Debates between humans and computers start with mechanical turk. That’s an historical autonomous chess player costructed in 18th century. However, that’s a fake one. The mechanism allows to hide a chess player inside the machine. Thus, the turk operates while hiding master playing chess. (Yes, just like Athony Deniels and Kenny Baker hid inside of 3PO and R2D2 in Star Wars). So, there is no intelligence for this ancient example. Still, this fake machine shows expectations of 18th century people for an intelligent system to involve in daily life.
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IBM Deep Blue is first chess playing computer won against a world champion. Garry Kasparov were defeated by Deep Blue in 1997. Interestingly, development of Deep Blue has began in 1985 at Carnegie Mellon University (remember this university). In other words, with 12 years study comes success.
AI challenges has been at a standstill for a long time after Deep Blue, but it has accelerated in recent years.
Chess victory turns the page to a new challanges and AI gazes at Game Go. Although, Game Go has simple rules, it is much more complex than chess. Actually, it possesses more possibilities than the total number of atoms in the universe. Recently, Google DeepMind won the Game Go against a world champion. It defeated World Game Go Champion Lee Sedol in 2016.
Even so, board games are based on moves. Every move reveals new moves. AI still could consider all the move combinations because all the information is clear.
Herein, poker pays AI’s attention. Because, intelligent system is unaware of opponent’s hand. That’s why, decisions have to be made based on imperfect information. Still, AI remember past plays. Moreover, it can predict one’s bluffing based on speed and delay time. So, Libratus is poker playing AI system developed by Carnegie Mellon University (sound familiar?). It just beaten four of the best professional poker players. Furthermore, intelligent system won $1.8M in the tournament.
End users
On the other hand, tech companies’ve run against to announce their own machine learning APIs in the recent year. For instance, Google introduced TensorFlow in 2015 Nov. The software underlies the Google’s image search and speech recognition. Then, Microsoft released a ML software, Azure ML, in following days. After then, IBM announced SystemML to apply machine learning. Afterwards, Facebook developed both hardware and software for machine learning. Prophet is a forecasting tool whereas Big Sur is a hardware for AI computing. Finally, Amazon shared the source code of their deep learning software DSSTNE (should be pronounced as destiny). Interestingly, most of these products are open source.
So, it seems that challenges would continue without slowing down. Moreover, advances in providing APIs and releasing open source software show that AI will be adapted into every personal requirement in near future. Thus, there would be no boundaries. We could hope to live long and prosper to come true.
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