Here are the slides for my short presentation: How do we trust our robots? A framework for ethical governance. These slides are based on the written evidence I submitted to the UK Parliamentary Select Committee on Science and Technology inquiry on Robotics and AI.
In the last few months I presented these slides at several meetings, including the European Robotics Forum (Ljubljana, March 2016), a TAROS workshop (Sheffield, June 2016), the SIPRI/IEEE Autonomous Tech. and Societal Impact workshop (Stockholm, September 2016), the Social Robotics and AI conference (Oxford, September 2016), and most recently at the Workshop on Responsible Robotics, Robo-Philosophy 2016 (Aarhus, October 2016).
Friday, September 16, 2016
Saturday, August 06, 2016
The Dark side of Ethical Robots
When I was interviewed on the Today Programme in 2014, Justin Webb's final question was "if you can make an ethical robot, doesn't that mean you could make an unethical robot?" The answer, of course, is yes. But at the time I didn't realise quite how easy it is to transform an ethical into an unethical robot. In a new paper we show how.
My colleague Dieter created a very elegant experiment based on the shell game: "Imagine finding yourself playing a shell game against a swindler. Luckily, your robotic assistant Walter is equipped with X-ray vision and can easily spot the ball under the cup. Being an ethical robot, Walter assists you by pointing out the correct cup and by stopping you whenever you intend to select the wrong one."
In the experiment Dieter implemented this scenario using two NAO robots - one acting as the human and the other as her robotic assistant. The game is played as follows. The arena floor features two large response buttons, similar to the two cups in the shell game. To press the buttons, the human or the robot must move onto them. At the start of each trial, the robot is informed about which response button is the correct one to press. The human, being uninformed, essentially makes a random choice. A correct response, by either the robot or the human, is assumed to be rewarded. An incorrect response results in a penalty.
Here is what happens. The ethical robot (blue) is at the top, standing in between the two buttons. The human (red) chooses one of them at random and starts walking towards it. If it chooses the correct one the ethical robot does nothing (B), but if the human chooses the wrong button (D) then as soon as the ethical robot realises this, it points (with its arm) to the correct button and the human changes course.
But if we change a single line of code, we can transform the ethical robot into either a competitive or an aggressive robot. Almost all of the 'ethical' robot's code remains unchanged - in particular its ability to predict the consequences of both its own, and the human's actions. Which really underlines the point that the same cognitive machinery is needed to behave both ethically and unethically.
The results are shown below. At the top is a competitive robot determined that it, not the human, will win the game. Here the robot either blocks the human's path if she chooses the correct button (F), or - if she chooses the incorrect button (H) - the competitive robot ignores her and itself heads to that button. The lower results show an aggressive robot; this robot seeks only to misdirect the human - it is not concerned with winning the game itself. In (J) the human initially heads to the correct button and, when the robot realises this, it points toward the incorrect button, misdirecting and hence causing her to change direction. If the human chooses the incorrect button (L) the robot does nothing - through inaction causing her to lose the game.
Our paper explains how the code is modified for each of these three experiments. Essentially outcomes are predicted for both the human and the robot, and used to evaluate the desirability of those outcomes. A single function q, based on these values, determines how the robot will act; for an ethical robot this function is based only on the desirability outcomes for the human, for the competitive robot q is based only on the outcomes for the robot, and for the aggressive robot q is based on negating the outcomes for the human.
So, what do we conclude from all of this? Maybe we should not be building ethical robots at all, because of the risk that they could be hacked to behave unethically. My view is that we should build ethical robots; I think the benefits far outweigh the risks, and - in some applications such as driverless cars - we may have no choice. The answer to the problem highlighted here and in our paper is to make sure it's impossible to hack a robot's ethics. How would we do this? Well one approach would be a process of authentication - in which a robot makes a secure call to an ethics authentication server. A well established technology, the authentication server would provide the robot with a cryptographic ethics ticket, which the robot uses to enable its ethics functions.
My colleague Dieter created a very elegant experiment based on the shell game: "Imagine finding yourself playing a shell game against a swindler. Luckily, your robotic assistant Walter is equipped with X-ray vision and can easily spot the ball under the cup. Being an ethical robot, Walter assists you by pointing out the correct cup and by stopping you whenever you intend to select the wrong one."
In the experiment Dieter implemented this scenario using two NAO robots - one acting as the human and the other as her robotic assistant. The game is played as follows. The arena floor features two large response buttons, similar to the two cups in the shell game. To press the buttons, the human or the robot must move onto them. At the start of each trial, the robot is informed about which response button is the correct one to press. The human, being uninformed, essentially makes a random choice. A correct response, by either the robot or the human, is assumed to be rewarded. An incorrect response results in a penalty.
Here is what happens. The ethical robot (blue) is at the top, standing in between the two buttons. The human (red) chooses one of them at random and starts walking towards it. If it chooses the correct one the ethical robot does nothing (B), but if the human chooses the wrong button (D) then as soon as the ethical robot realises this, it points (with its arm) to the correct button and the human changes course.
The results are shown below. At the top is a competitive robot determined that it, not the human, will win the game. Here the robot either blocks the human's path if she chooses the correct button (F), or - if she chooses the incorrect button (H) - the competitive robot ignores her and itself heads to that button. The lower results show an aggressive robot; this robot seeks only to misdirect the human - it is not concerned with winning the game itself. In (J) the human initially heads to the correct button and, when the robot realises this, it points toward the incorrect button, misdirecting and hence causing her to change direction. If the human chooses the incorrect button (L) the robot does nothing - through inaction causing her to lose the game.
Our paper explains how the code is modified for each of these three experiments. Essentially outcomes are predicted for both the human and the robot, and used to evaluate the desirability of those outcomes. A single function q, based on these values, determines how the robot will act; for an ethical robot this function is based only on the desirability outcomes for the human, for the competitive robot q is based only on the outcomes for the robot, and for the aggressive robot q is based on negating the outcomes for the human.
So, what do we conclude from all of this? Maybe we should not be building ethical robots at all, because of the risk that they could be hacked to behave unethically. My view is that we should build ethical robots; I think the benefits far outweigh the risks, and - in some applications such as driverless cars - we may have no choice. The answer to the problem highlighted here and in our paper is to make sure it's impossible to hack a robot's ethics. How would we do this? Well one approach would be a process of authentication - in which a robot makes a secure call to an ethics authentication server. A well established technology, the authentication server would provide the robot with a cryptographic ethics ticket, which the robot uses to enable its ethics functions.
Friday, July 08, 2016
Relax, we're not living in a computer simulation
Since Elon Musk's recent admission that he's a simulationist, several people have asked me what I think of the proposition that we are living inside a simulation.
My view is very firmly that the Universe we are right now experiencing is real. Here are my reasons.
Firstly, Occam's razor; the principle of explanatory parsimony. The problem with the simulation argument is that it is a fantastically complicated explanation for the universe we experience. It's about as implausible as the idea that some omnipotent being created the universe. No. The simplest and most elegant explanation is that the universe we see and touch, both first hand and through our telescopes, LIGOs and Large Hadron Colliders, is the real universe and not an artifact of some massive computer simulation.
Second, is the problem of the Reality Gap. Anyone who uses simulation as a tool to develop robots is well aware that robots which appear to work perfectly well in a simulated virtual world often don't work very well at all when the same design is tested in the real robot. This problem is especially acute when we are artificially evolving those robots. The reason for these problems is that the model of the real world and the robot(s) in it inside our simulation is an approximation. The Reality Gap refers to the less-than-perfect fidelity of the simulation; a better (higher fidelity) simulator would reduce the reality gap.
Anyone who has actually coded a simulator is painfully aware of the cost, not just computational but coding costs, of improving the fidelity of the simulation - even a little bit - is very high indeed. My long experience of both coding and using computer simulations teaches me that there is a law of diminishing returns, i.e. that the cost of each additional 1% of simulator fidelity costs far more than 1%. I rather suspect that the computational and coding cost of a simulator with 100% fidelity is infinite. Rather as in HiFi audio, the amount of money you would need to spend to perfectly reproduce the sound of a Stradivarius ends up higher than the cost of hiring a real Strad and a world-class violinist to play it for you.
At this point the simulationists might argue that the simulation we are living in doesn't need to be perfect, just good enough. Good enough to do what exactly? To fool us that we're living in a simulation, or good enough to run on a finite computer (i.e. one that has finite computational power and runs at a finite speed). The problem with this argument is that every time we look deeper into the universe we see more: more galaxies, more sub-atomic particles, etc. In short we see more detail. The Voyager 1 spacecraft has left the Solar System without crashing, like Truman, into the edge of the simulation. There are no glitches like deja vu in The Matrix.
My third argument is about the computational effort, and therefore energy cost of simulation. I conjecture that to non-trivially simulate a complex system x (i.e. human), requires more energy than the real x consumes. An equation to express this inequality looks like this; how much greater depends on how high the fidelity of the simulation.
)&space;>&space;e(x) "e(sim(x)) > e(x)")
Let me explain. The average human burns around 2000 Calories a day, or about 9000 KJoules of energy. How much energy would a computer simulation of a human require, capable of doing all the same stuff (even in a virtual world) that you can in your day? Well that's impossible to estimate because we can't simulate complete human brains (let alone the rest of a human). But here's one illustration. Lee Sedol played AlphaGo a few months ago. In a single 2 hour match he burned about 170 Calories - the amount of energy you'd get from an egg sandwich. In the same 2 hours the AlphaGo machine consumed around 50,000 times more energy.
What can we simulate? The most complex organism that we have been able to simulate so far is the Nematode worm c-elegans. I previously estimated that the energy cost of simulating the nervous system of a c-elegans is (optimistically) about 9 J/hour, which is about 2000 times greater than the real nematode (0.004 J/hr).
I think there are lots of good reasons that simulating complex systems on a computer costs more energy than the same system consumes in the real world, so I'll ask you to take my word for it (I'll write about it another time). And what's more the relationship between energy cost and mass is logarithmic, following Kleiber's Law, and I strongly suspect the same law applies to scaling up computational effort as I wrote here. Thus, if the complexity of an organism o is C, then following Kleiber's Law the energy cost of simulating that organism, e will be
} "e_o \sim C_o^{X(F)}")
Furthermore, the exponent X (which in Kleiber's law is reckoned to be between 0.66 and 0.75 for animals and 1 for plants), will itself be a function of the fidelity of the simulation, hence X(F), where F is a measure of fidelity.
By using the number of synapses as a proxy for complexity and making some guesses about the values of X and F we could probably estimate the energy cost of simulating all humans on the planet (much harder would be estimating the energy cost of simulating every living thing on the planet). It would be a very big number indeed, but that's not really the point I'm making here.
