Where will artificial general intelligence come from?
docs.google.com
docs.google.com
Saw their Ted talk, looks promising, but seems they delayed a lot.
Neuroscience-Inspired Artificial Intelligence http://www.cell.com/neuron/abstract/S0896-6273(17)30509-3
The starting place is simulation. We use games (and other kinds of sims) to learn to act intelligently in a virtual environment. In such a place we can define many tasks and a learning curriculum.
What you say about people reflects cooperative behavior that drives reproductive success for shared gene complexes.
In fact what nature invented was a persistent colony organism with external memory.
Wild solo humans are only a little smarter than wolves individually, but being able to share and externalise invention and learning created a massive advantage.
Humans are successful because although only a tiny minority of individuals are any good at invention, the fact that information persists and is shared means the entire population benefits.
The problem for AI is modelling the learning and invention process. Classifiers and recognisers are getting better, but they're not really learning in the human sense, which is a combination of abstraction, mimicry, and occasional invention.
IMO there's no chance of AGI developing until there's a persistent, transferable, abstracted model generated as an output from classifier systems and other learning machines which is a symbolic - not just a statistical - summary of the learning.
It can be compressed to "until AIs can talk".
Another way of putting it - the source of meaning is life, or death (prolonging life, avoiding death as much as possible). Reproduction is just the start of life. From this game of life and death come reward signals that teach us how to act in the world (our values).
In a neural net we do just that - maximize diversity by splitting the signal over many neurons, each with different weights, computing different things. Integration is maximized by the mixing together of signals from other neurons and training them together with a common loss function.
Even the internet as a medium requires diversity and integration to be successful. For example, net neutrality is related to diversity. Integration is related to national firewalls, copyright barriers, filter bubbling effect (where one sees only content from parts of the internet they agree with), walled gardens (like the app stores), and other things that cut the connection between people.
You can apply diversity and integration to other fields as well, for example, in politics/governance. We can compare a federal system (more diversity) with a centrally planned system (less diversity) and see the effects. With integration - we can compare free trade with regulated trade. The same principles apply to free speech - where diversity and integration are basically promised by the constitution.
Insects and especially social insects like ants are good examples of very successful survivors with very little general intelligence.
> Wild solo humans are only a little smarter than wolves individually, but being able to share and externalise invention and learning created a massive advantage.
This seems to resonate with a strain of Philosophy of Mind https://plato.stanford.edu/entries/content-externalism/ which deals with our mental content being distributed not only around the brain and body, but on paper, computers and relations with other people.
In some ways it is a search through program space. But then I think we as intelligences do a guided search through program space as we develop. It seems important to use what information you can to guide you e.g. culture and copying other people who have already been navigating the space of programs to be.
Evolution created a goal from nothing, which is self-replication. It works on many levels - self replicating DNA, self replicating cells, self replicating ideas (memes), self replicating ecosystem, even the economy has become a self replicator.
- The former can be debugged. If your image analysis system embarrassingly tags black people as gorillas, you can go in and find the bug and fix it. That's not so easy if you're using a black box model.
- The black box approach is tremendously enervating. You code up a neural network architecture, launch a bunch of cloud GPU instances, and start training. If the results are bad... you try a new architecture. In the function decomposition world, you can actually use your knowledge and understanding of the system as an engineer to figure out what went wrong and why.
<rant>This exemplifies the issue I have with current day philosophy. It's too blissfully unaware of the discoveries in AI. While they redefine consciousness the 1000th time, the AI researchers make "Reinforcement Learning Agents" that play Go, drive cars, paint, draw and can take a pizza order from you. Philosophers, get more concrete.</>
Interested to hear about these. I can honestly not remember any.
In fact, cognitive science largely split off as a separate field but there's a school of thought that algorithms and big data are only going to take things to a certain point and you're not going to get to things like fully autonomous vehicles under general conditions without different approaches that involve better understanding the physical world.
I do agree that different approaches are needed.
In the case of flight it was the realization that the airfoil is important and the rest is window dressing.
https://en.m.wikipedia.org/wiki/Fluid_dynamics
Nothing to do with origins of flight but interesting and related to why flight works.
