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When Things Start to Think
One underlying theme dear to Gershenfeld's heart is the death of traditional academic distinctions between physics and engineering, or between academia and commerce. Applied research is real research.
Another major theme is that older technologies should be treated with respect as we seek to supplement or replace them. For example, a laptop's display is much harder to read in most light than the paper in a book.
The book starts by drawing a contrast between Digital Revolution and Digital Evolution. Digital Revolution is the already-tired metaphor for universal connectivity to infinite information and memory via personal computers, the Internet, etc. Digital Evolution describes a more democratic future, from Gershenfeld's point of view, when computers are so smart, cheap, and ubiquitous that they do many ordinary chores to help ordinary people. When things talk to things, human beings are set free to do work they find more appealing.
"What are things that think?" asks the first section of the book.
Gershenfeld's whizbang examples won't be big news to Slashdot readers. My favorite, the Personal Fabricator, ("a printer that outputs working things instead of static objects")-- whose relationship to a full machine shop analog is like that of the Personal Computer to the old-fashioned mainframe. Gershenfeld actually has one of these in his lab (it outputs plastic doohickeys)--seeing it was one of the high points of my visit there.
"Why should things think?" asks the second section.
My favorite here is the Bill of Rights for machine users. (In true Baby-Boom style, it's of list of wants arbitrarily declared to be rights.) "You have the right to
Have information available when you want it, where you want it, and in the form you want it
Be protected from sending or receiving information that you don't want
Use technology without attending to its needs"
Under the heading "Bad Words," Gershenfeld offers a snide but useful summary of many high-tech pop-sci buzzwords, showing how they get misused by people who don't understand their real content or context.
"How will things that think be developed?"
By making them small and cheap. By getting industry to pay the bills for targeted, practical research, using the Media Lab model TTT ("Things That Think.") By reorganizing education on the model of the Media Lab, where students learn things as they need them for practical projects, not all at once in a huge, abstract lump.
The book concludes with directions to various websites, including the Physics and Media Group (One of their projects these days is "Intrabody Signaling.") Slashdotters might also be interested in Gershenfeld's textbooks The Nature of Mathematical Modeling and The Physics of Information Technology.
You can purchase When Things Start To Think from bn.com, and Amazon has the book paperback discounted to $11.20. Slashdot welcomes readers' book reviews -- to see your own review here, read the book review guidelines, then visit the submission page.
I mean, come'on. We have pattern recognition, and bots that have huge libraries of information. We aren't anywhere near true AI, and won't be for several decades, unless some huge breakthrough occurs in learning algorithms.
I think we are going to look back a hundered years from now and say how silly we were to ever believe computers could think like we do.
How is a computer program ever going to adopt abstract thinking and creativity? Is a computer program ever going to invent mathematics without previous knowledge of it just because it finds it to be a useful utility for solving problems?
Heck, if someone could write a decent language translation program I might think there is a hope.
Humans already have loads of free time now and what do we do? We piss it away watching Jerry Springer and WWF eating cheezy poof's on the sofa turning into fat slobs.
For me, I'd rather spend a little more time outside and with real people instead of wiring myself more than I already am.
Technology has it's place...serving me not usurping me.
"TV, a medium as it is neither rare nor well done." Ernie Kovacs
...when computers are so smart, cheap, and ubiquitous that they do many ordinary chores to help ordinary people. When things talk to things, human beings are set free to do work they find more appealing.
This is the same old nonsense that's been touted ever since the age of the washing machine. Considering the thousands of labor-saving devices we've acquired throughout the 20th century, by this logic we ought to be living lives of perfect leisure now. But this isn't what happens. In industrial societies, "labor-saving" devices don't. Work expands to fill the time available. When things think, I'm sure you and I will be freed from the tedious chores of cooking, driving, cleaning, and living. We can become machines ourselves, consumed with work until we burn out or die.
(More at Talbot's Netfuture, if you're interested.)
He who refuses to do arithmetic is doomed to talk nonsense.
How will things that think be developed?"
By making them small and cheap.
The invisible addendum to this sentence is expendable. Small, cheap, and expendable - the mantra of the Japanese economy. Someday we'll be so deep in silicon poisoning that it will be a worldwide crisis, and we'll have to have a resolution like the Kyoto Protocol so that our president can ignore it. But like our automobile industry fifty years ago, we should march relentlessly ahead with abandon until we reach a crisis point, rather than attempt to head it off now.
If machines could truly think they would be screaming at us: "Don't Throw Us Out!!!".
