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Go Champion Quits Because of AI



Go Champion Quits Because of AI

Lee Se-dol, the first and only human to beat Google’s algorithm at the Chinese strategy game Go, has decided to quit due to artificial intelligence (AI). According to the South Korean champion, machines “cannot be defeated.

Back in 2016. Lee Se-dol took part in a five-match competition with Google’s artificial intelligence program AlphaGo, which caused a big publicity boom surrounding the game. It was also during that time when the fears of machines and their endless learning capacity increased. 

Prior to the matchups, Lee publicly stated that he would beat AlphaGo in a “landslide.” After the major losses, he went on to publicly apologize to the public. 

“I failed,” he said. “I feel sorry that the match is over and it ended like this. I wanted it to end well.”

In those matches, Lee Se-dol only defeated the AI once. Since then, the algorithm has gotten even better and teaches itself. That algorithm crushed its predecessor 100 games to none, and it is called AlphaGo Zero. 

Lee spoke to Yonhap news agency about his decision and the future of machines.

“Even if I become the number one, there is an entity that cannot be defeated,” he said. 

“With the debut of AI in Go games, I’ve realised that I’m not at the top even if I become the number one.”

AlphaGo Zero improved by playing against itself continuously, and it only took three days of paying at superhuman speeds to drastically surpass its predecessor. At that time, DeepMind said that AlphaGo was likely the strongest Go player to ever exist. 

According to a statement given to The Verge, DeepMind’s CEO Demis Hassabis praised Lee as having “true warrior spirit,” and went on to say that “On behalf of the whole AlphaGo team at DeepMind, I’d like to congratulate Lee Se-dol for his legendary decade at the top of the game, and wish him the very best for the future…I know Lee will be remembered as one of the greatest Go players of his generation.”

Lee will go on to participate in other ventures dealing with AI, and in December he will go against HanDol, a South Korean AI program. HanDol has outperformed the top five players in the country.

He will be given a two-stone advantage in the first game, but he believes he will still lose. 

“Even with a two-stone advantage, I feel like I will lose the first game to HanDol. These days, I don’t follow Go news. I wanted to play comfortably against HanDol as I have already retired, though I will do my best,” he said.

Go was created in China around 3,000 years ago, and it has continued to be played since. It is most popular in China, Japan, and South Korea. The game consists of a square board with a 19X19 grid, and players take turns placing black or white stones on it. The winner is whoever takes the most territory wins. 

While the rules sound simple, the game is actually extremely complex. Some say that there are more combinations of move configurations than atoms in the universe. 

Lee began to play Go when he was five, and became a pro at the age of 12. 

Even though he is a master player, Lee has said that his AlphaGo win was the result of a bug that appeared after his play. 

“My white 78 was not a move that should be countered straightforwardly,” he said.


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Alex McFarland is a historian and journalist covering the newest developments in artificial intelligence.

Artificial General Intelligence

China Beginning to Implement AI in Court System



China Beginning to Implement AI in Court System

China is beginning to experiment with artificial intelligence and other technologies within their court system. It is another example of how artificial intelligence is becoming important in every aspect of society. 

The nation is using artificial intelligence judges, cyber courts, and verdicts that are delivered on chat apps in developing their new system. Digitization helps streamline case-handling, and cyberspace and technologies such as blockchain and cloud computing are being used as well. 

These new developments were announced by the nation’s Supreme People’s Court in a new policy paper. 

One of the aspects of the new court system is a “mobile court” that is offered on WeChat, a social media platform popular in the country. WeChat has already taken up over 3 million legal cases or other judicial procedures since it began in March, according to the Supreme People’s Court.       

In a demonstration that was given, authorities showed the way the Hangzhou Internet Court operates. It has an online interface that contains an AI judge portrayed with an on-screen avatar. Litigants appear before the AI judge on video chat, and it prompts them to present their cases.

“Does the defendant have any objection to the nature of the judicial blockchain evidence submitted by the plaintiff?” the virtual judge said as it sat under China’s national emblem. 

“No objection,” a human plaintiff answered. 

Some of the cases that are handled at the Hangzhou court include online trade disputes, copyright cases and e-commerce product liability claims. The civil complaints can be registered by litigants, and they can later log on to attend their court hearing. 

According to officials, the simple functions fall into the realm of the virtual judge, which helps the human justices who are responsible for making the major rulings in each case. 

One of the main reasons for implementing digitization into the court system is to help keep up with the growing number of cases that come from mobile payments and e-commerce in China. The nation has about 850 million mobile internet users, the most of any nation in the world. 

“(Concluding cases) at a faster speed is a kind of justice, because justice delayed is justice denied,” Hangzhou Internet Court Vice President Ni Defeng said.

According to Ni, blockchain technology is especially useful. It helps streamline and create clearer records of the legal process. 

Similar chambers have now been created in Beijing and Guangzhou in the south. In total, the branches have accepted 118,764 cases and concluded 88,401, according to the Supreme People’s Court. 

The “mobile court” option on WeChat allows users to complete case filings, hearings, and evidence exchange without every physically appearing in a courtroom.

The program has been launched in 12 provinces and regions, and courts all around the nation are using other online tools as well. Zhou Qiang, chief justice and president of the Supreme People’s Court, told a panel that 90 percent of China’s courts had handled some type of online case since October. 

At the same time, President Xi Jinping is pushing China forward in the AI space, and he is trying to turn them into the world’s leader in technology. All of this is done with a direct link to the government, and that is what concerns other nations like the United States. 




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Facebook’s AI Takes on Hanabi Game



Facebook’s AI Takes on Hanabi Game

Facebook AI Research (FAIR) has developed a new AI that produced extremely impressive results when put up against Hanabi. The new development is a major step forward for Facebook’s AI. 

