- This article is golden knowledge about the current state of artificial intelligence and the potential path for radical advancement.
- g-f(2)153 The Big Picture of Business Artificial Intelligence (3/3/2021) in a Single “g-f KBP” Chart
- g-f(2)82 THE BIG PICTURE OF THE DIGITAL AGE, MIT TASK FORCE ON THE WORK OF THE FUTURE, AI Today, and the General Intelligence of Work.
- g-f(2)133 THE BIG PICTURE OF THE DIGITAL AGE, Quanta Magazine, Artificial Neural Nets Finally Yield Clues to How Brains Learn.
- Key points
- Neuroscientist and tech entrepreneur Jeff Hawkins claims he’s figured out how intelligence works—and he wants every AI lab in the world to know about it.
- Today’s deep neural networks are loosely inspired by the way interconnected neurons fire in the brain. But loose inspiration is typically as far as it goes.
- Most people in AI don’t care too much about the details, says Jeff Hawkins. He wants to change that.
- Hawkins’s ideas have inspired big names in AI, including Andrew Ng, and drawn accolades from the likes of Richard Dawkins, who wrote an enthusiastic foreword to Hawkins’s new book A Thousand Brains: A New Theory of Intelligence, published March 2.
- Jeff Hawkins' ideas could revolutionize AI.
- Why do you think AI is heading in the wrong direction at the moment?
- That’s a complicated question. Hey, I’m not a critic of today’s AI. I think it’s great; it’s useful. I just don’t think it’s intelligent.
- We’re going to need to build machines that work along similar principles. The only examples we have of intelligent systems are biological systems. Why wouldn’t you study that?
- So what is it brains do that’s crucial to intelligence that you think AI needs to do too?
- There are four minimum attributes of intelligence, a kind of baseline.
- How do most AI researchers feel about these ideas?
- The vast majority of AI researchers don’t really embrace the idea that the brain is important.
- Most people aren’t trying to replicate the brain. It’s just whatever works, works. And today’s neural networks are working well enough.
- Most people in AI have very little understanding of neuroscience. It’s not surprising, because it’s really hard.
Category 2: The Big Picture of the Digital Age
[genioux fact produced, deduced or extracted from MIT Technology Review]
Type of essential knowledge of this “genioux fact”: Essential Analyzed Knowledge (EAK).
Type of validity of the "genioux fact".
- Inherited from sources + Supported by the knowledge of one or more experts + Supported by research.
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