13 ms·

Where will artificial general intelligence come from?

389 points by nshr 9y ago

indifferentalex 9y ago
Not on their radar, or their slides at least: Natural Language Processing based rule-based brute-force artificial intelligence (that could be augmented through sensors/motors that allow interaction with the external world). A Vulcan-like (Star Trek) AI, what do you think? Might be easier to simulate the entire brain, on the other hand it might be doable and bridge the gap to general AI.
- visarga 9y ago
  It's called the self driving car - an AI that interacts with the world. It will be a launching pad to other AI agents.
  - shagie 9y ago
    At a more general level, you may find the book Society of the Mind interesting ( https://en.wikipedia.org/wiki/Society_of_Mind https://en.wikipedia.org/wiki/Society_of_Mind ). In this, Minsky proposes simple (mindless) agents that then are combined together to form a mind. That our mind is interacting with the world and launching agents to deal with things - that our own mind may be built this way. Yes, its a bit old (1986) and the current machine learning techniques have dated it - what was theory at one time is reality in places. Still a good book to read and think about.
    - taneq 9y ago
      Minsky's 'society of mind' is actually not far from modern techniques, where deep neural nets have multiple distinct parts that solve different parts of the problem using each others' outputs. (Not to mention explicitly multi-part methods like actor-critic).
    - fspear 9y ago
      Interesting read...I actually came to this very conclusion after an amazing mushroom trip when I was watching the movie "her". I actually drew schematics of how I envisioned the whole system would work lol.
- nopinsight 9y ago
  Rule-based NLP has been tried for several decades and has (very) limited success in the real world. Current systems based on deep learning beat it for most complex tasks. DeepL, which was on HN front page a few days, is the latest example: https://news.ycombinator.com/item?id=15122764 https://news.ycombinator.com/item?id=15122764
  - orthoganol 9y ago
    You're going to have to elaborate on complex tasks. I would argue the majority of successful, money generating software based in NLP/ NLU, i.e. the majority of the industry, is "rule based" (used in a general sense to mean non DL). Personal assistants, search, chatbots, etc.
- crypticlizard 9y ago
  With regards to Vulcan emergence, I do believe we are in for that soonish; it's an archetypal depiction that consumers desire. Biotech, my friend, genetic enhancement. We can make ourselves smarter with genetic enhancement. We can even give ourselves the vulcan mind meld, and everything else exceptional about Spock. I do think the human brain substrate is exceptional, worthy of improving upon. I wish we talked more about so called "brainpower extension technologies", hopefully one day. I bet cats talk in ten years, it's only logical, consumers love cats & the companies can sell them by the millions.
  - red75prime 9y ago
    "I want to go out". The door is opened. "Lemme think a bit. Nah, I just want the door open". We can't have the door open at all times! "Who do you think you are for me to care? You are a human, invent something."
wonderwonder 9y ago
I think a very interesting aspect of general AI is that while an incredibly complex technology, it is not unrealistic that it could be created for the first time in someone's home office. Unlike many other earth changing technologies there is nothing that a massive corporation has that a home tinkerer does not (besides the obvious of money and many engineers). With the rise of cloud computing and open source; everything I need I have instant access too, all that's lacking is the core software which can be written (of course not a trivial task). While unlikely, it is still quite amazing that in a few years an AI could awaken 3 doors down at my neighbor Bob's house. No idea what happens after that, hopefully Bob was a fan of the 3 laws and has a couple more up his sleeve.
- qaq 9y ago
  Cloud computing will not help home tinkerer much if you need 100K/hour worth of compute to run an experiment.
  - CuriouslyC 9y ago
    If we move towards hierarchical model composition, you don't need to rebuild the visual object recognition module to experiment in learning spaces that incorporate visual information.
- klochner 9y ago
  [X] Open Source Tools [ ] Massive Data Sets [ ] $Millions in Computing Resources I'd put it roughly on par with finding a general cure for cancer. While unlikely, quite amazing that the cure for one of the largest causes of death could be solved 3 doors down with lab supplies from amazon and a handful of mice.
  - mrfusion 9y ago
    Well the data sets are all around your house and yard. Think of baby human agi's. And the computing costs could be boot strapped by the ai. Put it to work earning its keep and expanding resources.
exratione 9y ago
I feel that those who argue that any approach other than running human brain emulations and then reverse engineering them or speculatively modifying them is the most likely way to get to AGI has a pretty steep hill to climb in order to justify that point of view. Nothing else that is going on now or even on the agenda or even foreseeable offers a plausible, definitive plan to get to AGI. Whereas brain emulation is clearly going to achieve that goal fairly shortly after the maps are good enough and the computational capacity large enough, and the following experimentation is a far more reliable way to determine the underpinnings of intelligence than present efforts at de novo construction.
- visarga 9y ago
  I disagree. It's too expensive to run a low level brain sim. In the meantime deep learning based AI achieved superhuman or close to human results in many tasks, such as image recognition, voice recognition, translation, car driving and Go. The AGI will be a reinforcement learning agent, as it will need to be able to perceive and act in the physical world. Thus the path to AGI is the path of RL. The most essential piece in RL will be the development of environment simulators. AlphaGo was a trivial simulator - simple rules in a simple world - but we need real world simulators in order for the AI agents to learn to act. Fortunately simulation is almost the same as gaming and there is huge interest in it both for humans and AI, so it will be developed fast. So instead of simulating the brain, simulate the world (imperfectly) and run deep neural net based RL to learn to act on top of it.
  - grwthckrmstr 9y ago
    "I disagree. It's too expensive to run a low level brain sim." Interesting. Could you tell me Why is it too expensive? If it wasn't expensive, would that change things drastically, and make brain sim a viable option?
    - visarga 9y ago
      The brain has 10^14 synapses (100 trillion) synapses. Current day neural nets barely reach a hundred million, with very few exceptions. Then, besides compute, there is data movement - currently the bottleneck in AI is moving data around, not computing. Imagine the interconnect for a brain-size neural net.
Will_Do 9y ago
That was really interesting. I'm interested why he's so pessimistic about the simulating a brain approach. Yes it's the boring and obvious approach but it also seems the most direct. Also found this quote interesting > Might have to make it illegal to evolve AI strains or an upper bound of computation per person and closely track all computational resources on earth.
- tw1010 9y ago
  I think the main problem with the brain simulation approach is that we don't yet have a really good model of how the brain actually works.
  - ethbro 9y ago
    We know how components of the brain work. Is it inconceivable that we luck into the proper arrangements and interrelationships?
- visarga 9y ago
  The brain is slow and redundant. It has to be like that because it is not produced in a factory - it is created by self replication. Self replication imposes strict limits and requirements on the type of brain that can be created. AI neurons, on the other hand, are perfect - they never get old or tired and always remember. A neural net like ResNet-150 is capable of doing essentially what 1/3 of the brain is doing (vision). We can achieve superhuman results in vision with much less neurons, and faster. This is the kind of logic that makes brain emulation a far flung possibility compared to the current day deep neural nets. That and the fact that the brain simulation guys don't have anything to show for. There are no human-level tasks that could be replicated by this approach yet.
  - CuriouslyC 9y ago