Dr. Joseph Hairston
Superintendent, CCBC
Every time a book review appears on Slashdot, he posts an Amazon link, complete with his ID, and doesn't tell the Slashdot community he's getting a commission. Truly disgusting behavior.
Materialism can never offer a satisfactory explanation of the world.
For every attempt at an explanation must begin with the formation of
thoughts about the phenomena of the world.
Materialism thus begins with the thought of matter or material
processes. But, in doing so, it is already confronted by two different
sets of facts: the material world, and the thoughts about it.
The materialist seeks to make these latter intelligible by regarding
them as purely material processes. He believes that thinking takes
place in the brain, much in the same way that digestion takes place in
the animal organs. Just as he attributes mechanical and organic
effects to matter, so he credits matter in certain circumstances with
the capacity to think.
He overlooks that, in doing so, he is merely shifting the problem from
one place to another. He ascribes the power of thinking to matter
instead of to himself.
And thus he is back again at his starting point. How does matter come
to think about its own nature? Why is it not simply satisfied with
itself and content just to exist?
The materialist has turned his attention away from the definite
subject, his own I, and has arrived at an image of something quite
vague and indefinite. Here the old riddle meets him again. The
materialistic conception cannot solve the problem; it can only shift
it from one place to another.
(Philosophy of Freedom, Chapter 2)
To quote Joe vs. the Volcano: '99% of people go through life asleep; the remaining 1% walk around in a state of constant amazement.'
To add to that I'd say: 99% of people *think* they're awake; the remaining 1% know they've got some waking up to do.
There you have it, your Zen moment of the day.
To be quite honest, if I'm still waiting for a Photoshop render, or a level to load in RTCW, our machines aren't ready to think.
The difference is that 100 years ago, you might have worked 10-12hours a day to earn enough money to feed your family, and you wife would work at home all day doing landry, mending clothes, cooking, etc... Now with many chores automated we get to own TV's, A/C etc. It not the elimination of work, it removing some work so that we can focus on other things. History has shown that people don't use the extra free time machines gove them to loaf around, they use it to produce more, and make their lives better, cleaner, and healthier.
Spencer Ogden
To quote the [bad] movie Runaway:
"Humans aren't perfect so why should machines be perfect?"
Honestly, I see engineers and developers walking down the hall with their shirt half-tucked in and their shoes untied. A sign that either
- they can't think for themselves
- they don't care enough
Now, both of those indicators give me serious pause when I consider that they may be designing machines that "think." If the developers can't think for himself/herself, how is his/her "thinking" machine going to think? If the developer doesn't even care enough to tie his/her shoes, do they care enough to engineer a "thinking" machine to the very high degree it requires and can I trust them to care enough?I dunno. Maybe I'd feel better about all this if every time I turn around I didn't see Yet Another stack-overflow or buffer-overrun bug (yes, the quality of code is getting better but there is still too much of this crap.) Maybe I'm just a pessimistic pisser. Perhaps I enjoy laughing at an engineer when they fall flat on their face after tripping over their untied shoelace.
"Those who would sacrifice liberty for security deserve neither!"
Personally, I don't see why RedWolves2 shouldn't post a link to Amazon and make a dollar if you follow that link.
If you don't like it, don't click. If he were offering free porn and you went to his site from which he makes advertising dollars, would you feel the same?
RedWolves2's post is on-topic and for some /.'ers a service.
"Thinking" has been ascribed to mechanical devices for quite some time. Watt's flyball governor for steam engines yielded such comments in its day. Railroad switch and signal interlocking systems were said to "think" early in the 20th century. At that level, we can do "things that think".
But strong AI seems further away than ever. After years in the AI field, and having met most of the big names, I'm now convinced that we don't have a clue. Logic-based AI hit a wall decades ago; mapping the world into the right formalism is the hard part, not crunching on the formalism. Hill-climbing in spaces dominated by local minima (which includes neural nets, genetic algorithms, and simulated annealing) works for a while, but doesn't self-improve indefinitely. Reactive, bottom-up systems without world models (i.e. Brooks) can do insect-level stuff, but don't progress beyond that point.
I personally think that we now know enough to start developing something with a good "lizard brain", with balance, coordination, and a local world model. That may be useful, but it's still a long way from strong AI. And even that's very hard. But we're seeing the beginnings of it from game developers and from a very few good robot groups.
Related to this is that we don't really understand how evolution works, either. We seem to understand how variation and selection result in minor changes, but we don't understand the mechanism that produces major improvements. If we did, genetic algorithm systems would work a lot better. (Koza's been working on systems that evolve "subroutines" for a while now, trying to crack this, but hasn't made a breakthrough.)
