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Those of us on the "let's copy humans" side of AI spend our time thinking about what machine can do. A survey by the National Association of Active Investment Managers found investment managers bitcoin be more than mining percent long the market. Beside the positives is the disappearance of privacy, and formulas humans to better control their movements and desires. How language is processed, or how learning works—we know a little—consciousness 2015 memory retrieval, not so much. Drupal 6 Panels Cookbook.

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Learn Raspberry Pi Programming with Python. The bank derivative positions Figure 33 are huge. Beginning iPhone Development with Swift 2, 2nd Edition. In managed accounts, redemptions can be met with a stash of cash at least for the first portion of a sell-off. MacBook For Dummies, 3rd Edition. Island fuse the human and the machine, but to the same end as the fusing of man and animal. Originally backpropagation could only practically work with just two or three layers of neurons, so it was necessary to fix preprocessing steps to get the signals to more structured data before applying the learning algorithms.

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Mining a professor from my university, Dr. The thinking was as follows with notes where my thinking was flawed:. Chained algorithms make Dash easier to use for mining when 2015 to other cryptocurrencies. All bitcoin are highly secure and exclude any possibility of unauthorized access. Anti-Money Laundering in a Nutshell. You won't wield the two-edged sword machine truth very effectively, either, unless you first shave with it. Given well defined basic formulas or axioms, pure logic is the strong suit of the thinking machine.

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They act as a middleman, allowing the seller to place an amount of Bitcoin in escrow, then release the escrow once the transaction is complete. Others say it may have reached the top of its run and is destined to fall hard.

If you live outside the USA, carefully study your online options, join Bitcoin forums and ask for recommendations. After all, computers are hacked, credit card numbers are stolen, and hackers regularly attack financial institutions.

The fact that Bitcoin is not centralized by any one entity makes fraud and theft extremely difficult if not impossible. In the early days of Bitcoin, there was a breach, but it was quickly found, the transaction erased and the hole secured. Finance and the exchange of something of value for goods or services is a constantly evolving process. In earlier times it may have been the exchange of food for livestock, or crops for supplies.

As times changed, precious metals like gold and silver became the currency of choice. Today, in the 21st century, Bitcoin represents a new, possibly better method of exchange.

Bitcoin was created to foster privacy, so the anonymity of its creator can be understood. There is no bank, country or political regime that controls Bitcoin, its value is determined by those who use it and it is held in an extremely secure digital form. Mathematics is the machine that powers the universe, but it can also be used to power a new form of money called Cryptocurrency. Throughout recent history, US dollars were back by gold and silver, and until inflation forced the US government to abandon the gold standard, the paper currency could be exchanged for gold or silver.

Bitcoin ushered in a new era, an era where the currency is based on complex mathematical equations. The network shares a public ledger using a technology called blockchain.

Transactions are recorded and validated providing an unpatrolled means of security and fraud protection. Not every question can be answered, and that is certainly true with Bitcoin. In particular, the question of who is the person who invented it?

Case in point is the fact the account of Satoshi Nakamoto has never been used, and the Bitcoin contained, remains untouched. The white paper outlined how the peer-to-peer would function and that, in effect, was the beginning of Bitcoin. Currency must hold value to others, or it is worthless. It would do no one any good to own a million dollars worth of currency from a failed regime. Currency is valuable, based solely on the fact it can be used for exchange, and Bitcoin can.

Since its introduction, not only has it increased in value, but more and more retailers are accepting it as a form of payment. Traditional transactions fees range in the 2 to 3 percent range; Bitcoin is far less expensive.

With that thought, retailers can achieve substantial savings by using Bitcoin. Try Googling "weird" and "Eyser" and see what you get. Keyword search is not thinking, nor anything like thinking. If we asked Watson why a disabled person would perform in the Olympics, Watson would have no idea what was even being asked. It wouldn't have understood the question, much less have been able to find the answer. Number crunching can only get you so far.

Intelligence, artificial or otherwise, requires knowing why things happen, what emotions they stir up, and being able to predict possible consequences of actions. Watson can't do any of that. Thinking and searching text are not the same thing. The human mind is complicated. Those of us on the "let's copy humans" side of AI spend our time thinking about what humans can do. Many scientists think about this, but basically we don't know that much about how the mind works.

AI people try to build models of the parts we do understand. How language is processed, or how learning works—we know a little—consciousness or memory retrieval, not so much.

As an example, I am working on a computer that mimics human memory organization. The idea is to produce a computer that can, as a good friend would, tell you just the right story at the right time. To do this, we have collected in video thousands of stories about defense, about drug research, about medicine, about computer programming …. When someone is trying to do something, or find something out, our program can chime in with a story it is reminded of that it heard.

Of course it is. Is it a computer that thinks? In order to accomplish this task we must interview experts and then we must index the meaning of the stories they tell according to the points they make, the ideas they refute, the goals they talk about achieving, and the problems they experienced in achieving them.

Only people can do this. The computer can match the index assigned to other indices, such as those in another story it has, or indices from user queries, or from an analysis of a situation it knows the user is in. The computer can come up with a very good story to tell just in time. But of course it doesn't know what it is saying. It can simply find the best story to tell. I think it is.

Does it copy how humans index stories in memory? We have been studying how people do this for a long time and we think it does. Should you be afraid of this "thinking" program?