    The brain is slow in "cycles"/second, but the amount of computation done by each cycle isn't directly comparable to that done by a computer. Forgetting isn't a bug, it's a feature. Forgetting is basically like dimensionality reduction on input data - we extract the principle components/exemplars, remember a weighting, and trash the redundancy. Training a ML model is a lot faster on smaller data, and the same is true for us. Don't compare ANNs with the brain strictly on a time/time basis. Time isn't the only factor, power consumption and heat production are also factors, and if you include them the brain comes out way ahead. People with an engineering background almost universally underestimate how freaking awesome biology is. Our brains are self-constructing, self-replicating, self repairing (mostly) hyper-efficient pattern recognition systems. The more we learn about them the more awesome we realize they are. Don't be so arrogant as to assume a few hundred years of engineering will universally eclipse hundreds of millions of years of evolution.
bryananderson 9y ago
When he says "artificial life", is he referring to reinforcement learning?
- poppingtonic 9y ago
  No, probably evolution strategies. https://blog.openai.com/evolution-strategies/ https://blog.openai.com/evolution-strategies/
- visarga 9y ago
  Yes. Evolution strategies are just an alternative approach to RL. Reinforcement learning is basically solving the problem of being an intelligent agent in the world, moving about, achieving goals.
- letlambda 9y ago
  http://www.alife.org/ http://www.alife.org/ Artificial Life is a field with very fuzzy boundaries. Roughly, computer systems that look like biological or ecological systems. From an AL perspective, life evolves to function in it's ecology. The problem is not building an AGI, it's building an ecology in which AGI will emerge. Oh... and hopefully, also one in which ruthlessly destroying other intelligent agents isn't a good survival strategy.
CuriouslyC 9y ago
Artificial general intelligence won't be invented, it will emerge, just general intelligence did in the wild. AGI is just going to be a hierarchical arrangement of specialized tools. The first step is highly specialized AI tools for very specific problem domains. The second step is AI tools that use other AI tools as components but address slightly larger problem domains. The third and successive steps are recursions of the second step. Additionally, we won't be able to tell right away when we've crossed the threshold. We can't even say for sure where "intelligence" stops in animals. We used to think we were the line, but now the bar has been pushed down to include primates, cetaceans, a number of birds, possibly some members of the bear family, etc. The reality is that it is a gradient and there is no clear line.
- tokipin 9y ago
  > Artificial general intelligence won't be invented, it will emerge, just general intelligence did in the wild. I think so as well, but for a specific reason: That people are not as "AGI" as we think, and that our general intelligence is the result of immense neuron-computing resources committed to satisfying a few simple drives. In other words, narrow AI with a lot of resources. Not to mention many specialized subcomponents. And to the degree that consciousness is a selected trait, that means it has a purpose. I think the work on attention/focus in neural networks hints at that purpose.
  - CuriouslyC 9y ago
    Why do you think consciousness is a selected trait?
    - tokipin 9y ago
      Well I don't know what consciousness is, so I couldn't answer that. But I think the opposite stance of consciousness being some kind of majestic oracle is silly. There is a reason why we perceive consciousness, even if it is just as a side effect of something else or a random fluke that is useless or an illusion for coordinating our limbs or whatever. These types of discussions do get pretty wonky. When I say consciousness is selected for, I mean more whatever practical apparatus (if any) it is emergent from, not the perception of consciousness itself.
WheelsAtLarge 9y ago
I'm waiting for the day when there is an IA OS. Basically there would be a Natural Language processor that determines what sub AI app to run. It's not true general AI but if it's done broad enough it will seem like it.
- visarga 9y ago
  Google and FB have most to gain from a capable conversational agent. The moment this system will appear, it will start replacing the old interfaces, and pretty soon eat the G-FB pie. If they are not on top of the wave then, they'll lose. Current state of the art in dialogue AI is an agent that can reason based on images, documents or tables with data. There is a lot of research into attention and memory augmented neural nets. I put my bet on graph based neural nets, that can better represent object and relations in reasoning tasks.
rdlecler1 9y ago
Background: I did AI and Philosophy of Mind in undergrad, an MSc focused on ALife, then a PhD at Yale under a Macarthur Fellow who developed the theoretical framework for the 'evolution of Evolvability' where I worked on computational evolutionary biology. I can say we're not going to blindly brute force our way forward, but instead we'll need to reverse engineer nature's core algorithms to generate hard AI. Every time a major advance is made in AI the computational neuroscientist say: "why didn't you talk to us 15 years ago? We could have told you that!". Those ingredients will be embodiment, evolution, genetics (genotype-phenotype encoding), neirogenesis(gene regulatory networks directing phenotypic development from a single cell to a multicellular neural network), and ecology (evolving in adversarial and cooperative environments). And we'll need a lot of theoretical work in how to represent nature's algorithms in code. For example my PhD work just focused on how to use evolutionary algorithms to evolve simple gene regulatory networks and how that leads to properties of modularity in the genotype-phenotype map. That alone is life's work but a necessary ingredient. I don't expect to see this solved in my lifetime given how we're attacking the problem (head on) today. And until then we're going to continue to run into these dark winters of AI.
- heimatau 9y ago
  Hmm, reading your experience clarifies to me (a Maths guy) why current deep learning efforts are producing real fruit from their labor. Linear Regression and Vector/dot products are just constructs to re-create the evolutionary biology seen in nature. When looking at the slow process of evolution, it's the nuances that matter and over time, they compound exponentially. Pretty neat. By breaking down a task into abstract constructs and sifting problems out with a sigmoid function.
- eggie 9y ago
  I'd love to read your thesis. Would you please a link?
- Jedi72 9y ago
  Can you link to some of your work, this is very interesting stuff
- return0 9y ago
deleted 9y ago
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eb3c90 9y ago
I'm working on a technology that I think might enable either IA or AI. Basically intelligences manage their own programs and the computational resources allocated to those programs. So I'm looking at doing that with markets. With IA the user acts as feedback to the market about what is good or bad. Ideally it would act as an external brain lobe. More information on my approach is on this blog https://improvingautonomy.wordpress.com/2017/07/25/why-study-resource-allocation/ https://improvingautonomy.wordpress.com/2017/07/25/why-study...
- eb3c90 9y ago
  This might be a better link. It explains how we might get to IA. TL;DR It is a mix of machine learning with different parameters and inputs/outputs and language translation into programs with the economy acting as the force to guide this evolving set of programs. https://improvingautonomy.wordpress.com/2017/08/22/a-possible-path-to-intelligence-augmentation/ https://improvingautonomy.wordpress.com/2017/08/22/a-possibl...
  - stephengillie 9y ago
    Interesting idea. Instead of thinking of computing as an authoritarian schema, it could be a community schema, using a kanban or currency system to communicate resource needs between units. It's also similar to negotiating memory over commitment in virtualization. VMWare's driver on the VM "inflates a memory bubble" to communicate host memory constraints to all client VMs. This is often done when VMs have allocated 125%-300% of the host's physical RAM, and forces clients to swap more.
    - eb3c90 9y ago
      Yep, while people have to worry about the memory/cpu usage of the programs inside a computer, we are probably going to be stuck with just narrow AI. General Intelligence needs the ability to trade off resources between different programs doing different things (these may be learning different things, processing data or doing other computational tasks). Also we get malware because we expect the user to be a good knowledgeable authoritarian manager of a system and never run a bad program and be able to get rid of a bad program when it occurs. This just isn't realistic.
nether 9y ago
Goldman Sachs
throwaway00100 9y ago
Where will the philosopher's stone come from?
NumberCruncher 9y ago
