Samsung and LG go head to head with AI-powered fridges that recognize food – The Verge

Get ready for a smart fridge showdown at CES 2020, because Samsung and LG will both be unveiling fridges with added artificial intelligence capabilities this year. Samsungs latest edition of its Family Hub refrigerator and LGs second-generation InstaView ThinQ fridge both tout AI-equipped cameras that can identify food. The idea is that the cameras can scan whats inside and let users know what items theyre short on, even making meal suggestions based on the ingredients they still have.

Samsungs Family Hub smart fridge was first unveiled at CES 2016, and since then, the company has been rolling out updated iterations with Bixby support, SmartThings integration, and AKG speakers. The latest edition adds software upgrades to enable AI image recognition in its View Inside cameras.

Before, the cameras let users see whats in their fridges from their smartphones, a useful feature if you happen to be out grocery shopping and cant remember what you need to stock up on. With the AI-enabled updates, Family Hub will supposedly make these recommendations for you on its own, identifying which ingredients youre low on. Though its to be determined how well the image recognition will work for example, how will it deal with ingredients stored in tubs of Tupperware?

The software upgrades also include improved meal planning with the help of Whisk, a food tech startup Samsung acquired last year. Whisk lets users plan meals for up to a week and then creates smart shopping lists using ingredients that apply to multiple recipes.

Finally, the huge built-in touchscreen that can be used as a virtual bulletin board can now support video clips, as well as mirror content from Samsung TVs and phones. That means you can watch vertical videos like IGTV on your Samsung fridge, as God intended.

LG is showing off two models of its InstaView fridges, both of which feature a 22-inch display that can turn transparent to let users see whats inside without opening the door and letting the cold air out. Theres the AI-equipped InstaView ThinQ and the InstaView with Craft Ice, which makes fancy, two-inch spherical ice balls. Those are supposed to melt slower than regular ice, if thats a problem that you have. The InstaView with Craft Ice was released in the US last year, but will now be available in more markets.

Theres no pricing information yet, but based on the prices for LG and Samsungs previous fridge models, customers can expect prices to range from $4,500 to $6,000. Samsung says its Family Hub updates will be available in the spring.

Im not opposed to the idea of a huge Wi-Fi-connected touchscreen on a fridge in fact, it seems like a genuinely useful way to look up recipes or display cute photos and videos. Im skeptical how well the AI will identify different ingredients, and whether using a computer to see what items youre low on is really better than just taking a look for yourself.

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Samsung and LG go head to head with AI-powered fridges that recognize food - The Verge

SumUp co-founders are back with bookkeeping AI startup Zeitgold – TechCrunch

The next time you go to your favorite restaurant or cocktail bar, talk with the manager about bookkeeping. Chances are that theyll tell you that they waste a ton of time collecting and recording various documents. German startup Zeitgold wants to automate this pesky process so that you can spend more time on your actual business.

The startup was founded by two of the co-founders behind SumUp Stefan Jeschonnek and Jan Deepen who left the mobile payment company in 2014. Kobi Eldar is the third co-founder. Zeitgold just raised $4.5 million from Battery Ventures and Holtzbrinck Ventures Invest (4.2 million).

So what is Zeitgold exactly? Its an all-in-one financial solution for small shop owners. The company sends you a physical box. You can put all your receipts, bills and invoices in this box. Somebody will come and pick up the box every week. The startup then scans and archives all these documents to process them Zeitgold also accepts digital documents.

This way, Zeitgold can build a structured database of all this paperwork and provide you actionable information. For instance, you can then open the Zeitgold app and accept all payment requests from your suppliers. The startup can also help you when it comes to invoice collection, payroll and everything your tax advisor will need.

You can also use the app as your one-stop shop to search for that one document youre looking for. Thanks to OCR, you can just type keywords. Zeitgold also tells you if there are missing documents.

Eventually, Zeitgold wants to automate most of it with optical character recognition, text analytics and a bunch of algorithms. Its not there yet, and a lot of work is still done by humans. But the startups team of accounting experts will train Zeitgolds algorithms over time so that it relies less on humans.

We spent the last 18 months on R&D. Now, our AI is able to create clean data sets out of unsorted physical documents, Jeschonnek told me.

The company also didnt just want to tackle one aspect of the financial paperwork. This is an interesting approach and Im curious to see if it can scale indeed.

The startup is launching in Germany first, but Zeitgold is already thinking about expanding to other countries around Germany as all shop owners face the same issues.

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SumUp co-founders are back with bookkeeping AI startup Zeitgold - TechCrunch

Elon Musk: AI is a ‘fundamental existential risk for human civilisation’ and creators must slow down – The Independent

Elon Musk has branded artificial intelligencea fundamental existential risk for human civilisation.

He says we mustnt wait for a disaster to happen before deciding to regulate it, and that AI is, in his eyes, the scariest problem we now face.

He also wants the companies working on AI to slow down to ensure they dont unintentionally build something unsafe.

The CEO of Tesla and SpaceX was speaking on-stage at the National Governors Association at the weekend.

I have exposure to the most cutting-edge AI and I think people should be really concerned about it, he said. I keep sounding the alarm bell but until people see robots going down the street killing people, they dont know how to react because it seems so ethereal.

I think we should be really concerned about AI and I think we should AIs a rare case where I think we need to be proactive in regulation instead of reactive. Because I think by the time we are reactive in AI regulation, its too late.

Normally the way regulations are set up is that a whole bunch of bad things happen, theres a public outcry, and then after many years, a regulatory agency is set up to regulate that industry. Theres a bunch of opposition from companies who dont like being told what to do by regulators. It takes forever.

That, in the past, has been bad but not something which represented a fundamental risk to the existence of civilisation. AI is a fundamental risk to the risk of human civilisation, in a way that car accidents, airplane crashes, faulty drugs or bad food were not. They were harmful to a set of individual in society, but they were not harmful to society as a whole.

AI is a fundamental existential risk for human civilisation, and I dont think people fully appreciate that.

However, he recognises that this will be easier said than done, since companies dont like being regulated.

Also, any organisation working on AI will be crushed by competing companies if they dont work as quickly as possible, he said. It would be up to a regulator to control all of them.

When its cool and regulators are convinced that its safe to proceed, then you can go. But otherwise, slow down.

He added: I think wed better get on [introducing regulation] with AI, pronto. Therell certainly be a lot of job disruption because whats going to happen is robots will be able to do everything better than us. Im including all of us.

Earlier this year, Mr Musk said that humans will have to merge with machines to avoid becoming irrelevant.

Ray Kurzweil, a futurist and Googles director of engineering, believes that computers will have human-level intelligence by 2029.

However, he believes machines will improve humans, making us funnier, smarter and even sexier.

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Elon Musk: AI is a 'fundamental existential risk for human civilisation' and creators must slow down - The Independent

New AI tool will help you sleep better – VentureBeat

A good nights sleep is the foundation for a healthy life. Insufficient sleep leads to daytime drowsiness and impaired cognitive function, and it can put you at risk for more serious conditions such as heart disease, high blood pressure, and diabetes. Understanding sleep and the issues around it is an essential public health concern.

When we become sleep-deprived, the effects are astonishing, said Dr. Sujay Kansagra, director of Duke Universitys sleep medicine program and sleep health consultant for Mattress Firm. Even a single night of disrupted or shortened sleep can wreak havoc on our bodies. In addition to the sleep you need every night, lost sleep will accumulate, causing you to need to sleep even more to make up for any hours previously missed.

Due to the importance of sleep to public health, researchers are racing to find new ways to monitor and improve sleep habits, including using artificial intelligence (AI). While there are a number of AI sleep technologies to keep an eye on, one in particular stands out a new sensor that uses radio waves translated by machine-learning algorithms to monitor your sleep phases without the need for any intrusive wearable devices.

Researchers at MIT and Massachusetts General Hospital, including professors of engineering and computer science Dina Katabi and Tommi Jaakkola, as well as Matt Bianchi, Chief of the hospitals Division of Sleep Medicine, joined forces to develop and test the new technology. A wireless device, similar to a Wi-Fi router, emits low-power radio frequency signals, which bounce off of the body. The AI algorithms analyze the data and translate the measurements of pulse, breathing, and other factors into the major sleep stages light sleep, deep sleep, and REM sleep.

Previous attempts to use radio waves to evaluate the quality of sleep have maxed out at 65% accuracy, but the MIT/MGH team has achieved an 80% success rate in accurately measuring sleep stages, comparable to the accuracy rate of the electroencephalography (EEG) machines currently used in scientific sleep studies. This technology may lead to a greater understanding of sleep and take sleep studies out of the lab and into the real world.

