AI News, Facebook AI chief Yann LeCun on the future of artificial intelligence ... artificial intelligence

Intel’s neuro guru slams deep learning: ‘it’s not actually learning’

'Backpropogation doesn't correlate to the brain,' insists Mike Davies, head of Intel's neuromorphic computing unit, dismissing one of the key tools of the species of A.I.

'For that reason, 'it's really an optimizations procedure, it's not actually learning.'  Davies made the comment during a talk on Thursday at the International Solid State Circuits Conference in San Francisco, a prestigious annual gathering of semiconductor designers.  Davies was returning fire after Facebook's Yann LeCun, a leading apostle of deep learning, earlier in the week dismissed Davies's own technology during LeCun's opening keynote for the conference.

In contrast, so-called back-prop, invented in the 1980s, is a mathematical technique used to optimize the response of artificial neurons in a deep learning computer program.  Although deep learning has proven 'very effective,' Davies told a ballroom of attendees, 'there is no natural example of back-prop,' he said, so it doesn't correspond to what one would consider real learning.  Also: Facebook's Yann LeCun reflects on the enduring appeal of convolutions Davies then went on to give a talk about 'Loihi,' his team's computer model of a neural network that uses so-called spiking neurons that activate only when they receive an input sample.

The contention of neuromorphic computing advocates is that the approach more closely emulates the actual characteristics of the brain's functioning, such as the great economy with which the brain transmits signals.  He chided LeCun for failing to value the strengths of that approach.

Hence, some of Intel's chip business with large customers like Facebook might be in jeopardy if the company faces competition from those very customers because they have a different vision.  The Facebook AI researcher during his talk Monday morning had critiqued spiking neuron work broadly, saying that the technology had not yielded algorithms that could produce practical results.

(It's worth noting that the Loihi chip, however, actually delivers lower accuracy, as described in the full version of the Applied Science paper, which is available on the arXiv pre-print server.)  In another example, Davies noted that a basic classifier task performed by Loihi was forty times as fast as a conventional GPU-based deep learning network, with 8% greater accuracy.  Davies told the audience that Loihi gets 'much more efficient' as it is tasked with larger and larger networks.

Yann LeCun Cake Analogy 2.0

Facebook AI Chief Yann LeCun introduced his now-famous “cake analogy” at NIPS 2016: “If intelligence is a cake, the bulk of the cake is unsupervised learning, the icing on the cake is supervised learning, and the cherry on the cake is reinforcement learning (RL).” The quip rippled across the AI community and confirmed LeCun a strong advocate of unsupervised learning, a machine learning technique that finds patterns in unlabeled data.

As LeCun says: “Prediction is the essence of Intelligence.” The French Scientist defines unsupervised/predictive learning as “predicting any part of the past, present, or future percepts from whatever information is available.” He explains that “the number of samples required to train a large learning machine for any task depends on the amount of information we ask it to predict.

The more you ask of the machine, the larger it can be.” Machine learning techniques such as supervised learning (which only predicts human-provided labels), and reinforcement learning (which only predicts a value function), are too narrow to create human-level intelligent machines.

At NIPS 2017 UC Berkeley Professor Pieter Abbeel escalated the dessert warfare, including the DeepMind cherry cake image in his presentation and joking “I prefer to eat a cake with a lot of cherries because I like reinforcement learning.” Since LeCun’s criticism on pure reinforcement learning methods mainly focuses on sparse reward signals, Abbeel illustrated his point with Hindsight Experience Replay, a novel, sample-efficient learning technique that tries to get a reward signal from any experience by simply assuming the goal equals whatever happened.

3 things we learned from Facebook’s AI chief about the future of artificial intelligence

This market is becoming a core focus for a lot of technology companies and, as it matures and as the machines become more skilled, we will see AI start to work its way into more of our everyday lives.

Where we, as MIS students, might be able to fit into this market is in designing the use cases for AI, deciding how AI should make its decisions, and helping to code the algorithms to help advance an entire industry.

Facebook's AI chief researching new breed of semiconductor

(Bloomberg) --Facebook Inc.’s chief AI researcher has suggested the company is working on a new class of semiconductor that would work very differently than most existing designs.

Yann LeCun said that future chips used for training deep learning algorithms, which underpin most of the recent progress in artificial intelligence, would need to be able to manipulate data without having to break it up into multiple batches.

In April, Bloomberg reported that Facebook was hiring a hardware team to build its own chips for a variety of applications, including artificial intelligence as well as managing the complex workloads of the company’s vast datacenters.

For the moment the most commonly-used chips for training neural networks -- a kind of software loosely based on the way the human brain works -- are graphical processing units from companies such as Nvidia Corp., originally designed to handle the computing intensive workloads of rendering images for video games.

LeCun said that for the moment, GPUs would remain important for deep learning research, but the chips were ill-suited for running the AI algorithms once they were trained, whether that was in datacenters or on devices like mobile phones or home digital assistants.

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