If you take a look at the evolution of the most advanced non artificial general intelligence, eg. human intelligence, it is strongly connected to the evolution of communication. It is a question of efficiency whether you learn through your own experience and failures or through the experience and failures of others. This teaching/learning process was boosted through the use of pictures, spoken language, hand written and printed books. This is why I believe the artificial general intelligence will we teached by an other artificial general intelligence and this evolution will be somehow connected to language processing. As far as I know Google tries to train its AIs through human imput, eg. to recognize animals drawn by humans. I consider it as one of the first steps in the right direction.
- viewtransform 9y ago
  I would argue that intelligence is connected to the evolution of sensing at a distance. Vision in particular, allowed life to evaluate the state of the environment at a distance and allowed for the evolution of strategies to predict and respond to the environment in real time. The progression in intelligence from sponge to amphibian to mammal is related to the evolution of finer sensing of the environment at a distance: vision, smell, sound etc.
  - naikrovek 9y ago
    'Situational awareness' is maybe a better phrase. Predicting the future was probably next. Knowing that sunset is soon or that rain is coming requires situational awareness, long term memory, short-term memory, and all kinds of other stuff. A realistic set of stimuli, a LOT of artificial neurons, and a lot of time will probably get there, eventually.
FullMtlAlcoholc 9y ago
However it originates, it will need a body to experience sensations firsthand, not pre-recorded or simulated data. Perhaps connected IoT devices will be sufficient. Also, AGI will not be invented. It will arise as an emergent phenomena, and it may have already achieved what we call consciousness. Somewhat off topic: Another phenomenon that people should be on the watch for is "Articial Out-telligence", a phrase coined by Eric Weinstein. [0] It describes strategies used by organisms with no known brain to get more intelligent creatures to do its bidding, wittingly or unwittingly. The cordyceps fungus, toxoplasma gondii, and pollinating plants that need insects to spread their seed are examples of how an organism with no known neurological network can "outsmart" more advanced organisms. A scenario involving AI may be one that is developed to maximize each individual users time on a site/app by using online data about that person to find their particular addictions. [0] https://www.youtube.com/watch?v=Wu8s0tp9yzY https://www.youtube.com/watch?v=Wu8s0tp9yzY
novaleaf 9y ago
what is the point of slides without the underlying presentation? slides are glorified notes, NOT presentations nor papers.
- ilaksh 9y ago
  Well these slides seemed to give all of the most relevant details.
ThomPete 9y ago
Intelligence is emerging just like it was with humans. It's not a thing so it wont come from somewhere. As always solving the small problems will eventually allow solutions to emerge we weren't aware of and that might turn into human like or more probably technology like intelligence far surpassing humans. I always find it fascinating that we have no problem accepting that we became intelligent over time and out of nowhere (Unless you are religious which is a whole other discussion) we even have no issues imagening that life and intelligence could have happened other places in the universe. But the idea of a non-carbon based intelligence is a big debate as if it's somehow unimaginable to think that AI could emerge from human hand while having no problem entertaining the idea that we our intelligence is somehow a unique snowflake.
- AsyncAwait 9y ago
  I think the problem is not that we cannot accept evolving general AI by solving much smaller problems first, rather we're very impatient and don't want to wait for the evolution to take place.
  - ThomPete 9y ago
    Yes impatience is probably one of the most underestimated issues when it comes with humans and progress in general.
chanakya 9y ago
What is the best book/reference to understand why there seems to be general agreement that AGI/"broad" AI will happen? TFA compares the relative likelihood of the various approaches, but says nothing about the absolute likelihood of any of them. Are there signs of AGI we can see today? Is there an argument/data which links the huge improvements we're seeing in narrow AI to the likelihood of AGI?
- ktRolster 9y ago
  The best argument I've heard is that we can use a computer to model any physical process.....the brain is a physical process, therefore we can use a computer to model the brain. If you think that there is some process in the brain that would be theoretically impossible for computers to model, that would be an interesting topic of discussion.
  - chanakya 9y ago
    The brain is a physical entity, yes, so in theory we should be able to model it, assuming we know all the laws it works on with enough precision. This is a big if, but even if that's granted, is there anything which indicates that this is imminent?
    - ktRolster 9y ago
      is there anything which indicates that this is imminent? This article seems to be arguing "no." The biggest thing missing I think is an understanding of how human memory works.
    - red75prime 9y ago
      Market demands. Anyway, given that GI already happened, better question would be "Are there obstacles which could prevent creation of an AGI?". I think the answer is unlikely.
    - fasquoika 9y ago
      I think it depends on how you define "imminent". If we're talking a hundred years, well, a lot can happen in that time. We didn't even have computers a hundred years ago, and now they can do certain things that are considered particularly "human", like have a fairly coherent conversation
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temp-defualt 9y ago
is there a link to the video of this talk ???
kowdermeister 9y ago
Would someone be so kind to translate / explain the math on slides 53, 54 to simplish english? What are the symbols (burst pipe, µ) representing on slide 55? And why are the exclamation marks there on the next one?
- letlambda 9y ago
  Consider every action that can be taken at this moment. For each possible action, consider every possible future (out to infinity) weighted by it's likelihood. There are exclamation marks because some of these terms present minor practical problems. The whole, all possibilities out to the end of forever part of it, is easier said than done.
sabujp 9y ago
need video
psadri 9y ago
Do we consider simpler brains to exhibit general intelligence (e.g. A crow's). Is it a more tractable problem to replicate crow level AI first before tackling humans?
- AsyncAwait 9y ago
  We can't even model worms at the moment, so a crow might be far off still.
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- ilaksh 9y ago
  I think the answer is yes and yes. And I believe this idea to be key. His artificial life slides do show starting with simulating very simple animals. That reminds me of something I was thinking a few years ago which I wrote in this comment: https://www.reddit.com/r/artificial/comments/8uwcq/are_worms_intelligent/ https://www.reddit.com/r/artificial/comments/8uwcq/are_worms... See this article https://www.inverse.com/article/35862-a-i-ben-medlock-machine-intelligence-cell-not-brain https://www.inverse.com/article/35862-a-i-ben-medlock-machin... I think Medford is right when he points out > “It comes back, I think, to what intelligence actually is,” reasons Medlock. “Intelligence is not the ability to play chess or to understand speech. More generally, it’s the ability to process data from the environment and then act in the environment. The cell really is the start of intelligence, of all organic intelligence, and it’s very much a data processing machinery.” > The organic intelligence, he says, confers an embodied model of the world for the conscious organism. “The data that’s coming in [through the senses] only really matters at the point where it violates something in the model that I’m already predicting.” So I believe that we should be emulating the capabilities of much simpler organisms. For me I would look at something like a lizard or simple mammal first for a practical starting point, rather than simulating billions of cells and DNA machinery. But the core aspects of intelligence are right there in the cell as he says -- the embodiment, the complex model, prediction and adaptability. To me crows are too smart for a starting point. Personally I think that what typically we think of as general intelligence or strong AI is really just a very smart animal (human), but that is mainly a matter of degree of performance rather than a totally different type of intelligence from animals. What is missing from our computer programs is the type of things that a crow, your cat, or probably even a lizard, all do very naturally. And we may be able to technically bring that down to worms or the cell even as far as core capabilities (but not practical targets for emulating). Can we build an artificial lizard that is able to process the same high bandwidth stream of sensory data as that animal? That can output the same high bandwidth stream of motor outputs? That can see part of a predator behind a rock and realize that it must move, and plan an escape route? That can do these things in completely arbitrary novel environments? That can perform that species' mating dance to attract a mate? These are the types of capabilities I believe we should start with, based on broadly adaptable systems like advanced neural networks. So I think his artificial life slide is mostly right, but we should aim to just emulate animals as a serious goal, with the types of high bandwidth inputs and outputs and complex environments, and make sure that all of the capabilities he lists on that slide like attention etc. are derived from/integrated with powerful general purpose adaptive computation like advanced neural nets so they can handle real world complexity and performance requirements.
