Category Archives: AI

Your smart home is trying to reprogram you

Murray Goulden, University of Nottingham

A father finds out his daughter is pregnant after algorithms identify tell-tale patterns in the family’s store card data. Police charge suspects in two separate murder cases based on evidence taken from a Fitbit tracker and a smart water meter. A man sues Uber for revealing his affair to his wife.

Stories such as these have been appearing in ever greater numbers recently, as the technologies involved become ever more integrated into our lives. They form part of the Internet of Things (IoT), the embedding of sensors and internet connections into the fabric of the world around us. Over the last year, these technologies, led by Amazon’s Alexa and Google’s Home, have begun to make their presence felt in our domestic lives, in the form of smart home devices that allow us to control everything in the house just by speaking.

We might look at stories like those above as isolated technical errors, or fortuitous occurrences serving up justice. But behind them, something much bigger is going on: the development of an entire class of technologies seeking to remake the fundamentals of our everyday lives.

Breaking the social order

These technologies want to be ubiquitous, seamlessly spanning the physical and virtual worlds, and awarding us frictionless control over all of it. The smart home promises a future in which largely hidden tech provides us with services before we’ve even realised we want them, using sensors to understand the world around us and navigate it on our behalf. It’s a promise of near limitless reach, and effortless convenience.

It’s also completely incompatible with social realities. The problem is, our lives are full of limits, and nowhere is this better demonstrated than in the family home, which many of these technologies target. From the inside, these places often feel all too chaotic but they’re actually highly ordered. This a world full of boundaries and hierarchies: who gets allowed into which rooms, who gets the TV remote, who secrets are shared with, who they are hidden from.

Much of this is mundane, but if you want to see how important these kind of systems of order are to us, consider the “breaching experiments” of sociologist Erving Goffman in the 1960s. Goffman set out to deliberately break the rules behind social order in order to reveal them. Conducting the most humdrum interaction in the wrong way was shown to elicit reactions in others that ranged from distress to outright violence. You can try this yourself. When sat round the dinner table try acting entirely normal save for humming loudly every time someone starts speaking, and see how long it is before someone loses their temper.

The technologies of the smart home challenge our orderings in countless small ways. A primary limitation is their inability to recognise boundaries we take for granted. I had my own such experience a week ago while sitting in my front room. With the accidental slip of a finger I streamed a (really rather sweary) YouTube video from my phone onto my neighbour’s TV, much to the surprise of their four-year-old daughter in the middle of watching Paw Patrol.

Slip of the finger.

A finger press was literally all it took, of a button that can’t be disabled. That, and the fact that I have their Wi-Fi password on my phone as I babysit for them from time to time. To current smart home technology, those who share Wi-Fi networks share everything.

Of course, we do still have passwords to at least offer some crude boundaries. And yet smart home technologies excel at creating data that doesn’t fit into the neat, personalised boxes offered by consumer technologies. This interpersonal data concerns groups, not individuals, and smart technologies are currently very stupid when it comes to managing it. Sometimes this manifests itself in humorous ways, like parents finding “big farts” added to their Alexa-generated shopping list. Other times it’s far more consequential, as in the pregnant daughter story above.

In our own research into this phenomena, my colleagues and I have discovered an additional problem. Often, this tech makes mistakes, and if it does so with the wrong piece of data in the wrong context, the results could be disastrous. In one study we carried out, a wife ended up being informed by a digital assistant that her husband had spent his entire work day at a hotel in town. All that had really happened was an algorithm had misinterpreted a dropped GPS signal, but in a relationship with low trust, a suggestion of this kind could be grounds for divorce.

Rejecting the recode

These technologies are, largely unwittingly, attempting to recode some of the most basic patterns of our everyday lives, namely how we live alongside those we are most intimate with. As such, their placement in our homes as consumer products constitute a vast social experiment. If the experience of using them is too challenging to our existing orderings, the likelihood is we will simply come to reject them.

This is what happened with Google Glass, the smart glasses with a camera and heads-up-display built into them. It was just too open to transgressions of our notions of proper behaviour. This discomfort even spawned the pejorative “Glasshole” to describe its users.

