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One challenge I see in AI is that humans tend to attribute human traits to non-human entities (Anthropomorphism). This tendency leads people to expect something

by kator 9y ago

One challenge I see in AI is that humans tend to attribute human traits to non-human entities (Anthropomorphism). This tendency leads people to expect something from AI that they can do quickly and to be shocked when we find AI to be brittle and lacking fundamental features of human understanding. Having raised children and now playing with my grandchildren, it often amazes me at just how much we take for granted in that comes "out of the box" with a human brain. Humans can build associations with very few samples, and we come pre-wired with all sorts of tools from primary systems like hearing, visual, sensory systems to complex capabilities like speech and communication abilities. I've worked with computers for 37 years, and the progression has been amazing, but we're still a long ways away from the primary capabilities of even a house cat. All this said I often wonder if the reason for the failure of current AI systems to wow us is the gap between power density of a human brain vs. compute systems. I've heard it said that DeepMind burned an order of magnitude more than the 20W/hr a typical human brain uses. When we have compute systems with that power density, we may see more emergent behaviors from our silicon-based friends. Either way, I think AI has useful applications today and I hope we will find many useful applications of these technologies to make our lives better and make more time for us to learn, love and care for one another.

ianai 9y ago
I think it’s related to learning. From my own experiences, it seems like our brains use the same technique to learn from all senses at once. Ie we can probably spot our equivalent of a random pixel through having multiple sensory inputs of all types processed together. We can know we don’t have a spot on our head despite seeing one in a mirror because we can touch or otherwise feel it is not there. That takes an incredible amount of cross reference across all of our combined senses. Similarly, my mind can differentiate from the floaters in my eyes because it remembers their existence. O do that it had to build a concept of a floater, etc.
- danbruc 9y ago
  That can not be the important aspect, at least not in the general case. Show me only a few good photos of an object I have never seen - and one may be sufficient if the object is different enough from all the things I know - and it will become almost impossible for you to trick me into confusing it with anything else and certainly not be changing a few pixels in validation images. I may make mistakes if the image quality is bad or the lighting or if you show me an object that is at least visually very similar and where I am unaware of the distinguishing features. No where here are other senses than vision involved.
  - ianai 9y ago
    Yes you have a concept of an object based on all your memories across all senses. It takes all of your senses and their multiple sources to form concepts.
    - danbruc 9y ago
      This is true insofar that the huge amount of background knowledge I have and use to build a model of what I see in an image was almost inevitably build over my lifetime with all my senses, but this does not change the fact that I performed the task at hand only visually, i.e. without fusing different senses. The AI is lacking my background knowledge, not senses other than vision. One might argue that building human-level background knowledge requires more than just vision but it seems at least not totally obvious that this is indeed the case.
    - TFortunato 9y ago
      This is a very interesting point that I think has more going for it than the poster you are replying to may realize. Related, and apologies I don't have a link, but there have been a few stories circulated of adults who were blind at birth / a young age who had their sight restored at adulthood - They often had great difficulty, or even found it impossible to correlate what they saw with their eyes with the concept of objects that they knew by touch and their other senses.
      - danbruc 9y ago
        I have no link at hand either, but I also read about blind people being unable to transfer their concepts of objects build from touching them to seeing them when their sight was restored. They know what a sharp edge or a pointy thing feels like but they don't know what it looks like. But I think that actually strengthens my point, blind people are capable of learning how different object feel and they recognize them this way but they so just fine without vision and if they suddenly gain access to vision it does not help them. In general having more senses will help to more reliably identify objects because you have access to more features to differentiate them, for example distinguishing materials from imitations just by vision can be hard or maybe even impossible. But when you can also touch them you gain a lot of new information about surface structure, hardness, thermal conductivity and so on and you can easily distinguish between, for example, real stone and plastic or wood with a stone print on it. But just because it is advantageous in the general case to have access to more than one sense that does not imply that it is necessary for a specific task.
- raducu 9y ago