The opportunity is very big because we dont understand sleep well, and a high fraction of the population has sleep problems, says Mingmin Zhao, an MIT graduate student working on the project. We have this technology that, if we can make it work, can move us from a world where we do sleep studies once every few months in the sleep lab to continuous sleep studies at home.

Previous attempts to use radio waves and AI to measure sleep stages have been hampered by the extra information, not related to sleep, that confuse the AI algorithms. The MIT/MGH team came up with a combination of three deep neural network algorithms to get the measurements they were looking for. The first uses a neural network for image recognition to parse snapshots of the data. The second uses a neural net for temporal pattern measurement to calculate the dynamics of the various sleep stages light, deep, and REM. A third refines the analysis to make it comparable across test subjects. The team tested the sensor and algorithms on 25 healthy patients, measuring sleep stages with the aforementioned 80% accuracy.

The researchers intend to also study how Parkinsons disease affects sleep. Both sleep issues and cognition are important, but often overlooked, contributors to the burden of the disease, says Joyce Oberdorf, President and CEO of the National Parkinson Foundation. Further research may be able to lessen those burdens.

Other potential uses include the study of sleep apnea, chronic insomnia, and even hard-to-detect mid-sleep epileptic seizures, along with studying sleep disorders that can be precursors for other problems. Beyond sleep application, similar combinations of sensors using radio waves monitored by AI algorithms may be used to measure and predict the decline of function in a number of other health areas. One thing is for surean increase in available health information is imminent.

Alice Williams is a freelance writer that specializes in tech and business.

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New AI tool will help you sleep better - VentureBeat

Enterprises Increased AI Spending By 62% Last Year – Forbes

Getty

These and many other insights are from MIT Sloan Management Reviews recent study completed in collaboration with SAS, How AI Changes the Rules: New Imperatives for the Intelligent Organization. A copy of the survey can be downloaded here (PDF, 24 pp., no opt-in). Its a quick, interesting read that provides examples from enterprises actively adopting AI today, sharing their lessons learned. The methodology is based on a global online survey completed during June and July 2019, interviewing 2,280 survey respondents from MIT Sloan Management Review readers. 80% of respondents hold C-suite, board, or management roles distributed across 110 countries. 37% of respondents are from North America, 22% from Asia, and 20% from Europe. MIT Sloan Management followed up with interviews with analytics experts, including practitioners, consultants, and academics. These individuals provided insight into how the drive to implement AI is changing organizational culture, technology strategy, and technology governance.

The following are the key insights from the report:

MIT Sloan Management Review & SAS Study, How AI Changes the Rules: New Imperatives for the Intelligent Organization.

MIT Sloan Management Review & SAS Study, How AI Changes the Rules: New Imperatives for the Intelligent Organization.

MIT Sloan Management Review & SAS Study, How AI Changes the Rules: New Imperatives for the Intelligent Organization.

MIT Sloan Management Review & SAS Study, How AI Changes the Rules: New Imperatives for the Intelligent Organization.

MIT Sloan Management Review & SAS Study, How AI Changes the Rules: New Imperatives for the Intelligent Organization.

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Enterprises Increased AI Spending By 62% Last Year - Forbes

Amazon Alexa Can Now Help Diagnose What Ails You – Fortune

Amazon Echo with its microphone disabled. Jason Cipriani

Amazon Alexa users with health issues can now have Alexa call Healthtap's Doctor AI to help figure out what's wrong and direct them to act accordingly.

Doctor AI initially supported Apple iOS and Android devices and its first voice application, Talk to Docs launched for those same devices in 2013. But support for Alexa, the smart voice-activated software that debuted with the Amazon Echo connected speaker, brings Dr. AI to home users who might not be adept at using screens.

"We'd been doing text and video before, then expanded into voice and that's exciting in healthcare because we serve many populations that are older, disabled, or frail," said Ron Gutman, founder and chief executive of Palo Alto, California-based digital health company which claims 107,000 doctors in its network. Records and data from those doctors make up Dr. AI's health care data trove.

"Voice is cool, but more important, it's fills a real need of people who have difficulty using their hands or whose eyesight is not that great," Gutman added.

Healthtap's staff worked to make voice interactions natural and conversational, Gutman said. Healthtap used Amazon's tool set to build an Alexa SkillAmazon ( amzn ) lingo for an app. But the really sophisticated part lays in the backend where Healthtap's artificial intelligence technology parses data from all those medical records to consider what the user's symptoms most likely mean and convey that information in a human-like way.

Dr. AI will provide feedback on the user's stated problem and, if it deems necessary, connect the user with on of Healthtap's associated physicians.

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Healthtap also offers what it calls a Health Operating System (HOPES) cloud-based software to hospitals and other large healthcare organizations. It's goal is to make sense of myriad services used to track appointments, patient records, and lab results. In that arena it competes with companies like AthenaHealth ( athn ) and McKesson ( mck ) .

For more on Healthtap, watch:

A basic version of the Dr. A.I. and Alexa is now available via the Amazon Echo or mobile devices. More advanced services that connect to the Health Operating System are also available for clinics, hospital systems, and insurance companies.

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Amazon Alexa Can Now Help Diagnose What Ails You - Fortune

The Go-To Glossary for Marketers Needing to Brush Up on AI – AdAge.com

Every corner of marketing and technology is being shaped by AI. Credit: Bakal/iStock

It's not enough for marketers to collect petabytes of data; it takes a sharp mind to make sense of it all. Actually it takes a nonhuman one.

That's why artificial intelligence has invaded the marketing world, with Facebook, Google, Salesforce, IBM, Amazon and others building machine learning into their platforms.

Now marketers must understand the lingo if they're going to survive the machines. To help, here's a guide to the terminology around A.I.

Machine learning

When a machine teaches itself with minimal programming needed. Google showed off the powers of machine learning when it created a computer that learned the rules to the ancient game Go. Machine learning can be helpful in direct marketing and email marketing, in particular, by ingesting sense of vast sets of consumer data and using it to determine things such as the best times to send emails. Proponents say it can also identify the clients or customers that would be most receptive to given messages. And machine learning is used in ad targeting to help deliver messages to those audiences.

Image recognition

AI looks for patterns in images. Machines can obviously analyze many more images than humans, and with machine learning they can identify what's in the images and reveal patterns that people would never detect. Brands can use image recognition technology, for example, to find every photo online in which their logos appear. That could help brands locate their most loyal customers and tease out other actionable marketing insights. "Computer vision" is a term associated with image recognition, and refers to computer programs that analyze and categorize digital images.

Clusters

A fancy word for a group of peopleor anythingsharing a common characteristic. AI programs can identify clusters within mountains of data, uncovering patterns that humans alone couldn't perceive or connections that people alone wouldn't draw. Clusters can lead to developing audiences or segments for marketing purposes, creating a group of people with common traits and targeting them with ads.

Unstructured data

A term for disorganized pools of data that appear random and unconnected. Machine learning can make sense of the data, and it can identify clusters within it and other patterns that could be useful when making marketing decisions. Marketers gather all kinds of data, even if it doesn't seem relevant at the time, because they never know when something interesting is going to pop up.

Natural Language Processing

The technology that enables machines to interpret what people are saying in words or in text. Sophisticated AI can decipher speech, not just understanding the words but the context. Advanced natural language processing, or NLP, could detect sarcasm and other subtle human tones. Natural language processing is essential to the automated customer service of the future.

Chatbots

The programs running inside messaging apps and on websites that help consumers perform simple tasks. Chatbots were popularized by apps like Facebook Messenger and Kik, and they operate like apps within the messaging services. Brands and publishers build chatbots to do things like deliver news stories and facilitate e-commerce transactions. The smarter chatbots become, the better they understand language, the more useful they can be. Chatbots could become the personal assistants of the future, booking flights, making reservations and handling schedules.

Deep Learning

A more advanced branch of machine learning, where a computer teaches itself with only minimal amounts of programming. With deep learning, marketers can make the most use of data and apply it to make predictions about consumer behavior.

Neural Networks

Artificial intelligence programs modeled after the human brain. They incorporate deep learning and natural language processing to perform functions like recognizing handwriting and faces in photos.

Dynamic Pricing

One of the common tasks that deep learning-based programs can perform, setting prices based on consumer data. Dynamic pricing means that each consumer is presented with a price based on their own particular circumstances, depending on the time of day, their financial situation and other factors. Dynamic pricing can come into play in rates for plane tickets, for instance. AI can help craft personalized prices that are most likely to ensure a sale at the most efficient rate.

