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Artificial intelligence -- which encompasses everything from service robots to medical diagnostic tools to your Alexaspeaker -- is a fast-growing field that is increasingly playing a more critical role in many aspects of our lives. A country's AI prowess has major implications for how its citizens live and work -- and its economic and military strength moving into the future.

With so much at stake, the narrative of an AI "arms race" between the US and China has been brewing for years. Dramatic headlines suggest that China is poised to take the lead in AI research and use, due to its national plan for AI domination and the billions of dollars the government has invested in the field, compared with the US' focus on private-sector development.

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But the reality is that at least until the past year or so, the two nations have been largely interdependent when it comes to this technology. It's an area that has drawn attention and investment from major tech heavy hitters on both sides of the Pacific, including Apple, Google and Facebook in the US and SenseTime, Megvii and YITU Technology in China.

Generation China is a CNET series that looks at the areas of technology where the country is looking to take a leadership position.

"Narratives of an 'arms race' are overblown and poor analogies for what is actually going on in the AI space," said Jeffrey Ding, the China lead for the Center for the Governance of AI at the University of Oxford's Future of Humanity Institute. When you look at factors like research, talent and company alliances, you'll find that the US and Chinese AI ecosystems are still very entwined, Ding added.

But the combination of political tensions and the rapid spread of COVID-19 throughout both nations is fueling more of a separation, which will have implications for both advances in the technology and the world's power dynamics for years to come.

"These new technologies will be game-changers in the next three to five years," said Georg Stieler, managing director of Stieler Enterprise Management Consulting China. "The people who built them and control them will also control parts of the world. You cannot ignore it."

You can trace China's ramp up in AI interest back to a few key moments starting four years ago.

The first was in March 2016, when AlphaGo -- a machine-learning system built by Google's DeepMind that uses algorithms and reinforcement learning to train on massive datasets and predict outcomes -- beat the human Go world champion Lee Sedol. This was broadcast throughout China and sparked a lot of interest -- both highlighting how quickly the technology was advancing, and suggesting that because Go involves war-like strategies and tactics, AI could potentially be useful for decision-making around warfare.

The second moment came seven months later, when President Barack Obama's administration released three reports on preparing for a future with AI, laying out a national strategic planand describing the potential economic impacts(all PDFs). Some Chinese policymakers took those reports as a sign that the US was further ahead in its AI strategy than expected.

This culminated in July 2017, when the Chinese government under President Xi Jinping released a development plan for the nation to become the world leader in AI by 2030, including investing billions of dollars in AI startups and research parks.

In 2016, professional Go player Lee Sedol lost a five-game match against Google's AI program AlphaGo.

"China has observed how the IT industry originates from the US and exerts soft influence across the world through various Silicon Valley innovations," said Lian Jye Su, principal analyst at global tech market advisory firm ABI Research. "As an economy built solely on its manufacturing capabilities, China is eager to find a way to diversify its economy and provide more innovative ways to showcase its strengths to the world. AI is a good way to do it."

Despite the competition, the two nations have long worked together. China has masses of data and far more lax regulations around using it, so it can often implement AI trials faster -- but the nation still largely relies on US semiconductors and open source software to power AI and machine learning algorithms.

And while the US has the edge when it comes to quality research, universities and engineering talent, top AI programs at schools like Stanford and MIT attract many Chinese students, who then often go on to work for Google, Microsoft, Apple and Facebook -- all of which have spent the last few years acquiring startups to bolster their AI work.

China's fears about a grand US AI plan didn't really come to fruition. In February 2019, US President Donald Trump released an American AI Initiative executive order, calling for heads of federal agencies to prioritize AI research and development in 2020 budgets. It didn't provide any new funding to support those measures, however, or many details on how to implement those plans. And not much else has happened at the federal level since then.

Meanwhile, China plowed on, with AI companies like SenseTime, Megvii and YITU Technology raising billions. But investments in AI in China dropped in 2019, as theUS-China trade war escalated and hurt investor confidence in China, Su said. Then, in January, the Trump administration made it harder for US companies to export certain types of AI software in an effort to limit Chinese access to American technology.

Just a couple weeks later, Chinese state media reported the first known death from an illness that would become known as COVID-19.

In the midst of the coronavirus pandemic, China has turned to some of its AI and big data tools in attempts to ward off the virus, including contact tracing, diagnostic tools anddrones to enforce social distancing. Not all of it, however, is as it seems.

"There was a lot of propaganda -- in February, I saw people sharing on Twitter and LinkedIn stories about drones flying along high rises, and measuring the temperature of people standing at the window, which was complete bollocks," Stieler said. "The reality is more like when you want to enter an office building in Shanghai, your temperature is taken."

A staff member introduces an AI digital infrared thermometer at a building in Beijing in March.

The US and other nations are grappling with the same technologies -- and the privacy, security and surveillance concerns that come along with them -- as they look to contain the global pandemic, said Elsa B. Kania, adjunct fellow with the Center for a New American Security's Technology and National Security Program, focused on Chinese defense innovation and emerging technologies.

"The ways in which China has been leveraging AI to fight the coronavirus are in various respects inspiring and alarming," Kania said. "It'll be important in the United States as we struggle with these challenges ourselves to look to and learn from that model, both in terms of what we want to emulate and what we want to avoid."

The pandemic may be a turning point in terms of the US recognizing the risks of interdependence with China, Kania said. The immediate impact may be in sectors like pharmaceuticals and medical equipment manufacturing. But it will eventually influence AI, as a technology that cuts across so many sectors and applications.

Despite the economic impacts of the virus, global AI investments are forecast to grow from $22.6 billion in 2019 to $25 billion in 2020, Su said. The bigger consequence may be on speeding the process of decoupling between the US and China, in terms of AI and everything else.

