Looks good in principle. I'd like to see some diagrams.The Architecture is the key.
~PM. > Date: Fri, 2 Nov 2012 03:08:36 -0400 > From: [email protected] > To: [email protected] > Subject: [agi] superintelligence. > > > > > > Note to ZS readers: save this message for Monday, I just had to get it > off my HD tonight. (too lazy to hold it). > > > > > > > > om > > Today I've decided, foolishly, to spill the beans on some of my more > advanced concepts for AI. I'm going to cover the parts of my Hypermind > architecture that I can remember right now and discuss their feasibility > and the technical problems in implementing them on both hardware and as > a bio-retrofit. > > The basic capabilities I'll be discussing are distinct but closely related: > > -> Algorithmic learning. > -> multi-session intelligence with interleaved sleep and wake sessions. > -> multi-configuration intelligence > -> distributed intelligence > -> High performance intelligence. > > om > > One of the annoyingist things about learning is repetition, or having to > go through ginormous training sets to learn a simple idea. Neural nets, > both natural and artificial use what is called adaptive learning. > Obviously, it isn't all that great, even in the high performance neurons > we humans get to use. What is going on is that our populations of > neurons have to be slowly nudged into the shape we want them to be in. > This takes practice, repetition, and lots of Dr. Pepper. (not that I use > that specific drink). > > What if we could learn algorithmically? What if we only needed to be > exposed to enough input to reasonably disambiguate the information to be > learned and then be able to use that knowledge, with masterful skill > until we run out of disk space and are forced to prune back the > less-useful stuff... The human memory system appears to be able to > buffer information in short term memory long enough for the long-term > neural nets to learn it, intellectually at least (with pathetically > limited bandwidth)... Skill memory is much trickier because it involves > wiring up what amount to new processor elements. So a student of math, > at any level, must work through many related problems before the skill > pathways begin to form, the long term memory is mostly useless for this > kind of skill... The amount of practice required to master a musical > instrument is legendary. > > The first step is to identify what exactly needs to be learned. That is > why the human brain relies on its imagination as it's primary mode of > perception. It compares the actual scene with the imagined scene to > identify surprising stimuli which it has special neurons rigged to > detect and then focus as learning targets. The most annoying kinds of > sounds are sounds that can't be modeled by the brain and therefore are > constantly hitting this surprise circuit. The brain is certainly much > more advanced than current neural networks but can we go any further? We > need is a much more efficient means of generating motor programs. One of > the issues is that the brain is pattern based, not math based, a math > co-processor, much more precise and reliable than the cerebellum could > provide the means for much more precise mental rehearsals. Furthermore, > if the information is stored in discreet data structures rather than a > matrix-blob as current neural networks are (or as a physically fixed > cortical circuit), then an algebra and optimization process can be > performed on them to much more quickly derive a more optimal > representation that would otherwise take years to develop through practice. > > Another problem with the matrix representation is that it simulates > neural plasticity with a vector of weights. In larger networks, this > vector is almost sure to be relatively sparse and therefore considerable > amounts of computation will be wasted computing N*0 . Therefore the > conventional systems are inherently unscaleable in addition to having > the previously mentioned weaknesses. > > In a biological context we need pattern processing elements that can be > re-configured programatically and we need an organ that can inspect and > re-organize these pattern processing elements on a continual basis. > > om > > One of the crippling limitations of our current neural architecture is > that we can only do one thing at a time. The best we can do is rapidly > time-share our brains between two tasks. This will almost inevitably > lead to confusion as I frequently try to read an article and listen to a > video talk or internet radio talk show at the same time. Our brains also > must spend about 1/3rd of their time doing house-keeping tasks and > simulations to rehearse behaviors and consolidate memories. What if we > could treat our knowledge and memories as a mainframe hosted database > and run tens of thousands of fully isolated concurrent processes over > it. It might cut out useful forms of confusion but it would also allow > us to fire up as many sessions as we need to process our 21st century > information diet. This, again, requires us to be able to process > memories and skills as discreet units that can be treated symbolically > so that cached copies in different partitions can be re-integrated, as > it has been noted that this won't work in neural nets. > > So we need a cognitive architecture that can be multi-tasked out to the > limit of the hardware... Conceptually, this is no different than how an > operating system carries out multitasking, it is only necessary to be > able to virtualize the hardware and run the virtual instances > concurrently. Then RDBMS techniques can be applied to the stored > abstractions and there you go... > > In a biological context, it is necessary to be able to tag and index > activation patterns so that they can be processed independently against > the set of abstractions. > > om > > When one thinks about all the kinds of things one would want to extend > one's mind into in all of transhumandom (real world