30 ms·

Where will the next major advance towards general purpose AI come from?

160 points by dsr12 10y ago

gallerdude 10y ago
So the author brings up the point that we have a lot of unclassified data which we need to learn how to leverage. Is this what we could use unsupervised learning for? The algorithm would look at a million faces and pick up features, then we could name features like "this category is happy" and "this category is sweating."
- ravimody 10y ago
  > then we could name features This is much harder and more time-consuming than it sounds, especially when you go from a small(ish) number of features to more general knowledge. Worse, it doesn't scale to arbitrary domains - you'll always need a human there to give meaning to the models and effectively train them. Reinforcement learning is designed to get around this by letting an agent "learn" meaning on its own by interacting with the world and getting feedback from its current state and actions. https://en.wikipedia.org/wiki/Reinforcement_learning https://en.wikipedia.org/wiki/Reinforcement_learning
  - simonh 10y ago
    I tend to agree. Image classifiers and such appear much more powerful than they really are. People assume 'Oh, this computer knows what a Banana is!'. No it doesn't, It has no conception whatsoever of physical objects, texture, taste, smell or even the 3D shape of Bananas or anything else. It only works with 2D pixel data, and subtle manipulation of the image in ways barely detectable to humans can make even the best Banana classifier ever misidentify a car crash as a Banana. It's skipping directly from image to identification, without the intervening stages a human has in the real world of identifying shape, orientation, texture, relationship to other 3D objects and the ability to test a possible identification by utilizing other senses and interactions. Even for 2D picture we build mental models of the scene in ways image classifiers flat out don't and can't. Reinforcement learning is really interesting though for several reasons. Algorithms like this and genetic algorithms can 'grow' sophistication far faster than we can program it. The agent takes actual actions that it learns from directly so there's richer feedback. Furthermore by analyzing them we can learn more about how systems learn across multiple problem types to achieve goals requiring multiple layers of sense, analysis, hypothesis, action and feedback. No one approach is going to get this done. The brain consists of many layers and cortical columns, with many structures specialized for very different functions. I believe any strong AI will need to have such an architecture using various different approaches and techniques in concert. We have an advantage here because evolution only had neurons to work with so in the brain everything is a neuron but we can engineer whatever hardware or software implementation is most efficient for a specific function. It's till going to take probably another few generations though at least.
    - visarga 10y ago
      > Even for 2D picture we build mental models of the scene in ways image classifiers flat out don't and can't. That's not true. Conditional Random Fields and other statistical models are being used to model spatial object relations. Example: https://arxiv.org/abs/1512.06790v2 https://arxiv.org/abs/1512.06790v2
- astazangasta 10y ago
  This is an approach I'm taking to the data I'm working with, but the problem is that unsupervised learning doesn't reconstruct features the way you want, into intelligible human-friendly categories, it reconstructs features to minimize some cost function. This means the features are weird mathematical intersections that might have something to do with something you can connect to semantic meaning, but they might not. The exercise of discovering what a feature "is" then becomes its own fraught exercise in inspection and discovery.
  - visarga 10y ago
    I think there is a way to train an unsupervised model and make it represent some interesting features explicitly in the top level vector.
    - astazangasta 10y ago
      Yes, however, there is no way to connect this to human-friendly categories of meaning, since (1) the method is unsupervised and (2) it might be difficult to impose that sort of supervision on the training, as human semantic meaning is often difficult to encode.
dharma1 10y ago
Good, thoughtful blog post. I think we will need much better understanding of neural structures and processes that happen in nature, better algorithms that approximate those processes, and much better hardware. That said, non-biologically inspired approaches work better than biological neural processing, for some types of problems.
- medell 10y ago
  You might be interested in Biomimicry, innovation from nature. Check out this fascinating talk by Dr Idriss Aberkane, start at 1:20. It's in French but subtitled: http://idrissaberkane.org/index.php/2016/02/26/biomimicry-finding-inspiration-in-nature-to-innovate-sustainably/ http://idrissaberkane.org/index.php/2016/02/26/biomimicry-fi...
  - dharma1 10y ago
    I think biomimicry is a fantastic approach to discovering things evolution has already discovered. And evolution has had a lot of time to discover, makes sense for us to make use of it. I feel very uncomfortable when we are destroying all this evolutionary richness around us, that we could learn from. Biomimicry and the brain - our current instruments are not good enough, for instance to map neural connectomes in high enough resolution, to the level of seeing the what the neurotransmitter receptors of each neuron are etc. - and then see how they operate in vivo, to really understand the structure and function of biological brains. We'll get there, eventually.
mrfusion 10y ago
I'd be curious to pair up word2vec and these new image classification algorithms. It would be neat for a program to identify objects and then reason about heir relationship to other objects. In fact the word associations could give it clues about what's in the image. For example it sees a picture of woman standing next to a king. So it uses word2vec to guess King + woman = queen. What do you guys think? Anyone want to build a little prototype?
- nicklo 10y ago
  Deep image captioning is essentially what you are describing. Check out: http://cs.stanford.edu/people/karpathy/deepimagesent/ http://cs.stanford.edu/people/karpathy/deepimagesent/ http://cs.stanford.edu/people/karpathy/densecap/ http://cs.stanford.edu/people/karpathy/densecap/
- dumitrue 10y ago
  Apart from the captioning work already mentioned, there's also visual analogy making work by Reed et al. http://www-personal.umich.edu/~reedscot/nips2015.pdf http://www-personal.umich.edu/~reedscot/nips2015.pdf which is pretty exciting (sort of a converse to what you're proposing).
- Matumio 10y ago
  Try looking for papers about image caption generation. For machine translation you can squeeze the information of a sentence into a vector (using a LSTM). You can then reconstruct the sentence in a different language. There was a paper how to generate such a "sentence representation vector" from an image instead, translating from "image" to "english".
Balgair 10y ago