Hanabi is a card game similar to Solitaire. While most games that are used for this technology place AI against humans directly, specifically chess or Go, Hanabi requires players to work with each other towards a common goal. 

Facebook employed bots to work together in the game until they outperformed previously used AI systems. The most recent best AI system achieved a score of 23.92 out of 25 in the game, while the new one reached 24.61 out of 25. 

Back in February, A Hanabi benchmark was proposed by researchers from Google, DeepMind, Carnegie Mellon University, and Oxford. They also included the creation of additional AI capable of playing the game, and they called it “a new frontier for AI research.” 

Researchers are excited about the new development since the same AI used to help the bots could possibly be used in other areas. One possible use is to improve the way that virtual assistants interact with people. 

Noam Brown, a Facebook AI researcher, spoke about the new AI system. 

“One of the really exciting things about this is that the improvement we’re observing is really orthogonal to the improvements that are being observed with deep reinforcement learning: You can add this on top of any strategy, and it will make it much stronger,” Bown said in an interview he gave to VentureBeat. “We’re seeing that the results are far beyond what we or other researchers expected. In fact, the benefits that we get from search are stronger than the benefits that have been gained through all of the deep reinforcement learning algorithms that have been used in the past.”

The new development with Facebook’s AI comes at a time when researchers are continuing to create software capable of going up against some of the most complex games. In 2016, Google’s DeepMind’s AI system beat the best human players in the Chinese board game Go. 

Hanabi is now considered the best game for testing AI since it is built around teamwork and strategy, a major milestone for AI to reach. When used in this environment, AI can improve and become more sophisticated.

Adam Lerer is a Facebook researcher and contributor to the paper. 

“One of the reasons we’re moving to these cooperative games is that I think we’re kind of at the point where there’s no games left at least in terms of competitive games,” he said. 

Hanabi has teams of two to five players who are given random cards. The cards are different colors and contain different numbers, and the teams place them on a table, by color and in the correct numerical order. 

Players are not able to see their own cards, but their teammates can. Players are permitted to give hints to others. For example, a teammate can give a hint about colors, leading to the other to play or discard the card. 

One of the more complex aspects of the game is that a player has to figure out the clues and what they mean. This part of the game is difficult for a bot to figure out with the information that they have. 

The bots were able to build a strategy due to the techniques and reinforcement learning that Facebook used. Facebook believes that this technology could be used in other applications like robotics, self-driving vehicles, and other systems. 

“This is something that comes very naturally to humans, this idea of being able to put yourself in the shoes of another person and understand why they’re taking the actions they’re taking, what they’re thinking, and even if they don’t know certain things. But it’s something that AI has historically really struggled with,” he said. “There’s been this long debate about whether primates have theory of mind and at what age do humans babies develop theory of mind, and I think it’s really fascinating to finally be seeing this sort of behavior in AI. And I think that that’s going to be really important if we want to deploy AI in the real world to interact with humans because humans expect this behavior.”


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How Can Artificial Intelligence Learn About The Learning Process?




How Can Artificial Intelligence Learn About The Learning Process?

To make new leaps in advancing artificial intelligence, AI would, as author Jun Wu puts it in Forbes, have to ‘learn to learn’. What would that mean?

As Wu explains, “humans have the unique ability to learn from any situation or surrounding.” Humans can adapt their process of learning. To be able to have such a flexible quality AI needs Artificial General Intelligence – it would have to learn about the learning process, what is called Meta-Learning.

There is one very specific contrast in the learning process between humans and artificial intelligence. While the human capacity for learning is limited, AI has many more resources such as its computational power. Human brainpower has its limits and it also has limited time to learn. But, while AI “learns from more data than the data our human brains use, processing these vast amounts of data requires immense computational power.”

Wu explains that“as the complexity of AI’s tasks grows, there’s also an exponential increase in computational power.” This would mean that even if the cost of computational power is low, “exponential increase is never the scenario that we want.” This is the main reason that at the moment “AI is designed to be specific-purpose learners,” making their learning process more efficient.

But as AI started to learn more, “learning to learn” it started to “infer from data with increasing complexity.” To avoid the exponential increase in computational power, a more efficient learning path had to be devised, and AI had to remember that path.

The whole problem got even more complex when researchers and technologists started to assign multi-tasking problems to AI. To be able to do that, AI “needs to be able to evaluate independent sets of data in parallel. It also needs to relate pieces of data and infers connections on that data.” As one task is being done, AI needs to update its knowledge so that it can apply it in other situations. “Since tasks are interrelated, the evaluations for the tasks will need to be done by the whole network.”

Google developed one such model, MultiModel, which is an AI system that “learned to perform eight different tasks simultaneously. MultiModel can detect objects in images, provide captions, recognize speech, translate between four pairs of languages, and perform grammatical constituency parsing.

While Google’s achievement is a big leap forward, AI still needs to make further strides so that it can become a general-purpose learner. To be able to achieve this it would need to further develop meta-reasoning and meta-learning. As Wu explains, “meta-reasoning focuses on the efficient use of cognitive resources. Meta-learning focuses on human’s unique ability to efficiently use limited cognitive resources and limited data to learn.”

Currently, there are studies being conducted to figure out the gaps between human cognition and the way AI learns such as awareness of internal states, the accuracy of memory or confidence.

All this means that “becoming an artificial generalized learner requires extensive research on how humans learn as well as research on how AI can mimic the way that humans learn. To adapt to new situations such as having the ability to “multitask”, and the ability to make “strategic decisions” with limited resources, are just a few of the hurdles that AI researchers will overcome along the way.”

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