Undoubtedly, the tech giants selling these products will continue to tweak them in the hope of avoiding similar outcomes. Yet a fundamental challenge remains: how can technologies that sell themselves on convenience be taught the complexities and nuances of our private worlds? At least without needing us to constantly hand-hold them, entirely negating their aim of making our lives easier.

The ConversationTheir current approach – to ride roughshod over the social terrain of the home – is not a sustainable approach. Unless and until the day we have AI systems capable of comprehending human social worlds, it may be that the smart home promised to us ends up being a lot more limited than its backers imagine. Right now, if you’re taking part in this experiment, the advice must be to proceed with caution, because when it comes to social relationships, the smart home remains pretty dumb. And be very careful not to stream things to your neighbour’s TV.

Murray Goulden, Research Fellow, University of Nottingham

This article was originally published on The Conversation. Read the original article.

Google’s latest Go victory shows machines are no longer just learning, they’re teaching

Mark Robert Anderson, Edge Hill University

Just over 20 years ago was the first time a computer beat a human world champion in a chess match, when IBM’s Deep Blue supercomputer beat Gary Kasparov in a narrow victory of 3½ games to 2½. Just under a decade later, machines were deemed to have conquered the game of chess when Deep Fritz, a piece of software running on a desktop PC, beat 2006 world champion Vladimir Kramnik. Now the ability of computers to take on humanity has taken a step further by mastering the far more complex board game Go, with Google’s AlphaGo program beating world number one Ke Jie twice in a best-of-three series. The Conversation

This signifcant milestone shows just how far computers have come in the past 20 years. DeepBlue’s victory at chess showed machines could rapidly process huge amounts of information, paving the way for the big data revolution we see today. But AlphaGo’s triumph represents the development of real artificial intelligence by a machine that can recognise patterns and learn the best way to respond to them. What’s more, it may signify a new evolution in AI, where computers not only learn how to beat us but can start to teach us as well.

Go is considered one of the world’s most complex board games. Like chess, it’s a game of strategy but it also has several key differences that make it much harder for a computer to play. The rules are relatively simple but the strategies involved to play the game are highly complex. It is also much harder to calculate the end position and winner in the game of Go.

It has a larger board (a 19×19 grid rather than an 8×8 one) and an unlimited number of pieces, so there are many more ways that the board can be arranged. Whereas chess pieces start in set positions and can each make a limited number of moves each turn, Go starts with a blank board and players can place a piece in any of the 361 free spaces. Each game takes on average twice as many turns as chess and there are six times as many legal move options per turn.

Each of these features means you can’t build a Go program using the same techniques as for chess machines. These tend to use a “brute force” approach of analysing the potential of large numbers of possible moves to select the best one. Feng-Hsiung Hsu, one of the key contributors to the DeepBlue team, argued in 2007 that applying this strategy to Go would require a million-fold increase in processing speed over DeepBlue so a computer could analyse 100 trillion positions per second.

Learning new moves

The strategy used by AlphaGo’s creators at Google subsidiary DeepMind was to create an artificial intelligence program that could learn how to identify favourable moves from useless ones. This meant it wouldn’t have to analyse all the possible moves that could be made at each turn. In preparation for its first match against professional Go player Lee Sedol, AlphaGo analysed around 300m moves made by professional Go players. It then used what are called deep learning and reinforcement learning techniques to develop its own ability to identify favourable moves.

But this wasn’t enough to enable AlphaGo to defeat highly ranked human players. The software was run on custom microchips specifically designed for machine learning, known as tensor processing units (TPUs), to support very large numbers of computations. This seems similar to the approach used by the designers of DeepBlue, who also developed custom chips for high-volume computation. The stark difference, however, is that DeepBlue’s chips could only be used for playing chess. AlphaGo’s chips run Google’s general-purpose AI framework, Tensorflow, and are also used to power other Google services such as Street View and optimisation tasks in the firm’s data centres.

Lesson for us all

The other thing that has changed since DeepBlue’s victory is the respect that humans have for their computer opponents. When playing chess computers, it was common for the human players to adopt so-called anti-computer tactics. This involves making conservative moves to prevent the computer from evaluating positions effectively.

