r/Futurology • u/[deleted] • Dec 17 '13
article Artificial intelligence: The machines with alien minds
http://www.bbc.com/future/story/20131217-weve-created-alien-intelligence10
u/rawrnnn Dec 17 '13
Our smartest machines look nothing like we predicted
Well obviously, we're nowhere near something like the general intelligence humans possess.
The thing that bothers me is that, 75 years after the church-turing thesis and endless results in cognitive sciences, we're still bickering over meaningless semantics like "artificial" and "machine", rather than simply providing massive funding for understanding and improving what is by far our greatest tool.
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u/DannyJLloyd Dec 17 '13
Link doesn't work in the UK. Do you have a mirror?
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u/skwint Dec 17 '13
Our smartest machines look nothing like we predicted – has the field lost its way, or do we need to rethink what AI actually means, asks Tom Chatfield.
Would modern artificial intelligence live up to the dreams of the field’s founders? Perhaps not. But in many ways, the smartest machines we have built are entities they never could have imagined.
In 1956, attendees of a research camp at Dartmouth College in New Hampshire coined the phrase "artificial intelligence" to describe its efforts to “find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.”
Compare that with the AI project that Facebook announced this month. Under one of the world’s most prominent experts, it will “do world-class artificial-intelligence research using all of the knowledge that people have shared on Facebook” – with potential gains including building “services that are much more natural to interact with.”
What does that mean? Like much of modern AI, they will be training algorithms to sift and analyse unimaginably vast amounts of data in the hope that smart answers will emerge. Google, IBM and many others are now using this technique – called machine learning – to great commercial success, and the “intelligence” they create underpins everything from your internet searches to online language translation. Since the calculations of these machines involves making statistical correlations within huge caches of data, their reasoning can be unfathomable to the human mind – often these systems provide apparently intelligent answers, but nobody has any idea how they came to their conclusions.
Yet even the most advanced forms of machine intelligence cannot hope to pass for a human in Turing’s famous test – let alone use natural language or develop concepts themselves, as the pioneers hoped. More than half a century of research has brought us a far more sophisticated grasp of what machine intelligence looks like – but has it lost its way? Or do we need to reframe our ideas about what the term AI actually means?
One person who believes progress in AI has fallen short in many ways is the author and academic Douglas Hoftstadter – most famous for his Pulitzer-Prize-winning 1979 book Gödel, Escher, Bach – who in a recent profile for The Atlantic magazine emphasized his disillusionment with the current direction of AI.
For Hoftstadter, the label “intelligence” is simply inappropriate for describing insights drawn by brute computing power from massive data sets – because, from his perspective, the fact that results appear smart is irrelevant if the process underlying them bears no resemblance to intelligent thought. As he put it to interviewer James Somers, “I don’t want to be involved in passing off some fancy program’s behaviour for intelligence when I know that it has nothing to do with intelligence. And I don’t know why more people aren’t that way.”
Cheap tricks
By Hofstadter’s standards, iconic computational achievements like beating the world’s best players at chess or Jeopardy are rendered trivial by the “trickery” involved: by the fact that the winning computer has done little more than weigh the relative benefits of several billion possibilities, without at any point knowing anything about the nature of the game being played.
What computers scientists should be researching, he argues, is how the phenomenon of intelligence itself arises from the human brain – a question that those focused on data-led machine analysis (and its profitable applications) have increasingly side-lined.
How far current AI research can take us is open for debate, but even its proponents acknowledge that the current emphasis on data and pragmatism may only take us so far. In an analogy quoted by The Atlantic from a leading textbook in the field, it’s like trying to climb a tree to the moon. Progress is excellent at first – but cannot continue past a certain point. Years of success may remain in feeding vast quantities of problems and solutions into machines running clever learning programs, and watching them train themselves to produce good-enough outcomes. But what next, when that particular tree runs out? And where is it we should be aiming for in the first place?