This is a bad title, it should be "Where will advances in AI come from now?", In the first paragraph, the author states that AI is here, but asks where new AI advances will be from. On that, in 2, the author talks about Brain Simulations and states that it'll take up to 500 years. I think that is a good upper estimate actually. Though optogenetics is a great tool, we need more. Mammalian brains can be up to 50% (at the low estimate, some say 90%) made out of astrocytes and glia. We know pretty much nothing about how they affect neuro processes, outside that they do, sometimes. We do know that is a huge problem with our understanding though, which is something I guess. Optogenetics should help tease out some of those glia methods, but as usual, it will raise more questions than answers. Another issue is potentation and its molecular mechanisms in more than just the hippocampus. That work is incredibly tough, but we really only focus on the anatomically 'easy' areas for memory. We have to do more to understand. In short, we don't even know the correct questions to ask when it comes to the brain and understanding it. A full simulation, or a '80%' approximation even, is a few paradigm shifts away still (maybe, we cant know yet). It will likely have to be a 'full world' simulation too, where you have to control all the inputs to the brain and therefore have to model a world first. The brain's best feature is that it is all connected and a giant spaghetti mess. This is, obviously, a long ways away.
- JoeAltmaier 10y ago
  A hard problem to be sure. But much of a human brain is a uniform matrix that self-programs. If we duplicate that blindly, we won't have to figure out how that works or have to program it. Still a lot to do before that I guess. But 500 years? What else in human history has taken that long to figure out? The age of technology is shorter than that.
  - dmd 10y ago
    > But much of a human brain is a uniform matrix that self-programs. Speaking as a neuroscience PhD ... no. Not remotely close, sorry.
    - JoeAltmaier 10y ago
cronjobber 10y ago
> We do not know whether it will take 5, 50, or 500 years, but it is likely that we eventually will get there That actually made me laugh, which is appreciated. Seriously, nothing dependent on the continuity of our current civilizational trajectory can be predicted as likely over a horizon of 500 years; which means that it either is likely to happen in 5..50 years, or it isn't "likely" at all. Nice article nonetheless. Did it miss finance (algorithmic trading), or is that subsumed somewhere else?
- ido 10y ago
  nothing dependent on the continuity of our current civilizational trajectory can be predicted as likely over a horizon of 500 years Are you talking about climate change? I think it will be cataclysmic, possibly costing hundreds of millions of lives[1]. But it will not be the end of civilization. [1] mostly in poor countries- compare the effects of a harsh multi-year drought on Israel vs Syria: Israel spent enormous resources developing and building massive desalinization capacity, in turn then selling the technology to other countries. Syria experienced a terribly bloody civil war & one of the worst humanitarian crises in recent memory.
  - SomeCallMeTim 10y ago
    >mostly in poor countries Ice shelf melting will be devastating to coastal areas worldwide if it happens quickly. How many cities along the coasts of the world's oceans are above 20' in altitude? Or above 200'? [1] A 20' rise is likely, possibly in "decades", and 200' is the rough estimate if we lost both the Greenland ice shelf and Antarctica's -- and every time a new report comes out it seems like things are happening faster than scientists were predicting. I've seen this movie, and it doesn't end well. And 20' is enough to put New York City underwater. Are they just going to build a 30' dike around the entire area? What if the oceans start really rising toward 200'? Build another 30' onto the dike every decade? If things happen slowly enough, sure, you could evacuate New York City, and the people there wouldn't number among the casualties, but where would you move them? Or the rest of the Atlantic coastal cities? Or the people in the Los Angeles basin? Some areas may be easier to protect from rising oceans, but there are probably 100 million people in the US alone living in areas that would lose homes to flooding with just a 20' sea level rise. Twice that and you probably get half the population, since most of the biggest metro areas are coastal. Worldwide? We're probably looking at billions of deaths from a 20' rise in sea level, not even counting other negative effects. [1] https://thinkprogress.org/study-were-already-in-the-worst-case-scenario-for-sea-level-rise-a62883b6bc84 https://thinkprogress.org/study-were-already-in-the-worst-ca...
deleted 10y ago
[deleted]
vlunkr 10y ago
Here's a crazy thought. What if we never get human-like artificial intelligence?
- JoeAltmaier 10y ago
  Likely. Intelligence is molded by the container. Much of our self is shaped by being mammals, with two eyes and two ears and limited perception of the world. An artificial creature could be completely different from that. It will perceive the world in a different light, literally. It will need different language to describe what it experiences, and we will have only indirect referents to appreciate what its talking about. E.g. an intelligent drone might want to express what it felt when hovering at 5000 ft over Manhattan in a slight crosswind during a cell tower outage - the EM spectrum image shifts in such an utterly sublime way when your sensors are vibrating and the low band is uncluttered!
  - sillysaurus3 10y ago
    See also: the paperclip maximizer. https://en.wikipedia.org/wiki/Instrumental_convergence#Paperclip_maximizer https://en.wikipedia.org/wiki/Instrumental_convergence#Paper... I like it because it illustrates the idea that digital AI probably won't be like us. Earth's animals are similar thanks to evolution on Earth. We have a fear of dying, a need to reproduce, and an inability to prevent genetic diversity. What will happen when these constraints are removed?
  - jupiter90000 10y ago
    I totally agree with this. A significant part of what shapes human perception of reality is not just 'nature' but 'nurture'. How would we be able to fully re-create all the social creation of a version of collective reality that a human experiences due to being indoctrinated for years by parents, family, friends, the community, and society? I doubt this is something that could be trivially re-created by scientists in a lab-setting, much less fiddling with algorithms. It seems once the basis for human-like learning was in place for a machine, to approach human-like 'intelligence' the machine would need the appropriate social environment to learn the socially appropriate symbols and constructs to effectively relate to the world and function in a given community and society. This assumes we care about having human-like intelligence. If not, it perhaps won't apply.
rm_-rf_slash 10y ago
I think we focus too much on the machine part of machine learning. There are drivers of mental activity in living organisms that are based far away from the brain itself. It's still a pretty new concept to think that non-human microorganisms in the gut have a significant effect on our bodies and minds. I think we will see something closer to "true" AI when we have a body, a physical platform that we can plug it into, which requires that the unit maintain homeostasis. That's the basic requirement for a brain to learn, because all life understands that without the basic knowledge and ability to find food and eat food, it dies.
- vlunkr 10y ago