bluetwo 9y ago
Assuming a lot of people here are working on an AI or ML problem for work or fun, what are you working on?
jboggan 9y ago
Intelligence is an emergent property of self-replicating systems. I would file that under "something else" since that seems so different from all the approaches listed here.
Torai 9y ago
If this is a talk, is there any video of it?
bra-ket 9y ago
Artificial intelligence will come from understanding natural intelligence.
- viewtransform 9y ago
  We generally accept that we 'know' something when the model used to explain the system is simpler than the system itself. The brain is a very high-dimensional non-linear dynamical system. The number of neurons in the brain is on the order of the number of trees in the amazon rain forest and the number of synapses the number of leaves on those trees. https://youtu.be/8FHBh_OmdsM?t=1165 https://youtu.be/8FHBh_OmdsM?t=1165 We do not have the mathematical tools to understand such systems in general. What if reductionism doesn't work and the best model of natural intelligence is as complicated as the system itself ? Can we say we understand natural intelligence? It could be the case in a distance future that we evolve an artifical intelligence purely as a computation that is capable of understanding us but not us them.
Torai 9y ago
So, when when say AGI, what do we mean? Is it about creating a new intelligent "being" or mimicking what we perceive as human intelligence inside some hardware? I guess it's the the first one. And I guess AGI would be just 1 intelligent being because there is no need for more as they would communicate and share intelligence, so de facto being only 1. Can all human intelligence also be understood as only 1 in some sense, as an isolated human without access to culture wouldn't be more than surviving animal? And when defining intelligence's ingredients, isn't necessary some sort of "motivation" than drives someone to get better at something? Humans have, genetic (survival), social, personal... motivations. How does that translate to AGI, what could be it's motivation?
karpathy 9y ago
I gave this talk a while ago to a small group of attendees. It was not recorded (I saw some ask below). It's based on a document I wrote a while ago called "You suck at writing AI" (never published). The basic argument was that people are comically inadequate at writing complex code. You can't write the code to detect a cat in an image and the correct thing to do is to give up, write down an objective that measures the desiderata and pay with compute to search a function space for solutions. In the same vain, the idea of writing an AGI and all of its cognitive machinery is preposterous and the correct thing to do is to give up, think about the objective and search the program space for solutions. Unfortunately, the mindset of decomposition by function (see Brooks ref), which has worked so well for us in so many areas of scientific inquiry, is just about the most misleading mindset when it comes to AGI.
- olegkikin 9y ago
  Fortunately you don't have to write an AGI yourself. We have a very powerful tool called evolution that can do all the heavy lifting for us, we just have to set up the environment and the goals. I'm pretty sure we could create an AGI, given enough computational power and time, we're basically hardware-limited.
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  - goatlover 9y ago
    Nature had a planet and several billion years. Of course the goal wasn't AGI, it was survival, and horseshoe crabs have done a pretty good job of that. So have beatles, with their large number of species. How would you select for only AGI? It would be like selecting for only the greatest eyes (Mantis shrimp), but doing it over the entire tree of life. You still need a way to narrow down to the best eyes.
    - lgas 9y ago
      Survival is not a goal, it's just the score keeping system.
scottlocklin 9y ago
Speculating on where AGI will come from is sort of like speculating where Faster than Light travel will come from. Except FTL has some vaguely plausible physics behind it, and AGI-wise, we really have no idea what the "I" in AGI means. The mere fact that biological neural networks are rate encoded might turn out to be the one crucial thing that's practically impossible to simulate in a VN computer. My vote: "we have no idea; probably not in my lifetime."
- FLUX-YOU 9y ago
  Since you likely can't prove it, the existence of AGI will be a marketing exercise.
staunch 9y ago
IMHO: If an AGI from the future came back to 2017, it could almost certainly create a new AGI from scratch on current hardware. What would it type into its terminal?
- novalis78 9y ago
  Staunch ;-)
- syrrim 9y ago
  We, humans, are general intelligences, and we are incapable of creating AGIs on modern hardware. What makes you think artificial variants would be any more capable?
  - taneq 9y ago
    'Incapable' the way humans from the 1800s were 'incapable' of building heavier-than-air flying machines? Every human invention in history was 'impossible' until we pulled it off.
aabajian 9y ago
These arguments about AGI all seem to overlook that our computational model is still very Turing-constrained. It's a clock-based, sequential model where each calculation is taken linearly in time. Even with multi-core and distributed computing, you're still bottlenecked by the final integration step (two cores sharing the result of their calculation). There is no central place in our brains where thoughts begin and end. A CPU's clock and ALU are simple not analogous to the human mind. As far as we know, human intelligence is a constant, dynamic interaction between all neurons in our brains, any one of which is capable of originating a signal. I personally think we will develop AGI, but with a different computational model. I don't know enough about quantum computing to even comment on it, but I do have a background in medicine (MD) and computer science (MSCS).
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fourfaces 9y ago
I know where it will not come from. It will not come from the mainstream AI community. They are married to and madly in love with deep learning. Deep learning, the supervised kind, is a red herring. AGI will require a revolutionary breakthrough, most likely from a maverick, probably a lone wolf rebel, who is used to thinking outside the box.
smegma 9y ago
Certainly not from a black person.
markan 9y ago
The presentation briefly mentioned simulating the brain, but I think what's more likely to succeed is mimicking the mind at a high level of abstraction (i.e. a level we can study with introspective or even linguistic methods rather than neuroscience). There's some precedent for this with projects like Soar and ACT-R (and even some recent interest from mathematicians [1]). IMHO this kind of methodology could be pushed much further. [1] https://arxiv.org/abs/1309.4501 https://arxiv.org/abs/1309.4501
segmondy 9y ago
AGI will come from one or two people working by themselves, outside of academia, no more than 100k line of codes.
aaronsnoswell 9y ago
Can someone explain where the gif image on slide 69 comes from?
cerealbad 9y ago
can a data center be shrunk down to the size of a consumer product within the next 50 years? will we all own one and store massive amounts of information for purely selfish or inane reasons? yes/yes - ai comes out of that. no/no - we hit computing plateaus and ai becomes dm (decision maker), and we all own a pdm.
joeldg 9y ago
This slideshow is a mess.. it needs some kind of narrative.
yahyaheee 9y ago
Really interesting slides, would love to see a talk or a more in depth write up!
subru 9y ago
Oh you humans. Genetic engineering, coupled with advances in digital/consciousness interfaces will yield spontaneously appearing brains with an API. Good luck.
evc123 9y ago
What are "something(s) not on our radar"?