The US still has advantages in areas like semiconductors and AI chips. But in the midst of the trade war, the Chinese government is reducing its reliance on foreign technologies, developing domestic startups and adopting more open-source solutions, Su said. Cloud AI giants like Alibaba, for example, are using open-source computing models to develop their own data center chips. Chinese chipset startups like Cambricon Technologies, Horizon Robotics and Suiyuan Technology have also entered the market in recent years and garnered lots of funding.

But full separation isn't on the horizon anytime soon. One of the problems with referring to all of this as an AI arms race is that so many of the basic platforms, algorithms and even data sources are open-source, Kania said. The vast majority of the AI developers in China use Google TensorFlow or Facebook PyTorch, Stieler added -- and there's little incentive to join domestic options that lack the same networks.

The US remains the world's AI superpower for now, Su and Ding said. But ultimately, the trade war may do more harm to American AI-related companies than expected, Kania said.

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"My main concern about some of these policy measures and restrictions has been that they don't necessarily consider the second-order effects, including the collateral damage to American companies, as well as the ways in which this may lessen US leverage or create much more separate or fragmented ecosystems," Kania said. "Imposing pain on Chinese companies can be disruptive, but in ways that can in the long term perhaps accelerate these investments and developments within China."

Still, "'arms race' is not the best metaphor," Kania added. "It's clear that there is geopolitical competition between the US and China, and our competition extends to these emerging technologies including artificial intelligence that are seen as highly consequential to the futures of our societies' economies and militaries."

Excerpt from:
The US, China and the AI arms race: Cutting through the hype - CNET

Artificial intelligence systems have become increasingly well-adapted to a host of basic board games. Now, DeepMind is hoping to teach agents the art of collaboration using Diplomacy.

IMAGE: iStock/MaksimTkachenko

From Turochamp to DeepBlue, human-vs.-computer competition has captivated audiences for decades fueling plenty of hyperbole along the way. In recent years, artificial intelligence (AI) systems have claimed supremacy across a variety of classic games. The AI research and development company DeepMind has been behind many of these systems at the bleeding edge of innovation.

In March 2016, one such bout of bytes vs. brains pitted DeepMind's AI system, AlphaGo against Go legend and 18-time world titleholder Lee Sedol. With millions tuning in around the globe, the unthinkable slowly unfolded as AlphaGo picked apart arguably the best player of the abstract strategy board game of the past decade with surgical precision. The stunning AlphaGo victory awarded the AI system a 9 dan ranking, the highest such certification.

Now the company has set its sights on training an AI agent on another of mankind's mysterious board games; this time trying its hand at Diplomacy. After all, it was only a matter of time before we trained AI the skillful art of negotiation en route to global domination.

Unlike more rudimentary games, Diplomacy involves a complex level of strategy and scheming. In a game like checkers, for example, a player has a rather limited decision about where to move an individual piece at any given time. The nuances and complexities, of course, increase with chess as a player must assign value to pieces and orchestrate a cohesive series of moves for success. In the esoteric world of boardgames, Diplomacy presents its own set of challenges for AI.

"Diplomacy has seven players and focuses on building alliances, negotiation, and teamwork in the face of uncertainty about other agents. As a result, agents have to constantly reason about who to cooperate with and how to coordinate actions," said Tom Eccles, a research engineer at DeepMind.

SEE:Building the bionic brain (free PDF)(TechRepublic)

AI systems have proved to be far superior to even the best human beings at zero-sum games like chess and Go. In this type of gameplay, there can only be one winner and one loser. Dissimilarly, Diplomacy requires agents to build alliances and foster collaboration.

"On the one hand, it is difficult to make progress in the game without the support of other players, but on the other hand, only one player can eventually win. This means it is more difficult to achieve cooperation in this environment. The tension between cooperation and competition in Diplomacy makes building trustworthy agents in this game an interesting research challenge," said Tom Anthony, a research scientist at DeepMind.

The ability to expeditiously vanquish a human player in a zero-sum game is certainly impressive, however, a richer layering of skills opens up another world of AI potential. Our day-to-day lives involve an intricate patchwork of balanced synergies; our individual needs often packaged within a larger group effort. That said, this research could enhance agents' ability to collaborate with us and one another, leading to a vast spectrum of real-world applications.

"In real-life, we often work in teams and have to both compete and cooperate. From simple decisions such as scheduling a meeting or deciding where to eat out with friends, to complex decisions such as negotiating with suppliers or clients or assigning tasks in a joint project, we constantly reason about how to best work with others. It seems likely that as AI systems become more complex, we'd need to provide them with better tools for effectively cooperating with others," said Yoram Bachrach, a research scientist at DeepMind.

Organizational workflows are typically hinged on collaboration and teamwork. As digital transformation takes hold across industries, organizations are increasingly utilizing a host of autonomous systems to increase efficiency and streamline operations. Enhancing agents with artificial soft skills related to teamwork and cooperation may be key moving forward.

"Artificial Intelligence is increasingly being applied to more complex tasks. This could mean that a number of different autonomous systems must work together, or at least in the same environment, in order to solve a task. As such, understanding how autonomous systems learn, act, and adapt to each other, is a growing area of research." Eccles said.

SEE:Managing AI and ML in the enterprise 2020: Tech leaders increase project development and implementation(TechRepublic Premium)

It's important to note that this research focused on understanding the interactions in a "many-agent setting," and used a limited No-Press version of gameplay, which does not allow communication. Further research and development will allow future agents to participate in full Diplomacy gameplay, leveraging communication to build alliances and negotiate with other players.