Which means that copying birds is fine, you just have to know what parts to copy (mechanisms to control direction in all 3 axes) and which to ignore (flapping wings isn't practical given the power sources and construction materials we have, but soaring on stationary wings is a good place to start).
Which in turn means that saying "Just copy nature, they've been doing this for 1bn years" or "don't bother copying nature, we need different stuff" both aren't very useful.
The analogy between acheiving AGI and achieving flight is invalid on its face and yields no useful information. Nothing can be drawn from the latter about the former.
Flight is mechanical. AGI is not. However, until now, ML and other AI techniques have focused largely on mechanics (stuffing data into an algorithm), which is why such analogies with nature are tempting. But, that approach won't suffice for AGI.
The breakthrough for AGI will come through an approach that backs away from brute force and attacks the problem from a meta-perspective; building on the core blocks of what intelligence is. The approach will be relatively simple, once the core foundation of the integrative learning process is understood, which is the much more difficult task. Execution will then involve establishing a base case that implements, not learning, but the capacity to learn. This will then be replicated across generations, compounding exponentially until what we recognize as AGI emerges.
As well, the required computing power is likely to be significantly less than is commonly believed. This raw brute-force ML-style approach again misleads us here.
I just disagree that we have to intentionally create them -- I think there are a lot of places we can experimentally bruteforce the implementation instead of having a solid theoretical understanding if our only goal is creating an AGI.
I don't think we need any theoretical breakthroughs, per se, but rather, a lot of computing power and time. No one seems willing to take a million nodes and run them for a decade to brute force some of the mechanisms -- everyone wants results on the short timelines that grants or Wall St operate on. I get why, but that bias towards quantifiable short-term gains fundamentally limits the search algorithms we're implementing in our quest for AI, and likely means that we won't get there in a meaningful way, because intentionally implementing the necessary requirements is a fool's errand of complexity.
In short: we're not smart enough to do the simple, but slow thing, so we're trying the highly complex one with demonstrable incremental results. I expect this approach to continuously fail to develop AGI even while it demonstrates results on discrete problems.
Like when it got cold for humans, rather than just dying and waiting until they evolved fur, they just made clothes. Evolution is inherently able to do great things but is very inefficient. We have the power to reverse engineer nature and use that knowledge to our advantage, and if our goal is to create general AI, then not trying to understand the one sitting in everyone heads and just trying to throw more computing behind it just seems like a waste of a tool that has allowed us to get this far.
Humans already accidentally invented AI at least a few times, sort of. To talk meaningfully about this topic, I think it's better to step away from the topic of "intelligence" and instead speak about "reasoning ability". It's still fuzzy, but seems to sidestep a lot of the "can submarines swim" arguments.
Things like societies, governments, corporations, economies, etc all show similar features to machine learning algorithms and reasoning animals in their ability to dynamically problem solve -- the main difference is that they run on human computers instead of electronic computers. (Or photonic, quantum, etc computers.) Well, in modern times, they're actually sort of cyborgs, but that's a side issue.
The quest for an AGI is more about translating the notion of a corporation (or government, etc) meaningfully into fully electronic computer terms than it is in replicating the human mind. We know enough about the supra-structure of the corporation to meaningfully bound the search, and it's much more prudent to spend a decade on bruteforcing each component of a corporation than it is to navel gaze about reinventing minds. The truth is that human brains (or animal brains) aren't the only general intelligence that we know -- so reinventing them isn't the only path to AGI, though it's a worthwhile project in its own right.
I just worry that many of the meaningful contributions of the people working on reinventing minds will be lost on the birth of the actual first AGI through corporate cyberization. (And that many of us will fail to recognize corporate cyberization for what it is -- an AGI. Any (successful) corporation which can act fully by algorithm must be a general intelligence.)
2. How is it that you know that the reasoning abilities of the structures that we create are because of the structure, and not because we are the pieces that make up the larger structure?