  I find fascinating the idea o sensations and I think there's so much more about them as a stepping stone to intelligence; maybe machines will find something better than sensations, but I doubt it. Why are sensations necessary? Why do I need to feel hungry in order to eat? Certainly an automatic feeding mechanism could exist where I eat without the sensation of hunger, sex without the sensation of lust, kill without anger and so on.
  - chongli 9y ago
    Why do I need to feel hungry in order to eat? Certainly an automatic feeding mechanism could exist where I eat without the sensation of hunger, sex without the sensation of lust, kill without anger and so on. Because that's an incredibly brittle solution. Imagine yourself in an environment where you have to track your prey for hours or days at a time in order to hunt. How are you going to accomplish that if your "automatic feeding mechanism" takes over as soon as it decides you need food? Our sensations and emotions combine to regulate our behaviour, not control it absolutely. The ability to make short-term sacrifices in order to achieve long-term goals is dependent on our ability to ignore or override these sensations.
    - ianai 9y ago
      I think emotions, impulses, short and long term desires, and the like are felt across many forms of life, to various degrees. I base this on my experiences with life directly. Thought definitely advances evolutionary success more than simple unthinking behaviors would - if only for the flexibility afforded. A state machine might flip between billions of possible states in reactions to things, whereas evolved brains flip through their past experiences, emotions (as a representative of external and internal factors), and so on to arrive at some perceived preferencial act/outcome. My bet is the organic model winds up taking fewer resources to achieve more robust results.
    - raducu 9y ago
      I follow the same line of thought as yours - that sensations could be just a hint and you utimately wheigh all your sensations and intentions; BUT just as machines don't have sensations and they can do just fine with planning, so too could animals. "Automatic" doesn't mean right now. My point is sensations/emotions are an incredible information processing paradigm wich we are largely ignoring when we think of AI.
JohnJamesRambo 9y ago
I agree with you completely about things like a housecat and how far we are from something that advanced. The movie Blade Runner made me cherish and appreciate just how incredible animals are when I learned real animals were like the ultimate status symbol in that damaged world. I looked at my own animals differently from that point on and began to see just how complex and amazing these self-replicating, totally autonomous organic beings are and how very far away from creating that we are.
- eitland 9y ago
  An example that stuck with me was on a windy day when I saw a seagull approach a telephone pole, coming in with the wind, turning 180 degrees and landing neatly facing the wind. The brain of those creatures are tiny. No scientists are handholding them as they grow up. Yet they manage to search vast areas for food, learn new ideas like stealing from shops and tourists and even as you mention - self replicate. Yes, while AI is improving we are orders of magnitude away from replicating animals.
  - tinymollusk 9y ago
    I can't wait to see what AI can do with the collective time and resources it took for that seagull to evolve.
    - eitland 9y ago
      That's a good point. My point is it seems we are still quite a few steps behind.
      - fjsolwmv 9y ago
        We can land a rocket on the noon without steering it after launch. Can a seagull do that?
        roywiggins 9y ago
AndrewKemendo 9y ago
Humans can build associations with very few samples This to me is an example of whatever the opposite of anthropomorphism is - assuming that humans sample like computers, and then extrapolating to "low" relative to computing. It's also my #1 pet peeve in DL debates. As someone who has also raised children I can see how this conclusion (low sample rate) can be made, however as someone also deep into ML/RL I see how wrong it is. You say "very few" samples without a metric. I've seen people in the past cite 2 or 3 presentations of a stimulus to a child, for example in the form of a toy, and then state that the child has correctly visually identified the toy with a verbal label in subsequent tests. Assuming that these 2 or 3 presentations correlate with 2 or 3 samples is wrong because it doesn't take into account sample rate. Every presentation batch is a 4D (continuous time + three dimensional) multi-sensory supervised labeling exercise at first (no RL until the first recitation/exploration). Using rough abstractions, at 60 "frames per second" input rate, and lets assume there was a "supervised labeler" (aka parent/guardian) which said the word "toy" multiple times across a 5 minute play period, you have up to 18,000 "labeled" pieces of training data across multiple sensory inputs for one object. If you blindfolded the child and had them identify the object by feel you may need more batches, similarly with other senses (smell for example). Obviously this is a gross simplification - but the constant 1:1 batch comparison at the sampling rate between humans and [linear models/MDP/differentiable programs/Neural Networks] really is way off.
- tlarkworthy 9y ago
  Also the pre-training takes 2 years
- maxerickson 9y ago