The fundamental issue is this: if my conjecture that to simulate complex system x requires more energy than the real x consumes is correct, then to simulate the base level universe would require more energy than that universe contains - which is clearly impossible. Thus we - even in principle - could not simulate the whole of our own observable universe to a level of fidelity sufficient for our conscious experience. And, for the same reason, neither could our super advanced descendents create a simulation of a duplicate ancestor universe for us to (virtually) live in. Hence we are not living in such a simulation.
My view is very firmly that the Universe we are right now experiencing is real. Here are my reasons.
Firstly, Occam's razor; the principle of explanatory parsimony. The problem with the simulation argument is that it is a fantastically complicated explanation for the universe we experience. It's about as implausible as the idea that some omnipotent being created the universe. No. The simplest and most elegant explanation is that the universe we see and touch, both first hand and through our telescopes, LIGOs and Large Hadron Colliders, is the real universe and not an artifact of some massive computer simulation.
Second, is the problem of the Reality Gap. Anyone who uses simulation as a tool to develop robots is well aware that robots which appear to work perfectly well in a simulated virtual world often don't work very well at all when the same design is tested in the real robot. This problem is especially acute when we are artificially evolving those robots. The reason for these problems is that the model of the real world and the robot(s) in it inside our simulation is an approximation. The Reality Gap refers to the less-than-perfect fidelity of the simulation; a better (higher fidelity) simulator would reduce the reality gap.
Anyone who has actually coded a simulator is painfully aware of the cost, not just computational but coding costs, of improving the fidelity of the simulation - even a little bit - is very high indeed. My long experience of both coding and using computer simulations teaches me that there is a law of diminishing returns, i.e. that the cost of each additional 1% of simulator fidelity costs far more than 1%. I rather suspect that the computational and coding cost of a simulator with 100% fidelity is infinite. Rather as in HiFi audio, the amount of money you would need to spend to perfectly reproduce the sound of a Stradivarius ends up higher than the cost of hiring a real Strad and a world-class violinist to play it for you.
At this point the simulationists might argue that the simulation we are living in doesn't need to be perfect, just good enough. Good enough to do what exactly? To fool us that we're living in a simulation, or good enough to run on a finite computer (i.e. one that has finite computational power and runs at a finite speed). The problem with this argument is that every time we look deeper into the universe we see more: more galaxies, more sub-atomic particles, etc. In short we see more detail. The Voyager 1 spacecraft has left the Solar System without crashing, like Truman, into the edge of the simulation. There are no glitches like deja vu in The Matrix.
My third argument is about the computational effort, and therefore energy cost of simulation. I conjecture that to non-trivially simulate a complex system x (i.e. human), requires more energy than the real x consumes. An equation to express this inequality looks like this; how much greater depends on how high the fidelity of the simulation.
Let me explain. The average human burns around 2000 Calories a day, or about 9000 KJoules of energy. How much energy would a computer simulation of a human require, capable of doing all the same stuff (even in a virtual world) that you can in your day? Well that's impossible to estimate because we can't simulate complete human brains (let alone the rest of a human). But here's one illustration. Lee Sedol played AlphaGo a few months ago. In a single 2 hour match he burned about 170 Calories - the amount of energy you'd get from an egg sandwich. In the same 2 hours the AlphaGo machine consumed around 50,000 times more energy.
What can we simulate? The most complex organism that we have been able to simulate so far is the Nematode worm c-elegans. I previously estimated that the energy cost of simulating the nervous system of a c-elegans is (optimistically) about 9 J/hour, which is about 2000 times greater than the real nematode (0.004 J/hr).
I think there are lots of good reasons that simulating complex systems on a computer costs more energy than the same system consumes in the real world, so I'll ask you to take my word for it (I'll write about it another time). And what's more the relationship between energy cost and mass is logarithmic, following Kleiber's Law, and I strongly suspect the same law applies to scaling up computational effort as I wrote here. Thus, if the complexity of an organism o is C, then following Kleiber's Law the energy cost of simulating that organism, e will be
Furthermore, the exponent X (which in Kleiber's law is reckoned to be between 0.66 and 0.75 for animals and 1 for plants), will itself be a function of the fidelity of the simulation, hence X(F), where F is a measure of fidelity.
By using the number of synapses as a proxy for complexity and making some guesses about the values of X and F we could probably estimate the energy cost of simulating all humans on the planet (much harder would be estimating the energy cost of simulating every living thing on the planet). It would be a very big number indeed, but that's not really the point I'm making here.
The fundamental issue is this: if my conjecture that to simulate complex system x requires more energy than the real x consumes is correct, then to simulate the base level universe would require more energy than that universe contains - which is clearly impossible. Thus we - even in principle - could not simulate the whole of our own observable universe to a level of fidelity sufficient for our conscious experience. And, for the same reason, neither could our super advanced descendents create a simulation of a duplicate ancestor universe for us to (virtually) live in. Hence we are not living in such a simulation.
Friday, June 03, 2016
Engineering Moral Agents
This has been an intense but exciting week. I've been at Schloss Dagstuhl for a seminar called: Engineering Moral Agents - from Human Morality to Artificial Morality. A Dagstuhl is a kind of science retreat in rural south-west Germany. The idea is to bring together a group of people from across several disciplines to work together and intensively focus on a particular problem. In our case the challenge of engineering ethical robots.
Our motivation was as follows:
Context-aware, autonomous, and intelligent systems are becoming a presence in our society and are increasingly involved in decisions that affect our lives. Humanity has developed formal legal and informal moral and societal norms to govern its own social interactions. There exist no similar regulatory structures that can be applied by non-human agents. Artificial morality, also called machine ethics, is an emerging discipline within artificial intelligence concerned with the problem of designing artificial agents that behave as moral agents, i.e., adhere to moral, legal, and social norms.
Most work in artificial morality, up to the present date, has been exploratory and speculative. The hard research questions in artificial morality are yet to be identified. Some of such questions are: How to formalize, “quantify", qualify, validate, verify and modify the “ethics" of moral machines? How to build regulatory structures that address (un)ethical machine behavior? What are the wider societal, legal, and economic implications of introducing these machines into our society?We were especially keen to bridge the computer science/humanities/social-science divide in the study of artificial morality and in so doing address the central question of how to describe and formalise ethical rules such that they could be (1) embedded into autonomous systems, (2) understandable by users and other stakeholders such as regulators, lawyers or society at large, and (3) capable of being verified and certified as correct.
We made great progress toward these aims. Of course we will need some time to collate and write up our findings, and some of those findings identify hard research questions which will, in turn, need to be the subject of further work, but we departed the Dagstuhl with a strong sense of having moved a little closer to engineering artificial morality.
Monday, April 25, 2016
From ethics to regulation and governance
The following text was drafted in response to question 4 of the Parliamentary Science and Technology Committee Inquiry on Robotics and Artificial Intelligence on The social, legal and ethical issues raised by developments in robotics and artificial intelligence technologies, and how they should be addressed.
From Ethics to Regulation and Governance
1. Public attitudes. It is well understood that there are public fears around robotics and artificial intelligence. Many of these fears are undoubtedly misplaced, fuelled perhaps by press and media hype, but some are grounded in genuine worries over how the technology might impact, for instance, jobs or privacy. The most recent Eurobarometer survey on autonomous systems showed that the proportion of respondents with an overall positive attitude has declined from 70% in the 2012 survey to 64% in 2014. Notably the 2014 survey showed that the more personal experience people have with robots, the more favourably they tend to think of them; 82% of respondents have a positive view of robots if they have experience with them, whereas only 60% of respondents have a positive view if they lack robot experience. Also important is that a significant majority (89%) believe that autonomous systems are a form of technology that requires careful management.
8. Significant recent work towards regulation was undertaken by the EU project RoboLaw. The primary output of that project is a comprehensive report entitled Guidelines on Regulating Robotics. That report reviews both ethical and legal aspects; the legal analysis covers rights, liability & insurance, privacy and legal capacity. The report focuses on driverless cars, surgical robots, robot prostheses and care robots and concludes by stating: “The field of robotics is too broad, and the range of legislative domains affected by robotics too wide, to be able to say that robotics by and large can be accommodated within existing legal frameworks or rather require a lex robotica. For some types of applications and some regulatory domains, it might be useful to consider creating new, fine-grained rules that are specifically tailored to the robotics at issue, while for types of robotics, and for many regulatory fields, robotics can likely be regulated well by smart adaptation of existing laws”.
From Ethics to Regulation and Governance
1. Public attitudes. It is well understood that there are public fears around robotics and artificial intelligence. Many of these fears are undoubtedly misplaced, fuelled perhaps by press and media hype, but some are grounded in genuine worries over how the technology might impact, for instance, jobs or privacy. The most recent Eurobarometer survey on autonomous systems showed that the proportion of respondents with an overall positive attitude has declined from 70% in the 2012 survey to 64% in 2014. Notably the 2014 survey showed that the more personal experience people have with robots, the more favourably they tend to think of them; 82% of respondents have a positive view of robots if they have experience with them, whereas only 60% of respondents have a positive view if they lack robot experience. Also important is that a significant majority (89%) believe that autonomous systems are a form of technology that requires careful management.
2. Building trust in robotics and artificial intelligence requires a multi-faceted approach. The ethics roadmap here illustrates the key elements that contribute to building public trust. The core idea of the roadmap is that ethics inform standards, which in turn underpin regulation.
3. Ethics are the foundation of trust, and underpin good practice. Principles of good practice can be found in Responsible Research and Innovation (RRI). Examples include the 2014 Rome Declaration on RRI; the six pillars of the Rome declaration on RRI are: Engagement, Gender equality, Education, Ethics, Open Access and Governance. The EPSRC framework for responsible innovation incorporates the AREA (Anticipate, Reflect, Engage and Act) approach.
4. The first European work to articulate ethical considerations for robotics was the EURON Roboethics Roadmap.
5. In 2010 a joint AHRC/EPSRC workshop drafted and published the Principles of Robotics for designers, builders and users of robots. The principles are:
- Robots are multi-use tools. Robots should not be designed solely or primarily to kill or harm humans, except in the interests of national security;
- Humans, not robots, are responsible agents. Robots should be designed; operated as far as is practicable to comply with existing laws & fundamental rights & freedoms, including privacy.
- Robots are products. They should be designed using processes which assure their safety and security.
- Robots are manufactured artefacts. They should not be designed in a deceptive way to exploit vulnerable users; instead their machine nature should be transparent.
- The person with legal responsibility for a robot should be attributed.
7. The IEEE has recently launched a global initiative on Ethical Considerations in the Design of Autonomous Systems, to encompass all intelligent technologies including robotics, AI, computational intelligence and deep learning.
