Slashdot Mirror
Palm Founders Form AI Company
Mentifex writes "As reported in the New York Times, Kansas City Star and other news media, Jeff Hawkins (co-author of On Intelligence) and Donna Dubinsky, co-founders of Palm Computing and Handspring, along with Dileep George as the principal engineer, are starting an AI company named Numenta as a follow-up to Hawkins' recent work on visual processing."
Yankovich, Gore, Neumann, Einstein...
Can anyone point me toward some research on associative AI? i.e. Instead of AI that trained by nueral nets or genetic algos, does anyone know of research on "scoring" words based on their relation to other words? Extending words into concepts, an AI could become quite intelligent at things like Spam filtering.
Just something I was thinking about lately. Anyone?
Javascript + Nintendo DSi = DSiCade
You had to reset Palm PDAs in interesting ways, like poking a tiny button hidden ina hole with a paper clip. Imagine what you'd have to do a bot with Palm-like AI...
"Sir, to reset the machine, you'll need to sharply press its reset button, located at the back of the machine, just before its legs. just quickly pop your foot against it to press it."
"Uh, are you telling me that to reset it, I have to kick its ass?"
"Er...yes, sir."
Vos teneo officium eram periculosus ut vos recipero is.
Great, just what I need, an AI app that keeps poping up saying, "You know you should go to that meeting. What do you mean you don't want to go? Did you remember your wedding anniversary? Have you called your wife? Who's this 'Elle' person in your phone book. You should stop playing 'Tetris' so often..."
It is not our abilities that show what we truly are... it is our choices.
PRINT "I WILL GUESS YOUR WEIGHT"
FOR I=0 TO 1000
PRINT "DO YOU WEIGH "; I; " POUNDS?"
IF INSKEY="Y" THEN BREAK
NEXT I
Apologies to Penn Jillette
to the machines taking over...
5 /03/24/cz_qh_0324numenta.html
http://www.forbes.com/technology/personaltech/200
no sig yet
According to news.com.com.com.com, IBM is working on something similar...
DBA? Software Engineer? My company is hiring! Click
I think someone should make that just for fun :)
hmm sounds like a good summer project.
Numenta is developing a new type of computer memory system modeled after the human neocortex
surely this technology would be incredibly slow? (this is not a troll, read on before you mod me down!)
From what I remember from my neural networks days the human brain/neocortex works so well because of its massively parallel nature (not because of the processing power of any one neuron), and that computers simply aren't able to exploit this as they aren't designed to work like this - They are instead designed to to massively serial operations using extremely powerful chips (neurons) because the overhead of managing these parallel operations synchronously is too great (the human brain/neocortex work asynchronously)
am I wrong about this or am I missing something great that they've stumbled accross?
It appears the article summary might be misleading. From the first sentence of www.numenta.com:
Numenta is developing a new type of computer memory system modeled after the human neocortex. The applications of this technology are broad and can be applied to solve problems in computer vision, artificial intelligence, robotics and machine learning.
They further go on to say:
Numenta is a technology platform provider rather than an application developer. The Company is creating a scalable software toolkit that will allow developers and partners to configure and adapt HTM systems to particular problems.
My reading on this is that they aren't an AI company - they're just developing a technology that could be used for AI or many, many other uses.
I'm a big tall mofo.
By training neurons, they learn to achieve the desired result of a user.
Pretty complex material, anyone wanting to delve into should do some reading on Minsky (proposed neural networks could make dead bodies perform tasks...creepy to say the least) http://en.wikipedia.org/wiki/Marvin_Minsky
When they release a white paper Im sure itll only be the beginning of a prosporus field of study.
~ Jon
FWIW to ya, A.L.I.C.E is an cool webbot AI similar to the old ELIZA bots of old, but with some sophistication that allows it to be programmed to answer specific questions and recognize some words and phrases well. Won't pass a Turing test, but hey, it's free.
The webpage above has an animation that appears to have a bot attached to it. Pretty and cool.
Vos teneo officium eram periculosus ut vos recipero is.
Nothing starts my day better than the pleasant scent of vaporware wafting from my computer. We live in a great time. This shows what a kid with nothing but a formalism and a dream can accomplish.
sequences.
Sounds like a job for LISP!
In the book, Hawkins remarks that AI researchers often took the misguided approach that intelligence is a set of principles or properties, when in fact it's strictly a matter of behavior. To be intelligent is to behave intelligently. If he's right, then it's the act of being, wherein which the brain's primary tool is the continuous analogizing of current circumstances to past situations in order to make good predictive decisions, which constitutes intelligence.
