There is a nice piece out in Tech Republic that recounts a conversation between reporter Nick Heath and Google Deepmind co-founder Demis Hassabis. What struck us in the conversation are two things that we have talking and writing about for awhile – the applicability of deep learning and the need for AI to power major scientific breakthroughs. Let’s take them in turn.
First off, deep learning is a boon to the field. Powered by algorithmic progress by academics and systems innovations from the likes of Nvidia, storage from the cloud providers and broad, open-source libraries of algorithms the performance of these frameworks has grown alongside the interest in the field.
But, like the graph, things have plateaued for deep learning. The fact of the matter is that DL is not a fit for many tasks, yet like the proverbial hammer, for many, everything looks like nail when you are wedded to that approach.
Deep Learning struggles with many of the data challenges we find in enterprises today. Perhaps most importantly, is the fact that the majority of data in the enterprise is unlabeled. This makes it harder to find massive training sets to train performant deep learning models.
What Demis points out is that while DL is a useful tool, it is but one avenue, one pathway – there are many others. He notes that “Deep learning is an amazing technology and hugely useful in itself, but in my opinion it’s definitely not enough to solve AI, [not] by a long shot. I would regard it as one component, maybe with another dozen or half-a-dozen breakthroughs we’re going to need like that. There’s a lot more innovations that’s required.”
We could not agree more. We think that in order to be successful with AI, you need a range of capabilities – from the unsupervised to the semi-supervised and ultimately to the supervised.
In fact, Gunnar Carlsson’s work in the deep learning space is proving this out – that to generate material improvements in deep learning, it is very useful to apply techniques like topological data analysis as a starting point.
The net of it is that the alpha and omega of AI is not deep learning. It is merely a letter – you need other concepts to truly change the world.
Speaking of changing the world, Hassabis also talks about the promise of AI to solve some of the world’s great challenges. He’s right. AI needs to be judged against its ability to solve the real problems that face mankind – from healthcare to climate change.
“This is what I’m really excited about and I think what we’re going to see over the next 10 years is some really huge, what I would call Nobel Prize-winning breakthroughs in some of these areas.”
He goes onto note that DeepMind is looking protein folding and quantum chemistry among other areas. We agree wholeheartedly.
Our work to date has produced over 285 peer reviewed publications in places like Nature, Science and Cell. Our unique brand of AI (topological data analysis), has produced breakthroughs with diabetes, asthma, cancer, disease states and traumatic brain injury.This is where AI interacts with the real world – with powerful effects. Like Hassabis, we are incredibly optimistic on what the future holds with regard to AI. If you want to learn more about why – just ask us and we will find some time.