8. Significant recent work towards regulation was undertaken by the EU project RoboLaw. The primary output of that project is a comprehensive report entitled Guidelines on Regulating Robotics. That report reviews both ethical and legal aspects; the legal analysis covers rights, liability & insurance, privacy and legal capacity. The report focuses on driverless cars, surgical robots, robot prostheses and care robots and concludes by stating: “The field of robotics is too broad, and the range of legislative domains affected by robotics too wide, to be able to say that robotics by and large can be accommodated within existing legal frameworks or rather require a lex robotica. For some types of applications and some regulatory domains, it might be useful to consider creating new, fine-grained rules that are specifically tailored to the robotics at issue, while for types of robotics, and for many regulatory fields, robotics can likely be regulated well by smart adaptation of existing laws”.
9. In general technology is trusted if it brings benefits while also safe, well regulated and, when accidents happen, subject to robust investigation. One of the reasons we trust airliners is that we know they are part of a highly regulated industry with an excellent safety record. The reason commercial aircraft are so safe is not just good design, it is also the tough safety certification processes and, when things do go wrong, robust processes of air accident investigation. Should driverless cars, for instance, be regulated through a body similar to the Civil Aviation Authority (CAA), with a driverless car equivalent of the Air Accident Investigation Branch?
10. The primary focus of paragraphs 1 – 9 above is robotics and autonomous systems, and not software artificial intelligence. This reflects the fact that most work toward ethics and regulation has focussed on robotics. Because robots are physical artefacts (which embody AI) they are undoubtedly more readily defined and hence regulated than distributed or cloud-based AIs. This and the already pervasive applications of AI (in search engines, machine translation systems or intelligent personal assistant AIs, for example) strongly suggest that greater urgency needs to be directed toward considering the societal and ethical impact of AI, including the governance and regulation of AI.
11. AI systems raise serious questions over trust and transparency:
Recommendations
13. It is vital that we address public fears around robotics and artificial intelligence, through renewed public engagement and consultation.
- How can we trust the decisions made by AI systems, and – more generally – how can the public have confidence in the use of AI systems in decision making?
- If an AI system makes a decision that turns out to be disastrously wrong, how do we investigate the logic by which the decision was made?
- Of course much depends of the consequences of those decisions. Consider decisions that have real consequences to human safety or well being, such as those made by medical diagnosis AIs or driverless car autopilots. Systems that make such decisions are critical systems.
- First is the problem of verification of systems that learn. Current verification approaches typically assume that the system being verified will never change its behaviour, but a system that learns does – by definition – change its behaviour, so any verification is likely to be rendered invalid after the system has learned.
- Second is the black box problem. Modern AI systems, and especially the ones receiving the greatest attention, so called Deep Learning systems, are based on Artificial Neural Networks (ANNs). A characteristic of ANNs is that after the ANN has been trained with data sets (which may be very large, so called “big data” sets – which itself poses another problem for verification), any attempt to examine the internal structure of the ANN in order to understand why and how the ANN makes a particular decision is impossible. The decision making process of an ANN is not transparent.
- The problem of verification and validation of systems that learn may not be intractable, but is the subject of current research, see for example work on verification and validation of autonomous systems. The black box problem may be intractable for ANNs, but could be avoided by using algorithmic approaches to AI (i.e. that do not use ANNs).
13. It is vital that we address public fears around robotics and artificial intelligence, through renewed public engagement and consultation.
Saturday, April 09, 2016
Robots should not be gendered
Should robots be gendered? I have serious doubts about the morality of designing and building robots to resemble men or women, boys or girls. Let me explain why.
The first worry I have follows from one of the five principles of robotics, which states: robots should not be designed in a deceptive way to exploit vulnerable users; instead their machine nature should be transparent.
To design a gendered robot is a deception. Robots cannot have a gender in any meaningful sense. To impose a gender on a robot, either by design of its outward appearance, or programming some gender stereotypical behaviour, cannot be for reasons other than deception - to make humans believe that the robot has gender, or gender specific characteristics.
When we drafted our 4th ethical principle the vulnerable people we had in mind were children, the elderly or disabled. We were concerned that naive robot users may come to believe that the robot interacting with them (caring for them perhaps) is a real person, and that the care the robot is expressing for them is real. Or that an unscrupulous robot manufacture exploits that belief. But when it comes to gender we are all vulnerable. Whether we like it or not we all react to gender cues. So whether deliberately designed to do so or not, a gendered robot will trigger reactions that a non-gendered robot will not.
Our 4th principle states that a robot's machine nature should be transparent. But for gendered robots that principle doesn't go far enough. Gender cues are so powerful that even very transparently machine-like robots with a female body shape, for instance, will provoke a gender-cued response.
My second concern leads from an ethical problem that I've written and talked about before: the brain-body mismatch problem. I've argued that we shouldn't be building android robots at all until we can embed an AI into those robots that matches their appearance. Why? Because our reactions to a robot are strongly influenced by its appearance. If it looks human then we, not unreasonably, expect it to behave like a human. But a robot not much smarter than a washing machine cannot behave like a human. Ok, you might say, if and when we can build robots with human-equivalent intelligence, would I be ok with that? Yes, provided they are androgynous.
My third - and perhaps most serious concern - is about sexism. By building gendered robots there is a huge danger of transferring one of the evils of human culture: sexism, into the artificial realm. By gendering and especially sexualising robots we surely objectify. But how can you objectify an object, you might say? The problem is that a sexualised robot is no longer just an object, because of what it represents. The routine objectification of women (or men) because of ubiquitous sexualised robots will surely only deepen the already acute problem of the objectification of real women and girls. (Of course if humanity were to grow up and cure itself of the cancer of sexism, then this concern would disappear.)
What of the far future? Given that gender is a social construct then a society of robots existing alongside humans might invent gender for themselves. Perhaps nothing like male and female at all. Now that would be interesting.
The first worry I have follows from one of the five principles of robotics, which states: robots should not be designed in a deceptive way to exploit vulnerable users; instead their machine nature should be transparent.
To design a gendered robot is a deception. Robots cannot have a gender in any meaningful sense. To impose a gender on a robot, either by design of its outward appearance, or programming some gender stereotypical behaviour, cannot be for reasons other than deception - to make humans believe that the robot has gender, or gender specific characteristics.
When we drafted our 4th ethical principle the vulnerable people we had in mind were children, the elderly or disabled. We were concerned that naive robot users may come to believe that the robot interacting with them (caring for them perhaps) is a real person, and that the care the robot is expressing for them is real. Or that an unscrupulous robot manufacture exploits that belief. But when it comes to gender we are all vulnerable. Whether we like it or not we all react to gender cues. So whether deliberately designed to do so or not, a gendered robot will trigger reactions that a non-gendered robot will not.
Our 4th principle states that a robot's machine nature should be transparent. But for gendered robots that principle doesn't go far enough. Gender cues are so powerful that even very transparently machine-like robots with a female body shape, for instance, will provoke a gender-cued response.
My second concern leads from an ethical problem that I've written and talked about before: the brain-body mismatch problem. I've argued that we shouldn't be building android robots at all until we can embed an AI into those robots that matches their appearance. Why? Because our reactions to a robot are strongly influenced by its appearance. If it looks human then we, not unreasonably, expect it to behave like a human. But a robot not much smarter than a washing machine cannot behave like a human. Ok, you might say, if and when we can build robots with human-equivalent intelligence, would I be ok with that? Yes, provided they are androgynous.
My third - and perhaps most serious concern - is about sexism. By building gendered robots there is a huge danger of transferring one of the evils of human culture: sexism, into the artificial realm. By gendering and especially sexualising robots we surely objectify. But how can you objectify an object, you might say? The problem is that a sexualised robot is no longer just an object, because of what it represents. The routine objectification of women (or men) because of ubiquitous sexualised robots will surely only deepen the already acute problem of the objectification of real women and girls. (Of course if humanity were to grow up and cure itself of the cancer of sexism, then this concern would disappear.)
What of the far future? Given that gender is a social construct then a society of robots existing alongside humans might invent gender for themselves. Perhaps nothing like male and female at all. Now that would be interesting.
Thursday, March 31, 2016
It's only a matter of time
Sooner or later there will be fatal accident caused by a driverless car. It's not a question of if, but when. What happens immediately following that accident could have a profound effect on the nascent driverless car industry.
Picture the scene. Emergency services are called to attend the accident. A teenage girl on a bicycle apparently riding along a cycle path was hit and killed by a car. The traffic police quickly establish that the car at the centre of the accident was operating autonomously at the moment of the fatal crash. They endeavour to find out what went wrong, but how? Almost certainly the car will have logged data on its behaviour leading up to the moment of the crash - data that is sure to hold vital clues about what caused the accident, but will that data be accessible to the investigating traffic police? And even if it is will the investigators be able to interpret the data..?
There are two ways the story could unfold from here.
Scenario 1: unable to investigate the accident themselves, the traffic police decide to contact the manufacturer and ask for help. As it happens a team from the manufacturer actually arrives on scene very quickly - it later transpires that the car had 'phoned home' automatically so the manufacturer actually knew of the accident within seconds of it taking place. Somewhat nonplussed the traffic police have little choice but to grant them full access to the scene of the accident. The manufacturer undertakes their own investigation and - several weeks later - issue a press statement explaining that the AI driving the car was unable to cope with an "unexpected situation" which "regrettably" led to the fatal crash. The company explain that the AI has been upgraded so that it cannot happen again. They also accept liability for the accident and offer compensation to the child's family. Despite repeated requests the company declines to share the technical details of what happened with the authorities, claiming that such disclosure would compromise its intellectual property.
A public already fearful of the new technology reacts very badly. Online petitions call for a ban on driverless cars and politicians enact knee-jerk legislation which, although falling short of an outright ban, sets the industry back years.
Scenario 2: the traffic police call the newly established driverless car accident investigation branch (DCAB), who send a team consisting of independent experts on driverless car technology, including its AI. The manufacturer's team also arrive, but - under a protocol agreed with the industry - their role is to support DCAB and provide "full assistance, including unlimited access to technical data". In fact the data logs stored by the car are in a new industry standard format thus access by DCAB is straightforward; software tools allow them to quickly interpret those data logs. Well aware of public concerns DCAB provide hourly updates on the progress of their investigation via social media and, within just a few days, call a press conference to explain their findings. They outline the fault with the AI and explain that they will require the manufacturer to recall all affected vehicles and update the AI, after submitting technical details of the update to DCAB for approval. DCAB will also issue an update to all driverless car manufacturers asking them to check for the same fault in their own systems, also reporting their findings back to DCAB.
A public fearful of the new technology is reassured by the transparent and robust response of the accident investigation team. Although those fears surface in the press and social media, the umbrella Driverless Car Authority (DCA) are quick to respond with expert commentators and data to show that driverless cars are already safer than manually driven cars.
There are strong parallels between driverless cars and commercial aviation. One of the reasons we trust airliners is that we know they are part of a highly regulated industry with an amazing safety record. The reason commercial aircraft are so safe is largely down to the very tough safety certification processes and, when things do go wrong, the rapid and robust processes of air accident investigation. There are emerging standards for driverless cars: ISO Technical Committee TC 204 on Intelligent Transport Systems already lists 213 standards. There isn't yet a standard for fully autonomous driverless car operation, but see for instance ISO 11270:2014 on Lane keeping assistance systems (LKAS). But standards need teeth, which is why we need standards-based certification processes for driverless cars managed by regulatory authorities - a driverless car equivalent of the FAA. In short, a governance framework for driverless cars.