In the full version, "communication is used to broker deals and form alliances, but also to misrepresent situations and intentions," according to the paper. Teaching an agent to utilize other players as collaborative pawns to ensure victory does bring up a series of concerns.

In one such scenario, the authors of the report explain that "agents may learn to establish trust, but might also exploit that trust to mislead their co-players and gain the upper hand." The researchers reiterate the importance of testing these agents in an isolated environment to better understand developments and pinpoint detrimental behaviors if they arise.

"We start from the premise that all AI applications should remain under meaningful human control, and be used for socially beneficial purposes. Our teams working on technical safety and ethics aim to ensure that we are constantly anticipating short- and long-term risks, exploring ways to prevent these risks from happening, and finding ways to address them if they do." Anthony said.

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DeepMind sets AI loose on Diplomacy board game, and collaboration is key - TechRepublic

Artificial super-intelligence (ASI) is a software-based system with intellectual powers beyond those of humans across an almost comprehensive range of categories and fields of endeavor.

The reality is that AI has been with here for a long time now, ever since computers were able to make decisions based on inputs and conditions. When we see a threatening Artificial Intelligence system in the movies, its the malevolence of the system, coupled with the power of some machine that scares people.

However, it still behaves in fundamentally human ways.

The kind of AI that prevails today can be described as an Artificial Functional Intelligence (AFI). These systems are programmed to perform a specific role and to do so as well or better than a human. They have also become more successful at this in a short period which no one has ever predicted. For example, beating human opponents in complex games like Go and StarCraft II which knowledgeable people thought wouldnt happen for years, if not decades.

However, Alpha Go might beat every single human Go player handily from now until the heat death of the Universe, but when it is asked for the current weather conditions there the machine lacks the intelligence of even single-celled organisms that respond to changes in temperature.

Moreover, the prospect of limitless expansion of technology granted by the development of Artificial Intelligence is certainly an inviting one. While investment and interest in the field only grow by every passing year, one can only imagine what we might have to come.

Dreams of technological utopias granted by super-intelligent computers are contrasted with those of an AI lead dystopia, and with many top researchers believing the world will see the arrival of AGI within the century, it is down to the actions people take now to influence which future they might see. While some believe that only Luddites worry about the power AI could one-day hold over humanity, the reality is that most tops AI academics carry a similar concern for its more grim potential.

Its high time people must understand that no one is going to get a second attempt at Powerful AI. Unlike other groundbreaking developments for humanity, if it goes wrong there is no opportunity to try again and learn from the mistakes. So what can we do to ensure we get it right the first time?

The trick to securing the ideal Artificial Intelligence utopia is ensuring that their goals do not become misaligned with that of humans; AI would not become evil in the same sense that much fear, the real issue is it making sure it could understand our intentions and goals. AI is remarkably good at doing what humans tell it, but when given free rein, it will often achieve the goal humans set in a way they never expected. Without proper preparation, a well-intended instruction could lead to catastrophic events, perhaps due to an unforeseen side effect, or in a more extreme example, the AI could even see humans as a threat to fully completing the task set.

The potential benefits of super-intelligent AI are so limitless that there is no question in the continued development towards it. However, to prevent AGI from being a threat to humanity, people need to invest in AI safety research. In this race, one must learn how to effectively control a powerful AI before its creations.

The issue of ethics in AI, super-intelligent or otherwise, is being addressed to a certain extent, evidenced by the development of ethical advisory boards and executive positions to manage the matter directly. DeepMind has such a department in place, and international oversight organizations such as the IEEE have also created specific standards intended for managing the coexistence of highly advanced AI systems and the human beings who program them. But as AI draws ever closer to the point where super-intelligence is commonplace and ever more organizations adopt existing AI platforms, ethics must be top of mind for all major stakeholders in companies hoping to get the most out of the technology.

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Is Dystopian Future Inevitable with Unprecedented Advancements in AI? - Analytics Insight

Next month, CIIs annual summit will explore the digital transformation of knowledge societies. To be held entirely online from July 6-8, the forum is titled CII Global Knowledge Virtual Summit 2020: Knowledge in the Age of Artificial Intelligence.

The conference is also supported by the KM Global Network (KMGN), and will feature the awards ceremony for the Most Innovative Knowledge Enterprise (MIKE). AFCONS, Infosys, Wipro, Cognizant, and Tata Chemicals are winners of the MIKE Awards at the India and global levels.

YourStory is the media partner for the summit this year as well (see Part I and Part II of our 2019 summit articles). Topics addressed this year include the rise of AI/ML, knowledge integration, gamification, and storytelling.

In this series of preview articles, YourStory presents insights from the speakers and organisers of the CII 2020 summit, as well as experts from KMGN (see Part I and Part II of our ongoing coverage of the 2020 edition). The knowledge movement has particular urgency in the wake of COVID-19 to speed up effective knowledge-sharing across sectoral and national boundaries.

In a chat with YourStory, Jennifer Mecherippady, Senior Vice-President of CGI, shows a number of AI benefits that have been realised by her company. These include digital transformation of AM/IM (application/infrastructure management) operations through its Intelligent Automation Platform, responding to RFPs based on insights from specifications and past data, and digitisation of industry-specific needs in banking and HR.

A number of case studies of AI have shown broader impacts across industries, explains Sameer Dhanrajani, CEO of AIQRATE. He is also the author of AI and Analytics: Accelerating Business Decisions (see my book review here).

The case studies cover AI impacts in media (innovative content creation via hyper-personalisation and micro-segmenting), insurance (transformation of the business value chain in claims processing, telematics, risk management, actuarial valuations), and manufacturing (predictive asset maintenance to pre-empt wear and tear).

