Sentences with phrase «alphago neural net»

Having studied experimental psychology as an undergraduate at Cambridge, Hinton was enthusiastic about neural nets, which were software constructs that took their inspiration from the way networks of neurons in the brain were thought to work.

What's changed is that today computer scientists have finally harnessed both the vast computational power and the enormous storehouses of data — images, video, audio, and text files strewn across the Internet — that, it turns out, are essential to making neural nets work well.

In the cat experiment, researchers exposed a vast neural net — spread across 1,000 computers — to 10 million unlabeled images randomly taken from YouTube videos, and then just let the software do its thing.

Neural nets are good at recognizing patterns — sometimes as good as or better than we are at it.

With «unsupervised learning,» by contrast, a neural net is shown unlabeled data and asked simply to look for recurring patterns.

Although the Internet was awash in it, most data — especially when it came to images — wasn't labeled, and that's what you needed to train neural nets.

Despite all the strides, in the mid-1990s neural nets fell into disfavor again, eclipsed by what were, given the computational power of the times, more effective machine - learning tools.

«It was not formulated in those terms,» LeCun recalls, «because it was very difficult at that time actually to publish a paper if you mentioned the word «neurons» or «neural nets.»

Neural nets offered the prospect of computers» learning the way children do — from experience — rather than through laborious instruction by programs tailor - made by humans.

So it seemed to me that neural nets were a much better paradigm for how intelligence would work than logic was.»

Neural nets aren't new.

The most remarkable thing about neural nets is that no human being has programmed a computer to perform any of the stunts described above.

«His paper was basically the foundation of the second wave of neural nets,» says LeCun.

At the time, neural nets were out of favor.

We already know that neural nets work well for image recognition, observes Vijay Pande, a Stanford professor who heads Andreessen Horowitz's biological investments unit, and «so much of what doctors do is image recognition, whether we're talking about radiology, dermatology, ophthalmology, or so many other «- ologies.»»

Almost every deep - learning product in commercial use today uses «supervised learning,» meaning that the neural net is trained with labeled data (like the images assembled by ImageNet).

No electro - chemical pathways lighting up, no neural net traffic, no genius creative problem solving ideas or thoughts generated.

With deep learning, organizations can feed enormous quantities of data into so - called neural nets designed to loosely mimic the way the human brain understands information.

Brin then shared an anecdote: A few years ago, he underestimated and largely disregarded Google's research into artificial intelligence, believing that the concept of «neural nets» had been proven infeasible back in the 1990s.

With deep learning, researchers can feed huge amounts of data into software systems called neural nets that learn to recognize patterns within the vast information faster than humans.

Now, its neural net (where all the algorithms work together like a brain's neurons) can «reinforce» its learning model with chords and melodies to influence the complexity of the final compositions.

To make sense of all of this data, a new onboard computer with over 40 times the computing power of the previous generation runs the new Tesla - developed neural net for vision, sonar and radar processing software.

The first thing many of us think about when it comes to the future relationship between artificial intelligence (AI) and cybersecurity is Skynet — the fictional neural net - based group mind from the «Terminator» movie franchise.

Plus the implications for these clips on training their neural nets, HD maps, and redundancy for sensing.

Instead, their neurons connect in a decentralized neural net.

And since the brain stores memories in the strength of connections between neurons, inside the neural net itself, it requires no energy - draining bus.

The signals leap from the axons across a synapse, or gap, to the dendrites of the next nerve cell in the neural net.

When the layers communicated with each other by passing signals over the synapse, that was said to model (roughly) a living neural net.

Just as the neural net revival was picking up steam, Modha entered India's premier engineering school, the Indian Institute of Technology in Bombay.

When Google's AlphaGo neural net played go champion Lee Sedol last year in Seoul, it made a move that flummoxed everyone watching, even Sedol.

The hope is to create a prototypical neural net that will help build transparency into autonomous systems like drones or unmanned vehicles.

For instance, an experimental neural net at Mount Sinai called Deep Patient can forecast whether a patient will receive a particular diagnosis within the next year, months before a doctor would make the call.

To extract a more meaningful — if less exacting — explanation, Fern's team proposes probing a neural net with a second neural net.

«It's very difficult to find out why [a neural net] made a particular decision,» says Alan Winfield, a robot ethicist at the University of the West of England Bristol.

This flexibility allows neural nets to outperform other forms of machine learning — which are limited by their relative simplicity — and sometimes even humans.

Bonsai seeks to open the box by changing the way neural nets learn.

If that prediction is wrong, a neural net will then tweak the links between nodes, steering the system closer to the right result.

This figure compares a traditionally trained algorithm to Aarabi and Guo's heuristically trained neural net.

What makes today's deep neural nets at once powerful and capricious is their ability to find patterns in huge amounts of data.

The next step: extract the specific features that make the difference, from the neural net.

Sure, you could, in theory, look under the hood and review every position of every knob — that is, every parameter — in AlphaGo's artificial brain, but even a programmer would not glean much from these numbers because their «meaning» (what drives a neural net to make a decision) is encoded in the billions of diffuse connections between nodes.

«[Deep neural nets] can be really good but they can also fail in mysterious ways,» says Anders Sandberg, a senior research fellow at the University of Oxford's Future of Humanity Institute.

For example, by carving up an image of a cat and feeding a neural net the pieces one at a time, a programmer can get a good idea of which parts — tail, paws, fur patterns or something unexpected — lead the computer to make a correct classification.

Neural nets process information by passing it through a hierarchy of interconnected layers, somewhat akin to the brain's biological circuitry.

This is an artificial neural net existing of several layers and determining over nine million parameters.

Because neural nets essentially program themselves, however, they often learn enigmatic rules that no human can fully understand.

By tuning the knobs to satisfy millions of examples, the neural net creates a structured set of relationships — a model — that can classify new images or perform actions under conditions it has never encountered before.

You can now hold neural nets in the palm of your hand.

Facebook has announced a neural net that can run on a phone.

«The neural networks we tested — three publicly available neural nets and one that we developed ourselves — were able to determine the properties of each lens, including how its mass was distributed and how much it magnified the image of the background galaxy,» said the study's lead author Yashar Hezaveh, a NASA Hubble postdoctoral fellow at KIPAC.