Landing AI: Unlocking The Power Of Data-Centric Artificial Intelligence
Artificial intelligence (AI) has been hugely transformative in industries with access to huge datasets and trained algorithms to analyze and interpret them. Probably the most obvious examples of this success can be found in consumer-facing internet businesses like Google, Amazon, Netflix, or Facebook.
Over the last two decades, companies such as these have grown into some of the world’s largest and most powerful corporations. In many ways, their growth can be put down to their exposure to the ever-growing volumes of data being churned out by our increasingly digitized society.
But if AI is going to unlock the truly world-changing value that many believe it will – rather than simply making some very smart people in Silicon Valley very rich – then businesses in other industries have to consider different approaches. Companies in, for example, healthcare, agriculture, manufacturing, or logistics simply are not going to have millions (or billions) of people signing up to freely share volumes of data, in the way that they do with Google or Facebook – the models of interaction between consumers and the businesses are completely different.
For the last decade, it’s generally been assumed that AI – and in particular, deep learning, which relies on complex deep neural networks – requires enormous volumes of data in order to surface the insights needed for genuine transformation. But what if we could get insights from smaller volumes of data?
This is the starting point taken by Dr. Andrew Ng with his current venture, Landing AI. Ng will need no introduction to those familiar with the recent history of AI. For the benefit of those who aren’t, he was the founder of Google’s deep learning research group, Google Brain, and chief scientist at Baidu’s Artificial Intelligence Group. He also co-founded the online learning portal Coursera, is a former director of Stanford University’s AI Lab, and is widely considered a pioneer in the field of AI – in particular, deep learning.
Having played a leading role in integrating AI into some of the industries where it is obviously proving highly transformational, with Landing AI he decided to shift focus to some of the areas where its impact is yet to be fully felt. There’s a good reason for this, he believes – and it’s down to the heterogeneous nature of operations. Once you move beyond the world of public-facing consumer web services, industry is no longer built around homogenous infrastructure staples – web browsers, cloud servers, mobile apps, and a small number of standard operating systems. This means that “plugging in” AI-as-a-service solutions becomes less straightforward, and the customization that’s necessary becomes expensive.
The solution? Focus on the data, rather than the models and the technology, he tells me, when we got together recently for a conversation.
“I think machine learning has transformed the consumer software companies”, Ng says. “Google and Baidu have AI teams, also Microsoft, Facebook – but once you look into other industries, candidly, I’m not seeing the potential value of AI being realized yet today.”
Shortly after deciding to take his mission to unlock the value of AI to legacy industries like manufacturing, healthcare, and agriculture, Ng realized that perhaps he had been, in his own words, “a little naive.”
“That recipe that a lot of us had collectively built at [internet companies] – that recipe doesn’t work at all these other industries.
“So at Landing AI, one of the things we’ve been doing is working out that recipe … I’m excited about that; it’s a big thing that those of us in AI have to figure out.”
To start with, Ng and the rest of the team at Landing AI are focusing their efforts on the manufacturing sector. In order to do this, they have just completed a series A funding round, raising $57 million from investors, including industrial IoT-focused fund McRock Capital, as well as Insight Partners, Intel Capital, and Samsung Investment Fund.
