Sentences with phrase «of neural nets»

Ivan Del Duca, the studio's technical director, has strong views about the use of neural nets in creating its upcoming racers.

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

If you think of this neural net as a sequence of steps, where you're processing information at each step and feeding it to the next one, then one of the goals from the algorithmic standpoint is to reduce that to the smallest number of steps yet get the same results.

As the PR2 moves its joints and manipulates objects, the algorithm calculates good values for the 92,000 parameters of the neural net it needs to learn.

Even less is known about the assembly of the neural net within the mouse olfactory system, which, in the end, enables the individual to distinguish one smell from another with astonishing specificity and to remember such distinctions over time.

Not exact matches

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.

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.

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.

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

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.»»

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.

It's kind of chilling when you begin to suspect that such things are explainable as perhaps inevitable consequences of being the kind of creatures that we are — that we might point to some part of the brain and say it resides there as nothing more than a convoluted net of neural circuits.

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.

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

«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.

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

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.

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.

«Deep» nets contain anywhere from three to hundreds of layers, the last of which distills all of this neural activity into a singular prediction: This is a picture of a cat, for example.

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

«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.

There aren't yet any instruments that can measure what a large, complicated neural net is doing in detail, especially while it is part of a living brain, so scientists have to find indirect ways of testing their ideas about what's going on in there.

«In some domains neural nets are actually superhuman, like they're beating human performance,» says Anish Athalye, a Massachusetts Institute of Technology graduate student who researches AI.

The trained neural nets performed with 90 % and 96 % accuracy respectively (or 94 % and 99 % if the most challenging specimens were discarded), confirming that deep learning is a useful and important technology for the future analysis of digitized museum collections.

This paper builds on research from Bengio's lab on a more biologically plausible way to train neural nets and an algorithm developed by Lillicrap that further relaxes some of the rules for training neural nets.

Neural nets are intrinsically probabilistic: An object - recognition system fed an image of a small dog, for instance, might conclude that the image has a 70 percent probability of representing a dog and a 25 percent probability of representing a cat.

Contestants could feed the program their own sound files and analyze the neural net's simulated bursts of activity, or they could look at archived responses to sound files that Brody and Hopfield had presented to the neural net.

During training, a neural net continually readjusts thousands of internal parameters until it can reliably perform some task, such as identifying objects in digital images or translating text from one language to another.

But on their own, the final values of those parameters say very little about how the neural net does what it does.

Once the system includes more neurons and the kinks are worked out, it could supply data centers, autonomous cars, and national security services with neural nets that are orders of magnitude faster than existing designs, while using orders of magnitude less power, according to the study's two primary authors, Yichen Shen, a physicist, and Nicholas Harris, an electrical engineer, both at MIT.

Scientists have used optical equipment to build simple neural nets, but these setups required tabletops full of sensitive mirrors and lenses.

The DeepMind team also tried its neural net on a language - based task, in which it received sets of statements such as, «Sandra picked up the football» and «Sandra went to the office.»

«Compared to conventional neural probes, the much - reduced dimensions of our NET - e probes allow us to implant the devices at previously unattainable high densities without damage to brain tissue,» Wei tells nanotechweb.org.

But the development of «neural nets» have enabled breakthroughs in machine learning, and there's hope that driverless cars will carry image identification software that can distinguish a motorcycle from a bicycle and a pedestrian from an orangutan that's just escaped from the local zoo.

An endless array of allusions springs to mind — shredded fishnet stockings, spider webs, hammocks, neural pathways, nomadic dwellings, acrobatic safety nets, kids» makeshift forts, the flight patterns of pigeons, river systems, air traffic control maps, the electrical grid, and so on.

A reminder that Tsonis et all using a neural net model to simulate chaos and the behavior of ocean currents, give predictions for the next century.

How exactly did the Coca Cola Company plan to accomplish the huge task of being a «net - carbon neural sponsor»?