389 points by nshr 9y ago

Not on their radar, or their slides at least: Natural Language Processing based rule-based brute-force artificial intelligence (that could be augmented through sensors/motors that allow interaction with the external world). A Vulcan-like (Star Trek) AI, what do you think? Might be easier to simulate the entire brain, on the other hand it might be doable and bridge the gap to general AI.

visarga 9y ago
It's called the self driving car - an AI that interacts with the world. It will be a launching pad to other AI agents.
- shagie 9y ago
  At a more general level, you may find the book Society of the Mind interesting ( https://en.wikipedia.org/wiki/Society_of_Mind https://en.wikipedia.org/wiki/Society_of_Mind ). In this, Minsky proposes simple (mindless) agents that then are combined together to form a mind. That our mind is interacting with the world and launching agents to deal with things - that our own mind may be built this way. Yes, its a bit old (1986) and the current machine learning techniques have dated it - what was theory at one time is reality in places. Still a good book to read and think about.
  - taneq 9y ago
    Minsky's 'society of mind' is actually not far from modern techniques, where deep neural nets have multiple distinct parts that solve different parts of the problem using each others' outputs. (Not to mention explicitly multi-part methods like actor-critic).
  - fspear 9y ago
    Interesting read...I actually came to this very conclusion after an amazing mushroom trip when I was watching the movie "her". I actually drew schematics of how I envisioned the whole system would work lol.
nopinsight 9y ago
Rule-based NLP has been tried for several decades and has (very) limited success in the real world. Current systems based on deep learning beat it for most complex tasks. DeepL, which was on HN front page a few days, is the latest example: https://news.ycombinator.com/item?id=15122764 https://news.ycombinator.com/item?id=15122764
- orthoganol 9y ago
  You're going to have to elaborate on complex tasks. I would argue the majority of successful, money generating software based in NLP/ NLU, i.e. the majority of the industry, is "rule based" (used in a general sense to mean non DL). Personal assistants, search, chatbots, etc.
crypticlizard 9y ago
With regards to Vulcan emergence, I do believe we are in for that soonish; it's an archetypal depiction that consumers desire. Biotech, my friend, genetic enhancement. We can make ourselves smarter with genetic enhancement. We can even give ourselves the vulcan mind meld, and everything else exceptional about Spock. I do think the human brain substrate is exceptional, worthy of improving upon. I wish we talked more about so called "brainpower extension technologies", hopefully one day. I bet cats talk in ten years, it's only logical, consumers love cats & the companies can sell them by the millions.
- red75prime 9y ago
  "I want to go out". The door is opened. "Lemme think a bit. Nah, I just want the door open". We can't have the door open at all times! "Who do you think you are for me to care? You are a human, invent something."

wonderwonder 9y ago

I think a very interesting aspect of general AI is that while an incredibly complex technology, it is not unrealistic that it could be created for the first time in someone's home office. Unlike many other earth changing technologies there is nothing that a massive corporation has that a home tinkerer does not (besides the obvious of money and many engineers). With the rise of cloud computing and open source; everything I need I have instant access too, all that's lacking is the core software which can be written (of course not a trivial task). While unlikely, it is still quite amazing that in a few years an AI could awaken 3 doors down at my neighbor Bob's house. No idea what happens after that, hopefully Bob was a fan of the 3 laws and has a couple more up his sleeve.

qaq 9y ago
Cloud computing will not help home tinkerer much if you need 100K/hour worth of compute to run an experiment.
- CuriouslyC 9y ago
  If we move towards hierarchical model composition, you don't need to rebuild the visual object recognition module to experiment in learning spaces that incorporate visual information.
klochner 9y ago
[X] Open Source Tools [ ] Massive Data Sets [ ] $Millions in Computing Resources I'd put it roughly on par with finding a general cure for cancer. While unlikely, quite amazing that the cure for one of the largest causes of death could be solved 3 doors down with lab supplies from amazon and a handful of mice.
- mrfusion 9y ago
  Well the data sets are all around your house and yard. Think of baby human agi's. And the computing costs could be boot strapped by the ai. Put it to work earning its keep and expanding resources.

exratione 9y ago

I feel that those who argue that any approach other than running human brain emulations and then reverse engineering them or speculatively modifying them is the most likely way to get to AGI has a pretty steep hill to climb in order to justify that point of view. Nothing else that is going on now or even on the agenda or even foreseeable offers a plausible, definitive plan to get to AGI. Whereas brain emulation is clearly going to achieve that goal fairly shortly after the maps are good enough and the computational capacity large enough, and the following experimentation is a far more reliable way to determine the underpinnings of intelligence than present efforts at de novo construction.

visarga 9y ago
I disagree. It's too expensive to run a low level brain sim. In the meantime deep learning based AI achieved superhuman or close to human results in many tasks, such as image recognition, voice recognition, translation, car driving and Go. The AGI will be a reinforcement learning agent, as it will need to be able to perceive and act in the physical world. Thus the path to AGI is the path of RL. The most essential piece in RL will be the development of environment simulators. AlphaGo was a trivial simulator - simple rules in a simple world - but we need real world simulators in order for the AI agents to learn to act. Fortunately simulation is almost the same as gaming and there is huge interest in it both for humans and AI, so it will be developed fast. So instead of simulating the brain, simulate the world (imperfectly) and run deep neural net based RL to learn to act on top of it.
- grwthckrmstr 9y ago
  "I disagree. It's too expensive to run a low level brain sim." Interesting. Could you tell me Why is it too expensive? If it wasn't expensive, would that change things drastically, and make brain sim a viable option?
  - visarga 9y ago
    The brain has 10^14 synapses (100 trillion) synapses. Current day neural nets barely reach a hundred million, with very few exceptions. Then, besides compute, there is data movement - currently the bottleneck in AI is moving data around, not computing. Imagine the interconnect for a brain-size neural net.

Will_Do 9y ago

That was really interesting. I'm interested why he's so pessimistic about the simulating a brain approach. Yes it's the boring and obvious approach but it also seems the most direct. Also found this quote interesting > Might have to make it illegal to evolve AI strains or an upper bound of computation per person and closely track all computational resources on earth.

tw1010 9y ago
I think the main problem with the brain simulation approach is that we don't yet have a really good model of how the brain actually works.
- ethbro 9y ago
  We know how components of the brain work. Is it inconceivable that we luck into the proper arrangements and interrelationships?
visarga 9y ago
The brain is slow and redundant. It has to be like that because it is not produced in a factory - it is created by self replication. Self replication imposes strict limits and requirements on the type of brain that can be created. AI neurons, on the other hand, are perfect - they never get old or tired and always remember. A neural net like ResNet-150 is capable of doing essentially what 1/3 of the brain is doing (vision). We can achieve superhuman results in vision with much less neurons, and faster. This is the kind of logic that makes brain emulation a far flung possibility compared to the current day deep neural nets. That and the fact that the brain simulation guys don't have anything to show for. There are no human-level tasks that could be replicated by this approach yet.
- CuriouslyC 9y ago
  The brain is slow in "cycles"/second, but the amount of computation done by each cycle isn't directly comparable to that done by a computer. Forgetting isn't a bug, it's a feature. Forgetting is basically like dimensionality reduction on input data - we extract the principle components/exemplars, remember a weighting, and trash the redundancy. Training a ML model is a lot faster on smaller data, and the same is true for us. Don't compare ANNs with the brain strictly on a time/time basis. Time isn't the only factor, power consumption and heat production are also factors, and if you include them the brain comes out way ahead. People with an engineering background almost universally underestimate how freaking awesome biology is. Our brains are self-constructing, self-replicating, self repairing (mostly) hyper-efficient pattern recognition systems. The more we learn about them the more awesome we realize they are. Don't be so arrogant as to assume a few hundred years of engineering will universally eclipse hundreds of millions of years of evolution.