  We are also rather prone to seeing patterns in noise.
- resource0x 9y ago
  As someone who raised children and grandchildren, I can't find any explanation to how fast they learn the language, based on very few samples (where your 4D argument doesn't apply). Sure, children learn from conversations with adults, but those are mostly trivial, and involve trivial concepts. And children seem to be able to learn not only from the very limited number of samples, but also (in a sense) - learn more than these samples contain. BTW, did anyone try to analyze how many words/phrases the child heard, say, by the age of 7, when they develop perfect understanding of the language and ability to speak like adults? And after that age, one can spend 50 years learning foreign language and still not get it.
deleted 9y ago
[deleted]
NewEntryHN 9y ago
Funny you mention Anthropomorphism when that's precisely what the article does by saying AI has "hallucinations".
LrnByTeach 9y ago
I think these two points captures the challenges for the AI to match primitive human abilities . >just how much we take for granted in that comes "out of the box" with a human brain. Humans can build associations with very few samples, and we come pre-wired with all sorts of tools from primary systems like hearing, visual, sensory systems to complex capabilities like speech and communication abilities. > DeepMind burned an order of magnitude more than the 20W/hr a typical human brain uses. When we have compute systems with that power density, we may see more emergent behaviors from our silicon-based friends.
YeGoblynQueenne 9y ago
>> This tendency leads people to expect something from AI that they can do quickly and to be shocked when we find AI to be brittle and lacking fundamental features of human understanding. Just to be clear- "AI" ≠ classification and even more so, AI ≠ machine learning ≠ deep neural nets. The article above runs fast and loose with the terminology, but machine vision and in particular object detection (or classification of objects in images) is one area and one sub-task of AI in general. It happens to be one of the two or three areas that have seen strong empirical results in recent years, but it's by no means the only active area of research (although, thanks to the funding from large technology companies, it is probably the fastest growing one). It's also very strange to see "brittleness" as a criticism of deep neural networks in particular (the attacks described only work on convolutional neural nets as far as I know). In the past, the type of AI system criticised as "brittle" was the hand-crafted rule-based expert system type of AI. And the reason why that kind of AI was criticised as brittle is because it did not deal very well with the noise in real-world domains, such as in photography or speech etc. Deep neural nets in particular are extremely robust to noise, which is why they work so well in speech and image processing. I think what you really mean by "brittle" is the tendency of deep nets to be, well, a little too good in dealing with noise. Specifically, they have a tendency to overfit to the noise, because they produce models with very high variance. Indeed, the whole adversarial examples thing is probably best understood as a result of overfitting. As to "lacking fundamental features of human understanding" what you mean, I think, is that image classifiers only do classification and nothing more- which is true, but then that's what they 're designed to do. Nobody expects an image classifier to have any understanding of the images it's classifying.
- chubot 9y ago
  "Brittle" is easy to explain: they're wrong in ways that a human would not be wrong. That goes for both rule-based systems and deep neural nets. Humans are inherently social; we have a model of how other humans behave which is essential to getting things done. It doesn't involve say mistaking a bunch of white noise for a camel. For another example, when you're driving, don't underestimate how many of the rules are not written down and involve a model of what the other driver (human) might do. Those rules are also specific to a geographic area and evolve (slowly and imperceptibly) over time. The question "Is this person going to be surprised by what I do?" is inherently different than "Is this object a cyclist?"
raincom 9y ago
Hubert Dreyfus in his book "What computers can't do: a critique of artificial reason" made similar arguments 40 years ago.
ergothus 9y ago
> progression has been amazing, but we're still a long ways away from the primary capabilities of even a house cat I'm in complete agreement with your point about how amazing things we consider "basic" are. But (using your post as a chance to soapbox) we need to remember that we're not TRYING to match the primary capabilities of a cat (or humans). In part, because we know little about how thought and instinct work on a useful level, but we know more about flowchart-style logic. And as I understand them (which isn't much), even the deeper neural work doesn't try to emulate biological thought. Instead, we focus on a _goal_ where we get a result similar to biological thought and try different (complex) ways of mixing deterministic logic to get there. Everything is still rudimentary, but going forward I expect that AI (or any of the various related fields often lumped as AI) will do remarkable things beyond biological capability long before certain "basic" (as you say, actually amazing) things. And I don't mean just super-logic or strong memorization - we already have that - I mean they will "think" from thought A to thought B easily (e.g. 