Geoffrey Hinton Quotes

I think the first wave of deep learning progress was mainly big companies with a ton of data training very large neural networks, right? So if you want to build a speech recognition system, train it on 100,000 hours of data.

AIs are only as good as the data they are trained on.

Graphics has lately made a great shift towards machine learning, which itself is about understanding data.

The thing that's going to make artificial intelligence so powerful is its ability to learn, and the way AI learns is to look at human culture.

Deep learning allows you to create predictive models at a level of quality and sophistication that was previously out of reach. And so deep learning also enhances the product function of data science because it can generate new product opportunities.

When you have a large amount of data that is labeled so a computer knows what it means, and you have a large amount of computing power, and you're trying to find patterns in that data, we've found that deep learning is unbeatable.

A lot of what people are calling 'artificial intelligence' is really data analytics - in other words, business as usual. If the hype leaves you asking 'What is A.I., really?,' don't worry, you're not alone.

The paradigm shift of the ImageNet thinking is that while a lot of people are paying attention to models, let's pay attention to data. Data will redefine how we think about models.

Pretend I asked, now answer the question.

The neural network is this kind of technology that is not an algorithm, it is a network that has weights on it, and you can adjust the weights so that it learns. You teach it through trials.

In deep learning, the algorithms we use now are versions of the algorithms we were developing in the 1980s, the 1990s. People were very optimistic about them, but it turns out they didn't work too well.

Good morning, Dr. Chandra. This is Hal. I am ready for my first lesson.

If you just have a single problem to solve, then fine, go ahead and use a neural network. But if you want to do science and understand how to choose architectures, or how to go to a new problem, you have to understand what different architectures can and cannot do.

There is huge demand for artificial intelligence technologies.

When you use Google, do you get more than one answer? Of course you do. Well, that's a bug. We have more bugs per second in the world. We should be able to give you the right answer just once. We should know what you meant.

If you want to solve very complex problems, you will have to end up letting machines work out a lot of the details for themselves, and in ways that we don't understand what they are doing.

As commentators like the American psychologist Gary Marcus have noted, it's extremely difficult to teach a computer to recognise cats. And that's not for want of trying.

A graphic representation of data abstracted from the banks of every computer in the human system. Unthinkable complexity. Lines of light ranged in the nonspace of the mind, clusters and constellations of data. Like city lights, receding.

My mission at Google is to develop natural language understanding with a team and in collaboration with other researchers at Google.

Mimicking the intricacies of the human brain, a neuro-inspired computer would work in a fashion similar to the way neurons and synapses communicate. It could potentially learn or develop memory.

Search is an unsolved problem.

I'm a geek through and through. My last job at Microsoft was leading much of the search engine relevance work on Bing. There we got to play with huge amounts of data, with neural networks and other AI techniques, with massive server farms.

Data helps solve problems.

People are saying, 'Big Data is the new oil.'

There will always be plenty of things to compute in the detailed affairs of millions of people doing complicated things.