bryananderson 9y ago

When he says "artificial life", is he referring to reinforcement learning?

poppingtonic 9y ago
No, probably evolution strategies. https://blog.openai.com/evolution-strategies/ https://blog.openai.com/evolution-strategies/
visarga 9y ago
Yes. Evolution strategies are just an alternative approach to RL. Reinforcement learning is basically solving the problem of being an intelligent agent in the world, moving about, achieving goals.
letlambda 9y ago
http://www.alife.org/ http://www.alife.org/ Artificial Life is a field with very fuzzy boundaries. Roughly, computer systems that look like biological or ecological systems. From an AL perspective, life evolves to function in it's ecology. The problem is not building an AGI, it's building an ecology in which AGI will emerge. Oh... and hopefully, also one in which ruthlessly destroying other intelligent agents isn't a good survival strategy.

CuriouslyC 9y ago

Artificial general intelligence won't be invented, it will emerge, just general intelligence did in the wild. AGI is just going to be a hierarchical arrangement of specialized tools. The first step is highly specialized AI tools for very specific problem domains. The second step is AI tools that use other AI tools as components but address slightly larger problem domains. The third and successive steps are recursions of the second step. Additionally, we won't be able to tell right away when we've crossed the threshold. We can't even say for sure where "intelligence" stops in animals. We used to think we were the line, but now the bar has been pushed down to include primates, cetaceans, a number of birds, possibly some members of the bear family, etc. The reality is that it is a gradient and there is no clear line.

tokipin 9y ago
> Artificial general intelligence won't be invented, it will emerge, just general intelligence did in the wild. I think so as well, but for a specific reason: That people are not as "AGI" as we think, and that our general intelligence is the result of immense neuron-computing resources committed to satisfying a few simple drives. In other words, narrow AI with a lot of resources. Not to mention many specialized subcomponents. And to the degree that consciousness is a selected trait, that means it has a purpose. I think the work on attention/focus in neural networks hints at that purpose.
- CuriouslyC 9y ago
  Why do you think consciousness is a selected trait?
  - tokipin 9y ago
    Well I don't know what consciousness is, so I couldn't answer that. But I think the opposite stance of consciousness being some kind of majestic oracle is silly. There is a reason why we perceive consciousness, even if it is just as a side effect of something else or a random fluke that is useless or an illusion for coordinating our limbs or whatever. These types of discussions do get pretty wonky. When I say consciousness is selected for, I mean more whatever practical apparatus (if any) it is emergent from, not the perception of consciousness itself.

WheelsAtLarge 9y ago

I'm waiting for the day when there is an IA OS. Basically there would be a Natural Language processor that determines what sub AI app to run. It's not true general AI but if it's done broad enough it will seem like it.

visarga 9y ago
Google and FB have most to gain from a capable conversational agent. The moment this system will appear, it will start replacing the old interfaces, and pretty soon eat the G-FB pie. If they are not on top of the wave then, they'll lose. Current state of the art in dialogue AI is an agent that can reason based on images, documents or tables with data. There is a lot of research into attention and memory augmented neural nets. I put my bet on graph based neural nets, that can better represent object and relations in reasoning tasks.

rdlecler1 9y ago

Background: I did AI and Philosophy of Mind in undergrad, an MSc focused on ALife, then a PhD at Yale under a Macarthur Fellow who developed the theoretical framework for the 'evolution of Evolvability' where I worked on computational evolutionary biology. I can say we're not going to blindly brute force our way forward, but instead we'll need to reverse engineer nature's core algorithms to generate hard AI. Every time a major advance is made in AI the computational neuroscientist say: "why didn't you talk to us 15 years ago? We could have told you that!". Those ingredients will be embodiment, evolution, genetics (genotype-phenotype encoding), neirogenesis(gene regulatory networks directing phenotypic development from a single cell to a multicellular neural network), and ecology (evolving in adversarial and cooperative environments). And we'll need a lot of theoretical work in how to represent nature's algorithms in code. For example my PhD work just focused on how to use evolutionary algorithms to evolve simple gene regulatory networks and how that leads to properties of modularity in the genotype-phenotype map. That alone is life's work but a necessary ingredient. I don't expect to see this solved in my lifetime given how we're attacking the problem (head on) today. And until then we're going to continue to run into these dark winters of AI.

heimatau 9y ago
Hmm, reading your experience clarifies to me (a Maths guy) why current deep learning efforts are producing real fruit from their labor. Linear Regression and Vector/dot products are just constructs to re-create the evolutionary biology seen in nature. When looking at the slow process of evolution, it's the nuances that matter and over time, they compound exponentially. Pretty neat. By breaking down a task into abstract constructs and sifting problems out with a sigmoid function.
eggie 9y ago
I'd love to read your thesis. Would you please a link?
Jedi72 9y ago
Can you link to some of your work, this is very interesting stuff
return0 9y ago

deleted 9y ago

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eb3c90 9y ago

I'm working on a technology that I think might enable either IA or AI. Basically intelligences manage their own programs and the computational resources allocated to those programs. So I'm looking at doing that with markets. With IA the user acts as feedback to the market about what is good or bad. Ideally it would act as an external brain lobe. More information on my approach is on this blog https://improvingautonomy.wordpress.com/2017/07/25/why-study-resource-allocation/ https://improvingautonomy.wordpress.com/2017/07/25/why-study...

eb3c90 9y ago
This might be a better link. It explains how we might get to IA. TL;DR It is a mix of machine learning with different parameters and inputs/outputs and language translation into programs with the economy acting as the force to guide this evolving set of programs. https://improvingautonomy.wordpress.com/2017/08/22/a-possible-path-to-intelligence-augmentation/ https://improvingautonomy.wordpress.com/2017/08/22/a-possibl...
- stephengillie 9y ago
  Interesting idea. Instead of thinking of computing as an authoritarian schema, it could be a community schema, using a kanban or currency system to communicate resource needs between units. It's also similar to negotiating memory over commitment in virtualization. VMWare's driver on the VM "inflates a memory bubble" to communicate host memory constraints to all client VMs. This is often done when VMs have allocated 125%-300% of the host's physical RAM, and forces clients to swap more.
  - eb3c90 9y ago
    Yep, while people have to worry about the memory/cpu usage of the programs inside a computer, we are probably going to be stuck with just narrow AI. General Intelligence needs the ability to trade off resources between different programs doing different things (these may be learning different things, processing data or doing other computational tasks). Also we get malware because we expect the user to be a good knowledgeable authoritarian manager of a system and never run a bad program and be able to get rid of a bad program when it occurs. This just isn't realistic.

nether 9y ago

Goldman Sachs

throwaway00100 9y ago

Where will the philosopher's stone come from?

