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A year after AlphaGo defeated Lee Sedol, an 18-time world champion at Go, the AI faced Ke Jie, who is currently considered the worlds best Go player. AlphaGo beat Ke Jie with only half a point difference--the smallest possible--but that may be due to the AIs safer winning strategy.

The AlphaGo AI is built on the core DeepMind technology that that has already been used to cut Googles data center cooling costs and to discover better treatments for some diseases.

AlphaGo is a special version of DeepMind AI that only knows how to play Go. Google has shown it (as it learns through computer vision) millions of matches between professional (human) Go players, and the company also made AlphaGo play against itself, so it could learn from its own mistakes. Basically, AlphaGo learns similarly to how a human would learn, by watching others do a task well and then repeating that task over and over until its skill at the task improves.

This machine learning technique was not just an innovative way to teach AlphaGo how to play Go well, but it was quite necessary for AlphaGos successful learning. Thats because it wasnt possible to program an artificial intelligence to play and win at Go in the same way chess-focused artificial intelligence was programmed before it.

There are more possible Go moves than atoms in the universe, according to Google, which would have made it impossible for the AI to play ahead all the moves until it found the winning ones. This is also why experts thought having an AI win at Go was going to take at least another decade, before AlphaGo was created.

By learning how humans play best, AlphaGo was able to not only replicate those winning moves in various Go-playing scenarios, but also create its own patterns for what a winning move would look like.

Ultimately, this allowed AlphaGo to beat even the best Go players in the world, such as Lee Sedol, and now Ke Jie, too. However, Ke Jie still has two more tries left before a final winner is declared.

Although AlphaGo and Lee Sedol played five Go matches against each other, there will be only three matches between the AI and Ke Jie this time around.

In the first natch, Ke Jie lost to AlphaGo by the smallest margin possible: half a point. However, as is often the case in sports, the final score doesn't always clearly indicate how close a competition really was. According to Demis Hassabis, DeepMinds founder, AlphaGo is not as interested in winning by large margins, as its interested in winning period. At the end of the day, winning is what matters in Go, and this seems to be what AlphaGo cares about, too.

Unlike Lee Sedol, who didnt really know what to expect from AlphaGo, Ke Jie was more prepared for this match. Hed seen the matches against Sedol, as well as other matches played by AlphaGo online under the nickname of Master, so he could understand a little better how the Google AI likes to play.

Ke Jie tried to use a strategy hes seen AlphaGo use online before, but that didnt work out for him in the end. Jie shouldve probably known that AlphaGo must have already played such moves against itself when training, which should also mean that it should know how to defeat itself in such scenarios.

A more successful strategy against AlphaGo may be one that AlphaGo hasnt seen before. However, considering Google has shown it millions of matches from top players, coming up with such unseen moves may be difficult, especially for a human player who cant watch millions of hours of video to train.

However, according to Hassabis, the AlphaGo AI also seems to have liberated Go players when thinking about Go strategies, by making them think that no move is impossible. This could lead to Go players trying out more innovative moves in the future, but it remains to be seen if Ke Jie will try that strategy in future matches against AlphaGo.

Although Google hasnt mentioned anything about this yet, its likely that both AlphaGos neural networks as well as the hardware doing all the computations have received significant upgrades from last year. Google recently introduced the Cloud TPU, its second-generation Tensor Processing Unit, which should have not only have much faster inference performance, but now it comes with high training performance, too. As Google previously used the TPUs to power AlphaGo, it may have also used the next-gen versions to power AlphaGo in the match against Ke Jie.

The next match between Ke Jie and AlphaGo will happen on Thursday, and then the final one will be streamed on Saturday. At the Future of Go Summit in Wuzhen, China, where these matches take place, there will also be a match between five human players and one AlphaGo AI, as well as a match between two humans who are both assisted by AlphaGo AI instances.

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AlphaGo' AI Scores Narrow Win Against Ke Jie, World's Top ...

AlphaGos historic defeat of Lee Sedol in their 2016 match and its latest successes against world champion Go player Ke Jie during last months Future of Go Summit in Wuzhen, China have demonstrated the power and potential of artificial intelligence. Although the summit was presented as an opportunity for AlphaGo to explore the mysteries of Go with leading Go players, the contest was seen as a battle of man and machine and the result a triumph of artificial over human intelligence. Ke Jie was shocked and deeply impressed by his opponent, including because certain of the AIs moves would never happen in a human to human match.

