Artificial Intelligence Google DeepMind: Three (3) truths about AI

Artificial Intelligence
Google DeepMind: Three truths about AI

One of the creators of the AI research company famed for building the pioneering AlphaGo AI spells out the technology's impact and future development.

The 2016 victory by a Google-built AI at the notoriously complex game of Go was a bold demonstration of the power of modern machine learning.

While, that triumphant AlphaGo system, created by AI research group Google DeepMind, confounded expectations that computers were years away from beating a human champion, although, as significant as that achievement was, DeepMind's co-founder Demis Hassabis expects it will be dwarfed by how AI will transform society in the years to come.

Hassabis spelt out his vision for the future of AI at the Economist Innovation Summit in London.

AI will save us from ourselves, "I'm very optimistic"....

"I would actually be very pessimistic about the world if something like AI wasn't coming down the road," he said.

"The reason I say that is that if you look at the challenges that confront society: climate change possible cause for world's end, sustainability, mass inequality and so forth — which is getting worse — diseases, contagion and healthcare, we're not making progress anywhere near fast enough in any of these areas, to forestall Armageddon.

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"Either we need an exponential improvement in human behavior — less selfishness, less short-termism, more cooperation and collaboration, more generosity more long-termism— or we need an exponential improvement in technology.

"If you look at current geopolitics, I don't think we're going to be getting an exponential improvement in human behavior any time soon.... countries are out-of-order.

"That's why we need a quantum leap in technology like AI."

AI will lead to Nobel Prize-winning scientific breakthroughs, mark my words.

Hassabis' confidence that AI can offset the worst effects of human averice, greed and selfishness which stems from how readily the technology can be applied to solving intractable problems, such as preventing catastrophic climate change and world's end.

Google Deepmind co-founder Demis Hassabis.

Image: Google

"I think about AI as a very powerful tool. What I'm most excited about is applying those tools to science and accelerating breakthroughs," he said.

Today's machine-learning and related AI technologies make it possible to carry out tasks such as image recognition and to find patterns in vast amounts of big data, he said.

But he's particularly enthused about the potential applications of AI's ability to optimize tasks that would otherwise be overwhelmingly complex, as demonstrated by AlphaGo's success at a game where there are more potential moves than there are atoms in the universe.

"You can think about huge combinatorial spacetimes and you're trying to find a path through. Obviously, games like Chess and Go are like that, there's such a huge number of possibilities you can't brute force the right solution.

"There are lots of areas in science that have a similar structure. I think about areas like materiale, cell DNA editing and drug design, where often what you're doing is painstakingly putting together all sorts of combination of compounds and/ or genomes and testing them for their properties."

The impact of breakthroughs in areas like material design, disease and cancer cures could be profound, according to Hassabis.

"It's hypothesized, for example, there could be a room-temperature superconductor that could revolutionize power and energy, but we don't know what that compound is currently, but we're working on it.

"This is what I'm really excited about and I think what we're going to see over the next 10 - 50 years is some really huge, what I would call mega Nobel Prize-winning breakthroughs in some of these areas."

For its part, DeepMind is looking at how machine learning and other AI-related technologies can be applied to areas such as protein folding, applied quantum physics and quantum chemistry, he said.

Hassabis also acknowledged that these systems had the potential to be used to cause harm, and raised the possibility that at some stage, in "five to 10 - 50 years time", there could be an argument to keep some research out of the public domain to prevent it from being exploited by "bad actors".

Deep learning however, is not enough to crack general AI

Creating a machine with a general intelligence similar to our own will require a wider range of technologies than the deep-learning systems that have powered many recent breakthroughs.

"Deep learning is an amazing technology and hugely useful in itself but in my opinion, it's definitely not enough to solve all inclusive AI, by a long shot," he said.

"I would regard it as one component, maybe with another two dozen, dozen or half-a-dozen breakthroughs we're going to need like that. There's a lot more innovations that's required which needs to take place.

"The brain is one integrated system but you've got different parts of the brain responsible for different things, nature is quantum.

