by non-governmental organisations (NGO) "SOS Mediterranee" and "Medecins Sans Frontieres" (Doctors Without Borders) in the Mediterranean Sea, 30 nautic miles from the Libyan coast, on August 2, 2017.

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Two children hold a placard picturing a plane as they take part in a demonstration in central Athens outside the German embassy with others refugees and migrants to protest against the limitation of reunification of families in Germany, on August 2, 2017.

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Flames erupt as clashes break out while the Constituent Assembly election is being carried out in Caracas, Venezuela, July 30, 2017. REUTERS/Carlos Garcia Rawlins

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People in the village of Gabarpora carry the remains of Akeel Ahmad Bhat, a civilian who according to local media died following clashes after two militants were killed in an encounter with Indian security forces in Hakripora in south Kashmir's Pulwama district, August 2, 2017. REUTERS/Danish Ismail

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- Incumbent Rwandan President Paul Kagame gestures as he arrives for the closing rally of the presidential campaign in Kigali, on August 2, 2017 while supporters greet him. Rwandans go the polls on August 4, 2017 in a presidential election in which strongman Paul Kagame is widely expected to cruise to a third term in office.

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Soldiers of China's People's Liberation Army (PLA) get ready for the military parade to commemorate the 90th anniversary of the foundation of the army at Zhurihe military training base in Inner Mongolia Autonomous Region, China.

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Cyclists at the start of the first stage of the Tour de Pologne cycling race, over 130km from Krakow's Main Market Square, Poland

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Israeli border guards keep watch as Palestinian Muslim worshippers pray outside Jerusalem's old city overlooking the Al-Aqsa mosque compound

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A supporter of Pakistan's Prime Minister Nawaz Sharif passes out after the Supreme Court's decision to disqualify Sharif in Lahore

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Australian police officers participate in a training scenario called an 'Armed Offender/Emergency Exercise' held at an international passenger terminal located on Sydney Harbour

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North Korean soldiers watch the south side as the United Nations Command officials visit after a commemorative ceremony for the 64th anniversary of the Korean armistice at the truce village of Panmunjom in the Demilitarized Zone (DMZ) dividing the two Koreas

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Bangladeshi commuters use a rickshaw to cross a flooded street amid heavy rainfall in Dhaka. Bangladesh is experiencing downpours following a depression forming in the Bay of Bengal.

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The Soyuz MS-05 spacecraft for the next International Space Station (ISS) crew of Paolo Nespoli of Italy, Sergey Ryazanskiy of Russia, and Randy Bresnik of the U.S., is transported from an assembling hangar to the launchpad ahead of its upcoming launch, at the Baikonur Cosmodrome in Baikonur, Kazakhstan

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A protester shouts at U.S. President Donald Trump as he is removed from his rally with supporters in an arena in Youngstown, Ohio

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Indian supporters of Gorkhaland chant slogans tied with chains during a protest march in capital New Delhi. Eastern India's hill resort of Darjeeling has been rattled at the height of tourist season after violent clashes broke out between police and hundreds of protesters of the Gorkha Janmukti Morcha (GJM) a long-simmering separatist movement that has long called for a separate state for ethnic Gorkhas in West Bengal. The GJM wants a new, separate state of "Gorkhaland" carved out of eastern West Bengal state, of which Darjeeling is a part.

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Demonstrators clash with riot security forces while rallying against Venezuela's President Nicolas Maduro's government in Caracas, Venezuela. The banner on the bridge reads "It will be worth it"

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The Heathcote river as it rises to high levels in Christchurch, New Zealand. Heavy rain across the South Island in the last 24 hours has caused widespread damage and flooding with Dunedin, Waitaki, Timaru and the wider Otago region declaring a state of emergency.

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A mourner prays at a memorial during an event to commemorate the first anniversary of the shooting spree that one year ago left ten people dead, including the shooter in Munich, Germany. One year ago 18-year-old student David S. shot nine people dead and injured four others at and near a McDonalds restaurant and the Olympia Einkaufszentrum shopping center. After a city-wide manhunt that caused mass panic and injuries David S. shot himself in a park. According to police David S., who had dual German and Iranian citizenship, had a history of mental troubles.

