This video interview is a good summary of my take on what we’re trying to do in the European HumanE AI project (humane-ai.eu) – and thus also what can/should be done at Stanford HAI (hai.stanford.edu). Of course I meant to say “overestimate” not “underestimate” near the end!
It’s been a while.
The May 6th, 2019 UK edition of Metro published an article entitled “Can we trust machines to predict the stock market with 100% accuracy?“, by Sonya Barlow.
The piece, which is more in-depth and better-researched than one might expect, included a single sentence from me, which was a portion of my response to this question from Sonya: “The more AI [is] used in predictions, the less that AI can predict – Can that really be the case?”
Here is my response in full:
It’s not in general true that the more AI is used in predictions, the less it can predict, if the AI is not a part of the system it is predicting (predicting sunspot activity, for example). But in the kinds of cases many people are interested in, such as AI for financial prediction, it’s a different matter, because in finance, the AI is typically part of the system it is trying to predict. That is, the predictions the AI makes are used to take action (buy, sell, short, whatever) in that system. And the presence of predictors (machine or human) in a system, taking action in that same system on the basis of their predictions, makes the system more difficult to predict (by machines or humans).Why is this so? To see why, consider a relatively simple, one-on-one system with two members: you and your opponent. The best way to predict what your opponent is going to do is to model them: figure out what their strategy is, and predict that they will do whatever that strategy recommends in the current situation. You then choose your best action given what you predict they will do. But if they are also a predictor like you, then you both have a problem. Even if you know what your opponent’s strategy is — it’s to predict what you are going to do, and act appropriately — predicting what they will do depends on what they predict you will do, which in turn depends on your prediction of what they are going to do, which is back where we started. Thus, the behaviour of the system is an unstable, chaotic circle.This doesn’t mean that we’ll stop using AIs to predict — on the contrary, they will become (even more) obligatory, just to stay in the predictive arms race. To fail to use them would make you more easily predictable, and thus at a relative disadvantage.
Today MindShare hosts their annual London Huddle (http://www.mindshareworld.com/uk/huddle). As part of the event, at 3pm I’ll be chatting with LoopMe’s Head of Agency, Jack Edmonds, about some of the ethical issues concerning, and implications of, artificial intelligence:
The September 2017 issue of Viva Lewes magazine features a two-page spread by Jacqui Bealing on the robot opera project that Evelyn Ficarra, Ed Hughes and I have been collaborating on (as detailed in earlier updates on this blog). The article is available at:
For convenience, I include a copy of the article below.
I’ve just posted on LinkedIn a rare (for me!) piece of near-futurology:
This article is an expansion of “The Shift From Conscious To Unconscious Data” that I wrote earlier this year for Culture Vulture Issue 09: Smart Living, pp 48-49, MindShare.
For convenience, I’ve included the text here.
The move to unconscious data and AI beyond deep learning will require substantial algorithmic – and ethical – innovation
In a way, the hype is right: the robots are here. It might not look like it, but they are. If we understand robots to be artificial agents that can, based on information they revceive from their environment, autonomously take action in the world, then robots are in our cars, homes, hospitals, schools, workplaces, and our own bodies, even if they don’t have the expected humanoid shape and size. And more are on the way. What will it be like to live in the near-future world full of sensing, adapting, and acting technologies? Will it be like things are now, but more so (whatever that might mean)? Or will it be qualitatively different?
There are several indications that the technological changes about to occur will result in qualitative shifts in the structure of our lives.
We can expect a dramatic increase in the quantity, kinds, and temporal resolution of the sensors our embedded smart technologies will use.
One example involves sensors. We can expect a dramatic increase in the quantity, kinds, and temporal resolution of the sensors our embedded smart technologies will use. And in many cases these sensors will be aimed directly at us, the users. Most significantly, we will see a shift from technologies that solely use symbolic, rational-level data that we consciously provide (our purchasing history, our stated preferences, the pages we “like”, etc.) to ones that use information about us that is even more revealing, despite (or because) it is unconscious/not under our control. It will start with extant, ubiquitous input devices used in novel ways (such as probing your emotional state or unexpressed preferences by monitoring the dynamics of your mouse trajectories over a web page), but will quickly move to an uptake and exploitation of sensors that more directly measure our bio-indicators, such as eye trackers, heart rate monitors, pupillometry, etc.