2. Well, this is somewhat more complicated to answer --
Obviously the reasoning abilities are (partly) because we're part of the structure. But in a way that we know can be decoupled from the reasoning ability of the structure -- see modern CS on which problems are tractable to mechanize and the success of ML at individual tasks -- because modern corporate theory has formulated a system of corporate governance and execution that only depends on individual people for one or a few tasks. The fixation of modern corporate management on replaceable, cheap, low-skill workers makes it a perfect template to replace components one at a time with bruteforce discovered ML components (and in fact, we already see this happening -- because it is part of the reason that corporations are designed that way).
After all, from the perspective of the corporation as a reasoning entity, increased cyberization is increased efficiency, which ultimately means increased competitiveness (and hence survival).
Electronic computers were designed to replace human computers in the military, and we have a wealth of information on their numeric and string processing capabilities. They're specifically designed to emulate in another medium some of humans' reasoning abilities, particularly those around logical deduction and numeric calculation (which in some senses are similar). Since modern corporations have reduced much of their operation to machines (eg, factories), string manipulation and calculation, it seems perfectly conceivable to have a corporation control all of its "core functions" via electronic computer after a period of increasing cyberization, with contracted delivery services and on-site technicians viewed something like a doctor.
Corporate cyberization is, if you take my first claim that corporations represent a "general" reasoning structure of made of specialized components as true, an open and direct path to an AGI that we seem to be stumbling down.
I mean, what is the problem we are trying to solve here? Fully emulate a human? That's worthless, we have an overabundance of humans already and it's easy to make more.
Or are we trying to come up with something that performs our tasks without us having to think or do any work and presumably makes life richer and more pleasant? Well that's a different problem, and yes, we have been automating that forever and are arguably getting better at it. (I say arguably because "richer and more pleasant" are relative as it turns out).
The argument over what constitutes "intelligence" is not an insignificant one either. My dog likely thinks I'm stupid because I don't eat butter all day and shave and go to work. And come to think of it, he might be right. Point being, we often conceive of intelligence as "having better ways to do or get what we would". But then there is abstraction, longer term planning, higher order thinking, which in the end almost certainly will be incomprehensible to humans.
That's an astoundingly terrible idea, considering what powerful corporations and governments have done to the world.
The problem is that, at the risk of trivializing a lot of good research, science related to the brain/how we think/etc. has seen a whole lot more money spent and the effort of smart people expended than it has seen useful results. There are still debates going on about a lot of aspects of learning and other aspects of cognitive science that were probably already happening when computers were made from vacuum tubes or even earlier.
Can you name one time that has happened? I can't think of any.
Is your opinion independent of evidence?
I know of no biological forms that have evolved wheels. But wheels have turned out to be a hell of a lot faster than fins or legs. I see no reason "intelligence" has to follow the pathways or limitations of biology or neurology at all. Although certainly it may be a place to look for some ideas.
That said, evolutionary optimization is generally considered unreliable(convergence) and inefficient.
EDIT: Please explain your downvotes here? Don't you people see that this, coupled with there being no useful intermediate stage before developing a full wheel, prevents evolution from "inventing" it.
See: Wheelbarrow; amongst many wheeled devices I can think of immediately.
Also, try running around with a wheelbarrow in a dense forest.
Flagella use propeller motion for propulsion.[1]
Lots of animals use jets.[2]
I'd consider the combustion engine to be the most novel 'human' design with regards to aircraft propulsion -- or the way we achieve jet propulsion; that's pretty unique.
[0]: https://en.wikipedia.org/wiki/List_of_soaring_birds
On certain terrain. And much slower - sometimes useless - on others. Wheels aren't simply better legs, they're a different way of moving, with its own benefits and drawbacks.
Likewise, if we're talking about computational ability, we already have things that are faster than the human brain in certain areas (say, numerical computation). When we talk about AGI, we're talking about human-like intelligence (at least to a certain degree).
So we already have the wheel, but now we want to develop something that does what the leg does. Now, maybe that doesn't have to be an actual leg, but that's usually what people usually end up with when they try to find something that will have a similar functionality as a leg.
There is nothing intelligent about artificial intelligence. You might as well call it artificial stupidity, but that doesn't make you sound sophisticated.
Memorizing the results of a pre-defined concept faster due to a lower barrier of entry does not make you smarter or more intelligent. You can process all of the traffic signals in the world in a blink of an eye only to get stomped by a dancing traffic director.
