Andrew Ng Quotes Quotes

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.
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.
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.
AI has been making tremendous progress in machine translation, self-driving cars, etc. Basically, all the progress I see is in specialised intelligence. It might be hundreds or thousands of years or, if there is an unexpected breakthrough, decades.
AI is creating tremendous economic value today.
AI is witnessing an early innings in India. It has a thoughtful government, and India can race ahead if it chooses to.
Animals see a video of the world. If an animal were only to see still images, how would its vision develop? Neuroscientists have run experiments in cats in a dark environment with a strobe so it can only see still images - and those cats' visual systems actually underdevelop. So motion is important, but what is the algorithm?
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.
Baidu and Google are great companies, but there are a lot of things you can do outside them. Just as electricity and the Internet transformed the world, I think the rise of modern A.I. technology will create a lot of opportunities both for new startups and for incumbent companies to transform.
Baidu Research has three labs - two in Beijing that are already largely built up, and the Silicon Valley one is being built from scratch. We're hiring pretty rapidly, about one person a week, but we are about a month in, so honestly, we haven't done that much work yet.
Baidu's AI is incredibly strong, and the team is stacked up and down with talent; I am confident AI at Baidu will continue to flourish. After Baidu, I am excited to continue working toward the AI transformation of our society and the use of AI to make life better for everyone.
Beyond helping other people build AI systems with Deeplearning.ai, I also hope to build some AI systems myself!
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.
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.
Despite all the hype and excitement about AI, it's still extremely limited today relative to what human intelligence is.
Education is not about thinning the herd. Education is about helping every student succeed.
Education is one of the industry categories with a big potential for AI. And Coursera is already doing some of this work.
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.
Even companies like Baidu and Google, which have amazing AI teams, cannot do all the work needed to get us to an AI-powered society. I thought the best way to get us there would be creating courses to welcome more people to deep learning.
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.
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Andrew Ng
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