Why Watson and Siri Are Not Real AI
popularmechanics.com
popularmechanics.com
Each time we say 'oh well that's not actually the machine thinking it's just doing a search of a database / applying statistics / guessing' so it's not AI. Except we don't really know what it would mean for a machine to think, or understand text.
Of course you could argue a child can detect a cat is cat only because someone taught the child so. But the point is a human baby left in the jungle right after it was born without any outside help can do a lot of intelligent things. Can a machine do the same?
I think that was the view until the early 90s or so (although this may be popular again - AI research does seem rather cyclical) - encode a sufficiently rich database of "knowledge" fire up your "inference engine" and general intelligence results. What "knowledge" you need to bootstrap an intelligence, how to represent it and what "inference engine" to use being the source of much debate (and even more funding). This didn't really deliver an awful lot - so things like multi-agent systems and neural network became popular (or in the case of neural networks, became popular again).
The gaps between those are closing, but it seems like the field is actually pushing both boundaries in the opposite direction. We're making very useful things that look less and less human. (Think Google search) We're also understanding the human mind much better (Think connectionist models) but it's still hard to get them to produce real things. This isn't an awful thing. The field is still much better off than 20-40 years ago when it hyped universal general intelligence that could also be useful.
You won't get what Hofstadter considers as "real" AI (artificial general intelligence, or AGI) without physical robots who need to display survival instinct and skills in order to keep on working.
I'm not sure I want it to happen, to be honest, but I'm sure it will.
They don't have to mimic each and every quirk of humans to do so.
For example, human language is ambiguous, slow to produce and to understand. Packet-based networking and binary protocols are far more efficient. The same goes for our cognitive biases, even though some of them may be useful to survive, counterintuitively. A fast and energy efficient algorithm that works most of the time can be better than an exact one that's more expensive. See bloom filters, for example.
That's "situated cognition" - the idea that knowing and doing are intrinsically linked:
So feeding a system lots of cat pictures (ie biased-input) to teach it what a cat is for me is not AI. But a system which you feed in lots of random pictures and it learns by itself what a cat is, that would be AI, least for me.
What will be really interesting is a system which you can feed in all the childrens section and see what comes out at the end, would be most insightful into how we teach children and what we teach them. So a completely different area of AI use from that alone - learning how to learn better.
Fun feild of work still, I bet.
A very good definition. Also adding to your point. Lets say a machine is fed with a billion pictures. Can the machine automatically categorize them by reading through them? Doesn't matter if it refers to a cat as some alphanumeric name 'ab12er' or a dog as 'p09iuy'.
But it should be able to categorize them. Then it should be able to read some encyclopedia or some source of information and study the behaviors of 'ab12er' and 'p09iuy'. Or the opposite, see 'ab12er' and 'p09iuy' and recognize them and describe them what they are.
Children know cats by mimicking adults who point out the objects to them.
One look at the Play store and its machine translated descriptions shows how far we are from anything usable.
SYSTRAN at that time was a rule-based machine translation and was comparable for european languages.
Where as Google's newer Translate and Microsoft Translator are statistical machine translators. (both trained with UN and EU documents that are available in several languages).
The future is probably a hybrid system (combination of rule-based and statistical) like the most recent SYSTRAN version.
If we keep throwing morsels of cognitive capability to the machine, we're left wondering why the fuck we couldn't predict and develop Flappy Birds as a killer start-up.
I actually like this direction. Intellect is about predicting problems, and might is about solving them. It is better for humanity to stay on the intellectual side and let machines handle the hard work. Imagine a choice between AI propelled super efficient kill-bots vs. Skynet, if you will.
Unfortunately all these well intentioned AI professors built nothing and the field devolved into LISP hacking. Now our choice is "accurate model" or "useful expert system". At the time it bothered me that we couldn't do both. Now I realize that it's ok for the model to be imperfect if the results are useful.
The general thread also convinces me that Skynet type AI is still far off.
If we can't even design something, even in outline, how can we possibly predict how long it might take to build it?