AlphaGo, designed by a team of researchers at Alphabet Inc.s Google DeepMind, utilizes deep neural networks trained through both supervised learning from the games of human experts and reinforcement learning from self-played games. While its value networks evaluate the board positions, policy networks select its moves. In the course of its career in Go, AlphaGo has displayed the capability to formulate unique, creative tactics undiscovered and unanticipated by human players. Although AlphaGo itself represents only narrow AI or weak AItailored to a specific task, rather than capable of generalized intelligencethe techniques used for its development will be applied to new contexts and challenges after its retirement from the game. Ultimately, the strategic ramifications of AlphaGos successes extend far beyond Go itself.

The definitive defeat of Chinas best human Go players by foreign AI could have been seen as awkward or problematic by the Chinese leadership, demonstrating the limitations of not only human intellect but also Chinese technology. (Reportedly, at one point, scientists from the China Computer Go team planned to challenge AlphaGo with their own AI, but apparently did not manage to do so.) Despite extensive coverage of the event in official media, Chinese censorship instructions forbade websites from live streaming the match, emphasizing it may not be broadcast live in any form and without exception. This decision could reflect the sensitivity of Google itself, which had withdrawn from the market in 2010 due to concerns over censorship and cyber espionage and remains blocked. Googles hosting of the summit has been characterized as a charm offensive, in the context of overtures towards reentry. However, its prospects for future market access remain questionable, particularly given Chinas intensified drive for indigenous innovation and the success of national champions.

Indeed, the rapidity of recent Chinese advances in AI has indicated its ability to keep pace withor perhaps even overtakethe U.S. in this critical emerging technology. The dynamism of private sector efforts in China is clearly demonstrated by the successes of major Chinese companies, including Baidu, Alibaba, and Tencent, and even start-ups such as Iflytek, Uisee Technology, or Turing Robot. From speech recognition to self-driving cars, Chinese public and private efforts in AI are cutting-edge. The magnitude of research, as reflected by the number of papers published and cited, has already surpassed that of the U.S. However, for the time being, AlphaGo represents a high-profile demonstration of the sophistication of U.S. AI.

Looking forward, the Chinese leadership aspires to achieve a dominant position in AI, surpassing the U.S. in the process, in order to take advantage of the unique advantages that AI could confer to Chinas economic competitiveness and military capabilities. To date, China has released several national science and technology plans involving AI and established a national deep learning lab, headed by Baidu. In particular, Chinas new AI 2.0 mega-project will advance an ambitious agenda for research and development, including economic and national security applications.

At the highest levels, the Chinese Peoples Liberation Army (PLA) also recognizes and intends to take advantage of the transformation of todays informatized () ways of warfare into future intelligentized () warfare. According to Lieutenant General Liu Guozhi, director of the Central Military Commissions (CMC) Science and Technology Commission, the world is on the eve of a new scientific and technological revolution, and we are entering the era of intelligentization due to rapid advances in AI and its impactful military applications. He believes that AI will result in fundamental changes to military units programming, operational styles, equipment systems, and models of combat power generation, even leading to a profound military revolution. The PLA might have a unique opportunity to take advantage of these trends through leveraging the dynamism of Chinese advances in AI through a national agenda of military-civil integration ().

While the lessons learned by the PLA from other nations wars have traditionally informed its approach to military modernization, its current thinking on the military implications of AI has, in fact, been influenced not by a war but by a game. AlphaGos initial defeat of Lee Sedol appears to have captured the PLAs imagination at the highest levels, resulting in the convening of high-level seminars and symposiums on the topic. PLA thinkers apparent fascination with AlphaGo presents early indications of its initial thinking on and potential future employment of AI in warfare, with applications ranging from autonomous unmanned systems and swarm intelligence to command decision-making. The PLA appears to see AlphaGos mastery of the complex tactics associated with the game as an apt demonstration of its future military utility.

From the perspective of PLA strategists, this great war of man and machine () decisively displayed AIs potential to take on an integral role in command decision-making in future intelligentized warfare. The successes of AlphaGo are considered a turning point that demonstrated the potential of AI to engage in complex analyses and strategizing comparable to that required in warfarenot only equaling human cognitive capabilities but even enabling a distinctive advantage that may surpass the human mind.