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Palestinians react following tear gas that was shot by Israeli forces after Friday prayer on a street outside Jerusalem's Old City

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Ousted former Thai prime minister Yingluck Shinawatra greets supporters as she arrives at the Supreme Court in Bangkok, Thailand

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AI is coming to war, regardless of Elon Musk's well-meaning concern - The Independent

Amwell is looking to evolve virtual care beyond just imitating in-person care.

To do that, the telehealth companyexpects to use its latestpartnership with Google Cloud toenable it to tap into artificial intelligence and machine learning technologies to create a better healthcare experience, according to Peter Antall, M.D., Amwell's chief medical officer.

"We have a shared vision to advance universal access to care thats cost-effective. We have a shared vision to expand beyond our borders to look at other markets. Ultimately, its a strategic technology collaboration that were most interested in," Antall said of the company's partnership with the tech giant during a STATvirtual event Tuesday.

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"What we bring to the table is that we can help provide applications for those technologiesthat will have meaningful effects on consumers and providers," he said.

The use of AI and machine learning can improve bot-based interactions or decision support for providers, he said. The two companies also want to explore the use of natural language processing and automated translation to provide more "value to clients and consumers," he said.

Joining a rush of healthcare technology IPOs in 2020, Amwell went public in August, raising$742 million. Google Cloud and Amwell also announced amultiyear strategic partnership aimed at expanding access to virtual care, accompanied by a$100 million investmentfrom Google.

During an HLTH virtual event earlier this month, Google Cloud director of healthcare solutions Aashima Gupta said cloud and artificial intelligence will "revolutionize telemedicine as we know it."

RELATED:Amwell files to go public with $100M boost from Google

"There's a collective realization in the industry that the future will not look like the past," said Gupta during the HTLH panel.

During the STAT event, Antall said Amwellis putting a big focus onvirtual primary care, which has become an area of interest for health plans and employers.

"It seems to be the next big frontier. Weve been working on it for three years, and were very excited. So much of healthcare is ongoing chronic conditions and so much of the healthcare spend is taking care ofchronic conditionsandtaking care of those conditions in the right care setting and not in the emergency department," he said.

The companyworks with 55 health plans, which support over 36,000 employers and collectively represent more than 80million covered lives, as well as 150 of the nations largest health systems. To date, Amwell says it has powered over 5.6million telehealth visits for its clients, including more than 2.9million in the six months ended June 30, 2020.

Amwell is interested in interacting with patients beyond telehealth visits through what Antall called "nudges" and synchronous communication to encouragecompliance with healthy behaviors, he said.

RELATED:Amwell CEOs on the telehealth boom and why it will 'democratize' healthcare

It's an area where Livongo, recently acquired by Amwell competitor Teladoc,has become the category leader by using digital health tools to help with chronic condition management.

"Were moving into similar areas, but doing it in a slightly different matter interms of how we address ongoing continuity of care and how we address certain disease states and overall wellness," Antallsaid, in reference to Livongo's capabilities.

The telehealth company also wants to expand into home healthcare through the integration of telehealth and remote care devices.

Virtual care companies have been actively pursuing deals to build out their service and product lines as the use of telehealth soars. To this end, Amwell recently deepened its relationship with remote device company Tyto Care. Through the partnership, the TytoHome handheld examination device that allows patients to exam their heart, lungs, skin, ears, abdomen, and throat at home, is nowpaired withAmwells telehealth platform.

Looking forward, there is the potential for patients to getlab testing, diagnostic testing, and virtual visits with physicians all at home, Antall said.

"I think were going to see a real revolution in terms ofhow much more we can do in the home going forward," he said.

RELATED:Amwell's stock jumps on speculation of potential UnitedHealth deal: media report

Amwell also is exploring the use of televisions in the home to interact with patients, he said.

"We've done work with some partners and we're working toward a future where, if it's easier for you to click your remote and initiate a telehealth visit that way, thats one option. In some populations, particularly the elderly, a TV could serve as a remote patient device where a doctor or nurse could proactively 'ring the doorbell' on the TV and askto check on the patient," Antall said.