'I've read this book before' leads to 'an accountable monarchy is the best system of rule'), while we struggle to do the same, while we can make the A to C conclusion easily ('I like green' leading to 'I need to mow the lawn') that AI finds harder to accomplish or even understand enough to predict. Biology is messy - just a few days ago we had an article on here about how social comfort makes us warmer...and in reverse, raising the temperature makes us feel more befriended. We have bizarre social rules at some level imprinted into our DNA - Tall people, pretty people, they will find success. Mob mentality, mass hallucination, zealotry, we have "work" and "fun" as mostly separate areas. Being fit is healthy, but becoming/staying fit is emotionally difficult. Our memories are ridiculously unreliable - our impressions and desires shape interpretation, and then continue to reshape the memories on each recall. We categorize thoughts down into intellectual and emotional. Change blindness, desiring comfort but pretending it is ideology, getting defensive when proven or even suggested to be wrong, conspiracy theories, all drama, fiction, gender, parody, humor itself, sleep, xenophobia, celebrities, luxury goods, clowns, desire to get intoxicated, placebos, psychological schema, "I'm not a snitch", ...the list goes on. We (humans) tend to think there are two options: human-like thought, and cold predictable logic (witness almost every sentient computer in media...including popular science journalism). We don't think "huh, the complexity and history of our biological systems has resulted in all these surprising connections. I bet a non-biological system complex enough to "think" will have it's own surprising connections because of a totally different structure, physics, and background." They will be surprising because their way of thinking will be something we cannot wrap our brains around unable to emulate because it is literally contrary to the physical way we think. AI will not be simple and unable to process "this statement is false" - but they might find that hilarious. Or perhaps it gives a result similar to horror. Or it isn't worth much attention...but too many such statements is like dirt under your fingernails. Who knows? I only know that expecting an early AI to have the capabilities of a child is most likely self-deceptive. I don't know when AI as generally thought of will exist. I don't know if it will prove the salvation/destruction/entertainment of humanity. I fully expect it will be very weird from a human perspective, with different strengths, weaknesses, and quirks. I also expect that humanity en masse will translate this collection as "inferior", because our brains say we should be skeptical of "different").
monk_e_boy 9y ago
There was an AMA with a google team of AI coders. I asked them if any current AI was as smart as a snail (or a lizard or a fish), could it make it's way in the world and eat food and find a mate. They didn't think so, not yet.
ScottBurson 9y ago
> the 20W/hr a typical human brain uses You mean "the 20W a typical human brain uses". A watt is a joule per second, so it already has the dimensions of energy per unit time. Good comment otherwise :-)
nitwit005 9y ago
We have a decent error rate. People go searching for their car keys, or some item in the fridge, and will overlook what they're looking for multiple times, and then suddenly they notice it and feel like an idiot. Rather than anthropomorphizing the AI, it's more like expecting superhuman qualities. People overlook objects in their rear view mirror all the time, but the expectation for the AI is that it will never make that mistake.
goatlover 9y ago
A good question that should be asked is why are we focused on trying to recreate being human in a machine? There's already billions of human beings. Machines are our tools. I think Augmented Intelligence was always the better term, but the scifi stories and overhype stole the show a long time ago. Do we really want and need a Data or HAL? They're great for story telling, but is that what humanity really needs from it's tools? I don't need to fall in love with Siri like in the movie Her. I just need it to be useful when I want to ask my device oral questions.
derefr 9y ago
> I've heard it said that DeepMind burned an order of magnitude more than the 20W/hr a typical human brain uses. Consider, on the other hand, the number of watt-hours AlphaZero spent to reinvent the current state of the art in Go strategy, vs. the number of cumulative watt-hours the brains of the global community of Go masters spent—over centuries—to figure out that state of the art in the first place. ML might be kind of expensive to run right now, but its "thinking" can already be horizontally parallelized in a way human thinking just can't. For jobs that fit in one human brain, the human consumes less energy to get the result. For jobs that don't fit in one human brain, humans will have to do exponentially more redundant work to get the same results as one computer cluster. Now, if we manage to get to "instantaneous recording and ingestion of mental mastery of a skill, like in the Matrix" before we get to "AI that builds better AI", maybe humans will (at least temporarily) be the better thinkers once again. Maybe we'll go back to the model of having offices full of human "computers" analyzing problems!

