AI News, Machine-Learning Maestro Michael Jordan on the Delusions of Big Data and Other Huge Engineering Efforts
- On Sunday, June 3, 2018
- By Read More
Machine-Learning Maestro Michael Jordan on the Delusions of Big Data and Other Huge Engineering Efforts
IEEE Spectrum: I infer from your writing that you believe there’s a lot of misinformation out there about deep learning, big data, computer vision, and the like.
And one of the problems with both the previous wave, that has unfortunately persisted in the current wave, is that people continue to infer that something involving neuroscience is behind it, and that deep learning is taking advantage of an understanding of how the brain processes information, learns, makes decisions, or copes with large amounts of data.
Spectrum: It’s always been my impression that when people in computer science describe how the brain works, they are making horribly reductionist statements that you would never hear from neuroscientists.
But for issues of higher cognition—how we perceive, how we remember, how we act—we have no idea how neurons are storing information, how they are computing, what the rules are, what the algorithms are, what the representations are, and the like.
Spectrum: In addition to criticizing cartoon models of the brain, you actually go further and criticize the whole idea of “neural realism”—the belief that just because a particular hardware or software system shares some putative characteristic of the brain, it’s going to be more intelligent.
I think in the early 1980s, computer science was dominated by sequential architectures, by the von Neumann paradigm of a stored program that was executed sequentially, and as a consequence, there was a need to try to break out of that.
But people tend to lump that particular success story together with all the other attempts to build brainlike systems that haven’t been nearly as successful.
A second area involves what you were describing and is aiming to get closer to a simulation of an actual brain, or at least to a simplified model of actual neural circuitry, if I understand correctly.
As I alluded to before, back in the 1980s, it was actually helpful to say, “Let’s move out of the sequential, von Neumann paradigm and think more about highly parallel systems.” But in this current era, where it’s clear that the detailed processing the brain is doing is not informing algorithmic process, I think it’s inappropriate to use the brain to make claims about what we’ve achieved.
- On Saturday, September 21, 2019
Google's Deep Mind Explained! - Self Learning A.I.
Subscribe here: Become a Patreon!: Visual animal AI: .
9 Proofs You Can Increase Your Brain Power
The human brain is probably the most mysterious organ in our body. Scientists keep learning new facts about its work, but it still hides lots of secrets. There are a ...
But what *is* a Neural Network? | Chapter 1, deep learning
Subscribe to stay notified about new videos: Support more videos like this on Patreon: Special .
The Science of Learning: How to Turn Information into Intelligence | Barbara Oakley
Cramming for a test and having a hard time understanding something? Might be best to go away and come back after a while. Your brain is constantly fluctuating ...
This Is How Your Brain Powers Your Thoughts
Scientists have figured out how our brains process thoughts and the explanation will blow your mind. Can Shocking Your Brain Make You Smarter?
How Machines Learn
How do all the algorithms around us learn to do their jobs? Bot Wallpapers on Patreon: Discuss this video: ..
Google's DeepMind AI Just Taught Itself To Walk
Google's artificial intelligence company, DeepMind, has developed an AI that has managed to learn how to walk, run, jump, and climb without any prior guidance ...
The Learning Brain
One of the 9 films available in Successful Learners How does my brain work? What happens in my brain when Im learning? What stops my brain from learning?
MarI/O - Machine Learning for Video Games
MarI/O is a program made of neural networks and genetic algorithms that kicks butt at Super Mario World. Source Code: "NEAT" ..
Breaking Deep Learning Systems With Adversarial Examples | Two Minute Papers #43
Artificial neural networks are computer programs that try to approximate what the human brain does to solve problems like recognizing objects in images. In this ...