It's very frustrating.
I haven't worked with Gershenfeld, but have followed the Media Lab with some interest. At first, I approached news about the Media Lab with the awe that I believed appropriate to an elite institution, but after comparing what I knew from working in the technology field (in companies that are producing real products) with what Negroponte and others were saying it became apparent that most of what the Media Lab spins about the future is pure marketing hype at best and total bullshit at worst. The Media Lab should be called the Media Playground. Mostly its a bunch of talented people who play with technology. Playing with technology is fine and valuable things can come from it especially in basic research, howevever, by the very fact that it is grounded in play (i.e. something without an end or telos), rather than work, it is not going to be a good indicator of where society will be in 10, 20, or 100 years because society, for the most part, is driven by economics, and economics has a very definite end, profit. Essentially the folks at the Media Lab are parlaying MIT's well-deserved reputation as an excellent engineering school into a claim of credibility in an unrelated field, product marketing, in order to attract funding. How many products developed in the Media Lab actually make money? I don't mean how many products that have passed through the Media Lab (they do see a lot of the cool stuff first), but how many products that are based on research that originated in the Media Lab are making money? I am willing to bet fairly few, but I haven't run the numbers myself. That's why this quote is the funniest one in the whole review:
"By reorganizing education on the model of the Media Lab, where students learn things as they need them for practical projects, not all at once in a huge, abstract lump."
What a joke! It looks like the Media Lab is getting a little nervous about Olin college, whose focus is exactly that which is described, or his definition of "practical projects" is a little different than mine.
Is it that these guys can't learn, or won't learn? They have been preaching the same delirant projections for some three decades now, and look where we are. Have you guys tried to interact with ALICE, the most recent Loebner Prize winner? It's really pathetic.
To the AI practitioners: You guys are no closer to understanding how human-level intelligence works today than you were thirty years ago, when the spectacular results that you got on very specific, well-defined problems made your head swell up.
In my view, the guy takes a large chunk of the blame is Marvin Minsky, who, after having seen not many (if any) of his extravagant forecasts realized, he still refuses to adopt a more circumspect attitude. I am sure he was an AI guru during the 60s, but he has shown little capabilities to adapt and learn - and to stop making silly public announcements.
I can't comment on the nature of the Physics and Media Group's actual work in physics. But the comment that the rest of the Media Lab's work is "90% bullshit" is unfair.
I've been interested in the Media Lab's work ever since reading "When Things Start to Think" a couple years ago. I was puzzled by the 'bullshit' factor to until I realized that the proper precedent for understanding the Media Lab isn't Bell Labs, but the Bauhaus!
The Bauhaus came about when people realized early in the 20th century that 1) with the new industrial materials, artists, artisans, and architects could build what seemed like *anything*, and that furthermore, 2) with the movement towards abstraction and Modernism, they were being given permission to do just that (ie, build forget tradition and start building "just anything")
Nobody knew how to do this. So they put talented people together and they taught each other (the master teachers commented often on how much they learned from their students)
The key to understanding the Media Lab's work is that having computation built into everything means that once again we have the heady freedom of being able to build "just anything"--but nobody really knows *what* to build. So they put talented people together to figure it out.
Yeah, entrepreneurs and technically-minded research organizations will build the things that the economy demands. These are often the things that business types and engineer types can think of --you might say these reflect the needs of Motorola to sell more product. Ideally, the Media Lab--and institutions like IVREA in Italy, PLAY Research in Sweden, the Berkeley Institute of Design, the ITP at NYU--will give talented people the latitutde to design *concepts* that relfect the products needed by the society we'd like to find ourselves in.
Check out especially the work of Mitchel Resnick's group and Hiroshi Ishii's groups at the Media Lab.
Sorry I don't have time to post links,
Rich K.
rpk-at-NOSPAM-pobox-dot-com
What's missing is the drive (not the hard drive, but the will to survive).AI will only evolve if it has to, just like natural intelligence evolves because it has to.
First find a way for your computer to feel fear. Make it afraid of being destroyed. Then only, it might start thinking about how to avoid death. If it does survive (as natural selection might come into play here ;-), over time it will not only survive, it will start improving its life
Intelligence (artifical or not) can only come from within life itself. Simulating a series of logical steps, be it a thousand or 10 trillion steps, is not all there is to it.
It's the drive that's missing.
BTW: Computer viruses are not enough to make your computer afraid :)