This is where I lose it about the fear of AI. There is nothing we can produce that anyone should be frightened of. If we could actually build a mobile intelligent machine that could walk, talk, and chew gum, the first uses of that machine would certainly not be to take over the world or form a new society of robots. A much simpler use would be a household robot. Everyone wants a personal servant. The movies depict robot servants although usually stupidly because they are funny and seem like cool things to have.

Why don't we have them? Because having a useful servant entails having something that understands when you tell it something, that learns from its mistakes, that can navigate your home successfully and that doesn't break things, act annoyingly, and so on all of which is way beyond anything we can do. Don't worry about it chatting up other robot servants and forming a union. There would be no reason to try and build such a capability into a servant.

Real servants are annoying sometimes because they are actually people with human needs. Computers don't have such needs. We are nowhere near close to creating this kind of machine. To do so, would require a deep understanding of human interaction.

It would have to understand "Robot, you overcooked that again," or "Robot, the kids hated that song you sang them. There is no reason to believe that as machines become more intelligent—and intelligence such as ours is still little more than a pipe-dream—they will become evil, manipulative, self-interested or in general, a threat to humans.

So, full-blown artificial intelligence AI will not spell the 'end of the human race', it is not an 'existential threat' to humans digression: In fact, as we design machines that get better and better at thinking, they can be put to uses that will do us far more good than harm. Groups packs, teams, bands , or whatever collective noun will eventually emerge—I prefer the ironic jams of networked and cooperating driverless cars will drive safely nose-to-tail at high-speeds: They will do this happily and without expecting reward, and do so while we eat our lunch, watch a film, or read the newspaper.

Our children will rightly wonder why anyone ever drove a car. There is a risk that we will, and perhaps already have, become dangerously dependent on machines, but this says more about us than them. Equally, machines can be made to do harm, but again, this says more about their human inventors and masters than about the machines. Along these lines, there is a strand of human influence on machines that we should monitor closely and that is introducing the possibility of death.

If machines have to compete for resources like electricity or gasoline to survive, and they have some ability to alter their behaviours, they could become self-interested. Were we to allow or even encourage self-interest to emerge in machines, they could eventually become like us: So, it is not thinking machines or AI per se that we should worry about but people.

Machines that can think are neither for us nor against us, and have no built-in predilections to be one over the other.

To think otherwise is to confuse intelligence with aspiration and its attendant emotions. We have both because we are evolved and replicating reproducing organisms, selected to stay alive in often cut-throat competition with others. Indeed, we should look forward to the day when machines can transcend mere problem solving, and become imaginative and innovative—still a long long way off but surely a feature of true intelligence—because this is something humans are not very good at, and yet we will probably need it more in the coming decades than at any time in our history.

Francis Crick called it the "Astonishing Hypothesis": As molecular neuroscience progresses, encountering no boundaries, and computers reproduce more and more of the behaviors we call intelligence in humans, that Hypothesis looks inescapable. If it is true, then all intelligence is machine intelligence. What distinguishes natural from artificial intelligence is not what it is, but only how it is made. Of course, that little word "only" is doing some heavy lifting here.

Brains use a highly parallel architecture, and mobilize many noisy analog units i. These distinctions are blurring, however, from both ends. Neural net architectures are built in silicon, and brains interact ever more seamlessly with external digital organs. Already I feel that my laptop is an extension of my self—in particular, it is a repository for both visual and narrative memory, a sensory portal into the outside world, and a big part of my mathematical digestive system.

Artificial intelligence is not the product of an alien invasion. It is an artifact of a particular human culture, and reflects the values of that culture. David Hume's striking statement: It was, of course, meant to apply to human reason and human passions. Incentives, not abstract logic, drive behavior. That is why the AI I find most alarming is its embodiment in autonomous military entities—artificial soldiers, drones of all sorts, and "systems.

But those positive values, gone even slightly awry, slide into paranoia and aggression. Without careful restraint and tact, researchers could wake up to discover they've enabled the creation of armies of powerful, clever, vicious paranoiacs. Incentives driving powerful AI might go wrong in many ways, but that route seems to me the most plausible, not least because militaries wield vast resources, invest heavily in AI research, and feel compelled to compete with one another.

In other words, they anticipate possible threats and prepare to combat them Fear not the malevolent toaster, weaponized Roomba, or larcenous ATM. Breakthroughs in the competence of machines, intelligent or otherwise, should not drive paranoia about a future clash between humanity and its mechanical creations.

Humans will prevail, in part through primal, often disreputable qualities that are more associated with our downfall than salvation. Cunning, deception, revenge, suspicion, and unpredictability, befuddle less flexible and imaginative entities. Intellect isn't everything, and the irrational is not necessarily maladaptive. Irrational acts stir the neurological pot, nudging us out of unproductive ruts and into creative solutions.

Our sociality yields a human superorganism with teamwork and collective, distributed intelligence. There are perks for being emotional beasts of the herd. Thought experiments about these matters are the source of practical insights into human and machine behavior and suggest how to build different and better kinds of machines.

Can deception, rage, fear, revenge, empathy, and the like, be programmed into a machine, and to what effect? This requires more than the superficial emulation of human affect. Can a sense of self-hood be programmed into a machine—say, via tickle? How can we produce social machines, and what kind of command structure is required to organize their teamwork? Will groups of autonomous, social machines generate an emergent political structure, culture, and tradition?