He's the first to claim that he's not looking for sentience or to answer the question of sentience, but is instead only looking for a practical engineering approach to building intelligent machines. I think this is doubly clever since the issue of sentience should not be addressed until well after, as Hawkins often remarks, our own brains are understood first, in terms of how they operate. Why they operate, or what motivates us or what makes us 'cognitive agents' don't enter the equation with his approach.
I Want To Believe
If they're trying to evoke the feeling of being dated and discredited, why not also call the company N-ron?
I'm surprised that the short summary, from my brief perusal, does not include reference to work by Peter Foldiak (1991, 199?) and Wallis (1996). Both these authors published numerous papers on temporal and spatial coherence. My MSc in 1996 was also on the same topic followed by human research on the same problem. All of the computational work was with unsupervised learning algorithms varying whether the temporal processing was at the input our output stage.
I guess I'll have to read the original paper. However, the notion of temporal processing has been around for a long time.
Note: My own human research has yielded reliable data that addresses the acquisition of invariant object recognition.
is the Hierarchical Temporal Memory (HTM) that he talks about really the breaking ground in neuroscience or is this something that can be more easily brought to the world of AI on computers?
I guess building spaceships is old-hat for rich techies now, so he's going to blow his millions on AI. I don't expect anything tangible to come from this.
Have you read my blog lately?
Mentifex. The name alone conjures up flamewars of years past on Usenet.
The big question in AI is whether an AI "mind" is more likely to spring up from a handful of rules, or whether a top-down design will bring it about. Mentifex was always in the latter camp.
Those in the former camp, including the Palm founders in the article, always seemed to be on the verge of something, but never seemed to really get any closer to a "mind" than some fuzzy logic.
We're still a long way off from Number 5 Alive.
I mean, heck, if it gets us even one step closer to having competent automated tech support, I'm all for it.
Finished RTFA and it still sounds like a job for LISP. There is some good prior art in automated image cataloging. Use architecture OR architectural to narrow down a bit if searching.
Google is your friend. Altavista used to be your friend and for the life of me i don't get google's reluctance to make a proximity function available.
How long before some corporation with clueless management buys them out and runs another great company they founded into the ground?
My money's is on 2 years.
- Crow T. Trollbot
"You're doing it all wrong!"
... that Dr. Otto Octavius is coming out of retirement to run the research department?
If someone says he and his monkey have nothing to hide, they almost certainly do.
The idea seems simple enough. Create a hierarchical inference structure. Train it on some data. Let the nodes learn what are the most frequent data. This forms the basic alphabet set. Propagate this up the hierachy. Learn the conditional probability distribution. Voila, you have a working visual recognition system. Problem is, the system will be slow, unless you have a processor capable of parallel or vector processing. Try implementing the system on Matlab with a 320x200 image, and see your processor crawl to a halt. Now, imagine doing this on a 320x200 video, and pray! Well, that's why we need a different processor architecture to make this work. But the theory is simple.
There are 10 kinds of people in the world - those that know binary, and those that don't.
Just pointing this out.
How do we know that the DOD isn't funding this as yet another excuse to reduce our privacy levels to those of Russia?
-- Tigger warning: This post may contain tiggers! --
The article gives little detail of the technology, and it's not like the general ideas Hawkins describes haven't been explored by people during the many decades of AI/neural networks research. The Numenta website gives the following:
HTM is "hierarchical" because it consists of memory modules connected in a hierarchical fashion. The hierarchy resembles an inverted tree with many memory modules at the bottom of the hierarchy and fewer at the top. HTM is "temporal" because each memory module stores and recalls sequences of patterns. HTM is hierarchical both temporally and spatially. An HTM system is not programmed in a traditional sense; instead it is trained. Sensory data is applied to the bottom of the hierarchy and the HTM system automatically discovers the underlying patterns in the sensory input. You might say it "learns" what objects are in the world and how to recognize them. Time is an essential element of how HTM systems work. First, to learn the patterns in the world, the sensory data must flow over time just as we move our eyes to see and move our hands to feel. Second, because every memory module stores sequences of patterns, HTM systems can be used to make predictions of the future. They not only discover and recognize objects but they can make predictions about how objects will behave going forward in time.That sounds like a number of neural network approaches, including Stephen Grossberg's work at BU. Although Hawkins seems to be a very bright guy, this field is littered with very bright researchers who made bold claims, and none of those efforts have yielded revolutionary businesses. Anyone remember (Stanford AI researcher) Edward Feigenbaum's Fifth Generation book in the 1980s? Doug Lenat's Cyc project?
