Here are the slides from my York Festival of Ideas keynote yesterday, which introduced the festival focus day Artificial Intelligence: Promises and Perils.
I start the keynote with Alan Turing's famous question: Can a Machine Think? and explain that thinking is not just the conscious reflection of Rodin's Thinker but also the largely unconscious thinking required to make a pot of tea. I note that at the dawn of AI 60 years ago we believed the former kind of thinking would be really difficult to emulate artificially and the latter easy. In fact it has turned out to be the other way round: we've had computers that can expertly play chess for over 20 years, but we can't yet build a robot that could go into your kitchen and make you a cup of tea (see also the Wozniak coffee test).
In slides 5 and 6 I suggest that we all assume a cat is smarter than a crocodile, which is smarter than a cockroach, on a linear scale of intelligence from not very intelligent to human intelligence. I ask where would a robot vacuum cleaner be on this scale and propose that such a robot is about as smart as an e-coli (single celled organism). I then illustrate the difficulty of placing the Actroid robot on this scale because, although it may look convincingly human (from a distance), in reality the robot is not very much smarter than a washing machine (and I hint that this is an ethical problem).
In slide 7 I show how apparently intelligent behaviour doesn't require a brain, with the Solarbot. This robot is an example of a Braitenberg machine. It has two solar panels (which look a bit like wings) acting as both sensors and power sources; the left hand panel is connected to the right hand wheel and vice versa. These direct connections mean that Solarbot can move towards the light and even navigate its way through obstacles, thus showing that intelligent behaviour is an emergent property of the interactions between body and environment.
In slide 8 I ask the question: What is the most advanced AI in the world today? (A question I am often asked.) Is it for example David Hanson's robot Sophia (which some press reports have claimed as the world's most advanced)? I argue it is not, since it is a chatbot AI - with a limited conversational repertoire - with a physical body (imagine Alexa with a humanoid head). Is it the DeepMind AI AlphaGo which famously beat the world's best Go player in 2016? Although very impressive I again argue no since AlphaGo cannot do anything other than play Go. Instead I suggest that everyday Google might well be the world's most advanced AI (on this I agree with my friend Joanna Bryson). Google is in effect a librarian able to find a book from an immense library for you - on the basis of your ill formed query - more or less instantly! (And this librarian is poly lingual too.)
In slides 9 I make the point that intelligence is not one thing that animals, robots and AIs have more or less of (in other words the linear scale shown on slides 5 and 6 is wrong). Then in slides 10 - 13 I propose four distinct categories of intelligence: morphological, swarm, individual and social intelligence. I suggest in slides 14 - 16 that if we express these as four axes of a graph then we can (very approximately) compare the intelligence of different organisms, including humans. In slide 17 I show some robots and argue that this graph shows why robots are so unintelligent; it is because robots generally only have two of the four kinds of intelligence whereas animals typically have three or sometimes all four. A detailed account of these ideas can be found in my paper How intelligent is your intelligent robot?
In the next segment, slides 18-20 I ask: how do we make Artificial General Intelligence (AGI)? I suggest that the key difference between current narrow AI and AGI is the ability - which comes very naturally to humans - to generalise knowledge learned in one context to a completely different context. This I think is the basis of human creativity. Using Data from Star Trek the next generation as a SF example of an AGI with human-equivalent intelligence as what we might be aiming for in the quest for AGI I explain that there are 3 approaches to getting there: by design, using artificial evolution or by reverse engineering animals. I offer the opinion that the gap between where we are now and Data like AGI is about the same as the gap between current space craft engine technology and warp drive technology. In other words not any time soon.
In the fourth segment of the talk (slides 21-24) I give a very brief account of evolutionary robotics - a method for breeding robots in much the same way farmers have artificially selected new varieties of plants and animals for thousands of years. I illustrate this with the wonderful Golem project which, for the first time, evolved simple creatures then 3D printed the most successful ones. I then introduce our new four year EPSRC funded project Autonomous Robot Evolution: from cradle to grave. In a radical new approach we aim to co-evolve robot bodies and brains in real-time and real-space. Using techniques from 3D printing new robot designs will literally be printed, before being trained in a nursery, then fitness tested in a target environment. With this approach we hope to be able to evolve robots for extreme environments, however because the energy costs are so high I do not think evolution is a route to truly thinking machines.
