Many of us may know Andrew Ng as the founder of the Google Brain team or the former chief scientist at Baidu. He has taught countless students, curious listeners, and business leaders about the principles of machine learning through his online courses.
Now in his latest venture, Landing AI, he is exploring how businesses without giant data sets to draw on can still join in the AI revolution. Ng joined MIT Technology Review’s virtual EmTech Digital annual AI event, to share the lessons he’s learned.
Andrew started by explaining that in terms of how he executes the business, he tends to be customer-led or mission-led, and almost never technology-led.
He shared that after heading the AI teams at Google and Baidu, he realized that AI has transformed software consumer internet. However, Ng wanted to take AI to all of the other industries, which is an even bigger part of the economy, so he decided to focus on manufacturing. According to Andrew, one huge difference in consumer software internet is the fact that there we have a billion users and a huge amount of data. But he pointed out that in manufacturing, no factory has manufactured a billion or even a million scratched smartphones. Hence the challenge is, can we get an AI to work with a hundred images… Well, in his opinion “it often turns out we can.”
For Andrew, machine learning is so diverse that it’s becoming really hard to give one-size-fits-all answers.
He thinks that today building AI systems are still very manual. There are a few brilliant machine-learning engineers and data scientists who do things on a computer and then push things to production. There’s a lot of manual steps in the process.
He also pointed out that if we look at a lot of the typical business problems there’s a lot of room for automation and efficiency improvement. Andrew hopes that the AI community can look at the biggest social problems too – eg. climate change, homelessness or poverty. He added:
“It’s more important to start quickly, and it’s okay to start small. My first meaningful business application at Google was speech recognition, not web search or advertising. But by helping the Google speech team make speech recognition more accurate, that gave the Brain team the credibility and the wherewithal to go after bigger and bigger partnerships. So Google Maps was the second big partnership where we used computer vision—to read house numbers to geolocate houses on Google maps. And only after those first two successful projects did I have a more serious conversation with the advertising team. So I think I see more companies fail by starting too big than fail by starting too small. It’s fine to do a smaller project to get started as an organization to learn what it feels like to use AI, and then go on to build bigger successes.”
Ng recommended that if our company isn’t already making pretty aggressive and smart investments, this is a good time because AI is causing a shift in the dynamics of many industries.