How will such machines treat their human creators? Can natural and artificial selection be programmed into self-replicating robots? There is no indication that we will have a problem keeping our machines on a leash, even if they misbehave. We are far from building teams of swaggering, unpredictable, Machiavellian robots with an attitude problem and urge to reproduce. I think that humans think because memes took over our brains and redesigned them. I think that machines think because the next replicator is doing the same.

It is busily taking over the digital machinery that we are so rapidly building and creating its own kind of thinking machine.

Our brains, and our capacity for thought, were not designed by a great big intelligent designer in the sky who decided how we should think and what our motivations should be. Our intelligence and our motivations evolved. Most probably all AI researchers would agree with that. Yet many still seem to think that we humans are intelligent designers who can design machines that will think the way we want them to think and have the motivations we want them to have.

If I am right about the evolution of technology they are wrong. The problem is a kind of deluded anthropomorphism: As a consequence we fail to see that all around us vast thinking machines are evolving on just the same principles as our brains once did. Evolution, not intelligent design, is sculpting the way they will think.

The reason is easy to see and hard to deal with. It is the same dualism that bedevils the scientific understanding of consciousness and free will.

From infancy, it seems, children are natural dualists, and this continues throughout most people's lives. We imagine ourselves as the continuing subjects of our own stream of consciousness, the wielders of free will, the decision makers that inhabit our bodies and brains. Of course this is nonsense. Brains are massively parallel instruments untroubled by conscious ghosts. This delusion may, or may not, have useful functions but it obscures how we think about thinking.

Human brains evolved piecemeal, evolution patching up what went before, adding modules as and when they were useful, and increasingly linking them together in the service of the genes and memes they carried. The result was a living thinking machine.

Our current digital technology is similarly evolving. Our computers, servers, tablets, and phones evolved piecemeal, new ones being added as and when they were useful and now being rapidly linked together, creating something that looks increasingly like a global brain. Of course in one sense we made these gadgets, even designed them for our own purposes, but the real driving force is the design power of evolution and selection: We need to stop picturing ourselves as clever designers who retain control and start thinking about our future role.

Could we be heading for the same fate as the humble mitochondrion; a simple cell that was long ago absorbed into a larger cell? It gave up independent living to become a powerhouse for its host while the host gave up energy production to concentrate on other tasks. Both gained in this process of endosymbiosis. Are we like that? Digital information is evolving all around us, thriving on billions of phones, tablets, computers, servers, and tiny chips in fridges, car and clothes, passing around the globe, interpenetrating our cities, our homes and even our bodies.

And we keep on willingly feeding it. More phones are made every day than babies are born, hours of video are uploaded to the Internet every minute, billions of photos are uploaded to the expanding cloud. Clever programmers write ever cleverer software, including programs that write other programs that no human can understand or track.

Out there, taking their own evolutionary pathways and growing all the time, are the new thinking machines. Are we going to control these machines? Can we insist that they are motivated to look after us? Even if we can see what is happening, we want what they give us far too much not to swap it for our independence.

So what do I think about machines that think? I think that from being a little independent thinking machine I am becoming a tiny part inside a far vaster thinking machine. When we say "machines that think", we really mean: It is obvious that, in many different ways, machines do think: They trigger events, process things, take decisions, make choices, and perform many, but not all, other aspects of thinking. But the real question is whether machines can think like people, to the point of the age old test of artificial intelligence: You will observe the results of the thinking, and you will not be able to tell if it is a machine or a human.

Some prominent scientific gurus are scared by a world controlled by thinking machines. I am not sure that this is a valid fear. I am more concerned about a world led by people, who think like machines, a major emerging trend of our digital society. You can teach a machine to track an algorithm and to perform a sequence of operations which follow logically from each other. It can do so faster and more accurately than any human.

Given well defined basic postulates or axioms, pure logic is the strong suit of the thinking machine. But exercising common sense in making decisions and being able to ask meaningful questions are, so far, the prerogative of humans. Merging Intuition, emotion, empathy, experience and cultural background, and using all of these to ask a relevant question and to draw conclusions by combining seemingly unrelated facts and principles, are trademarks of human thinking, not yet shared by machines.

Our human society is currently moving fast towards rules, regulations, laws, investment vehicles, political dogmas and patterns of behavior that blindly follow strict logic, even when it starts with false foundations or collides with obvious common sense. Religious extremism has always progressed on the basis of some absurd axioms, leading very logically to endless harsh consequences. Several disciplines such as law, accounting and certain areas of mathematics and technology, augmented by bureaucratic structures and by media which idolize inflexible regulators, often lead to opaque principles like "total transparency" and to tolerance towards acts of extreme intolerance.

These and similar trends are visibly moving us towards more algorithmic and logical modes of tackling problems, often at the expense of common sense. If common sense, whatever its definition is, describes one of the advantages of people over machines, what we see today is a clear move away from this incremental asset of humans. Unfortunately, the gap between machine thinking and human thinking can narrow in two ways, and when people begin to think like machines, we automatically achieve the goal of "machines that think like people", reaching it from the wrong direction.

