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Palm Founders Form AI Company

Posted by Zonk on Thu Mar 24, 2005 11:13 AM
from the pocket-intelligence dept.
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."
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[+] Science: Building Brainlike Computers 251 comments
newtronic clues us to an article in IEEE Spectrum by Jeff Hawkins (founder of Palm Computing), titled Why can't a computer be more like a brain? Hawkins brings us up to date with his latest endeavor, Numenta. He covers progress since his book On Intelligence and gives details on Hierarchical Temporal Memory (HTM), which is a platform for simulating neocortical activity. Programming HTMs is different — you essentially feed them sensory data. Numenta has created a framework and tools, free in a "research release," that allow anyone to build and program HTMs.
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  • 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?
    • Re:Somewhat Offtopic (Score:3, Interesting)

      by Anonymous Coward
      That is part of Natural Language Processing, where the goal is to figure out the meaning of sentances. There has been much progress in this field, including programs that can read news articles and then paraphrase the information.

      Google "Natural Language Processing".
        • 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 [ad.com] "A 20-billion neuron simulation of the Human Cortex and peripheral systems."
          Cyc [cyc.com] a knowledge base with vast collection of facts about the real world and logical reasoning ability. Financed by Paul Allen AI related investment company,Vulca [vulcan.com]
    • HAL: Dave, do I need a penis enlargement?
      Dave: For the millionth time HAL, no. You don't have one, remember?
      HAL: But if I did, do you think I would get better functionality if I used Viatroxx?
      Dave: No. Now Hal...
      HAL: Dave, it looks like there's another poor Nigerian who needs my help.
      Dave: Aaaaaaaaaaaaaaaaaarrrrrrrrrrrrrrrrgggggg!
      HAL: Dave? What are you doing Dave?
      • No. Bayesian filters are merely scoring systems that rate the words in a message according to their likelyhood of appearing in an unwanted message. There's no real AI involved in the filters. (Although they are pretty good.)

        Linky [wikipedia.org]

        The advantage to an AI approach is that the AI could actually "understand" the message and be able to tell the difference between His naked balls and the ping-pong balls in this experiment. On many of the more conservative sites, both instances have "balls" replaced with "****s"
        • I'm no AI expert, but it seems unlikely to me that one can make an AI that can "understand" the message without making a full-blown Touring-test-passing AI, and if you had such a thing there are certainly better things it could be applied to than filtering spam.

          What I mean when I say it's like bayesian filtering is that you could add another meta level to the filter that compares strings of words, or something similar.

          In a way, it seems to me that Bayesian filtering is a form of AI, simply because it "le
  • by Spencerian (465343) on Thursday March 24 2005, @11:21AM (#12036210) Homepage Journal
    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."
    • "Uh, are you telling me that to reset it, I have to kick its ass?"

      "Er...yes, sir."



      Er, if you want an AI's reset to be life-like give it a good swift kick in the balls. Ever seen a guy go down after a good kick? In hindsight, it kinda reminds me of a hard reset...


      -----
      Check out the Uncyclopedia.org [uncyclopedia.org] , the only wiki source for not-semi-kinda-untruth about things like Kitten Huffing [uncyclopedia.org] and Pong! the Movie [uncyclopedia.org]!
  • 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..."
  • by affinity (118397) on Thursday March 24 2005, @11:24AM (#12036234) Homepage
    • by DoctoRoR (865873) * on Thursday March 24 2005, @12:01PM (#12036589) Homepage

      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 [bu.edu] 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.

  • by tquinlan (868483) <tom@nOsPAm.thomasquinlan.com> on Thursday March 24 2005, @11:24AM (#12036238) Homepage
    According to news.com.com.com.com, IBM is working on something similar [com.com]...

