Posted on 03/15/2016 1:44:53 PM PDT by SeekAndFind
It was an intense competition between man and machine, and in the end, the machine took home the title.
On Tuesday, Google's AlphaGo artificial intelligence program won the fifth and final round game of Go in a bout with Lee Sedol, one of the greatest Go players in the world. AlphaGo took four out of the five games in a contest that marked a noteworthy leap forward for computer smarts.
That's not to say the computer was perfect. After losing the fourth game, AlphaGo hit a rough patch in game five, but continued on to defeat Lee, who resigned 280 moves in.
"It was difficult to say at what point AlphaGo was ahead or behind, a close game throughout," commentator Michael Redmond said. "AlphaGo made what looked like a mistake with move 48, similar to the mistake in game four in the middle of the board. After that AlphaGo played very well in the middle of the board, and the game developed into a long, very difficult end game."
The brainchild of Google's DeepMind team, AlphaGo uses sophisticated machine learning technologies such as neural networks to evaluate board positions and determine which moves to make.
The ancient game of Go may look simple on the surface as two players move black and white stones around a board in an attempt to occupy the greater number of spaces. The complexity lies in the vast number of ways the stones can be set up and the variety of possible moves and outcomes, and that makes Go a greater challenge for AI than chess.
AlphaGo's wins came as a surprise to many who thought artificial intelligence wouldn't be able to compete against top human Go players for years. But for all the current activity in AI, including its use in everyday technology such as the image recognition that Facebook performs, it remains quite limited compared with people's ability to adapt, make decisions and solve real-world problems.
The same methods that DeepMind employed to master Go could one day be applied toward pressing tasks such as climate modeling and disease analysis. Yet such luminaries as SpaceX CEO Elon Musk, Microsoft co-founder Bill Gates and physicist Stephen Hawking have expressed concerns about AI getting too smart for our own good.
"One can imagine such technology outsmarting financial markets, out-inventing human researchers, out-manipulating human leaders, and developing weapons we cannot even understand," Hawking said in an article he co-wrote in May 2014 for The Independent. "Whereas the short-term impact of AI depends on who controls it, the long-term impact depends on whether it can be controlled at all."
AlphaGo's victory came with $1 million in prize money, an amount Google DeepMind will donate to Unicef, STEM (science, technology, engineering, and math) charities and Go organizations.
This isn't the first time AlphaGo has defeated a human opponent at the game of Go. In January, it swept the boards against European champion Fan Hui a five-game series.
Bbbwwwwaaaahhhhhhaaaaaaaahaaaaaaa..........
The machine isn’t THAT good - it didn’t figure out that a bit 3-of-5 match is over if one side wins the first 3 games...
I’m not worried about the apocalyptic predictions about AI. We barely even understand our own minds, so there is no possible way that we would be able to invent an artificial mind that can match all of our capabilities, much less supersede us.
In certain areas, computers can overtake us, but those areas are not enough to create something that can truly be called “intelligent”.
We don’t understand how the brain works but we understand what it does on a utility level: Self learning of tasks/commands by watching others, reading a learned language and practicing.
I used to believe what you said just last year and didn’t think machines would be able to accomplish that. But as an engineer, I’ve had the chance to witness some cutting edge AI and it is startling stuff. The machines are truly beginning to learn tasks on their own without any task-based programming.
The machines now have the ability to process large masses of information through their sensors and make sense of it without being told every minute motion. Learning purely by watching others and then perfecting their own techniques automatically. Basically programming themselves in real time while trying new things. Exactly how children and animals begin to learn.
I believe the advancement will be exponential from now on and the machines will surpass the mental ability of most humans in 10-20 years.
They also have the advantage of a global neural network that allows them to cross exchange new knowledge instantly. Plus no knowledge would ever be forgotten or lost due to death. The possibilities are incredible and also frightening...
“We don’t understand how the brain works but we understand what it does on a utility level: Self learning of tasks/commands by watching others, reading a learned language and practicing.”
This is only one portion of the human mind though. We could assume that portion (which we still can only approximate) is the totally, but it would be an unfounded assumption.
Heck, we cannot even rightfully assert that “the brain”, which you are referring to, is the only component that makes up “the mind” that we actually subjectively experience. Materialists assert such, but they do so with no evidence or argument, so it’s simply an assumption. However, you can’t write a computer simulation based on unfounded assumptions. Garbage in, garbage out.
The Turk
John Henry
Skynet
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