Certain PLA thinkers anticipate that the intelligentization of warfare will result in a trend towards a battlefield singularity, such that human intelligence may prove unable to keep pace with the new operational tempo of machine-age warfare. At that point, as warfare occurs at machine speed, keeping humans in the loop for the employment of weapons systems or even certain aspects of decision-making could become a liability, rather than an asset. Consequently, AI could necessarily take on a greater role in command and control.

The PLAs response to AlphaGo thus raises criticaland, for the time being, unanswerablequestions about how its future approach to autonomous weapons and other military applications of AI might differ from that of U.S, including on issues of meaningful human control. Indeed, as the U.S. and Chinese militaries compete to advance their capabilities in this critical technological domain, the resulting legal and normative issuesas well as potential adverse implications for strategic stabilitywill merit further analysis and bilateral engagement.

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AlphaGo and Beyond: The Chinese Military Looks to Future ...

Google Deepmind head Demis Hassabis speaks during a press conference ahead of the Google DeepMind Challenge Match in Seoul on March 8, 2016.

Jung Yeon-Je | AFP |Getty Images | Getty Images

In 2016, DeepMind, an Alphabet-owned AI unit headquartered in London, was riding a wave of publicity thanks to AlphaGo, its computer program that took on the best player in the world at the ancient Asian board game Go and won.

Photos of DeepMind's leader, Demis Hassabis, were splashed across the front pages of newspapers and websites, and Netflix even went on to make a documentary about the five-game Go match between AlphaGo and world champion Lee SeDol. Fast-forward four years, and things have gone surprisingly quiet about DeepMind.

"DeepMind has done some of the most exciting things in AI in recent years. It would be virtually impossible for any company to sustain that level of excitement indefinitely," said William Tunstall-Pedoe, a British entrepreneur who sold his AI start-up Evi to Amazon for a reported $26 million. "I expect them to do further very exciting things."

AI pioneer Stuart Russell, a professor at the University of California, Berkeley, agreed it was inevitable that excitement around DeepMind would tail off after AlphaGo.

"Go was a recognized milestone in AI, something that some commentators said would take another 100 years," he said. "In Asia in particular, top-level Go is considered the pinnacle of human intellectual powers. It's hard to see what else DeepMind could do in the near term to match that."

DeepMind's army of 1,000 plus people, which includes hundreds of highly-paid PhD graduates, continues to pump out academic paper after academic paper, but only a smattering of the work gets picked up by the mainstream media. The research lab has churned out over 1,000 papers and 13 of them have been published by Nature or Science, which are widely seen as the world's most prestigious academic journals. Nick Bostrom, the author of Superintelligence and the director of the University of Oxford's Future of Humanity Institute described DeepMind's team as world-class, large, and diverse.

"Their protein folding work was super impressive," said Neil Lawrence, a professor of machine learning at the University of Cambridge, whose role is funded by DeepMind. He's referring to a competition-winning DeepMind algorithm that can predict the structure of a protein based on its genetic makeup. Understanding the structure of proteins is important as it could make it easier to understand diseases and create new drugs in the future.

The World's top human Go player, 19-year-old Ke Jie (L) competes against AI program AlphaGo, which was developed by DeepMind, the artificial intelligence arm of Google's parent Alphabet. Machine won the three-game match against man in 2017. The AI didn't lose a single game.

VCG | Visual China Group | Getty Images

DeepMind is keen to move away from developing relatively "narrow" so-called "AI agents," that can do one thing well, such as master a game. Instead, the company is trying to develop more general AI systems that can do multiple things well, and have real world impact.

It's particularly keen to use its AI to leverage breakthroughs in other areas of science including healthcare, physics and climate change.

But the company's scientific work seems to be of less interest to the media.In 2016, DeepMind was mentioned in 1,842 articles, according to media tracker LexisNexis. By 2019, that number had fallen to 1,363.

One ex-DeepMinder said the buzz around the company is now more in line with what it should be. "The whole AlphaGo period was nuts," they said. "I think they've probably got another few milestones ahead, but progress should be more low key. It's a marathon not a sprint, so to speak."

DeepMind denied that excitement surrounding the company has tailed off since AlphaGo, pointing to the fact that it has had more papers in Nature and Science in recent years.

"We have created a unique environment where ambitious AI research can flourish. Our unusually interdisciplinary approach has been core to our progress, with 13 major papers in Nature and Science including 3 so far this year," a DeepMind spokesperson said. "Our scientists and engineers have built agents that can learn to cooperate, devise new strategies to play world-class chess and Go, diagnose eye disease, generate realistic speech now used in Google products around the world, and much more."