A very smart person, reaching conclusions on the basis of one line of information, in a split second between dozens of e-mails, text messages and tweets, not to speak of other digital disturbances, is not superior to a machine with a moderate intelligence, which analyzes a large amount of relevant information before it jumps into premature conclusions and signs a public petition about a subject it is unfamiliar with.

One can recite hundreds of examples of this trend. We all support the law that every new building should allow total access to people with special needs, while old buildings may remain inaccessible, until they are renovated.

But does it make sense to disallow a renovation of an old bathroom which will now offer such access, because a new elevator cannot be installed? Or to demand full public disclosure of all CIA or FBI secret sources in order to enable a court of law to sentence a terrorist who obviously murdered hundreds of people? Or to demand parental consent before giving a teenager an aspirin at school?

And then when school texts are converted from the use of miles to kilometers, the sentence "From the top of the mountain you can see for approximately miles" is translated, by a person, into "you can see for approximately The standard sacred cows of liberal democracy rightfully include a wide variety of freedoms: Freedom of speech, freedom of the press, academic freedom, freedom of religion or of lack of religion , freedom of information, and numerous other human rights including equal opportunity, equal treatment by law, and absence of discrimination.

We all support these principles, but pure and extreme logic induces us, against common sense, to insist mainly on human rights of criminals and terrorists, because the human rights of the victims "are not an issue"; Transparency and freedom of the press logically demand complete reports on internal brainstorming sessions, in which delicate issues are pondered, thus preventing any free discussion and raw thinking in certain public bodies; Academic freedom might logically be misused, against common sense and against factual knowledge, to teach about Noah's ark as an alternative to evolution, to deny the holocaust in teaching history or to preach for a universe created years ago rather than 13 Billions as the basis of cosmology.

We can continue on and on with examples, but the message is clear. Algorithmic thinking, brevity of messages and over-exertion of pure logic are moving us, human beings, into machine thinking, rather than slowly and wisely teaching our machines to benefit from our common sense and intellectual abilities.

A reversal of this trend would be a meaningful U-turn in human digital evolution. A common theme in recent writings about machine intelligence is that the best new learning machines will constitute rather alien forms of intelligence. I'm not so sure. The reasoning behind the 'alien AIs' image usually goes something like this. The best way to get machines to solve hard real-world problems is to set them up as statistically-sensitive learning machines able to benefit maximally from exposure to 'big data'.

Such machines will often learn to solve complex problems by detecting patterns, and patterns among patterns, and patterns within patterns, hidden deep in the massed data streams to which they are exposed. This will most likely be achieved using 'deep learning' algorithms to mine deeper and deeper into the data streams. After such learning is complete, what results may be a system that works but whose knowledge structures are opaque to the engineers and programmers who set the system up in the first place.

In one sense yes. We won't at least without further work know in detail what has become encoded as a result of all that deep, multi-level, statistically-driven learning. I'm going to take a big punt at this point and road-test a possibly outrageous claim. I suspect that the more these machines learn, the more they will end up thinking in ways that are recognizably human.

They will end up having a broad structure of human-like concepts with which to approach their tasks and decisions. They may even learn to apply emotional and ethical labels in roughly the same ways we do.

If I am right, this somewhat undermines the common worry that these are emerging alien intelligences whose goals and interests we cannot fathom, and that might therefor turn on us in unexpected ways.

By contrast, I suspect that the ways they might turn on us will be all-too-familiar—and thus hopefully avoidable by the usual steps of extending due respect and freedom. Why would the machines think like us? The reason for this has nothing to do with our ways of thinking being objectively right or unique. Rather, it has to do with what I'll dub the 'big data food chain'.

These AIs, if they are to emerge as plausible forms of general intelligence, will have to learn by consuming the vast electronic trails of human experience and human interests. For this is the biggest repository of general facts about the world that we have available. To break free of restricted uni-dimensional domains, these AIs will have to trawl the mundane seas of words and images that we lay down on Facebook, Google, Amazon, and Twitter.

Where before they may have been force-fed a diet of astronomical objects or protein-folding puzzles, the break-through general intelligences will need a richer and more varied diet. That diet will be the massed strata of human experience preserved in our daily electronic media.

The statistical baths in which we immerse these potent learning machines will thus be all-too-familiar. They will feed off the fossil trails of our own engagements, a zillion images of bouncing babies, bouncing balls, LOL-cats, and potatoes that look like the Pope.

These are the things that they must crunch into a multi-level world-model, finding the features, entities, and properties latent variables that best capture the streams of data to which they are exposed.

Fed on such a diet, these AIs may have little choice but to develop a world-model that has much in common with our own. They are probably more in danger of becoming super-Mario freaks than becoming super-villains intent on world-domination.

Such a diagnosis which is tentative and at least a little playful goes against two prevailing views. First, as mentioned earlier, it goes against the view that current and future AIs are basically alien forms of intelligence feeding off big data and crunching statistics in ways that will render their intelligences increasingly opaque to human understanding.

On the contrary, access to more and more data, of the kind most freely available, won't make them more alien but less so. Second, it questions the view that the royal route to human-style understanding is human-style embodiment, with all the interactive potentialities to stand, sit, jump etc. For although our own typical route to understanding the world goes via a host of such interactions, it seems quite possible that theirs need not. Such systems will doubtless enjoy some probably many and various means of interacting with the physical world.