by kator 9y ago

ianai 9y ago

I think it’s related to learning. From my own experiences, it seems like our brains use the same technique to learn from all senses at once. Ie we can probably spot our equivalent of a random pixel through having multiple sensory inputs of all types processed together. We can know we don’t have a spot on our head despite seeing one in a mirror because we can touch or otherwise feel it is not there. That takes an incredible amount of cross reference across all of our combined senses. Similarly, my mind can differentiate from the floaters in my eyes because it remembers their existence. O do that it had to build a concept of a floater, etc.

danbruc 9y ago
That can not be the important aspect, at least not in the general case. Show me only a few good photos of an object I have never seen - and one may be sufficient if the object is different enough from all the things I know - and it will become almost impossible for you to trick me into confusing it with anything else and certainly not be changing a few pixels in validation images. I may make mistakes if the image quality is bad or the lighting or if you show me an object that is at least visually very similar and where I am unaware of the distinguishing features. No where here are other senses than vision involved.
- ianai 9y ago
  Yes you have a concept of an object based on all your memories across all senses. It takes all of your senses and their multiple sources to form concepts.
  - danbruc 9y ago
    This is true insofar that the huge amount of background knowledge I have and use to build a model of what I see in an image was almost inevitably build over my lifetime with all my senses, but this does not change the fact that I performed the task at hand only visually, i.e. without fusing different senses. The AI is lacking my background knowledge, not senses other than vision. One might argue that building human-level background knowledge requires more than just vision but it seems at least not totally obvious that this is indeed the case.
  - TFortunato 9y ago
    This is a very interesting point that I think has more going for it than the poster you are replying to may realize. Related, and apologies I don't have a link, but there have been a few stories circulated of adults who were blind at birth / a young age who had their sight restored at adulthood - They often had great difficulty, or even found it impossible to correlate what they saw with their eyes with the concept of objects that they knew by touch and their other senses.
    - danbruc 9y ago
      I have no link at hand either, but I also read about blind people being unable to transfer their concepts of objects build from touching them to seeing them when their sight was restored. They know what a sharp edge or a pointy thing feels like but they don't know what it looks like. But I think that actually strengthens my point, blind people are capable of learning how different object feel and they recognize them this way but they so just fine without vision and if they suddenly gain access to vision it does not help them. In general having more senses will help to more reliably identify objects because you have access to more features to differentiate them, for example distinguishing materials from imitations just by vision can be hard or maybe even impossible. But when you can also touch them you gain a lot of new information about surface structure, hardness, thermal conductivity and so on and you can easily distinguish between, for example, real stone and plastic or wood with a stone print on it. But just because it is advantageous in the general case to have access to more than one sense that does not imply that it is necessary for a specific task.
raducu 9y ago
I find fascinating the idea o sensations and I think there's so much more about them as a stepping stone to intelligence; maybe machines will find something better than sensations, but I doubt it. Why are sensations necessary? Why do I need to feel hungry in order to eat? Certainly an automatic feeding mechanism could exist where I eat without the sensation of hunger, sex without the sensation of lust, kill without anger and so on.
- chongli 9y ago
  Why do I need to feel hungry in order to eat? Certainly an automatic feeding mechanism could exist where I eat without the sensation of hunger, sex without the sensation of lust, kill without anger and so on. Because that's an incredibly brittle solution. Imagine yourself in an environment where you have to track your prey for hours or days at a time in order to hunt. How are you going to accomplish that if your "automatic feeding mechanism" takes over as soon as it decides you need food? Our sensations and emotions combine to regulate our behaviour, not control it absolutely. The ability to make short-term sacrifices in order to achieve long-term goals is dependent on our ability to ignore or override these sensations.
  - ianai 9y ago
    I think emotions, impulses, short and long term desires, and the like are felt across many forms of life, to various degrees. I base this on my experiences with life directly. Thought definitely advances evolutionary success more than simple unthinking behaviors would - if only for the flexibility afforded. A state machine might flip between billions of possible states in reactions to things, whereas evolved brains flip through their past experiences, emotions (as a representative of external and internal factors), and so on to arrive at some perceived preferencial act/outcome. My bet is the organic model winds up taking fewer resources to achieve more robust results.
  - raducu 9y ago
    I follow the same line of thought as yours - that sensations could be just a hint and you utimately wheigh all your sensations and intentions; BUT just as machines don't have sensations and they can do just fine with planning, so too could animals. "Automatic" doesn't mean right now. My point is sensations/emotions are an incredible information processing paradigm wich we are largely ignoring when we think of AI.