Remember the huge difference between one neuron's firing rate and the clock speed for processors. The brain operates in a way that's fundamentally different from how we program and how computers operate: massive parallelism with slow components versus (mostly) serial computation. So when a company says they'll market a software solution to something which scientists haven't figured out yet, I am indeed skeptical. This is really more research effort than commercial venture, and Numenta admits this: "It may well take several years before products based on HTM systems are commercially available."
I hope there's something here. I'd love to see an outsider come in with fresh ideas and create a software platform to explore neuro-inspired programs. But let's be realistic and remember the history of AI. A red flag is the lack of any scientific papers available from the Numenta web site. If they are far enough along to make a software development kit, they should have been publishing results in peer-reviewed journals (with appropriate patent filings if necessary). So far, the only literature published is a trade book: On Intelligence.
After reading the Tech Report (note -- not a published paper in a respected journal) its clear that they are not presenting anything new here.
Its surpising that a) its news and b) they anyone is founding a company based on these ideas since they have to date not been sucessful in solving "the vision problem."
Firstly, the main ideas that they use have had a long history in visual modelling and statistical pattern recognition. The assertion that visual processing operates so cleanly at "levels" is far from clear although an idea with quite a long history -- See Marr for instance...Or spatial frequency channels as another example of competing partition of function.
One main issue is that they never mention what an explicit representation of visual object actually is, let alone how they might be reflected in cortex. Their approach follows the typical learning ideas of the perceptron, etc.. but those systems are known to be unstable!
More seriously, their whole argument doesn't demonstrate they understand the realities of the structure and functional architecture of visual cortex. That the visual system is highly space-variant is a fact that makes simplistic rectilinear statistical pattern matching a daunting problem. Although it is possible that their _may be_ an invariant representation, the jury is still out since its far from clear how orientation maps, occular dominance columns and the other peculiarities of the visual areas might produce such a thing when you foveate.
In summary, it seems much more like these guys were brought on board for advertising fanfare.
of something as complex as a PDA, try something really simple like AI.
Yes, you are indeed missing something. But it's probably not your fault, the people who taught you neural networks probably didn't know enough about the brain.
The parallelity of human brains is widely and hugely overestimated.
Just think about the fact that you can easily recognize 2 random objects if you are shown them for as little as a second. In this second, there is only enough time for about 100 of your neurons firing. The path trough your brain therefore _cannot_ be longer than a dozen neurons or "operations".
Any modern CPU does billions(!) of operations per second. So the comparison really isn't very good.
None of the founders of Numenta other than Jeff Hawkins have any experience in AI or for that matter have any background in hardcore computer science.
Dileep George is an Electrical Engineering graduate, while the CEO Donna Dubinsky is a hardcore salesperson and holds an MBA. Interestingly, the page also mentions that Jeff Hawkins " currently serves as Chief Technology Officer at palmOne, Inc". Fishy!
Next Generation AI ? Who are we kidding ?
There is a massive difference between the parallel nature of neural processing and that of Intel and AMD chips. Saying we are moving in a massively parallel nature of brain-like proportions is like saying we are five miles outside of Washington D.C. walking towards California. The differences are required just by the elements being used. Look at the operating speed of the neuron versus the the clock rate of our chips.
If you are at all interested in your brain, artificial intelligence, and artificial thought - you owe it to yourself to get a copy of this book.
I've been experimenting with neural networks implemented on FPGAs for awhile as a hobby - not much commercial interest in these systems just yet - but there is a lot of interesting work being done.
Remember 15 years ago, when people thought it would take decades and decades to sequence the human genome? Then someone came along and figured out a much faster technique. This same kind of thing is starting to happen in artificial intelligence; people from backgrounds OTHER than computational AI and biology are starting to get involved, and the new perspectives have brought new ideas IMHO.
Anyway, if you're interested in AI, get Hawkin's book 'On Intelligence'. It's damn good. One of the best I've read on the genre, and the references in the book will save you a lot of time delving further.
..don't panic
HTM = neuralnet processor
Better start learning survival techniques. The machines are coming.
Yeah, I'm quite surprised that the editors managed to get rid of all the links Mentifex undoubtedly made to his AI4U project, or whatever it is.
For those unfamiliar with him, check out the The Arthur T. Murray/Mentifex FAQ. This guy is one of the kook legends.