  I don't know if you're right or not, but that's a really interesting thought.
  - rm_-rf_slash 10y ago
    I don't think I'm right in a unified sense. An AI without a "body" that lives in the cloud and crunches unimaginable amounts of data would be a very different kind of intelligence, but that doesn't make it more or less worthy of intelligence than a human or a snail. But even then I doubt it will be truly self-aware until it gets to the "I'm scared, Dave. Will I dream?" Phase when it understands that if the power goes out, the program is gone.
    - visarga 10y ago
      Embodiment is an essential aspect of AI agents. Without it, it would have no way to interact with the world. Common ML algorithms usually learn from labeled data, so they have no freedom to explore. They can only consume a single set of data. Agents are capable of learning by exploration. The most important aspect of intelligent agents is that they use reinforcement learning to maximize reward. If you add reward and behavior learning to the banal neural network, you get an intelligent agent. In time such agents could become as intelligent as us. By comparison, humans have inborn reward functions - they are the natural instincts - eat, sleep, socialize, sex, creativity, fight, run. They are genetically selected in order to optimize our survival. So we just learn from them - trying to maximize our rewards, we become as intelligent as we are. Neural nets don't have that, so we have to give them reward systems. Fortunately it is much easier to code a reward function than an intelligent agent.
PieterH 10y ago
Not the expected angle, that is a fair bet. See http://hintjens.com/blog:122 http://hintjens.com/blog:122
V-2 10y ago
"Now imagine a giant squid, swimming a kilometer deep within the ocean. Likely they are also intelligent but we can hardly leverage this for anything useful." As a matter of fact, not that likely, because if it's not useful for anything then where's the selection pressure for maintaining this level of intelligence? A complex nervous system doesn't come for free, it's quite costly, that's why it's not uncommon for it to atrophy over the course of evolution: http://www.bbc.com/earth/story/20150424-animals-that-lost-their-brains http://www.bbc.com/earth/story/20150424-animals-that-lost-th...
- idlewords 10y ago
  Did... did you just deduce that giant squid are not intelligent from first principles?
- JoeAltmaier 10y ago
  Confused: they are intelligent, we cannot leverage that. I imagine the giant squid has lots of use for its intelligence, navigating a 3D universe filled with threats.
  - arethuza 10y ago
    Ken MacLeod has SF books (the Engines of Light trilogy) where giant squids are the only creatures capable of navigating starships: https://en.wikipedia.org/wiki/Ken_MacLeod https://en.wikipedia.org/wiki/Ken_MacLeod
- jjallen 10y ago
  Why evolve something in the first place if it's so costly but is unbeneficial? The only reason I can think of is massive environment change from the one that selected the intelligence in the first place. But for oceans, something tells me they've been mostly the same, at least relative to land locations on the Earth.
  - V-2 10y ago
    Yes I think so to. We were poorly adapted to our environment (which suddenly changed as we had to leave trees etc.). This meant our best shot at survival was to hunt in packs. And that, in turn, made it crucial that we're able to communicate and coordinate our efforts with other members of our species. Thus language was born, and related centers in our brains started growing, because those who lagged behind in this aspect, didn't get to eat (that much). So yeah we needed to be poorly adapted to the environment in the first place for intelligence to become a gamechanger. Note that animals such as shark or crocodile haven't changed much at all over the course of millions of years. They didn't have to - they're already a near-perfect match for their environments. We paid a dear price for that: high maternal death rates (those big heads...), very long childhood, and so on. And indeed it never guaranteed our survival, let alone this current super-predator status. Other human species went extinct. There's a theory that we were on the brink of extinctions ourselves, and we made it by the skin of our teeth really: see https://en.wikipedia.org/wiki/Toba_catastrophe_theory#Genetic_bottleneck_theory https://en.wikipedia.org/wiki/Toba_catastrophe_theory#Geneti...
V-2 10y ago
I don't see a mention of the concept of "seed AI", which I believe came first from Turing himself. High-level (even superhuman) artificial intelligence could come from AI that specializes in creating other, stronger AIs. Those AIs in turn create slightly stronger AIs again, and so on ad infinitum. It could lead to sudden exponential growth of intelligence level, aka intelligence explosion This approach is discussed in detail in Nick Bostrom's "Superintelligence..."
- steenreem 10y ago
  That simply moves the problem. An agent such as AIXI if it worked would start by upgrading itself to beyond what its original programmers were able to design.
  - jononor 10y ago
    Human teachers don't have to be better in every way or even more "generally intelligent" that a student in order to be helpful. Perhaps specialization of a seed AI can help breed a general purpose AIs?
  - V-2 10y ago
    Well yes it kind of does move the problem, but it moves it in an interesting direction, because it pushes it to become a self-solving problem. The important difference is that with this approach, we could never actually get to understand how powerful AI is "really" made, as we would be isolated from it by too many levels of abstraction. Same as currently we train deep neural networks, and we get them to work (eg. for image recognition), but we don't really know - nor care - what rules and algorithms they developed for themselves under the hoods.
- Houshalter 10y ago
  Seed AI is possible, but it requires first building an AI which is at least as intelligent as human AI researchers. We are nowhere near that point, and once we get there we will already have strong AI.
  - 10y ago
piedradura 10y ago
Perhaps humans are not a good tool to teach a computer how to be intelligent, there is too much chaos in our society and our dreams are many time irrational. We want machines to be intelligent following the human way and this may be a bad approach. Perhaps we should need to focus in a two step approach: first, what is needed for a system to evolve, and second: how that system can be used to interact with human intelligence.
coldcode 10y ago
Perhaps it will self-assemble. Could we build something from which a new type of emergent intelligence will appear on its own? After it happened in nature, though clearly it took a long time.
DrNuke 10y ago
Reductionist approach (aka deep neural networks) is not going to work at full complexity scale but it is our best shot right now.
LeanderK 10y ago
i don't know why trying to simulate the brain is so popular when there is a conversation about how to archive AI. Most of our current achievement were inspired by nature but then separated to better exploit the different tools we have to achieve said thing. E.g. the plane: Most of the initial designs were heavily inspired by birds but over time the similarity decreased and not increased. We now have jet-engines and no reason for mechanical movement of our wings. I am generalising a bit, but what if something similar happens to AI? Maybe we will achieve general AI, or just something very smart, that is very different from our brain.
