Jefferson Han Quotes

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.

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.

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

Machine learning allows us to build software solutions that exceed human understanding and shows us how AI can innervate every industry.

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.

Pretend I asked, now answer the question.

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

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.

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.

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.

We are going to completely change what it means to do advanced analytics with our data solutions. We have machine-learning stuff that is about really bringing advanced analytics and statistical machine learning into data-science departments everywhere.

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.

I came in as an engineer and worked on artificial intelligence at Google. I worked on related sites and matching advertising to queries with some of our earliest ads.

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

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.

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.

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

At Harvard, I worked for some time as a researcher in a lab for computer graphics and spatial analysis, which is one of the birthplaces for what we do.

I'm fascinated by artificial intelligence.

I want to build the next CNN with 'VICE' - it's within my grasp.

AI does not keep me up at night. Almost no one is working on conscious machines. Deep learning algorithms, or Google search, or Facebook personalization, or Siri or self driving cars or Watson, those have the same relationship to conscious machines as a toaster does to a chess-playing computer.

Interactive computers and software will, I think, provide a less costly method of doing some kinds of inquiry, in knowledge acquisition and even reasoning and interaction.

Every part of your business will change based on what I consider predictive analytics of the future.