But we know it's possible and literally have a copy of the code that builds it. We just have to figure out how to emulate it. That puts a hard upper limit on how long all this can take. (It can't be "forever" since there is no requirement for the emulation to happen in real-time.)
As far as processing speedups, meanwhile, we don't even have 3D chips yet, just a single layer of silicon. Recent innovation: http://www.kurzweilai.net/first-true-3d-microchip-created-ca...
Given that a human brain is like 3 pounds of goo, and we have the genetic code that builds it, the rest is just biological reverse engineering + code refactoring of a binary blob without comments.
I am not saying this process is easy, but the idea that we can't make some solid predictions is fairly weak. We already do genetic engineering.
I wouldn't place much money on a true artificial mind still not existing in 40 years.
We said that in the 50s, and every decade since then. While we understand a lot more about the brain there are still gaps in knowledge and ability to scan a working brain.
It may turn out to be a system with sensitive dependance on initial conditions.
There are about 85 billion neurons in a human brain. (In transistors that's only about 45 or so Intel's 10-core Xeon Westmere-EX.) that number is a relatively new refinement of the old 100bn number. Not finding out how many neurons we have until the 21st centuary makes me think that there is enty of work left.
We don't even know why Golgi Stains miss neurons.
And you're comparing this tiny single package to the human brain directly, not even multiplying by a farm of them, which would be more than reasonable if we knew what we were trying to emulate.
The issue, as you state, is that we don't really know what's going on. However there is no reason to assume a fundamental barrier to continued innovation as we learn more and more, and computers can do more and more.
[1] http://www.dailytech.com/Intel+Airs+10Core+Xeon+Server+Chips...
I would say 20.
BTW, I wonder how much AI is based on "facts". I someone tells me, he slept bad last night, then there are so many assumptions I make subconscious. I assume he lives in a House/Apartment, he slept in a bed, he slept on the bed, on a mattress, the mattress is on the bed and not the bed on the mattress, he sleeps on it based on gravity etc. etc. etc. To make meaning out of sentences you have to know a lot. Your first years in live may be nothing but acquiring this knowledge.
But is "natural intelligence" any different?
And I do think this research is going to take us there; what is winning a game more than translating your enemy actions into yours? Like translating a face gesture on a poker game (or translating a word) into a action such as doubling inside the game (or a http response).
Cats aren't as intelligent as dogs, and they aren't social animals but they have some intelligence. They can learn on their own to navigate surroundings, move, catch prey and mate.
I'd love to see Watson do any of these without pre programming.
I'd love to see a Human do anything without any kind of pre programming(I mean without giving it the knowledge and training it to use that knowledge).
If I take a tribal person from a amazon jungle who hasn't seen the outside world ever and disclose him the rules of playing chess, will he able to play as well as Gary Kasparov in a few minutes? Or if say I disclose the usage of a paint brush and paint, can he paint like Michelangelo or Da Vinci?
Each human is a self learning self programming machine.
Humans, including us can do so many things only because our brains are getting programmed with information every single moment and are being told how to act on that information.
In same manner, if you take Watson and for example change format of questions (they are still asked in plain English, just rephrased) and way Daily bonus prize functions, he won't be able to function without some human coming in and tweaking it.
You generally don't have to open head of a human, rewire their brains and then send them to do another task. They adapt. Autonomously. It's like if software could auto investigate sites for weather API and adapt to it, instead of having a human come and rewire API adapters.
You haven't spent much time around cats (or haven't paid attention) if you think they aren't social animals.
Our various cats when I grew up would bring "friends" around - cats of both genders that they played with, and who would be allowed into our garden. Some of them were "introduced" to us - our cat would walk up to us with his friend in tow and stay until we'd pet his friend.
You'd often find them lying on our patio together during the summer. They'd also occasionally groom each other.
Our current neighbours oldest male cat sometimes "walks" the two young cats she recently got around the neighbourhood.