We are being ushered into an AI era, an algorithm-led economy wherein self-intuitive and ML- enabled algorithms sit at the core of every business model and in the organisational DNA, delivering end-to-end transformative impact, he explains.

Machines are great at evaluating huge volumes of data and generating clever visualisations from these. AI is also good at finding trends that humans cant immediately see due to the volume of data and possible interfering counter patterns, explains Arthur Shelley, Founder of Intelligent Answers.

A number of other experts have documented specific impacts of AI and ML in companies like Amazon, GE, Bosch, Nike, Caterpillar, Spotify, Netflix, SAP, Cisco, IBM, Siemens, Verizon, Unilever, P&G, GSK, Novartis, SalesForce.com, DBS Bank, RioTinto, Lowes, AllState, and AlphaGo. See my book reviews of Prediction Machines; What to do when Machines do Everything; Machine, Platform, Crowd; The AI Advantage; and Human + Machine.

Every five years or so, the field of KM undergoes a metamorphosis, absorbing the latest trends into its practices and thereby delivering continuing value, explains Rudolph D'souza, Chair of KMGN and Chief Knowledge Officer of AFCONS Infrastructure. He cites the rise of the internet, social media, and enterprise digital platforms as examples of such waves.

The same is going to happen with AI, automation, and machines. What will change is the pace, the sources of knowledge, and in this new era the application of knowledge, Rudolph says. The role of KM is to absorb the latest applications to serve organisation needs to compete effectively.

This is already happening, mainly in the form of simple decision support where the implications are not catastrophic. But some use cases of higher-end applications have been around, as in the case of using machines to analyse scans in oncology departments and assist specialists, Rudolph observes.

Knowledge creation and management is a critical differentiator for the industry. With AI making great strides in generating knowledge from raw video, image, voice, and social media text, knowledge creation and management has to be redefined, explains Gopichand Katragadda, Chairman, Global Knowledge Summit 2020, and Founder and CEO at Myelin Foundry.

The rise of AI and automation will lead to the increasing embedding of relevant knowledge about decisions, design, and processes right into the code, according to Ravi Shankar Ivaturi, Business Operations Senior Director, Products and Platforms, Unisys. This can lead to positive and negative effects, he cautions.

Structured KM lays the foundation on which AI, machine learning, and automation can thrive, according to Ved Prakash, Chief Knowledge Officer of Trianz. The role of KM is only going to increase in the emerging scenarios where deep understanding of knowledge and data will be a key skill, he adds.

The role of KM is going to be that of a connective tissue across systems, machines, and humans. The game is still about insights, explains Balaji Iyer, Director of Knowledge Management and Enterprise Transformation at Grant Thornton.

Many processes are automated in a HUMBOT framework where humans work closely with bots to get the desired outcomes. There is a crucial knowledge play in areas of machine teaching, human-bot hand-offs, and solving the right problems, he adds

The more AI makes a lot of the processes appear like black boxes for business leaders, the more pronounced the need for a next-gen KM program, Balaji says. He also draws attention to the re-imagination of KM systems using AI as a backbone for an AI-driven world, with KM products like Microsofts Cortex as an example.

AI will continue to be used to replicate human cognitive functions such as memory, learning, evaluation, decision making, and problem solving, says Zeba Khan, Managing Partner, Xenvis Solutions. The role of the human factor in aspects of creativity, intuition and in other soft skills cannot be replaced by technology. AI will not replace human jobs but will redefine them, she emphasises.

AI needs knowledge to properly operate and produce valuable results. KM will help producing the raw material for AI and support the AI process at every stage, explains Vincent Ribire, Managing Director and Co-founder of the Institute for Knowledge and Innovation Southeast Asia (IKI-SEA), hosted by Bangkok University.

Every organisation using AI aims to have knowledge embedded into a system to perform the roles humans do at lightning speed, observes Rajesh Dhillon, President, Knowledge Management Society (KMS), Singapore. Knowledge sharing, collaboration, reuse and learning are the impetus for implementing KM and keeping AI relevant.

AI-assisted collaboration tools can take knowledge management to another level, observes Refiloe Mabaso, Deputy Chairperson of Knowledge Management South Africa (KMSA). AI and KM combined can help teams and organisations operate even more intelligently.

What AI is not (yet) great at is finding the gaps or creatively connecting the insights that may be possible. The future is about what is possible in future and this is informed from what currently is and cant be done, explains Arthur Shelley of Intelligent Answers.

This is where collaboration between AI and human creativity offers more than either alone can achieve, he adds. Based in Melbourne, Arthur is the producer of the Creative Melbourne conference, and author of KNOWledge SUCCESSion, Being a Successful Knowledge Leader, and The Organizational Zoo.

AI and automation can be beneficial, but humane and responsible automation is important for balancing the unemployment and cost, cautions Sudip Mazumder, Head of Engineering and Construction, Digital at L&T NxT, and General Manager, L&T Group. AI may lead to dehumanised processes as peoples behavioural drivers may not be mapped in an AI model, he explains.

There will be realignment of the human-machine equation in the context of AI proliferation in the Industry 4.0 era, explains Sameer Dhanrajani of AIQRATE. However, akin to all three previous revolutions, AI progress will redefine jobs and human roles a few notches up, he adds.

He foresees a change in workforce composition with menial and trivial jobs getting redefined with AI and redesigned with human-machine combinations. However, platform aggregators and the gig economy will open up new work opportunities for the workforce.

A world that was hurtling at a relentless pace towards automation, AI, and ML has been forced to stop in its tracks and take cognizance of the human in the process. And, it took a virus to do that, cautions Rajib Chowdhury, Founder of The Gamification Company.

Working from home is ineffective without emotional trust, a sense of ownership, self-motivation, and measures of accountability, he adds. Let us not forget that we humans are fundamentally social beings. Technology is but a medium that plays a role of enabler to the process, he emphasises.