Recommendations/Content Curation

AI is useful in figuring out what goods to recommend to shoppers based on data, the way Amazon volunteers products that a visitor to its site might want. When a consumer visits a sneaker website, for instance, AI can help determine which the person might like based on their past browsing habits and other factors. A website could be customized for each visitor, much as Facebook's algorithm personalizes the feed users see, tapping AI to order its content in the way most likely to appeal.

Weak/Narrow AI

AI that's limited to specific tasks. Basically all of the AI found in marketing is weak. Most AI overallthe code behind everything from virtual assistants to self-driving carsis actually considered weak. The next evolution is artificial general intelligence, bringing us closer to the long-anticipated, futuristic robots that could outperform any human at any task.

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The Go-To Glossary for Marketers Needing to Brush Up on AI - AdAge.com

A.I. Versus the Coronavirus – The New York Times

Advanced computers have defeated chess masters and learned how to pick through mountains of data to recognize faces and voices. Now, a billionaire developer of software and artificial intelligence is teaming up with top universities and companies to see if A.I. can help curb the current and future pandemics.

Thomas M. Siebel, founder and chief executive of C3.ai, an artificial intelligence company in Redwood City, Calif., said the public-private consortium would spend $367 million in its initial five years, aiming its first awards at finding ways to slow the new coronavirus that is sweeping the globe.

I cannot imagine a more important use of A.I., Mr. Siebel said in an interview.

Known as the C3.ai Digital Transformation Institute, the new research consortium includes commitments from Princeton, Carnegie Mellon, the Massachusetts Institute of Technology, the University of California, the University of Illinois and the University of Chicago, as well as C3.ai and Microsoft. It seeks to put top scientists onto gargantuan social problems with the help of A.I. its first challenge being the pandemic.

The new institute will seek new ways of slowing the pathogens spread, speeding the development of medical treatments, designing and repurposing drugs, planning clinical trials, predicting the diseases evolution, judging the value of interventions, improving public health strategies and finding better ways in the future to fight infectious outbreaks.

Condoleezza Rice, a former U.S. secretary of state who serves on the C3.ai board and was recently named the next director of the Hoover Institution, a conservative think tank on the Stanford campus, called the initiative a unique opportunity to better manage these phenomena and avert the worst outcomes for humanity.

The new institute plans to award up to 26 grants annually, each featuring up to $500,000 in research funds in addition to computing resources. It requires the principal investigators to be located at the consortiums universities but allows partners and team members at other institutions. It wants coronavirus proposals to be submitted by May and plans to award its first grants in June. The research findings are to be made public.

The institutes co-directors are S. Shankar Sastry of the University of California, Berkeley, and Rayadurgam Srikant of the University of Illinois, Urbana-Champaign. The computing power is to come from C3.ai and Microsoft, as well as the Lawrence Berkeley National Laboratory at the University of California and the National Center for Supercomputing Applications at the University of Illinois. The schools run some of the worlds most advanced supercomputers.

Successful A.I. can be extremely hard to deliver, especially in thorny real-world problems such as self-driving cars. When asked if the institute was less a plan for practical results than a feel-good exercise, Mr. Siebel replied, The probability of something good not coming out of this is zero.

In recent decades, many rich Americans have sought to reinvent themselves as patrons of social progress through science research, in some cases outdoing what the federal government can achieve because its goals are often unadventurous and its budgets unpredictable.

Forbes puts Mr. Siebels current net worth at $3.6 billion. His First Virtual Group is a diversified holding company that includes philanthropic ventures.

Born in 1952, Mr. Siebel studied history and computer science at the University of Illinois and was an executive at Oracle before founding Siebel Systems in 1993. It pioneered customer service software and merged with Oracle in 2006. He founded what came to be named C3.ai in 2009.

The first part of the companys name, Mr. Siebel said in an email, stands for the convergence of three digital trends: big data, cloud computing and the internet of things, with A.I. amplifying their power. Last year, he laid out his thesis in a book Digital Transformation: Survive and Thrive in an Era of Mass Extinction. C3.ai works with clients on projects like ferreting out digital fraud and building smart cities.

In an interview, Eric Horvitz, the chief scientist of Microsoft and a medical doctor who serves on the spinoff institutes board, likened the push for coronavirus solutions to a compressed moon shot.

The power of the approach, he said, comes from bringing together key players and institutions. We forget who is where and ask what we can do as a team, Dr. Horvitz said.

Seeing artificial intelligence as a good thing perhaps a lifesaver is a sharp reversal from how it often gets held in dread. Critics have assailed A.I. as dangerously powerful, even threatening the enslavement of humanity to robots with superhuman powers.

In no way am I suggesting that A.I. is all sweetness and light, Mr. Siebel said. But the new institute, he added, is a place where it can be a force for good.

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A.I. Versus the Coronavirus - The New York Times

AI Researchers Hate The "Terminator" Movies With a Passion – Futurism

Science Fiction

Theres a new Terminator movie coming out and artificial intelligence researchers really hope you dont see it.

The issue lies in the films misrepresentation of AI, several experts told BBC News in a fascinating new story, which they worry could stir up irrational fears about the tech the same way Jaws once made moviegoers afraid to go in the water.

[The films] paint a picture which is really not coherent with the current understanding of how AI systems are built today and in the foreseeable future, AI pioneer Yoshua Bengio told the outlet.

The reality is that todays AI systems are hyper-specialized, with programmers often designing an AI to excel at just one task, such as playing a board game. Researchers havent come close to creating a Terminator-level artificial general intelligence capable of acting beyond the control of its creator.

We are very far from super-intelligent AI systems, Bengio told BBC News, and there may even be fundamental obstacles to get much beyond human intelligence.

Still, while todays AI systems may not resemble those of the Terminator films, that doesnt mean the public shouldnt consider the harm AI is already inflicting upon society and the dangers lurking ahead.

AI is already helping us destroy our democracies and corrupt our economies and the rule of law, Joanna Bryson, an AI expert at the University of Bath, told BBC News, adding that it is good to get people thinking about the problems of autonomous weapons systems.

READ MORE: Why Terminator: Dark Fate is sending a shudder through AI labs [BBC News]

More on autonomous weapons: Experts: Itd Be Relatively Easy to Deploy Killer Robots by 2021

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AI Researchers Hate The "Terminator" Movies With a Passion - Futurism

Squirrel AI Learning appears at Top European Education Summit OEB and gives a Keynote Speech as the Only Educational Technology Company from China -…

Dr. Wei Cui, a co-founder and chief scientist of Squirrel AI Learning, shared the podium with global educational and technological hotshots such as Edith Hooge, Chairman of the Education Committee of the Netherlands, Bryan Alexander, a writer and teacher at the Georgetown University and a futurologist and research fellow at the Bryan Alexander Consulting, Olivier Crouze, a co-founder of the "42" Educational Institution, Iyad Rahwan, the founder of the Human and Machine Center of Max Planck Institute for Evolutionary Anthropology and an associate professor at the MIT Media Lab, as well as writers Paul Kirschner and Audrey Watters. They discussed how the development of AI and other new technologies would change the learning styles and solve the difficult problems of professional skill training and how education should be developed to cope with future changes.

After more than ten years of development since its establishment in 1995, OEB Global has gradually grown into Europe's largest annual educational technology summit with energetic support from the Federal Ministry of Education and Research of Germany and the Europe Education and Culture Administration. OEB Global has great influence in the global educational circles.

What is education able to provide in an era of fast technological changes? What new changes have ever taken place? Themed "Shaping the Future of Learning", this year's OEB Global was held to discuss topics about future education, including the application of data and technology in learning, the development of personalized data-driven education, the development of educational robotics, the training of future-oriented skills and the way to learn for practice in the future. The participating experts expressed different ideas and opinions on future development.

Instrumental in strengthening personalized learning, AI is essential for future skill training

As a leading global AI adaptive education provider and the only Chinese educational technology company to be invited to the summit, Dr. Wei Cui, a co-founder and chief scientist of Squirrel AI Learning, expressed his views at the summit. He emphasized the importance of data and technology to the future development of learning solutions.

As China's first AI unicorn company to apply an AI adaptive learning technology in the K-12 education field, Squirrel AI Learning has successfully developed the country's first advanced algorithm-based AI adaptive learning engine with completely independent intellectual property rights.

The co-founder and chief scientist of Squirrel AI Learning Dr. Wei Cui gave his opinion about the influence of AI on the education industry. He pointed out, "Personalized learning is of vital importance to the development of key skills in the future. For now, the AI technology can be used to upgrade education to make personalized education a possibility."

"Along with the fast updating of technology, earthshaking changes will take place in professions in the future. The problem with the traditional education is that teachers cannot make a prejudgment of existing professions or forthcoming professions. Current education is unlikely to provide training on all future professional skills. Nor can teachers quickly acquire the key skills required by these professions, but AI technology can change all this."