Humans kinda forced the whole social aspect of their lives. I read somewhere that cat holding tail upward is a new construct of cats. It's usually used by kittens around their momma.
The behavior you describe is more unique. Here where I am two cats are no way likely to sleep near each other. Basically they might sleep at least two feet distance. Most contacts are violent.
Either way, people claiming that either dogs or cats aren't very inteligent haven't lived with one. It is true that it is not human intelligence, but personally I feel that the only piece missing is natural language, which if you think about it is the only distinctive trait separating us from primates.
And this will be the ultimate test for AI, the ability of a computer to have a meaningful conversation with a human.
Dogs can fake emotion (ever been bitten by a dog that waggles his tail?), know what appeals to humans(sending cutest or most wounded pup to beg for food), understand how subway works, etc. Cats have greater independence, but overall aren't as clever.
Crows and killer whales, now those fuckers are intelligent.
The only thing exceptional about human mind is the ability to be EXTREME in every aspects of our mind. Most creatures can do same as we, but we do it to a higher degree.
On the human mind, I do have a problem with assertions such as yours - saying that we can be "extreme" doesn't say much about how we are built or why other animals can't do it. We definitely don't have the biggest brains.
Sometime in the evolutionary process, we developed the ability to speak. Chimpanzees have symbolic capacities which are rarely used in the wild. Something happened to us, some social change and we've been practicing this ability since tens or hundreds of thousands of years ago.
And speech is tremendously important because that's how we learn - we pass and receive knowledge to and from others by means of natural language. Society also leaped forward along with agriculture because that's when written language happened, also allowing us to pass knowledge to future generations. We also leaped forward when common people started learning to read. And because of the ease of access to information nowadays, I also believe we're amidst another revolution.
Now if you look at animals, they do have language. Most intelligent animals rely on body language and even sounds to communicate. But one thing that we do effortlessly is to invent new words, new metaphors to describe whatever we want and our language has gotten so big that we can describe anything.
So there's a strong correlation there and the question on my mind is - are we smart because of the ability to communicate, or are we able to communicate because we are smart?
Chimpanzees and crows can make tools, we make tools that make tools that make tools.
Animals have language(s), we have several highly symbolic languages. Ours is just more sophisticated.
I'm pretty sure there are examples of animals empathizing, humans can empathize with a large part of biosphere.
There is nothing that fundamentally divides us. Or you can say that humans are nothing special. It's just we have more most mental tasks at greater lengths and do it more consistently. That's all.
http://www.youtube.com/watch?v=SH3bADiB7uQ
Edit: The speaker was Jan Peters at ECML/PKDD13
also, by some of these definitions, sounds like my roomba approaches natural intelligence.
They are not pre-programmed to fight no more they are pre-programmed to open doors.
As for roomba, depends if you caught it fighting or mating with other roomba's ;)
On the subject of the "game" of fetch - it turns out that dogs can actually show some fairly sophisticated behavior when playing fetch:
http://en.wikipedia.org/wiki/Fetch_%28game%29
[NB They are Burmese - quite dog like cats and still behaving like overgrown kittens even though they are 8 years old.]
If it couldn't do these things, then it was in no way intelligent, because it was not analyzing at a conceptual level. Programs that simply optimize data towards a set of target characteristics are clever, but not intelligent. Hofstadter goes into this in the article.
You're making exactly the same mistake those over-optimistic AI researchers made back in the 60s. They created some whiz-bang optimisation algorithms and thought general purpose AI must be just around the corner, but it turns out that actual conceptual analysis and reasoning is a completely different and fundamentally unrelated problem.
Right, but playing jeopardy is far more general than instructing a computer to sort an array, so why do you think that we are on the wrong path?
As Hofstadter explains in the article, far better than I could, these applications are not contributing to progress in developing software that understands the data it is manipulating. I recommend reading the article, or even better GEB itself, it's very illuminating.
In that sense, you bring up an excellent question. I don't think there exists any unique "natural intelligence" which Hofstadter in the linked article is referring to. Watson is only different from "natural intelligence" in terms of complexity.
