41–50 of 189 posts
Most of the AI progress in last years is just tuning pattern recognition algorithms. We can not expect these algorithms to produce results like humans, because humans have a lot of information not just from percieving the world, but their patterns of thinking are also vastly dependend on the underlying structure of brain, that has developed over milions of years of evolution. If there is a cliff, toddlers are scared…
We (humanity) have made huge progress to understand images in terms of content and emotions of people. Imagenet is truly a gift to the world. However, that has brought us only a small but important step forward. Clearly expectation has to catch up to reality. However, all these solutions are becoming quickly more accessible to the laymen bringing another boost to operational efficiencies for companies worldwide.
> Clearly expectation has to catch up to reality. Woah, you feel expectations are behind reality? I feel there's a lot of news lately predicting AGI.
We (humanity) have made huge progress to understand images in terms of content and emotions of people. Imagenet is truly a gift to the world. However, that has brought us only a small but important step forward. Clearly expectation has to catch up to reality. However, all these solutions are becoming quickly more accessible to the laymen bringing another boost to operational efficiencies for companies worldwide.
> Clearly expectation has to catch up to reality. Woah, you feel expectations are behind reality? I feel there's a lot of news lately predicting AGI.
Earlier quoted context omitted.
How is an analog computer "easier to program" than a digital computer? Making neural networks do what you want is hard enough with the help of tons of libraries, decent scripting languages, the ability to dump the weights into a file and inspect them, etc. Programming with an analog computer, which I'm guessing would be something like programming with FPGAs, sounds like a nightmare in comparison.
Because neural networks are fundamentally dynamic systems that are much easier to model with continuous signals than discrete bits. A lot of the hardness comes from the fact that you are discretizing fundamentally continuous signals.
It starts badly:
> Artificial Intelligence is colossally hyped these days, but the dirty little secret is that it still has a long, long way to go
This is not a secret, let alone a dirty one. Even 5 minutes casual research into the state of AI will reveal what it can do and what it can't.
It says:
> Such systems can neither comprehend what is going on in complex visual scenes (“Who is chasing whom and why?”) nor follow simple instructions (“Read this story and summarize what it means”).
In fact comprehension of (very) simple stories is now more or less a solved problem. I wrote about performance on the bAbI tests here:
https://blog.plan99.net/the-science-of-westworld-ec624585e47
Summarisation of stories is also something with good recent results:
https://research.googleblog.com/2016/08/text-summarization-w...
Summarisation of arbitrary video is harder but given that object and path extraction already works well, it doesn't seem very implausible that we'll see some good research results in video summarisation systems within a few years. Extrapolation from what's happening to hypothesised explanations is a lot harder but not hard to imagine it being possible given the direction research is going.
> My daughter had never seen anyone else disembark in quite this way; she invented it on her own. Presumably, my daughter relied on an implicit theory of how her body moves, along with an implicit theory of physics — how one complex object travels through the aperture of another. I challenge any robot to do the same.
Challenge accepted:
https://www.youtube.com/watch?v=gbYiKMisbME
And for the imagination component:
http://www.wired.co.uk/article/googles-deepmind-creates-an-a...
> To get computers to think like humans, we need a new A.I. paradigm
That's not clear at all, given recent research. It is an odd statement from someone who has worked in AI. But then as the author is not a computer scientist, perhaps not that odd.
Modern neural networks are so similar to how humans think that psychological techniques are being used to understand and "debug" them:
https://deepmind.com/blog/cognitive-psychology/
I'm not sure how "think like humans" can be easily defined, but using strategies developed to understand human thinking on robots seems like a good starting point. Making mistakes similar to what you'd expect humans to make is also a good sign.
> But it is no use when it comes to top-down knowledge. If my daughter sees her reflection in a bowl of water, she knows the image is illusory; she knows she is not actually in the bowl
She does now. But it takes time for babies to learn how to interpret mirrors.
http://www.thoughtfulparent.com/2009/10/child-psychology-cla...
Animals usually never learn this, though a few very intelligent species can.
I don't see any obvious theoretical reason why image recognition engines shouldn't be able to understand mirrors, given sufficient research.
> Corporate labs like those of Google and Facebook have the resources to tackle big questions, but in a world of quarterly reports and bottom lines, they tend to concentrate on narrow problems like optimizing advertisement placement or automatically screening videos for offensive content.
Another bizarre statement given the author's background. Google and Facebook have been investing massively in very long term AI research and building many things along the way of no direct commercial value, like AIs that play games. I don't see Google's public AI research focusing on the cited problems, although it would not surprise me if there are parallel efforts to apply research breakthroughs in these areas.
> An international A.I. mission focused on teaching machines to read could genuinely change the world for the better — the more so if it made A.I. a public good, rather than the property of a privileged few.
And here we have it ladies and gentlemen .... the reason the article is so filled with factually false and logically dubious statements. It is an advocacy piece for new social policy: a vast new government research investment in academia, in which presumably Mr Marcus would like to be employed (rather than at Uber).