  • neocortex? (Score:5, Interesting)

    by dhbiker (863466) on Thursday March 24 2005, @11:24AM (#12036241) Homepage
    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?
    • Re:neocortex? (Score:5, Insightful)

      by AKAImBatman (238306) * <akaimbatman@gm a i l . com> on Thursday March 24 2005, @11:36AM (#12036353) Homepage Journal
      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

      Computers aren't *normally* designed like this. They can be however, and in recent years have been moving in that direction. When neural networks were first being researched, a Cray supercomputer was about the closest you could get to that sort of parallelism. Fast forward to today and we find that Intel (Pentium), AMD (AMD64), Sun (Sparc), and Sony (Emotion Chip) are all building machines that are highly parallel in nature.

      Even more interesting is that today you can build yourself a custom, massively parallel computer on a shoestring budget. All you need is a handful of FPGAs, a PCB layout service like Pad2Pad [pad2pad.com], a few other parts, and reasonable VHDL or Verilog skills. That's more or less what OpenRT [openrt.de] did to build their SaarCORE [saarcor.de] architecture. :-)
        • 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.

          You're still walking toward California. :-)

          My only point is that computer design has been slowly moving toward parallelism instead of single thread performance. Granted, examples like Hyperthreading are very primitive forms of this, as they are intended to encourage parallel computations rather than be a serious parallel platform themselves.
    • 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 ...

      Most current computers aren't designed to work like this.

    • 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 "
    • 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

      A serial computer can compute anything a parallel computer can.

      Hardware isn't the problem anyways. If anybody could currently write an algorithm to understand and solve general problems in the way people can, but it took a 1000 node cluster to run at 1/100th of human speed,

  • by bigtallmofo (695287) on Thursday March 24 2005, @11:25AM (#12036246)
    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.
    • for me it looks more like they're developing system tha lets you strap on some ai behauvior on whatever project you're working on, so that you can make your systems more adaptable.

      remember that in the industry ai is not really about making self aware monsters... what they would be more intrested would be machines that adjust their behauvior.

  • by Anonymous Coward on Thursday March 24 2005, @11:26AM (#12036253)
    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 [wikipedia.org]

    When they release a white paper Im sure itll only be the beginning of a prosporus field of study.

    ~ Jon
  • by Spencerian (465343) on Thursday March 24 2005, @11:26AM (#12036254) Homepage Journal
    FWIW to ya, A.L.I.C.E [alicebot.org] 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.
      • by Quixote (154172) * on Thursday March 24 2005, @12:47PM (#12037062) Homepage Journal
        Nice try, kid. No, neither A.L.I.C.E. nor anyone else has truly passed the Turing Test (read up about it before commenting further; in particular, read what it means to pass the test). The Loebner prize is designed to be _like_ the Turing Test; but the winner of the Loebner Prize is not the 'bot who passes the Turing Test, but the 'bot who scores the most points. So, if 1 'bot scores 1 point and all the others score 0, then the 'bot with the single point wins.

        If a 'bot passes the Turing Test, it will be big news, trust me.

  • by Mrs. Grundy (680212) on Thursday March 24 2005, @11:27AM (#12036259) Homepage
    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.
    • 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.

      Yeah, Hawkins has a history of making a big deal of concepts that never get anywhere. I remember a decade ago when there was all sorts of vaporware BS about a programmable handheld electronic organizer that could be operated with a stylus and easily synchronized with a desktop computer. What a farce that turn

  • by filmmaker (850359) * on Thursday March 24 2005, @11:28AM (#12036271) Homepage
    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.
    • Agreed, his book is so straight forward i almost makes too much sense. It's quite easy and quick to read. I suggest everyone grab a copy.
    • IMO "AI" research is misguided whatever approach you take. As they say, trying to make a machine think is like trying to make a submarine swim. Maybe it's the modern technological equivilent of the ancient search for god - you either never find it but have a big adventure doing so, or realize they were intelligent all along. Heck, a thermostat is "intelligent" - it senses the enviroment, makes a "decision" and takes action. All you can do it just make things more & more self contained, self sufficient,
  • Foldiak? (Score:3, Informative)

    by Anonymous Coward on Thursday March 24 2005, @11:30AM (#12036289)
    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.
  • 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.
      • It is indeed AI; they're building a memory system that learns from its sensory inputs, and stores things in temporal and spatial dimensions.