You can't have AI without a human, and you can't have AI with a human.
Silly to reduce all rotary motion to wheels.
Less complex to roll as a ball -- the benefit of rotary motion without facilitating the additional complexity of adding a steering mechanism.
> But wheels have turned out to be a hell of a lot faster than fins or legs.
Nature doesn't somehow cares about hyper-optimizing for a single characteristic like 'speed', rather the characteristics arise from individual (species) successes between genetics lines.
https://en.wikipedia.org/wiki/Rotating_locomotion_in_living_...
I know that the "feelings" part is most of the times neglected in these types of presentations (you cannot put them into any abstract representation, like you can draw a wheel using geometry), but it's what most of us, people who are not in this field, expect from AIG. Great Science Fiction works (novels, movies) have talked about this subject (AI and human feelings) long enough, but it somehow never makes it into academic presentations.
Just think for a second of the self-driving cars problem. Someone on HN (or maybe reddit) explained a couple of days ago that even if we 'd manage to devise such a system that would be considerably better compared to humans we'd still not be ok with these machines killing us on the roads. Now, imagine these machines possessing the feeling of "guilt", or of "compassion", I'm pretty sure that the percentage of humans that would become ok with some AIG driving our cars for us will rise dramatically (I know I would stand on the AIG's side). We don't mind the fact that machines kill us, accidents happen, we mind that machines kill us and they don't "realize" what they've done and they don't support the consequences.
Re-reading my comment I feel like it sounds a little pop-sciency, didn't mean it to go into that direction, it's just my interpretation on how I think things are. I for myself would love, really love, if we'd somehow "make" an AIG capable of telling genuine, funny jokes.
That said, biological evolution tends to get stuck on local maxima very easily (see convergent evolution of eyeballs), and wheels are kind of hard to evolve because of the difficulty of making large bearings biologically.
Not to mention wheels kind of suck unless you're on pavement (look at tanks, etc)
I'm not sure which advances you are talking about, but the modern successes of Deep Learning are primarily due to backpropagation and convolution. Neither of the two is considered to be biologically plausible.
Some people are actively trying to come up with alternatives to backpropagation that would be biologically plausible though.
See for example: Bengio et al., "Towards Biologically Plausible Deep Learning" https://arxiv.org/abs/1502.04156
That doesn't contradict what I wrote.
ConvNets require the synchronization of weights between neurons, which is not considered to be biologically plausible. Some aspects of the architecture (the receptive fields, in this case) may well be plausible, with the complete architecture still implausible.
The major advances are the implementation details, and I think many would consider the network topology research constrained not by our imagination but by our implementations.
I've been following AI for years but this is the first time I found such a concept. So basically a cell is like a small neural net with as many neurons as genes, each gene having (chemical) input and outputs signals. That means a cell's DNA is much more dynamic than I previously imagined. It's a self-replicating m.f. computer, that's what it is. We can only dream of similar accomplishments.
Previously I was aware of the huge workload carried out by DNA - for every protein in the body, DNA replicates the blueprints - an amazing amount of fine detailed work. It's not just sitting there waiting for reproduction. Seeing it not just as a factory, but also as a neural net is another level.
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.
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.
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.
It sounds to me like you're referring to cognition rather than consciousness.
The fact that we don't know how higher-order intelligence works in general is exactly why it will be emergent rather than designed.
You shouldn't worry so much about consensus, but instead use your senses and your brain to make up your own mind. That is the approach that gave us the enlightenment.
You know what French Enlightenment thinkers were also against? Making headstrong claims (as an authority) without empirical evidence on your side :)
Human intelligence definitely did not arise "in one day" and any anthropologist positing such a theory hasn't looked at the last 30 years of research in statistical genetics. Intelligence is an incredibly complex trait resulting from the interaction of many, many genes.
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...
https://improvingautonomy.wordpress.com/2017/08/22/a-possibl...
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.
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.
A realistic set of stimuli, a LOT of artificial neurons, and a lot of time will probably get there, eventually.
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.
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.
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.