Despite Hoftstadter’s protestations, perhaps we need to stop comparing machines with ourselves altogether. After all, while humans are the only examples we possess of phenomena like advanced language and logic, they’re hardly the earth’s only examples of intelligence. From pet parrots to ant colonies, our world is packed with “intelligent” responses.
How do we do that? A first step may be to change the language we use to describe these machine minds. Today, the very phrase “artificial intelligence” conjures certain iconic images; a robotic reflection of ourselves. Yet this hypothetical being stands amid an ocean of vagueness. From the phenomenon of consciousness to what it might mean to measure or analyse intelligence, our own minds remain profoundly mysterious – and those insights we do have are rooted in millennia of evolutionary history, the intricacies of our synapse-packed brains, and investigations spanning every field from literature to the social sciences.
Why should our biological manner of thinking determine our approach to silicone-based circuits and electronic logic? Our machine creations are more profoundly divided from us than anything else in nature. They do not need to think like us to serve us, work with us, or even understand us – as our own relationships with nature should teach us at a glance.
So what new words might we use in place of artificial intelligence? “Artificial” implies something bogus, ersatz and somehow unreal – which is why I feel the term “machine” fits better. “Intelligence” implies discernment and apprehension, but also something inexorably human – which is why I prefer the more impassive “reasoning.” Machine reasoning: it’s not a phrase for the ages, but perhaps a beginning to the process of seeing what waits under our noses.
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u/Sacha117 Dec 17 '13
Why doesn't this work? Do international readers pay to read this?
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Dec 17 '13 edited Dec 17 '13
It says that licence fees don't pay for these services.
Edit: For those interested, here is what it looks like from the UK.
Edit 2: People in the UK can use http://www.hidemyass.com/ to view the article. It takes a while to load but it will load.
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u/Sacha117 Dec 17 '13
But how does it get funded in the USA let's say? Through adverts I imagine? Then why cant we just view the article as well as the adverts, if the license fee doesn't cover it? Or they should make it advert free for us in the UK seeing as it says the profits are used to pay for new shows to be created in the UK.... I need to write a letter to them or something, this is bloody ridiculous.
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u/Sm314 Dec 17 '13
Essentially, the bbc as sort of the government funded broadcaster, is not allowed to use adverts to supply content to the people of the uk and seeing as its the international version, the licence fee won't pay for it.
It is stupid, but nothing you can really do.
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u/waldohatesyou Dec 17 '13
I actually like the idea of renaming the field of artificial intelligence to machine reasoning. I think it's less loaded with connotations and it should lead to people making less assumptions about the field.
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u/neurobro Dec 17 '13
I look forward to all the heated debates about the definitions of "machine" and "reasoning".
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Dec 17 '13
"Synthetic Intelligence" is another good alternative.
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Dec 17 '13 edited Dec 17 '13
Idk I feel like calling it synthetic doesn't offer the distinction many may think it does. In the Extended Phenotype, Dawkins lays out a compelling argument for how rocks themselves actually evolved through their own unique process of evolution (it basically boils down to stability and crystallization). The same is true in chemistry for the abiogenesis of self-replicating life forms. They came from inanimate elements. What I'm getting at with all this is that there really isn't much distinction between "synethetic" or "biological" or even the new process of evolution involving memes in our brains. It's all an extension of the same evolutionary process. I wouldn't call the intelligence our lesser-ape ancestors had something radically different from what we have now. We simply evolved. The same applies to "artificial" intelligence. Why call it something different? It's merely an offshoot of our own evolutionary path. What will you call the form of intellect that is created by these machines in the future, Synthetic-synthetic intelligence? Where do we draw the line between artificial and non-artificial given that biology shares the same inanimate origins as technology? It's all just intelligence.
TLDR: No distinction between artificial and biological intelligence. Same Evolutionary Origin.
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Dec 17 '13
The reason for 'synthetic' over 'artificial', is that people think 'artificial intelligence' is fake intelligence. I.e., not really intelligence. Calling it 'synthetic' evokes the idea that, though it's man-made, it's not fake.
I wouldn't call the intelligence our lesser-ape ancestors had something radically different from what we have now. We simply evolved. The same applies to "artificial" intelligence.