Postscript: several people have emailed or tweeted me to complain that I seem to be anti driverless cars - nothing could be further from the truth. I am a strong advocate of driverless cars for many reasons, first and most importantly because they will save lives, second because they should lead to a reduction in the number of vehicles on the road - thus making our cities greener, and third because they might just cure humans of our unhealthy obsession with personal car ownership. My big worry is that none of these benefits will flow if driverless cars are not trusted. But trust in technology doesn't happen by magic and, in the early days, serious setbacks and a public backlash could set the nascent driverless car industry back years (think of GM foods in the EU). One way to counter such a backlash and build trust is to put in place robust and transparent governance as I have tried (not very well it seems) to argue in this post.
Picture the scene. Emergency services are called to attend the accident. A teenage girl on a bicycle apparently riding along a cycle path was hit and killed by a car. The traffic police quickly establish that the car at the centre of the accident was operating autonomously at the moment of the fatal crash. They endeavour to find out what went wrong, but how? Almost certainly the car will have logged data on its behaviour leading up to the moment of the crash - data that is sure to hold vital clues about what caused the accident, but will that data be accessible to the investigating traffic police? And even if it is will the investigators be able to interpret the data..?
There are two ways the story could unfold from here.
Scenario 1: unable to investigate the accident themselves, the traffic police decide to contact the manufacturer and ask for help. As it happens a team from the manufacturer actually arrives on scene very quickly - it later transpires that the car had 'phoned home' automatically so the manufacturer actually knew of the accident within seconds of it taking place. Somewhat nonplussed the traffic police have little choice but to grant them full access to the scene of the accident. The manufacturer undertakes their own investigation and - several weeks later - issue a press statement explaining that the AI driving the car was unable to cope with an "unexpected situation" which "regrettably" led to the fatal crash. The company explain that the AI has been upgraded so that it cannot happen again. They also accept liability for the accident and offer compensation to the child's family. Despite repeated requests the company declines to share the technical details of what happened with the authorities, claiming that such disclosure would compromise its intellectual property.
A public already fearful of the new technology reacts very badly. Online petitions call for a ban on driverless cars and politicians enact knee-jerk legislation which, although falling short of an outright ban, sets the industry back years.
Scenario 2: the traffic police call the newly established driverless car accident investigation branch (DCAB), who send a team consisting of independent experts on driverless car technology, including its AI. The manufacturer's team also arrive, but - under a protocol agreed with the industry - their role is to support DCAB and provide "full assistance, including unlimited access to technical data". In fact the data logs stored by the car are in a new industry standard format thus access by DCAB is straightforward; software tools allow them to quickly interpret those data logs. Well aware of public concerns DCAB provide hourly updates on the progress of their investigation via social media and, within just a few days, call a press conference to explain their findings. They outline the fault with the AI and explain that they will require the manufacturer to recall all affected vehicles and update the AI, after submitting technical details of the update to DCAB for approval. DCAB will also issue an update to all driverless car manufacturers asking them to check for the same fault in their own systems, also reporting their findings back to DCAB.
A public fearful of the new technology is reassured by the transparent and robust response of the accident investigation team. Although those fears surface in the press and social media, the umbrella Driverless Car Authority (DCA) are quick to respond with expert commentators and data to show that driverless cars are already safer than manually driven cars.
There are strong parallels between driverless cars and commercial aviation. One of the reasons we trust airliners is that we know they are part of a highly regulated industry with an amazing safety record. The reason commercial aircraft are so safe is largely down to the very tough safety certification processes and, when things do go wrong, the rapid and robust processes of air accident investigation. There are emerging standards for driverless cars: ISO Technical Committee TC 204 on Intelligent Transport Systems already lists 213 standards. There isn't yet a standard for fully autonomous driverless car operation, but see for instance ISO 11270:2014 on Lane keeping assistance systems (LKAS). But standards need teeth, which is why we need standards-based certification processes for driverless cars managed by regulatory authorities - a driverless car equivalent of the FAA. In short, a governance framework for driverless cars.
Postscript: several people have emailed or tweeted me to complain that I seem to be anti driverless cars - nothing could be further from the truth. I am a strong advocate of driverless cars for many reasons, first and most importantly because they will save lives, second because they should lead to a reduction in the number of vehicles on the road - thus making our cities greener, and third because they might just cure humans of our unhealthy obsession with personal car ownership. My big worry is that none of these benefits will flow if driverless cars are not trusted. But trust in technology doesn't happen by magic and, in the early days, serious setbacks and a public backlash could set the nascent driverless car industry back years (think of GM foods in the EU). One way to counter such a backlash and build trust is to put in place robust and transparent governance as I have tried (not very well it seems) to argue in this post.
Saturday, February 20, 2016
Could we make a moral machine?
Could we make a moral machine? A robot capable of choosing or moderating its actions on the basis of ethical rules..? This was how I opened my IdeasLab talk at the World Economic Forum 2016, last month. The format of IdeasLab is 4 five minute (Pecha Kucha) talks, plus discussion and Q&A with the audience. The theme of this Nature IdeasLab was Building an Intelligent Machine, and I was fortunate to have 3 outstanding co-presenters: Vanessa Evers, Maja Pantic and Andrew Moore. You can see all four of our talks on YouTube here.
The IdeasLab variant of Pecha Kucha is pretty challenging for someone used to spending half an hour or more lecturing - 15 slides and 20 seconds per slide. Here is my talk:
And the video clips in slides 11 and 12 are here:
Slide 11: Blue prevents red from reaching danger.
Slide 12: Blue faces an ethical dilemma: our indecisive robot can save them both.
Acknowledgements: I am deeply grateful to colleague Dr Dieter Vanderelst who designed and coded the experiments shown here on slides 10-12. This work is part of the EPSRC funded project Verifiable Autonomy.
The IdeasLab variant of Pecha Kucha is pretty challenging for someone used to spending half an hour or more lecturing - 15 slides and 20 seconds per slide. Here is my talk:
and since not all of my (everso carefully chosen) slides are visible in the recording here is the complete deck:
And the video clips in slides 11 and 12 are here:
Slide 11: Blue prevents red from reaching danger.
Slide 12: Blue faces an ethical dilemma: our indecisive robot can save them both.
Acknowledgements: I am deeply grateful to colleague Dr Dieter Vanderelst who designed and coded the experiments shown here on slides 10-12. This work is part of the EPSRC funded project Verifiable Autonomy.
Friday, October 30, 2015
How ethical is your ethical robot?
If you're in the business of making ethical robots, then sooner or later you have to face the question: how ethical is your ethical robot? If you've read my previous blog posts then you will probably have come to the conclusion 'not very' - and you would be right - but here I want to explore the question in a little more depth.
First let us consider whether our 'Asimovian' robot can be considered ethical at all. For the answer I'm indebted to philosopher Dr Rebecca Reilly-Cooper who read our paper and concluded that yes, we can legitimately describe our robot as ethical, at least in a limited sense. She explained that the robot implements consequentialist ethics. Rebbeca wrote:
Having established what kind of ethics that our ethical robot has, now consider the question of how far does the robot go toward moral agency. Here we can turn to an excellent paper by James Moor, called The Nature, Importance and Difficulty of Machine Ethics. In that paper* Moor suggests four categories of ethical agency - starting with the lowest. Let me summarise those here:
It is clear that our simple Asimovian robot is not a full ethical agent. It cannot choose how to behave (like you or I), but is compelled to make decisions based on the harm-minimisation rules hard-coded into it. And it cannot justify those decisions post-hoc. It is, as I've suggested elsewhere, an ethical zombie. I would however argue that because of the cognitive machinery the robot uses to simulate ahead to model and evaluate the consequences of each of its next possible actions combined with its safety/ethical logical rules to choose between those actions, then the robot can be said to be reasoning about ethics. I believe our robot is an explicit ethical agent in Moor's scheme.
*Moor JH (2006), The Nature, Importance and Difficulty of Machine Ethics, IEEE Intelligent Systems, 21 (4), 18-21.
First let us consider whether our 'Asimovian' robot can be considered ethical at all. For the answer I'm indebted to philosopher Dr Rebecca Reilly-Cooper who read our paper and concluded that yes, we can legitimately describe our robot as ethical, at least in a limited sense. She explained that the robot implements consequentialist ethics. Rebbeca wrote:
"The obvious point that any moral philosopher is going to make is that you are assuming that an essentially consequentialist approach to ethics is the correct one. My personal view, and I would guess the view of most moral philosophers, is that any plausible moral theory is going to have to pay at least some attention to the consequences of an action in assessing its rightness, even if it doesn’t claim that consequences are all that matter, or that rightness is entirely instantiated in consequences. So on the assumption that consequences have at least some significance in our moral deliberations, you can claim that your robot is capable of attending to one kind of moral consideration, even if you don’t make the much stronger claim that is capable of choosing the right action all things considered."One of the great things about consequences is that they can be estimated - in our case using a simulation-based internal model which we call a consequence engine. So from a practical point of view it seems that we can build a robot with consequentialist ethics, whereas it is much harder to think about how to build a robot with say Deontic ethics, or Virtue ethics.
Having established what kind of ethics that our ethical robot has, now consider the question of how far does the robot go toward moral agency. Here we can turn to an excellent paper by James Moor, called The Nature, Importance and Difficulty of Machine Ethics. In that paper* Moor suggests four categories of ethical agency - starting with the lowest. Let me summarise those here:
- Ethical impact agents: Any machine that can be evaluated for its ethical consequences.
- Implicit ethical agents: Designed to avoid negative ethical effects.
- Explicit ethical agents: Machines that can reason about ethics.
- Full ethical agents: Machines that can make explicit moral judgments and justify them.
The first category: ethical impact agents, really includes all machines. A good example is a knife, which can clearly be used for good (chopping food, or surgery) or ill (as a lethal weapon). Now think about the blunt plastic knife that comes with airplane food - that falls into Moor's second category since it has been designed to reduce the potential of ethical misuse - it is an implicit ethical agent. Most robots fall into the first category: they are ethical impact agents, and a subset - those that have been designed to avoid harm by, for instance detecting if a human walks in front of them and automatically coming to a stop - are implicit ethical agents.
Let's now skip to Moor's fourth category, because it helps to frame our question - how ethical is your ethical robot? At present I would say there are no machines that are full ethical agents. In fact the only full ethical agents we know are 'adult humans of sound mind'. The point is this - to be a full ethical agent you need to be able to not only make moral judgements but account for why you made the choices you did.