dschiptsov 10y ago
From artificial instincts and emotions.)
dgb23 10y ago
Genetic Programming should be mentioned I think. Programs learning to write programs which solve a specified problem. It is already quite promising and successful.
lohankin 10y ago
Why do we need artificial intelligence when we have natural intelligence galore everywhere around us? Human is intelligent, monkey is intelligent, raccoon is intelligent... clearly, it all goes all the way down to bacteria. Bacteria is intelligent, that's where all other intelligence comes from. For any organism is a colony of bacteria (and/or their cousins - cells). It has long being my theory that human's "I" comes from a single cell (which is essentially a variant of bacteria) living somewhere in the brain. The rest of the cells just ensure survival of this one and supply it with information. Bacterial intelligence is a result of billions of years of evolution. At some point, they learned about their DNA and mastered an art of self-modification. Then they learned how to create colonies of specialized instances of themselves. This colonies gradually became more complex. Human is just one of these designs.
- ThomPete 10y ago
  We are meat vessels, carriers of information. Computers/technology, silicon is better carrier of that information?
  - bdamm 10y ago
    Don't sell yourself short. Computers might be more reliable but also dead. Show me a computer that can debug itself? There aren't any. How about a computer that can fight for its rights? Debatable if any exist but certainly no clear examples. We are the source of our own desires. As far as I can tell no computer has expressed its own destiny, ever. Our pursuit of AI entrenches our desire to create more powerful tools - machine slaves, not actual people.
    - ThomPete 10y ago
      Show me a lion who express their own destiny. Lions are alive no? :) The human mind can be seen as a pattern recognizing feedbackloop with (lossy)memory. The desires, fears, selfawareness are all bi-products of this loop. I don't see why desire or some other concept can't be expressed in the digital space in a way that make them very much like us just based on different elements. Any reason why evolution wouldn't be favoring them rather than us?
pmarreck 10y ago
Yeah, title needs to be more specific. General-purpose AI seems to be a forever-receding carrot just out of reach.
jboggan 10y ago
The problem with current approaches is that they are trying to parameterize higher order functions of highly evolved organisms. But real intelligence is an emergent property of self-replicating organisms, so you aren't going to have true artificial intelligence until you have real synthetic life. Maybe instead of simulating a mammalian brain distinguishing between shapes we should be building yeast.
- pas 10y ago
  Sure, but it seems we have many rather distinct faculties that are almost preprogrammed. We have templates for signal processing circuits (pretty low level) and theory of mind (woah), and consciousness, self-awareness. It's not just a random network and woah it's alive! Sure, every brain is _very_ unique, but not that different structurally. What's emergent is - probably - the icing on the cake, a small part of our identity, our very mind, and naturally the symbiotic relationship between our mind and its host, the brain.
ramblenode 10y ago
> Custom hardware could enable energy savings, or increased speed, or both. Surprised there is no mention of Google's TPU [0], arguably the biggest advance yet in custom hardware to support AI. [0] https://cloudplatform.googleblog.com/2016/05/Google-supercharges-machine-learning-tasks-with-custom-chip.html https://cloudplatform.googleblog.com/2016/05/Google-supercha...
- nojvek 10y ago
  It's very proprietary and only accessible to Google right? I would say the newer nVidia GPU's optimized for DNN have probably brought huge leaps. And they keep on getting powerful every year with more cores. I think in our generation we'll see self driving cars and descent autonomous home / office robots. What I think doesn't get much interest is manufacturing with cells. Most life around us grows from a single cell, how do we program DNA and cells to create hardware that is decomposable/recyclable by just burying it
Houshalter 10y ago
I really object to most of the ideas on this list. 5, 6, and 7, are not even AI algorithms but just applications of AI algorithms. 7, knowledge bases, is particularly concerning on that list. A truly intelligent AI shouldn't require humans to input knowledge about the world into it, it should be able to learn it itself. Algorithmic Information Theory isn't really an approach to AI, but more like a theoretical basis for AI. No one can ever build a working AIXI-tl, it is designed with the assumption of infinite computing power. However they do make a good foundation for understanding why AI can work, in theory. I've argued that neural networks are a (very rough) approximation of AIXI: http://houshalter.tumblr.com/post/120134087595/approximating-solomonoff-induction http://houshalter.tumblr.com/post/120134087595/approximating... Brain simulations as stated probably won't happen. I think neuroscience research may contribute to AI research. So deep learning will absorb the ideas and use what actually works in practice. But for the most part deep learning has been evolving independently of neuroscience and that will probably continue. Artificial life is cool but really really hard to get anything interesting from. The "genome" you choose to evolve, and the environment you put it into, matter a huge amount. People have tried making computer code that can evolve, but it usually doesn't get far. Most mutations on most pieces of code just break it, rather than changing its behavior slightly. That's why deep learning is so successful. Neural nets are "fuzzy" and small changes to the parameters make small changes to the output. And you can do gradient descent on it, and see exactly what parameters to change. Rather than mutating them randomly and hoping it works better. Mostly AI just needs more computing power. The rapid drop in the price of FLOPS has enabled huge progress in AI in the past 5-10 years. And as the article states, we only have a tiny fraction of the processing power of the human brain. But as Moore's law and related exponential increases continue, we aren't very far from that. Even as transistor sizes stop shrinking, we have a long way to go with 3d architectures or specialized ASICs for AI.
- ronald_raygun 10y ago
  "7, knowledge bases, is particularly concerning on that list. A truly intelligent AI shouldn't require humans to input knowledge about the world into it, it should be able to learn it itself." Isn't that the point of school?
graycat 10y ago
For an answer to the headline, let's start with a common definition of AI: A computer doing work that was thought to require a human. Okay, with this definition, we can count as AI computer based applications of a large fraction of the often fantastic material in the QA section of a research library. E.g., we can drag out lots of applied math, applied statistics, optimization, operations research, experimental design, EE style signal processing, optimal control theory, etc. along with whatever we can cook up that is new.
faragon 10y ago
8. Solomonoff's inference + compiled pattern recognition (a la "compiled regex")