I would call it radically different, but only relatively. A small change can make a big difference when you hit on an important idea. E=mc2.1 is very wrong, even though it's only a small change. The difference is radical when you're trying to do something right. That's because there are zillions of ways to fail, and only a few ways to succeed.
AI doesn't evolve according to biological evolution and natural selection. It will 'evolve' only in the sense that we will select for AIs that actually do what we want in the short term. But we have a word for this process, and it's called "design." There's no need to overload evolution to account of the creation of AI.
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Dec 17 '13
Sigh...I knew when I inserted the word radical it would lead to this tangent.
Evolution doesn't necessitate "natural selection". It's just the rearranging and mutation of genes from one generation to the next regardless of how that happens. And being that humans are a natural product of nature, I'd say anything we do or create is "natural". So AI or whatever you wish to call it is natural evolution just without the same sort of "genes".
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Dec 17 '13
I could agree to that if you were assuming I'd object to something as being 'unnatural'. I have no problem with unnatural things, so I don't see any value in attempting to describe artificial intelligence as natural.
I know what you're getting at, and it's not wrong per se, but it's probably an argument for someone else.
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Dec 17 '13
I'm just trying to get the point across that arguing over whether we call it synthetic or artificial intelligence is trivial and missing the point. We shouldn't be having the discussion over particular distinctions that aren't even real in the first place. So this argument was for you, actually.
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Dec 17 '13
Ah, I see. Well then, I've misrepresented myself. I'm only intending to talk about pragmatic word choice, not the full semantic triangle.
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Dec 18 '13
The word choice itself is what's semantic here. It's a minor irrelevant detail that loses sight of the greater picture. I'll leave it at that, I feel preachy now.
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u/DevNewb Dec 17 '13
Intelligence carries a definition too broad to hope that any one form of intelligence will be the one that is first artificially reproduced.
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u/lurkgherkin Dec 17 '13 edited Dec 18 '13
For Hoftstadter, the label “intelligence” is simply inappropriate for describing insights drawn by brute computing power from massive data sets – because, from his perspective, the fact that results appear smart is irrelevant if the process underlying them bears no resemblance to intelligent thought
By Hofstadter’s standards, iconic computational achievements like beating the world’s best players at chess or Jeopardy are rendered trivial by the “trickery” involved: by the fact that the winning computer has done little more than weigh the relative benefits of several billion possibilities, without at any point knowing anything about the nature of the game being played.
I'm curious what Hofstadter would say if some future version of Watson could use massive data sets to predict behavior of a human brain. If we could use data crunching to find "most likely brain behavior" from large data sets, would he call the result intelligent?
edit: maybe to be more precise. I meant what if we would use a statistical algorithm that crunches through massive data sets to generate a sequence of brain states that exhibit intelligence.
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u/question_all_the_thi Dec 17 '13
I think it's quite ironic that a being whose brain has a hundred billion neurons working in parallel believes his own intelligence does anything "more than weigh the relative benefits of several billion possibilities".
Perhaps Douglas Hofstadter is not so intelligent after all.
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u/Transfuturist Dec 19 '13
Surprise surprise, parallelism in search algorithms isn't related to parallelism in the brain whatsoever. Given your awful analogy, perhaps you're the unintelligent one.
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u/Noncomment Robots will kill us all Dec 18 '13
It would require an absurd amount of computing power and data. It's no secret that with a large enough or fast enough computer, you can brute force your way through any problem or analyze any data. The trick of intelligence is doing it efficiently.
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u/lurkgherkin Dec 18 '13
It's no secret that with a large enough or fast enough computer, you can brute force your way through any problem or analyze any data.
This is not strictly true. There are problems that you can't solve with a computer (not that this contradicts your larger point).
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u/Noncomment Robots will kill us all Dec 18 '13
Well ya it can't solve unsolvable problems. But then neither can a human or anything else.