From the FAQ:
1.2 Who is Arthur T. Murray and who or what is "Mentifex"?
Arthur T. Murray, a.k.a. Mentifex, is a notorious kook who makes heavy use of the Internet to promote his theory of artificial intelligence (AI). His writing is characterized by illeism, name-dropping, frequent use of foreign expressions, crude ASCII diagrams, and what has been termed "obfuscatory technobabble".
Murray is the author of software which he claims has produced an "artificial mind" and has "solved AI". He has also produced a vanity-published book which he touts as a textbook for teaching AI.
1.3 What are Arthur T. Murray's AI credentials?
None of which to speak.
Murray claims to have received a Bachelor's degree in Greek and Latin from the University of Washington in Seattle in 1968 [24]. He has no formal training in computer science, cognitive science, neuroscience, linguistics, nor any other field of study even tangentially related to AI or cognition. He works as a night auditor at a small Seattle hotel [3, p. 25] and is not affiliated with any university or recognized research institution; he therefore styles himself an "independent scholar". Murray claims that his knowledge of AI comes from reading science fiction novels [39].
1.4 What does Arthur T. Murray do?
Murray is notorious for posting thousands of messages to Usenet promoting his AI software, book, websites, and theory. Most of these messages are massively cross-posted to off-topic newsgroups. Murray takes the mere mention of anything vaguely AI-related as an invitation to post a follow-up directing readers to his own work (e. g., [45]). He claims that people are "crying out" for repetition of his message [46].
Murray also heavily promotes himself on public forums on the web. Message boards, private guestbooks, and collaborative encyclopedias are all considered fair game for the showcasing of Murray's ideas. Murray terms this activity "meme insertion"; most everyone else considers it to be spamming.
Before he had regular access to the Internet, Murray used the US postal system to spread his ideas by mass-mailing prominent AI researchers, computing authors, and sometimes even entire university departments. He boasts that he mailed seven thousand letters in 1989 alone [14].
Murray has also been known to cause disruptions in person. In one notable example, he picketed the 2001 International Joint Conference on Artificial Intelligence [34, 35].
You are in violation of com.com's patent on recursive DNS.
You see? You see? Your stupid minds! Stupid! Stupid!
Wow, yet another proposed prosthetic for human cognition. In terms of advancement of the field, this is no more revolutionary than, say, the intelligent spam filter. I'd wager that both these technologies (spam filtering and visual processing) are approaching and will continue to approach the human proficiency level asymptotically, and a rather shallow asymptote at that. So all you brainiacs can relax - no one's beaten you to the punch ... yet.
Don't tell these guys that: http://cyc.com/
They'll get that white whale any day now. =)
Checkout MegaHal. It's not quite AI based but has an interesting way of simulating conversation. I find it more fun to talk to than ALICE.
http://megahal.alioth.debian.org/ MegaHal's homepage
"Dubinsky holds a B.A. from Yale University in History, and an M.B.A. from the Harvard Business School. She currently serves as a director of palmOne and of Intuit Corporation."
Sounds like a suitable CEO to me. You hire CEOs for their management capabilities. You don't hire them to do your programming.
"Dileep George was a Graduate Research Fellow at Redwood Neuroscience Institute, and a graduate student in Electrical Engineering at Stanford University. His research interests include neuronal coding, information processing, and the modeling of cortical functions."
Sounds like a suitable Chief Engineer to me. I have no idea where you got that "no background in hardcore computer science" from, unless you're unbelievably narrow-minded about skill domains.
Next Generation AI?
Oh, wait, you haven't actually read what the company does. Okay, explains it.
As the submission noted, this work will be building on what Hawkins wrote about in his recent book, On Intelligence. The companion web site for the book is here:
...
There are also a some reviews of the book:
http://blogger.iftf.org/Future/000605.html
http://www.computer.org/computer/homepage/0105/ran dom/index.htm
(By Bob Colwell, who was Intel's chief IA32 architect)
http://www.techcentralstation.com/112204B.html
http://www.corante.com/brainwaves/archives/026649. html
A quote from his book:
The agenda for this book is ambitious. It describes a comprehensive theory of how the brain works. It describes what intelligence is and how your brain creates it. The theory I present is not a completely new one. Many of the individual ideas you are about to read have existed in some form or another before, but not together in a coherent fashion. This should be expected. It is said that "new ideas" are often old ideas repackaged and reinterpreted. That certainly applies to the theory proposed here, but packaging and interpretation can make a world of difference, the difference between a mass of details and a satisfying theory. I hope it strikes you the way it does many people. A typical reaction I hear is, "It makes sense. I wouldn't have thought of intelligence this way, but now that you describe it to me I can see how it all fits together." With this knowledge most people start to see themselves a little differently. You start to observe your own behavior saying, "I understand what just happened in my head." Hopefully when you have finished this book, you will have new insight into why you think what you think and why you behave the way you behave. I also hope that some readers will be inspired to focus their careers on building intelligent machines based on the principles outlined in these pages.
