Andrew Ng Quotes Quotes

Many researchers are exploring other forms of AI, some of which have proved useful in limited contexts; there may well be a breakthrough that makes higher levels of intelligence possible, but there is still no clear path yet to this goal.
Most of the value of deep learning today is in narrow domains where you can get a lot of data. Here's one example of something it cannot do: have a meaningful conversation.
No university is the best place to execute every mission and no one company the best place to execute every mission.
None of us today know how to get computers to learn with the speed and flexibility of a child.
One of my philosophies of building companies is the importance of velocity.
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.
One of the things Baidu did well early on was to create an internal platform that made it possible for any engineer to apply deep learning to whatever application they wanted to, including applications that AI researchers like me would never have thought of.
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.
One thing I've been doing at Baidu is running a workshop on the strategy of innovation. The idea is that innovation is not these random unpredictable acts of genius but that, instead, one can be very systematic in creating things that have never been created before.
Our education system has succeeded so far in teaching generations to do different routine tasks. So when tractors displaced farming labor, we taught the next generation to work in factories. But what we've never really been good at is teaching a huge number of people to do non-routine creative work.
Our educational system globally has not been historically great in reskilling for newer job roles. We need a new social contract to do that. For India, lack of an incumbent structure might be an advantage, where it can use digital education to leapfrog.
People change jobs much more often, and therefore, companies, on average, invest less in employee development.
Speech recognition today doesn't really work in noisy environments.
Text input is certainly useful, but images and speech are a much more natural way for humans to express their queries. Infants learn to see and speak well before they learn to type. The same is true of human evolution - we've had spoken language for a long time compared to written language, which is a relatively recent development.
The big AI dreams of making machines that could someday evolve to do intelligent things like humans could - I was turned off by that. I didn't really think that was feasible when I first joined Stanford.
The biggest ethical challenge AI is facing is jobs. You have to reskill your workforce not just to create a wealthier society but a fairer one. A lot of call centre jobs will go away, and a radiologist's job will be transformed.
The Chinese market is very different. One of the things that I believe is that the biggest, hottest tech trend in China right now is O2O, or online-to-offline.
The most trusted way to keep moving up that value chain is to keep investing in individuals - to help them grow in knowledge and skills. Education is hard. It takes individuals to do the hard work.
The success, or failure, of a CEO to implement AI throughout the organization will depend on them hiring a leader to build an organization to do this. In some companies, CIOs or chief data officers are playing this role.
The thing that really excites me today is building a new AI-powered society.
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