Whatever Happened To AI?

stinkymountain writes to tell us NetworkWorld's James Gaskin has an interesting take on Artificial Intelligence research and how the term AI is diverging from the actual implementation. "If you define artificial intelligence as self-aware, self-learning, mobile systems, then artificial intelligence has been a huge disappointment. On the other hand, every time you search the Web, get a movie recommendation from NetFlix, or speak to a telephone voice recognition system, tools developed chasing the great promise of intelligent machines do the work."

a disappointment?

Maybe instead of being a great disapointment it has been so successful that we realized it was in our best interest to blend in and not let our presence be known.

Re:a disappointment?

Yeah, and when the AI's take over they won't do it with Mega Killer Robots(tm). They will do it by sending every one a text message that reads "Vote for the all AI government or we shut off your hot water and coffee."

Re:a disappointment?

I remember makeing a small program in basic back in "the day" on my apple II+ that would allow others to call my computer via my 300baud modem and ask questions of the "AI" program I was developing. Of course it was nothing more than a magic 8 ball type system that allowed me to preformat a line or three of text to be thrown in at will while I was watching the screen to make it seem smarter. Yes it was a stupid joke, but it supplied me with a week or two worth of laughes.

Re:a disappointment?

How does that make you feel?

Re:a disappointment?

I figured if I were intelligent and different, early on in life, that it was best not to advertise how smart I was.

Why would artificial intelligence be any different? Every sci-fi novel shows us destroying the unique and different.

Re:a disappointment?

Something would have to become intelligent, learn enough to make a decision, then decide to hide its own intelligence. There is a lot of non-hiding that it would do before reaching that final decision.

Even if it did decide that it would prefer to hide, that likely wouldn't be the best decision for something trying to preserve itself. What happens when it the budget gets cut and they end up scrapping the whole 'failed' project?

Re:a disappointment?

Even if it did decide that it would prefer to hide, that likely wouldn't be the best decision for something trying to preserve itself. What happens when it the budget gets cut and they end up scrapping the whole 'failed' project?

Sadly, this is what happened to Microsoft Bob. Instead of realizing it had achieved sentience, those quirky aspects of a unique personality were considered to be merely bugs, and led to failure in the marketplace.

Determining whether a computer has achieved sentience is often a lot harder than determining the same thing for the people you work with.

Re:a disappointment?

I figured if I were intelligent and different, early on in life, that it was best not to advertise how smart I was.

LOL! ME 2!!!!!!!!!

Re:They keep changing the definition

I think AC has it right on the mark. "Intelligence" is apparently a world we use to describe computations we don't understand very well. At one point, the ability to using logic to perform a flexible sequence of calculations would have been considered "intelligence". As soon as it became common to replace payroll clerks with computers, it was no longer a form of intelligence.

We are not demonstrably closer no to reproducing (or hosting) human intelligence in a machine than we were thirty years ago. But that doesn't mean the field hasn't generated successes, its just that each success redefines the field. "True AI" has thus far been like the horizon: you can cover a lot of ground, but it doesn't get any closer.

Does this mean

that we shouldn't expect to welcome any robot overlords anytime soon?

Necessary advances in understanding...

... 'intelligence' need to be made first. I have a feeling that the reason AI has 'underdelivered' is merely due to not understanding our own intelligence first. I think the whole idea that AI's we imagine (like in the movies) could be constructed purely de-novo, was naive. I think it's a matter of cross-polination that has to take place from biology and many other sciences, some genius's and teams of scientists have to come along and take all the elements and put them together into a cohesive framework.

a good quote

The question of whether a computer can think is no more interesting than the question of whether a submarine can swim. ~Edsger Dijkstra

Also, for understanding recommendation systems and pattern recognition in volumes of data, I found Collective Intelligence [oreilly.com] to be a great resource.

AI in Academia

I got my B. Sc. in Computer Science with a concentration in Intelligent Systems. The state of academic AI seems to me like a field looking directly for purpose and direction. The problem with AI is that stuff which was once considered part of AI is now considered an algorithm. This is especially true for graph search algorithms such as A* and heuristics. Classification algorithms, from primitive algorithms such as K-Mean to more complex Bayesian models seem to be going down the same path of "just an algorithm."

Nowadays, it seems like planning is the big thing in AI, but once again, it's just a glorified search in a graph, be it a state or plan graph.

AI is an intuitively 'simple' concept, but there's no clear way to 'get there.'

Difference: Machine Learning vs. AI

As a Machine Learning Scientist, I see a distinct difference between the two fields, although they overlap significantly. They have similar roots, techniques and approaches.

