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Andrew Ng: Why AI Is the New Electricity

When you ask Siri for directions, peruse Netflix’s recommendations, or get a fraud alert from your bank, these interactions are led by computer systems using large amounts of data to predict your needs.

Still, computer scientist and Coursera co-founder Andrew Ng says, fears that AI will replace humans are misplaced: “Despite all the hype and excitement about AI, it’s still extremely limited today relative to what human intelligence is.” Ng, who is chief scientist at Baidu Research and teaches at Stanford, spoke to the Stanford Graduate School of Business community as part of a series presented by the Stanford MSx Program.

“Just as electricity transformed almost everything 100 years ago, today I actually have a hard time thinking of an industry that I don’t think AI will transform in the next several years,” Ng says.

“Worrying about evil AI killer robots today is a little bit like worrying about overpopulation on the planet Mars.” The real concern regarding AI is societal impact.

“I think government should give people a safety net, but pay the unemployed to study, to provide the structure to help the unemployed study, so as to increase the odds of gaining skills needed to re-enter the workforce.” Want to learn more?

What Artificial Intelligence and Machine Learning Can Do—And What It Can’t

In most of the cases where organizations failed with AI or Machine Learning, they used those techniques in the wrong context. Machine Learning models are not very helpful if you only have one big decision to make. Analytics can still give you easier access to the data you need to make a decision by presenting the data in a consumable fashion. At the end of the day, those single, big decisions are often very strategic. Building a machine learning model or AI to help you make this decision is just not worth the effort.

Here is where ML and AI can help. Machine Learning and Artificial Intelligence delivers the most value when you need to make lots of similar decisions quickly. Some good examples for this include: You can see that a human with access to all relevant data could make those decisions—only they can’t without AI or ML, since they would need to make this decision millions of times, every day. Imagine sifting through your customer base of 50 million clients every day to identify those with a high churn risk. That’s impossible for any human, but it’s not a problem at all for a ML model.

If you’re looking for more ways that machine learning can help your business, check out my recent webinar How to Ruin Your Business with Data Science and Machine Learning where I talk about how to apply machine learning to your business and show how surprisingly simple it is to draw completely wrong conclusions from statistical models.

QA: Andrew Ng, the Authority on A.I.

ANDREW NG, A 42-YEAR-OLD COMPUTER SCIENCE professor at Stanford University, made his name leading artificial intelligence efforts at two of the world’s biggest tech companies, Google and Baidu.

courses called deeplearning.ai, a business called Landing AI that develops artificial intelligence for manufacturing companies, and an incubator for startups known as the AI Fund.

Fortune spoke with Ng about why he left the world of Big Tech, what’s next for his projects and his fi eld, and what the rise of artificial intelligence might mean for the rest of us.

Look at the three teams I’m involved in: The AI Fund is involved in creating new companies from scratch, Landing AI is involved in helping incumbent companies do A.I., and deeplearning.ai is for everyone else.

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.

Some people would argue that it’s difficult for startups to compete because the Internet and other effects help big companies compound their power.

I think what’s changed is that instead of doing manual work, a lot of these people are operations people and software engineers—highly skilled and working with machines.

Rather than unconditional basic income, where you pay people for anything, I think we should pay people to study because that creates the structure for them to gain the skills they need to reenter the workforce.

Because people are better at optimizing for the short term, I think basic income can be conditional basic income where we pay people to study.

I would like the world to be fairer, I would like everyone to have better access to opportunities and education, I would like to relieve people of menial tasks, I would like democracy to run better.

AI veteran Andrew Ng just can’t stop launching new companies

Ng writes that his startup and Foxconn will “jointly develop and deploy AI solutions and training globally.” Among the AI researchers credited with architecting today’s AI boom, Ng one of the most active in preaching the technology’s benefits.

“One in which our physical needs, health care, transportation, food, and lodging are more accessible through AI, and where every person is freed from repetitive mental drudgery.” This time, for a change, Ng is also highlighting the potential for job loss as AI makes manufacturing more efficient and reduces the need for human workers;

How artificial intelligence and data add value to businesses

Artificial intelligence will transform many companies and create completely new types of businesses.

In this video, recorded at the Aspen Ideas Festival in June, Andrew Ng, cofounder of Coursera, AI Fund, and Landing.AI, discusses the difference between an AI-enabled business versus a true AI company, and how businesses can organize, hire, and make use of AI to add value.

Almost all the economic value created by AI is through one type of technology, which learns inputs, outputs, or maybe A-to-B mappings, such as you might input an email, telling you it’s spam or not.

For a self-driving car, input a picture of what’s in front of your car and your radar readings and output the position of the other cars.

For example, if you look at any mobile app, there was probably a product manager that drew a simplified diagram called a wireframe to design that app.

For a lot of the companies, the best hope might be to try to hire one strong AI leader and then build a centralized AI organization that you can then matrix into your various business units.

There are tons of resources on the Internet, but to use resources like that to level up your whole employee base, that would make your whole organization more effective at maybe working with your centralized AI organization.

After that comes some data science, where you start to get more insights, and then also AI, because it’s only after you have the digital data that AI is very efficient in coming in to eat that data to create value.

If you can just have enough data to launch a product that’s good enough, that allows you to enter a positive feedback loop in which your users help you generate more data.

For example, today the large web-search engines have an incredibly valuable data asset of what web pages people click on when they search on certain things.

The sophisticated AI organizations are definitely playing these multiyear chess games, doing multiyear strategic planning in order to play out data acquisition.

There’s so much data in the world that I don’t think any one company today has a reasonable strategy for acquiring the majority of valuable data.

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