Why Watson and Siri Are Not Real AI

by slacka • 12 years ago

45 80 12 years ago

lsnape• 12 years ago

One hypothesis is real ai will be achieved by simulating biological brains, bottom up AI, instead of designing intelligent algorithms top down

catshirt3• 12 years ago

is there any more viable hypothesis?

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.

arethuza6• 12 years ago

Personally, having worked on AI research in the late 80s and early 90s I don't observe a lot of progress in replicating general intelligence. Of course, there has been a huge amount of progress in special purpose systems leveraging the huge amount of computing power we have now - but the fundamentals of general intelligence look as puzzling now as they did 20 years ago. I don't believe that we really have the right algorithms and it's simply a matter of scaling up the underlying hardware.

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).

chegra• 12 years ago

That is like trying to create a bird instead of understanding the principle of flight. By understanding the principle of intelligence we can do better than our current level intelligence. One contender for understanding this principle is AIXI: http://www.youtube.com/watch?feature=player_detailpage&v=V6u....

mehwoot2• 12 years ago

Well if we spent 60 years failing to fly we probably would be looking at recreating what a bird does pretty closely.

sgk284• 12 years ago

We may not need to replicate the mind.

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).

bnegreve3• 12 years ago

Well, artificial intelligence is a slippery term. It could refer to just getting machines to do things that seem intelligent on the surface.

But is "natural intelligence" any different?

simonh4• 12 years ago

Yes, because it is general purpose intelligence. Google Translate can't even play the simplest of games, let alone Chess or Jeopardy. Watson can't even begin to tackle translation or Chess. Deep Blue has absolutely no capability towards translation or playing Jeopardy. They are fixed-function, single-task algorithms optimized towards a single very tightly defined problem domain. That sort of research isn't going to get us to general purpose AI.

stdbrouw• 12 years ago

Isn't the brain itself a good mix of general-purpose and special-purpose intelligence?

ivanca• 12 years ago

Cats cannot play the simplest of games; does that mean that they do not have general purpose intelligence?

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).

bnegreve3• 12 years ago

Yes, because it is general purpose intelligence.

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?

chongli• 12 years ago

You could run all of these programs on one computer. Comparing a person to a piece of software is apples to oranges. Build a computer big enough to run all of the software in the world (and all of the data, too) and compare those capabilities to a human.

jkldotio• 12 years ago

It's worth pointing out Google just bought the startup with the guys who wrote the "Playing Atari with Deep Reinforcement Learning" paper which is supposedly just an algorithm working on the raw pixels and then beating all other programs and half of the human experts.[1]

[1] http://arxiv.org/pdf/1312.5602v1.pdf

ximeng• 12 years ago

A computer recently designed a published board game called Yavalath, by deciding whether randomly generated games are interesting or not. That algorithm wouldn't be difficult to integrate into Google if they decided to do it. As you add more and more capabilities to Google at a more abstract level (design something "fun" like here), it's closer and closer to general purpose AI. It's not just that, see also http://www.gamesbyangelina.org/ .

ibelong2u• 12 years ago

In my opinion, it's not. Natural intelligence is another way of saying that dynamics and interactions of millions of neurons in our brain is too complicated for us to understand. Our brain is just a complex machine, and the complexity at the level of its individual components is based on very fundamental laws of physics and chemistry.

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.

simonh4• 12 years ago

This is why I am so skeptical of the claims of people like Ray Kurzweil. I honestly don't think we yet have even an outline of an idea of how a true artificial mind might be architected, let alone implemented.

If we can't even design something, even in outline, how can we possibly predict how long it might take to build it?

logicallee2• 12 years ago

Skeptical or not, we've already sequenced a complete blueprint for it that is running on billions of copies of firmware in the wild. True, we can't even emulate the firmware ourselves.

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.

DanBC• 12 years ago

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.

twobits2• 12 years ago

I wouldn't place much money on a true artificial mind still not existing in 40 years.

I would say 20.

tiatia• 12 years ago

Two AI Pioneers. Two Bizarre Suicides. What Really Happened? http://www.wired.com/techbiz/people/magazine/16-02/ff_aimyst...