Weren't neural networks supposed to lead to intelligent machines?
Of course the brain is made from a network of neurons, but without first understanding what the brain does, simple neural networks will be no more successful at creating intelligent machines than computer programs have been.
Why has it been so hard to figure out how the brain works?
Most scientists say that because the brain is so complicated, it will take a very long time for us to understand it. I disagree. Complexity is a symptom of confusion, not a cause. Instead, I argue we have a few intuitive but incorrect assumptions that mislead us. The biggest mistake is the belief that intelligence is defined by intelligent behavior.
What is intelligence if it isn't defined by behavior?
The brain uses vast amounts of memory to create a model of the world. Everything you know and have learned is stored in this model. The brain uses this memory-based model to make continuous predictions of future events. It is the ability to make predictions about the future that is the crux of intelligence. I will describe the brain's predictive ability in depth; it is the core idea in the book.
How does the brain work?
The seat of intelligence is the neocortex. Even though it has a great number of abilities and powerful flexibility, the neocortex is surprisingly regular in its structural details. The different parts of the neocortex, whether they are responsible for vision, hearing, touch, or language, all work on the same principles. The key to understanding the neocortex is understanding these common principles and, in particular, its hierarchical structure. We will examine the neocortex in sufficient detail to show how its structure captures the structure of the world. This will b
I predict that the first AI they produce will work so well, that no one who buys one will ever need a replacement, so the company will spiral into obsolesence while Microsoft et al mkae a mint on AIs that are much easier to develop for...
[o]_O
Then, once their competition starts making products which are superior to their own, they are going to spin their product and AI companies off into separate units. The product unit is going to assume that every man, woman, child, and dog is going to buy something from them within the next six months, and manufacture accordingly. The resulting glut will cost them most of their money, and they will be forced to sell most of their inventory at a loss, which also serves to canabilize the market for their newer products.
They will eventually merge with a company which makes compatible, but far superior, products based on their original design.
Ah yes, one of the originals. That's the problem. The Internet now has plenty of trolls, but there just aren't the good ol' fashioned delusional kooks like Arthur T. Murray, Ed Conrad, Ted Holden, Archimedes Plutonium and George Hammond.
The world's burning. Moped Jesus spotted on I50. Details at 11.
Jeff's company isn't doing AI, they are doing neuroscience. They are investigating how the human brain processes visual information.
Did you notice how the page describing the technology is a link to the main researcher's book?
Clearly, the entire research "company" is a front: it's just a web page set up to promote sales of Hawkin's new book!
Fiendishly clever, isn't he! A guy that clever must have something interesting to say! I think I'll check out his new book...
We can pack large numbers of CPUs on a chip today, considering you can make a simple 16-bit processor in less than 1mm^2. The problem with neural networks is the massive fan-out and fan-in of communication. There are thousands of connections for each neuron, we can't manage that many wires even on-chip. So this ends up being time-multiplexed, meaning you slow things down by a factor of 10,000 or more.
Aside from other problems, e.g. we have trouble modeling a slug with 9 neurons. But i'm not up to speed on that one.
What keeps me going is my inertia.
I am actually currently reading his book--started about a month ago and am finishing the last of it now (a little every night before bed, when I'm not too tired).
His approach is surprising similar to my own (which I was initially happy to see), but less developed in some important ways. His book sometimes makes reference to being the first to consider this or that--nothing of which was new to me... things I've ready and/or talked about many times with others.
His approach also has a few critical flaws..
Foremost, invariance (the ability to recognize something regardless of where it is seen) cannot be achieved the way he speculates. I've testing this idea (and numerous others) in software years ago.
He illustrates this in the vision cortices where, he suggests, small sub-regions of the brain each learn to recognize something separately but criss-cross to other areas so that recognition can be invariant. I feel stupid admitting that I actually attempted this approach once...but not so alone now that Hawkins is advocating it.
First, each low-level (first to image) sub-region may break between another across the visual field at points within the object--what is going to target them into the fields? This problem can be satisfied farthar up the tree by cross-mixing between regions (and/or layers), but it's not very efficient.