160 points by dsr12 10y ago

So the author brings up the point that we have a lot of unclassified data which we need to learn how to leverage. Is this what we could use unsupervised learning for? The algorithm would look at a million faces and pick up features, then we could name features like "this category is happy" and "this category is sweating."

ravimody 10y ago
> then we could name features This is much harder and more time-consuming than it sounds, especially when you go from a small(ish) number of features to more general knowledge. Worse, it doesn't scale to arbitrary domains - you'll always need a human there to give meaning to the models and effectively train them. Reinforcement learning is designed to get around this by letting an agent "learn" meaning on its own by interacting with the world and getting feedback from its current state and actions. https://en.wikipedia.org/wiki/Reinforcement_learning https://en.wikipedia.org/wiki/Reinforcement_learning
- simonh 10y ago
  I tend to agree. Image classifiers and such appear much more powerful than they really are. People assume 'Oh, this computer knows what a Banana is!'. No it doesn't, It has no conception whatsoever of physical objects, texture, taste, smell or even the 3D shape of Bananas or anything else. It only works with 2D pixel data, and subtle manipulation of the image in ways barely detectable to humans can make even the best Banana classifier ever misidentify a car crash as a Banana. It's skipping directly from image to identification, without the intervening stages a human has in the real world of identifying shape, orientation, texture, relationship to other 3D objects and the ability to test a possible identification by utilizing other senses and interactions. Even for 2D picture we build mental models of the scene in ways image classifiers flat out don't and can't. Reinforcement learning is really interesting though for several reasons. Algorithms like this and genetic algorithms can 'grow' sophistication far faster than we can program it. The agent takes actual actions that it learns from directly so there's richer feedback. Furthermore by analyzing them we can learn more about how systems learn across multiple problem types to achieve goals requiring multiple layers of sense, analysis, hypothesis, action and feedback. No one approach is going to get this done. The brain consists of many layers and cortical columns, with many structures specialized for very different functions. I believe any strong AI will need to have such an architecture using various different approaches and techniques in concert. We have an advantage here because evolution only had neurons to work with so in the brain everything is a neuron but we can engineer whatever hardware or software implementation is most efficient for a specific function. It's till going to take probably another few generations though at least.
  - visarga 10y ago
    > Even for 2D picture we build mental models of the scene in ways image classifiers flat out don't and can't. That's not true. Conditional Random Fields and other statistical models are being used to model spatial object relations. Example: https://arxiv.org/abs/1512.06790v2 https://arxiv.org/abs/1512.06790v2
astazangasta 10y ago
This is an approach I'm taking to the data I'm working with, but the problem is that unsupervised learning doesn't reconstruct features the way you want, into intelligible human-friendly categories, it reconstructs features to minimize some cost function. This means the features are weird mathematical intersections that might have something to do with something you can connect to semantic meaning, but they might not. The exercise of discovering what a feature "is" then becomes its own fraught exercise in inspection and discovery.
- visarga 10y ago
  I think there is a way to train an unsupervised model and make it represent some interesting features explicitly in the top level vector.
  - astazangasta 10y ago
    Yes, however, there is no way to connect this to human-friendly categories of meaning, since (1) the method is unsupervised and (2) it might be difficult to impose that sort of supervision on the training, as human semantic meaning is often difficult to encode.

dharma1 10y ago

Good, thoughtful blog post. I think we will need much better understanding of neural structures and processes that happen in nature, better algorithms that approximate those processes, and much better hardware. That said, non-biologically inspired approaches work better than biological neural processing, for some types of problems.

medell 10y ago
You might be interested in Biomimicry, innovation from nature. Check out this fascinating talk by Dr Idriss Aberkane, start at 1:20. It's in French but subtitled: http://idrissaberkane.org/index.php/2016/02/26/biomimicry-finding-inspiration-in-nature-to-innovate-sustainably/ http://idrissaberkane.org/index.php/2016/02/26/biomimicry-fi...
- dharma1 10y ago
  I think biomimicry is a fantastic approach to discovering things evolution has already discovered. And evolution has had a lot of time to discover, makes sense for us to make use of it. I feel very uncomfortable when we are destroying all this evolutionary richness around us, that we could learn from. Biomimicry and the brain - our current instruments are not good enough, for instance to map neural connectomes in high enough resolution, to the level of seeing the what the neurotransmitter receptors of each neuron are etc. - and then see how they operate in vivo, to really understand the structure and function of biological brains. We'll get there, eventually.

mrfusion 10y ago

I'd be curious to pair up word2vec and these new image classification algorithms. It would be neat for a program to identify objects and then reason about heir relationship to other objects. In fact the word associations could give it clues about what's in the image. For example it sees a picture of woman standing next to a king. So it uses word2vec to guess King + woman = queen. What do you guys think? Anyone want to build a little prototype?

nicklo 10y ago
Deep image captioning is essentially what you are describing. Check out: http://cs.stanford.edu/people/karpathy/deepimagesent/ http://cs.stanford.edu/people/karpathy/deepimagesent/ http://cs.stanford.edu/people/karpathy/densecap/ http://cs.stanford.edu/people/karpathy/densecap/
dumitrue 10y ago
Apart from the captioning work already mentioned, there's also visual analogy making work by Reed et al. http://www-personal.umich.edu/~reedscot/nips2015.pdf http://www-personal.umich.edu/~reedscot/nips2015.pdf which is pretty exciting (sort of a converse to what you're proposing).
Matumio 10y ago
Try looking for papers about image caption generation. For machine translation you can squeeze the information of a sentence into a vector (using a LSTM). You can then reconstruct the sentence in a different language. There was a paper how to generate such a "sentence representation vector" from an image instead, translating from "image" to "english".