In his first match against AlphaGo, however, Ke Jie, adopted tactics that had previously been used by his opponent to beat it at its own game. Although this attempt failed, it demonstrates a change in approach for leading human players taking on computers. Instead of trying to stifle the machine, they have begun trying to learn from how it played in the past.

In fact, the machine has already influenced the professional game of Go, with grandmasters adopting AlphaGo’s strategy during their tournament matches. This machine has taught humanity something new about a game it has been playing for over 2,500 years, liberating us from the experience of millennia.

What then might the future hold for the AI behind AlphaGo? The success of DeepBlue triggered rapid developments that have directly impacted the techniques applied in big data processing. The benefit of the technology used to implement AlphaGo is that it can already be applied to other problems that require pattern identification.

For example, the same techniques have been applied to the detection of cancer and to create robots that can learn to do things like open doors, among many other applications. The underlying framework used in AlphaGo, Google’s TensorFlow, has been made freely available for developers and researchers to build new machine-learning programs using standard computer hardware.

More excitingly, combining it with the many computers available through the internet cloud creates the promise of delivering machine-learning supercomputing. When this technology matures then the potential will exist for the creation of self-taught machines in wide-ranging roles that can support complex decision-making tasks. Of course, what may be even more profound are the social impacts of having machines that not only teach themselves but teach us in the process.

Mark Robert Anderson, Professor in Computing and Information Systems, Edge Hill University

This article was originally published on The Conversation. Read the original article.

No problem too big #1: Artificial intelligence and killer robots

Adam Hulbert, UNSW

This is the first episode of a special Speaking With podcast series titled No Problem Too Big, where a panel of artists and researchers speculate on the end of the world as though it has already happened. The Conversation

It’s not the world we grew up in. Not since artificial intelligence. The machines have taken control.

Three fearless researchers gather in the post-apocalyptic twilight: a computer scientist, a mechanical engineer and a sci-fi author.

Together, they consider the implications of military robots and autonomous everything, and discover that the most horrifying post-apocalyptic scenario might look something like unrequited robot love.

Joanne Anderton is an award-winning author of speculative fiction stories for anyone who likes their worlds a little different. More information about Joanne and her novels can be found here.

No Problem Too Big is created and hosted by Adam Hulbert, who lectures in media and sonic arts at the University of New South Wales. It is produced with the support of The Conversation and University of New South Wales.

Sound design by Adam Hulbert.

Theme music by Phonkubot.

Additional music:

Beast/Decay/Mist by Haunted Me (via Free Music Archive)

Humming Ghost by Haunted Me (via Free Music Archive)

Additional audio:

Stephen Hawking interview, BBC News

Adam Hulbert, Sonic Arts Convener, UNSW

This article was originally published on The Conversation. Read the original article.

Why using AI to sentence criminals is a dangerous idea

Christopher Markou, University of Cambridge

Artificial intelligence is already helping determine your future – whether it’s your Netflix viewing preferences, your suitability for a mortgage or your compatibility with a prospective employer. But can we agree, at least for now, that having an AI determine your guilt or innocence in a court of law is a step too far?

Worryingly, it seems this may already be happening. When American Chief Justice John Roberts recently attended an event, he was asked whether he could forsee a day “when smart machines, driven with artificial intelligences, will assist with courtroom fact finding or, more controversially even, judicial decision making”. He responded: “It’s a day that’s here and it’s putting a significant strain on how the judiciary goes about doing things”.

Roberts might have been referring to the recent case of Eric Loomis, who was sentenced to six years in prison at least in part by the recommendation of a private company’s secret proprietary software. Loomis, who has a criminal history and was sentenced for having fled the police in a stolen car, now asserts that his right to due process was violated as neither he nor his representatives were able to scrutinise or challenge the algorithm behind the recommendation.

The report was produced by a software product called Compas, which is marketed and sold by Nortpointe Inc to courts. The program is one incarnation of a new trend within AI research: ones designed to help judges make “better” – or at least more data-centric – decisions in court.

