AI News, AlphaGo artificial intelligence

Demis Hassabis

(born 27 July 1976) is a British artificial intelligence researcher, neuroscientist, video game designer, entrepreneur, and world-class games player.[5][1][8][9][10][11][12][13]

Hassabis began his computer games career at Bullfrog Productions, first level designing on Syndicate and then at 17 co-designing and lead programming on the 1994 game Theme Park, with the games designer Peter Molyneux.

published in the Proceedings of the National Academy of Sciences of the United States of America, showed systematically for the first time that patients with damage to their hippocampus, known to cause amnesia, were also unable to imagine themselves in new experiences.

Hassabis developed a new theoretical account of the episodic memory system identifying scene construction, the generation and online maintenance of a complex and coherent scene, as a key process underlying both memory recall and imagination.[32]

More concretely, DeepMind aims to meld insights from neuroscience and machine learning with new developments in computing hardware to unlock increasingly powerful general-purpose learning algorithms that will work towards the creation of an artificial general intelligence (AGI).

The company has focused on training learning algorithms to master games, and in December 2013 it famously announced that it had made a pioneering breakthrough by training an algorithm called a Deep Q-Network (DQN) to play Atari games at a superhuman level by only using the raw pixels on the screen as inputs.[40]

Since the Google acquisition, the company has notched up a number of significant achievements, perhaps the most notable being the creation of AlphaGo, a program that defeated world champion Lee Sedol at the complex game of Go.

Go had been considered a holy grail of AI, for its high number of possible board positions and resistance to existing programming techniques.[45][46]

“This is a lighthouse project, our first major investment in terms of people and resources into a fundamental, very important, real-world scientific problem,” Hassabis said to the Guardian.[53]

In particular, the company has made significant advances in deep learning and reinforcement learning, and pioneered the field of deep reinforcement learning which combines these two methods.[54]


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]

This neural net improves the strength of tree search, resulting in higher quality of move selection and stronger self-play in the next iteration.

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 alpha–beta 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]

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.

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.[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.[28]

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.[31]

In June 2016, at a presentation held at a university in the Netherlands, Aja Huang, one of the Deep Mind team, revealed that it 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 and Lee's move was not credited as the one which won the game, but caused the program's computing powers to be diverted and confused.[38]

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.[39]

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'.[40][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.[45]

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.[53]

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.[54]

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.[59][60]

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.

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]

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.[20]

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;

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.

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.

Some commentators believe AlphaGo's victory makes for a good opportunity for society to start discussing preparations for the possible future impact of machines with general purpose intelligence.

(As noted by entrepreneur Guy Suter, AlphaGo itself 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'[68]) In March 2016, AI researcher Stuart Russell stated that 'AI methods are progressing much faster than expected, (which) makes the question of the long-term outcome more urgent,' adding that 'in order to ensure that increasingly powerful AI systems remain completely under human control...

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.[82]

DeepZenGo, a system developed with support from video-sharing website Dwango and the University of Tokyo, lost 2–1 in November 2016 to Go master Cho Chikun, who holds the record for the largest number of Go title wins in Japan.[92][93]

Pharma’s AlphaGo Moment: For the First Time, Artificial Intelligence Has Designed a New Drug in 21 Days - Deep Knowledge Analytics

“This is the first time that an AI company has designed a novel drug from scratch, synthesized it and preclinically validated it end-to-end in days rather than years –

The data-driven approach will give better and faster results than the traditional methods, leading to faster drug discovery and safer, more reliable results than clinical trials on their own.

Insilico Medicine starts with molecular leads that have been specifically designed, in terms of their pharmacokinetic and pharmacodynamic properties, and therefore have a higher probability of being effective for specific disease targets.

“This newest achievement made by Insilico Medicine, a leading AI for drug discovery and longevity company and an official partner of Ageing Research at King’s, demonstrates the truly disruptive potential that AI holds in terms of accelerating the pace of progress in drug discovery.

It is also quite notable that the team released the code behind their algorithm in an open-source format, allowing other researchers to apply their techniques and build upon their achievements for the advancement of the entire field of AI for drug design, ageing research and longevity” said Richard Siow, Ph.D., Director of Ageing Research at King’s and former Vice-Dean (International), Faculty of Life Sciences &

Another paper, published in Molecular Pharmaceutics in 2016, demonstrated the proof of concept of the application of deep neural networks for predicting the therapeutic class of the molecule using the transcriptional response data.

For example, deep learning and GANs (Generative adversarial networks) are providing opportunities for reducing the timeline for molecule hit discovery in a matter of weeks when compared to years with the traditional approach.

Sullivan created a new award category for this specific area of research and development is indicative of the interest and support that AI for drug discovery is garnering from the drug development community.

WuXi AppTec is committed to enabling innovative collaborators to bring innovative healthcare products to patients, and to fulfilling WuXi’s dream that every drug can be made and every disease can be treated.  “As far as I know, this marks the first ever demonstration that AI can generate entirely novel, synthesizable, active molecules against a specific pharmacological target.

Since then they have been the first to use cutting edge deep learning techniques like Generative Adversarial Networks to design novel drug candidates from scratch with specified molecular properties in 2016, and in 2018 to succeed in designing, synthesizing and validating a new drug end to end in less than 2 months.

Pharma Division of Deep Knowledge Analytics is the leading analytical agency specifically focused on deep intelligence of the pharma industry and the AI for Drug Discovery sector, and a specialized department of Deep Knowledge Analytics, a DeepTech-focused analytical company focusing on advanced industry analytics on the topics of Artificial Intelligence, GovTech, Blockchain, FinTech, Invest-Tech and Frontier Technologies.

Knowledge Ventures is a leading investment fund focused on the synergetic convergence of DeepTech, frontier technologies and technological megatrends, known for its use of sophisticated analytical system for investment target identification and due-diligence.

Deep Knowledge Ventures led Insilico Medicine’s seed funding round in 2014, and has remained a close advisor in the company’s journey towards becoming a global leader in the application of advanced AI for aging research and the extension of healthy human longevity.

DKV’s AI-Pharma Specialized Fund combines the profitability of venture funds with the liquidity of hedge funds significantly de-risking the interests of LP’s and simultaneously providing the best and most promising AI companies with a relevant amount of investment.