Besides, even this last paragraph is disingenuous. There does not seem to be any risk of AI becoming "the property of the few". In fact the large corporate research labs are doing fantastically well at publishing research papers and making the results of their work publicly available and useful ... in fact given the relative quality of corporate vs academic open source releases I'd say they're doing better than academia is. It's hard to imagine universities producing something as robust and well documented as TensorFlow.
Click-baity. AI tech isn't stuck. There are many forthcoming breakthroughs, particularly in medicine, which should really benefit humanity. Radiology is poised to let CNNs make radiologists a lot more efficient. We just need to build the labeled datasets. If we invest heavily in some AI tech, let it be to produce huge medical datasets. The software and hardware is ready. We're only lacking sufficient data to make mor…
So AI isn't stuck. It's also mostly working on well defined, targeted problems.
Life, on the other hand, works towards a very ill-defined objective function (survive, collectively) over millions of years; all of the emergent behavior we're astonished by is maybe just side effects of working on that objective. (This is a crass viewpoint, but let's stick with it for the sake of argument.)
We mostly aren't working on such objective functions, partly because it's hard to compare results, partly because there aren't clear milestones for success (indeed the goal posts for AGI shift as far as AI advances) and partly because skynet.
In fact, we are consistently surprised by the AI we already have. It finds ways to exploit our fitness functions constantly, and fine tricks and heuristics to gain a couple points on the final score constantly. Click bait comes to mind: we want to surface good content, use clicks as a proxy for quality, and get what we see for instead of what we wanted. Which somehow takes us directly to president trump... (Sure your kid can find a cool way to get out of a chair, but call me when she inadvertantly threatens the basis of the US democracy in the process. And then we can talk about the pressing need for AGI.)
Some of the best image-recognition systems, for example, can successfully distinguish dog breeds, yet remain capable of major blunders, like mistaking a simple pattern of yellow and black stripes for a school bus. That's exactly the problem. Robots lack sanity checks because they lack real understanding. If you cannot recognize an object that is far away, you are instantly aware of your inability to identify this obj…
You too can mistake yellow and black stripes for a school bus or see an actual leopard in Poland. That's when you put what you've seen in context that you rule out the idea. And if you really want to see something in a picture, you will, especially with faces.
It is no different with computers. You train your algorithm so see school buses exclusively and it will see school buses everywhere. Conversely can also teach it context, for example by taking account of the webpage hosting the image.
Computers algorithms usually have a confidence rating too. They can tell "definitely a school bus (99%)" or "looks vaguely like a school bus (30%), but it may also be a wasp (10%)", so they can be aware of their own flaws. In fact, confidence intervals are often a key part of machine learning.
> An international A.I. mission focused on teaching machines to read could genuinely change the world for the better — the more so if it made A.I. a public good, rather than the property of a privileged few. > author: Gary Marcus is a professor of psychology and neural science at New York University. Not sure what he has in mind. There are already a lot of smart people building Q&A systems. We need tests to establish…
No systems can really understand what they read or translate yet. They are basically sophisticated pattern matching systems. Check out Winograd Schema: https://en.wikipedia.org/wiki/Winograd_Schema_Challenge Overview by an expert: http://www.cs.nyu.edu/faculty/davise/papers/WinogradSchemas/... An example: The city councilmen refused the demonstrators a permit because they [feared/advocated] violence. When you switch…
To give the proper answer in the example you use, a human (or a system) needs to know how such permits are issued and what are the common reasons for refusing such permits. As such, a sufficiently sophisticated pattern matching system is perfectly sufficient to answer such questions - there's a simple pattern difference that fearing violence causes you to refuse permits but advocating violence causes you to get refused. It's worth thinking about where do humans learn this? For the Winograd schemas like putting a trophy in suitcase, it's the basic childhood experience of putting stuff in boxes that we all share, but a machine won't (unless it's raised as a child-robot). For schemas like this one, it's understanding how our society works learned by participating in our society for years, which we all share, but a machine won't (unless we allow machines to participate in our society). I.e. it's not so much a measure of intelligence as a measure of shared background experiences. A human from a hunter-gatherer tribe wouldn't be able to answer the councilman-permit schema, but that doesn't mean he/she isn't intelligent.
The difficulty there is caused mainly by the need to have domain-specific knowledge in a wide range of domains - we will perceive systems as "dumb" unless they share the same background knowledge that most humans have gained by being part of our society and basic schooling, and since the machines won't do that (yet), we're looking for "unnatural" ways of getting common sense knowledge without the direct experimentation and participation that we do.
But watching my 1yo learn to toddle around and navigate does show just how limited current AI is. With tons of training and battery, we can coax a computer to barely do what my 1yo does on a belly of cherrios and a few hours of trial and error.
There's lots of great stuff and some terrifying stuff happening in AI and I don't doubt more to come, but watching kids learn puts it in perspective for me.