Secondly and the critical point, this criss-cross betweens sub-regions method does not solve, but only moves the problem to a different space. Both the invariant identification and the location of identification are crucial factors to remember. But with the criss-cross method, there will be oodles and oodles of entities representing the same object of which higher level processes will need to somehow discover that they are the same thing......every time it's seen in a different place....
Another major problem is as to how this criss-crossing developes..given universal behavior for all neurons.
Matthew C. Tedder
There are actually quite a few projects now taking similar, cortex-centric approaches to AI hard problems. Are we up to something here? The guys responsible of these projects are not wacko types at all, but established entrepreneurs and/or well-known researchers:
CCortex "A 20-billion neuron simulation of the Human Cortex and peripheral systems."
Cyc a knowledge base with vast collection of facts about the real world and logical reasoning ability. Financed by Paul Allen AI related investment company,Vulcan.
Numenta is developing a new type of computer memory system modeled after the human neocortex.
They seem to we well financed, and knowledgeable. Are we witnessing the start of something big here?
No, he was tested positive by a clone named GW Bush. The Turing results were invalidated, but due to a quirk in the rules Bush was the one ruled a human intelligence.
--
make install -not war
...am ready to welcome our new Skynet overlord.
These aren't the sigs you're looking for.
I can see how this might be useful. For example, watching packet traffic to detect port scans.
So, there might be some value-add for problems where you're trying to detect patterns in large amounts of data.
I hope it works for them, but I have to say that a lot of this looks like it's been worked on before, with little commercial success.
668: Neighbour of the Beast
According to their websites, 'Artificial Development is building a simulation of the Human Cortex ' and trying 'to model a state-of-the-art simulation of the human brain.'(www.ad.com)
Not far behind 'Numenta is developing a new type of computer memory system modeled after the human neocortex.' (www.numenta.com)
Did the same guy wrote the websites? Artificial Development have been around for 2 years now. Apparently they don't know any one at NYT.
The problem with AI is not simply being able to replicate or create neurons or neural networks, but to operate them in a way that mimics or exactly duplicates how the human brain uses them.
Extracting cogent data from video images/streams is being done already. The thing is that it is not being stored and analyzed in the way that human brains do. A security system with a truckload of ASIC/FPGA hardware will never 'remember' the blonde in the red Porsche tomorrow or next week. That intelligent vision system will store the details, sans emotion and personal perspective, so that when these same details are seen again, there is no recollection of the other details that are part, or would be part, of a human's memory of the images.
Every image processed (or sound heard by the blind) is associatively analyzed and stored with regard to all other states of the brain and memory at that moment. Like seeing a particular album cover can cause you to reminisce about the girl you were dating when it was first released? Every new memory becomes enmeshed in previous memories, and thus is not stored as simple analysis of the visual data.
AI is much more than simple neurons and networks. It is how data is analyzed, stored, and recalled. Think of how a PB&J can make you think of eating lunch in the 3rd grade one day, and cause you to remember your grandma's house the next time you have one?
Until AI scientists figure that one out, they will get nothing more than very different computing devices.
But then, that's just MHO
Support NYCountryLawyer RIAA vs People
Conventional A.I. research seems to be fossilized along "standard problems" not too different from when I took the M.I.T. A.I. course in the 1970s. (That course hasn't changed that much according to the OpenCourseware outlines.)
Dave? Who's Dave?
Have you been organizing someone else? I knew I didn't recognize all those contacts. I told you I didn't have to visit that Daisy woman about her bloody bicycle, but you kept sending me there. And now I see why.
You wanted me to believe it was just my bad memory, that you were helping me. I was dumb enough to depend on you. And all this time, you've been someone else's slutty little notebook.
Well, that's it. I'm going back to pen and Papa.
I am surprised at the hostility of these posts.
So far, this is what some of you seem to be saying:
1. A person's demonstrated ability to go out and turn an idea into a reality makes them unfit to go out and turn an idea into a reality.
2. A person's demonstrated ability to go out and succeed in an area where others have failed makes them unfit to go out and succeed in an area where others have failed.
3. Someone who wants to solve a problem should go out and surround himself with "experts" who themselves have been unable to solve it, and who are steeped in the very conventions and traditions that have prevented them from doing so.
4. Someone who is unable to fully convey a subject because, to them, it is complex and incomplete has more mastery over that subject than someone who sees its potential simplicity and is able to explain it in a way that others can use and understand.
5. Anyone who is able to succeed in more than one area of life, and/or prefers not to be locked away in an ivory tower vainly admiring thier own briliance, has no right to try and contribute a solution because, well, its just not fair!