The human factor is still key in a world of AI, explains Jennifer Mecherippady of CGI. This includes identifying potential problems and measurable metrics, providing the right data sets, attributes, and values, and finally evaluating the business outcomes.

The screaming need for KM in the age of automation, ML, and AI is to formulate and implement frameworks for the Governance of Human and Machine Knowledge, emphasises Arthur Murray, CEO of Applied Knowledge Sciences, in Washington DC.

Knowledge, whether human or automated, does not manage itself. It requires, as we like to say, adult supervision, he explains. In a recent column, he shows how these challenges manifested themselves in Microsofts aborted Twitter chatbot Tay.

KM practitioners should strategically work with executive management to measure and update performance impacts of AI, advises Moria Levy, CEO, ROM Knowledgeware. They should examine how AI can, or cannot, support critical decisions. This involves knowledge validation, sense-making, and risk analysis.

A number of experts have weighed in on broader ethical dimensions of AI with respect to embedded bias, monopolistic practices, global governance, and lack of transparency and accountability. See for example my book reviews of A Human's Guide to Machine Intelligence, Life 3.0, The Four, and The Platform Society.

Despite the presence of AI for decades, a number of myths and misconceptions persist, and get in the way of harnessing AI. Jennifer Mecherippady of CGI points to some such myths: AI will replace humans and overtake human intelligence, AI can make sense of any data and learn the way humans learn, and AI will give immediate business results.

Many companies are embracing digital transformation without fully understanding the key role of analytics and AI, cautions Sameer Dhanrajani of AIQRATE. The road to digital transformation is incomplete without AI being at the fulcrum of the business. Enterprises cannot adopt AI if the foundational aspects of analytics capability are not in place in the journey to AI, he emphasises.

Lack of awareness of AI impacts gets in the way of evangelising and democratising AI, he adds. AI calls for disrupting the business value chain of the enterprises and replacing it with high powered ML-enabled algorithms.

The speakers offer a range of tips for professionals and organisations to upskill themselves for a world of AI. You need to identify different groups of people and upskill them. For example, programmers need to be able to identify, implement, refine, and manage new models, Jennifer Mecherippady of CGI explains.

Business users should master how to effectively use intelligent systems for solving new business problems. Business consultants should be able to understand business problems and identify the right use cases to invest in AI, she adds. Use case identification, collaboration, and scaling call for a systematic learning process.

AI therefore should be owned by the teams invested in driving the benefits for customers, she adds. CGIs organisational model alignment emphasises a flattened structure consisting of just five level to business unit leaders.

Learning will not be a one-time effort. It will be a continual one and the market will unleash new exponential technologies, business practices, and disruptive scenarios in rapid time cycles, observes Sameer Dhanrajani of AIQRATE.

The basic needs for survival so far have been roti, kapda, makaan, and data. All professions will be forced to add the fifth element learning into their monthly budgets to ensure that they remain topical on skills and competencies, Sameer jokes.

The speakers offer a range of tips for businesses to harness AI. Continue looking for strong opportunities and business cases for AI. Make it a goal for your teams, advises Jennifer of CGI.

Many enterprises have only a short-term measure for AI adoption and focus only on PoCs or limited engagements. Instead, they need to make AI integral to the strategy of the enterprise and a rallying cry, Sameer of AIQRATE urges.

The COVID-19 crisis will accelerate AI adoption in totality and across industry segments. Customer preferences have drastically changed, and operational processes have been altered because of this Black Swan event, Sameer observes.

However, as the current running algorithms have been fed with historical and episodical instances of the past, the coronavirus crisis will compel enterprises to alter the algorithms with revised assumptions and variables. Otherwise, these pre-configured algorithms may create biases in the existing data sets and provide distorted recommendations to the stakeholders, Sameer cautions.

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An eye on AI CII Global Knowledge Summit explores impacts and strategies for the Age of the Algorithm - YourStory

Artificial intelligence that plays Go

AlphaGo is a computer program that plays the board game Go.[1] It was developed by DeepMind Technologies[2] which was later acquired by Google. AlphaGo had three far more powerful successors, called AlphaGo Master, AlphaGo Zero[3] and AlphaZero.

In October 2015, the original AlphaGo became the first computer Go program to beat a human professional Go player without handicap on a full-sized 1919 board.[4][5] In March 2016, it beat Lee Sedol in a five-game match, the first time a computer Go program has beaten a 9-dan professional without handicap.[6] Although it lost to Lee Sedol in the fourth game, Lee resigned in the final game, giving a final score of 4 games to 1 in favour of AlphaGo. In recognition of the victory, AlphaGo was awarded an honorary 9-dan by the Korea Baduk Association.[7] The lead up and the challenge match with Lee Sedol were documented in a documentary film also titled AlphaGo,[8] directed by Greg Kohs. It was chosen by Science as one of the Breakthrough of the Year runners-up on 22 December 2016.[9]

At the 2017 Future of Go Summit, its successor AlphaGo Master beat Ke Jie, the world No.1 ranked player at the time, in a three-game match (the even more powerful AlphaGo Zero already existed but was not yet announced). After this, AlphaGo was awarded professional 9-dan by the Chinese Weiqi Association.[10]

AlphaGo and its successors use a Monte Carlo tree search algorithm to find its moves based on knowledge previously "learned" by machine learning, specifically by an artificial neural network (a deep learning method) by extensive training, both from human and computer play.[11] A neural network is trained to predict AlphaGo's own move selections and also the winner's games. This neural net improves the strength of tree search, resulting in higher quality of move selection and stronger self-play in the next iteration.

