A Quote by Steve Jurvetson

By developing deep learning solutions that are faster, easier, and less expensive to use, Nervana is democratizing deep learning and fueling advances in medical diagnostics, image and speech recognition, genomics, agriculture, finance, and eventually across all industries.
I think people need to understand that deep learning is making a lot of things, behind-the-scenes, much better. Deep learning is already working in Google search, and in image search; it allows you to image search a term like "hug."
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.
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 will transform every single industry. Healthcare and transportation will be transformed by deep-learning. I want to live in an AI-powered society. When anyone goes to see a doctor, I want AI to help that doctor provide higher quality and lower cost medical service. I want every five-year-old to have a personalised tutor.
Genomics, Artificial Intelligence, and Deep Machine learning technologies are helping practitioners deliver better diagnosis and actually freeing up time for patient interaction.
Deep learning is already working in Google search and in image search; it allows you to image-search a term like 'hug.' It's used to getting you Smart Replies to your Gmail. It's in speech and vision. It will soon be used in machine translation, I believe.
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.
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.
Real learning, attentive, real learning, deep learning, is playful and frustrating and joyful and discouraging and exciting and sociable and private all the time, which is what makes it great.
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.
Deep learning is a really powerful metaphor for learning about the world.
Deep learning is a subfield of machine learning, which is a vibrant research area in artificial intelligence, or AI.
The best results are achieved by using the right amount of effort in the right place at the right time. And this right amount is usually less than we think we need. In other words, the less unnecessary effort you put into learning, the more successful you'll be... the key to faster learning is to use appropriate effort. Greater effort can exacerbate faulty patterns of action. Doing the wrong thing with more intensity rarely improves the situation. Learning something new often requires us to unlearn something old.
One of my relatives had been asking me on how he could break into AI. For him to learn AI - deep-learning, technically - a lot of facts exist on the Internet, but it is difficult for someone to go and read the right combination of research papers and find blog posts and YouTube videos and figure out themselves on how to learn deep-learning.
Learning to ask is like flexing a muscle. The more you do it, the easier it becomes. I started by learning how to ask for the small things in my life, and eventually I could make the Big Daunting Asks.
There is first the problem of acquiring content, which is learning. There is another problem of acquiring learning skills, which is not merely learning, but learning to learn, not velocity, but acceleration. Learning to learn is one of the great inventions of living things. It is tremendously important. It makes evolution, biological as well as social, go faster. And it involves the development of the individual.
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