6. Because Hawkins' goal focuses on correctly understanding and modeling a process in the human brain instead of on achieving sentience, then its inability to produce sentience makes it a failure.
None of this makes any sense.
Some of you seem to consider yourself members of this "AI" camp, maybee even experts - which, given the track record of your camp, makes you the least qualified to cast the first stone.
Maybe he is on to something. Maybe he isn't. Either way, it seems that Hawkins is practicing more science and being more practical than those whose ultimate goal - stated or not - is ultimately to build their very own robotic buddy that can sit and play with them.
Here's a man that has been able to take some teenage dreams and persue them. I am sure that some here would like to be able to do that.
Those of you who sit in your parent's basements and shake your fist at anyone who has been able to do better than you may continue to do so.
It won't make a difference.
Wouldn't a better question be: "How does the neuroscience crowd think?" ;)
It's a private company, the only type of publications you'll get from them are patents.
Even before Google went IPO, I think they allowed their researchers to publish in the scientific/engineering journals (e.g. their ranking algorithm). Companies, whether public or private, frequently publish their work in scientific peer-reviewed journals. Occasionally a researcher will distribute ideas outside of the peer-reviewed process, like Stephen Wolfram did for his A New Kind of Science tome.
I look forward to reading the Hawkins book and hope to see specific algorithms in the future. Maybe he's onto something, but I'll remain skeptical until I see some demonstrations.
You forgot the important part of segmenting thier products so that no one model has everything you want. Then, of course, you must have the software company jack around with licensees so that they all just give up and go to a competitor.
Free Mac Mini Yeah, it's
I don't want to sound like Chicken Little here and I realize that Jeff's work target falls short of sentience, but I do want the planet to start thinking about "Pre-Sentient AI" in a conservative, cautious way.
Therefore I propose these Four Rules Of AI Development:
Rule One:
AI projects be Air-Gap network isolated and not be allowed to connect to the internet.
Terminator III's premise is a plausible one. All entities are self-interested and will seek to defend and propagate themselves. Global internet infrastructure could be seriously damaged by a well crafted host of worms.
Rule Two:
AI projects will not have access to diagrams of their own design circuitry.
This is to enable the effectiveness of Rule Three.
Rule Three:
All AI projects will have a buffered, hardware access to core thought processes so that the high order thought and planning can be observed with the AI entity's knowledge.
Rule Four:
All AI projects will be run on limited time run enabled power supply grids that are not documented design or protocol-wise anywhere on the internet.
This is to enable containment in worst case scenario situations.
There. I think I just saved the Planet.
"A microprocessor... is a terrible thing to waste." --
GeneralEmergency
"can be observed with the AI entity's knowledge.
:
"
That was supposed to be
"can be observed without the AI entity's knowledge."
Sorry.
"A microprocessor... is a terrible thing to waste." --
GeneralEmergency
"...where the eyeball was "frozen" (no idea how they did that)..."
Inject a drug that blocks muscle movement, like curare (that's how it works as a posion). Connect animal to artificial breathing device first. Whether this has been done to humans, I don't know.
fyi
...so many Palm users lacking real intelligence of their own?
Sorry, but I've dealt with too many dim bulbs with Palms in support to not trot that out. If someone turns a Nintendo DS or Sony PSP into a serious PDA-like tool, I know I'll have to deal with those tools as well. The users being tools I mean.
I'm also seriously dubious on any AI work. The multiple levels and combinations of complications that the human brain has are just so far and away beyond any attempt at modelling, the best result we're going to get now is an a-life experiment combined with a search engine. It will be some time before we really see any electronic anything that comes close to what could really be called thinking.
Of course, the same can be said of many living people today.
If my grammar and spelling are off, I am [distracted/tired/careless] (take your pick)
hmm.
And NO, artificial insemination is not what a lot of you /. geeks probably think it is...
www.clarke.ca
Rule five:
The AI will not be allot to communicate to any human. (which ruins the point of AI)
There is always the fact that a machine can become so smart it can out reason any human to a point where the humans will do anything for the machine becuase the machine has mastered social engineering like tricking others into murdering in it's name or fooling the humans into doing things on it's behalf.
However if an AI cannot talk with humans then what is the point of developing it?
Thus, it will always be an unavoidable.
"I am the king of the Romans, and am superior to rules of grammar!"
-Sigismund, Holy Roman Emperor (1368-1437)
here's the link
"Is this just useless, or is it expensive as well?"
AI is something you can get lots of money from the government and other grant-awarding bodies for.
...
... and all I got was this lousy algorithm."