In Dr. Wei Cui's view, personalized learning program customization will play an irreplaceable role in promoting the training of the key skills required for future professions. The greatest value of AI to education is that it can improve the learning efficiency by making a knowledge diagnosis and designing an optimal learning plan. Moreover, personalized learning helps to promote the training of key professional skills and fashion different people into different types of personnel.

The Squirrel AI adaptive learning system is an intellectualized and personalized system with students at the core. The AI technology is used in the teaching process, including teaching, learning, evaluation, test and exercise, to mimic human teachers and finally outperform them. This product is cost-effective and able to teach students in accordance with their aptitude by combining AI with human teachers, thus effectively solving the problems with the traditional education including high education fees, insufficient famous teacher resources and low learning efficiency.

In the past, students had to do massive exercises to understand knowledge points. Squirrel AI Learning can precisely locate a student's weak points by scanning his knowledge graph, making it unnecessary for him to consolidate his knowledge by solving hundreds or thousands of examination questions. The system can quickly identify the knowledge points in which he has not ever acquired proficiency, thus improving his learning efficiency to the greatest extent.

Squirrel AI also evaluates students' mastery of knowledge in their real-time learning process. According to the initial diagnosis and real-time evaluation results, Squirrel AI draws a clear student portrait and then makes a personalized recommendation in accordance with the portrait to provide every student with an optimal personalized learning path and the most suitable learning content.

In addition, the Squirrel AI Learning MCM system can detect students' model of thinking, learning capacity and methodology. For students with the same exam performance, after the end of evaluation and detection, the MCM system can recognize their different learning capacities and rates and weak points, thus drawing a clear portrait for them. Squirrel AI's MCM system is able not only to help students with learning, but also to improve their lifelong thinking ability and method so as to tap their potential and make up for their weaknesses. Only in this way can they be fashioned into talents.

AI adds heft to skill training to narrow the income gap

Iyad Rahwan, the founder of the Human and Machine Center of Max Planck Institute for Evolutionary Anthropology and an associate professor at the MIT Media Lab, agreed with Dr. Wei Cui. They both believe that greater use of data and AI will help to provide a more effective and personalized learning solution and disseminate cognitive and social skills that may be needed in the future.

"Education is closely related to your living space," said Rahwan. According to him, a study has proven that "the middle class is already hollowed out."

"There will be fewer and fewer middle-income jobs," said he, "We have to think about what skills can be used to narrow the gap between high- and low-income jobs."

AI can solve many problems with education in the future. Squirrel AI Learning is always committed to exploring the field of "AI + education". According to disclosed information, Squirrel AI has applied more than ten algorithms and deep learning technologies to its intelligent adaptive system. It has also developed a number of globally unique AI application technologies such as MCM ability value training, mistake reasoning based knowledge map reconstruction, super-Nano knowledge point splitting, association probability of unassociated knowledge points, and MIBA (Multimodal Integrated Behavioral Analysis). AI is used in the teaching process, including teaching, learning, evaluation, test and exercise, to mimic human teachers and finally outperform them.

How many more effective and personalized learning solutions will data and technology provide for education? To achieve this goal, Squirrel AI Learning carries out continuous theoretical and academic research to promote the development of the education industry. Squirrel AI has founded a few laboratories. In recent years, it has cooperated with the SRI International in developing technologies and worked with the Institute of Automation, Chinese Academy of Sciences in establishing a joint laboratory of AI adaptive learning. This year, Squirrel AI has set up a joint laboratory with the Carnegie Mellon University (CMU) for experiments and research in order to improve global K-12 students' adaptive learning experience.

Epilogue

The co-founder and chief scientist of Squirrel AI Learning Dr. Wei Cui pointed out at the summit that the AI technology could personalize education and make it possible to teach all students in accordance with their aptitude as Confucius wanted to do more than 2,000 years ago. According to Dr. Wei Cui, the development of AI will bring about more technologies and better teachers, with education to be truly revolutionized. We believe that, with the efforts of Squirrel AI Learning, AI will be able to realize educational fairness by meeting future requirements and promoting skill training.

SOURCE Squirrel AI Learning

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Squirrel AI Learning appears at Top European Education Summit OEB and gives a Keynote Speech as the Only Educational Technology Company from China -...

What’s Still Missing From The AI Revolution – Co.Design (blog)

Artificial intelligence is a young field full of nearly unlimited potential that remains largely misunderstood by most people. We've come a long way since Watson won Jeopardy in 2011 and IBM formed the business unit with over $1 billion in investments. AI is no longer a one-trick pony. AI technology from IBM Watson and multiple companies such as WayBlazer and SparkCognition has moved firmly into the real world. It is now being used for a variety of daily applications including:

We have no doubt come a good distance on what is indeed a very long road. My colleagues at Intel believe that AI will be bigger than the Internet. Software that can understand context and learn about users as individuals is an entirely new paradigm for computing. But many dangers and problems lie ahead, if we don't look past the hype and focus on five key areas:

1. Applying AI It all starts with what you are trying to achieve. Companies are struggling to generate business value with AI. Data scientists are overwhelmed by the complexity and quantity of data, and line-of-business executives for their part are underwhelmed by the tangible output of those data scientists. (See the recently published Harvard Business Review article, "Why Youre Not Getting Value from Your Data Science.") Machine learning teams are struggling with what business problems to solve with clear outcomes. What is needed is a clear set of high-value use cases by industry and process domains where AI can create demonstrable business value.

2. Building AI. We have a global talent shortage, and the demand for data scientists continues to grow rapidly, far outpacing the anemic growth in supply. A McKinsey study predicts that by 2018 the number of data science jobs in the United States alone will exceed 490,000, but there will be fewer than 200,000 available data scientists to fill these positions. Globally, demand for data scientists is projected to exceed supply by more than 50 percent by 2018.

In addition, the training offered at universities is too focused on the mathematical and research aspects of AI and machine learning. Largely missing are strategy, design, insights, and change management. This oversight may have serious consequences for graduating students and their future employerswithout a multi-disciplinary approach, we will be graduating data scientists capable of designing an algorithm that is mathematically elegant, but doesnt make strategic sense for the business.

3. Testing AI. Quality assurance is one of the most important parts of software development. Products must pass a number of tests before they reach the real worldthese include unit testing, boundary testing, stress testing, and other practices. In addition, we need systems that deliver the required training data for machine learning of systems. AI is not deterministicmeaning you can receive different results from the same input data when training it. The software learns in different, nuanced ways each time it is trained. So we need new types of software testing that start with an initial "ground truth" and then verify whether the AI system is doing its job.

4. Governing AI. Every transformative tool that people have createdfrom the steam engine to the microprocessoraugments human capabilities. Successful use of these tools requires proper governance, and AI is no different; we need governance to ensure that AI is developed the right way and for the right reasons. As the UX designer Mark Rolston wrote last year on Co.Design, "The coming tidal wave of [AI-based decision support software] threatens to give very few people a phenomenal amount of suggestive power over a great many peoplethe kind of power that is hard to trace and almost impossible to stop."

AI systems should be manageable and able to clearly explain their actions. Algorithm development has so far been driven by the goal of improving performance, at the expense of credibility and traceability, which means we end up with opaque "black boxes." We are already seeing such black boxes rejected by users, regulators, and companies, as they fail the regulatory, compliance and risk requirements of corporations dealing with sensitive personal health and financial information. This issue will only get bigger as AI leads to new processes and longer chains of responsibility.

Last years White House report on "Preparing for the Future of Artificial Intelligence" outlined key areas of governance:

5. Experiencing AI. One of the biggest stories at the 2017 Consumer Electronics Show in Las Vegas was the exponential growth of Amazon's Alexa ecosystem. It foretold a future of endless smart home and office products accessible via voice, gesture, and other ways through Amazon Echo. Another tech giant, chipmaker Nvidia, presented an expansive vision for homes, offices, and cars controlled by AI assistants. Meanwhile holographic projection, VR headsets, and "merged" reality technologies like Intels Project Alloy showed that the fundamental way we experience computers is evolving.

When it comes to experiencing AI, researchers tend to focus on creating better algorithms. But theres really much more to be done here. The quality of the user experience determines both the usefulness of the product and its rate of adoption, and this is why I believe design is the next frontier of AI. At the machine intelligence firm CognitiveScale, where I'm chairman, we are facing this challenge with cognitive computing, the type of AI software we create for multinational banks, retailers, healthcare providers, and others. Like a lot of enterprise systems today our software is cloud-based. So how do you make something as nebulous sounding as a "cognitive cloud" something that a user would be thrilled to welcome into her daily life?