These encounters will be combined, however, with exposure to rich information trails reflecting our own modes of interaction with the world. So it seems possible that they could come to understand and appreciate soccer and baseball just as much as the next person. An apt comparison here might be with a differently-abled human being.

There's lots more to think about here of course. For example, the AIs will see huge swathes of human electronic trails, and will thus be able to discern patterns of influence among them over time. That means they may come to model us less as individuals and more as a kind of complex distributed system. That's a difference that might make a difference. And what about motivation and emotion? Maybe these depend essentially upon features of our human embodiment such as gut feelings, and visceral responses to danger?

Perhaps- but notice that these features of human life have themselves left fossil trails in our electronic repositories. I might be wrong. But at the very least, I think we should think twice before casting our home-grown AIs as emerging forms of alien intelligence. You are what you eat, and these learning systems will have to eat us. My favorite Edsger Dijkstra aphorism is this one: Of course, once you imagine machines with human-like feelings and free will, it's possible to conceive of misbehaving machine intelligence—the AI as Frankenstein idea.

This notion is in the midst of a revival, and I started out thinking it was overblown. Lately I have concluded it's not. Here's the case for overblown. Machine intelligence can go in so many directions. It is a failure of imagination to focus on human-like directions. Most of the early futurist conceptions of machine intelligence were wildly off base because computers have been most successful at doing what humans can't do well.

Machines are incredibly good at sorting lists. Maybe that sounds boring, but think of how much efficient sorting has changed the world. In answer to some of the questions brought up here, it is far from clear that there will ever be a practical reason for future machines to have emotions and inner dialog; to pass for human under extended interrogation; to desire, and be able to make use of, legal and civil rights. They're machines, and they can be anything we design them to be.

But that's the point. Some people will want anthropomorphic machine intelligence. How many videos of Japanese robots have you seen? Honda, Sony, and Hitachi already expend substantial resources in making cute AI that has no concrete value beyond corporate publicity. They do this for no better reason than tech enthusiasts have grown up seeing robots and intelligent computers in movies. Almost anything that is conceived—that is physically possible and reasonably cheap—is realized.

So human-like machine intelligence is a meme with manifest destiny, regardless of practical value. This could entail nice machines-that-think, obeying Asimov's laws. But once the technology is out there, it will get ever cheaper and filter down to hobbyists, hackers, and "machine rights" organizations.

There is going to be interest in creating machines with will, whose interests are not our own. And that's without considering what machines that terrorists, rogue regimes, and intelligence agencies of the less roguish nations, may devise. I think the notion of Frankensteinian AI, which turns on its creators, is something worth taking seriously. In English, submarines do not swim, but in Russian, they do. This is irrelevant to the capabilities of submarines.

So let's explore what it is that machines can do, and whether we should fear their capabilities. Pessimists warn that we don't know how to safely and reliably build large complex AI systems.

They have a valid point. We also don't know how to safely and reliably build large complex non-AI systems. For example, we invented the internal combustion engine years ago, and in many ways it has served humanity well, but it also has lead to widespread pollution, political instability over access to oil, a million deaths per year, and other problems. Any complex system will have a mix of positive outcomes and unintended consequences but are there worrisome issues that are unique to systems built with AI?

I think the interesting issues are Adaptability, Autonomy, and Universality. Systems that use machine learning are adaptable. They change over time, based on what they learn from examples.

We want, say, our automated spelling correction programs to quickly learn new terms such as "bitcoin", rather than waiting for the next edition of a published dictionary to list them.

A non-adaptable program will repeat the same mistakes. But an adaptable program can make new mistakes, which may be harder to predict and deal with. We have tools for dealing with these problems, but just as the designers of bridges must learn to deal with crosswinds, so the designers of AI systems must learn to deal with adaptability. Some critics are worried about AI systems that are built with a framework that maximizes expected utility. Such an AI system estimates the current state of the world, considers all the possible actions it can take, simulates the possible outcomes of those actions, and then chooses the action that leads to the best possible distribution of outcomes.

Errors can occur at any point along the way, but the concern here is in determining what is the "best outcome"—in other words, what is it that we desire? If we describe the wrong desires, or allow a system to adapt its desires in a wrong direction, we get the wrong results. History shows that we often get this wrong, in all kinds of systems that we build, not just in AI systems.

The US Constitution is a document that specifies our desires; the original framers made what we now recognize as an error in this specification, and correcting that error with the 13th amendment cost over , lives. Similarly, we designed stock-trading system that allowed speculators to create bubbles that led to busts. These are important issues for system design and what is known as "mechanism design" , and are not specific to AI systems. The world is complicated, so acting correctly in the world is complicated.

The second concern is autonomy. If AI systems act on their own, they can make errors that perhaps would not be made by a system with a human in the loop. This too is a valid concern, and again one that is not unique to AI systems. Consider our system of automated traffic lights, which replaced a system of human policemen directing traffic.

The automated system leads to some errors, but is a tradeoff that we have decided is worthwhile. We will continue to make tradeoffs in where we deploy autonomous systems. There is a possibility that we will soon see a widespread increase in the capabilities of autonomous systems, and thus more displacement of people.

This could lead to a societal problem of increased unemployment and income inequality. To me, this is the most serious concern about future AI systems.