Engineering ability however appears to be growing exponentially. It took millions of years for the brain to reach the place it is now but it only took a few thousand years to get to the moon and create the Internet. I wouldn't count out the power of engineering because biology is complex. Particularly given ever more powerful tools of computation and communication that have only recently (historically speaking, not in lifespans of javascript frameworks) come on line.
- The first part of a sigmoidal curve looks exponential.
- Evolution is massively parallel.
It is also limited by the efficiency that it must attain in order to operate under the energy conditions of our environment.
The energy efficiency of biological systems might be a hint that we should more-directly employ them in the "artificial" minds we build. You're right that the human brain is limited because of its context, but we'll only get superhuman hard artificial intelligence when we're able to build/grow a big one in a vat.
> A neural net like ResNet-150 is capable of doing essentially what 1/3 of the brain is doing (vision).
Is it? There is probably a lot more going on in that 1/3 of our brain than mapping images to words.
Oh come on.
I'd say ResNet-150 is far from solving human vision, in the edge cases.
Can it distinguish between a baseball texture and an actual baseball?
How about between an actual cat versus a toy cat? Sometimes you want the two to be in the same category, other times you don't.
The human can put the same two images in the same or in different categories, based on the higher task at hand. Neural networks are far from that, because they don't (yet) have a model of the world.
>[OpenWorm] project coordinator Stephen Larson estimates it as "only 20 to 30 percent of the way towards where we need to get. (wikipedia)
That said, figuring what we can about how the human brain works and trying to make a computer equivalent seems quite promising. For example modern artificial vision seems to process data in a way similar to human vision even if it doesn't simulate human neurons.
The goal of current HTMs is to capture as much of the functions of neurons and the network (as they are currently understood) within the capability of typical computers and in areas that can be made readily useful such as image processing. For example, feedback from higher levels and motor control are not attempted because it is not yet understood how to incorporate them and binary instead of variable synapses are used because they were determined to be sufficient in the current HTM capabilities.
It doesn't seem like they are even close to a simulation that could accurately model something like C. elegans.
[1] https://en.wikipedia.org/wiki/Hierarchical_temporal_memory
I wonder if nebulous fears about AI soon will be added to the ranks of famous justifications for horrendously overbearing laws, like stopping terrorism, the war on drugs, or thinking of the children.
While theoretically possible, I think going with that approach would be admitting we don't understand the origin of general intelligence, so let's just copy the wetvare.
First, the human brain is built on a computational substrate that is completely and utterly unlike silicon. It is extremely inefficient to effect computation by simulating a computing model on silicon that is almost pathological for silicon to express. The abstract computational model of the human brain necessarily has an equivalent direct expression in computing hardware we actually have thanks to Turing equivalence. It just may look nothing like a human brain once you build it with algorithms optimized for silicon.
Second, and related, the abstract mathematical nature of intelligence is well-understood and the human brain must be an expression of that. However, there is currently a huge gap between that abstract theory and reduction to practice i.e. our computer science for building intelligence from first principles is severely lacking. There are many things that are easy to express in mathematics that go for decades before some reduces it to practical computable algorithms and data structures. Given the fundamental limitations and inherent complexity of simulating (poorly) the human brain, many people feel that applying a similar amount of effort to this direct approach is much more likely to produce a viable result.
And in any case, a simulated human brain would be completely eclipsed eventually by a more pure design by virtue of being several orders of magnitude more efficient computationally. Simulating a human brain is not a particularly productive detour on the long-term path given this.
Analog CMOS circuits can approach the energy efficiency of real neurons.
Do you have any other resources about serious attempts at using subthreshold FETs to simulate neurons?
Analog neuromorphic approaches to not attempt to simulate neurons, they emulate them in silicon. Partly because of a belief that research in this area is required to produce ultra low power computational devices and partly to explore the real time dynamics of spiking neural networks.
There are very few research groups working on this, but you can look up Kwabena Boahen's group at Stanford. They do large scale real time emulation and are currently building a sub threshold neuron accelerator for the neuro engineering framework from Chris Eliasmith at Waterloo which is famously used to create SPAUN. There is also the Karlheinz Meier group at Heidelberg university which does wafer-scale networks of neurons in accelerated time. Giacomo Indiveri at ETH Zurich has silicon neurons with on-chip learning circuits that the others are missing.
