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NASA Goddard to Auction off Patents for Automated Software Code Generation - 0 views

www.spaceref.com/...viewpr.html nasa patents software fault-tolerance design swarm
shared by Luís F. Simões on 04 Nov 10 - No Cached
  • The technology was originally developed to handle coding of control code for spacecraft swarms, but it is broadly applicable to any commercial application where rule-based systems development is used.
  • Luís F. Simões on 04 Nov 10
     
    This is related to the "Verified Software" item in NewScientist's list of ideas that will change science. At the link below you'll find the text of the patents being auctioned: http://icapoceantomo.com/item-for-sale/exclusive-license-related-improved-methodology-formally-developing-control-systems :) Patent #7,627,538 ("Swarm autonomic agents with self-destruct capability") makes for quite an interesting read: "This invention relates generally to artificial intelligence and, more particularly, to architecture for collective interactions between autonomous entities." "In some embodiments, an evolvable synthetic neural system is operably coupled to one or more evolvable synthetic neural systems in a hierarchy." "In yet another aspect, an autonomous nanotechnology swarm may comprise a plurality of workers composed of self-similar autonomic components that are arranged to perform individual tasks in furtherance of a desired objective." "In still yet another aspect, a process to construct an environment to satisfy increasingly demanding external requirements may include instantiating an embryonic evolvable neural interface and evolving the embryonic evolvable neural interface towards complex complete connectivity." "In some embodiments, NBF 500 also includes genetic algorithms (GA) 504 at each interface between autonomic components. The GAs 504 may modify the intra-ENI 202 to satisfy requirements of the SALs 502 during learning, task execution or impairment of other subsystems."
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Neural Networks Designed to 'See' are Quite Good at 'Hearing' As Well - 2 views

www.popsci.com/...ee-are-quite-good-hearing-well ia neural_networks
shared by pacome delva on 08 Jun 10 - Cached
  • Neural networks -- collections of artificial neurons or nodes set up to behave like the neurons in the brain -- can be trained to carry out a variety of tasks, often having something to do with pattern or sequence recognition. As such, they have shown great promise in image recognition systems. Now, research coming out of the University of Hong Kong has shown that neural networks can hear as well as see. A neural network there has learned the features of sound, classifying songs into specific genres with 87 percent accuracy.
  • Similar networks based on auditory cortexes have been rewired for vision, so it would appear these kinds of neural networks are quite flexible in their functions. As such, it seems they could potentially be applied to all sorts of perceptual tasks in artificial intelligence systems, the possibilities of which have only begun to be explored.
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Research Blog: Inceptionism: Going Deeper into Neural Networks - 0 views

googleresearch.blogspot.de/...-going-deeper-into-neural.html FUN AI
shared by alekenolte on 18 Jun 15 - No Cached
  • alekenolte on 18 Jun 15
     
    Deep neural networks "dreaming" psychedelic images
  • ...3 more comments...
  • Paul N on 25 Jun 15
     
    Although that's not technically correct. The networks don't actually generate the images, rather the features that get triggered in the network already get amplified through some heuristic. Still fun tho`
  • Paul N on 25 Jun 15
     
    Now in real time: http://www.twitch.tv/317070
  • alekenolte on 26 Jun 15
     
    Yes, true for the later images, but for the first images they start with random noise and a 'natural image' prior, no? But I guess calling it "hallucinating" might have been more accurate ;)
  • Luís F. Simões on 02 Jul 15
  • Paul N on 08 Jul 15
     
    Funny how representation errors in NNs suddenly become art. God.... neo-post-modernism.
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Thinking wind turbines - 0 views

www.siemens.com/...e_inno_1407_1.htm neural network wind
shared by Thijs Versloot on 16 Mar 14 - No Cached
  • Thijs Versloot on 16 Mar 14
     
    Siemens is using neural networks to improve operation of wind turbines, reducing maintenaince needs and improving output by one precent. It seems even that Siemens has quite a large neural network study group, probably linked to german universities, with various examples in practice (see websie)
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http://www.ai-junkie.com/ann/evolved/nnt2.html - 0 views

www.ai-junkie.com/...nnt2.html ai neural network
shared by ESA ACT on 24 Apr 09 - Cached
  • ESA ACT on 24 Apr 09
     