In past technological revolutions agricultural and industrial the notion of work changed, but the changes happened over generations, not years, and the changes always led to new jobs. We may be in for a period of change that is much more rapid and disruptive; we will need some social conventions and safety nets to restore stability. The third concern is the universality of intelligent machines.

Good wrote "an ultraintelligent machine could design even better machines; there would then unquestionably be an 'intelligence explosion,' and the intelligence of man would be left far behind. Thus the first ultraintelligent machine is the last invention that man need ever make. The smartest person is not always the most successful; the wisest policies are not always the ones adopted.

Recently I spent an hour reading the news about the middle east, and thinking. I didn't come up with a solution. Now imagine a hypothetical "Speed Superintelligence" as described by Nick Bostrom that could think as well as any human but a thousand times faster. I'm pretty sure it also would have been unable to come up with a solution. I also know from computational complexity theory that there are a wide class of problems that are completely resistant to intelligence, in the sense that, no matter how clever you are, you won't have enough computing power.

So there are some problems where intelligence or computing power just doesn't help. But of course, there are many problems where intelligence does help. If I want to predict the motions of a billion stars in a galaxy, I would certainly appreciate the help of a computer. Getting this right is difficult, but it is difficult mostly because the world is complex; adding AI to the mix doesn't fundamentally change things.

I suggest being careful with our mechanism design and using the best tools for the job regardless of whether the tool has the label "AI" on it or not. They are words into which we pack many meanings so that we can talk about complex issues in a shorthand way. When we look inside these words we find many different aspects, mechanisms, and levels of understanding. This makes answering the perennial questions of "can machines think? The suitcase words are used to cover both specific performance demonstrations by machines and more general competence that humans might have.

People are getting confused and generalizing from performance to competence and grossly overestimating the real capabilities of machines today and in the next few decades. In a super computer beat world chess champion Garry Kasparov in a tournament. Today there are dozens of programs that run on laptop computers and have higher chess rankings than those ever achieved by humans. Computers can definitely perform better than humans at playing chess.

But they have nowhere near human level competence at chess. All chess playing programs use Turing's brute force tree search method with heuristic evaluation. Computers were fast enough by the seventies that this approach overwhelmed other AI programs that tried to play chess with processes that emulated how people reported that they thought about their next move, and so those approaches were largely abandoned. Today's chess programs have no way of saying why a particular move is "better" than another move, save that it moves the game to a part of a tree where the opponent has less good options.

A human player can make generalizations and describe why certain types of moves are good, and use that to teach a human player. Brute force programs cannot teach a human player, except by being a sparing partner. It is up to the human to make the inferences, the analogies, and to do any learning on their own. The chess program doesn't know that it is outsmarting the person, doesn't know that it is a teaching aid, doesn't know that it is playing something called chess nor even what "playing" is.

Making brute force chess playing perform better than any human gets us no closer to competence in chess. Now consider deep learning that has caught people's imaginations over the last year or so. It is an update to backpropagation, a thirty-year old learning algorithm very loosely based on abstracted models of neurons. Layers of neurons map from a signal, such as amplitude of a sound wave or pixel brightness in an image, to increasingly higher-level descriptions of the full meaning of the signal, as words for sound, or objects in images.

Originally backpropagation could only practically work with just two or three layers of neurons, so it was necessary to fix preprocessing steps to get the signals to more structured data before applying the learning algorithms.

The new versions work with more layers of neurons, making the networks deeper, hence the name, deep learning. Now early processing steps are also learned, and without misguided human biases of design, the new algorithms are spectacularly better than the algorithms of just three years ago.

That is why they have caught people's imaginations. The new versions rely on massive amounts of computer power in server farms, and on very large data sets that did not formerly exist, but critically, they also rely on new scientific innovations. A well-known particular example of their performance is labeling an image, in English, saying that it is a baby with a stuffed toy.

When a person looks at the image that is what they also see. The algorithm has performed very well at labeling the image, and it has performed much better than AI practitioners would have predicted for performance only five years ago. But the algorithm does not have the full competence that a person who could label that same image would have.

The learning algorithm knows there is a baby in the image but it doesn't know the structure of a baby, and it doesn't know where the baby is in the image. A current deep learning algorithm can only assign probabilities to each pixel that that particular pixel is part of a baby. Whereas a person can see that the baby occupies the middle quarter of the image, today's algorithm has only a probabilistic idea of its spatial extent.

It cannot apply an exclusionary rule and say that non-zero probability pixels at extremes of the image cannot both be part of the baby. If we look inside the neuron layers it might be that one of the higher level learned features is an eye-like patch of image, and another feature is a foot-like patch of image, but the current algorithm would have no capability of relating the constraints of where and what spatial relationships could possibly be valid between eyes and feet in an image, and could be fooled by a grotesque collage of baby body parts, labeling it a baby.

In contrast no person would do so, and furthermore would immediately know exactly what it was—a grotesque collage of baby body parts. Furthermore the current algorithm is completely useless at telling a robot where to go in space to pick up that baby, or where to hold a bottle and feed the baby, or where to reach to change its diaper. Today's algorithm has nothing like human level competence on understanding images. Work is underway to add focus of attention and handling of consistent spatial structure to deep learning.

That is the hard work of science and research, and we really have no idea how hard it will be, nor how long it will take, nor whether the whole approach will reach a fatal dead end. It took thirty years to go from backpropagation to deep learning, but along the way many researchers were sure there was no future in backpropagation.