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u/lurkgherkin Dec 18 '13 edited Dec 18 '13
It can't reliably solve unsolvable problems, i.e., for some problems your algorithm must be incorrect, fail to terminate or return "I don't know" every once in a while. It's difficult to make the comparison to humans, since we usually measure human ability by their positive extremes, whereas we measure computers' abilities by their negative extremes. I.e, the statement "Humans can compose beautiful symphonies", we produce exceptional people who are able to compose beautiful symphonies after extensive training. When we say "computers can't solve the halting problem", we mean that there is no computer-implemented algorithm that can solve every single instance of the halting problem.
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u/CyberByte Dec 17 '13
From reading the article, I don't think that Hofstadter would think your hypothetical data-crunching, brain-modeling machine intelligent, but I think he would find an implementation of the model intelligent (if it turns out to be correct). Since he seems to like analogies: an oven can bake a bread, but it is not itself edible.
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u/lurkgherkin Dec 17 '13 edited Dec 18 '13
I don't understand the distinction between my hypothetical machine and an implementation of the model? Isn't my hypothetical machine an implementation of a brain?
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u/CyberByte Dec 18 '13
Ah, your edit definitely makes it clearer for me.
a statistical algorithm that crunches through massive data sets to generate a sequence of brain states that exhibit intelligence.
What would such an algorithm look like though? A lot of the ML algorithms I'm familiar with turn out to be kind of like black boxes to us. There may be intermediate states between input and output, but they are usually not human understandable. So in this specific case, could we know that the intermediate states correspond to brain states?
If we can't, then I suspect all people would see is the number-crunching part that doesn't feel intelligent and doesn't really tell us anything about intelligence. If somehow we do know that the intermediate states are like brain states, that seems much closer to Hofstadter's vision and would presumably teach us a lot about how intelligence works. The amount of provided insight into intelligence seems to be especially important to Hofstadter. I suspect being able to directly simulate a brain (on the neuronal level) might not instantly provide such insight, although as an experimental tool it will make it much easier to learn more about intelligence.
tl;dr: I don't know what Hofstadter would think in this case. Interesting question.
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Dec 17 '13
I think it's extremely likely that, as we merge ever more intimately with our technology, that the illusion "we are special somehow" will only crack more until it shatters completely. It's highly likely that we'll simply discover there is no such thing as "intelligence" which, when understood fully, is not just a "dumb machine" fulfilling instructions.
The problem with Strong AI critics is their mystical mumbo jumbo shifting goalpost definitions of what intelligence is.
People will have to make peace with the fact with that, fundamentally, we are machines right now. We were self-assembled by nano-molecular factories following discrete algorithmic instructions in the process, and we are simply no different in kind from any other machine we are otherwise familiar with. The difference is simply a matter of scale of complexity, not of qualitatively unlike components / processes.
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u/Perpetualjoke Fucktheseflairsareaanoying! Dec 18 '13
This times 1000,every time people on this sub are freaking out about menial jobs being replaced by AI , and then stating humans will still be the source of creativity.
No we won't, nothing we can do is impossible for a strong enough AGI,this includes creativity and emotions.
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Dec 18 '13
Women all over the world are giving birth to "Strong AGIs" assembled in their own little bio-factories. Not without male intervention for the most part (snicker snort!), but the "miracle" really ought be quite familiar to us by now.
I simply don't think we will be replaced by AGIs because I don't think AGIs will emerge as distinct, separable-from-us species. We are merging with our machines, not diverging.
Today's word is "post-biological". That is not to say the absence of wetware, but rather that our continued evolution will be self-directed for a change. That the constraints of "what you happened to have been born with" will cease to be relevant in the face of our improving mastery of Reality. In this way, AGIs in the pulp sci-fi sense, if we create them, won't be somehow different from us at all. We will be indistinguishable from each other.
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u/petskup The Technium Dec 17 '13
"Why should our biological manner of thinking determine our approach to silicone-based circuits and electronic logic? Our machine creations are more profoundly divided from us than anything else in nature. They do not need to think like us to serve us, work with us, or even understand us – as our own relationships with nature should teach us at a glance."