It is difficult to think of a major industry that AI will not transform. This includes healthcare, education, transportation, retail, communications, and agriculture. There are surprisingly clear paths for AI to make a big difference in all of these industries.
Andrew Ng
Education is not about thinning the herd. Education is about helping every student succeed.
We're making this analogy that AI is the new electricity. Electricity transformed industries: agriculture, transportation, communication, manufacturing.
Elon Musk is worried about AI apocalypse, but I am worried about people losing their jobs. The society will have to adapt to a situation where people learn throughout their lives depending on the skills needed in the marketplace.
We can build a much brighter future where humans are relieved of menial work using AI capabilities.
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.
A single neuron in the brain is an incredibly complex machine that even today we don't understand. A single 'neuron' in a neural network is an incredibly simple mathematical function that captures a minuscule fraction of the complexity of a biological neuron.
If we can make computers more intelligent - and I want to be careful of AI hype - and understand the world and the environment better, it can make life so much better for many of us. Just as the Industrial Revolution freed up a lot of humanity from physical drudgery I think AI has the potential to free up humanity from a lot of the mental drudgery.
In my own life, I found that whenever I wasn't sure what to do next, I would go and learn a lot, read a lot, talk to experts. I don't know how the human brain works, but it's almost magical: when you read enough or talk to enough experts, when you have enough inputs, new ideas start appearing. This seems to happen for a lot of people that I know.
If you have a lot of data and you want to create value from that data, one of the things you might consider is building up an AI team.
If a typical person can do a mental task with less than one second of thought, we can probably automate it using AI either now or in the near future.
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.
I think that, hundreds of years from now, if people invent a technology that we haven't heard of yet, maybe a computer could turn evil. But the future is so uncertain. I don't know what's going to happen five years from now. The reason I say that I don't worry about AI turning evil is the same reason I don't worry about overpopulation on Mars.
In Silicon Valley, there are a lot of startups using computer vision for agriculture or shopping - there are a lot for clothes shopping. At Baidu, for example, if you find a picture of a movie star, we actually use facial recognition to identify that movie star and then tell you things like their age and hobbies.
Every company has messy data, and even the best of AI companies are not fully satisfied with their data. If you have data, it is probably a good idea to get an AI team to have a look at it and give feedback. This can develop into a positive feedback loop for both the IT and AI teams in any company.
Despite all the hype and excitement about AI, it's still extremely limited today relative to what human intelligence is.
As leaders, it is incumbent on all of us to make sure we are building a world in which every individual has an opportunity to thrive. Understanding what AI can do and how it fits into your strategy is the beginning, not the end, of that process.
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.
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.
As the founding lead of the Google Brain team, former director of the Stanford Artificial Intelligence Laboratory, and now overall lead of Baidu's AI team of some 1,200 people, I've been privileged to nurture many of the world's leading AI groups and have built many AI products that are used by hundreds of millions of people.
The way AI complements people's work, it actually creates a lot of new jobs, a lot of demand. For example, if a automatic visual inspection technology helps spot flaws in manufacturing parts, I think that in some cases, this does create a lot more demand for people to come in to rework or to fix some of the parts that an AI has found to be flawed.
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.
The two things I'm most excited about are self-driving cars and speech. Speech doesn't sound like that much, but it's one of those technologies with the potential to change everything. Steve Jobs didn't invent the touch screen. He just made it work very well, and that's changed everything.
I want an AI-powered society because I see so many ways that AI can make human life better. We can make so many decisions more systematically or automate away repetitive tasks and save so much human time.
When you become sufficiently expert in the state of the art, you stop picking ideas at random. You are thoughtful in how to select ideas and how to combine ideas. You are thoughtful about when you should be generating many ideas versus pruning down ideas.
There are two companies that the AI Fund has invested in - Woebot and Landing AI - and the AI Fund has a number of internal teams working on new projects. We usually bring in people as employees, work with them to turn ideas into startups, then have the entrepreneurs go into the startup as founders.
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.
It seemed really amazing that you could write a few lines of code and have it learn to do interesting things.
With the Google Brain project, we made the decision to build deep learning processes on top of Google's existing infrastructure.
A lot of the game of AI today is finding the appropriate business context to fit it in. I love technology. It opens up lots of opportunities. But in the end, technology needs to be contextualized and fit into a business use case.
I think that solving the job impact of AI will require significant private and public efforts. And I think that many people actually underestimate the impact of AI on jobs. Having said that, I think that if we work on it and provide the skill training needed, then there will be many new jobs created.
India has a large base of tech talent, and I hope that a lot of AI machine learning education online will allow Indian software professionals to break into AI.
Speech recognition today doesn't really work in noisy environments.
There's a very long tail of all sorts of creative products - beyond our core web search, image search and advertising businesses - that are powered by deep learning.
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.
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.
Deep learning is a very capital-intensive area, and it's rare to find a company with both the necessary resources and a company structure where things can get done without having to pass through too many channels and committee meetings.
A lot of the progress in machine learning - and this is an unpopular opinion in academia - is driven by an increase in both computing power and data. An analogy is to building a space rocket: You need a huge rocket engine, and you need a lot of fuel.
Some of the most successful businesses succeed by exploiting their users.
Machine learning is the most popular course for people from India. There is a window of time when India can embrace and capture a large fraction of the AI opportunity. But it will not remain open for ever.
I think the world will just be better if AI is helping us. It will reduce the cost of goods, giving us good education, changing the way we run hospitals and the health-care system - there's just a long list of things.
Silicon Valley and Beijing are the leading hubs of AI, followed by the U.K. and Canada. I am seeing a lot of excitement in India, going by the number of people who are taking Coursera courses on AI.
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.
I think the next massive wave of value creation will be when you can get a manufacturing company or agriculture devices company or a health care company to develop dozens of AI solutions to help their businesses.
I believe that the ability to innovate and to be creative are teachable processes. There are ways by which people can systematically innovate or systematically become creative.
I think the Indian AI ecosystem is growing rapidly. A lot of Indian entrepreneurs reach out to me seeking feedback about startups and products. And some of them have very interesting business ideas.
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.
It takes a government to set up public-private partnerships and develop university programmes. I think this is the best path for India, given the rapid progress the country has already made and given the rapid progress we all hope India will continue to make.
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.
In terms of building consumer products, the U.S. and China are ahead of India. The interesting opportunity for India is whenever there is a disruption in technology, it gives every country a chance to leapfrog and take a lead. To take an example, China is leaping ahead in growing the China electric vehicle ecosystem.