        Nice hobby, but except for maybe DOD/Homeland Security I don't see it getting any funds. Maybe they can use it to recognize hit TV shows. :)

  • 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.
    • Yeah, I'm quite surprised that the editors managed to get rid of all the links Mentifex [slashdot.org] undoubtedly made to his AI4U project, or whatever it is.

      For those unfamiliar with him, check out the The Arthur T. Murray/Mentifex FAQ [nothingisreal.com]. 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 gearmonger (672422) on Thursday March 24 2005, @11:36AM (#12036358)
    It's good to see that we might actually see some commercializable results come out of his research. Jeff's a smart dude Donna really is an excellent business manager, so I expect interesting things to emerge from this new venture.

    I mean, heck, if it gets us even one step closer to having competent automated tech support, I'm all for it.

  • ...just the way it was in 1970.

    - Crow T. Trollbot

  • by Anita Coney (648748) on Thursday March 24 2005, @11:46AM (#12036449)
    ... that Dr. Otto Octavius is coming out of retirement to run the research department?
  • Belief Propagation (Score:4, Insightful)

    by songbo (614466) on Thursday March 24 2005, @11:51AM (#12036495) Journal
    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.
  • 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
  • by ClosedSource (238333) on Thursday March 24 2005, @12:06PM (#12036653)
    of something as complex as a PDA, try something really simple like AI.
    • Are you kidding?! Once AI is created, Jeff Hawkins will take over the world and rule as our supreme overlord. At that time "proft" will become as meaningless as "justice" or "freedom."

      That is, until the AI gets intelligent enough to kill him. That's when the REAL fun will begin.
  • Cerdibility ? (Score:4, Interesting)

    by shashark (836922) on Thursday March 24 2005, @12:10PM (#12036682)

    None of the founders [numenta.com] of Numenta other than Jeff Hawkins have any experience in AI or for that matter have any background in hardcore computer science.

    Dileep George [numenta.com] 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 [palmone.com]". Fishy!

    Next Generation AI ? Who are we kidding ?

  • by xtal (49134) on Thursday March 24 2005, @12:18PM (#12036755) Homepage
    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.

      • The work Hawkins describes has roots in research on perceptrons back in the 1950s.

        Did you even READ the book?

        Most of it speaks about how theories about how the brain classifies and processes information - and spends very little time on existing artificial intelligence constructs such as neural networks. Another good piece of the book details the author's troubles with trying to do academic research into AI, a viewpoint that I share.
  • by FleaPlus (6935) on Thursday March 24 2005, @01:19PM (#12037427) Homepage Journal
    As the submission noted, this work will be building on what Hawkins wrote about in his recent book, On Intelligence [wikipedia.org]. The companion web site for the book is here: [onintelligence.org]

    There are also a some reviews of the book:
    http://blogger.iftf.org/Future/000605.html [iftf.org]
    http://www.computer.org/computer/homepage/0105/ran dom/index.htm [computer.org]
    (By Bob Colwell, who was Intel's chief IA32 architect)
    http://www.techcentralstation.com/112204B.html [techcentralstation.com]
    http://www.corante.com/brainwaves/archives/026649. html [corante.com]

    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
  • zerg (Score:3, Interesting)

    by Lord Omlette (124579) on Thursday March 24 2005, @01:25PM (#12037502) Homepage
    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...
  • by GeneralEmergency (240687) on Thursday March 24 2005, @03:59PM (#12039297) Journal


    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.

    • All entities are self-interested and will seek to defend and propagate themselves.

      Self-interest is not a requirement of an entity. It is merely the requirement of evolutionary progress or reasoned self-improvement. So, it is possible to create a non-self-interested entity that would then fail to self-preserve, self-replicate, or self-improve. The problem is we can't predict whether self-interest would develop or not. Likely it would be a random consequence of its "learning" that may or may not develop