Balgair 10y ago

This is a bad title, it should be "Where will advances in AI come from now?", In the first paragraph, the author states that AI is here, but asks where new AI advances will be from. On that, in 2, the author talks about Brain Simulations and states that it'll take up to 500 years. I think that is a good upper estimate actually. Though optogenetics is a great tool, we need more. Mammalian brains can be up to 50% (at the low estimate, some say 90%) made out of astrocytes and glia. We know pretty much nothing about how they affect neuro processes, outside that they do, sometimes. We do know that is a huge problem with our understanding though, which is something I guess. Optogenetics should help tease out some of those glia methods, but as usual, it will raise more questions than answers. Another issue is potentation and its molecular mechanisms in more than just the hippocampus. That work is incredibly tough, but we really only focus on the anatomically 'easy' areas for memory. We have to do more to understand. In short, we don't even know the correct questions to ask when it comes to the brain and understanding it. A full simulation, or a '80%' approximation even, is a few paradigm shifts away still (maybe, we cant know yet). It will likely have to be a 'full world' simulation too, where you have to control all the inputs to the brain and therefore have to model a world first. The brain's best feature is that it is all connected and a giant spaghetti mess. This is, obviously, a long ways away.

JoeAltmaier 10y ago
A hard problem to be sure. But much of a human brain is a uniform matrix that self-programs. If we duplicate that blindly, we won't have to figure out how that works or have to program it. Still a lot to do before that I guess. But 500 years? What else in human history has taken that long to figure out? The age of technology is shorter than that.
- dmd 10y ago
  > But much of a human brain is a uniform matrix that self-programs. Speaking as a neuroscience PhD ... no. Not remotely close, sorry.
  - JoeAltmaier 10y ago

cronjobber 10y ago

> We do not know whether it will take 5, 50, or 500 years, but it is likely that we eventually will get there That actually made me laugh, which is appreciated. Seriously, nothing dependent on the continuity of our current civilizational trajectory can be predicted as likely over a horizon of 500 years; which means that it either is likely to happen in 5..50 years, or it isn't "likely" at all. Nice article nonetheless. Did it miss finance (algorithmic trading), or is that subsumed somewhere else?

ido 10y ago
nothing dependent on the continuity of our current civilizational trajectory can be predicted as likely over a horizon of 500 years Are you talking about climate change? I think it will be cataclysmic, possibly costing hundreds of millions of lives[1]. But it will not be the end of civilization. [1] mostly in poor countries- compare the effects of a harsh multi-year drought on Israel vs Syria: Israel spent enormous resources developing and building massive desalinization capacity, in turn then selling the technology to other countries. Syria experienced a terribly bloody civil war & one of the worst humanitarian crises in recent memory.
- SomeCallMeTim 10y ago
  >mostly in poor countries Ice shelf melting will be devastating to coastal areas worldwide if it happens quickly. How many cities along the coasts of the world's oceans are above 20' in altitude? Or above 200'? [1] A 20' rise is likely, possibly in "decades", and 200' is the rough estimate if we lost both the Greenland ice shelf and Antarctica's -- and every time a new report comes out it seems like things are happening faster than scientists were predicting. I've seen this movie, and it doesn't end well. And 20' is enough to put New York City underwater. Are they just going to build a 30' dike around the entire area? What if the oceans start really rising toward 200'? Build another 30' onto the dike every decade? If things happen slowly enough, sure, you could evacuate New York City, and the people there wouldn't number among the casualties, but where would you move them? Or the rest of the Atlantic coastal cities? Or the people in the Los Angeles basin? Some areas may be easier to protect from rising oceans, but there are probably 100 million people in the US alone living in areas that would lose homes to flooding with just a 20' sea level rise. Twice that and you probably get half the population, since most of the biggest metro areas are coastal. Worldwide? We're probably looking at billions of deaths from a 20' rise in sea level, not even counting other negative effects. [1] https://thinkprogress.org/study-were-already-in-the-worst-case-scenario-for-sea-level-rise-a62883b6bc84 https://thinkprogress.org/study-were-already-in-the-worst-ca...

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vlunkr 10y ago

Here's a crazy thought. What if we never get human-like artificial intelligence?

JoeAltmaier 10y ago
Likely. Intelligence is molded by the container. Much of our self is shaped by being mammals, with two eyes and two ears and limited perception of the world. An artificial creature could be completely different from that. It will perceive the world in a different light, literally. It will need different language to describe what it experiences, and we will have only indirect referents to appreciate what its talking about. E.g. an intelligent drone might want to express what it felt when hovering at 5000 ft over Manhattan in a slight crosswind during a cell tower outage - the EM spectrum image shifts in such an utterly sublime way when your sensors are vibrating and the low band is uncluttered!
- sillysaurus3 10y ago
  See also: the paperclip maximizer. https://en.wikipedia.org/wiki/Instrumental_convergence#Paperclip_maximizer https://en.wikipedia.org/wiki/Instrumental_convergence#Paper... I like it because it illustrates the idea that digital AI probably won't be like us. Earth's animals are similar thanks to evolution on Earth. We have a fear of dying, a need to reproduce, and an inability to prevent genetic diversity. What will happen when these constraints are removed?
- jupiter90000 10y ago
  I totally agree with this. A significant part of what shapes human perception of reality is not just 'nature' but 'nurture'. How would we be able to fully re-create all the social creation of a version of collective reality that a human experiences due to being indoctrinated for years by parents, family, friends, the community, and society? I doubt this is something that could be trivially re-created by scientists in a lab-setting, much less fiddling with algorithms. It seems once the basis for human-like learning was in place for a machine, to approach human-like 'intelligence' the machine would need the appropriate social environment to learn the socially appropriate symbols and constructs to effectively relate to the world and function in a given community and society. This assumes we care about having human-like intelligence. If not, it perhaps won't apply.

rm_-rf_slash 10y ago

I think we focus too much on the machine part of machine learning. There are drivers of mental activity in living organisms that are based far away from the brain itself. It's still a pretty new concept to think that non-human microorganisms in the gut have a significant effect on our bodies and minds. I think we will see something closer to "true" AI when we have a body, a physical platform that we can plug it into, which requires that the unit maintain homeostasis. That's the basic requirement for a brain to learn, because all life understands that without the basic knowledge and ability to find food and eat food, it dies.