    Explanation of artificial neural networks (explains very nicely the basics of the programming behind Christos' PhD thesis)
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Miguel Nicolelis Says the Brain Is Not Computable, Bashes Kurzweil's Singularity | MIT ... - 9 views

www.technologyreview.com/...the-brain-is-not-computable Singularity AI Brain
shared by Dario Izzo on 28 Feb 13 - No Cached
LeopoldS liked it
  • Dario Izzo on 28 Feb 13
     
    As I said ten years ago and psychoanalysts 100 years ago. Luis I am so sorry :) Also ... now that the commission funded the project blue brain is a rather big hit Btw Nicolelis is a rather credited neuro-scientist
  • ...14 more comments...
  • LeopoldS on 28 Feb 13
     
    nice article; Luzi would agree as well I assume; one aspect not clear to me is the causal relationship it seems to imply between consciousness and randomness ... anybody?
  • Luís F. Simões on 01 Mar 13
     
    This is the same thing Penrose has been saying for ages (and yes, I read the book). IF the human brain proves to be the only conceivable system capable of consciousness/intelligence AND IF we'll forever be limited to the Turing machine type of computation (which is what the "Not Computable" in the article refers to) AND IF the brain indeed is not computable, THEN AI people might need to worry... Because I seriously doubt the first condition will prove to be true, same with the second one, and because I don't really care about the third (brains is not my thing).. I'm not worried.
  • Dario Izzo on 01 Mar 13
     
    In any case, all AI research is going in the wrong direction: the mainstream is not on how to go beyond Turing machines, rather how to program them well enough ...... and thats not bringing anywhere near the singularity
  • Luís F. Simões on 01 Mar 13
     
    It has not been shown that intelligence is not computable (only some people saying the human brain isn't, which is something different), so I wouldn't go so far as saying the mainstream is going in the wrong direction. But even if that indeed was the case, would it be a problem? If so, well, then someone should quickly go and tell all the people trading in financial markets that they should stop using computers... after all, they're dealing with uncomputable undecidable problems. :) (and research on how to go beyond Turing computation does exist, but how much would you want to devote your research to a non existent machine?)
  • Dario Izzo on 01 Mar 13
     
    [warning: troll] If you are happy with developing algorithms that serve the financial market ... good for you :) After all they have been proved to be useful for humankind beyond any reasonable doubt.
  • Ma Ru on 04 Mar 13
     
    Two comments from me: 1) an apparently credible scientist takes Kurzweil seriously enough to engage with him in polemics... oops 2) what worries me most, I didn't get the retail store pun at the end of article...
  • Dario Izzo on 04 Mar 13
     
    True, but after Google hired Kurzweil he is de facto being taken seriously ... so I guess Nicolelis reacted to this.
  • Ma Ru on 04 Mar 13
     
    Crazy scientist in residence... interesting marketing move, I suppose.
  • Luzi Bergamin on 05 Mar 13
     
    Unfortunately, I can't upload my two kids to the cloud to make them sleep, that's why I comment only now :-). But, of course, I MUST add my comment to this discussion. I don't really get what Nicolelis point is, the article is just too short and at a too popular level. But please realize that the question is not just "computable" vs. "non-computable". A system may be computable (we have a collection of rules called "theory" that we can put on a computer and run in a finite time) and still it need not be predictable. Since the lack of predictability pretty obviously applies to the human brain (as it does to any sufficiently complex and nonlinear system) the question whether it is computable or not becomes rather academic. Markram and his fellows may come up with a incredible simulation program of the human brain, this will be rather useless since they cannot solve the initial value problem and even if they could they will be lost in randomness after a short simulation time due to horrible non-linearities... Btw: this is not my idea, it was pointed out by Bohr more than 100 years ago...
  • Dario Izzo on 05 Mar 13
     