NumberCruncher 9y ago

If you take a look at the evolution of the most advanced non artificial general intelligence, eg. human intelligence, it is strongly connected to the evolution of communication. It is a question of efficiency whether you learn through your own experience and failures or through the experience and failures of others. This teaching/learning process was boosted through the use of pictures, spoken language, hand written and printed books. This is why I believe the artificial general intelligence will we teached by an other artificial general intelligence and this evolution will be somehow connected to language processing. As far as I know Google tries to train its AIs through human imput, eg. to recognize animals drawn by humans. I consider it as one of the first steps in the right direction.

viewtransform 9y ago
I would argue that intelligence is connected to the evolution of sensing at a distance. Vision in particular, allowed life to evaluate the state of the environment at a distance and allowed for the evolution of strategies to predict and respond to the environment in real time. The progression in intelligence from sponge to amphibian to mammal is related to the evolution of finer sensing of the environment at a distance: vision, smell, sound etc.
- naikrovek 9y ago
  'Situational awareness' is maybe a better phrase. Predicting the future was probably next. Knowing that sunset is soon or that rain is coming requires situational awareness, long term memory, short-term memory, and all kinds of other stuff. A realistic set of stimuli, a LOT of artificial neurons, and a lot of time will probably get there, eventually.

FullMtlAlcoholc 9y ago

However it originates, it will need a body to experience sensations firsthand, not pre-recorded or simulated data. Perhaps connected IoT devices will be sufficient. Also, AGI will not be invented. It will arise as an emergent phenomena, and it may have already achieved what we call consciousness. Somewhat off topic: Another phenomenon that people should be on the watch for is "Articial Out-telligence", a phrase coined by Eric Weinstein. [0] It describes strategies used by organisms with no known brain to get more intelligent creatures to do its bidding, wittingly or unwittingly. The cordyceps fungus, toxoplasma gondii, and pollinating plants that need insects to spread their seed are examples of how an organism with no known neurological network can "outsmart" more advanced organisms. A scenario involving AI may be one that is developed to maximize each individual users time on a site/app by using online data about that person to find their particular addictions. [0] https://www.youtube.com/watch?v=Wu8s0tp9yzY https://www.youtube.com/watch?v=Wu8s0tp9yzY

novaleaf 9y ago

what is the point of slides without the underlying presentation? slides are glorified notes, NOT presentations nor papers.

ilaksh 9y ago
Well these slides seemed to give all of the most relevant details.

ThomPete 9y ago

Intelligence is emerging just like it was with humans. It's not a thing so it wont come from somewhere. As always solving the small problems will eventually allow solutions to emerge we weren't aware of and that might turn into human like or more probably technology like intelligence far surpassing humans. I always find it fascinating that we have no problem accepting that we became intelligent over time and out of nowhere (Unless you are religious which is a whole other discussion) we even have no issues imagening that life and intelligence could have happened other places in the universe. But the idea of a non-carbon based intelligence is a big debate as if it's somehow unimaginable to think that AI could emerge from human hand while having no problem entertaining the idea that we our intelligence is somehow a unique snowflake.

AsyncAwait 9y ago
I think the problem is not that we cannot accept evolving general AI by solving much smaller problems first, rather we're very impatient and don't want to wait for the evolution to take place.
- ThomPete 9y ago
  Yes impatience is probably one of the most underestimated issues when it comes with humans and progress in general.

chanakya 9y ago

What is the best book/reference to understand why there seems to be general agreement that AGI/"broad" AI will happen? TFA compares the relative likelihood of the various approaches, but says nothing about the absolute likelihood of any of them. Are there signs of AGI we can see today? Is there an argument/data which links the huge improvements we're seeing in narrow AI to the likelihood of AGI?

ktRolster 9y ago
The best argument I've heard is that we can use a computer to model any physical process.....the brain is a physical process, therefore we can use a computer to model the brain. If you think that there is some process in the brain that would be theoretically impossible for computers to model, that would be an interesting topic of discussion.
- chanakya 9y ago
  The brain is a physical entity, yes, so in theory we should be able to model it, assuming we know all the laws it works on with enough precision. This is a big if, but even if that's granted, is there anything which indicates that this is imminent?
  - ktRolster 9y ago
    is there anything which indicates that this is imminent? This article seems to be arguing "no." The biggest thing missing I think is an understanding of how human memory works.
  - red75prime 9y ago
    Market demands. Anyway, given that GI already happened, better question would be "Are there obstacles which could prevent creation of an AGI?". I think the answer is unlikely.
  - fasquoika 9y ago
    I think it depends on how you define "imminent". If we're talking a hundred years, well, a lot can happen in that time. We didn't even have computers a hundred years ago, and now they can do certain things that are considered particularly "human", like have a fairly coherent conversation

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temp-defualt 9y ago

is there a link to the video of this talk ???

kowdermeister 9y ago

Would someone be so kind to translate / explain the math on slides 53, 54 to simplish english? What are the symbols (burst pipe, µ) representing on slide 55? And why are the exclamation marks there on the next one?

letlambda 9y ago
Consider every action that can be taken at this moment. For each possible action, consider every possible future (out to infinity) weighted by it's likelihood. There are exclamation marks because some of these terms present minor practical problems. The whole, all possibilities out to the end of forever part of it, is easier said than done.

sabujp 9y ago

need video

psadri 9y ago

Do we consider simpler brains to exhibit general intelligence (e.g. A crow's). Is it a more tractable problem to replicate crow level AI first before tackling humans?

AsyncAwait 9y ago
We can't even model worms at the moment, so a crow might be far off still.
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ilaksh 9y ago
I think the answer is yes and yes. And I believe this idea to be key. His artificial life slides do show starting with simulating very simple animals. That reminds me of something I was thinking a few years ago which I wrote in this comment: https://www.reddit.com/r/artificial/comments/8uwcq/are_worms_intelligent/ https://www.reddit.com/r/artificial/comments/8uwcq/are_worms... See this article https://www.inverse.com/article/35862-a-i-ben-medlock-machine-intelligence-cell-not-brain https://www.inverse.com/article/35862-a-i-ben-medlock-machin... I think Medford is right when he points out > “It comes back, I think, to what intelligence actually is,” reasons Medlock. “Intelligence is not the ability to play chess or to understand speech. More generally, it’s the ability to process data from the environment and then act in the environment. The cell really is the start of intelligence, of all organic intelligence, and it’s very much a data processing machinery.” > The organic intelligence, he says, confers an embodied model of the world for the conscious organism. “The data that’s coming in [through the senses] only really matters at the point where it violates something in the model that I’m already predicting.” So I believe that we should be emulating the capabilities of much simpler organisms. For me I would look at something like a lizard or simple mammal first for a practical starting point, rather than simulating billions of cells and DNA machinery. But the core aspects of intelligence are right there in the cell as he says -- the embodiment, the complex model, prediction and adaptability. To me crows are too smart for a starting point. Personally I think that what typically we think of as general intelligence or strong AI is really just a very smart animal (human), but that is mainly a matter of degree of performance rather than a totally different type of intelligence from animals. What is missing from our computer programs is the type of things that a crow, your cat, or probably even a lizard, all do very naturally. And we may be able to technically bring that down to worms or the cell even as far as core capabilities (but not practical targets for emulating). Can we build an artificial lizard that is able to process the same high bandwidth stream of sensory data as that animal? That can output the same high bandwidth stream of motor outputs? That can see part of a predator behind a rock and realize that it must move, and plan an escape route? That can do these things in completely arbitrary novel environments? That can perform that species' mating dance to attract a mate? These are the types of capabilities I believe we should start with, based on broadly adaptable systems like advanced neural networks. So I think his artificial life slide is mostly right, but we should aim to just emulate animals as a serious goal, with the types of high bandwidth inputs and outputs and complex environments, and make sure that all of the capabilities he lists on that slide like attention etc. are derived from/integrated with powerful general purpose adaptive computation like advanced neural nets so they can handle real world complexity and performance requirements.

bluetwo 9y ago

Assuming a lot of people here are working on an AI or ML problem for work or fun, what are you working on?

jboggan 9y ago

Intelligence is an emergent property of self-replicating systems. I would file that under "something else" since that seems so different from all the approaches listed here.