"Its video technology that'salready there in most homes, you just need a camera to go with it and a little bit of software.Its one part of our strategy to be available for the whole spectrum of care and be able to interact in a variety of ways," he said.

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Amwell CMO: Google partnership will focus on AI, machine learning to expand into new markets - FierceHealthcare

The race for artificial intelligence technology is on, and while tech giants like Google and Facebook snap up top talent to build out their own AI-powered products, a new startup has just raised a huge round of funding to help the rest of us.

Element AI a Montreal-based platform and incubator that wants to be the go-to place for any and all companies (big or small) that are building or want to include AI solutions in their businesses, but lack the talent and other resources to get started is announcing a mammoth Series A round of $102 million. It plans to use the funding for hiring talent, for business development, and also, to put some money where its mouth is, by selectively investing in some of the solutions that will be built within its doors.

Our goal remains to lower the barrier for entry for commercial applications in AI, said Jean-Franois Gagn, the CEO of Element AI, in an interview. Everyone wants to have these capabilities, its hard for most companies to pull it off because of the lack of talent or access to AI technology. That is the opportunity. The company currently has 105 employees and the plan is to ramp that up to 250 in the next couple of months, he said.

The round was led by the prolific investorData Collective, with participation from a wide range of key financial and strategic backers. They include Fidelity Investments Canada, Koreas Hanwha, Intel Capital, Microsoft Ventures, National Bank of Canada, NVIDIA, Real Ventures, and several of the worlds largest sovereign wealth funds.

This large Series A has been swift: it comes only six months after Element AI announced a seed round from Microsoft Ventures (of an undisclosed amount), and only eight months after the company launched.

Weve asked Gagn and Element AIs investors, but no one is disclosing the valuation.However, what we do know is that the startup already has several companies signed up as customers and working on paid projects; and it has hundreds of potential companies on its list for more work.

As weve been engaging with corporates and startups [to be in our incubator] we have realized that being engaged in both at the same time is not easy, Gagn said. Weve started to put together a business network, including taking positions in startups to help them by investing capital, resources, providing them with technology and bringing them all the tools they need to accelerate the development of their apps and help them connect with large corporates who are their customers. The aim is to back up to 50 startups in the field, he said.

The strategic investors also fit into different parts of Element AIs business funnel. Some like Nvidia are working as partners for business in its case, using its deep learning platform, according to Jeff Herbst, VP of business development for NVIDIA. Element AI will benefit by continuing to leverage NVIDIAs high performance GPUs and software at large scale to solve some of the worlds most challenging issues, he said in a statement. Others, likeHanwha, are coming in as customer-investors, there to take advantage of some of the smarts.

AI in its early days may have been the domain of tech companies like Google, Apple and IBM when it came to needing and commercializing it, but these days, the wide range of solutions that can be thought of as AI-based, and applications for it, can touch any and all aspects of a business, from back-office functions and customer-facing systems, through to cybersecurity and financial transactions, to manufacturing, logistics and transportation, and robotics.

But the big issue has been that up to now, the most innovative startups in these areas are getting snapped up by the large tech giants (sometimes directly from the universities where they form, sometimes a bit later).

Then consider those that are independent and arent getting acquired (yet). There still remains a gap for most companies between what skills are out on the market to be used, and what would be the most useful takeaway for their own businesses.

In other words, many considering how to use AI in their businesses are effectively starting from scratch. Longer term, that disparity between the AI haves and have-nots could prove to be disastrous for the idea of democratising intellectual power and all the spoils that come with it.

There is not a lot left in the middle, Data Collectives Matt Ocko said in an interview. The issue with corporations, governments and others trapped in that no mans land of AI have-nots is that their rivals with superior AI-powered decision making and signal processing will dominate global markets.

The idea of building an AI incubator or safe space where companies that might even sometimes compete against each other, are now sitting alongside each other talking to the same engineers to build their new products, may be an industry first.

But the basic model is not: Element AI is tackling this problem essentially by leaning on trends in outsourcing: systems integrators, business process outsourcers, and others have built multi-billion dollar businesses by providing consultancy or even fully taking the reins on projects that businesses do not consider their core competency.

