AI News, Program artificial intelligence
Business and artificial intelligence come together in new program
“I thought I was eventually going to have to do a computer science degree to be able to fill in those gaps because I just didn’t think there was a hybrid that existed until I found [the MMAI] program.” Artificial intelligence (AI), cryptocurrency and blockchain are no longer terms reserved for nerdy subsets of technology.
They are front and centre to many aspects of industry, and business education programs around the world are trying to keep up with demand from graduates and their companies who want a hybrid education in business and these technologies.
“And that’s what our program is designed to do, to create a graduate that can speak to both sides and create the link between the two.” The 12-month course is designed for working professionals, holding courses in the evenings and weekends, and requires three years of work experience.
It involves using machine learning algorithms to construct a mathematical relationship between the features of the input and the target label.” Demand for these specialized business programs has been increasing all over the world, especially in cryptocurrencies and the blockchain technology behind them – both of which have seen a significant surge in market recognition in 2018.
“The target for me, when I designed the course, was people in established organizations who wondered how blockchain works and how they could do stuff with the technology to make what they’re already doing more efficient,” recalls Dr. Rowell, who adds that many of these topics remain mysterious and all but unheard of for many.
“I really enjoy the business aspect of my current role,” she says, “so I’m hoping I can leverage both that business side and the knowledge I’ve gained in school to help organizations reap the benefits of these technology opportunities.”
Artificial Intelligence Program Helps Fold Proteins
DeepMind, a company dedicated to artificial intelligence (AI) research, recently tested an advancement that might one day save biologists time and anxiety when it comes to “protein-folding,” The Next Web reported.
however, those methods take years worth of trial and error to land on a solution, and “cost tends of thousands of dollars per structure.” However, AlphaFold AI might be able to fix these issues, according to The Next Web: “AlphaFold was built by training a neural network with thousands of proteins whose structures were known, until the software could predict the 3D structures of proteins from their amino acid sequence alone…
Once AlphaFold is provided a new protein, it uses its neural network to predict the distances between pairs of its constituent amino acids, and the angles between their connecting chemical bonds, forming a draft structure.
promising outlook for decision makers in STEM: Based on the results of protein-folding competition, The Next Web suggests that AlphaFold might bring in a new wave of protein-folding technology that will make researchers’ jobs easier, especially biologists and other STEM fields.
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.
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.
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,
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 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.
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 rectified the problem 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 accurately 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.
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.
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.
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  in collaboration with Go organizations and experts'.
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.
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.
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.
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.
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.
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.
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.
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.
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') 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.
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.
- On 26. oktober 2020
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