    I guess chaos is what you are referring to. Stuff like the Lorentz attractor. In which case I would say that the point is not to predict one particular brain (in which case you would be right): any initial conditions would be fine as far as any brain gets started :) that is the goal :)
  • Luzi Bergamin on 05 Mar 13
     
    Kurzweil talks about downloading your brain to a computer, so he has a specific brain in mind; Markram talks about identifying neural basis of mental diseases, so he has at least pretty specific situations in mind. Chaos is not the only problem, even a perfectly linear brain (which is not a biological brain) is not predictable, since one cannot determine a complete set of initial conditions of a working (viz. living) brain (after having determined about 10% the brain is dead and the data useless). But the situation is even worse: from all we know a brain will only work with a suitable interaction with its environment. So these boundary conditions one has to determine as well. This is already twice impossible. But the situation is worse again: from all we know, the way the brain interacts with its environment at a neural level depends on his history (how this brain learned). So your boundary conditions (that are impossible to determine) depend on your initial conditions (that are impossible to determine). Thus the situation is rather impossible squared than twice impossible. I'm sure Markram will simulate something, but this will rather be the famous Boltzmann brain than a biological one. Boltzman brains work with any initial conditions and any boundary conditions... and are pretty dead!
  • Dario Izzo on 05 Mar 13
     
    Say one has an accurate model of a brain. It may be the case that the initial and boundary conditions do not matter that much in order for the brain to function an exhibit macro-characteristics useful to make science. Again, if it is not one particular brain you are targeting, but the 'brain' as a general entity this would make sense if one has an accurate model (also to identify the neural basis of mental diseases). But in my opinion, the construction of such a model of the brain is impossible using a reductionist approach (that is taking the naive approach of putting together some artificial neurons and connecting them in a huge net). That is why both Kurzweil and Markram are doomed to fail.
  • johannessimon81 on 06 Mar 13
     
    I think that in principle some kind of artificial brain should be feasible. But making a brain by just throwing together a myriad of neurons is probably as promising as throwing together some copper pipes and a heap of silica and expecting it to make calculations for you. Like in the biological system, I suspect, an artificial brain would have to grow from a small tiny functional unit by adding neurons and complexity slowly and in a way that in a stable way increases the "usefulness"/fitness. Apparently our brain's usefulness has to do with interpreting inputs of our sensors to the world and steering the body making sure that those sensors, the brain and the rest of the body are still alive 10 seconds from now (thereby changing the world -> sensor inputs -> ...). So the artificial brain might need sensors and a body to affect the "world" creating a much larger feedback loop than the brain itself. One might argue that the complexity of the sensor inputs is the reason why the brain needs to be so complex in the first place. I never quite see from these "artificial brain" proposals in how far they are trying to simulate the whole system and not just the brain. Anyone? Or are they trying to simulate the human brain after it has been removed from the body? That might be somewhat easier I guess...
  • Luís F. Simões on 06 Mar 13
     
    Johannes: "I never quite see from these "artificial brain" proposals in how far they are trying to simulate the whole system and not just the brain." In Artificial Life the whole environment+bodies&brains is simulated. You have also the whole embodied cognition movement that basically advocates for just that: no true intelligence until you model the system in its entirety. And from that you then have people building robotic bodies, and getting their "brains" to learn from scratch how to control them, and through the bodies, the environment. Right now, this is obviously closer to the complexity of insect brains, than human ones. (my take on this is: yes, go ahead and build robots, if the intelligence you want to get in the end is to be displayed in interactions with the real physical world...) It's easy to dismiss Markram's Blue Brain for all their clever marketing pronouncements that they're building a human-level consciousness on a computer, but from what I read of the project, they seem to be developing a platfrom onto which any scientist can plug in their model of a detail of a detail of .... of the human brain, and get it to run together with everyone else's models of other tiny parts of the brain. This is not the same as getting the artificial brain to interact with the real world, but it's a big step in enabling scientists to study their own models on more realistic settings, in which the models' outputs get to effect many other systems, and throuh them feed back into its future inputs. So Blue Brain's biggest contribution might be in making model evaluation in neuroscience less wrong, and that doesn't seem like a bad thing. At some point the reductionist approach needs to start moving in the other direction.
  • Luzi Bergamin on 06 Mar 13
     