While specific details of Loomis’ report remain sealed, the document is likely to contain a number of charts and diagrams quantifying Loomis’ life, behaviour and likelihood of re-offending. It may also include his age, race, gender identity, browsing habits and, I don’t know … measurements of his skull. The point is we don’t know.

What we do know is that the prosecutor in the case told the judge that Loomis displayed “a high risk of violence, high risk of recidivism, high pretrial risk.” This is standard stuff when it comes to sentencing. The judge concurred and told Loomis that he was “identified, through the Compas assessment, as an individual who is a high risk to the community”.

The Wisconsin Supreme Court convicted Loomis, adding that the Compas report brought valuable information to their decision, but qualified it by saying he would have received the same sentence without it. But how can we know that for sure? What sort of cognitive biases are involved when an all-powerful “smart” system like Compas suggests what a judge should do?

Unknown use

Now let’s be clear, there is nothing “illegal” about what the Wisconsin court did – it’s just a bad idea under the circumstances. Other courts are free to do the same.

Worryingly, we don’t actually know the extent to which AI and other algorithms are being used in sentencing. My own research indicates that several jurisdictions are “trialling” systems like Compas in closed trials, but that they cannot announce details of their partnerships or where and when they are being used. We also know that there are a number of AI startups that are competing to build similar systems.

However, the use of AI in law doesn’t start and end with sentencing, it starts at investigation. A system called VALCRI has already been developed to perform the labour-intensive aspects of a crime analyst’s job in mere seconds – wading through tonnes of data like texts, lab reports and police documents to highlight things that may warrant further investigation.

The UK’s West Midlands Police will be trialling VALCRI for the next three years using anonymised data – amounting to some 6.5m records. A similar trial is underway from the police in Antwerp, Belgium. However, past AI and deep learning projects involving massive data sets have been have been problematic.

Benefits for the few?

Technology has brought many benefits to the court room, ranging from photocopiers to DNA fingerprinting and sophisticated surveillance techniques. But that doesn’t mean any technology is an improvement.

Algorithms can be racist, too.
Vintage Tone/Shutterstock

While using AI in investigations and sentencing could potentially help save time and money, it raises some thorny issues. A report on Compas from ProPublica made clear that black defendants in Broward County Florida “were far more likely than white defendants to be incorrectly judged to be at a higher rate of recidivism”. Recent work by Joanna Bryson, professor of computer science at the University of Bath, highlights that even the most “sophisticated” AIs can inherit the racial and gender biases of those who create them.

What’s more, what is the point of offloading decision making (at least in part) to an algorithm on matters that are uniquely human? Why do we go through the trouble of selecting juries composed of our peers? The standard in law has never been one of perfection, but rather the best that our abilities as mere humans allow us. We make mistakes but, over time, and with practice, we accumulate knowledge on how not to make them again – constantly refining the system.

What Compas, and systems like it, represent is the “black boxing” of the legal system. This must be resisted forcefully. Legal systems depend on continuity of information, transparency and ability to review. What we do not want as a society is a justice system that encourages a race to the bottom for AI startups to deliver products as quickly, cheaply and exclusively as possible. While some AI observers have seen this coming for years, it’s now here – and it’s a terrible idea.

An open source, reviewable version of Compas would be an improvement. However, we must ensure that we first raise standards in the justice system before we begin offloading responsibility to algorithims. AI should not just be an excuse not to invest.

While there is a lot of money to be made in AI, there is also a lot of genuine opportunity. It can change a lot for the better if we get it right, and ensure that its benefits accrue for all and don’t just entrench power at the top of the pyramid.

I have no perfect solutions for all these problems right now. But I do know that when it comes to the role of AI in law we must ask in which context they are being used, for what purposes and with what meaningful oversight. Until those questions can be answered with certainty, be very very sceptical. Or at the very least know some very good lawyers.

Christopher Markou, PhD Candidate, Faculty of Law, University of Cambridge

This article was originally published on The Conversation. Read the original article.