In the final segment (slides 25-35) I return to the approach of trying to design rather than evolve thinking machines. I introduce the idea of embedding a simulation of a robot in that robot, so that it has the ability to internally model itself. The first example I give is the amazing anthropomimetic robot invented by my old friend Owen Holland, called ECCEROBOT. Eccerobot is able to learn how to control it's own very complicated and hard-to-control body by trying out possible movement sequences in its internal model (Owen calls this a 'functional imagination'). I then outline our own work to use the same principle - a simulation based internal model - to demonstrate simple ethical behaviours, first with e-puck robots, then with NAO robots. These experiments are described in detail here and here. I suggest that these robots - with their ability to model and predict the consequences of their own and others' actions, in other words anticipate the future - may represent the first small steps toward thinking machines.
Related blog posts:
60 years of asking can robot think?
How intelligent are intelligent robots?
Robot bodies and how to evolve them
Showing posts with label Alan Turing. Show all posts
Showing posts with label Alan Turing. Show all posts
Sunday, June 17, 2018
Tuesday, June 19, 2012
60 years of asking Can Robots Think?
Last week at the Cheltenham Science Festival we debated the question Can robots think? It's not a new question. Here, for instance, is a wonderful interview from 1961 on the very same question. So, the question hasn't changed. Has the answer?
Well it's interesting to note that I, and fellow panelists Murray Shanahan and Lilian Edwards, were much more cautious last week in Cheltenham, than our illustrious predecessors. Both on the question can present day robots think: answer No. And will robots (or computers) be able to think any time soon: answer, again No.
The obvious conclusion is that 50 years of Artificial Intelligence research has failed. But I think that isn't true. AI has delivered some remarkable advances, like natural speech recognition and synthesis, chess programs, conversational AI (chatbots) and lots of 'behind the scenes' AI (of the sort that figures out your preferences and annoyingly presents personalised advertising on web pages). But what is undoubtedly true was Weisner, Selfridge and Shannon were being very optimistic (after all AI had only been conceived a decade earlier by Alan Turing). Whereas today, perhaps chastened and humbled, most researchers take a much more cautious approach to these kinds of claims.
But I think there are more complex reasons.
One is that we now take a much stricter view of what we mean by 'thinking'. As I explained last week in Cheltenham, it's relatively easy to make a robot that behaves as if it is thinking (and, I'm afraid, also relatively easy to figure out that the robot is not really thinking). So, it seems that a simulation of thinking is not good enough*. We're now looking for the real thing.
That leads to the second reason. It seems that we are not much closer to understanding how cognition in animals and humans works than we were 60 years ago. Actually, that's unfair. There have been tremendous advances in cognitive neuroscience but - as far as I can tell - those advances have brought us little closer to being able to engineer thinking in artificial systems. That's because it's a very very hard problem. And, to add further complication, it remains a philosophical as well as a scientific problem.
In Cheltenham Murray Shanahan brilliantly explained that there are three approaches to solving the problem. The first is what we might call a behaviourist approach: don't worry about what thinking is, just try and make a machine that behaves as if it's thinking. The second is the computational modelling approach: try and construct, from first principles, a theoretical model of how thinking should work, then implement that. And third, the emulate real brains approach: scan real brains in sufficiently fine detail and then build a high fidelity model with all the same connections, etc, in a very large computer. In principle, the second and third approaches should produce real thinking.
What I find particularly interesting is that the first of these 3 approaches is more or less the one adopted by the conversational AI programs entered for the Loebner prize competition. Running annually since 1992, the Loebner prize is based on the test for determining if machines can think, famously suggested by Alan Turing in 1950 and now known as the Turing test. To paraphrase: if a human cannot tell whether she is conversing with a machine or another human - and it's a machine - then that machine must be judged to be thinking. I strongly recommend reading Turing's beautifully argued 1950 paper.
No chatbot has yet claimed the $100,000 first prize, but I suspect that we will see a winner sooner or later (personally I think it's a shame Apple hasn't entered Siri). But the naysayers will still argue that the winner is not really thinking (despite passing the Turing test). And I think I would agree with them. My view is that a conversational AI program, however convincing, remains an example of 'narrow' AI. Like a chess program a chatbot is designed to do just one kind of thinking: textual conversation. I believe that true artificial thinking ('general' AI) requires a body.
And hence a new kind of Turing test: for an embodied AI, AKA robot.
And this brings me back to Murray's 3 approaches. My view is that the 3rd approach 'emulate real brains' is at best utterly impractical because it would mean emulating the whole organism (of course, in any event, your brain isn't just the 1300 or so grammes of meat in your head, it's the whole of your nervous system). And, ultimately, I think that the 1st (behaviourist - which is kind of approaching the problem from the outside in) and 2nd (computational modelling - which is an inside out approach) will converge.