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.

icebraining• 12 years ago

That idea has been kicking in the AI community for a long time. One of the biggest projects for encoding every day knowledge in a machine readable way is Cyc: http://en.wikipedia.org/wiki/Cyc

richardwhiuk• 12 years ago

AI is repeatedly redefined to exclude things that we can do, that were previously considered AI. Examples include image and character recognition, speech recognition, machine translation.

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.

panacea• 12 years ago

And the domain of things we can empirically do better in the discrete task realm keeps diminishing ie. 'design', unsolvable capchas.

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.

cubancigar11• 12 years ago

This is where I find X-Men to be an awesome concept. What we are chasing in AI right now is Magneto (the might), but every time we reach there, we compare ourselves with Prof. X (the intellect).

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.

glomph• 12 years ago

I think Douglas Hofstadter has kept his idea of what AI is more constant than most folks.

Zenst3• 12 years ago

Me personaly, I define AI as a system that can be fed non-biased input and learn from that input to derive conclusions that we as humans can relate too.

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.

kamaal3• 12 years ago

>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.

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.

StavrosK2• 12 years ago

You're describing supervised vs unsupervised learning. We have both systems, one isn't really defined more as AI than the other.

thewarrior• 12 years ago

This is called Deep Learning. It can automatically form clusters from unlabelled training data and that is why there is a lot of excitement around it right now.

Houshalter3• 12 years ago

That's called unsupervised learning and it already exists. Supervised learning is more practical though. I'm not sure why you distinguish them because they are both AI and sometimes even the same algorithms.

axilmar2• 12 years ago

For that to happen the AI needs to be pointed to what is a cat.

Children know cats by mimicking adults who point out the objects to them.

kamaal3• 12 years ago

I thought AI was about one algorithm or something like that could do general thinking like a human. Its like you recognize images, characters, speech and then? What will you do with that?

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?

arethuza6• 12 years ago

"I thought AI was about one algorithm or something like that could do general thinking like a human."

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).

_pmf_• 12 years ago

machine translation

One look at the Play store and its machine translated descriptions shows how far we are from anything usable.

nodata• 12 years ago

I think it shows how far we've come. Remember altavista's machine translation from babelfish?

pygy_2• 12 years ago

I would say that these algorithms display intelligence to the extent of their input and output abilities.

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.

arethuza6• 12 years ago

"without physical robots who need to display survival instinct and skills in order to keep on working."

That's "situated cognition" - the idea that knowing and doing are intrinsically linked:

http://en.wikipedia.org/wiki/Situated_cognition

Houshalter3• 12 years ago

There is no reason a robot body is necessary for intelligence. It might even hinder it as the real world is much more messy and complicated than cleaned up and relevant data fed to machine learning algorithms.

mathattack2• 12 years ago

You are very right. That's why I divide the world between Cognitive Psychology, which is "How intelligence really works" and Artificial Intelligence which is "Replicating and improving intelligence with machines." The former is a very long drawn out field. (The research projects that the professor is talking about have been going on for 20+ years.) The latter is difficult. So far they can only tackle "Making useful things that don't represent how humans do it" or "Modeling how humans do things in a way too simple to be useful".

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.

mathattack2• 12 years ago

I might say though, that 30 to 40 years ago, when the field was really young, artificial intelligence wasn't about making money, and the people in the field weren't driven by developing products. It was about understanding how the mind works and trying to get computers to do things that the mind can do. The mind is very fluid and flexible, so how do you get a rigid machine to do very fluid things? That's a beautiful paradox and very exciting, philosophically.

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.

aaron695• 12 years ago

Normal FUD.

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.

qbrass• 12 years ago

If you ask Ken Jennings what his favorite song is, he'll probably tell you, or he'll say that he doesn't have a favorite.

If you ask Watson the same question, it will probably give you a reference to someone being asked that question in a magazine.

TheCoelacanth• 12 years ago

This is completely moving the goalposts. Of course Watson and Siri are AI. They aren't strong AI, but they are definitely AI.

axilmar2• 12 years ago

The way intelligence works, in my opinion, is this:

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.

KaoruAoiShiho• 12 years ago

Surprised at the traction that this interview is gaining. This isn't all that different from the amatuer blog post from a few days ago saying the same thing where everyone educated the poster on the subject of modern AI development.

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.

Why Watson and Siri Are Not Real AI

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