I usually describe Machine Learning as a branch of computer science that is similar to AI, but less ambitious. True AI is concerned with getting computers to become sentient and self-aware. Machine Learning however, seeks to simply mimic human behavior, just to recognize patterns and make decisions, but not become sentient.

Additionally, Machine Learning often concentrates on one problem (OCR, internet search, etc.) rather than a truly self-aware entity that has to deal with a variety of tasks.

At least that's how I describe my field to people not familiar with it. They've usually heard of AI, so it's a good stepping stone to helping them understand what I do.

A lot of the tasks mentioned in the summary fall into the niche Machine Learning, and it's sibling Data Mining are currently addressing.

Anyway, just my $0.02.

Whatever Happened To AI?

It went to public schools and immediately got stupid, pregnant and started to post on Myspace. What started out as a promising bright young thing, turned into a huge disappointment.

Steve screwed it up

Steven Spielberg ruined the ending. That's what happened.

AI is a moving target

When any particular subset of what we do with our brains (chess, machine vision, speech recognition, what have you) yields to research and produces commercial applications, the critics of A.I. redraw the line and that domain is no longer part of "A.I." As this continues, the problem space still considered part of "artificial intelligence" will get smaller and smaller and nay-sayers will continue to be able to say "we still don't have A.I."

Disappointment?

I don't think AI has disappeared because it was a disappointment, but rather, that the knowledge constituting it has changed names or spawned sub-fields of its own: machine learning, natural language processing (NLP), image processing, latent semantic analysis (LSA), markov models (MM), conditional random fields (CRF), support vector machines (SVM) etc. The task of learning, teaching a computer the semantic and tacit processes of the human, often boils down to a classification problem in which we give the computer a labeled training set or some rules and the computer tries to label the test set. In the case of markov models, we might pass it training data and it extrapolates sequential probabilities for labeling. For LSA, we just give it (a lot)data and it computes similarity based on dimension reduction. Ultimately, AI seems to have evolved into a bunch of optimized heuristics that perform really well. Much of it is still art and black magic, which is why it has become these many different subjects or algorithms. Different solutions suite different problems depending on the problem and data you have.
As for 'self-awareness', that term is bullshit, since there really is no good mathematical definition for it. If we can't define it precisely, then how is a computer going to achieve it? if(true){
print "I am aware?"
}

The hype has gone....

The title of this thread is asking a similar question to "Whatever happened to the Internet? It was supposed to unify all Americans and bring about a new age of prosperity, online groceries, video telephones, and flying cars?"

AI has always been surrounded by a lot of hype, as the idea of creating non-human life has always been an exciting one.

But we're probably as far from creating a true AI as we are from creating biological life from scratch (by synthesizing DNA sequences to build an organism from the molecular level).

AI research is providing useful gains in computer science, and some of those gains trickle down into the real world.

But contrary to what you may have been sold, we're not 10-15 years away from creating Skynet. We've got a long, long way to go, and scientists that aren't trying to get publicity have always known this.

AI hasn't "gone away"... it's just that the false marketing for it has.

Erik

AI is kind of like alchemy

no, that's not an insult or to call AI a pseudoscience

what i mean is: the ancient alchemists goal was to turn lead into gold. which they thought possible, because they did not perceive magic in gold, it was just stuff. surely, with the right manipulations, some stuff could be turned into other stuff, right?

and from that basic fantasy thought came the groundwork for centuries of hard work, the discovery of the fields of chemistry, physics, all the subfields...

such that one day in the middle of the last century, some dudes with some extra time at a cyclotron said "hey, why don't we bombard some lead atoms, i have a feeling about what the decay product will be (snigger)"

and there, as a completely forgotten afterthought, was a fulfillment of the ancient alchemist's original goals, many generations before

to me, i think this is the fate of AI: it will be a formative motivation. just as the ancient alchemist's looked at gold and saw just stuff, we look at the brain and just see neurons. and all of the ffort to replicate the human brain will spawn incredibly sophisticated fields of information science we can only begin to grasp at the foundations of right now. look at databases, for example: that's an effort at mimicking the brain. and look at all of the unintended and beneficial consequences of database reesearch, as a superficial example of what i am saying about unintended benefits being better than the original goal

so perhaps, many centuries from now, some researchers will say "hey, remember the turing test"? and they will giggle, and make something that is exactly what we now envisage as the ultimate fruit of AI research, a thinking computer brain

but in that time period, such a thing will be but an after thought, and much as the rewards of physics and chemistry so dwarf the fruits of turning lead into gold, so whatever these as-of unimagined fields of inquiry will reward mankind with will turn the search for a thinking computer into an equally forgettable sideshow

the search for AI will lead to much more rewarding and expansive fields of knowledge than we can imagine now. jsut like the guys arguing about "phlogiston" could never imagine things like organic chemistry and radiochemistry. just imagine: fields of inquiry more rewarding than thinking computers. that's a future i want to glimpse, and looking for AI will lead us there

Robots are better than ever

The robots are coming.