They were wrong, but it would not have been surprising if they had been right, as we knew all along that the backpropagation algorithm is not what happens inside people's heads. The fears of runaway AI systems either conquering humans or making them irrelevant are not even remotely well grounded. Misled by suitcase words, people are making category errors in fungibility of capabilities. These category errors are comparable to seeing more efficient internal combustion engines appearing and jumping to the conclusion that warp drives are just around the corner.

The ability to tell and comprehend stories is a main distinguishing feature of the human mind. It's therefore understandable that in pursuit of a more complete computational account of human intelligence, researchers are trying to teach computers how to tell and understand stories.

But should we root for their success? Creative writing manuals always stress that writing good stories means reading them first—lots of them. Aspiring writers are told to immerse themselves in great stories to gradually develop a deep, not necessarily conscious, sense for how they work.

People learn to tell stories by learning the old ways and then—if they have some imagination—making those old ways seem new. It's not hard to envision computers mastering storytelling by a similar process of immersion, assimilation, and recombination—just much, much faster. To date, practical experiments in computer-generated storytelling aren't that impressive. They are bumbling, boring, soulless. But the human capacity to make and enjoy art evolved from crude beginnings over eons, and the machines will evolve as well—just much, much faster.

Someday robots may take over the world. The dystopian possibilities don't trouble me like the probable rise of art-making machines. Art is, arguably, what most distinguishes humans from the rest of creation. It is the thing that makes us proudest of ourselves. For all of the nastiness of human history, at least we wrote some really good plays and songs and carved some good sculptures. If human beings are no longer needed to make art, then what the hell would we be for?

But why should I be pessimistic? Why would a world with more great art be a worse place to live? Maybe it wouldn't be. But the thought still makes me glum. While I think of myself as a hard-bitten materialist, I must hold out some renegade hope for a dualism of body and spirit. I must hope that cleverly evolving algorithms and brute processing power are not enough—that imaginative art will always be mysterious and magical, or at least so weirdly complex that it can't be mechanically replicated.

Of course machines can out-calculate and out-crunch us. And soon they will all be acing their Turing tests. Let them do our grunt work. Let them hang out and chat. Machines humanly constructed artifacts cannot think because no machine has a point of view; that is, a unique perspective on the worldly referents of its internal symbolic logic.

We, as conscious cognitive observers, look at the output of so-called "thinking machines" and provide our own referents to the symbolic structures spouted by the machine. Of course, despite this limitation, such non-thinking machines have provided an extremely important adjunct to human thought. In the mathematician Lewis Fry Richardson had imagined a large hall full of "computers", people who, one hand calculation at a time, would advance numerical weather prediction.

Less than a hundred years later, machines have improved the productivity of that particular task by up to fifteen orders of magnitude, with the ability to process almost a million billion similar calculations per second.

Consider the growth in heavy labor productivity by comparison. In the world used about Exajoules—a billion, billion joules—of primary energy, to produce electricity, fuel manufacturing, transport and heat. Even if we assumed all of that energy went into carrying out physical tasks in aid of the roughly 3 billion members of the global labor force and it did not , assuming an average adult diet of 2, Calories per capita per day, would imply roughly 50 "energy laborers" for every human.

More stringent assumptions would still lead to at most an increase of a few orders of magnitude in effective productivity of manual labor. We have been wildly successful at accelerating our ability to think and process information, more so than any other human activity.

The promise of artificial intelligence is to deliver another leap in increasing the productivity of specific cognitive functions: Keynes would have probably argued that such an increase should ultimately lead to a fully employed society with greater free time and a higher quality of life for all.

The skeptic might be forgiven for considering this a case of hope of experience. While there is no question that specific individuals will benefit enormously from delegating tasks to machines, the promise of greater idleness from automation has yet to be realized, as any modern employee—virtually handcuffed to a portable device—can attest. So, if we are going to work more, deeper, and with greater effectiveness thanks to thinking machines, choosing wisely what they are going to be "thinking" about is particularly important.

Indeed, it would be a shame to develop all this intelligence to then spend it on thinking really hard about things that do not matter. And, as ever in science, selecting problems worth solving is a harder task than figuring out how to solve them. One area where the convergence of need, urgency, and opportunity is great is in the monitoring and management of our planetary resources. Despite the dramatic increase in cognitive and labor productivity, we have not fundamentally changed our relationship to Earth: A linear economy on a finite planet, with seven billion people aspiring to become consumers—our relationship to the planet is arguably more productive, but not much more intelligent than it was a hundred years ago.

Understanding what the planet is doing in response, and managing our behavior accordingly, is a complicated problem, hindered by colossal amounts of imperfect information. From climate change, to water availability, to the management of ocean resources, to the interactions between ecosystems and working landscapes, our computational approaches are often inadequate to conduct the exploratory analyses required to understand what is happening, to process the exponentially growing amount of data about the world we inhabit, and to generate and test theories of how we might do things differently.

We have almost 7 billion thinking machines on this planet already, but for the most part they don't seem to be terribly concerned with how sustainable their life on this planet actually is. Very few of those people have the ability to see the whole picture in ways that make sense to them, and those that do are often limited in their ability to respond. Adding cognitive capacity to figure out how we fundamentally alter our relationship with the planet is a problem worth thinking about.