The same is happening here.Element AI says that initial products that can be picked up there include predictive modeling, forecasting models for small data sets, conversational AI and natural language processing, image recognition and automatic tagging of attributes based on images, aggregation techniques based on machine learning, reinforcement learning for physics-based motion control, compression of time-series data, statistical machine learning algorithms, voice recognition, recommendation systems, fluid simulation, consumer engagement optimization and computational advertising.

I asked, and I was told multiple times, that essentially colocating their R&D next to other first, for now, is not posing a problem for the companies who are getting involved. If anything, for those who understand the big-data aspect of AI intelligence, they can see that the benefit for one will indirectly benefit the rest, and speed everything up.

That model is what made Yoshua Bengio the godfather of machine learning so excited about co-founding this company, Ocko said. That massive research advantage leads Element AI to be able to deliver technically advantaged, increasingly cost effective solutions. It means they dont have to treat AI decision making capability as a scare resource, wielded like a club on everyone else.

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Element AI, a platform for companies to build AI solutions, raises ... - TechCrunch

As AI has grown from a menagerie of research projects to include a handful of titanic, industry-powering models like GPT-3, there is a need for the sector to evolve or so thinks Dario Amodei, former VP of research at OpenAI, who struck out on his own to create a new company a few months ago. Anthropic, as its called, was founded with his sister Daniela and its goal is to create large-scale AI systems that are steerable, interpretable, and robust.

The challenge the siblings Amodei are tackling is simply that these AI models, while incredibly powerful, are not well understood. GPT-3, which they worked on, is an astonishingly versatile language system that can produce extremely convincing text in practically any style, and on any topic.

But say you had it generate rhyming couplets with Shakespeare and Pope as examples. How does it do it? What is it thinking? Which knob would you tweak, which dial would you turn, to make it more melancholy, less romantic, or limit its diction and lexicon in specific ways? Certainly there are parameters to change here and there, but really no one knows exactly how this extremely convincing language sausage is being made.

Its one thing to not know when an AI model is generating poetry, quite another when the model is watching a department store for suspicious behavior, or fetching legal precedents for a judge about to pass down a sentence. Today the general rule is: the more powerful the system, the harder it is to explain its actions. Thats not exactly a good trend.

Large, general systems of today can have significant benefits, but can also be unpredictable, unreliable, and opaque: our goal is to make progress on these issues, reads the companys self-description. For now, were primarily focused on research towards these goals; down the road, we foresee many opportunities for our work to create value commercially and for public benefit.

The goal seems to be to integrate safety principles into the existing priority system of AI development that generally favors efficiency and power. Like any other industry, its easier and more effective to incorporate something from the beginning than to bolt it on at the end. Attempting to make some of the biggest models out there able to be picked apart and understood may be more work than building them in the first place. Anthropic seems to be starting fresh.

Anthropics goal is to make the fundamental research advances that will let us build more capable, general, and reliable AI systems, then deploy these systems in a way that benefits people, said Dario Amodei, CEO of the new venture, in a short post announcing the company and its $124 million in funding.

That funding, by the way, is as star-studded as you might expect. It was led by Skype co-founder Jaan Tallinn, and included James McClave, Dustin Moskovitz, Eric Schmidt and the Center for Emerging Risk Research, among others.

The company is a public benefit corporation, and the plan for now, as the limited information on the site suggests, is to remain heads-down on researching these fundamental questions of how to make large models more tractable and interpretable. We can expect more information later this year, perhaps, as the mission and team coalesces and initial results pan out.

The name, incidentally, is adjacent to anthropocentric, and concerns relevancy to human experience or existence. Perhaps it derives from the Anthropic principle, the notion that intelligent life is possible in the universe because well, were here. If intelligence is inevitable under the right conditions, the company just has to create those conditions.

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Anthropic is the new AI research outfit from OpenAIs Dario Amodei, and it has $124M to burn - TechCrunch

Atul Ajoy, left, and Michael Royzen, right, are high school students and software developers whove launched their own startups. (GeekWire Photo / Todd Bishop)

Michael Royzen and Atul Ajoy have several things in common, besides being high-school students in the Seattle region.

