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Opinion: Artificial Intelligence Hits the Barrier of Meaning (nytimes.com)
Machine learning algorithms don't yet understand things the way humans do -- with sometimes disastrous consequences. Melanie Mitchell, a professor of Computer Science at Portland State University, writes: As someone who has worked in A.I. for decades, I've witnessed the failure of similar predictions of imminent human-level A.I., and I'm certain these latest forecasts will fall short as well. The challenge of creating humanlike intelligence in machines remains greatly underestimated. Today's A.I. systems sorely lack the essence of human intelligence: understanding the situations we experience, being able to grasp their meaning. The mathematician and philosopher Gian-Carlo Rota famously asked, "I wonder whether or when A.I. will ever crash the barrier of meaning." To me, this is still the most important question.
The lack of humanlike understanding in machines is underscored by recent cracks that have appeared in the foundations of modern A.I. While today's programs are much more impressive than the systems we had 20 or 30 years ago, a series of research studies have shown that deep-learning systems can be unreliable in decidedly unhumanlike ways. I'll give a few examples. "The bareheaded man needed a hat" is transcribed by my phone's speech-recognition program as "The bear headed man needed a hat." Google Translate renders "I put the pig in the pen" into French as "Je mets le cochon dans le stylo" (mistranslating "pen" in the sense of a writing instrument). Programs that "read" documents and answer questions about them can easily be fooled into giving wrong answers when short, irrelevant snippets of text are appended to the document.
Similarly, programs that recognize faces and objects, lauded as a major triumph of deep learning, can fail dramatically when their input is modified even in modest ways by certain types of lighting, image filtering and other alterations that do not affect humans' recognition abilities in the slightest. One recent study showed that adding small amounts of "noise" to a face image can seriously harm the performance of state-of-the-art face-recognition programs. Another study, humorously called "The Elephant in the Room," showed that inserting a small image of an out-of-place object, such as an elephant, in the corner of a living-room image strangely caused deep-learning vision programs to suddenly misclassify other objects in the image.
The lack of humanlike understanding in machines is underscored by recent cracks that have appeared in the foundations of modern A.I. While today's programs are much more impressive than the systems we had 20 or 30 years ago, a series of research studies have shown that deep-learning systems can be unreliable in decidedly unhumanlike ways. I'll give a few examples. "The bareheaded man needed a hat" is transcribed by my phone's speech-recognition program as "The bear headed man needed a hat." Google Translate renders "I put the pig in the pen" into French as "Je mets le cochon dans le stylo" (mistranslating "pen" in the sense of a writing instrument). Programs that "read" documents and answer questions about them can easily be fooled into giving wrong answers when short, irrelevant snippets of text are appended to the document.
Similarly, programs that recognize faces and objects, lauded as a major triumph of deep learning, can fail dramatically when their input is modified even in modest ways by certain types of lighting, image filtering and other alterations that do not affect humans' recognition abilities in the slightest. One recent study showed that adding small amounts of "noise" to a face image can seriously harm the performance of state-of-the-art face-recognition programs. Another study, humorously called "The Elephant in the Room," showed that inserting a small image of an out-of-place object, such as an elephant, in the corner of a living-room image strangely caused deep-learning vision programs to suddenly misclassify other objects in the image.
I wonder if these AI vision systems that input millions of images are actually doing a deep learning, or are just canvassing pretty much every image possibility such that any possible live image is just a tiny automated delta calculation away from an answer.
This would explain why tweaking the input in the described ways would throw the AI into a tizzy -- the tweaked input isn't within a tiny delta of any of the millions of categorized images.
(-1: Post disagrees with my already-settled worldview) is not a valid mod option.
"...programs that recognize faces and objects, lauded as a major triumph of deep learning, can fail dramatically when their input is modified even in modest ways by certain types of lighting, image filtering and other alterations that do not affect humans' recognition abilities in the slightest." Now tell me again what a great idea self-driving cars are!
I've abandoned my search for truth; now I'm just looking for some useful delusions.
The Turing test has led us down a rocky road and we have a very long way to go. Artificial human-like intelligence IMHO is still a long way away. Most people make shoot from the hip assumptions about how the brain works and after doing some basic math about Moore's law assume super intelligence is right around the corner.
The brain is way more complicated than we know.
For example: there are two stable isotopes of lithium. Chemically they are identical, but they do not have the same effect on the brain. One is useful as a drug to treat mental illness and the other is not. This means there is something more subtle about how our brain works than interconnections and electrochemistry.
It is however a worthy challenge because the journey will teach us much about who we really are and how we work.
Greed is the root of all evil.
Thanks for the link. I read the article and many of the comments.
What do you think about this one?
The same thing I think about anyone who claims that a major moment in human history boils down to 1 factor - it's bullshit, man.
Yes, slavery was a factor, but not the only factor. Consider the tariffs I linked to, then ask yourself: under those trade rules, how would the Southern states have managed to survive without the use of slave labor? The fact is, they wouldn't have, so in a way the Northern states forced the South to rely on slavery, then punished those states for it.
The fact is, our American Civil War was complicated, both the reasons for it's beginning and continuation (fun fact - Lincoln floated the idea of leaving slavery legal in some states, to preserve the union). What's interesting as an American is that the angle historians take on the conflict tends to be defined by where you get the education: Northern states tend to teach the "Civil War was about slavery" concept, Southern states lean towards the "state's rights" ideology, and Border states (like where I'm from) tend to take a more middle-of-the-road, "both of you are assholes" mentality.
An enigma, wrapped in a riddle, shrouded in bacon and cheese
Entropy. Compression. Same thing. The whole world is thermodynamics and your state of knowledge about the world is also limited by thermodynamics. There will never be a computer that can predict the future of the universe before the universe arrives simply because you can't store a representation of the universe inside the universe itself.
Lossy Compression is therefore how we get around that and be able to compute/think/predict what an approximate future state of the universe is.
What the goal is to align the losses of the compression into the input space which does not exist. For example, if there is no possibly image of a living room of size smaller than an elephant that could contain the elephant then any mapping of images with and without elephants to the same compressed reprensention is a good compression. To say it differently the compresses state is a many to one mapping back to the original state. If for every compressed state there is only one realizable original state then it's invertable. THe images in the original space that could never happen are also mapped to the same compressed state but because they could never exist we lose nothing by ignoring them.
Thus compression and prediction are the same thing.
AI fails when it either over-compresses to a space too small to hold every realizable state. Or it compresses poorly so that in unnessarily conflates two possible real states. For example, the uber car that thought the woman in the road was blowing trash.
On the otherhand, it's often very valuable to overcompress as long as you are tolerant of mistakes on the prediction. That is, the uber car in question was able to do a great job of driving most of the time because it made fact choices that were nearly always good enough. The Cheetah can't just chase the antelope, it needs to try to guess and cut corners a bit. As long as most of it's guesses are good it wins. In the case of the cheetah, a mistake just means a missed meal, which is tolerable. But in the case of the uber driver or an ICBM nuclear missile failsafe system, then our tolerance for error is a bit lower.
Thus a little overcompression is acutally good for generalizing rather than parroting.
A lot of overcompression leads to bad predictions.
Some drink at the fountain of knowledge. Others just gargle.