"Cognitive design" is the subject of a longer article, but here I will hint that a key strategy is to focus on the micro-interactions between man and machinethe fleeting moments that add up to make engagement with an AI system delightful. Just as designers use tools like journey maps to develop a human-centered experience around a particular product or service, companies must practice "cognitive design thinking"creating an experience between man and machine that builds efficacy, trust, and an emotional bond. In the end, outcomes are determined as much by the human element as by the software element.

All of this only touches the surface of the issues and difficulties that lie ahead. AI isnt just software, and it isnt just about making things easier. Its potential for radical social and economic change is enormous, and it will touch every aspect of our personal and public lives, which is why we need to think carefully and ethically about how we apply, build, test, govern, and experience machine intelligence.

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What's Still Missing From The AI Revolution - Co.Design (blog)

NVIDIA Releases a $59 Jetson Nano 2GB Kit to Make AI More Accessible to Developers – InfoQ.com

NVIDIA recently debuted the Jetson Nano 2GB developer kit. For $59, the kit includes a credit-card-sized single-board computer with a quad-core ARM CPU and a 128 core Maxwell GPU. It comes with the JetPack SDK, a Ubuntu Linux-based developer SDK, and comprehensive documentation. The Jetson Nano 2GB kit also includes online training and certification, making it an ideal developers kit for students and new developers who just started AI programming.

Deep learning is one of the most notable advancements in computer science in the past decade. Deep learning-based AI applications are now used everywhere, from computer vision to speech to natural language processing. However, as developers, learning AI skills for professional programming has been difficult. The barrier is the GPU. Most deep learning algorithms and frameworks are designed to run on the GPU. They are too slow on the CPU. Yet, most computers are CPU-based. While a personal computer typically contains a GPU to drive graphics, the operating system and software stack in the computer are designed to "hide" the GPU and only use it to drive the display graphics.

When a developer writes a deep learning application and runs it on a personal or cloud-based computer, there is a good chance the program is actually running on CPUs. With the Jetson series of devices and software SDKs, NVIDIA creates a coherent development environment to learn and develop GPU-based AI applications. The JetPack SDK provides a customized version of eLinux, which is based on and compatible with Ubuntu 18.04, and curated versions of key software packages, such as Python, TensorFlow, PyTorch, Numpy, OpenCV, to ensure that the whole software stack is optimized for the GPU and ARM CPU hardware on the development board. In addition to official learning resources from NVIDIA, there is a vibrant community of developers and hobbyists in the Jetson ecosystem. There is a wealth of YouTube videos, open-source projects, and online articles for these devices.

The entry-level Jetson device, called the Jetson Nano, was priced at $99, which is a little high compared with other single board computers such as the Raspberry Pi, which is priced at $40 without the GPU. The new Jetson Nano 2GB's $59 price point is much more reasonable for price-sensitive students and hobbyists learning AI programming.

Like the regular Jetson Nano, the Jetson Nano 2GB has a 64-bit quad-core ARM A57 CPU clocked at 1.43 GHz, and a 128 CUDA core Maxwell GPU. The GPU delivers 472 GFLOPS computing power for AI applications. In fact, for AI applications, the Jetson Nano 2GB is 8 to 73 times faster than the most advanced Raspberry Pi 4.

The Jetson Nano 2GB board has several USB 2/3 connectors, a power connector, an HDMI display connector, an ethernet connector, GPIO pins, a camera kit connector, as well as an M2 key E connector for a WiFi and Bluetooth card. The 2GB refers to the on-board memory space. You do need an additional microSD card for the operating system and files in order to boot up and use the Jetson Nano 2GB. Furthermore, you will need to connect the card to an HDMI display, keyboard, and mouse, as well as the network (either cable-based Ethernet or WiFi card) before you can use it as a computer.

With its small size and low cost, the Jetson Nano 2GB can power computer vision applications in robots or drones. Its GPUs can analyze video streams from the camera in real-time, recognize objects and faces in each video frame, and send out corresponding control commands through its GPIO pins or USB connectors. However, with the 10W power consumption of the GPU working in full video image recognition mode, it is also challenging to keep the device running on batteries for an extended period of time. Therefore, the Jetson Nano 2GB is indeed primarily a learning device.

You can pre-order the Jetson Nano 2GB from online retailers, and it should be available in late October. Follow the learning resources and tutorials from the NVIDIA website to start programming AI applications on your GPU!

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NVIDIA Releases a $59 Jetson Nano 2GB Kit to Make AI More Accessible to Developers - InfoQ.com

When Digital Ads Go Rogue, and the Quest for Nuanced AI – ExchangeWire

In this exclusive article written for ExchangeWire, Omar Amath, head of ad and data operations (pictured below), agenda21, highlights how third-party content verification platforms can assist in ensuring brand safety, while aiding publishers reclaim revenue lost to over-zealous block-listing.

News stories about badly placed digital ads, in which the brand, media owner ad agency involved are all pointing the finger at one another, are ten a penny these days. But this week saw a new take on the argument, in which they both the brand and the platform took issue with an ASA ban, denying anyone was at fault.

An audio ad for Warner Bros new film It Chapter Two was found to be playing on a Classical Lullabies playlist on Spotify. Which meant it was conceivable that toddlers up and down the country were having their slumber interrupted by the terrifying tones of Pennywise the clown lilting for 27 years I dreamt of you. I craved you before loudly and desperately shouting Oh I missed you!.

After a complaint from a listener, the Advertising Standards Authority banned the ad, saying it was likely to cause distress to young children, and that it should have been appropriately targeted to avoid the risk of them hearing it.

As Warner Bros and Spotify both deny responsibility, who is to blame?

Shockingly, no-one really. On the one side Warner Media claims to have selected targeting parameters of ages 18-44 and the Real-time, genre. Brand safety was exercised. Block lists were in place, with the ad not containing violence, offensive language, gore or any elements of the film. And the advertiser was buying in a trusted, safe and premium environment.

Omar Amath, head of ad and data operations, agenda21

So, what really went wrong? The context of the ad nestled amongst a lullaby playlist on the Spotify platform was the issue, not the content. In its defence, Spotify claimed that targeting parameters used by the advertiser were appropriate because Spotify themselves did not believe Classical Lullabies, were primarily directed at children. The ASA disagreed and upheld the complaint.

In the main, agencies and brands are aware of brand suitability versus safety, and the apparent, nuanced approach we are all taking to ensure ads are placed amongst appropriate content. This has overtaken the still mandatory need to ensure inappropriate content for the brand is avoided.

But what we are seeing now is the rise of third-party content targeting and verification solutions. Most recently, publisher Reach has launched a content verification platform, Mantis, that allows advertisers and marketers to navigate to appropriate content without the need for an exhaustive blocklist based on keywords or page labels. It offers up a solid solution for brand safety, suitability and sentiment. Their solution has been developed to sit on the end publisher, rather than with middleware providers or agencies and brands. Others, such as Peer 39, also exist in this space.

The issue has always been third-party platforms having greater visibility on content categorisation rather than the media owner itself. With Mantis, Peer 39, and those like it in the market, it could be a viable solution to this conundrum due to the way they leverage dynamic scanning of content via the CMS instead of the usual URL or page scans. This has the added advantages of both reducing the need for excessive blocklist usage, which severely restricts advertiser reach, and of being optimal for user experience and page load performance. It also inherently makes GDPR and privacy a non-concern in this instance.

Going forward we can expect to see the continued rise of contextual verification technology. At the end of 2019, Integral Ad Sciences acquisition of AdmantX signalled their intent to bolster their capabilities in the content verification and semantic targeting arena by purchasing their Natural Language Processing (NLP), technology.

With the prevalence of tech such as Mantis surface, were starting to see publishers act on a vast swath of revenue opportunities that have, up until now, been lost due to aggressive blocking by advertisers. Some numbers being touted are as high as a 40% reduction in blocking rates of content.

Blocking potentially great content in premium environments and on a brand-by-brand basis starts to unravel a level of complexity that must be addressed in order to provide the brand suitability factor needed to ultimately make AI-powered content verification platforms a true success and a genuine solution to the Spotify and Warner Bros scenario.

Lets hope, for parents everywhere, that classical lullabies and horror movies can live a separate life around content from now on.

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When Digital Ads Go Rogue, and the Quest for Nuanced AI - ExchangeWire

4 Hard-To-Ignore Reasons Why You Should Use AI To Make More Intelligent Products – Forbes

Thanks to the Internet of Things (IoT), artificial intelligence (AI), and advances in sensor technology, a whole host of everyday products are getting smarter. We have smart TVs and smartwatches. We have smart running shoes or rather, smart insoles that gather data on your running performance. You can even get smart nappies that send an alert to your phone when your babys nappy needs changing.