We can expect an initial phase of applications and systems that are designed to shift users into purchasing/adopting, becoming proficient with, and actively using these technologies: Entertainment will no doubt lead the way, but other uses of the collected data (perhaps buried in EULAs) will initially piggyback on them. Any intrusiveness or inconvenience of these sensors, initially tolerated for the sake of the game or interactive cinematic experience, will give way to familiarity and acceptance, allowing other applications to follow.
Any intrusiveness or inconvenience of these sensors, initially tolerated for the sake of the game or interactive cinematic experience, will give way to familiarity and acceptance, allowing other applications to follow.
The intimate, sub-rational, continuous, dynamic and temporally-precise data these sensors will provide will enable exquisitely precise user-modelling (or monitoring) of a kind previously unimaginable. This in turn will enable technologies that will be able (or at least seem) to understand our intentions and anticipate our needs and wants. Key issues will involve ownership/sharing/selling/anonymisation of this data, the technologies for and rights to shielding oneself from such sensing (e.g., in public spaces) and the related use of decoys (technologies designed to provide false readings to these sensors), and delimiting the boundaries of responsibility and informed consent in cases where technologies can side-step rational choice and directly manipulate preferences and attitudes.
The engine behind this embedded intelligence will be artificial intelligence. The recent (and pervasively covered) rise of machine learning has been mainly to with recent advances in two factors: 1) the enormous data sets the internet has created, and 2) blindingly fast hardware such as GPUs. We can continue to expect advances in 1) with the new kinds and quantities of data that the new sensors will provide. The second factor is hard to predict, with experts differing on whether we will continue to reap the benefits of Moore’s Law, and on whether quantum computation is capable of delivering on its theoretical promise anytime soon.
The algorithms exploiting these two factors of data and speed have typically been minor variations on and recombination of those developed in the 80s and 90s. Although quantum computation might (or might not) allow the increased hardware trend to continue, the addition of further kinds of data will allow novel technologies in all spheres that are exquisitely tuned to the user.
On the other hand, the increased quantity of data, especially its temporal resolution, will require advances in machine learning algorthims – expect a move beyond simple, feedforward architectures from the 90s to systems that develop expectations about what they will sense (and do), and that use these expectations as a way to manage information overload by attending only to the important parts of the data.
This will yield unprecedented degrees of dynamic integration between us and our technology. What is often neglected in thinking about the pros and cons of such technologies is the way we adapt to them. One of the most exciting prospects, but also unforeseen risks, and needs to be thought of carefully. In particular, new conceptual tools.
The sooner we can develop and deploy into society at large a machine ethics that locates responsibility with the correct humans, and not with the technologies themselves, the better.
Embedded, autonomous technology will lead to situations that, given our current legal and ethical systems, will appear ambiguous: who is to blame when something goes wrong involving a technology that has adapted to a user’s living patterns? Is it the user, for having a lifestyle that was too far outside of the “normal” lifestyles used in the dynamic technology’s testing and quality control? Or is the fault of the designer/manufacturer/retailer/provider/procurer of that technology, for not ensuring that the technology would yield safe results in a greater number of user situations, of for not providing clear guidelines to the user on what “normal” use is? Given this conundrum, the temptation will often be to blame neither, but blame the technology itself instead, especially if it is made to look humanoid, given a name, voice, “personality” etc. We might very well see a phase of cynical, gratuitous use of anthromorphism whose main function is misdirect potential blame by “scapegoating the robot”. The sooner we can develop and deploy into society at large a machine ethics that locates responsibility with the correct humans, and not with the technologies themselves, the better.