After the match between AlphaGo and Ke Jie, DeepMind retired AlphaGo, while continuing AI research in other areas.[12] Starting from a 'blank page', with only a short training period, AlphaGo Zero achieved a 100-0 victory against the champion-defeating AlphaGo, while its successor, the self-taught AlphaZero, is currently perceived as the world's top player in Go as well as possibly in chess.

Go is considered much more difficult for computers to win than other games such as chess, because its much larger branching factor makes it prohibitively difficult to use traditional AI methods such as alphabeta pruning, tree traversal and heuristic search.[4][13]

Almost two decades after IBM's computer Deep Blue beat world chess champion Garry Kasparov in the 1997 match, the strongest Go programs using artificial intelligence techniques only reached about amateur 5-dan level,[11] and still could not beat a professional Go player without a handicap.[4][5][14] In 2012, the software program Zen, running on a four PC cluster, beat Masaki Takemiya (9p) twice at five- and four-stone handicaps.[15] In 2013, Crazy Stone beat Yoshio Ishida (9p) at a four-stone handicap.[16]

According to DeepMind's David Silver, the AlphaGo research project was formed around 2014 to test how well a neural network using deep learning can compete at Go.[17] AlphaGo represents a significant improvement over previous Go programs. In 500 games against other available Go programs, including Crazy Stone and Zen, AlphaGo running on a single computer won all but one.[18] In a similar matchup, AlphaGo running on multiple computers won all 500 games played against other Go programs, and 77% of games played against AlphaGo running on a single computer. The distributed version in October 2015 was using 1,202 CPUs and 176 GPUs.[11]

In October 2015, the distributed version of AlphaGo defeated the European Go champion Fan Hui,[19] a 2-dan (out of 9 dan possible) professional, five to zero.[5][20] This was the first time a computer Go program had beaten a professional human player on a full-sized board without handicap.[21] The announcement of the news was delayed until 27 January 2016 to coincide with the publication of a paper in the journal Nature[11] describing the algorithms used.[5]

AlphaGo played South Korean professional Go player Lee Sedol, ranked 9-dan, one of the best players at Go,[14][needs update] with five games taking place at the Four Seasons Hotel in Seoul, South Korea on 9, 10, 12, 13, and 15 March 2016,[22][23] which were video-streamed live.[24] Out of five games, AlphaGo won four games and Lee won the fourth game which made him recorded as the only human player who beat AlphaGo in all of its 74 official games.[25] AlphaGo ran on Google's cloud computing with its servers located in the United States.[26] The match used Chinese rules with a 7.5-point komi, and each side had two hours of thinking time plus three 60-second byoyomi periods.[27] The version of AlphaGo playing against Lee used a similar amount of computing power as was used in the Fan Hui match.[28] The Economist reported that it used 1,920 CPUs and 280 GPUs.[29] At the time of play, Lee Sedol had the second-highest number of Go international championship victories in the world after South Korean player Lee Changho who kept the world championship title for 16 years.[30] Since there is no single official method of ranking in international Go, the rankings may vary among the sources. While he was ranked top sometimes, some sources ranked Lee Sedol as the fourth-best player in the world at the time.[31][32] AlphaGo was not specifically trained to face Lee nor was designed to compete with any specific human players.

The first three games were won by AlphaGo following resignations by Lee.[33][34] However, Lee beat AlphaGo in the fourth game, winning by resignation at move 180. AlphaGo then continued to achieve a fourth win, winning the fifth game by resignation.[35]

The prize was US$1 million. Since AlphaGo won four out of five and thus the series, the prize will be donated to charities, including UNICEF.[36] Lee Sedol received $150,000 for participating in all five games and an additional $20,000 for his win in Game 4.[27]

In June 2016, at a presentation held at a university in the Netherlands, Aja Huang, one of the Deep Mind team, revealed that they had patched the logical weakness that occurred during the 4th game of the match between AlphaGo and Lee, and that after move 78 (which was dubbed the "divine move" by many professionals), it would play as intended and maintain Black's advantage. Before move 78, AlphaGo was leading throughout the game, but Lee's move caused the program's computing powers to be diverted and confused.[37] Huang explained that AlphaGo's policy network of finding the most accurate move order and continuation did not precisely guide AlphaGo to make the correct continuation after move 78, since its value network did not determine Lee's 78th move as being the most likely, and therefore when the move was made AlphaGo could not make the right adjustment to the logical continuation.[38]

On 29 December 2016, a new account on the Tygem server named "Magister" (shown as 'Magist' at the server's Chinese version) from South Korea began to play games with professional players. It changed its account name to "Master" on 30 December, then moved to the FoxGo server on 1 January 2017. On 4 January, DeepMind confirmed that the "Magister" and the "Master" were both played by an updated version of AlphaGo, called AlphaGo Master.[39][40] As of 5 January 2017, AlphaGo Master's online record was 60 wins and 0 losses,[41] including three victories over Go's top-ranked player, Ke Jie,[42] who had been quietly briefed in advance that Master was a version of AlphaGo.[41] After losing to Master, Gu Li offered a bounty of 100,000 yuan (US$14,400) to the first human player who could defeat Master.[40] Master played at the pace of 10 games per day. Many quickly suspected it to be an AI player due to little or no resting between games. Its adversaries included many world champions such as Ke Jie, Park Jeong-hwan, Yuta Iyama, Tuo Jiaxi, Mi Yuting, Shi Yue, Chen Yaoye, Li Qincheng, Gu Li, Chang Hao, Tang Weixing, Fan Tingyu, Zhou Ruiyang, Jiang Weijie, Chou Chun-hsun, Kim Ji-seok, Kang Dong-yun, Park Yeong-hun, and Won Seong-jin; national champions or world championship runners-up such as Lian Xiao, Tan Xiao, Meng Tailing, Dang Yifei, Huang Yunsong, Yang Dingxin, Gu Zihao, Shin Jinseo, Cho Han-seung, and An Sungjoon. All 60 games except one were fast-paced games with three 20 or 30 seconds byo-yomi. Master offered to extend the byo-yomi to one minute when playing with Nie Weiping in consideration of his age. After winning its 59th game Master revealed itself in the chatroom to be controlled by Dr. Aja Huang of the DeepMind team,[43] then changed its nationality to the United Kingdom. After these games were completed, the co-founder of Google DeepMind, Demis Hassabis, said in a tweet, "we're looking forward to playing some official, full-length games later [2017] in collaboration with Go organizations and experts".[39][40]