Torai 9y ago

If this is a talk, is there any video of it?

bra-ket 9y ago

Artificial intelligence will come from understanding natural intelligence.

viewtransform 9y ago
We generally accept that we 'know' something when the model used to explain the system is simpler than the system itself. The brain is a very high-dimensional non-linear dynamical system. The number of neurons in the brain is on the order of the number of trees in the amazon rain forest and the number of synapses the number of leaves on those trees. https://youtu.be/8FHBh_OmdsM?t=1165 https://youtu.be/8FHBh_OmdsM?t=1165 We do not have the mathematical tools to understand such systems in general. What if reductionism doesn't work and the best model of natural intelligence is as complicated as the system itself ? Can we say we understand natural intelligence? It could be the case in a distance future that we evolve an artifical intelligence purely as a computation that is capable of understanding us but not us them.

Torai 9y ago

So, when when say AGI, what do we mean? Is it about creating a new intelligent "being" or mimicking what we perceive as human intelligence inside some hardware? I guess it's the the first one. And I guess AGI would be just 1 intelligent being because there is no need for more as they would communicate and share intelligence, so de facto being only 1. Can all human intelligence also be understood as only 1 in some sense, as an isolated human without access to culture wouldn't be more than surviving animal? And when defining intelligence's ingredients, isn't necessary some sort of "motivation" than drives someone to get better at something? Humans have, genetic (survival), social, personal... motivations. How does that translate to AGI, what could be it's motivation?

karpathy 9y ago

I gave this talk a while ago to a small group of attendees. It was not recorded (I saw some ask below). It's based on a document I wrote a while ago called "You suck at writing AI" (never published). The basic argument was that people are comically inadequate at writing complex code. You can't write the code to detect a cat in an image and the correct thing to do is to give up, write down an objective that measures the desiderata and pay with compute to search a function space for solutions. In the same vain, the idea of writing an AGI and all of its cognitive machinery is preposterous and the correct thing to do is to give up, think about the objective and search the program space for solutions. Unfortunately, the mindset of decomposition by function (see Brooks ref), which has worked so well for us in so many areas of scientific inquiry, is just about the most misleading mindset when it comes to AGI.

olegkikin 9y ago
Fortunately you don't have to write an AGI yourself. We have a very powerful tool called evolution that can do all the heavy lifting for us, we just have to set up the environment and the goals. I'm pretty sure we could create an AGI, given enough computational power and time, we're basically hardware-limited.
- deleted 9y ago
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- goatlover 9y ago
  Nature had a planet and several billion years. Of course the goal wasn't AGI, it was survival, and horseshoe crabs have done a pretty good job of that. So have beatles, with their large number of species. How would you select for only AGI? It would be like selecting for only the greatest eyes (Mantis shrimp), but doing it over the entire tree of life. You still need a way to narrow down to the best eyes.
  - lgas 9y ago
    Survival is not a goal, it's just the score keeping system.

scottlocklin 9y ago

Speculating on where AGI will come from is sort of like speculating where Faster than Light travel will come from. Except FTL has some vaguely plausible physics behind it, and AGI-wise, we really have no idea what the "I" in AGI means. The mere fact that biological neural networks are rate encoded might turn out to be the one crucial thing that's practically impossible to simulate in a VN computer. My vote: "we have no idea; probably not in my lifetime."

FLUX-YOU 9y ago
Since you likely can't prove it, the existence of AGI will be a marketing exercise.

staunch 9y ago

IMHO: If an AGI from the future came back to 2017, it could almost certainly create a new AGI from scratch on current hardware. What would it type into its terminal?

novalis78 9y ago
Staunch ;-)
syrrim 9y ago
We, humans, are general intelligences, and we are incapable of creating AGIs on modern hardware. What makes you think artificial variants would be any more capable?
- taneq 9y ago
  'Incapable' the way humans from the 1800s were 'incapable' of building heavier-than-air flying machines? Every human invention in history was 'impossible' until we pulled it off.

aabajian 9y ago

These arguments about AGI all seem to overlook that our computational model is still very Turing-constrained. It's a clock-based, sequential model where each calculation is taken linearly in time. Even with multi-core and distributed computing, you're still bottlenecked by the final integration step (two cores sharing the result of their calculation). There is no central place in our brains where thoughts begin and end. A CPU's clock and ALU are simple not analogous to the human mind. As far as we know, human intelligence is a constant, dynamic interaction between all neurons in our brains, any one of which is capable of originating a signal. I personally think we will develop AGI, but with a different computational model. I don't know enough about quantum computing to even comment on it, but I do have a background in medicine (MD) and computer science (MSCS).

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fourfaces 9y ago

I know where it will not come from. It will not come from the mainstream AI community. They are married to and madly in love with deep learning. Deep learning, the supervised kind, is a red herring. AGI will require a revolutionary breakthrough, most likely from a maverick, probably a lone wolf rebel, who is used to thinking outside the box.

smegma 9y ago

Certainly not from a black person.

markan 9y ago

The presentation briefly mentioned simulating the brain, but I think what's more likely to succeed is mimicking the mind at a high level of abstraction (i.e. a level we can study with introspective or even linguistic methods rather than neuroscience). There's some precedent for this with projects like Soar and ACT-R (and even some recent interest from mathematicians [1]). IMHO this kind of methodology could be pushed much further. [1] https://arxiv.org/abs/1309.4501 https://arxiv.org/abs/1309.4501

segmondy 9y ago

AGI will come from one or two people working by themselves, outside of academia, no more than 100k line of codes.

aaronsnoswell 9y ago

Can someone explain where the gif image on slide 69 comes from?

cerealbad 9y ago

can a data center be shrunk down to the size of a consumer product within the next 50 years? will we all own one and store massive amounts of information for purely selfish or inane reasons? yes/yes - ai comes out of that. no/no - we hit computing plateaus and ai becomes dm (decision maker), and we all own a pdm.

joeldg 9y ago

This slideshow is a mess.. it needs some kind of narrative.

yahyaheee 9y ago

Really interesting slides, would love to see a talk or a more in depth write up!

subru 9y ago

Oh you humans. Genetic engineering, coupled with advances in digital/consciousness interfaces will yield spontaneously appearing brains with an API. Good luck.

evc123 9y ago

What are "something(s) not on our radar"?