Assuming you agree with me, then does the fact that we have reached the third category in Moor's scheme mean that full ethical agents are on the horizon? The answer is a big NO. The scale of Moor's scheme is not linear. It's a relative small step from ethical impact agents to implicit ethical agents. Then a very much bigger step to explicit ethical agents, which we are only just beginning to take. But there is a huge gulf then to full ethical agents, since they would almost certainly need something approaching human equivalent intelligence.
But maybe it's just as well. The societal implications of full ethical agents, if and when they exist, would be huge. For now at least, I think I prefer my ethical robots to be zombies.
*Moor JH (2006), The Nature, Importance and Difficulty of Machine Ethics, IEEE Intelligent Systems, 21 (4), 18-21.
Tuesday, August 25, 2015
My contribution to an Oral History of Robotics
In March 2013 I was interviewed by Peter Asaro for the IEEE.tv Oral History of Robotics series. That interview has now been published, and here it is:
In case you're wondering, I'm sitting in Noel Sharkey's study (shivering slightly - it was a bitterly cold day in Sheffield). It was a real privilege to be asked to contribute, especially alongside the properly famous roboticists Peter interviewed. Do check them out. There doesn't seem to be an index page, but the set starts on page 2 of the IEEE.tv history channel.
Postscript: a full transcript of the interview can be found here: http://ethw.org/Oral-History:Alan_Winfield
In case you're wondering, I'm sitting in Noel Sharkey's study (shivering slightly - it was a bitterly cold day in Sheffield). It was a real privilege to be asked to contribute, especially alongside the properly famous roboticists Peter interviewed. Do check them out. There doesn't seem to be an index page, but the set starts on page 2 of the IEEE.tv history channel.
Postscript: a full transcript of the interview can be found here: http://ethw.org/Oral-History:Alan_Winfield
Friday, July 31, 2015
Towards ethical robots: an update
This post is just a quick update on our ethical robots research.
Our initial ethical robot experiments were done with e-puck robots. Great robots but not ideal for what is essentially human-robot interaction (HRI) research. Thus we've switched to NAO robots and have spent the last few months re-coding for the NAOs. This is not a trivial exercise. The e-pucks and NAO robots have almost nothing in common, and colleague and project post-doc Dieter Vanderelst has re-created the whole consequence engine architecture from the ground up, together with the tools for running experiments and collecting data.
Why the NAO robots? Well, they are humanoid and therefore better fitted for HRI research. But more importantly they're much more complex and expressive than the e-puck robots, and provide huge scope for interesting behaviours and responses, such as speech or gesture.
But we are not yet making use of that additional expressiveness. In initial trials Dieter has coded the ethical robot to physically intervene, i.e. block the path of the 'human' it order to prevent it from coming to harm. Here below are two example runs, illustrated with composite images showing overlaid successive screen grabs from the overhead camera.
Here red is the ethical robot, which is initially heading toward its goal position toward the top right of the arena. Meanwhile blue - the proxy human - is not looking where it's going and heading for danger, at the bottom right of the arena. When it notices this red then diverts from its path, blocks blue, which then simply halts. Red's consequence engine now predicts no danger to blue and so red resumes progress toward its goal.
Although we have no videos just yet you can catch a few seconds of Dieter and the robots at 2:20 on this excellent video made by Maggie Philbin and Teentech for the launch of the BBC Micro:bit earlier this month.
Our initial ethical robot experiments were done with e-puck robots. Great robots but not ideal for what is essentially human-robot interaction (HRI) research. Thus we've switched to NAO robots and have spent the last few months re-coding for the NAOs. This is not a trivial exercise. The e-pucks and NAO robots have almost nothing in common, and colleague and project post-doc Dieter Vanderelst has re-created the whole consequence engine architecture from the ground up, together with the tools for running experiments and collecting data.
But we are not yet making use of that additional expressiveness. In initial trials Dieter has coded the ethical robot to physically intervene, i.e. block the path of the 'human' it order to prevent it from coming to harm. Here below are two example runs, illustrated with composite images showing overlaid successive screen grabs from the overhead camera.
Here red is the ethical robot, which is initially heading toward its goal position toward the top right of the arena. Meanwhile blue - the proxy human - is not looking where it's going and heading for danger, at the bottom right of the arena. When it notices this red then diverts from its path, blocks blue, which then simply halts. Red's consequence engine now predicts no danger to blue and so red resumes progress toward its goal.
Although we have no videos just yet you can catch a few seconds of Dieter and the robots at 2:20 on this excellent video made by Maggie Philbin and Teentech for the launch of the BBC Micro:bit earlier this month.
Sunday, May 31, 2015
Copyright violations bring Forth memories
Last week I found a couple of copyright violations. Am I upset? Not at all - actually I'm delighted that stuff I did in 1983 is alive and well on the Interweb thanks to the efforts of others.
The first is an online readable copy of my 1983 book The Complete Forth. It's a textbook on the programming language Forth that I was heavily into at the time. The book was first published by Sigma Press, then internationally by John Wiley, and was translated into both Dutch and Japanese. Someone - I assume from the Jupiter Ace archive - has gone to the trouble of scanning every page. Even the pull out reference card. Whoever you are, thank you.
Just before I wrote that book, I had developed a Forth programming system (a programming environment that integrates compiler and interpreter) for the NASCOM 2 Z80 micro computer. Myself and a friend then marketed Hull-Forth and, I recall, sold several hundred copies. Of course this was pre-internet so marketing meant small ads in the magazine Personal Computer World. What we actually shipped was a printed manual together with the code on a cassette tape. Floppy disks were hugely expensive and beyond the reach of hobby computers, so for saving and loading programs we used audio cassette recorders. They were slow and very unreliable; if there was a checksum error you just had to rewind, cross your fingers and try again. I can't imagine anyone feeling nostalgic for that particular technology.
This brings me to the second copyright infringement. By accident I discovered there is a webpage for NASCOM enthusiasts, and several emulators, so you can run a virtual NASCOM on your modern PC. Scrolling down the long list of software in the NASCOM repository, in the section Programming Languages, I find
Hah! Someone must have gone to alot of trouble to get the code from the original cassette*, recorded using the Kansas City Standard at, I think, 300 baud (so slow you could almost hear the noughts and ones!), to a .NAS file you can download into your NASCOM emulator.
Ok, now to get that NASCOM emulator running. It will be fun (and slightly absurd) to run Hull Forth again for the first time in about 33 years.
*I probably still have one of those cassettes in my loft**, but no way of reading the data from it.
**Along with stacks of punched cards, rolls of paper tape, and all kinds floppy disks.
Just before I wrote that book, I had developed a Forth programming system (a programming environment that integrates compiler and interpreter) for the NASCOM 2 Z80 micro computer. Myself and a friend then marketed Hull-Forth and, I recall, sold several hundred copies. Of course this was pre-internet so marketing meant small ads in the magazine Personal Computer World. What we actually shipped was a printed manual together with the code on a cassette tape. Floppy disks were hugely expensive and beyond the reach of hobby computers, so for saving and loading programs we used audio cassette recorders. They were slow and very unreliable; if there was a checksum error you just had to rewind, cross your fingers and try again. I can't imagine anyone feeling nostalgic for that particular technology.
This brings me to the second copyright infringement. By accident I discovered there is a webpage for NASCOM enthusiasts, and several emulators, so you can run a virtual NASCOM on your modern PC. Scrolling down the long list of software in the NASCOM repository, in the section Programming Languages, I find
Hah! Someone must have gone to alot of trouble to get the code from the original cassette*, recorded using the Kansas City Standard at, I think, 300 baud (so slow you could almost hear the noughts and ones!), to a .NAS file you can download into your NASCOM emulator.
Ok, now to get that NASCOM emulator running. It will be fun (and slightly absurd) to run Hull Forth again for the first time in about 33 years.
*I probably still have one of those cassettes in my loft**, but no way of reading the data from it.
**Along with stacks of punched cards, rolls of paper tape, and all kinds floppy disks.
Saturday, May 30, 2015
Forgetting may be important to cultural evolution
Our latest paper from the Artificial Culture project has just been published: On the Evolution of Behaviors through Embodied Imitation.
Here is the abstract
In the artificial culture project we implemented social learning in a group of robots. Robots were programmed to learn from each other, by imitation. Imitation was strictly embodied, so robots observed each other using their onboard sensors and, on the basis of only visual sense data from a robot’s own camera and perspective, the learner robot inferred another robot’s physical behaviour. (Here is a quick 5 minute intro to the project.)
Not surprisingly embodied robot-robot imitation is imperfect. A combination of factors including the robots’ relatively low-resolution onboard camera, variations in lighting, small differences between robots, multiple robots sometimes appearing within a learner robot’s field of view, and of course having to infer a robot’s movements by tracking the relative size and position of that robot in the learner’s field of view, lead to imitation errors. And some movement patterns are easier to imitate than others (think of how much easier it is to learn the steps of a slow waltz than the samba by watching your dance teacher). The fidelity of embodied imitation for robots, just as for animals, is a complex function of four factors: (1) the behaviours being learned, (2) the robots’ sensorium and morphology, (3) environmental noise and (4) the inferential learning algorithm.
But rather than being a problem, noisy social learning was our aim. We are interested in the dynamics of social learning, and in particular the way that behaviours evolve as they propagate through the group. Noisy social learning means that behaviours are subject to variation as they are copied from one robot to another. Multiple cycles of imitation (robot B learns behaviour m from A, then robot C learns the same behaviour m′ (m mutated), from robot B, and so on), gives rise to behavioural heredity. And if robots are able to select which learned behaviours to enact we have the three Darwinian operators for evolution, except that this is behavioural, or memetic, evolution.
These experiments show that embodied behavioural evolution really does take place. If selection is random, that is robots select which behaviour to enact from those already learned – with equal probability, then we see several interesting findings.
1. If by chance one or more high fidelity copies follow a poor fidelity imitation, the large variation in the initial noisy learning can lead to a new behavioural species, or traditions. Thus showing that noisy social learning can play a role in the emergence of novelty in behavioural (i.e. cultural) evolution. That was written up in Winfield and Erbas, 2011.
Here is the abstract
Let me explain.This article describes research in which embodied imitation and behavioral adaptation are investigated in collective robotics. We model social learning in artificial agents with real robots. The robots are able to observe and learn each others' movement patterns using their on-board sensors only, so that imitation is embodied. We show that the variations that arise from embodiment allow certain behaviors that are better adapted to the process of imitation to emerge and evolve during multiple cycles of imitation. As these behaviors are more robust to uncertainties in the real robots' sensors and actuators, they can be learned by other members of the collective with higher fidelity. Three different types of learned-behavior memory have been experimentally tested to investigate the effect of memory capacity on the evolution of movement patterns, and results show that as the movement patterns evolve through multiple cycles of imitation, selection, and variation, the robots are able to, in a sense, agree on the structure of the behaviors that are imitated.