4 Hard-To-Ignore Reasons Why You Should Use AI To Make More Intelligent Products

And thats just the tip of the iceberg. For product manufacturers, theres no doubt weve reached a tipping point in the smart product trend, meaning its no longer possible (or wise) to ignore consumer demand for smart, AI-loaded products.

So, if you aren't already asking yourself, Could our products be improved with AI?, now is the time to start. To whet your appetite, here are four huge benefits of smart products:

1. Making your customers lives easier (and boosting customer satisfaction in the process)

The smart product trend has particularly taken hold in our homes. Because of the IoT, basic home electronic goods and appliances can gather information on whats going on around them and respond accordingly. For example, a smart thermostat can heat your home to the perfect temperature in time for your return from work, without you having to program it. Technology like this makes our homes more efficient, more automated, and more responsive to our needs helping to remove some of the annoying wrinkles and bugbears from everyday life.

This is key to the success of smart products. Rather than inserting AI for AIs sake, its all about solving customers problems and making their lives easier. And I dont just mean in the home. Todays consumers expect smart solutions to a whole host of everyday tasks and activities, including changing their babys nappy and training for a marathon.

2. Building better products (products your customers really want)

Making your products smarter is a fantastic way to build a more in-depth understanding of your customers. This knowledge can and should feed into your product design. In my experience, building a better understanding of customers and developing more desirable products is one of the most attractive benefits for most businesses.

How does this process work? In a nutshell, by building AI capabilities into your products, you have the ability to collect masses of data on your customers habits and preferences: how they use your product, how often they use it, when they typically use it, and more. All this data can be used to improve product design and develop new products that better meet your customers needs.

3. Responding to customers needs more quickly

The customer journey has been forever altered (and sped up) thanks to our permanent attachment to our mobile devices. Because life is so fast-paced these days, were constantly making quick decisions, looking up solutions on-the-fly, and seeking split-second answers to the things we want to know. Google calls these brief I want to know/do/buy/go/learn flashes micro-moments, and, according to Google at least, these micro-moments are becoming a vital part of marketing. In other words, as consumers, we increasingly expect brands to respond instantly and offer us exactly what we want in the here and now.

The more information you have on your customers, the better able you are to spot and respond to these all-important micro-moments. This is where smart products come into play. Because theyre capable of gathering a wealth of data, smart products help you understand your customers actions, preferences, and decision making.

4. Adding new revenue streams

A less obvious benefit of smart products is that they often enable add-on, AI-driven services. And these services can add a lucrative new revenue stream for your business, particularly if you can tailor them into a subscription model. For example, let's say you manufacture security systems and have been transitioning to smart security alarms that gather and transmit data on what's happening in the home. Providers of similar smart security systems are now offering subscriptions to monitor the home in real-time.

Likewise, Apple has transitioned from a straight product manufacturer into a provider of music and TV streaming services (services that are supported by Apples iconic products). In this way, theres a surprising amount of crossover between smart products and smart services. If you can make your own products smarter, it could pave the way for a lucrative move into services.

Everything is becoming smarter dont get left behind

I believe every product-based business must carefully consider this intelligent product trend. Those that dont risk being left behind. Thats not to say you should quickly load your products with unnecessary AI, just so you can label them as smart. As with any new technology, its really important to find those ways in which AI adds the most value. This will be different for each business, and must be driven by your overarching business strategy.

AI is going to impact businesses of all shapes and sizes, across all industries. Discover how to prepare your organization for an AI-driven world in my new book, The Intelligence Revolution: Transforming Your Business With AI.

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4 Hard-To-Ignore Reasons Why You Should Use AI To Make More Intelligent Products - Forbes

Artificial Intelligence (AI) in Supply Chain & Logistics Market : Business overview, Upcoming Trends and Top Company Analysis Forecast By 2025 -…

Global AI in Supply Chain & Logistics Market to reach USD 10157.17 Million by 2025.Global AI in Supply Chain & Logistics Market valued approximately USD 502.9 Million in 2017 is anticipated to grow with a healthy growth rate of more than 45.60 % over the forecast period 2018-2025. Growth in this market is mainly driven by factors such as growing big data, demand for greater visibility and transparency into supply chain data and processes, and adoption of AI for improving consumer services and satisfaction. The major restraint for the market is the limited number of the artificial intelligence technology experts. But the limited number of artificial intelligence technology experts is expected to restrict adoption, which in turn may limit market growth to a certain extent. And The artificial intelligence in supply chain market for software offerings is expected to hold a larger share. The continuous developments have been witnessed in AI software and related software development kits

Request To Download Sample of This Strategic Report:https://www.kennethresearch.com/sample-request-10012727

The regional analysis of GlobalAI in Supply Chain & Logistics Marketis considered for the key regions such as Asia Pacific, North America, Europe, Latin America and Rest of the World. The artificial intelligence in supply chain market in APAC is expected to grow at the highest CAGR during the forecast period. Market growth can be attributed to increasing adoption of deep learning and NLP technologies for automotive, retail, and manufacturing applications in APAC. Moreover, the presence of major players in the artificial intelligence in supply chain ecosystem results in the increasing adoption of artificial intelligence in APAC.

The objective of the study is to define market sizes of different segments & countries in recent years and to forecast the values to the coming eight years. The report is designed to incorporate both qualitative and quantitative aspects of the industry within each of the regions and countries involved in the study. Furthermore, the report also caters the detailed information about the crucial aspects such as driving factors & challenges which will define the future growth of the market. Additionally, the report shall also incorporate available opportunities in micro markets for stakeholders to invest along with the detailed analysis of competitive landscape and product offerings of key players. The detailed segments and sub-segment of the market are explained below:

By Component:

*Hardware*Software*Services

By Technology:

*Machine Learning*NLP*Context Aware Computing*Computer Vision

By Regions:*North Americao U.S.o Canada*Europeo UKo Germany*Asia Pacifico Chinao Indiao Japan*Latin Americao Brazilo Mexico*Rest of the World

Furthermore, years considered for the study are as follows:

Historical year 2015, 2016Base year 2017Forecast period 2018 to 2025

The industry is seeming to be fairly competitive. Some of the leading market players include IBM, BonVision Technology, Intel, SAP, General Electric, Amazon Web Services (AWS), Google, LogiNext, Transmetrics, Smarter Sorting, Crowdz, Marble, Evertracker, Deliverish, Avrios International, GlobalTranz and so on. Acquisitions and effective mergers are some of the strategies adopted by the key manufacturers. New product launches and continuous technological innovations are the key strategies adopted by the major players.

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Target Audience of the Global AI In Supply Chain & Logistics Market in Market Study:

*Key Consulting Companies & Advisors*Large, medium-sized, and small enterprises*Venture capitalists*Value-Added Resellers (VARs)*Third-party knowledge providers*Investment bankers*Investors

About Kenneth Research

Kenneth Research is a reselling agency providing market research solutions in different verticals such as Automotive and Transportation, Chemicals and Materials, Healthcare, Food & Beverage and Consumer Packaged Goods, Semiconductors, Electronics & ICT, Packaging, and Others. Our portfolio includes set of market research insights such as market sizing and market forecasting, market share analysis and key positioning of the players (manufacturers, deals and distributors, etc), understanding the competitive landscape and their business at a ground level and many more. Our research experts deliver the offerings efficiently and effectively within a stipulated time. The market study provided by Kenneth Research helps the Industry veterans/investors to think and to act wisely in their overall strategy formulation

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Artificial Intelligence (AI) in Supply Chain & Logistics Market : Business overview, Upcoming Trends and Top Company Analysis Forecast By 2025 -...

[Hearing from an AI Expert 6] AI and 5G: A Two-Pronged Revolution – Samsung Newsroom

One of the most exciting things about the times we live in is the fact that we stand on the precipice of several major technological shifts. Whats more, the individual innovations that make up these seismic changes are not happening independently, but rather are interweaving to inform and empower one another. As we stand here at the edge, no two innovations are enlivening and empowering the tech industry more than those of AI and 5G.

While AI is making technology smarter across the board, 5G is ensuring that connection speeds are fast enough to allow platforms to interface in real-time. So how exactly do AI and 5G work hand-in-hand to make each other, and the entire tech industry, stronger? Dr. Gregory Dudek, Head of the Samsung AI Center in Montreal, extrapolates on the interplay between the two innovations, and how they stand to change things for consumers.

