Geoffrey Hinton Quotes

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.

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

The deep learning techniques, while relatively easy to learn, are quite foreign to traditional engineering modalities. It takes a different mindset and a relaxation of the presumption of control. The practitioners are like magi, sequestered from the rest of a typical engineering process.

The revolution in deep nets has been very profound. It definitely surprised me, even though I was sitting right there.

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.

Beyond helping other people build AI systems with Deeplearning.ai, I also hope to build some AI systems myself!

With the Google Brain project, we made the decision to build deep learning processes on top of Google's existing infrastructure.

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.

We now think of internal representation as great big vectors, and we do not think of logic as the paradigm for how to get things to work. We just think you can have these great big neural nets that learn, and so, instead of programming, you are just going to get them to learn everything.

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.

Take any old classification problem where you have a lot of data, and it's going to be solved by deep learning. There's going to be thousands of applications of deep learning.

Deep learning is a subfield of machine learning, which is a vibrant research area in artificial intelligence, or AI.

All you need is lots and lots of data and lots of information about what the right answer is, and you'll be able to train a big neural net to do what you want.

Pretend I asked, now answer the question.

The strange flavour of AI work is that people try to put together long sets of rules in strict formalisms which tell inflexible machines how to be flexible.

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.

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.

I just thought making machines intelligent was the coolest thing you could do. I had a summer internship in AI in high school, writing neural networks at National University of Singapore - early versions of deep learning algorithms. I thought it was amazing you could write software that would learn by itself and make predictions.

Machine learning is the science of getting computers to learn without being explicitly programmed.

Want to train a machine translation system? Train it on a gazillion pairs of sentences of parallel corpora, and that creates a lot of breakthrough results. Increasingly, I'm seeing results on small data where you want to try to take in results even if you have 1,000 images.

One of the things that Baidu did well early on was to create an internal platform for deep learning. What that did was enable engineers all across the company, including people who were not AI researchers, to leverage deep learning in all sorts of creative ways - applications that an AI researcher like me never would have thought of.

We have a good sense of the pulse of the industry. We know what's going on out there. We know that deep learning adoption is broad. It's going into production at scale.

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

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.

Deep learning doesn't shine.