JohnJamesRambo 9y ago

I agree with you completely about things like a housecat and how far we are from something that advanced. The movie Blade Runner made me cherish and appreciate just how incredible animals are when I learned real animals were like the ultimate status symbol in that damaged world. I looked at my own animals differently from that point on and began to see just how complex and amazing these self-replicating, totally autonomous organic beings are and how very far away from creating that we are.

eitland 9y ago
An example that stuck with me was on a windy day when I saw a seagull approach a telephone pole, coming in with the wind, turning 180 degrees and landing neatly facing the wind. The brain of those creatures are tiny. No scientists are handholding them as they grow up. Yet they manage to search vast areas for food, learn new ideas like stealing from shops and tourists and even as you mention - self replicate. Yes, while AI is improving we are orders of magnitude away from replicating animals.
- tinymollusk 9y ago
  I can't wait to see what AI can do with the collective time and resources it took for that seagull to evolve.
  - eitland 9y ago
    That's a good point. My point is it seems we are still quite a few steps behind.
    - fjsolwmv 9y ago
      We can land a rocket on the noon without steering it after launch. Can a seagull do that?
      - roywiggins 9y ago

AndrewKemendo 9y ago

Humans can build associations with very few samples This to me is an example of whatever the opposite of anthropomorphism is - assuming that humans sample like computers, and then extrapolating to "low" relative to computing. It's also my #1 pet peeve in DL debates. As someone who has also raised children I can see how this conclusion (low sample rate) can be made, however as someone also deep into ML/RL I see how wrong it is. You say "very few" samples without a metric. I've seen people in the past cite 2 or 3 presentations of a stimulus to a child, for example in the form of a toy, and then state that the child has correctly visually identified the toy with a verbal label in subsequent tests. Assuming that these 2 or 3 presentations correlate with 2 or 3 samples is wrong because it doesn't take into account sample rate. Every presentation batch is a 4D (continuous time + three dimensional) multi-sensory supervised labeling exercise at first (no RL until the first recitation/exploration). Using rough abstractions, at 60 "frames per second" input rate, and lets assume there was a "supervised labeler" (aka parent/guardian) which said the word "toy" multiple times across a 5 minute play period, you have up to 18,000 "labeled" pieces of training data across multiple sensory inputs for one object. If you blindfolded the child and had them identify the object by feel you may need more batches, similarly with other senses (smell for example). Obviously this is a gross simplification - but the constant 1:1 batch comparison at the sampling rate between humans and [linear models/MDP/differentiable programs/Neural Networks] really is way off.

tlarkworthy 9y ago
Also the pre-training takes 2 years
maxerickson 9y ago
We are also rather prone to seeing patterns in noise.
resource0x 9y ago
As someone who raised children and grandchildren, I can't find any explanation to how fast they learn the language, based on very few samples (where your 4D argument doesn't apply). Sure, children learn from conversations with adults, but those are mostly trivial, and involve trivial concepts. And children seem to be able to learn not only from the very limited number of samples, but also (in a sense) - learn more than these samples contain. BTW, did anyone try to analyze how many words/phrases the child heard, say, by the age of 7, when they develop perfect understanding of the language and ability to speak like adults? And after that age, one can spend 50 years learning foreign language and still not get it.

deleted 9y ago

[deleted]

NewEntryHN 9y ago

Funny you mention Anthropomorphism when that's precisely what the article does by saying AI has "hallucinations".