What problems will AI solve in future? An old British gameshow can help explain

Ian Miguel, University of St Andrews and Patrick Prosser, University of Glasgow

The Crystal Maze, the popular UK television show from the early 1990s, included a puzzle that is very useful for explaining one of the main conundrums in artificial intelligence. The puzzle appeared a few times in the show’s Futuristic Zone, one of four zones in which a team of six contestants sought to win “time crystals” that bought time to win prizes at the Crystal Dome at the end of the show.

Never solved in the two-minute time frame, the puzzle was based on a network of connected red circles (see clip below). On the wall was written a clue: “No consecutive letters in adjacent circles”. The letters A to H were printed on circular plates which could be fitted onto each circle.

So what is the right approach? We might start by considering which circles are hardest to label. With a little thought, you might choose the two middle circles, since they have the most connections. Now consider which letters might best be put on them: A and H are natural candidates because they each have only one neighbour (B and G, respectively). We might put them into the grid like this:

Ian Miguel

We can now do some deduction to eliminate incompatible possibilities for the other circles. For example the top-left circle is connected to both of the central circles. Since no consecutive letters can appear in connected circles, it can’t now contain B or G. Similar reasoning can be applied to the top-right, bottom-left, and bottom-right circles:

Ian Miguel

The leftmost and rightmost circles have to be treated differently, since each is only adjacent to one central circle. On the left we can rule out B, and on the right we can rule out G:

Ian Miguel

Look carefully at the remaining options and only the leftmost circle still has G as a possibility, and only the rightmost circle has B. Once we put them in place, we can remove further possibilities from the adjacent circles:

Ian Miguel

It is now time to make another guess. It seems reasonable to start with the top-left circle and try its first possibility: C. This allows us to rule out D from the adjacent circle and C from the bottom left. If we now guess E for the top-right circle, the bottom-left circle has only one possibility left, D, which leaves just F for the bottom-right circle. We have a solution:

Ian Miguel

Decisions, decisions

This puzzle is an example of a much wider class of decision-making problems that arise in our lives, such as rostering decisions in a hospital or factory, scheduling buses or trains, or designing medical experiments. To save us the aggravation of coming up with the best solutions, one of the challenges for artificial intelligence is to develop a general way of representing and reasoning about them.

One method is known as the constraint satisfaction problem. Just like our Crystal Maze puzzle, problems that fit this model involve a set of required decisions (“cover each circle with a plate”); a fixed set of possibilities (“use the plates from A to H provided”); and a set of constraints that allow only certain combinations of possibilities (“no consecutive letters in adjacent circles”). If you input the requirements for your particular problem into a piece of software known as a constraint solver, it can then try to solve it. It will do this in much the same way as we solved the puzzle: it combines guessing (we call this “search”) with deduction, ruling out possibilities that cannot be part of a solution based on the decisions made so far.

The greatest challenge for programmers in this field is that as you increase the size of the input problem, it quickly becomes much harder to find solutions. This is directly related to how the software “guesses” the answer. Although our guesses proved correct in our simple puzzle, in AI they can often lead us down blind alleys. With large problems there can be a vast number of possibilities and a similarly vast number of dead ends.

One key question is whether there is some way of reaching solutions without going down these alleys. As yet, we don’t know. This directly relates to one of the most important open questions in computer science, the P vs NP problem, for which the Clay Mathematics Institute in the US is offering Us$1m (£657,000) for a solution. It essentially asks whether every problem whose answer can be checked quickly by a computer can also be quickly solved by a computer.

Until someone solves it, the prevailing view is that it cannot. If so, our software does have to search through all the possible guesses, in which case we need to make it as efficient as possible. One important factor here is the search strategy – which decision we tell the computer to focus on next and which value we assign to it. Also very important is what we decide are the requirements for the particular problem. Mapping our puzzle to a constraint satisfaction template was straightforward, but in real life there are often many different options. Choosing the right strategy and model can be the difference between finding a quick solution and failing in any practical amount of time.

We have now reached the stage where the latest constraint-solving software can solve far more complex practical problems than, say, ten years ago. It was used to plan the scientific activities of the Philae comet lander last year, for instance. It also offers a better way of organising evacuation schedules for large-scale disasters.