The Montreal AI Centers primary area of focus is AI-for-5G, as Dudek explains. Bringing the strength of AI to bear in order to make use of the full potential of 5G is the key focus of our research in Montreal, he relates, The area is a natural fit for our center, since Montreal is one of the worlds hotbeds for AI research, as well as having a telecommunications research community that has a decades-long history.

Dudek relates that 5G (and beyond 5G) telecommunications systems are very flexible, and can outperform older systems such as 4G, but that extensive configuration is required to take full advantage of them. In order to exploit 5G networks full potential, and make them applicable for a wide range of users, devices and needs, extensive automated reconfigurability is required, Dudek says, And that is where AI comes into the picture.

The impact that AI stands to have on almost every aspect of our lives, and on our technologies as a whole, is immense there are few areas of the industry that arent expected to be revamped by the introduction of artificial intelligence. Telecommunication systems have been getting steadily more complex since they were first developed, Dudek says, In almost all areas of digital communication, complex optimization problems arise and are solved by increasingly sophisticated solutions that I would often call AI. According to Dudek, the main thing that AI allows devices to do is adapt to changing conditions, and this can lead to those devices being optimized in ways that have rarely been seen before. He says that 5Gs richer protocols and abundance of cells provide an opportunity to enhance performance with learning-based AI algorithms, and claims that using AI for 5G is likely to prove more of a fundamental requirement for state-of-the-art performance than just an opportunity.

Just as AI can be used to optimize 5G networks, the enhanced performance characteristics of 5G will be important for many key applications of AI in our daily lives.

Dudek outlines that 5G will be an essential component in many of the key applications of AI. Among those applications, Dudek highlights the automotive, edge computing, robotics and medicine sectors, among others.

One of the clearest needs is in the domain of autonomous cars and delivery vehicles, he expands, Efficient coordination of automotive vehicles will depend on the reduced latency that 5G networks offer.

Dudek also touches on the area of edge computing, which is additionally set to empower and be empowered by the introduction of 5G. Edge computing means computing that is done very close to data sources, so that relays back and forth from the cloud are minimized. 5G will accelerate communication speeds between the edge and the public cloud, while edge computing, in turn, will improve cybersecurity on 5G networks, reduce the burden on the public cloud and lead to savings on storage and processing costs. Eventually, edge computing is expected to prove helpful in realizing AIs potential because it will allow computation to be distributed across more devices. 5G is also expected to contribute in this area by accommodating a potentially very large number of edge clients and allowing them to do real-time processing with low latency.

Dudek says that robotics will be another important application of 5G, pointing out that areas such as robotic vision, reasoning and action will all depend on the high-quality connectivity it provides. Many of the most exciting applications of robotics will combine edge computing, sensing, big data in the cloud and interactions between multiple devices, Dudek reports, The combination of bandwidth and low-latency will be critical for things like robotic telemedicine solutions as well.

Touching on other sectors that 5G connectivity is set to empower, Dudek highlights those of medicine (telemedicine, smart diagnostic capabilities and therapeutic technologies), leisure (multiple streams and viewpoints for sports events and rich, multi-person VR), public transportation and factory automation. In fact, Dudek concludes, if we see 5G and AI as a combined package, then there are very few areas of human activity where there will not be some impact.

The versatility and power of 5G networks will allow for new kinds of connectivity and the emergence of an all-new family of devices. And, as the dual rise of AI and 5G expands capabilities, the degree to which companies and individuals will be able to take advantage of the resulting solutions will depend on their access. That is why Samsungs position is such a promising one.

Samsung has successfully exploited successive waves of the most modern and rapidly-changing technologies, Dudek imparts, When it comes to these upcoming innovations not just AI and 5G, but edge computing, multi-device interactions, machine learning, robotics and personalized devices, a fluid combination of hardware and software will be crucial. The companys extensive experience in creating individual devices that can talk to one another and have overlapping functions means that Samsung is in an exceptional position to lead this family of emerging technologies.

Touching on other areas that are expected to change things for consumers, Dudek says that he also expects Samsung to be instrumental in the sphere of robotics, as well as when it comes to innovations regarding original technologies such as AI and 5G. It cannot be overlooked that robotics is expected to have a huge influence on our lives, Dudek says, And, as robotics is a synthesis of AI and mechatronics, this area is very well-matched to Samsungs strengths as well.

For most of us, our devices have already become crucial to going through our daily routines, but the dual inception of 5G and AI is set to make our devices even more complementary to our day to day lives.

In the near term, AI will make our home lives healthier, safer and more fun, Dudek says, But AI also has the potential to help people communicate more easily across linguistic and geographic boundaries.

A world where 5G-dependent smart devices become woven into our lives is much closer than most people expect, Dudek continues, I expect this embedded, interconnected intelligence to start playing a role in our lives in the near future.

When one considers the entirety of the picture that Dudek paints, it is hard not to start seeing AI and 5G as more of a necessity than a luxury. Asked how he envisions the combined inception of 5G and AI will change our lives, he says One of my goals as a researcher is to have a positive impact on the world, and to play a role in bringing important new technologies to life. He also says that he expects the introduction of 5G and AI to progressively take away more and more of the mundane tasks that people deal with day-to-day. People will expect much more from the objects around them, he says, and this will allow them to focus more on the aspects of their lives that they find more rewarding.

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[Hearing from an AI Expert 6] AI and 5G: A Two-Pronged Revolution - Samsung Newsroom

OpenAI makes an all-purpose API for its text-based AI capabilities – TechCrunch

If youve ever wanted to try out OpenAIs vaunted machine learning toolset, it just got a lot easier. The company has released an API that lets developers call its AI tools in on virtually any English language task.

Basically, if youve got a task that requires understanding words in English, OpenAI wants to help automate it. The various abilities of the GPT-3 family of natural language understanding models are at the disposal of developers, at least if you can get into the private beta. (Request access here.)

What that looks like in action is a little difficult to explain, because it depends on what you want it to do. For instance, you could rely on its ability to look through lots of text at once to answer a question about an article or find a relevant portion. In a short demo video, OpenAI shows how this works asking why is bread so fluffy on the Wikipedia entry for bread returns the portion of the article dedicated to breads texture and how its created.

Thats a simple one, but the famous AI Dungeon is more complex. The AI was fed a bunch of D&D sourcebooks and adventures and based on that, improvises a journey for the player based on their inputs. While previously this had to be accomplished using clumsy means, essentially running a version of the model locally, the inputs can now simply be submitted via API.

Basically this is just a much easier, simpler way to access the broad language understanding and generation capabilities of GPT-3. Its just text in, text out.

We hope that the API will greatly lower the barrier to producing beneficial AI-powered products, resulting in tools and services that are hard to imagine today, wrote the company in a blog post. Certainly it would have been difficult to imagine AI Dungeon a year or two ago.

Its also made clear that harmful or abusive uses of the API will be immediately banned, which to the company seems safer than releasing the model into the wild. Since it is hard to predict the downstream use cases of our models, it feels inherently safer to release them via an API and broaden access over time, rather than release an open source model where access cannot be adjusted if it turns out to have harmful applications, the post reads.

So far OpenAI has partnered with a dozen or so companies to test out the API ahead of offering it more widely. Chatbots, teaching aids, legal research theres no end to the applications of this sort of thing because language is used to define and record pretty much everything we do. Finding exactly where AI agents like this one are truly useful will, however, take a bit of experimentation.

Janelle Shane of AI Weirdness already made a bot based on dog-rating Twitter that eternally creates and rates dogs, so that idea is already taken.

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OpenAI makes an all-purpose API for its text-based AI capabilities - TechCrunch

Alibaba Recruits Big-Hitters for AI Research Labs – Caixin Global

Internet giant Alibaba has recruited two acclaimed Chinese artificial intelligence (AI) experts for senior positions at its AI research laboratory in a pivot toward more complex forms of human-computer interaction, the company said in a Wednesday press release.

Chen Ying, a former principal engineer at U.S. telecoms company Qualcomm and researcher at Finnish firm Nokia, has joined Alibaba AI Labs as its chief scientist specializing in AI and edge computing, the statement said.

In addition, Tan Ping, an associate professor of computer science at Canadas Simon Fraser University who specializes in realistic 3D reconstruction technology and previously worked in AI for Beijing-based internet security firm Qihoo 360, will head the labs computer vision section, according to the statement.

The appointments show that with the imminent arrival of 5G high-speed internet, Alibaba AI Labs have already started transitioning from voice interaction to the more information-heavy and imaginative field of visual interaction, the company said, adding that this may allow its smart speaker Tmall Genie to offer users surprising new visual functions in future.

Both Chen and Tan will earn million-dollar annual salaries as part of their arrangement with Alibaba, the company said.