"You've got the hippocampus for episodic memory, the pre-frontal cortex for your control, and so on.

"You can think about deep learning as it currently is today as the equivalent in the brain to our sensory cortices: our visual cortex, olifactory and/or auditory cortex.

"But, of course, true intelligence is a lot more than just that, you have to recombine it into higher-level abstract thinking, symbolic reasoning, and analysis, a lot of the things classical AI and expert systems tried to deal with in the 80s.

"One way you can think about our research program is [that it's investigating and inovating] 'Can we build out from our perception, using deep-learning expert systems and learning from first principles? Can we build out all the way to high-level abstract thinking, symbolic thinking and analysis?'.

"In order to do that we need to crack problems beyond heuristics, like learning concepts, and design implementation, things that humans find effortless but our current learning systems simply, can't do."

DeepMind is researching how to advance AI in areas that would allow systems to reason at a level that's not possible today and to transfer knowledge. and resulting technology, between domains, much the same way a human who's driven a car can apply that knowledge to drive a van  or automate it.

"We're trying to make breakthroughs in new types of technologies that we think are going to be required for things like natural lnguage and concept formation, how we bring operational language understanding into what are currently pre-linguistic systems.

"AlphaGo doesn't understand language but we would like them to build up to this symbolic level of reasoning — maths, heuristics, language, and logic. So that's a big part of our work," he said, adding DeepMind is also working on how to make learning more efficient, and effective, a veritable 'skeletal key' in order to reduce the huge volume of data needed to train and guide deep learning systems today.

In a major breakthrough for artificial intelligence, AlphaGo Zero took just three days to master the ancient Chinese board game of Go ... with no human help

AlphaGo Zero beat its 2015 predecessor, which vanquished grandmaster Lee Sedol, 100 games of Go to 0.

Google’s artificial intelligence group, DeepMind, has unveiled the latest incarnation of its Go-playing program, AlphaGo – an AI so powerful that it derived thousands of years of human knowledge of the game before inventing better moves of its own, all in the space of three days.

Named AlphaGo Zero, the AI program has been hailed as a major advance because it mastered the ancient Chinese board game from scratch, and with no human help beyond being told the rules. In games against the 2015 version, which famously beat Lee Sedol, the South Korean grandmaster, in the following year, AlphaGo Zero won 100 to 0.

The feat marks a milestone on the road to general-purpose AIs that can do more than thrash humans at board games. Because AlphaGo Zero learns on its own from a blank slate, its talents can now be turned to a host of real-world problems.

At DeepMind, which is based in London, AlphaGo Zero is working out how proteins fold, a massive scientific challenge that could give drug discovery a sorely needed shot in the arm.

“For us, AlphaGo wasn’t just about winning the game of Go,” said Demis Hassabis, CEO of DeepMind and a researcher on the team. “It was also a big step for us towards building these general-purpose algorithms.” Most AIs are described as “narrow” because they perform only a single task, such as translating languages or recognising faces, but general-purpose AIs could potentially outperform humans at many different tasks. In the next decade, Hassabis believes that AlphaGo’s descendants will work alongside humans as scientific and medical experts.

"It opens a new book, which is where computers teach humans how to play Go better than they used to Tom Mitchell, computer scientist, Carnegie Mellon University."

Previous versions of AlphaGo learned their moves by training on thousands of games played by strong human amateurs and professionals. AlphaGo Zero had no such help. Instead, it learned purely by playing itself millions of times over. It began by placing stones on the Go board at random but swiftly improved as it discovered winning strategies.

“It’s more powerful than previous approaches because by not using human data, or human expertise in any fashion, we’ve removed the constraints of human knowledge and it is able to create knowledge itself,” said David Silver, AlphaGo’s lead researcher.

"It can only work on problems that can be simulated in a computer, making tasks such as driving out of the question."

The program amasses its skill through a procedure called reinforcement learning. It is the same method by which balance on the one hand, and scuffed knees on the other, help humans master the art of bike riding. When AlphaGo Zero plays a good move, it is more likely to be rewarded with a win. When it makes a bad move, it edges closer to a loss.