    @ Dario: absolutely agree, the reductionist approach is the main mistake. My point: if you take the reductionsit approach, then you will face the initial and boundary value problem. If one tries a non-reductionist approach, this problem may be much weaker. But off the record: there exists a non-reductionist theory of the brain, it's called psychology... @ Johannes: also agree, the only way the reductionist approach could eventually be successful is to actually grow the brain. Start with essentially one neuron and grow the whole complexity. But if you want to do this, bring up a kid! A brain without body might be easier? Why do you expect that a brain detached from its complete input/output system actually still works. I'm pretty sure it does not!
  • johannessimon81 on 06 Mar 13
     
    @Luzi: That was exactly my point :-)
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Artificial Neural Nets Grow Brainlike Navigation Cells - 0 views

www.quantamagazine.org/like-navigation-cells-20180509 AI
shared by jaihobah on 11 May 18 - No Cached
LeopoldS liked it
  • jaihobah on 11 May 18
     
    Faced with a navigational challenge, neural networks spontaneously evolved units resembling the grid cells that help living animals find their way.
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Google's AI Wizard Unveils a New Twist on Neural Networks - 2 views

www.wired.com/...a-new-twist-on-neural-networks AI
shared by jaihobah on 01 Nov 17 - No Cached
  • jaihobah on 01 Nov 17
     
    "Hinton's new approach, known as capsule networks, is a twist on neural networks intended to make machines better able to understand the world through images or video. In one of the papers posted last week, Hinton's capsule networks matched the accuracy of the best previous techniques on a standard test of how well software can learn to recognize handwritten digits." Links to papers: https://arxiv.org/abs/1710.09829 https://openreview.net/forum?id=HJWLfGWRb&noteId=HJWLfGWRb
  • LeopoldS on 02 Nov 17
     
    impressive!
  • LeopoldS on 02 Nov 17
     
    seems a very impressive guy :"Hinton formed his intuition that vision systems need such an inbuilt sense of geometry in 1979, when he was trying to figure out how humans use mental imagery. He first laid out a preliminary design for capsule networks in 2011. The fuller picture released last week was long anticipated by researchers in the field. "Everyone has been waiting for it and looking for the next great leap from Geoff," says Kyunghyun Cho, a professor"
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Google neural network teaches itself to identify cats - 1 views

www.eetimes.com/...neural-network-identifies-cats FUN INF AI
shared by Marcus Maertens on 22 Mar 13 - No Cached
  • Marcus Maertens on 22 Mar 13
     
    Its already "old" news but kinda nice...
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Neural network speech recognition - 4 views

www.wired.com/...android-neural-network technology
shared by johannessimon81 on 18 Feb 13 - No Cached
  • johannessimon81 on 18 Feb 13
     
    On Android speech recognition but also with a very nice video: direct translation of English voice input to Chinese audio. Looks like it might be really useful eventually.
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Bold title ..... - 3 views

www.extremetech.com/...ow-learn-from-pure-observation
shared by Dario Izzo on 02 Sep 16 - No Cached
  • johannessimon81 on 02 Sep 16
     
    I got a fever. And the only prescription is more cat faces! ...../\_/\ ...(=^_^) ..\\(___) The article sounds quite interesting, though. I think the idea of a "fake" agent that tries to trick the classifier while both co-evolve is nice as it allows the classifier to first cope with the lower order complexity of the problem. As the fake agent mimics the real agent better and better the classifier has time to add complexity to itself instead of trying to do it all at once. It would be interesting if this is later reflected in the neural nets structure, i.e. having core regions that deal with lower order approximation / classification and peripheral regions (added at a later stage) that deal with nuances as they become apparent. Also this approach will develop not just a classifier for agent behavior but at the same time a model of the same. The later may be useful in itself and might in same cases be the actual goal of the "researcher". I suspect, however, that the problem of producing / evolving the "fake agent" model might in most case be at least as hard as producing a working classifier...
  • johannessimon81 on 05 Sep 16
     