applications always > being more interesting than VR...) one is boggled by the variety. On the > extreme, you might want a highly optimized flame-bot for getting into > IRC and mailing list flame-wars, its modalities would be primarily > text-based and would only have enough visualization to be able to > properly interpret the language. On the other extreme you would might > have a very large and highly complex virtual body with many conventional > and unconventional modalities for 3rd generation cyber-sex... Both > systems would act concurrently on the same database... The basic problem > is that you need to select and configure an ad-hoc network of functional > units as needed for the selected embodyment. The fixed white-matter > pathways one finds in conventional brains (and simulations thereof) > hardly provides for any flexibility. The only solution is to chuck it > and use computer data structures. > > A biologically embodied superintelligence wouldn't need to deal with as > much change as mentioned above. It would be sufficient that it merely > have a few spare general-purpose channels that can be tasked as > necessary. Additional capabilities would come through networking in > semi-sentient sub-units. > > om > > That brings us to distributed intelligence. For many reasons it is > desirable to spread a mind across geographically separated locations. > The physical security of any one site can never be perfect and it is > probable that low-latency interactions will be desirable in a number of > contexts. The first issue is latency, the second issue is the one Eugene > likes to beat us over the head with, synchronizing slower hosts with > faster hosts. There are two basic types of interactions here. The first > is the knowledge, skills, and memories. This level of knowledge, as I > proposed above, can be treated like a database and therefore the > standard synchronization techniques can be applied. Latency is not a > major problem though bandwidth is. As bandwidth becomes choked, each > side the link will have to prioritize traffic. This is not a problem at > all. What is a problem is the high level executive function, the thread > of consciousness who's modality is to manage all of the other parallel > operations. As the system becomes larger, it will be necessary to apply > a deeper and deeper hierarchy of executive functions. Unless there's a > major breakthrough in quantum communication, it appears that it will be > necessary that the top level executive function will necessarily be > quite slow. However, this does not place any constraints on the > subordinate exeucitives, or the workers. Unlike what you may have read > in sci-fi, the parts of the AI you actually interact with locally will > run at full speed but they will not have any special privileges in > communicating with other agents within the same mind on a distant planet. > > Eugene says that neural interfaces are pointless because human neurology > is so many orders of magnitude slower than an AI substrate (actually, he > claims this is true of uploads too!!!). He never goes past the quick > put-down. He assumes what he perceives to be perfectly obvious to be an > insurmountable, unavoidable obstacle. The question is, exactly how much > communication do you need between the "fast" parts of your mind and the > legacy parts of your mind? Is there sufficient bandwidth available and > does the latency create a bottleneck? > > As I explained above, when bandwidth is limited, then only the most > important messages are sent. This is an inherent limitation in the human > brain that simply cannot be overcome by any conceivable technique, > including uploading, because the neural architecture is too limited in > capacity and has the previously mentioned limitations in learning and > scalability. It is also acknowledged that a slow-time or even uploaded > human would have difficulty managing more than a small number of > interlinked agents. The only way around that is to use a more powerful > executive function external to the base neural architecture. This does > have the side effect of reducing you to an appendage of a larger being > but I'm pretty sure that's the best deal you can possibly get. The best > part of it, you get to live through the process! > > om > > Now for the last part of the discussion, pushing performance through the > roof. "clock speed" and even basic algorithmic tweaks are too trivial to > mention. Where the real gold is, is in expanding the architecture to be > able to grok a chemistry database of 2 * 10^6 unique chemicals. Or truly > understand 5-space physics. What you need is the ability to manipulate > and extend the architecture of the mind as the cognitive demands arise. > It's difficult to say when a new domain sub-processor would be > required... Maybe a frustration detector would be able to identify where > the existing system is failing, or maybe the problem would be optimized > out by assuming everything is higher-dimensional from the outset and > only reducing dimensions as the true eigen-spaces become apparent. > Regardless, the true power of superintelligence is the ability to expand > and reconfigure as the need arises. This approach, and only this > approach is capable of tackling the grand challenges of the universe. > > > > Conclusion: superintelligence looks nothing like an upload! =P > > > > -- > E T F > N H E > D E D > > Powers are not rights. > > > > ------------------------------------------- > AGI > Archives: https://www.listbox.com/member/archive/303/=now > RSS Feed: https://www.listbox.com/member/archive/rss/303/19999924-5cfde295 > Modify Your Subscription: https://www.listbox.com/member/?&; > Powered by Listbox: http://www.listbox.com ------------------------------------------- AGI Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/21088071-c97d2393 Modify Your Subscription: https://www.listbox.com/member/?member_id=21088071&id_secret=21088071-2484a968 Powered by Listbox: http://www.listbox.com