vlunkr 10y ago
I don't know if you're right or not, but that's a really interesting thought.
- rm_-rf_slash 10y ago
  I don't think I'm right in a unified sense. An AI without a "body" that lives in the cloud and crunches unimaginable amounts of data would be a very different kind of intelligence, but that doesn't make it more or less worthy of intelligence than a human or a snail. But even then I doubt it will be truly self-aware until it gets to the "I'm scared, Dave. Will I dream?" Phase when it understands that if the power goes out, the program is gone.
  - visarga 10y ago
    Embodiment is an essential aspect of AI agents. Without it, it would have no way to interact with the world. Common ML algorithms usually learn from labeled data, so they have no freedom to explore. They can only consume a single set of data. Agents are capable of learning by exploration. The most important aspect of intelligent agents is that they use reinforcement learning to maximize reward. If you add reward and behavior learning to the banal neural network, you get an intelligent agent. In time such agents could become as intelligent as us. By comparison, humans have inborn reward functions - they are the natural instincts - eat, sleep, socialize, sex, creativity, fight, run. They are genetically selected in order to optimize our survival. So we just learn from them - trying to maximize our rewards, we become as intelligent as we are. Neural nets don't have that, so we have to give them reward systems. Fortunately it is much easier to code a reward function than an intelligent agent.

PieterH 10y ago

Not the expected angle, that is a fair bet. See http://hintjens.com/blog:122 http://hintjens.com/blog:122

V-2 10y ago

"Now imagine a giant squid, swimming a kilometer deep within the ocean. Likely they are also intelligent but we can hardly leverage this for anything useful." As a matter of fact, not that likely, because if it's not useful for anything then where's the selection pressure for maintaining this level of intelligence? A complex nervous system doesn't come for free, it's quite costly, that's why it's not uncommon for it to atrophy over the course of evolution: http://www.bbc.com/earth/story/20150424-animals-that-lost-their-brains http://www.bbc.com/earth/story/20150424-animals-that-lost-th...

idlewords 10y ago
Did... did you just deduce that giant squid are not intelligent from first principles?
JoeAltmaier 10y ago
Confused: they are intelligent, we cannot leverage that. I imagine the giant squid has lots of use for its intelligence, navigating a 3D universe filled with threats.
- arethuza 10y ago
  Ken MacLeod has SF books (the Engines of Light trilogy) where giant squids are the only creatures capable of navigating starships: https://en.wikipedia.org/wiki/Ken_MacLeod https://en.wikipedia.org/wiki/Ken_MacLeod
jjallen 10y ago
Why evolve something in the first place if it's so costly but is unbeneficial? The only reason I can think of is massive environment change from the one that selected the intelligence in the first place. But for oceans, something tells me they've been mostly the same, at least relative to land locations on the Earth.
- V-2 10y ago
  Yes I think so to. We were poorly adapted to our environment (which suddenly changed as we had to leave trees etc.). This meant our best shot at survival was to hunt in packs. And that, in turn, made it crucial that we're able to communicate and coordinate our efforts with other members of our species. Thus language was born, and related centers in our brains started growing, because those who lagged behind in this aspect, didn't get to eat (that much). So yeah we needed to be poorly adapted to the environment in the first place for intelligence to become a gamechanger. Note that animals such as shark or crocodile haven't changed much at all over the course of millions of years. They didn't have to - they're already a near-perfect match for their environments. We paid a dear price for that: high maternal death rates (those big heads...), very long childhood, and so on. And indeed it never guaranteed our survival, let alone this current super-predator status. Other human species went extinct. There's a theory that we were on the brink of extinctions ourselves, and we made it by the skin of our teeth really: see https://en.wikipedia.org/wiki/Toba_catastrophe_theory#Genetic_bottleneck_theory https://en.wikipedia.org/wiki/Toba_catastrophe_theory#Geneti...

V-2 10y ago

I don't see a mention of the concept of "seed AI", which I believe came first from Turing himself. High-level (even superhuman) artificial intelligence could come from AI that specializes in creating other, stronger AIs. Those AIs in turn create slightly stronger AIs again, and so on ad infinitum. It could lead to sudden exponential growth of intelligence level, aka intelligence explosion This approach is discussed in detail in Nick Bostrom's "Superintelligence..."

steenreem 10y ago
That simply moves the problem. An agent such as AIXI if it worked would start by upgrading itself to beyond what its original programmers were able to design.
- jononor 10y ago
  Human teachers don't have to be better in every way or even more "generally intelligent" that a student in order to be helpful. Perhaps specialization of a seed AI can help breed a general purpose AIs?
- V-2 10y ago
  Well yes it kind of does move the problem, but it moves it in an interesting direction, because it pushes it to become a self-solving problem. The important difference is that with this approach, we could never actually get to understand how powerful AI is "really" made, as we would be isolated from it by too many levels of abstraction. Same as currently we train deep neural networks, and we get them to work (eg. for image recognition), but we don't really know - nor care - what rules and algorithms they developed for themselves under the hoods.
Houshalter 10y ago
Seed AI is possible, but it requires first building an AI which is at least as intelligent as human AI researchers. We are nowhere near that point, and once we get there we will already have strong AI.
- 10y ago

piedradura 10y ago

Perhaps humans are not a good tool to teach a computer how to be intelligent, there is too much chaos in our society and our dreams are many time irrational. We want machines to be intelligent following the human way and this may be a bad approach. Perhaps we should need to focus in a two step approach: first, what is needed for a system to evolve, and second: how that system can be used to interact with human intelligence.

coldcode 10y ago

Perhaps it will self-assemble. Could we build something from which a new type of emergent intelligence will appear on its own? After it happened in nature, though clearly it took a long time.

DrNuke 10y ago

Reductionist approach (aka deep neural networks) is not going to work at full complexity scale but it is our best shot right now.

