I analyze local news, business, and technology's influence on how we live our lives.
Cortigal Labs, a biological computing company in Australia, has developed 'synthetic biological intelligence', for the first time. This was demonstrated through a study of brain cells grown in a Petri dish that developed to play Pong, on their own.
The neurons were placed on high-density micro-electrode array chips that both record the electrical activity of the cells, and stimulate them. As the cells mature, they connect with each other, forming neuronal networks. From there, the data is written onto Cortigal Labs', Biological Intelligence Operating System also known as biOS.
BiOS records the actions and development of the brain cells, and notes whether actions were positive or negative; this helps identify and resolve programmatic margins for artificial intelligence development programming.
Big Think notes that Cortigal Labs' neural networking system that plays Pong is called, 'DishBrain'. DishBrain connects the microchips to a computer hosting the Pong game. Since the micro-electorde array chips can electrically stimulate the neurons on the chip, researchers gave the cells stimulus when the paddle connected with the ball, and gave unpredictable stimulus when the ball missed the paddle.
This method enabled the cells to learn the game's objectives, and improve their game play, within five minutes. It is noted that the more feedback the cells received, the greater the improvement. Under circumstance of no feedback given, the networks completely failed to learn to play the game.
Samples of the research were conducted with human and mouse cells, to which the human neurons outperformed those of mouse neurons. This demonstrates that human neurons have a greater capacity for information processing than rodents.
Cortigal calls this type of learning, the free-energy principle. According to a paper published by Karl Friston, the free-energy principle claims that,
Entropy is unpredictability, in general. The unpredictible signals sent to the neurons when the paddle misses the ball, increases entropy. Affirmative feedback or stimuli to the neurons, when the paddle hits the ball, reduces uncertainty in the learning program.
Future work in this type of artificial intelligence developmental research, and 'synthetic biological intelligence' as a research category, can ascribe to the computational power of the human neuron and improve machine learning algorithms.
Credits:
Big Think
Karl Friston
Cortigal Labs
I analyze local news, business, and technology's influence on how we live our lives.
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