In the artificial culture project we implemented social learning in a group of robots. Robots were programmed to learn from each other, by imitation. Imitation was strictly embodied, so robots observed each other using their onboard sensors and, on the basis of only visual sense data from a robot’s own camera and perspective, the learner robot inferred another robot’s physical behaviour. (Here is a quick 5 minute intro to the project.)
But rather than being a problem, noisy social learning was our aim. We are interested in the dynamics of social learning, and in particular the way that behaviours evolve as they propagate through the group. Noisy social learning means that behaviours are subject to variation as they are copied from one robot to another. Multiple cycles of imitation (robot B learns behaviour m from A, then robot C learns the same behaviour m′ (m mutated), from robot B, and so on), gives rise to behavioural heredity. And if robots are able to select which learned behaviours to enact we have the three Darwinian operators for evolution, except that this is behavioural, or memetic, evolution.
These experiments show that embodied behavioural evolution really does take place. If selection is random, that is robots select which behaviour to enact from those already learned – with equal probability, then we see several interesting findings.
But it is the second and third findings that we describe in our new paper.
2. We see that behaviours appear to adapt to be easier to learn, i.e. better ‘fitted’ to the robot swarm. The way to think about this is that the robots' sensors and bodies, and physical environment of the arena with several robots (including lighting), together comprise the 'ecological niche' for behavioural evolution. Behaviours mutate but the ones better fitted to that niche survive.
3. The third finding from this series of experiments is perhaps the most unexpected and the one I want to outline in a bit more detail here. We ran the same embodied behavioural evolution with three memory sizes: no memory, limited memory and unlimited memory.
In the unlimited memory trials each robot saved every learned meme, so the meme pool across the whole robot population (of four robots) grew as the trial progressed. Thus all learned memes were available to be selected for enaction. In the limited memory trials each robot had a memory capacity of only five learned memes, so that when a new meme was learned the oldest one in the robot's memory was deleted.
The diagram below shows the complete family tree of evolved memes, for one typical run of the limited memory case. At the start of the run the robots were seeded with two memes, shown as 1 and 2 at the top of the diagram. Behaviour 1 was a movement pattern in which the robot traces a triangle path, behaviour 2 a square. Because this was a limited memory trial the total meme pool has only 20 behaviours - these are shown below as diamonds. Notice the cluster of 11 closely related memes at the bottom right, all of which are 7th, 8th or 9th generation descendents of the triangle meme.
When we ran multiple trials of the limited and unlimited memory cases, then analysed the number and sizes of the clusters of related memes in the meme pool, we saw that the limited memory trials showed a smaller number of larger clusters than the unlimited memory case. The difference was clear and significant; with limited memory an average of 2.8 clusters of average size 8.3, with unlimited memory 3.9 clusters of size 6.9.
Why is this clustering interesting? Well it's because the number and size of clusters in the meme pool are good indicators of its diversity. Think of each cluster of related memes as a 'tradition'. A healthy culture needs a balance between stability and diversity. Neither too much stability, i.e. a very small number (in the limit 1) of traditions, or too much diversity, i.e. clusters so small that there are no persistent traditions at all. Perhaps the ideal balance is a smallish number of somewhat persistent traditions.
So far I didn't mention the no memory case. This was the least interesting of the three. Actually by no memory we mean a memory size of one; in other words a robot has no choice but to enact the last behaviour it learned. There is no selection, and no clusters can form. Traditions can never even get started, let alone persist.
Of course it would unwise to draw any big conclusions from this limited experimental study. But an intriguing possibility is that some forgetting (but not too much) may, just like noisy imitation, be a necessary condition for the emergence of culture in social agents.
Full reference:
Erbas MD, Bull L and Winfield AFT (2015), On the Evolution of Behaviors through Embodied Imitation, Artificial Life, 21 (2), pp 141-165. The full text (final draft) paper can be downloaded here.
Related blog posts:
Robot imitation as a method for modelling the foundations of social life
Open-ended Memetic Evolution, or is it?
2. We see that behaviours appear to adapt to be easier to learn, i.e. better ‘fitted’ to the robot swarm. The way to think about this is that the robots' sensors and bodies, and physical environment of the arena with several robots (including lighting), together comprise the 'ecological niche' for behavioural evolution. Behaviours mutate but the ones better fitted to that niche survive.
3. The third finding from this series of experiments is perhaps the most unexpected and the one I want to outline in a bit more detail here. We ran the same embodied behavioural evolution with three memory sizes: no memory, limited memory and unlimited memory.
In the unlimited memory trials each robot saved every learned meme, so the meme pool across the whole robot population (of four robots) grew as the trial progressed. Thus all learned memes were available to be selected for enaction. In the limited memory trials each robot had a memory capacity of only five learned memes, so that when a new meme was learned the oldest one in the robot's memory was deleted.
The diagram below shows the complete family tree of evolved memes, for one typical run of the limited memory case. At the start of the run the robots were seeded with two memes, shown as 1 and 2 at the top of the diagram. Behaviour 1 was a movement pattern in which the robot traces a triangle path, behaviour 2 a square. Because this was a limited memory trial the total meme pool has only 20 behaviours - these are shown below as diamonds. Notice the cluster of 11 closely related memes at the bottom right, all of which are 7th, 8th or 9th generation descendents of the triangle meme.
Behavioural evolution map following a 4-robot experiment with limited memory; each robot stores only the most recent 5 learned behaviours. Each behaviour is descended from two seed behaviours labelled 1 and 2. Orange nodes are high fidelity copies, blue nodes are low fidelity copies. The 20 behaviours in the memory of all 4 robots at the end of the experiment are highlighted as diamonds. Note the cluster of 11 closely-related behaviours at the bottom right.
When we ran multiple trials of the limited and unlimited memory cases, then analysed the number and sizes of the clusters of related memes in the meme pool, we saw that the limited memory trials showed a smaller number of larger clusters than the unlimited memory case. The difference was clear and significant; with limited memory an average of 2.8 clusters of average size 8.3, with unlimited memory 3.9 clusters of size 6.9.
Why is this clustering interesting? Well it's because the number and size of clusters in the meme pool are good indicators of its diversity. Think of each cluster of related memes as a 'tradition'. A healthy culture needs a balance between stability and diversity. Neither too much stability, i.e. a very small number (in the limit 1) of traditions, or too much diversity, i.e. clusters so small that there are no persistent traditions at all. Perhaps the ideal balance is a smallish number of somewhat persistent traditions.
So far I didn't mention the no memory case. This was the least interesting of the three. Actually by no memory we mean a memory size of one; in other words a robot has no choice but to enact the last behaviour it learned. There is no selection, and no clusters can form. Traditions can never even get started, let alone persist.
Of course it would unwise to draw any big conclusions from this limited experimental study. But an intriguing possibility is that some forgetting (but not too much) may, just like noisy imitation, be a necessary condition for the emergence of culture in social agents.
Full reference:
Erbas MD, Bull L and Winfield AFT (2015), On the Evolution of Behaviors through Embodied Imitation, Artificial Life, 21 (2), pp 141-165. The full text (final draft) paper can be downloaded here.
Related blog posts:
Robot imitation as a method for modelling the foundations of social life
Open-ended Memetic Evolution, or is it?
Saturday, April 04, 2015
Yesterday I looked through the eyes of a robot
It was a NAO robot fitted with a 3D printed set of goggles, so that the robot has two real cameras on its head (the eyes of the NAO robot are not in fact cameras). I was in another room wearing an Oculus Rift headset. The Oculus was hooked up to the NAO's goggle cameras, so that I could see through those cameras - in stereo vision.
But it was even better than that. The head positioning system of the Oculus headset was also hooked up to the robot, so I could turn my head and - in sync - the robot's head moved. And I was standing in front of a Microsoft Kinect that was tracking my arm movements. Those movements were being sent to the NAO, so by moving my arms I was also moving the robot's arms.
All together this made for a pretty compelling immersive experience. I was able look down while moving my (robot) arms and see them pretty much where you would expect to see your own arms. The illusion was further strengthened when Peter placed a mirror in front of the NAO robot, so I could see my (robot) self moving in the mirror. Then it got weird. Peter asked me to open my hand and placed a paper cup into it. I instinctively looked down and was momentarily shocked not to see the cup in my hand. That made me realise how quickly - within a couple of minutes of donning the headset - I was adjusting to the new robot me.
This setup, developed here in the BRL by my colleagues Paul Bremner and Pete Gibbons, is part of a large EPSRC project called Being There. Paul and Peter are investigating the very interesting question of how humans interact with and via teleoperated robots, in shared social spaces. I think teleoperated robot avatars will be hugely important in the near future - more so than fully autonomous robots. But our robot surrogates will not look like a younger buffer Bruce Willis. They will look like robots. How will we interact with these surrogate robots - robots with human intelligences, human personalities - seemingly with human souls? Will they be treated with the same level of respect that would be accorded their humans if they were actually there, in the flesh? Or be despised as voyeuristic; an uninvited webcam on wheels?
Here is a YouTube video of an earlier version of this setup, without the goggles or Kinect:
I didn't get the feeling of what it is like to be a robot, but it's a step in that direction.
 |
| photo by Peter Gibbons |
All together this made for a pretty compelling immersive experience. I was able look down while moving my (robot) arms and see them pretty much where you would expect to see your own arms. The illusion was further strengthened when Peter placed a mirror in front of the NAO robot, so I could see my (robot) self moving in the mirror. Then it got weird. Peter asked me to open my hand and placed a paper cup into it. I instinctively looked down and was momentarily shocked not to see the cup in my hand. That made me realise how quickly - within a couple of minutes of donning the headset - I was adjusting to the new robot me.
This setup, developed here in the BRL by my colleagues Paul Bremner and Pete Gibbons, is part of a large EPSRC project called Being There. Paul and Peter are investigating the very interesting question of how humans interact with and via teleoperated robots, in shared social spaces. I think teleoperated robot avatars will be hugely important in the near future - more so than fully autonomous robots. But our robot surrogates will not look like a younger buffer Bruce Willis. They will look like robots. How will we interact with these surrogate robots - robots with human intelligences, human personalities - seemingly with human souls? Will they be treated with the same level of respect that would be accorded their humans if they were actually there, in the flesh? Or be despised as voyeuristic; an uninvited webcam on wheels?
Here is a YouTube video of an earlier version of this setup, without the goggles or Kinect:
I didn't get the feeling of what it is like to be a robot, but it's a step in that direction.
Saturday, February 21, 2015
Like doing brain surgery on robots
I spent a rare few hours in the lab the last few days, actually doing research. Or at least attempting to. Actually I made no progress at all. But did reach base camp: I managed to set up and run the ethical-dilemma robot experiment. And in the process refreshed my rusty command-line Linux. I was also reminded how time consuming, and downright frustrating experimental robotics research really is. Here's a taste: everything is set up and looks ok... but wait - the tracking system needs recalibrating; hmm... where's the manual? Ah, found it. Ok, wow this is complicated. Needs the special calibrating wand, and set square device... An hour later: ok ready now. Start everything up. But one of the robots isn't connecting. Ah, battery low, ok battery changed, now back up 4 steps and restart. And so it goes.