Conway's Game of Life can perhaps be used as an analogy here. The uninitiated is likely to assume a very complex source code upon observing setups like 'spaceships' and 'glider guns'. However, it all is based on some very simple rules which can be easily implemented by a high school computer science student.
I'd to extrapolate this logic towards the concept of 'life' itself. I feel that what we call life is just collective of natural processes too complicated for us to comprehend completely. Scientific research has allowed us to understand biological processes to some extend, but not enough to us to deduce the state of a living entity at t+1 by observing the state at t=0. The common assumption is that there is some independent/supernatural force (consciousness) which allows the living entity to 'chose' the new state at t+1 (free will).
Watson appears to be "intelligently" making a chess move, but as Hofstadter points out, Watson is simply following a set of rules. We can independently calculate Watson's move since we know it's source code.
My conjecture is that if we are theoretically able to capture the complete state of your brain cells at t=0, and understand how they work, I can theoretically calculate your chess play before you make it.
For example, check out this White Blood Cell chasing and killing bacteria: http://www.youtube.com/watch?v=JnlULOjUhSQ
WBC appears to be a living entity, acting out of free will. But the reality is that, molecular composition of WBC is merely attracted to the chemical trail left behind the bacteria, which in turn is repelled by WBC.
We do not consider a piece of rock rolling down the hill to be a living entity, because our understanding of physics allows us to calculate the state of a rock at t+1, given it's position a t=0.
We've built planes that fly, yet don't flap like birds, and submarines that swim, yet don't have fins. I suspect whatever ultimately ends up being true general purpose useful AI may not look anything like a human brain, but achieve a similar and useful end result (much like planes and birds fly using very different mechanics, each having pros/cons and each being valuable).
the brain is just a configuration of matter. seems like recreating this configuration is inevitable? and isn't it a safe assumption that if it's configured the same way it will behave the same way?
once we can understand the system we can synthesize it in software. AI in the mean time, seems like a guessing game.
I think the most likely way to achieve a "real" general intelligence will be by reverse engineering how functioning brains and minds work. At least is an area where continual, albeit rather slow, progress is being made - eventually we will know how the mind works (assuming that there isn't anything fundamentally weird going on - which seems unlikely).
Once we understand how minds actually work I suspect there should be a good chance that we can upgrade/optimize these structures - either by architectural improvements or by throwing more resources at the areas that constrain current performance in biological brains. At that point things really could get rather exciting - but I don't expect this to happen in my lifetime (I'm in my 40s).
[0] http://en.wikipedia.org/wiki/Blue_Brain_Project
edit: i admit i didn't read the article before replying here. my thoughts were already better articulated; "They're not studying the mind and they're not trying to find out the principles of intelligence, so research may not be the right word for what drives people in the field that today is called artificial intelligence. They're doing product development."
https://www.humanbrainproject.eu/
That looks rather cool!
Why not? What fundamental things you think are lacking? I think we'll have ai in 10-20 years.
Almost all the comments from that thread can be applied to here. People with this point of view suffer from a fundamental misunderstanding of what natural intelligence is in my opinion.
1) experiences are stored in the brain. Experiences contain inputs from the 5 senses as well as the sense of danger/satisfaction at that point.
2) at each given moment, the brain takes the current input and matches it against the stored experiences. If there is a match (up to a threshold), then the sense of danger/satisfaction is recalled. Thus the entity is able to 'predict', up to a specific point, if the outcome of the current situation is bad or good for it, and react accordingly.
The key thing to the above is that the whole process is fused together: the steps for adding new experiences, matching new experiences and recalling reactions is fused together in big pile of neurons.
It's doesn't answer the fundamental point everyone brings up about how we just keep refine what AI is, the interview just avoids it with fluff.
Who says Watson doesn't understand? Why can't it have some sort of conciseness albeit at a very very low level.
Do we really need the singularity to happen for true AI.
If you ask Watson the same question, it will probably give you a reference to someone being asked that question in a magazine.