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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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AlphaGo - Wikipedia

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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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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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