The Ledger
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LeCun: Simulating biological brain neurons to achieve AI is 'completely nuts'
“There are people out there in Europe that, that have argued for the fact to kind of study neurons and neurons and neural networks natural neural networks in all the details, and then build giant supercomputers to simulate this very accurately, and somehow they…”
LeCun: Building chips for spiking neural networks is misguided
“The problem is, from the engineering point of view, nobody has actually demonstrated that spiking neurons actually work. Like there is no image recognition systems that are based on spiking neural networks. Why build chips for something we don't know works? So…”
LeCun: The window for deep learning startups will close quickly
“The window is going to close really quickly on, on deep learning you know, sort of opportunity for startups in deep learning.”
LeCun: Building superintelligent AI will take a long time
“Building machines that are super intelligent, you know, more intelligent than humans is going to take a long time.”
LeCun: The public thinks AI is more dangerous than it is
“The public thinks it's more dangerous than it actually is, so there is a whole segment of the public that will be extremely scared about the potential of AI, and that need, you know, the real dangers and the false dangers need to be explained.”
LeCun: Backprop designs vision systems better than human engineers
“In fact, those dumb learning algorithms, Backprop, namely, is better at designing vision systems than very experienced engineers and scientists are at designing vision systems.”
LeCun: Deep learning designs NLP systems better than linguists
“You know, those deep learning algorithms are better at designing NLP systems than linguists are, or natural language specialists.”
LeCun predicted in 2014 that AI would mediate human social interactions
“And started thinking about the next 10 years. What are the next 10 years going to be for social interactions? And it's pretty obvious to a lot of people that a lot of our interactions you know, with our friends and a lot of interactions with the digital world …”
LeCun predicted in 2014 deep learning would conquer natural language processing
“There's a kind of a sense that in the community that the next set of techniques to kind of fall to deep learning, if you want, will be natural language processing.”
LeCun: Deep learning's rapid industry adoption surprised early AI pioneers
“I mean, the whole success of deep learning, actually has taken some of us by surprise. We were all sort of Convinced from a long time ago that deep learning was going to take off at some point, but the speed at which has been picked up by, ah, industry and res…”
LeCun turned down Larry Page's offer to direct Google's research lab
“And they offered me the job, and I turned it down.”
LeCun: Apple, Android, and Microsoft mobile speech recognition use deep learning
“So probably many of you know that most You know, mobile speech recognition application search and stuff like that uses deep learning for the last two years. This is both for, you know, Apple, Android, Microsoft. All of those applications use deep learning for …”
LeCun: Facebook stole Word2Vec creator Tomasz Mikolov from Google
“I should mention that Tomasz Mikolov now works at Facebook AI Research. I stole him from Google.”
LeCun: Industry adopted deep learning faster than academia
“Industry picked up on deep learning faster than academia.”
LeCun: AT&T's neural net read 20% of US checks in late 1990s
“Which at some point in the nineties were, in the late nineties, was reading something like 20% of all the checks in the U.S.”
LeCun: Deep learning automates feature engineering by integrating representation with classification
“And deep learning basically opens the possibility that you, to automate the process of feature engineering. So, it essentially learns to, It integrates the process of producing good representations with the process of learning a classifier in one fell swoop.”
LeCun: ConvNet training times dropped from two weeks to two days
“The first large convolutional nets that are appropriate for object recognition came up about two and a half years ago, and it would take about two weeks to train. The latest versions with the latest software implementations, we can train in about two days.”