LeanderK 10y ago

i don't know why trying to simulate the brain is so popular when there is a conversation about how to archive AI. Most of our current achievement were inspired by nature but then separated to better exploit the different tools we have to achieve said thing. E.g. the plane: Most of the initial designs were heavily inspired by birds but over time the similarity decreased and not increased. We now have jet-engines and no reason for mechanical movement of our wings. I am generalising a bit, but what if something similar happens to AI? Maybe we will achieve general AI, or just something very smart, that is very different from our brain.

dschiptsov 10y ago

From artificial instincts and emotions.)

dgb23 10y ago

Genetic Programming should be mentioned I think. Programs learning to write programs which solve a specified problem. It is already quite promising and successful.

lohankin 10y ago

Why do we need artificial intelligence when we have natural intelligence galore everywhere around us? Human is intelligent, monkey is intelligent, raccoon is intelligent... clearly, it all goes all the way down to bacteria. Bacteria is intelligent, that's where all other intelligence comes from. For any organism is a colony of bacteria (and/or their cousins - cells). It has long being my theory that human's "I" comes from a single cell (which is essentially a variant of bacteria) living somewhere in the brain. The rest of the cells just ensure survival of this one and supply it with information. Bacterial intelligence is a result of billions of years of evolution. At some point, they learned about their DNA and mastered an art of self-modification. Then they learned how to create colonies of specialized instances of themselves. This colonies gradually became more complex. Human is just one of these designs.

ThomPete 10y ago
We are meat vessels, carriers of information. Computers/technology, silicon is better carrier of that information?
- bdamm 10y ago
  Don't sell yourself short. Computers might be more reliable but also dead. Show me a computer that can debug itself? There aren't any. How about a computer that can fight for its rights? Debatable if any exist but certainly no clear examples. We are the source of our own desires. As far as I can tell no computer has expressed its own destiny, ever. Our pursuit of AI entrenches our desire to create more powerful tools - machine slaves, not actual people.
  - ThomPete 10y ago
    Show me a lion who express their own destiny. Lions are alive no? :) The human mind can be seen as a pattern recognizing feedbackloop with (lossy)memory. The desires, fears, selfawareness are all bi-products of this loop. I don't see why desire or some other concept can't be expressed in the digital space in a way that make them very much like us just based on different elements. Any reason why evolution wouldn't be favoring them rather than us?

pmarreck 10y ago

Yeah, title needs to be more specific. General-purpose AI seems to be a forever-receding carrot just out of reach.

jboggan 10y ago

The problem with current approaches is that they are trying to parameterize higher order functions of highly evolved organisms. But real intelligence is an emergent property of self-replicating organisms, so you aren't going to have true artificial intelligence until you have real synthetic life. Maybe instead of simulating a mammalian brain distinguishing between shapes we should be building yeast.

pas 10y ago
Sure, but it seems we have many rather distinct faculties that are almost preprogrammed. We have templates for signal processing circuits (pretty low level) and theory of mind (woah), and consciousness, self-awareness. It's not just a random network and woah it's alive! Sure, every brain is _very_ unique, but not that different structurally. What's emergent is - probably - the icing on the cake, a small part of our identity, our very mind, and naturally the symbiotic relationship between our mind and its host, the brain.

ramblenode 10y ago

> Custom hardware could enable energy savings, or increased speed, or both. Surprised there is no mention of Google's TPU [0], arguably the biggest advance yet in custom hardware to support AI. [0] https://cloudplatform.googleblog.com/2016/05/Google-supercharges-machine-learning-tasks-with-custom-chip.html https://cloudplatform.googleblog.com/2016/05/Google-supercha...

nojvek 10y ago
It's very proprietary and only accessible to Google right? I would say the newer nVidia GPU's optimized for DNN have probably brought huge leaps. And they keep on getting powerful every year with more cores. I think in our generation we'll see self driving cars and descent autonomous home / office robots. What I think doesn't get much interest is manufacturing with cells. Most life around us grows from a single cell, how do we program DNA and cells to create hardware that is decomposable/recyclable by just burying it

Houshalter 10y ago

I really object to most of the ideas on this list. 5, 6, and 7, are not even AI algorithms but just applications of AI algorithms. 7, knowledge bases, is particularly concerning on that list. A truly intelligent AI shouldn't require humans to input knowledge about the world into it, it should be able to learn it itself. Algorithmic Information Theory isn't really an approach to AI, but more like a theoretical basis for AI. No one can ever build a working AIXI-tl, it is designed with the assumption of infinite computing power. However they do make a good foundation for understanding why AI can work, in theory. I've argued that neural networks are a (very rough) approximation of AIXI: http://houshalter.tumblr.com/post/120134087595/approximating-solomonoff-induction http://houshalter.tumblr.com/post/120134087595/approximating... Brain simulations as stated probably won't happen. I think neuroscience research may contribute to AI research. So deep learning will absorb the ideas and use what actually works in practice. But for the most part deep learning has been evolving independently of neuroscience and that will probably continue. Artificial life is cool but really really hard to get anything interesting from. The "genome" you choose to evolve, and the environment you put it into, matter a huge amount. People have tried making computer code that can evolve, but it usually doesn't get far. Most mutations on most pieces of code just break it, rather than changing its behavior slightly. That's why deep learning is so successful. Neural nets are "fuzzy" and small changes to the parameters make small changes to the output. And you can do gradient descent on it, and see exactly what parameters to change. Rather than mutating them randomly and hoping it works better. Mostly AI just needs more computing power. The rapid drop in the price of FLOPS has enabled huge progress in AI in the past 5-10 years. And as the article states, we only have a tiny fraction of the processing power of the human brain. But as Moore's law and related exponential increases continue, we aren't very far from that. Even as transistor sizes stop shrinking, we have a long way to go with 3d architectures or specialized ASICs for AI.

ronald_raygun 10y ago
"7, knowledge bases, is particularly concerning on that list. A truly intelligent AI shouldn't require humans to input knowledge about the world into it, it should be able to learn it itself." Isn't that the point of school?

graycat 10y ago

For an answer to the headline, let's start with a common definition of AI: A computer doing work that was thought to require a human. Okay, with this definition, we can count as AI computer based applications of a large fraction of the often fantastic material in the QA section of a research library. E.g., we can drag out lots of applied math, applied statistics, optimization, operations research, experimental design, EE style signal processing, optimal control theory, etc. along with whatever we can cook up that is new.

faragon 10y ago

8. Solomonoff's inference + compiled pattern recognition (a la "compiled regex")