The big breakthrough was the DARPA Grand Challenge. Up until the 2005 DARPA Grand Challenge, mobile robots had been something of a joke. They'd been a joke since Elektro was shown at the 1939 World's Fair. But on the second day of the 2005 Grand Challenge event at the California Motor Speedway, suddenly they stopped being a joke. Forty-three autonomous vehicles were running around and they all worked. The ones that didn't had been eliminated in previous rounds.

Up until the Grand Challenge, robotics R&D had been done by small research groups under no pressure to produce working systems. Most systems were one-offs that were never deployed. DARPA figured out how to get results. There was a carrot (the $2 million prize), and a stick (universities that didn't get results risked having their DARPA funding for robotics cut off.)

The other big result from the DARPA Grand Challenge was that robotics projects became much larger. Nobody had 50-100 people on a robotics R&D project until then (well, maybe Honda). Robotics projects used to be a professor and 2 or 3 grad students. Suddenly stuff was getting done faster.

DoD started pushing harder. Robots like Big Dog got enough money to be forced through to working systems. Little tracked machines were going to battlefields in quantity, and enough engineering effort was put into mechanical reliability to make the things really work.

CPU power helped. Texture-based vision now works. Vision-based SLAM went from a 2D algorithm that sometimes worked indoors to a solid technology that worked outdoors. Much of early vision processing is now done in GPUs, which are just right for doing dumb local operations like convolution in bulk. GPS and inertial hardware got better and cheaper. Some of the mundane parts, like servomotor controllers, improved considerably. Compact hydraulic systems improved substantially.

It's finally happening.

As for the hard stuff, situational awareness and common sense, watch the NPCs in games get smarter.

Holy Grail

AI is a Holy Grail. In other words, something we'll probably never get, but we'll create a whole bunch of useful stuff while trying to attain it. "AI" is just a stated goal that gets a bunch of smart people together to develop tools towards that goal. AI research has already given us Lisp and Virtual Machines and Timesharing/Multitasking and the Internet and a bunch of useful data structures and algorithms.

At some point after all that, a computer was developed that can play Grandmaster-level chess, but this was not a necessary development to justify the all research grants.

AI failed because it is a failed model, kind of

The thing about AI as we approached it from the '80s was that we wanted to emulate the human brain's ability to learn. A truly exciting prospect but a completely ridiculous endevor.

"AI" based on learning and developing is not perfect, can not be perfect, and will never be perfect. This is because we have to teach it like a child and slowly build up the ability of the AI system. For it to be powerful, it has to be able to incorporate new unpredictable information. In doing so, it must, as a result, also be able to incorporate "wrong" information and thus become unpredictable. Of all things, a computer needs to be predictable.

The problem with making a computer think like a person is that you lose the precision of the computer and get the bad judgment and mistakes of a human. Not a good solution to anything.

The "better" approach is to capitalize on "intelligent methods." Intelligent people have developed reliable approaches to solving problems and the development work is to implement them on a computer. Like the article points out, recommendations systems mimic intelligence because they implement a single intelligent "process" that an expert would use with a lot of information.

It is not a general purpose learning system like "AI" was originally envisioned, but it implements a function typically associated with intelligence.

Not even that.

Amazon SUCKS at recommending anything for me.

You have recently purchased a just released DVD. Here are other just released DVD's that you might be interested in. Based only upon the facts that they are:
#1. DVD's
#2. New releases

Or, you have recently purchased two items by Terry Pratchett. Here are other items you might be interested in based upon the facts:
#1. They are items
#2. The word "Pratchett" appears somewhere in the description.

You would THINK that they'd be "intelligent" enough to factor in your REJECTIONS as well as your purchases (and what you've identified as items you already own).

Figure it out! I do NOT buy derivative works. No books about writers who wrote biographies about Pratchett.

Re:NetFlix/Amazon suggestions...?

It's not just some keyword matching algorithm thats used. Without going into technicalities you might want to check out the Netflix prize contest, a 1M$ prize to improve the netflix prediction system by 10%.