Shulou(Shulou.com)11/24 Report--

CTOnews.com March 2 news, ChatGPT's global popularity has made more people feel the strength and development potential of artificial intelligence (AI), so which direction can AI move forward in the future? Scientists believe that one direction is to bring AI closer to the human brain.

"We call this new interdisciplinary field organ-like intelligence (OI)," said Professor Thomas Hartung of Johns Hopkins University. "A group of top scientists have gathered to develop and advance this technology, which we believe will usher in a new era of fast, powerful and efficient biological computing."

The core of this study is the cultivation of brain organs. Most cell cultures are flat, while quasi-organs have a three-dimensional structure. This property increases the cell density of the culture by 1000 times, which in turn allows neurons to form more connections.

CTOnews.com translated Hartung's view as follows: "computers are better at computing, while human brains are better at learning. AlphaGo (artificial intelligence that beat the world's first go player in 2017) received data training in 160000 games. If a person plays five hours a day, it will take more than 175 years to experience so many games."

Another obvious advantage of biological computers over traditional silicon-based computers is the amount of information that can be stored. Hartung estimates that the human brain has the ability to store about 2500TB data. He points out that silicon computers are beginning to reach the physical limit of the information they can store because only so many transistors can be encapsulated on a single microchip.

The current number of organ-like organs has about 50000 cells, and that number needs to be increased to about 10 million to produce OI, Hartung said. This group of scientists are also studying and developing techniques for communicating with similar organs. This technology basically sends organ-like information and then reads out what they are thinking. Various disciplines are being studied, such as bioengineering and machine learning.

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