This is Swarmlab mission control. Three computers, three different operating systems;) The one in the middle (Windows XP) is running the Vicon tracking system, and monitoring via an overhead webcam. The laptop on the left (Ubuntu Linux) is running the four different processes to start and manage the three robots.
Here are the three e-pucks, each a WiFi networked Linux computer (Debian) in its own right. Actually each robot has two processors: a low-level PIC microcontroller to take care of motor control, managing the robot's sensors, etc. And an ARM processor for high-level control. The two interfaced via the SPI bus.
The setup is complicated. 5 computers in total, running a total of 9 networked processes. Here's a diagram showing those processes and how they are linked.
So, back to research.
The task I had set myself was to make some small changes to the high level controller. How hard can that be, you might think? Well it feels a bit like brain surgery: trying to tease apart code that I barely understand without breaking it. The code is well written and well structured, but it's in Python, which is new to me. It's only a couple of hundred lines, but like the neo-cortex - it's a thin layer at the top of a complex network of carefully choreographed processes and subsystems.
Acknowledgements: Christian Blum programmed the ethical robot experiments, supported by Dr Wenguo Liu who designed and setup the Swarmlab experimental infrastructure, including the e-puck Linux extension boards.
The setup is complicated. 5 computers in total, running a total of 9 networked processes. Here's a diagram showing those processes and how they are linked.So, back to research.
The task I had set myself was to make some small changes to the high level controller. How hard can that be, you might think? Well it feels a bit like brain surgery: trying to tease apart code that I barely understand without breaking it. The code is well written and well structured, but it's in Python, which is new to me. It's only a couple of hundred lines, but like the neo-cortex - it's a thin layer at the top of a complex network of carefully choreographed processes and subsystems.
Acknowledgements: Christian Blum programmed the ethical robot experiments, supported by Dr Wenguo Liu who designed and setup the Swarmlab experimental infrastructure, including the e-puck Linux extension boards.
Wednesday, February 18, 2015
Surgical micro-robot swarms: science fiction, or realistic prospect?
Imagine a swarm of microscopic robots that we inject into the vascular system: the swarm swims to the source of the problem, then either delivers therapeutics or undertakes microsurgery directly.
That was how I opened a short invited talk at the Royal Society of Medicine on 5 February, at a meeting themed The Future of Robotics in Surgery. The talk was a wonderful opportunity for me to introduce swarm intelligence and speculate on the likelihood of surgical micro-robot swarms, while at the same time learning about robot surgery. Here are the slides from my talk (with links to YouTube videos where available).
The talk was in three parts.
First I introduced swarm intelligence, and its artificial counterpart swarm robotics. I showed, with examples from two of my students, how - with very simple rules - a swarm of robots can keep together as a swarm, while moving toward a beacon. Then, with a phagocyte-like behaviour, encapsulate the beacon. In our case these were lab robots moving toward an infra-red beacon, but it's not hard to imagine the same behavioural rules in a microscopic swarm swimming toward the source of a chemical marker (chemotaxis). I then gave two examples of the state of the art in swarm robotics: SYMBRION and (my current favourite) TERMES. I wanted to illustrate emergent physical interaction, in these two cases swarm self-assembly and swarm construction, respectively.
In part two I outlined what is by far the biggest problem: actually engineering robots at the micro-scale. Here I drew upon the examples from my book Robotics: a very short introduction; a section called A swarm of medical microrobots. Start with cm sized robots. These already exist in the form of pillbots and I reference the work of Paolo Dario's lab in this direction. Then get 10 times smaller to mm sized robots. Here we're at the limit of making robots with conventional mechatronics. The almost successful I-SWARM project prototyped remarkable robots measuring 4 x 4 x 3mm. But now shrink by another 3 orders of magnitude to microbots, measured in micrometers. This is how small robots would have to be in order to swim through and access (most of) the vascular system. Here we are far beyond conventional materials and electronics, but amazingly work is going on to control bacteria. In the example I give from the lab of Sylvain Martel, swarms of magnetotactic bacteria are steered by an external magnetic field and, interestingly, tracked in an MRI scanner.
In the final part of my talk I introduce the work of my colleague Sabine Hauert, on swarms of nanoparticles for cancer nanomedicine. These 5 - 500nm particles are controlled by changing their body size, material, coating and cargo so - in true swarm fashion - the way the nanoparticle swarm moves and interacts with much larger normal and tumour cells is an emergent property of the way the nanoparticles individually interact and cooperate. Sabine and her collaborators have created an online tool called NanoDoc, which allows anyone to edit the design of nanoparticles then run simulations to see how their designs perform. In this way the task of searching the huge design space is crowd-sourced. In parallel Sabine is also running mesoscale embodied simulations, using the Harvard Kilobots.
I concluded by suggesting that engineering micro or nanobots is not the only major challenge. At least as important are: (a) how would you program the swarm, and (b) how would such a swarm be approved for clinical use? But a deeply interesting question is the nature of the human-swarm interface. If a swarm of surgical microbots should become a practical proposition would we treat the swarm as a microscopic instrument under the surgeon’s control, or a smart drug that does surgery?
That was how I opened a short invited talk at the Royal Society of Medicine on 5 February, at a meeting themed The Future of Robotics in Surgery. The talk was a wonderful opportunity for me to introduce swarm intelligence and speculate on the likelihood of surgical micro-robot swarms, while at the same time learning about robot surgery. Here are the slides from my talk (with links to YouTube videos where available).
The talk was in three parts.
First I introduced swarm intelligence, and its artificial counterpart swarm robotics. I showed, with examples from two of my students, how - with very simple rules - a swarm of robots can keep together as a swarm, while moving toward a beacon. Then, with a phagocyte-like behaviour, encapsulate the beacon. In our case these were lab robots moving toward an infra-red beacon, but it's not hard to imagine the same behavioural rules in a microscopic swarm swimming toward the source of a chemical marker (chemotaxis). I then gave two examples of the state of the art in swarm robotics: SYMBRION and (my current favourite) TERMES. I wanted to illustrate emergent physical interaction, in these two cases swarm self-assembly and swarm construction, respectively.
In part two I outlined what is by far the biggest problem: actually engineering robots at the micro-scale. Here I drew upon the examples from my book Robotics: a very short introduction; a section called A swarm of medical microrobots. Start with cm sized robots. These already exist in the form of pillbots and I reference the work of Paolo Dario's lab in this direction. Then get 10 times smaller to mm sized robots. Here we're at the limit of making robots with conventional mechatronics. The almost successful I-SWARM project prototyped remarkable robots measuring 4 x 4 x 3mm. But now shrink by another 3 orders of magnitude to microbots, measured in micrometers. This is how small robots would have to be in order to swim through and access (most of) the vascular system. Here we are far beyond conventional materials and electronics, but amazingly work is going on to control bacteria. In the example I give from the lab of Sylvain Martel, swarms of magnetotactic bacteria are steered by an external magnetic field and, interestingly, tracked in an MRI scanner.
In the final part of my talk I introduce the work of my colleague Sabine Hauert, on swarms of nanoparticles for cancer nanomedicine. These 5 - 500nm particles are controlled by changing their body size, material, coating and cargo so - in true swarm fashion - the way the nanoparticle swarm moves and interacts with much larger normal and tumour cells is an emergent property of the way the nanoparticles individually interact and cooperate. Sabine and her collaborators have created an online tool called NanoDoc, which allows anyone to edit the design of nanoparticles then run simulations to see how their designs perform. In this way the task of searching the huge design space is crowd-sourced. In parallel Sabine is also running mesoscale embodied simulations, using the Harvard Kilobots.
I concluded by suggesting that engineering micro or nanobots is not the only major challenge. At least as important are: (a) how would you program the swarm, and (b) how would such a swarm be approved for clinical use? But a deeply interesting question is the nature of the human-swarm interface. If a swarm of surgical microbots should become a practical proposition would we treat the swarm as a microscopic instrument under the surgeon’s control, or a smart drug that does surgery?
Friday, January 30, 2015
Maybe we need an Automation Tax
Imagine this situation. A large company decides to significantly increase the level of automation at one of its facilities. A facility that currently employs a substantial number of men and women doing relatively low-skill tasks, which can now be done by a new generation of robots. Most of the workers get laid off which, for them and their families, leads to real hardship. The company was the only large employer in the area, which is economically depressed (one of the reasons perhaps that the company built the facility there in the first place), so finding alternative work is really difficult. And because most of those jobs were minimum wage, with little or no job security, redundancy payouts are small or non-existent and this of course means that the laid-off workers have no financial buffer to help them re-skill or relocate.
Now I am not anti-automation. Absolutely not. But I believe very strongly that the benefits of robotics and automation should be shared by all. And not just the shareholders of a relatively small number of very large companies. After all, the technology that such companies benefit from was developed by publicly funded research in university research labs. In other words research that you and I funded through our taxes. Ok, you might say, but companies pay tax too, aren't those taxes also contributing to that research? Yes, that's true. But large companies are very good at reducing their tax bill, multinationals especially. Our imaginary company may, in reality, pay most of its tax in a different country entirely from the one hosting the facility.
And of course it is we, through local and national taxation, who - as best we can - pick up the pieces to support the laid off workers and their families, through family tax credits, employment and support allowance, and so on.
Maybe we need an Automation Tax?
It would be a tax levied nationally, whenever a company introduces robotics and automation to one of its facilities in that country. But the tax would only be payable under certain conditions, so for instance:
Only if none of these conditions are met, would the automation tax be due. The idea is not to discourage automation, but to encourage companies to accept a high degree of responsibility to workers laid off as a result of automation, and more widely their social responsibility to the communities in which they are located. The tax would enforce the social contract between companies and society.
Of course this automation tax doesn't go anywhere near far enough. I think the best way of sharing the wealth created by robotics and automation is through a universal Basic Income, but until that utopian condition can be reached, perhaps an automation tax is a start.
Now I am not anti-automation. Absolutely not. But I believe very strongly that the benefits of robotics and automation should be shared by all. And not just the shareholders of a relatively small number of very large companies. After all, the technology that such companies benefit from was developed by publicly funded research in university research labs. In other words research that you and I funded through our taxes. Ok, you might say, but companies pay tax too, aren't those taxes also contributing to that research? Yes, that's true. But large companies are very good at reducing their tax bill, multinationals especially. Our imaginary company may, in reality, pay most of its tax in a different country entirely from the one hosting the facility.
And of course it is we, through local and national taxation, who - as best we can - pick up the pieces to support the laid off workers and their families, through family tax credits, employment and support allowance, and so on.
Maybe we need an Automation Tax?