Go experts were impressed by the program's performance and its nonhuman play style; Ke Jie stated that "After humanity spent thousands of years improving our tactics, computers tell us that humans are completely wrong... I would go as far as to say not a single human has touched the edge of the truth of Go."[41]

In the Future of Go Summit held in Wuzhen in May 2017, AlphaGo Master played three games with Ke Jie, the world No.1 ranked player, as well as two games with several top Chinese professionals, one pair Go game and one against a collaborating team of five human players.[44]

Google DeepMind offered 1.5 million dollar winner prizes for the three-game match between Ke Jie and Master while the losing side took 300,000 dollars.[45][46][47] Master won all three games against Ke Jie,[48][49] after which AlphaGo was awarded professional 9-dan by the Chinese Weiqi Association.[10]

After winning its three-game match against Ke Jie, the top-rated world Go player, AlphaGo retired. DeepMind also disbanded the team that worked on the game to focus on AI research in other areas.[12] After the Summit, Deepmind published 50 full length AlphaGo vs AlphaGo matches, as a gift to the Go community.[50]

AlphaGo's team published an article in the journal Nature on 19 October 2017, introducing AlphaGo Zero, a version without human data and stronger than any previous human-champion-defeating version.[51] By playing games against itself, AlphaGo Zero surpassed the strength of AlphaGo Lee in three days by winning 100 games to 0, reached the level of AlphaGo Master in 21 days, and exceeded all the old versions in 40 days.[52]

In a paper released on arXiv on 5 December 2017, DeepMind claimed that it generalized AlphaGo Zero's approach into a single AlphaZero algorithm, which achieved within 24 hours a superhuman level of play in the games of chess, shogi, and Go by defeating world-champion programs, Stockfish, Elmo, and 3-day version of AlphaGo Zero in each case.[53]

On 11 December 2017, DeepMind released AlphaGo teaching tool on its website[54] to analyze winning rates of different Go openings as calculated by AlphaGo Master.[55] The teaching tool collects 6,000 Go openings from 230,000 human games each analyzed with 10,000,000 simulations by AlphaGo Master. Many of the openings include human move suggestions.[55]

An early version of AlphaGo was tested on hardware with various numbers of CPUs and GPUs, running in asynchronous or distributed mode. Two seconds of thinking time was given to each move. The resulting Elo ratings are listed below.[11] In the matches with more time per move higher ratings are achieved.

In May 2016, Google unveiled its own proprietary hardware "tensor processing units", which it stated had already been deployed in multiple internal projects at Google, including the AlphaGo match against Lee Sedol.[56][57]

In the Future of Go Summit in May 2017, DeepMind disclosed that the version of AlphaGo used in this Summit was AlphaGo Master,[58][59] and revealed that it had measured the strength of different versions of the software. AlphaGo Lee, the version used against Lee, could give AlphaGo Fan, the version used in AlphaGo vs. Fan Hui, three stones, and AlphaGo Master was even three stones stronger.[60]

89:11 against AlphaGo Master

[62]

As of 2016, AlphaGo's algorithm uses a combination of machine learning and tree search techniques, combined with extensive training, both from human and computer play. It uses Monte Carlo tree search, guided by a "value network" and a "policy network," both implemented using deep neural network technology.[4][11] A limited amount of game-specific feature detection pre-processing (for example, to highlight whether a move matches a nakade pattern) is applied to the input before it is sent to the neural networks.[11]

The system's neural networks were initially bootstrapped from human gameplay expertise. AlphaGo was initially trained to mimic human play by attempting to match the moves of expert players from recorded historical games, using a database of around 30 million moves.[19] Once it had reached a certain degree of proficiency, it was trained further by being set to play large numbers of games against other instances of itself, using reinforcement learning to improve its play.[4] To avoid "disrespectfully" wasting its opponent's time, the program is specifically programmed to resign if its assessment of win probability falls beneath a certain threshold; for the match against Lee, the resignation threshold was set to 20%.[63]

Toby Manning, the match referee for AlphaGo vs. Fan Hui, has described the program's style as "conservative".[64] AlphaGo's playing style strongly favours greater probability of winning by fewer points over lesser probability of winning by more points.[17] Its strategy of maximising its probability of winning is distinct from what human players tend to do which is to maximise territorial gains, and explains some of its odd-looking moves.[65] It makes a lot of opening moves that have never or seldom been made by humans, while avoiding many second-line opening moves that human players like to make. It likes to use shoulder hits, especially if the opponent is over concentrated.[citation needed]

AlphaGo's March 2016 victory was a major milestone in artificial intelligence research.[66] Go had previously been regarded as a hard problem in machine learning that was expected to be out of reach for the technology of the time.[66][67][68] Most experts thought a Go program as powerful as AlphaGo was at least five years away;[69] some experts thought that it would take at least another decade before computers would beat Go champions.[11][70][71] Most observers at the beginning of the 2016 matches expected Lee to beat AlphaGo.[66]