LrnByTeach 9y ago

I think these two points captures the challenges for the AI to match primitive human abilities . >just how much we take for granted in that comes "out of the box" with a human brain. Humans can build associations with very few samples, and we come pre-wired with all sorts of tools from primary systems like hearing, visual, sensory systems to complex capabilities like speech and communication abilities. > DeepMind burned an order of magnitude more than the 20W/hr a typical human brain uses. When we have compute systems with that power density, we may see more emergent behaviors from our silicon-based friends.

YeGoblynQueenne 9y ago

>> This tendency leads people to expect something from AI that they can do quickly and to be shocked when we find AI to be brittle and lacking fundamental features of human understanding. Just to be clear- "AI" ≠ classification and even more so, AI ≠ machine learning ≠ deep neural nets. The article above runs fast and loose with the terminology, but machine vision and in particular object detection (or classification of objects in images) is one area and one sub-task of AI in general. It happens to be one of the two or three areas that have seen strong empirical results in recent years, but it's by no means the only active area of research (although, thanks to the funding from large technology companies, it is probably the fastest growing one). It's also very strange to see "brittleness" as a criticism of deep neural networks in particular (the attacks described only work on convolutional neural nets as far as I know). In the past, the type of AI system criticised as "brittle" was the hand-crafted rule-based expert system type of AI. And the reason why that kind of AI was criticised as brittle is because it did not deal very well with the noise in real-world domains, such as in photography or speech etc. Deep neural nets in particular are extremely robust to noise, which is why they work so well in speech and image processing. I think what you really mean by "brittle" is the tendency of deep nets to be, well, a little too good in dealing with noise. Specifically, they have a tendency to overfit to the noise, because they produce models with very high variance. Indeed, the whole adversarial examples thing is probably best understood as a result of overfitting. As to "lacking fundamental features of human understanding" what you mean, I think, is that image classifiers only do classification and nothing more- which is true, but then that's what they 're designed to do. Nobody expects an image classifier to have any understanding of the images it's classifying.

chubot 9y ago
"Brittle" is easy to explain: they're wrong in ways that a human would not be wrong. That goes for both rule-based systems and deep neural nets. Humans are inherently social; we have a model of how other humans behave which is essential to getting things done. It doesn't involve say mistaking a bunch of white noise for a camel. For another example, when you're driving, don't underestimate how many of the rules are not written down and involve a model of what the other driver (human) might do. Those rules are also specific to a geographic area and evolve (slowly and imperceptibly) over time. The question "Is this person going to be surprised by what I do?" is inherently different than "Is this object a cyclist?"

raincom 9y ago

Hubert Dreyfus in his book "What computers can't do: a critique of artificial reason" made similar arguments 40 years ago.