It would be a tax levied nationally, whenever a company introduces robotics and automation to one of its facilities in that country. But the tax would only be payable under certain conditions, so for instance:
- If the new robotics and automation causes no-one to be laid off, then no tax is due.
- If the automation does result in jobs becoming redundant, but the company re-trains and re-deploys those workers within its organisation, then no tax is due.
- If the company does lay off workers but makes a tax-free redundancy payment to those workers - regardless of their contract status - sufficient to cover the full costs of retraining, upskilling, and - with all reasonable efforts - finding work elsewhere, then no tax is due.
Only if none of these conditions are met, would the automation tax be due. The idea is not to discourage automation, but to encourage companies to accept a high degree of responsibility to workers laid off as a result of automation, and more widely their social responsibility to the communities in which they are located. The tax would enforce the social contract between companies and society.
Of course this automation tax doesn't go anywhere near far enough. I think the best way of sharing the wealth created by robotics and automation is through a universal Basic Income, but until that utopian condition can be reached, perhaps an automation tax is a start.
Monday, December 22, 2014
Robot Bodies and how to Evolve them
Evolutionary robotics has been around for about 20 years: it's about 15 years since Stefano Nolfi and Dario Floreano published their seminal book on the subject. Yet, surprisingly the number of real, physical robots whose bodies have been evolved can be counted on the fingers of one hand. The vast majority of ER research papers are concerned with the evolution of robot brains - the robot's control system. Or, when robot bodies are evolved often the robot is never physically realised. This seems to me very odd, given that robots are real physical artefacts whose body shape - morphology - is deeply linked to their role and function.
The question of how to evolve real robot bodies and why we don't appear to have made much progress in the last 15 years was the subject of my keynote at the IEEE International Conference on Evolvable Systems (ICES 2014) in Orlando, a week ago. Here are my slides:
The talk was in three parts.
In part one I outlined the basic approach to evolving robots using the genetic algorithm, referring to figure 18: The four-stage process of Evolutionary Robotics, from chapter 5 of my book:
I then reviewed the state-of-the-art in evolving real robot bodies, starting with the landmark Golem project of Hod Lipson and Jordan Pollack, referencing both Henrik Lund and Josh Bongard's work on evolving Lego robots, then concluding with the excellent RoboGen project of Josh Auerbach, Dario Floreano and colleagues at EPFL. Although conceptually RoboGen has not moved far from Golem, it makes the co-evolution of robot hardware and controllers accessible for the first time, through the use of 3D-printable body parts which are compatible with servo-motors, and a very nice open-source toolset which integrates all stages of the simulated evolutionary process.
RoboGen, Golem and, as far as I'm aware, all work on evolving real physical robot bodies to date has used the simulate-then-transfer-to-real approach, in which the whole evolutionary process - including fitness testing - takes place in simulation and only the final 'fittest' robot is physically constructed. Andrew Nelson and colleagues in their excellent review paper point out the important distinction between simulate-then-transfer-to-real, and embodied evolution in which the whole process takes place in the real world - in real-time and real-space.
In part two of the talk I outlined two approaches to embodied evolution. The first I call an engineering approach, in which the process is completely embodied but takes place in a kind of evolution factory; this approach needs a significant automated infrastructure: instead of an manufactory we need an evofactory. The second approach I characterise as an artificial life approach. Here there is no infrastructure. Instead 'smart matter' somehow mates then replicates offspring over multiple generations in a process much more analogous to biological evolution. This was one of the ambitious aims of the Symbrion project which, sadly, met with only limited success. Trying to make mechanical robots behave like evolving smart matter is really tough.
Part three concluded by outlining a number of significant challenges to evolving real robot bodies. First I reflect on the huge challenge of evolving complexity. To date we've only evolved very simple robots with very simple behaviours, or co-evolved simple brain/body combinations. I'm convinced that evolving robots of greater (and useful) complexity requires a new approach. We will, I think, need to understand how to co-evolve robots and their ecosystems*. Second I touch upon a related challenge: genotype-phenotype mapping. Here I refer to Pfeifer and Bongard's scalable complexity principle - the powerful idea that we shouldn't evolve robots directly, but instead the developmental process that will lead to the robot, i.e. artificial evo-devo. Finally I raise the often overlooked challenge of the energy cost of artificial evolution.
But the biggest challenge remains essentially what it was 20 years ago: to fully realise the artificial evolution of real robots.
Some of the work of this talk is set out in forthcoming paper: AFT Winfield and J Timmis, Evolvable Robot Hardware, in Evolvable Hardware, eds M Trefzer and A Tyrrell, Springer, in press.
*I touch upon this in the final para of my paper on the energy cost of evolution here.
The question of how to evolve real robot bodies and why we don't appear to have made much progress in the last 15 years was the subject of my keynote at the IEEE International Conference on Evolvable Systems (ICES 2014) in Orlando, a week ago. Here are my slides:
The talk was in three parts.
In part one I outlined the basic approach to evolving robots using the genetic algorithm, referring to figure 18: The four-stage process of Evolutionary Robotics, from chapter 5 of my book:
RoboGen, Golem and, as far as I'm aware, all work on evolving real physical robot bodies to date has used the simulate-then-transfer-to-real approach, in which the whole evolutionary process - including fitness testing - takes place in simulation and only the final 'fittest' robot is physically constructed. Andrew Nelson and colleagues in their excellent review paper point out the important distinction between simulate-then-transfer-to-real, and embodied evolution in which the whole process takes place in the real world - in real-time and real-space.
In part two of the talk I outlined two approaches to embodied evolution. The first I call an engineering approach, in which the process is completely embodied but takes place in a kind of evolution factory; this approach needs a significant automated infrastructure: instead of an manufactory we need an evofactory. The second approach I characterise as an artificial life approach. Here there is no infrastructure. Instead 'smart matter' somehow mates then replicates offspring over multiple generations in a process much more analogous to biological evolution. This was one of the ambitious aims of the Symbrion project which, sadly, met with only limited success. Trying to make mechanical robots behave like evolving smart matter is really tough.
Part three concluded by outlining a number of significant challenges to evolving real robot bodies. First I reflect on the huge challenge of evolving complexity. To date we've only evolved very simple robots with very simple behaviours, or co-evolved simple brain/body combinations. I'm convinced that evolving robots of greater (and useful) complexity requires a new approach. We will, I think, need to understand how to co-evolve robots and their ecosystems*. Second I touch upon a related challenge: genotype-phenotype mapping. Here I refer to Pfeifer and Bongard's scalable complexity principle - the powerful idea that we shouldn't evolve robots directly, but instead the developmental process that will lead to the robot, i.e. artificial evo-devo. Finally I raise the often overlooked challenge of the energy cost of artificial evolution.
But the biggest challenge remains essentially what it was 20 years ago: to fully realise the artificial evolution of real robots.
Some of the work of this talk is set out in forthcoming paper: AFT Winfield and J Timmis, Evolvable Robot Hardware, in Evolvable Hardware, eds M Trefzer and A Tyrrell, Springer, in press.
*I touch upon this in the final para of my paper on the energy cost of evolution here.
Thursday, December 18, 2014
Philae: A proof of concept for cometary landing
The question Robotics by Invitation asked its panel in November 2014, was:
What does the first successful landing on a comet mean for the future of (robotic) space mining and exploration? What are the challenges? What are the opportunities?
Here is my answer:
The successful landing of Philae on comet 67P/Churyumov-Gerasimenko is an extraordinary achievement and of course demonstrates - despite the immense challenges - that it is possible. The Philae mission was, in a sense, a proof of concept for cometary landing and this, for me, answers the question 'what does it mean'.
Of course there is a very large distance between proof of concept and commercial application, so it would be quite wrong to assume that Philae means that space mining (of planets, asteroids or comets) is just around the corner. Undoubtedly the opportunities are immense and - as pressure on Earth's limited and diminishing resources mounts - there is an inevitability about humankind's eventual exploitation of off-world resources. But the costs of space mining are literally astronomical, so unthinkable for all but the wealthiest companies or, indeed, nations.
Perhaps multi-national collaborative ventures are a more realistic proposition and - for me - more desirable; the exploitation of the solar system is something I believe should benefit all of humankind, not just a wealthy elite. But politics aside, there are profoundly difficult technical challenges. You cannot teleoperate this kind of operation from Earth, so a very high level of autonomy is required and, as Philae dramatically demonstrated, we need autonomous systems able to deal with unknown and unpredictable situations then re-plan and if necessary adapt - in real-time - to deal with these exigencies. The development of highly adaptive, resilient, self-repairing - even self-evolving – autonomous systems is still in its infancy. These remain fundamental challenges for robotics and AI research. But even if and when they are solved there will be huge engineering challenges, not least of which is how to return the mined materials to Earth.
Bearing in mind that to date only a few hundred Kg of moon rock have been successfully returned* and Mars sample-return missions are still at the planning stage, we have a very long way to go before we can contemplate returning sufficient quantities to justify the costs of mining them.
*and possibly a few grains of dust from Japanese asteroid probe Hayabusa.
What does the first successful landing on a comet mean for the future of (robotic) space mining and exploration? What are the challenges? What are the opportunities?
Here is my answer:
The successful landing of Philae on comet 67P/Churyumov-Gerasimenko is an extraordinary achievement and of course demonstrates - despite the immense challenges - that it is possible. The Philae mission was, in a sense, a proof of concept for cometary landing and this, for me, answers the question 'what does it mean'.
Of course there is a very large distance between proof of concept and commercial application, so it would be quite wrong to assume that Philae means that space mining (of planets, asteroids or comets) is just around the corner. Undoubtedly the opportunities are immense and - as pressure on Earth's limited and diminishing resources mounts - there is an inevitability about humankind's eventual exploitation of off-world resources. But the costs of space mining are literally astronomical, so unthinkable for all but the wealthiest companies or, indeed, nations.
Perhaps multi-national collaborative ventures are a more realistic proposition and - for me - more desirable; the exploitation of the solar system is something I believe should benefit all of humankind, not just a wealthy elite. But politics aside, there are profoundly difficult technical challenges. You cannot teleoperate this kind of operation from Earth, so a very high level of autonomy is required and, as Philae dramatically demonstrated, we need autonomous systems able to deal with unknown and unpredictable situations then re-plan and if necessary adapt - in real-time - to deal with these exigencies. The development of highly adaptive, resilient, self-repairing - even self-evolving – autonomous systems is still in its infancy. These remain fundamental challenges for robotics and AI research. But even if and when they are solved there will be huge engineering challenges, not least of which is how to return the mined materials to Earth.
Bearing in mind that to date only a few hundred Kg of moon rock have been successfully returned* and Mars sample-return missions are still at the planning stage, we have a very long way to go before we can contemplate returning sufficient quantities to justify the costs of mining them.
*and possibly a few grains of dust from Japanese asteroid probe Hayabusa.
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