So when, eventually, the first thinking robot passes the (as yet undefined) Turing test for robots I don't think it will matter very much whether the robot is behaving as if it's thinking - or actually is, for reasons of its internal architecture, thinking. Like Turing, I think it's the test that matters.
*Personally I think that a good enough behavioural simulation will be just fine. After all, an aeroplane is - in some sense - a simulation of avian flight but no one would doubt that it is also actually flying.
Well it's interesting to note that I, and fellow panelists Murray Shanahan and Lilian Edwards, were much more cautious last week in Cheltenham, than our illustrious predecessors. Both on the question can present day robots think: answer No. And will robots (or computers) be able to think any time soon: answer, again No.
The obvious conclusion is that 50 years of Artificial Intelligence research has failed. But I think that isn't true. AI has delivered some remarkable advances, like natural speech recognition and synthesis, chess programs, conversational AI (chatbots) and lots of 'behind the scenes' AI (of the sort that figures out your preferences and annoyingly presents personalised advertising on web pages). But what is undoubtedly true was Weisner, Selfridge and Shannon were being very optimistic (after all AI had only been conceived a decade earlier by Alan Turing). Whereas today, perhaps chastened and humbled, most researchers take a much more cautious approach to these kinds of claims.
But I think there are more complex reasons.
One is that we now take a much stricter view of what we mean by 'thinking'. As I explained last week in Cheltenham, it's relatively easy to make a robot that behaves as if it is thinking (and, I'm afraid, also relatively easy to figure out that the robot is not really thinking). So, it seems that a simulation of thinking is not good enough*. We're now looking for the real thing.
That leads to the second reason. It seems that we are not much closer to understanding how cognition in animals and humans works than we were 60 years ago. Actually, that's unfair. There have been tremendous advances in cognitive neuroscience but - as far as I can tell - those advances have brought us little closer to being able to engineer thinking in artificial systems. That's because it's a very very hard problem. And, to add further complication, it remains a philosophical as well as a scientific problem.
In Cheltenham Murray Shanahan brilliantly explained that there are three approaches to solving the problem. The first is what we might call a behaviourist approach: don't worry about what thinking is, just try and make a machine that behaves as if it's thinking. The second is the computational modelling approach: try and construct, from first principles, a theoretical model of how thinking should work, then implement that. And third, the emulate real brains approach: scan real brains in sufficiently fine detail and then build a high fidelity model with all the same connections, etc, in a very large computer. In principle, the second and third approaches should produce real thinking.
What I find particularly interesting is that the first of these 3 approaches is more or less the one adopted by the conversational AI programs entered for the Loebner prize competition. Running annually since 1992, the Loebner prize is based on the test for determining if machines can think, famously suggested by Alan Turing in 1950 and now known as the Turing test. To paraphrase: if a human cannot tell whether she is conversing with a machine or another human - and it's a machine - then that machine must be judged to be thinking. I strongly recommend reading Turing's beautifully argued 1950 paper.
No chatbot has yet claimed the $100,000 first prize, but I suspect that we will see a winner sooner or later (personally I think it's a shame Apple hasn't entered Siri). But the naysayers will still argue that the winner is not really thinking (despite passing the Turing test). And I think I would agree with them. My view is that a conversational AI program, however convincing, remains an example of 'narrow' AI. Like a chess program a chatbot is designed to do just one kind of thinking: textual conversation. I believe that true artificial thinking ('general' AI) requires a body.
And hence a new kind of Turing test: for an embodied AI, AKA robot.
And this brings me back to Murray's 3 approaches. My view is that the 3rd approach 'emulate real brains' is at best utterly impractical because it would mean emulating the whole organism (of course, in any event, your brain isn't just the 1300 or so grammes of meat in your head, it's the whole of your nervous system). And, ultimately, I think that the 1st (behaviourist - which is kind of approaching the problem from the outside in) and 2nd (computational modelling - which is an inside out approach) will converge.
So when, eventually, the first thinking robot passes the (as yet undefined) Turing test for robots I don't think it will matter very much whether the robot is behaving as if it's thinking - or actually is, for reasons of its internal architecture, thinking. Like Turing, I think it's the test that matters.
*Personally I think that a good enough behavioural simulation will be just fine. After all, an aeroplane is - in some sense - a simulation of avian flight but no one would doubt that it is also actually flying.
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