That's nice, but whenever I researched, I only got vague explanations involving recursion and unicorns.
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.464...
A stacked version of that in a 3D mesh could probably handle quite a bit of intelligence.
My take is that it's a lot of R&D work, and it's not clear which approach will get to a human level first nor which is safer. The emulation approach seems lower variance to me than an intelligence-from-scratch approach, even though there's tons of variance there too. We're just looking at a very uncertain future.
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.
This article seems to be arguing "no." The biggest thing missing I think is an understanding of how human memory works.
Because the responses are human-written, they can seem locally coherent, but since the model generally isn't tracking any state, the conversation never really goes anywhere. Also, if you try to talk to one of these response rankers about something it doesn't have any canned responses for, obviously it doesn't work.
Natural language generation on the other hand, where the model writes each response character-by-character or word-by-word, has the potential to do something much more interesting, but the state of the art there isn't quite at the level of "fairly coherent" AFAIK.
Hopefully we'll figure out a more useable level of abstraction that still allows for intelligent behavior.
It seems like the argument for AGI appearing reasonably soon isn't ultimately that long. It is just the ingredients seem to be assembling and there's no fundamental limit preventing it. We're getting closer to complexity of a brain, our machines are general purpose, we're getting more of the specific capacities of the brain. How we can get there seems a fundamental unknown but it's hard to see there being a fundamental barrier.
The problem is any more detailed argument has to involve one or another prescriptions concerning exactly how to create AGI. And such arguments are going to be much more controversial.
We have AI algorithms today like the DeepMind Atari playing stuff that are general and so you could call AGI but that are way below human intelligence. Basically things advance every year in hardware and software and so will probably get there one day.
And the brain really isn't that great. Signals travel through the brain about a million times slower than electrons through silicon. Neurons are large macroscale structures. Compare to our transistor technology that is approaching the limits of how small you can build things with atoms. These are just the hardware specs, I don't see any reason to believe the software is much better.
Correction. Electrons' drift velocity in conductors is quite slow and it doesn't affect signal transmission speed.
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.
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.
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.
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.
AGI will require a revolutionary breakthrough, most likely from a maverick, probably a lone wolf rebel, who is used to thinking outside the box.
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?
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.
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.
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?
I'm aiming for General Intelligence Augmentation, rather than AGI, but I it could be adapted.
I think the trick we are missing is always developing AI systems that need external programmers/maintainers. If we get away from that mindset, I think we will be more successful, even if it is not my particular vision.
What are the symbols (burst pipe, µ) representing on slide 55?
And why are the exclamation marks there on the next one?
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.
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."
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.
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...
See this article 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.
You can look for the topic of Artifical Life, Emergent Computation, Artifical Evolution. See the Santa Fe Institute conferences on ALife. There were some promising initial results but the field never managed to take off.
Good luck.
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.
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
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.
What would it type into its terminal?
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.
[X] Open Source Tools
[ ] Massive Data Sets
[ ] $Millions in Computing Resources
And the computing costs could be boot strapped by the ai. Put it to work earning its keep and expanding resources.
It's called Google, and is already filthy rich and has a ton of computation and privileged data at its disposal.
Part of the reasoning for the Kurzweil turing test by 2029 type of stuff is that once human level hardware falls in price to hobbyist levels, loads of people will hack away and someone will figure it out.
Look up interdiction of telomere lengthening. Different groups are looking into how to sabotage telomerase and ALT mechanisms. If both can be achieved, then any cancer can be shut down. Those are the only ways to lengthen telomeres, and cancers cannot live without them.
Finding an ALT drug candidate is as simple as running assays on the drug libraries; the assay hasn't existed for long, which is why this hasn't been done yet in any major way. The SENS Research Foundation raised $70k last year to run a preliminary scan of a few thousand compounds. That's about what it costs these days.
So not quite garage science yet, but getting close.
The biggest reason I suspect it will be solved by one person is it's "just" a math problem. Most of the hardest problems in mathematics were solved by a single person. Building off of the works of others of course, but rarely in some big team or corporate project. 9 women can't have a baby in 1 month. The research process of big corporations or even academia isn't really amenable to actually solving hard problems.