"More recently, we've been excited to see early signs of how we could use our progress in fundamental AI research to understand the world around us in a much deeper way. Our protein folding work is our first significant milestone applying artificial intelligence to a core question in science, and this is just the start of the exciting advances we hope to see more of over the next decade, creating systems that could provide extraordinary benefits to society."

The company, which competes with Facebook AI Research and OpenAI, did a good job of building up hype around what it was doing in the early days.

Hassabis and Mustafa Suleyman, the intellectual co-founders who have been friends since school, gave inspiring speeches where they would explain how they were on a mission to "solve intelligence" and use that to solve everything else.

There was also plenty of talk of developing "artificial general intelligence" or AGI, which has been referred to as the holy grail in AI and is widely viewed as the point when machine intelligence passes human intelligence.

But the speeches have become less frequent (partly because Suleyman left Deepmind and works for Google now), and AGI doesn't get mentioned anywhere near as much as it used to.

Larry Page, left, and Sergey Brin, co-founders of Google Inc.

JB Reed | Bloomberg | Getty Images

Google co-founders Larry Page and Sergey Brin were huge proponents of DeepMind and its lofty ambitions, but they left the company last year and its less obvious how Google CEO Sundar Pichai feels about DeepMind and AGI.

It's also unclear how much free reign Pichai will give the company, which cost Alphabet $571 million in 2018. Just one year earlier, the company had losses of $368 million.

"As far as I know, DeepMind is still working on the AGI problem and believes it is making progress," Russell said. "I suspect the parent company (Google/Alphabet) got tired of the media turning every story about Google and AI into the Terminator scenario, complete with scary pictures."

One academic who is particularly skeptical about DeepMind's achievements is AI entrepreneur Gary Marcus, who sold a machine-learning start-up to Uber in 2016 for an undisclosed sum.

"I think they realize the gulf between what they're doing and what they aspire to do," he said. "In their early years they thought that the techniques they were using would carry us all the way to AGI. And some of us saw immediately that that wasn't going to work. It took them longer to realize but I think they've realized it now."

Marcus said he's heard that DeepMind employees refer to him as the "anti-Christ" because he has questioned how far the "deep learning" AI technique that DeepMind has focused on can go.

"There are major figures now that recognize that the current techniques are not enough," he said. "It's very different from two years ago. It's a radical shift."

He added that while DeepMind's work on games and biology had been impressive, it's had relatively little impact.

"They haven't used their stuff much in the real world," he said. "The work that they're doing requires an enormous amount of data and an enormous amount of compute, and a very stable world. The techniques that they're using are very, very data greedy and real-world problems often don't supply that level of data."

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Why the buzz around DeepMind is dissipating as it transitions from games to science - CNBC

The Hardware in Microsofts OpenAI Supercomputer Is InsaneAndrew Wheeler posted on June 02, 2020 | The benefit to Elon Musks organization is not yet clear.

(Image courtesy of Microsoft.)

OpenAI, the San Francisco-based research laboratory founded by serial entrepreneur Elon Musk, is dedicated to ensure that artificial general intelligence benefits all of humanity. Microsoft invested $1 billion in OpenAI in June 2019 to build a platform of unprecedented scale. Recently, Microsoft pulled back the curtain on this project to reveal that its OpenAI supercomputer is up and running. Its powered by an astonishing 285,000 CPU cores and 10,000 GPUs.

The announcement was made at Microsofts Build 2020 developer conference. The OpenAI supercomputer is hosted by Microsofts Azure cloud and will be used to test massive artificial intelligence (AI) models.

Many AI supercomputing research projects focus on perfecting single tasks using deep learning or deep reinforcement learning as is the case with Googles various DeepMind projects like AlphaGo Zero. But a new wave of AI research focuses on how these supercomputers can perfect multiple tasks simultaneously. At the conference, Microsoft mentioned a few of these tasks that its AI supercomputer could tackle. These include having the companys AI supercomputer possibly examine huge datasets of code from GitHub (which Microsoft acquired in 2018 for $7.5 billion worth of stock) to artificially generate its own code. Another multitasking AI function could be the moderation of game-streaming services, according to Microsoft.

But is OpenAI going to benefit from this development? How would these services use Microsofts OpenAI supercomputer?

Users of Microsoft Teams benefit from real-time captioning via Microsofts development of Turing models for natural language processing and generation, so maybe OpenAI will pursue more natural language processing projects. But the answer is unknown at this point.