    This paper from 2014 seems discribe something pretty similar (except for not using physical robots, etc...): https://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf
  • Paul N on 13 Sep 16
     
    Yes, this IS basically adversarial learning. Except the generator part instead of being a neural net is some kind of swarm parametrization. I just love how they rebranded it, though. :))
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The Neural Network Zoo - The Asimov Institute (...love that name!) - 2 views

www.asimovinstitute.org/neural-network-zoo AI fun
shared by johannessimon81 on 15 Sep 16 - No Cached
  • johannessimon81 on 15 Sep 16
     
    Cute info-graphics on different machine learning architectures
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Will robots be smarter than humans by 2029? - 2 views

robohub.org/be-smarter-than-humans-by-2029 AI
shared by Guido de Croon on 10 Mar 14 - No Cached
  • Guido de Croon on 10 Mar 14
     
    Nice discussion about the singularity. Made me think of drinking coffee with Luis... It raises some issues such as the necessity of embodiment, etc.
  • ...9 more comments...
  • Luís F. Simões on 12 Mar 14
     
    "Kurzweilians"... LOL. Still not sold on embodiment, btw.
  • Ma Ru on 12 Mar 14
     
    The biggest problem with embodiment is that, since the passive walkers (with which it all started), it hasn't delivered anything really interesting...
  • Paul N on 13 Mar 14
     
    The problem with embodiment is that it's done wrong. Embodiment needs to be treated like big data. More sensors, more data, more processing. Just putting a computer in a robot with a camera and microphone is not embodiment.
  • Marcus Maertens on 14 Mar 14
     
    I like how he attacks Moore's Law. It always looks a bit naive to me if people start to (ab)use it to make their point. No strong opinion about embodiment.
  • johannessimon81 on 14 Mar 14
     
    @Paul: How would embodiment be done RIGHT?
  • Guido de Croon on 16 Mar 14
     
    Embodiment has some obvious advantages. For example, in the vision domain many hard problems become easy when you have a body with which you can take actions (like looking at an object you don't immediately recognize from a different angle) - a point already made by researchers such as Aloimonos.and Ballard in the end 80s / beginning 90s. However, embodiment goes further than gathering information and "mental" recognition. In this respect, the evolutionary robotics work by for example Beer is interesting, where an agent discriminates between diamonds and circles by avoiding one and catching the other, without there being a clear "moment" in which the recognition takes place. "Recognition" is a behavioral property there, for which embodiment is obviously important. With embodiment the effort for recognizing an object behaviorally can be divided between the brain and the body, resulting in less computation for the brain. Also the article "Behavioural Categorisation: Behaviour makes up for bad vision" is interesting in this respect. In the field of embodied cognitive science, some say that recognition is constituted by the activation of sensorimotor correlations. I wonder to which extent this is true, and if it is valid for extremely simple creatures to more advanced ones, but it is an interesting idea nonetheless. This being said, if "embodiment" implies having a physical body, then I would argue that it is not a necessary requirement for intelligence. "Situatedness", being able to take (virtual or real) "actions" that influence the "inputs", may be.
  • Ma Ru on 17 Mar 14
     
    @Paul While I completely agree about the "embodiment done wrong" (or at least "not exactly correct") part, what you say goes exactly against one of the major claims which are connected with the notion of embodiment (google for "representational bottleneck"). The fact is your brain does *not* have resources to deal with big data. The idea therefore is that it is the body what helps to deal with what to a computer scientist appears like "big data". Understanding how this happens is key. Whether it is the problem of scale or of actually understanding what happens should be quite conclusively shown by the outcomes of the Blue Brain project.
  • johannessimon81 on 17 Mar 14
     
    Wouldn't one expect that to produce consciousness (even in a lower form) an approach resembling that of nature would be essential? All animals grow from a very simple initial state (just a few cells) and have only a very limited number of sensors AND processing units. This would allow for a fairly simple way to create simple neural networks and to start up stable neural excitation patterns. Over time as complexity of the body (sensors, processors, actuators) increases the system should be able to adapt in a continuous manner and increase its degree of self-awareness and consciousness. On the other hand, building a simulated brain that resembles (parts of) the human one in its final state seems to me like taking a person who is just dead and trying to restart the brain by means of electric shocks.
  • Paul N on 17 Mar 14
     
