Clumps of human brain cells in a dish can learn to play Pong faster than an AI
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Re: Clumps of human brain cells in a dish can learn to play Pong faster than an AI
#2Re: Clumps of human brain cells in a dish can learn to play Pong faster than an AI
#3Re: Clumps of human brain cells in a dish can learn to play Pong faster than an AI
#4The concept of using human neural networks instead of artificial neural networks is both terrifying and intriguing.
Re: Clumps of human brain cells in a dish can learn to play Pong faster than an AI
#5> Using real neurons avoids several other difficulties that software-based neural networks have. For instance, to get artificial neural networks to start learning well, their programmers usually have to engage in a laborious process of manually adjusting the initial coefficients, or weights, that will be applied to each type of data point the network processes. Another challenge is to get the software to balance how much it should be trying to explore new solutions to a problem versus relying on solutions the network has already discovered that work well.
> “All these problems are completely eluded if you have a system that is based on biological neurons to begin with,” Friston said.
https://fortune.com/2020/03/30/startup-human-neurons-compute...
The other main advantage is low power consumption.
> AlphaGo, the deep-learning system DeepMind created to play Go and which beat the world’s best human player in that ancient strategy game in 2016, consumed one megawatt of power while playing the game, enough to power about 100 homes for a day, according to an estimate by technology company Ceva. By contrast, the human brain consumes about 20 watts of power, or 50,000 times less energy than AlphaGo used.
However, it's not clear whether these hybrid chips will have similarly low power consumption.
Re: Clumps of human brain cells in a dish can learn to play Pong faster than an AI
#6Re: Clumps of human brain cells in a dish can learn to play Pong faster than an AI
#7Paywalled. From an earlier article in Forbes: > Using real neurons avoids several other difficulties that software-based neural networks have. For instance, to get artificial neural networks to start learning well, their programmers usually have to engage in a laborious process of manually adjusting the initial coefficients, or weights, that will be applied to each type of data point the network processes. Another ch…