(Video courtesy of Microsoft.)

Bottom Line

Large-scale AI implementations from powerful and ultra-wealthy tech giants like Microsoft with access to tremendous datasets (this is the key for advanced AI beyond powerful software) could lead to the development of an AI programmer using the vast repositories of code on GitHub.

Microsofts Turing models for natural language processing use over 17 billion parameters for deciphering language. The number of CPUs and GPUs in Microsofts AI supercomputer is almost as staggering as the potential applications the company could create with access to such vast computing power. On that one note, Microsoft announced that its Turing models for natural language generation will become open source for human developers to use in the near future, but no exact date has been given.

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The Hardware in Microsofts OpenAI Supercomputer Is Insane - ENGINEERING.com

Here Are This Weeks Top Stories:1. Collaboration: Zooms Stock Is Skyrocketing But Is It Secure? 2. Security: Healthcare Hit By COVID-19 Cyber Attack 3. IT Strategy: Washington Signs Facial Tech Into Law Zoom's Stock Is Skyrocketing But Is It Secure?

Even as Zoom's active user count scales everyday with U.S. volumes touching 4.84 million on Monday, concerns around the solutions's security credentials have risen significantly. Elon Musk founded SpaceX shunned the videoconferencing app, citing significant privacy and security concerns. New York's Attorney General wrote to Zoom about its ability to secure massive workloads.

Big Picture: While stock is soaring for Zoom amid a global meltdown, the lack of end-to-end encryption will impact user growth. "It is not possible to enable E2E encryption for Zoom video meetings", Zoom spokesperson reportedly shared with The Intercept. Even though Zoom secures audio and video meetings using TCP and UDP connections, it can access unencrypted video and audio content of meetings. Another downsize Zoom sells user data to advertisers for targeted marketing.

Our Take: Considering that NASA has prohibited its employees from using Zoom and the FBI has observed instances of people invading school sessions on the service, organizations need to prevent employees from sharing links to team meetings publicly. Alternatively, organizations can also try other video conferencing services that boast better security features.

The coronavirus epidemic is weighing heavily on the security sector with a record spike in COVID-19 themed cyberattacks. In fact, the healthcare industry on the frontlines of the epidemic is facing a record surge of cyber attacks. As per reports, hackers targeted U.K. based Hammersmith Medicines Research, the test center preparing to perform medical trials on prospective COVID-19 vaccines. The test center was hit by a cyber attack on March 14 when hackers attempted to breach the system. Reports indicate some data was stolen and posted online for ransom. Additionally, Security researchers from Nokia's Threat Intelligence Lab uncovered a powerful malware disguised as a "coronavirus map" application that infects Windows computers and is disguised as software from John Hopkins University.

Big Picture: The coronavirus epidemic has become the new attack vector for cyber criminals who have jumped on the opportunity. The Coronavirus map app is one such malicious app- secretly stealing credit card numbers, browser history, cookies, usernames and passwords from the browser's cache without users noticing such actions.

Our Take: Cybercriminals are exploiting global concerns around COVID-19, targeting people and organizations on the front lines of the pandemic. The record increase in hacking attempts has prompted cybersecurity professionals to step up the plate and form a response group called Cyber Volunteers 19.

Both individuals and corporations need to put more guardrails against these cyber threats and ensure appropriate security frameworks and policies to keep threat actors at bay.

On Tuesday, the Washington state legislature passed a bill into law to regulate the use of facial recognition by government agencies. As per the new law, facial recognition technologies need to be regularly tested for fairness and accuracy and can only be used under warrant. However, another bill to regulate the commercial use of facial recognition was tabled but not passed.

Big Picture: Washington tech giant Microsoft that has been lobbying for regulations around the use of facial recognition tech welcomed the move. Microsoft President Brad Smith hailed the new law as a significant breakthrough and an early and important model to serve the public interest without impacting people's fundamental rights.

Our Take: Facial recognition offers many benefits but also poses a serious threat to privacy and security. Ethical use of such technologies should be enforced through legislation and should apply to both public and private entities. The scope and purposes of facial recognition tech should also be reviewed regularly to prevent misuse.

AlphaGo Developer Nabs ACM Prize!

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Latest Tech News This Week: Zoom Hit With Security Woes, Cyber Attacks on Healthcare Ramp Up|Weekly Rundown - Toolbox