On Monday Kit Bradshaw of Sky News Swipe interviewed some of us involved with the recent robot operas at the University of Sussex (see http://www.sussex.ac.uk/broadcast/read/40568).
The report is due to air this weekend on Sky News at the following times (UK):
- Friday: 2130
- Saturday: 1030, 1430 & 1630
- Sunday: 1130, 1430 & 1630
(Subject to cancellation if there are breaking news stories or other big events).
I hope one particular bit didn’t get left on the cutting room floor. When Kit asked the robot “How did it feel to sing in a robot opera?”, the robot replied “Hmm. Well I’m sure it was wonderful for the other performers and the audience, but not for me. I’m not conscious, so nothing feels like anything for me. In fact, I don’t even understand the words I am saying right now!”
As mentioned in a previous post, I was invited to speak at “AI: The Future of Us” at the British Museum earlier this month. Rather than give a lecture, it was decided that I should have a “fireside chat” with Stephen Upstone, the CEO and founder of LoopMe, the AI company hosting the event. We had fun, and got some good feedback, so we’re looking into doing something similar this Autumn — watch this space.
Our discussion was structured around the following questions/topics being posed to me:
- My background (what I do, what is Cognitive Science, how did I start working in AI, etc.)
- What is the definition of consciousness and at what point can we say an AI machine is conscious?
- What are the ethical implications for AI? Will we ever reach the point at which we will need to treat AI like a human? And how do we define AI’s responsibility?
- Where do you see AI 30 years from now? How do you think AI will revolutionise our lives? (looking at things like smart homes, healthcare, finance, saving the environment, etc.)
- So on your view, how far away are we from creating a super intelligence that will be better than humans in every aspect from mental to physical and emotional abilities? (Will we reach a point when the line between human and machine becomes blurred?)
- So is AI not a threat? As Stephen Hawking recently said in the Guardian “AI will be either the best or worst thing for humanity”. What do you think? Is AI something we don’t need to be worried about?
You can listen to our fireside chat here.
My piece on “What philosophy can offer AI” is now up at AI firm LoopMe’s blog. This is part of the run-up to my speaking at their event, “Artificial Intelligence: The Future of Us”, to be held at the British Museum next month. Here’s what I wrote (the final gag is shamelessly stolen from Peter Sagal of NPR’s “Wait Wait… Don’t Tell Me!”):
Despite what you may have heard, philosophy at its best consists in rigorous thinking about important issues, and careful examination of the concepts we use to think about those issues. Sometimes this analysis is achieved through considering potential exotic instances of an otherwise everyday concept, and considering whether the concept does indeed apply to that novel case — and if so, how.
In this respect, artificial intelligence (AI), of the actual or sci-fi/thought experiment variety, has given philosophers a lot to chew on, providing a wide range of detailed, fascinating instances to challenge some of our most dearly-held concepts: not just “intelligence”, “mind”, and “knowledge”, but also “responsibility”, “emotion”, “consciousness”, and, ultimately, “human”.
But it’s a two-way street: Philosophy has a lot to offer AI too.
Examining these concepts allows the philosopher to notice inconsistency, inadequacy or incoherence in our thinking about mind, and the undesirable effects this can have on AI design. Once the conceptual malady is diagnosed, the philosopher and AI designer can work together (they are sometimes the same person) to recommend revisions to our thinking and designs that remove the conceptual roadblocks to better performance.
This symbiosis is most clearly observed in the case of artificial general intelligence (AGI), the attempt to produce an artificial agent that is, like humans, capable of behaving intelligently in an unbounded number of domains and contexts
The clearest example of the requirement of philosophical expertise when doing AGI concerns machine consciousness and machine ethics: at what point does an AGI’s claim to mentality become real enough that we incur moral obligations toward it? Is it at the same time as, or before, it reaches the point at which we would say it is conscious? At when it has moral obligations of its own? And is it moral for us to get to the point where we have moral obligations to machines? Should that even be AI’s goal?
These are important questions, and it is good that they are being discussed more even though the possibilities they consider aren’t really on the horizon.