With games such as checkers (that has been "solved" by the Chinook draughts player team), chess, and now Go won by computers, victories at popular board games can no longer serve as major milestones for artificial intelligence in the way that they used to. Deep Blue's Murray Campbell called AlphaGo's victory "the end of an era... board games are more or less done and it's time to move on."[66]

When compared with Deep Blue or Watson, AlphaGo's underlying algorithms are potentially more general-purpose and may be evidence that the scientific community is making progress towards artificial general intelligence.[17][72] Some commentators believe AlphaGo's victory makes for a good opportunity for society to start preparing for the possible future impact of machines with general purpose intelligence. As noted by entrepreneur Guy Suter, AlphaGo only knows how to play Go and doesn't possess general-purpose intelligence; "[It] couldn't just wake up one morning and decide it wants to learn how to use firearms."[66] AI researcher Stuart Russell said that AI systems such as AlphaGo have progressed quicker and become more powerful than expected, and we must therefore develop methods to ensure they "remain under human control".[73] Some scholars, such as Stephen Hawking, warned (in May 2015 before the matches) that some future self-improving AI could gain actual general intelligence, leading to an unexpected AI takeover; other scholars disagree: AI expert Jean-Gabriel Ganascia believes that "Things like 'common sense'... may never be reproducible",[74] and says "I don't see why we would speak about fears. On the contrary, this raises hopes in many domains such as health and space exploration."[73] Computer scientist Richard Sutton said "I don't think people should be scared... but I do think people should be paying attention."[75]

In China, AlphaGo was a "Sputnik moment" which helped convince the Chinese government to prioritize and dramatically increase funding for artificial intelligence.[76]

In 2017, the DeepMind AlphaGo team received the inaugural IJCAI Marvin Minsky medal for Outstanding Achievements in AI. AlphaGo is a wonderful achievement, and a perfect example of what the Minsky Medal was initiated to recognise, said Professor Michael Wooldridge, Chair of the IJCAI Awards Committee. What particularly impressed IJCAI was that AlphaGo achieves what it does through a brilliant combination of classic AI techniques as well as the state-of-the-art machine learning techniques that DeepMind is so closely associated with. Its a breathtaking demonstration of contemporary AI, and we are delighted to be able to recognise it with this award.[77]

Go is a popular game in China, Japan and Korea, and the 2016 matches were watched by perhaps a hundred million people worldwide.[66][78] Many top Go players characterized AlphaGo's unorthodox plays as seemingly-questionable moves that initially befuddled onlookers, but made sense in hindsight:[70] "All but the very best Go players craft their style by imitating top players. AlphaGo seems to have totally original moves it creates itself."[66] AlphaGo appeared to have unexpectedly become much stronger, even when compared with its October 2015 match[79] where a computer had beaten a Go professional for the first time ever without the advantage of a handicap.[80] The day after Lee's first defeat, Jeong Ahram, the lead Go correspondent for one of South Korea's biggest daily newspapers, said "Last night was very gloomy... Many people drank alcohol."[81] The Korea Baduk Association, the organization that oversees Go professionals in South Korea, awarded AlphaGo an honorary 9-dan title for exhibiting creative skills and pushing forward the game's progress.[82]

China's Ke Jie, an 18-year-old generally recognized as the world's best Go player at the time,[31][83] initially claimed that he would be able to beat AlphaGo, but declined to play against it for fear that it would "copy my style".[83] As the matches progressed, Ke Jie went back and forth, stating that "it is highly likely that I (could) lose" after analysing the first three matches,[84] but regaining confidence after AlphaGo displayed flaws in the fourth match.[85]

Toby Manning, the referee of AlphaGo's match against Fan Hui, and Hajin Lee, secretary general of the International Go Federation, both reason that in the future, Go players will get help from computers to learn what they have done wrong in games and improve their skills.[80]

After game two, Lee said he felt "speechless": "From the very beginning of the match, I could never manage an upper hand for one single move. It was AlphaGo's total victory."[86] Lee apologized for his losses, stating after game three that "I misjudged the capabilities of AlphaGo and felt powerless."[66] He emphasized that the defeat was "Lee Se-dol's defeat" and "not a defeat of mankind".[25][74] Lee said his eventual loss to a machine was "inevitable" but stated that "robots will never understand the beauty of the game the same way that we humans do."[74] Lee called his game four victory a "priceless win that I (would) not exchange for anything."[25]

Facebook has also been working on its own Go-playing system darkforest, also based on combining machine learning and Monte Carlo tree search.[64][87] Although a strong player against other computer Go programs, as of early 2016, it had not yet defeated a professional human player.[88] Darkforest has lost to CrazyStone and Zen and is estimated to be of similar strength to CrazyStone and Zen.[89]

DeepZenGo, a system developed with support from video-sharing website Dwango and the University of Tokyo, lost 21 in November 2016 to Go master Cho Chikun, who holds the record for the largest number of Go title wins in Japan.[90][91]

A 2018 paper in Nature cited AlphaGo's approach as the basis for a new means of computing potential pharmaceutical drug molecules.[92]

AlphaGo Master (white) v. Tang Weixing (31 December 2016), AlphaGo won by resignation. White 36 was widely praised.

The AlphaGo documentary film[93][94] raised hopes that Lee Sedol and Fan Hui would have benefitted from their experience of playing AlphaGo, but as of May 2018 their ratings were little changed; Lee Sedol was ranked 11th in the world, and Fan Hui 545th.[95] On 19 November 2019, Lee announced his retirement from professional play, arguing that he could never be the top overall player of Go due to the increasing dominance of AI. Lee referred to them as being "an entity that cannot be defeated".[96]

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