Theyre both entrepreneurs and software developers who, despite being in their teens, have already launched their own technology startups and projects. And theyve both been invited to major software development conferences: Royzen, 17, attended Apples WWDC in San Francisco and Ajoy, 15, went to Microsoft Build in Seattle.

They had never met before, but it struck us that they would have a lot to talk about, and some interesting insights to share based on their similar but separate experiences. So we brought them together to talk about their experiences on the GeekWire podcast. And we werent disappointed.

Listen below, download the MP3, and keep reading for highlights.

Atul Ajoy will be a 10th grader next year at Redmond High School. A tech enthusiast and frequent blogger,hes the founder of a startup called Chromata, which is reimagining school fundraising with artificial intelligence, machine learning, and blockchain. He wrote about his experience attending Microsoft Build in a guest post on GeekWire earlier this year.

Michael Royzen will be a senior at the The Bush School. Hes a software developer and entrepreneur who founded and serves as CEO of his own company, Mlab Technologies, Inc., which makes apps for Apple platforms, including Rydeand RecipeReadr. A past GeekWire Geek of the Week, he attended Apples Worldwide Developer conference at Apples invitation.

Given their respective backgrounds and platforms of choice, we originally thought this might be a Microsoft vs. Apple conversation, but Royzen is actually interning this summer at Microsoft Research, giving him an understanding and appreciation for the Redmond tech giant, as well.

So at a time when many of their peers are focused on the Snapchats and Instagrams of the world, why are Microsoft and Apple relevant to these teen entrepreneurs? A big part of the answer is their developer platforms.

Microsofts really relevant today because not only do they care about their first-party applications, but their cloud platform Azure and then other services that they provide really help third-party companies even my own startup to get started and build products that anyone in the world can use, Ajoy said. Microsoft doesnt get enough credit for this, but theyre doing a lot of cool things that enable the next generation of technology.

He cited, as an example, the Microsoft HoloLens mixed reality headset, which he got a chance to experience first-hand at Microsoft Build.

Royzen agreed with many of Ajoys comments about Microsoft, citing the companys huge, huge focus on the cloud and artificial intelligence.

Apple, I think is a very different company, he said. Apple is mostly a hardware company thats focused on selling to consumers. They really ride waves of popular culture. One notable difference, as he pointed out, is that Apple is doing machine-learning processing on-device vs. in the cloud.

They both use Amazon Web Services, thanks to AWS credits they were able to get as students. But they said theyre also impressed with the ease-of-use, rapid release schedules and features of Microsoft Azure and Google Cloud.

Theyre also optimistic about artificial intelligence and expressed optimism that humanity can avoid the doomsday AI scenarios.

There is reason to be wary of whats coming, Ajoy acknowledged, but I think that if we band together and are responsible about what we do, well see new companies form that are just as successful as these, and see that technology can transform our lives, the way we live, the way we communicate, and the way we almost do anything.

I think it will be a smooth transition, just like it was from no Internet to Internet. Perhaps it will be quicker, but it wont be as if you wake up one day and robots have taken over the entire world, unless were incredibly irresponsible about AI. It will be a gradual transition, one towards a world where everything is more automated and hopefully people are more productive.

What advice would they give to other teens who want to get started in software development or startups?

Follow your passion, Royzen said. If you have this inkling of something you want to get started, that means theres something in you thats driving you.

What really helped me is to look inside and figure out why you want to do this, and to use that as a starting point. For me, I was really fascinated by iOS, Apples consumer devices, and I wanted to see how I could help people by creating software solutions for those devices, he said.

Ajoy agreed with that sentiment: Dont try until you know that you have a passion for it, he said. Once you do, its really easy to get to any point you want, because once you set your goal for yourself, if you want to do it, you can get there.

If you like what you do, it will become fun rather than hard, he said. Make sure this is your passion, decide what your passion is, and then follow it relentlessly, and youll definitely get as far as you want to go.

Listen to the entire conversation above or download the MP3 here.

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What these teen tech entrepreneurs think about Microsoft, Amazon, Apple, AI, the cloud and more - GeekWire