Less well-known is that philosophical sub-disciplines other than ethics have been, and will continue to be, crucial to progress in AGI.
It’s not just the philosophers that say so; Quantum computation pioneer and Oxford physicist David Deutsch agrees: “The whole problem of developing AGIs is a matter of philosophy, not computer science or neurophysiology”. That “not” might overstate things a bit (I would soften it to “not only”), but it’s clear that Deutch’s vision of philosophy’s role in AI will not be limited to being a kind of ethics panel that assesses the “real work” done by others.
What’s more, philosophy’s relevance doesn’t just kick in once one starts working on AGI — which substantially increases its market share. It’s an understatement to say that AGI is a subset of AI in general. Nearly all, of the AI that is at work now providing relevant search results, classifying images, driving cars, and so on is not domain-independent AGI – it is technological, practical AI, that exploits the particularities of its domain, and relies on human support to augment its non-autonomy to produce a working system. But philosophical expertise can be of use even to this more practical, less Hollywood, kind of AI design.
The clearest point of connection is machine ethics.
But here the questions are not the hypothetical ones about whether a (far-future) AI has moral obligations to us, or we to it. Rather the questions will be more like this:
– How should we trace our ethical obligations to each other when the causal link between us and some undesirable outcome for another, is mediated by a highly complex information process that involves machine learning and apparently autonomous decision-making?
– Do our previous ethical intuitions about, e.g., product liability apply without modification, or do we need some new concepts to handle these novel levels of complexity and (at least apparent) technological autonomy?
As with AGI, the connection between philosophy and technological, practical AI is not limited to ethics. For example, different philosophical conceptions of what it is to be intelligent suggest different kinds of designs for driverless cars. Is intelligence a disembodied ability to process symbols? Is it merely an ability to behave appropriately? Or is it, at least in part, a skill or capacity to anticipate how one’s embodied sensations will be transformed by the actions one takes?
Contemporary, sometimes technical, philosophical theories of cognition are a good place to start when considering what way of conceptualising the problem and solution will be best for a given AI system, especially in the case of design that has to be truly ground breaking to be competitive.
Of course, it’s not all sweetness and light. It is true that there has been some philosophical work that has obfuscated the issues around AI, thereby unnecessarily hindering progress. So, to my recommendation that philosophy play a key role in artificial intelligence, terms and conditions apply. But don’t they always?
I was interviewed by Verdict.co.uk recently about the ethics of AI in healthcare. One or
two remarks of mine from that interview
are included near the end of this piece that appeared last week:
My views are on this are considerably more nuanced than these quotes suggest, so I am thinking of turning my extensive prep notes for the interview into a piece to be posted here and/or on a site like TheConversation.com. These thoughts are completely distinct from the ones included in the paper Steve Torrance and I wrote a few years back, “Modelling consciousness-dependent expertise in machine medical moral agents“.
“We need to be worried about criminals using AI in three different ways. One is to evade detection: if one has some artificial intelligence technology, one might be able, for instance, to engage in certain kinds of financial crimes in a way that can be randomized in a particular way that avoids standard methods of crime detection. Or criminals could use computer programs to notice patterns in security systems that a human couldn’t notice, and find weaknesses that a human would find very hard to identify… And then finally a more common use might be of AI to just crack passwords and codes, and access accounts and data that people previously could leave secure. So these are just three examples of how AI would be a serious threat to security of people in general if it were in the hands of the wrong people.”
“I think it would be a tragedy if we let fear of remote possibilities of AI systems committing crimes, if that fear stopped us from investigating artificial intelligence as a positive technology that might help us solve some of the problems our world is facing now. I’m an optimist in that I think that AI as a technology can very well be used for good, and if we’re careful, can be of much more benefit than disadvantage.”
“I think that as long as legislators and law enforcement agencies understand what the possibilities are, and understand that the threat is humans committing crimes with AI rather than robots committing crimes, then I think we can head off any potential worries with the appropriate kinds of regulations and updating of our laws.”