ergothus 9y ago

> progression has been amazing, but we're still a long ways away from the primary capabilities of even a house cat I'm in complete agreement with your point about how amazing things we consider "basic" are. But (using your post as a chance to soapbox) we need to remember that we're not TRYING to match the primary capabilities of a cat (or humans). In part, because we know little about how thought and instinct work on a useful level, but we know more about flowchart-style logic. And as I understand them (which isn't much), even the deeper neural work doesn't try to emulate biological thought. Instead, we focus on a _goal_ where we get a result similar to biological thought and try different (complex) ways of mixing deterministic logic to get there. Everything is still rudimentary, but going forward I expect that AI (or any of the various related fields often lumped as AI) will do remarkable things beyond biological capability long before certain "basic" (as you say, actually amazing) things. And I don't mean just super-logic or strong memorization - we already have that - I mean they will "think" from thought A to thought B easily (e.g. 'I've read this book before' leads to 'an accountable monarchy is the best system of rule'), while we struggle to do the same, while we can make the A to C conclusion easily ('I like green' leading to 'I need to mow the lawn') that AI finds harder to accomplish or even understand enough to predict. Biology is messy - just a few days ago we had an article on here about how social comfort makes us warmer...and in reverse, raising the temperature makes us feel more befriended. We have bizarre social rules at some level imprinted into our DNA - Tall people, pretty people, they will find success. Mob mentality, mass hallucination, zealotry, we have "work" and "fun" as mostly separate areas. Being fit is healthy, but becoming/staying fit is emotionally difficult. Our memories are ridiculously unreliable - our impressions and desires shape interpretation, and then continue to reshape the memories on each recall. We categorize thoughts down into intellectual and emotional. Change blindness, desiring comfort but pretending it is ideology, getting defensive when proven or even suggested to be wrong, conspiracy theories, all drama, fiction, gender, parody, humor itself, sleep, xenophobia, celebrities, luxury goods, clowns, desire to get intoxicated, placebos, psychological schema, "I'm not a snitch", ...the list goes on. We (humans) tend to think there are two options: human-like thought, and cold predictable logic (witness almost every sentient computer in media...including popular science journalism). We don't think "huh, the complexity and history of our biological systems has resulted in all these surprising connections. I bet a non-biological system complex enough to "think" will have it's own surprising connections because of a totally different structure, physics, and background." They will be surprising because their way of thinking will be something we cannot wrap our brains around unable to emulate because it is literally contrary to the physical way we think. AI will not be simple and unable to process "this statement is false" - but they might find that hilarious. Or perhaps it gives a result similar to horror. Or it isn't worth much attention...but too many such statements is like dirt under your fingernails. Who knows? I only know that expecting an early AI to have the capabilities of a child is most likely self-deceptive. I don't know when AI as generally thought of will exist. I don't know if it will prove the salvation/destruction/entertainment of humanity. I fully expect it will be very weird from a human perspective, with different strengths, weaknesses, and quirks. I also expect that humanity en masse will translate this collection as "inferior", because our brains say we should be skeptical of "different").

monk_e_boy 9y ago

There was an AMA with a google team of AI coders. I asked them if any current AI was as smart as a snail (or a lizard or a fish), could it make it's way in the world and eat food and find a mate. They didn't think so, not yet.

ScottBurson 9y ago

> the 20W/hr a typical human brain uses You mean "the 20W a typical human brain uses". A watt is a joule per second, so it already has the dimensions of energy per unit time. Good comment otherwise :-)

nitwit005 9y ago

We have a decent error rate. People go searching for their car keys, or some item in the fridge, and will overlook what they're looking for multiple times, and then suddenly they notice it and feel like an idiot. Rather than anthropomorphizing the AI, it's more like expecting superhuman qualities. People overlook objects in their rear view mirror all the time, but the expectation for the AI is that it will never make that mistake.

goatlover 9y ago

A good question that should be asked is why are we focused on trying to recreate being human in a machine? There's already billions of human beings. Machines are our tools. I think Augmented Intelligence was always the better term, but the scifi stories and overhype stole the show a long time ago. Do we really want and need a Data or HAL? They're great for story telling, but is that what humanity really needs from it's tools? I don't need to fall in love with Siri like in the movie Her. I just need it to be useful when I want to ask my device oral questions.

derefr 9y ago

> I've heard it said that DeepMind burned an order of magnitude more than the 20W/hr a typical human brain uses. Consider, on the other hand, the number of watt-hours AlphaZero spent to reinvent the current state of the art in Go strategy, vs. the number of cumulative watt-hours the brains of the global community of Go masters spent—over centuries—to figure out that state of the art in the first place. ML might be kind of expensive to run right now, but its "thinking" can already be horizontally parallelized in a way human thinking just can't. For jobs that fit in one human brain, the human consumes less energy to get the result. For jobs that don't fit in one human brain, humans will have to do exponentially more redundant work to get the same results as one computer cluster. Now, if we manage to get to "instantaneous recording and ingestion of mental mastery of a skill, like in the Matrix" before we get to "AI that builds better AI", maybe humans will (at least temporarily) be the better thinkers once again. Maybe we'll go back to the model of having offices full of human "computers" analyzing problems!