Some people believe that humans were designed in the image of their creator. We’ve tried to do the same thing before when it comes to true ARTIFICIAL intelligence, which may be our greatest invention. A typical approach to ARTIFICIAL intelligence is a digital representation of the human brain. But leading scientists say inspiration will come from elsewhere. In fact, trying to mimic the human brain perfectly is a waste of time.
“We don’t really understand the human mind,” says Janna Levin, an astronomer at Barnard College in New York City who leads a panel on the technology and ethical future of ARTIFICIAL intelligence. “We thought we could understand the human mind through mapping, but that didn’t happen.”
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“How can we create artificial intelligence when we don’t understand the human mind?”
That’s a tricky problem, according to the group’s AI researchers. We can’t perfectly simulate the human brain. Instead, we should spend our time on the basic principles of unlocking intelligence.
Max Tegmark is a physicist at the Massachusetts Institute of Technology and director of the Future of Life Institute. Too much focus on the brain, he says, is merely “carbon chauvinism” (the idea that, as carbon-based creatures, it is hard for humans, who have not touched any extraterrestrial life, to conjecture up radically different biochemical theories). There’s nothing magical about how the brain works, though scientists have so far failed to figure out its secret. “We get so caught up in how the brain works,” Tegmark says. “I think it shows a lack of imagination.”
History bears him out. In the Victorian era, an engineer named Clement Ader built the first heavier-than-air aircraft. He modeled it after bats. These machines are just chairs with large bat wings on either side. The Ader flew hundreds of meters with this almost uncontrollable device. But if he was the first man to fly successfully, why is he known only about the Wright Brothers and not about him?
Ader version 3 aircraft. Although it can sustain flight, its steam-powered engines are completely uncontrollable.
While creating ARTIFICIAL intelligence in our own image is not a viable way to do it, the panel’s discussion that evening returned to biology. As Levin says, human intelligence and consciousness are still our best examples.
“You can take inspiration from biology, but you can’t mechanically copy it,” says Jan Lecun, Facebook’s head of AI research. “From an engineering point of view, it would be very difficult to trace evolution.” Because evolution lacks agency, it did not take conscious effort or decision to create intelligent apes. Instead, we got here because of millions of years of random mutations that allowed us to live long enough to reproduce. Maximizing or simplifying the intelligence and reasoning power of our brains has never been part of the problem. The human brain is extremely complex. It’s full of mechanisms that can self-configure in the womb and repair themselves over time. Machines don’t need this, because the processing of configuration is done by humans. It just needs to receive the data, process it, and learn from it.
Artificial intelligence Pioneer Jan Lecun speaks at Pioneer Works’ latest “Science Controversy” panel. Also speaking were renowned physicist Max Tegmark and moderator Janna Levin, an astronomer at Barnard College.
For more traditional methods of supervised learning, lecun explains, humans have to feed the system thousands of examples before the machine itself can do meaningful work. For example, an image recognition algorithm would need to see an infinite number of apples to identify one in a photo. The second approach is reinforcement learning, in which artificial intelligence systems or neural networks — brain-like algorithms — train each other. This approach is usually only applicable to games. An AI playing chess can play millions of times and learn the rules in minutes.
But neither approach is perfect. Neither approach will produce an ARTIFICIAL intelligence that truly enables it to understand the world. In supervised learning, humans are still doing all the grunt work, while chess-playing computers know nothing about it.
“We train neural networks in a very stupid way,” Lecun said. “It’s completely different from how humans and animals train themselves.” Babies learn about permanence by the time they are two months old. By the time they are six months old, they have an intuitive sense of how the physical world works. But we can’t start this kind of unsupervised learning in our machines (if anyone can pull it off, it’s likely to be Lecun and his Facebook team, since only large companies have the resources and architecture to train high-level neural networks). But during the panel discussion, he shrugged: “We can’t do that either.”
That’s why biological underpinnings, rather than perfect reconstructions of the human brain, are crucial for AI. There are no other schemas for programmers to refer to. The human brain is a scientific marvel, but it’s not the only answer. These researchers need to remember that there is nothing special about humans and supercomputers hidden in our skulls, and they should not be trying to create something new.
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Above, Chen Ruchu’s humble opinion! I’m sorry if I offended you.
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