Alibaba AI Labs is part of the companys Damo Academy, a scientific and technological research arm founded in 2017. The labs focus on voice-assisted technology, industrial design, intelligent manufacturing, and robotics, according to the Damo Academy website.

Contact reporter Matthew Walsh (matthewwalsh@caixin.com)

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Alibaba Recruits Big-Hitters for AI Research Labs - Caixin Global

Bringing the predictive power of artificial intelligence to health care – MIT News

An important aspect of treating patients with conditions like diabetes and heart disease is helping them stay healthy outside of the hospital before they to return to the doctors office with further complications.

But reaching the most vulnerable patients at the right time often has more to do with probabilities than clinical assessments. Artificial intelligence (AI) has the potential to help clinicians tackle these types of problems, by analyzing large datasets to identify the patients that would benefit most from preventative measures. However, leveraging AI has often required health care organizations to hire their own data scientists or settle for one-size-fits-all solutions that arent optimized for their patients.

Now the startup ClosedLoop.ai is helping health care organizations tap into the power of AI with a flexible analytics solution that lets hospitals quickly plug their data into machine learning models and get actionable results.

The platform is being used to help hospitals determine which patients are most likely to miss appointments, acquire infections like sepsis, benefit from periodic check ups, and more. Health insurers, in turn, are using ClosedLoop to make population-level predictions around things like patient readmissions and the onset or progression of chronic diseases.

We built a health care data science platform that can take in whatever data an organization has, quickly build models that are specific to [their patients], and deploy those models, says ClosedLoop co-founder and Chief Technology Officer Dave DeCaprio 94. Being able to take somebodys data the way it lives in their system and convert that into a model that can be readily used is still a problem that requires a lot of [health care] domain knowledge, and thats a lot of what we bring to the table.

In light of the Covid-19 pandemic, ClosedLoop has also created a model that helps organizations identify the most vulnerable people in their region and prepare for patient surges. The open source tool, called the C-19 Index, has been used to connect high-risk patients with local resources and helped health care systems create risk scores for tens of millions of people overall.

The index is just the latest way that ClosedLoop is accelerating the health care industrys adoption of AI to improve patient health, a goal DeCaprio has worked toward for the better part of his career.

Designing a strategy

After working as a software engineer for several private companies through the internet boom of the early 2000s, DeCaprio was looking to make a career change when he came across a project focused on genome annotation at the Broad Institute of MIT and Harvard.

The project was DeCaprios first professional exposure to the power of artificial intelligence. It blossomed into a six year stint at the Broad, after which he continued exploring the intersection of big data and health care.

After a year in health care, I realized it was going to be really hard to do anything else, DeCaprio says. Im not going to be able to get excited about selling ads on the internet or anything like that. Once you start dealing with human health, that other stuff just feels insignificant.

In the course of his work, DeCaprio began noticing problems with the ways machine learning and other statistical techniques were making their way into health care, notably in the fact that predictive models were being applied without regard for hospitals patient populations.

Someone would say, I know how to predict diabetes or I know how to predict readmissions, and theyd sell a model, DeCaprio says. I knew that wasnt going to work, because the reason readmissions happen in a low-income population of New York City is very different from the reason readmissions happen in a retirement community in Florida. The important thing wasnt to build one magic model but to build a system that can quickly take somebodys data and train a model thats specific for their problems.

With that approach in mind, DeCaprio joined forces with former co-worker and serial entrepreneur Andrew Eye, and started ClosedLoop in 2017. The startups first project involved creating models that predicted patient health outcomes for the Medical Home Network (MHN), a not-for-profit hospital collaboration focused on improving care for Medicaid recipients in Chicago.

As the founders created their modeling platform, they had to address many of the most common obstacles that have slowed health cares adoption of AI solutions.

Often the first problems startups run into is making their algorithms work with each health care systems data. Hospitals vary in the type of data they collect on patients and the way they store that information in their system. Hospitals even store the same types of data in vastly different ways.

DeCaprio credits his teams knowledge of the health care space with helping them craft a solution that allows customers to upload raw data sets into ClosedLoops platform and create things like patient risk scores with a few clicks.

Another limitation of AI in health care has been the difficulty of understanding how models get to results. With ClosedLoops models, users can see the biggest factors contributing to each prediction, giving them more confidence in each output.

Overall, to become ingrained in customers operations, the founders knew their analytics platform needed to give simple, actionable insights. That has translated into a system that generates lists, risk scores, and rankings that care managers can use when deciding which interventions are most urgent for which patients.

When someone walks into the hospital, its already too late [to avoid costly treatments] in many cases, DeCaprio says. Most of your best opportunities to lower the cost of care come by keeping them out of the hospital in the first place.

Customers like health insurers also use ClosedLoops platform to predict broader trends in disease risk, emergency room over-utilization, and fraud.

Stepping up for Covid-19

In March, ClosedLoop began exploring ways its platform could help hospitals prepare for and respond to Covid-19. The efforts culminated in a company hackathon over the weekend of March 16. By Monday, ClosedLoop had an open source model on GitHub that assigned Covid-19 risk scores to Medicare patients. By that Friday, it had been used to make predictions on more than 2 million patients.

Today, the model works with all patients, not just those on Medicare, and it has been used to assess the vulnerability of communities around the country. Care organizations have used the model to project patient surges and help individuals at the highest risk understand what they can do to prevent infection.

Some of it is just reaching out to people who are socially isolated to see if theres something they can do, DeCaprio says. Someone who is 85 years old and shut in may not know theres a community based organization that will deliver them groceries.

For DeCaprio, bringing the predictive power of AI to health care has been a rewarding, if humbling, experience.

The magnitude of the problems are so large that no matter what impact you have, you dont feel like youve moved the needle enough, he says. At the same time, every time an organization says, This is the primary tool our care managers have been using to figure out who to reach out to, it feels great.

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Bringing the predictive power of artificial intelligence to health care - MIT News

AI Is All the Rage. So Why Arent More Businesses Using It? – WIRED

The Census report found AI to be less widespread than some earlier estimates. The consulting firm McKinsey, for instance, reported in November 2018 that 30 percent of surveyed executives said their firms were piloting some form of AI. Another study, by PwC at the end of 2018, found that 20 percent of executives surveyed planned to roll out AI in 2019.

One reason for the difference is that those surveys were focused on big companies which are more likely to adopt new technology. Fortune 500 firms have the money to invest in expertise and resources, and often have more data to feed to AI algorithms.

For a lot of smaller companies, AI isnt part of the picturenot yet, at least. Big companies are adopting, says Brynjolfsson, but most companies in AmericaJoes pizzeria, the dry cleaner, the little manufacturing companythey are just not there yet.

Another reason for the discrepancy is that those who responded to the Census survey might not realize that their company is using some form of AI. Companies could use software that relies on some form of machine learning for tasks such as managing employees or customers without advertising the fact.

Even if AI isnt yet widespread, the fact that it is more common at larger companies is important, because those companies tend to drive an even greater proportion of economic activity than their size suggests, notes Pascual Restrepo, an assistant professor at Boston University who researches technology and the economy. He adds that job ads for AI experts increased significantly in 2019.

LinkedIn says that postings for AI-related roles grew 14 percent year over year for the 10 weeks before the Covid outbreak slowed hiring in early March. There has been a very rapid uptake in terms of hiring of people with skills related to AI, Restrepo says.

Another data point that suggests rapid growth in use of AI comes from Google. Kemal El Moujahid, director of product management for TensorFlow, Googles software framework for creating AI programs, says interest in the product has skyrocketed recently. The framework has been downloaded 100 million times since it was released five years agoincluding 10 million times in May 2020 alone.

The economic crisis triggered by the pandemic may do little to dim companies' interest in automating decisions and processes with AI. What can be accomplished is expanding really rapidly, and we're still very much in the discovery phase, says David Autor, an economist at MIT. I cant see any reason why, in the midst of this, people would say, Oh no, we need less AI.

But the benefits may not flow equally to all companies. One worrying aspect that this survey reveals, the report concludes, is that the latest technology adoption is mostly being done by the largest and older firms, potentially leading to increased separation between the typical firm and superstar firms.

As a general principle, says Restrepo of Boston University, when technology adoption concentrates amongst a handful of firms, the gains will not be fully passed to consumers.

Nicholas Bloom, a professor of economics at Stanford, isnt so sure. While the average small firm lags the average large firm, there are some elite adopters in small firms, Bloom says. These are the rapid innovators, who are creative and ambitious, often becoming the larger firms of the future.

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AI Is All the Rage. So Why Arent More Businesses Using It? - WIRED