    Actually on a neuronal level all information gets processed. Not all of it makes it into "conscious" processing or attention. Whatever makes it into conscious processing is a highly reduced representation of the data you get. However that doesn't get lost. Basic, low processed data forms the basis of proprioception and reflexes. Every step you take is a macro command your brain issues to the intricate sensory-motor system that puts your legs in motion by actuating every muscle and correcting every step deviation from its desired trajectory using the complicated system of nerve endings and motor commands. Reflexes which were build over the years, as those massive amounts of data slowly get integrated into the nervous system and the the incipient parts of the brain. But without all those sensors scattered throughout the body, all the little inputs in massive amounts that slowly get filtered through, you would not be able to experience your body, and experience the world. Every concept that you conjure up from your mind is a sort of loose association of your sensorimotor input. How can a robot understand the concept of a strawberry if all it can perceive of it is its shape and color and maybe the sound that it makes as it gets squished? How can you understand the "abstract" notion of strawberry without the incredibly sensible tactile feel, without the act of ripping off the stem, without the motor action of taking it to our mouths, without its texture and taste? When we as humans summon the strawberry thought, all of these concepts and ideas converge (distributed throughout the neurons in our minds) to form this abstract concept formed out of all of these many many correlations. A robot with no touch, no taste, no delicate articulate motions, no "serious" way to interact with and perceive its environment, no massive flow of information from which to chose and and reduce, will never attain human level intelligence. That's point 1. Point 2 is that mere pattern recogn
  • Ma Ru on 18 Mar 14
     
    All information *that gets processed* gets processed but now we arrived at a tautology. The whole problem is ultimately nobody knows what gets processed (not to mention how). In fact an absolute statement "all information" gets processed is very easy to dismiss because the characteristics of our sensors are such that a lot of information is filtered out already at the input level (e.g. eyes). I'm not saying it's not a valid and even interesting assumption, but it's still just an assumption and the next step is to explore scientifically where it leads you. And until you show its superiority experimentally it's as good as all other alternative assumptions you can make. I only wanted to point out is that "more processing" is not exactly compatible with some of the fundamental assumptions of the embodiment. I recommend Wilson, 2002 as a crash course.
  • Paul N on 24 Mar 14
     
    These deal with different things in human intelligence. One is the depth of the intelligence (how much of the bigger picture can you see, how abstract can you form concept and ideas), another is the breadth of the intelligence (how well can you actually generalize, how encompassing those concepts are and what is the level of detail in which you perceive all the information you have) and another is the relevance of the information (this is where the embodiment comes in. What you do is to a purpose, tied into the environment and ultimately linked to survival). As far as I see it, these form the pillars of human intelligence, and of the intelligence of biological beings. They are quite contradictory to each other mainly due to physical constraints (such as for example energy usage, and training time). "More processing" is not exactly compatible with some aspects of embodiment, but it is important for human level intelligence. Embodiment is necessary for establishing an environmental context of actions, a constraint space if you will, failure of human minds (i.e. schizophrenia) is ultimately a failure of perceived embodiment. What we do know is that we perform a lot of compression and a lot of integration on a lot of data in an environmental coupling. Imo, take any of these parts out, and you cannot attain human+ intelligence. Vary the quantities and you'll obtain different manifestations of intelligence, from cockroach to cat to google to random quake bot. Increase them all beyond human levels and you're on your way towards the singularity.
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Neural Networks (!) in OLCI - ocean colour sensor onboard Sentinel 3 - 3 views

sentinel.esa.int/..._Ocean_Colour_Turbid_Water.pdf NN algorithm eo optics phytoplankton ocean colour bio
shared by Athanasia Nikolaou on 01 Sep 14 - No Cached
LeopoldS and Paul N liked it
  • Athanasia Nikolaou on 01 Sep 14
     