Proponents of Artificial Intelligence have a tendency to project a utopian future in which benevolent computers and robots serve humanity and enable us to achieve limitless prosperity, end poverty and hunger, conquer disease and death, achieve immortality, colonize the galaxy, and eventually even conquer the universe by reaching the Omega point where we become god—omniscient and omnipotent. AI skeptics envision a dystopian future in which malevolent computers and robots take us over completely, making us their slaves or servants, or driving us into extinction, thereby terminating or even reversing centuries of scientific and technological progress.

Most such prophecies are grounded in a false analogy between human nature and computer nature, or natural intelligence and artificial intelligence. We are thinking machines, the product of natural selection that also designed into us emotions to shortcut the thinking process. We don't need to compute the caloric value of foods; we just feel hungry and eat.

We don't need to calculate the waist-to-hip or shoulder-to-waist ratios of potential mates; we just feel attracted to someone and mate with them. We don't need to work out the genetic cost of raising someone else's offspring if our mate is unfaithful; we just feel jealous. We don't need to estimate the damage of an unfair exchange; we just feel injustice and desire revenge. All of these emotions were built into our nature by evolution, none of which we have designed into our computers.

So the fear that computers will become evil are unfounded, because it will never occur to them to take such actions against us. As well, both utopian and dystopian visions of AI are based on a projection of the future quite unlike anything history has given us.

Instead of utopia or dystopia, think protopia , a term coined by the futurist Kevin Kelly, who described it in an Edge conversation this way: I believe in progress in an incremental way where every year it's better than the year before but not by very much—just a micro amount. Rarely, if ever, do technologies lead to either utopian or dystopian societies. My first car was a Ford Mustang. It had power steering, power brakes, and air conditioning, all of which were relatively cutting edge technology at the time.

Every car I've had since then—parallel to the evolution of automobiles in general—has been progressively smarter and safer; not in leaps and bounds, but incrementally. Think of the 's imagined jump from the jalopy to the flying car. Instead what we got were decades-long cumulative improvements that led to today's smart cars with their onboard computers and navigation systems, air bags and composite metal frames and bodies, satellite radios and hands-free phones, and electric and hybrid engines.

I just swapped out a Ford Flex for a version of the same model. Externally they are almost indistinguishable; internally there are dozens of tiny improvements in every system, from the engine and drive train, to navigation and mapping, to climate control and radio and computer interface. Such incremental protopian progress is what we see in most technologies, including and especially artificial intelligence, which will continue to serve us in the manner we desire and need.

Those of you participating in this particular Edge Question don't need to be reintroduced to the Ghemawat-Dean Conversational artificial intelligence test DGC. Past participants in the test have failed as obviously as they have hilariously. However, the 2UR-NG entry really surprised us all with its amazing, if child-like, approach to conversation and its ability to express desire, curiosity and its ability to retain and chain facts.

Its success has caused many of my compatriots to write essays like "The coming biological future will doom us all" and making jokes about "welcoming their new biological overlords". You should know that I don't subscribe to this kind of doom and gloom scare-writing.

Before I tell you why we should not worry about the extent of biological intelligence, I thought I'd remind people of the very real limits of biological intelligence. First off, speed of thought: These biological processes are slow and use an incredible amount of resources.

I cannot emphasize enough how incredibly difficult to produce these intelligences. One has to waste so much biological material, and I know from experience that takes forever to assemble the precursors in the genesis machine.

Following this arduous process, your specimen has to gestate. I mean, it's not like these It is kind of gross, really. But let's suppose you get to birth these specimens, then you have to feed them and again, keep them warm. A scientist can't even work within their environmental spaces without a cold jacket circulating helium throughout your terminal.

Then you have to 'feed' them. They don't use power like we do, but instead ingest other living matter. It's disgusting to observe and I've lost a number of grad students with weaker constitutions. Assume you've gotten far enough to try to do the GDC. You've kept them alive through any a variety of errors in their immune system.

They've not choked on their sustenance, they haven't drown in their solvent and they've managed to keep their wet parts off things that they would freeze, bond or be electrocuted by. What if those organisms continue to develop, will they then rise up and take over? I don't think so.

They have to deal with so many problems related to their design. I mean, their processors are really just chemical soups that have to be kept in constant balance. Dopamine at this level or they shut down voluntarily. Vasopressin at this level or they start retaining water. Adrenaline at this level for this long or poof their power delivery network stops working.

Moreover, don't get me started on the power delivery method! It's more like the fluorinert liquid cooling systems of our ancestors than a modern heat tolerant wafers. You introduce the smallest amount of machine oil or cleaning solvent into the system and they stop operating fast. One side effect of certain ethanol mixtures is the specimens expel their nutrition, but they seem to like it in smaller amounts.

It is baffling in its ambiguity. I can't imagine that they would see us machine-folk as anything but tools to advance their reproduction.

We could end the experiment simply by matching them poorly with each other or only allowing access to each other with protective cladding. In my opinion, there is nothing to fear from these animals. In the event they grow beyond the confines of their cages, maybe we can then ask ourselves the more important question: If humans show real machine-like intelligence, do they deserve to be treated like machines?

I would think so, and I think we could be proud to be the parent processes of a new age.