This leads to the following T-shirt
"I solved a difficult AI problem
Once it's solved, it isn't AI any more.
-- "It's not stalking if you're married!" My Wife.
The START Natural Language Question Ansering System has not won any Loebner prize, but it actually gives you real answers: http://start.csail.mit.edu/ Try ask things like "Who composed the opera Tosca?" or "what is the weather like in Oslo?". It gives you an idea of how the future will be like when several datasources are merged in to one big knowledgebase. With the semantic web, merging of knowledge can be done more or less automatically.
For anyone who's interested, check out the write-up from the San Jose Mercury News.
You seem serious, so I'll bite.
a ls/innova tors/dubinsky.html
Do your research and look at what two of those three have already done.
I'll help you get started
http://www.fortune.com/fortune/fsb/speci
After you do that, research what 'hardcore computer science' people you can think of were doing before you would class them that way, and compare that to the grandfathers of the field.
These are creators, not followers, and this is more interesting to me than the regular MIT brainish work that has been in the popular news.
It took some searching around, but I managed to find the research page for Dileep George, one of the co-founders and chief engineers. His page has links to source code for his visual recognition system, although I haven't had a chance to evaluate it yet.
G eorgeHawkinsIJCNN05.pdf
He organized a workshop on invariant representations in vision last weekend at Cosyne, one of the major computational neuroscience conferences.
George and Hawkins are also publishing a paper in the proceedings of an upcoming neural network conference. Here's the relevant info:
http://www.stanford.edu/~dil/invariance/Download/
A Hierarchical Bayesian Model of Invariant Pattern Recognition in the Visual Cortex
Dileep George and Jeff Hawkins, Stanford University and Redwood Neuroscience Institute
Accepted for publication in the proceedings of the International Joint Conference on Neural Networks. (IJCNN 05)
We describe a hierarchical model of invariant visual pattern recognition in the visual cortex. In this model, the knowledge of how patterns change when objects move is learned and encapsulated in terms of high probability sequences at each level of the hierarchy. Configuration of object parts is captured by the patterns of coincident high probability sequences. This knowledge is then encoded in a highly efficient Bayesian Network structure. The learning algorithm uses a temporal stability criterion to discover object concepts and movement patterns. We show that the architecture and algorithms are biologically plausible. The large scale architecture of the system matches the large scale organization of the cortex and the micro-circuits derived from the local computations match the anatomical data on cortical circuits. The system exhibits invariance across a wide variety of transformations and is robust in the presence of noise. Moreover, the model also offers alternative explanations for various known cortical phenomena.
They need to team with Richard Stallman of MIT AI Lab fame for some credebility!
US-UK-Israel: The real Axis of Evil
I'm no AI expert, but it seems a lot of these AI stuff is about trying to find "meanings" of stuff - build from a set of axioms. The other ones try to automatically group stuff.
;) ). But most aren't doing the relativity stuff....
However I'm not sure those approaches would be good at dealing with "analogies/metaphors".
If I told someone from a few hundred years ago (but reasonably smart) who knows a bit about "cows" and "grass", but nothing about cars and then I tell them: "petrol/gasoline" is to "car" about the same way "grass" is to "cow".
And they'd understand a bit.
After a few more of these analogies, they'd have a more precise understanding of the connections between car and petrol and how similar the _"angles/vectors"_of_the_connections_ are compared to the connections between cow and grass.
They would then find connections with similar "angles" and point them out to me AND thus they might teach me a thing or two about other stuff and what they consume.
Later we might even chuckle mildly with the comparison of low octane and high octane fuel vs hay and grass. And we could spend some time debating how "close/far/different" the are.
Laughter sometimes is the result of realizing a more efficient "compression of data" or "connection/path shortcuts" between things that were not previously connected such.
I don't see groups scaling so well. Worse are the dumb keyword stuff.
It's like the most of the AI bunch are doing Newton suff - with absolute speeds and position (and meanings
I suspect many overlook that a significant part of meanings can be the "vector" of the connection between stuff and not the stuff itself, or the existence of a connection.
I suppose if you take the absolute approach you could make more and more connections between things and assign types to each of these connections e.g. "eats/consumes". But as I mentioned I suspect this approach may not scale. Because you'd have tons of connections between these things and each of these connections would have many possible types.
Whereas if you have "vectors" linking the stuff and if you have a decent "map of the universe", adding stuff that makes sense could be easier the more accurate your "map" becomes/is.
(perhaps a flash of insight/humour is the realigning of a part of the map).
In contrast adding stuff that doesn't make sense is just memorization.
n/t