    Not easily digestible piece of esa document, but to prove Paul's point. And yes, they have already planned to train neural networks on a database of different water types, so that the satellite figures out from the combined retrieval of backscattering and absorption = f(λ) which type of water it is looking at. Type of water relates to οptical clarity of the water, a variable called turbidity. We could do this as well for mapping iron fertilization locations if we find its spectral signature. Lab time?????
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Training and operation of an integrated neural network based on memristors - 0 views

www.nature.com/...nature14441.html NAN science technology memristor AI neuromorphic
shared by jcunha on 07 May 15 - No Cached
LeopoldS liked it
  • jcunha on 07 May 15
     
    Almost in time for the workshop last week! This new Nature paper (e-mail me for full paper) claims training and usage of neural network implemented with metal-oxide memristors, without selector CMOS. They used it to implement a delta-rule algorithm for classification of 3x3 pixel black and white letters. Very impressive work!!!!
  • jcunha on 07 May 15
     
    For those not that much into the topic, see the Nature's News and View section www.nature.com/nature/journal/v521/n7550/full/521037a.html?WT.ec_id=NATURE-20150507 where they feature this article.
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MoNETA: A Mind Made from Memristors (IEEE Spectrum) - 0 views

spectrum.ieee.org/...0 darpa AI memristor neuromorphic animat
shared by Luís F. Simões on 07 Dec 10 - No Cached
  • Luís F. Simões on 07 Dec 10
     
    (don't forget to turn your hype-filters on...) MoNETA (http://cns.bu.edu/nl/moneta.html) stands for "MOdular Neural Exploring Traveling Agent". It is one of projects participating in the DARPA-funded SyNAPSE project ("Systems of Neuromorphic Adaptive Plastic Scalable Electronics"): http://www.darpa.mil/dso/thrusts/bio/biologically/synapse/index.htm http://www.darpa.mil/dso/solicitations/baa08-28.html
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All Optical Interface for Parallel, Remote, and Spatiotemporal Control of Neuronal Acti... - 0 views

pubs.acs.org/...nl072783t.html BIO biocontroller bmi
shared by ESA ACT on 24 Apr 09 - No Cached
  • ESA ACT on 24 Apr 09
     
    A key technical barrier to furthering our understanding of complex neural networks has been the lack of tools for the simultaneous spatiotemporal control and detection of activity in a large number of neurons.
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Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable ... - 4 views

arxiv.org/1412.1897 AI neural networks images deep learning
shared by anonymous on 10 Dec 14 - No Cached
  • anonymous on 10 Dec 14
     
    Other possible study: get a textbook example of an image of a pen, evolve it just enough so NN can't recognize it anymore, while minimizing the distance between the original and evolved images. EDIT: Its been done already: http://cs.nyu.edu/~zaremba/docs/understanding.pdf
  • Paul N on 11 Dec 14
     
    Of course, you can't really use them to extrapolate. The unknown unknown is always the trickiest :P They should just make another class "random bullshit", really and dump all of this stuff in there. I think there's a potential paper right there
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Real-Time Recognition and Profiling of Home Appliances through a Single Electricity Sensor - 3 views

csserver.ucd.ie/...Secon10_REAR.pdf Smart home neural networks energy
shared by Thijs Versloot on 22 Oct 14 - No Cached
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  • Thijs Versloot on 22 Oct 14
     
    A personal interest of mine that I want to explore a bit more in the future. I just bought a ZigBee electricity monitor and I am wondering whether from the signal of the mains one could detect (reliably) the oven turning on, lights, etc. Probably requires Neural Network training. The idea would be to make a simple device which basically saves you money by telling you how much electricity you are wasting. Then again, its probably already done by Google...
  • ...3 more comments...
  • LeopoldS on 24 Oct 14
     
    nice project!
  • Thijs Versloot on 28 Oct 14
     
    For those interested, this is what/where I ordered.. http://openenergymonitor.org/emon/
  • Thijs Versloot on 10 Nov 14
     
    Update two.. RF chip is faulty and tonight I have to solder a new chip into place.. That's open-source hardware for you!
  • jcunha on 09 Dec 14
  • Thijs Versloot on 10 Dec 14
     
    haha, yep, that's it... but we can do better than that right! :)
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