Constraint solving has found most success with scheduling problems, but there are other similar AI tools that are more useful for other types of questions. We won’t go into them here, but they include the likes of propositional satisfiability, evolutionary algorithms and mathematical programming techniques. The job of specialists is to analyse a problem, identify which combination of tools will be the most successful for a particular case, and put together a bespoke piece of software. Once computers can do this analysis and identification, hopefully only a few years in the future, we will have made a huge leap forward. Meanwhile, the battle to make each of these tools as powerful as possible continues.

Ian Miguel, Professor of Computer Science, University of St Andrews and Patrick Prosser, Senior Lecturer in Computer Science, University of Glasgow

This article was originally published on The Conversation. Read the original article.

How to teach Deep Blue to play poker and deliver groceries

Graham Kendall, University of Nottingham

Deep Blue gained world-wide attention in 1997 when it defeated the then chess world champion Garry Kasparov. But playing chess was all that Deep Blue could do. Ask it to play another game, even a simpler one, such as checkers, and Deep Blue would not even know how to play at beginner level. The same is also true of many other programs that can beat humans. Computers that can play poker cannot play bridge.

Royal Flush.
Images of Money

This type of tailored software development is also apparent in systems that we rely on every day. A system that produces nurse rosters may not be able to cope with producing shift patterns for a factory, even though they are both personnel scheduling systems. Programs that plan delivery routes of an online supermarket cannot usually be used to schedule appointments for servicing home appliances, even though they are both examples of a Vehicle Routing Problem.

In recent years there has been a growing interest in a field called hyper-heuristics, which aims to develop more general computer systems. The idea is to build systems that are not tailored for just one type of problem, but which can be reused for a wide range of problems.

The figure below shows a typical hyper-heuristic framework. Let’s assume that this framework is being used to tackle a nurse rostering problem, where we have to assign nurses to work a certain number of shifts over a certain time period, say a week.

Hyper-heuristic Framework.

If we start with a possible shift pattern (perhaps from the previous week), we can do certain things to improve it. For example, we could move a nurse from one shift to another, we could swap two nurses or we could remove all nurses from a certain shift (say the Wednesday evening shift) and replace them with nurses that do not meet their contractual arrangements, just to give a few examples. These changes to the shift pattern are usually called heuristics.

The important thing is that we have a number of these low-level heuristics that we can use to improve the current roster. All these heuristics are placed in the bottom of the framework. We now choose one of these heuristics and execute it (for instance, swap one nurse with another). We repeat the process of choosing and executing a heuristic over and over again, in the hope that we will gradually get a better roster. The quality of the roster is measured by the evaluation function, which checks the outcome.

The key to this approach is to decide in which order to execute the low-level heuristics. This is where the top part of the framework comes into play. The hyper-heuristic looks at the state of the system and decides which heuristic to execute. This is repeated until we decide to stop (maybe after a certain period of time, or after we have executed the low-level heuristics a certain number of times).

What makes a hyper-heuristic different, from other heuristic-selecting algorithms, is the “domain barrier”. This stops the higher level hyper-heuristic knowing anything about the problem it is trying to solve. The hyper-heuristic only has access to data that is common to any problem. This includes how long each low-level heuristic took to execute, the track record of each low-level heuristic (how well it has performed), how pairs of low-level heuristics work with each other, to give just a few examples.

The benefit of the domain barrier is that we can replace the low-level heuristics, and the evaluation function, with another type of problem. As the hyper-heuristic has no knowledge of the problem being tackled we would hope that we can use the same higher level algorithm to tackle this new problem. And, indeed, this has been shown to be the case in a large number of scientific problems.

The challenge in hyper-heuristics lies in developing a robust high-level strategy that is able to adapt to as many different problems as possible. We are still some way off having a hyper-heuristic that is able to produce nurse rosters, plan deliveries and play poker, but, given the pace of progress in this field, we hope to achieve this goal in the not-too-distant future.

Graham Kendall, Professor of Operations Research and Vice-Provost, University of Nottingham

This article was originally published on The Conversation. Read the original article.