Does not look like there is a nice formatting option. They do have an enable conversations setting, but I do not thing that provides formatting and indentation. If I have some free time -- which I have very little of unfortunately -- I will look.
________________________________ From: Terren Suydam <[email protected]> To: [email protected] Sent: Friday, September 5, 2014 4:02 PM Subject: Re: Fwd: The Machine Intelligence Research Institute Blog I left Yahoo mail five years ago because they do such a terrible job of engineering. I have embraced the Google. Thanks for whatever you can do. Usually email clients offer a couple of modes for how to include the original email... Is there a different mode you can try? Terren On Sep 5, 2014 5:50 PM, "'Chris de Morsella' via Everything List" <[email protected]> wrote: Terren - You should forward your concerns to the folks who code the yahoo webmail client... when I am at work I use its webmail client, which does a poor job of threading a conversation. Will try to remember that and put in manual '>>' marks to show what I am replying to. > > > >________________________________ > From: Terren Suydam <[email protected]> >To: [email protected] >Sent: Friday, September 5, 2014 12:47 PM >Subject: Re: Fwd: The Machine Intelligence Research Institute Blog > > > >Chris, is there a way you can improve your email client? Sometimes your >responses are very hard to detect because they're at the same indentation and >font as the one you are reply to, as below. Someone new to the conversation >would have no way of knowing that Brent did not write that entire thing, as >you didn't sign your name. > > >Thanks, Terren > > > > > > >On Fri, Sep 5, 2014 at 2:15 PM, 'Chris de Morsella' via Everything List ><[email protected]> wrote: > > >> >> >> >> >>________________________________ >> From: meekerdb <[email protected]> >>To: EveryThing <[email protected]> >>Sent: Friday, September 5, 2014 9:47 AM >>Subject: Fwd: The Machine Intelligence Research Institute Blog >> >> >> >>For you who are worried about the threat of artificial intelligence, MIRI >>seems to make it their main concern. Look up their website and subscribe. >>On my list of existential threats it comes well below natural stupidity. >> >> >>On mine as well... judging by how far the google car still has to go before >>it does not drive straight into that pothole or require that its every route >>be very carefully mapped down to the level of each single driveway. Real >>world AI is still mired in the stubbornly, dumb as sand nature of our silicon >>based deterministic logic gate architecture. >>Much higher chance that we will blow ourselves up in some existentially >>desperate final energy war, or so poison our earth's biosphere that systemic >>collapse is triggered and the deep ocean's flip into an anoxic state favoring >>the hydrogen sulfide producing microorganisms that are poisoned by oxygen, >>resulting in another great belch of poisonous (to animals and plants) >>hydrogen sulfide into the planet's atmosphere -- as occurred during the great >>Permian extinction. >>Speaking of which has anyone read the recent study that concludes the current >>anthropocene boundary layer extinction rate is more than one thousand times >>the average extinction level that prevailed from the last great extinction >>(Jurassic) until now. See: Extinctions during human era one thousand times >>more than before >> >>Brent >> >> >> >> >>-------- Original Message -------- >>Subject: The Machine Intelligence Research Institute Blog >>Date: Fri, 05 Sep 2014 12:07:00 +0000 >>From: Machine Intelligence Research Institute » Blog <[email protected]> >>To: [email protected] >> >> >>The Machine Intelligence Research Institute Blog >> >>________________________________ >> >>John Fox on AI safety >>Posted: 04 Sep 2014 12:00 PM PDT >> John Fox is an interdisciplinary scientist with theoretical interests in AI >> and computer science, and an applied focus in medicine and medical software >> engineering. After training in experimental psychology at Durham and >> Cambridge Universities and post-doctoral fellowships at CMU and Cornell in >> the USA and UK (MRC) he joined the Imperial Cancer Research Fund (now Cancer >> Research UK) in 1981 as a researcher in medical AI. The group’s research was >> explicitly multidisciplinary and it subsequently made significant >> contributions in basic computer science, AI and medical informatics, and >> developed a number of successful technologies which have been commercialised. >>In 1996 he and his team were awarded the 20th Anniversary Gold Medal of the >>European Federation of Medical Informatics for the development of PROforma, >>arguably the first formal computer language for modeling clinical decision >>and processes. Fox has published widely in computer science, cognitive >>science and biomedical engineering, and was the founding editor of the >>Knowledge Engineering Review (Cambridge University Press). Recent >>publications include a research monograph Safe and Sound: Artificial >>Intelligence in Hazardous Applications (MIT Press, 2000) which deals with the >>use of AI in safety-critical fields such as medicine. >>Luke Muehlhauser: You’ve spent many years studying AI safety issues, in >>particular in medical contexts, e.g. in your 2000 book with Subrata Das, Safe >>and Sound: Artificial Intelligence in Hazardous Applications. What kinds of >>AI safety challenges have you focused on in the past decade or so? >>________________________________ >> >>John Fox: From my first research job, as a post-doc with AI founders Allen >>Newell and Herb Simon at CMU, I have been interested in computational >>theories of high level cognition. As a cognitive scientist I have been >>interested in theories that subsume a range of cognitive functions, from >>perception and reasoning to the uses of knowledge in autonomous >>decision-making. After I came back to the UK in 1975 I began to combine my >>theoretical interests with the practical goals of designing and deploying AI >>systems in medicine. >>Since our book was published in 2000 I have been committed to testing the >>ideas in it by designing and deploying many kind of clinical systems, and >>demonstrating that AI techniques can significantly improve quality and safety >>of clinical decision-making and process management. Patient safety is >>fundamental to clinical practice so, alongside the goals of building systems >>that can improve on human performance, safety and ethics have always been >>near the top of my research agenda. >>________________________________ >> >>Luke Muehlhauser: Was it straightforward to address issues like safety and >>ethics in practice? >>________________________________ >> >>John Fox: While our concepts and technologies have proved to be clinically >>successful we have not achieved everything we hoped for. Our attempts to >>ensure, for example, that practical and commercial deployments of AI >>technologies should explicitly honor ethical principles and carry out active >>safety management have not yet achieved the traction that we need to achieve. >>I regard this as a serious cause for concern, and unfinished business in both >>scientific and engineering terms. >>The next generation of large-scale knowledge based systems and software >>agents that we are now working on will be more intelligent and will have far >>more autonomous capabilities than current systems. The challenges for human >>safety and ethical use of AI that this implies are beginning to mirror those >>raised by the singularity hypothesis. We have much to learn from singularity >>researchers, and perhaps our experience in deploying autonomous agents in >>human healthcare will offer opportunities to ground some of the singularity >>debates as well. >>________________________________ >> >>Luke: You write that your “attempts to ensure… [that] commercial deployments >>of AI technologies should… carry out active safety management” have not yet >>received as much traction as you would like. Could you go into more detail on >>that? What did you try to accomplish on this front that didn’t get adopted by >>others, or wasn’t implemented? >>________________________________ >> >>John: Having worked in medical AI from the early ‘seventies I have always >>been keenly aware that while AI can help to mitigate the effects of human >>error there is a potential downside too. AI systems could be programmed >>incorrectly, or their knowledge could prescribe inappropriate practices, or >>they could have the effect of deskilling the human professionals who have the >>final responsibility for their patients. Despite well-known limitations of >>human cognition people remain far and away the most versatile and creative >>problem solvers on the planet. >>In the early ‘nineties I had the opportunity to set up a project whose goal >>was to establish a rigorous framework for the design and implementation of AI >>systems for safety critical applications. Medicine was our practical focus >>but the RED project1 was aimed at the development of a general architecture >>for the design of autonomous agents that could be trusted to make decisions >>and carry out plans as reliably and safely as possible, certainly to be as >>competent and hence as trustworthy as human agents in comparable tasks. This >>is obviously a hard problem but we made sufficient progress on theoretical >>issues and design principles that I thought there was a good chance the >>techniques might be applicable in medical AI and maybe even more widely. >>I thought AI was like medicine, where we all take it for granted that medical >>equipment and drug companies have a duty of care to show that their products >>are effective and safe before they can be certificated for commercial use. I >>also assumed that AI researchers would similarly recognize that we have a >>“duty of care” to all those potentially affected by poor engineering or >>misuse in safety critical settings but this was naïve. The commercial tools >>that have been based on the technologies derived from AI research have to >>date focused on just getting and keeping customers and safety always takes a >>back seat. >>In retrospect I should have predicted that making sure that AI products are >>safe is not going to capture the enthusiasm of commercial suppliers. If you >>compare AI apps with drugs we all know that pharmaceutical companies have to >>be firmly regulated to make sure they fulfill their duty of care to their >>customers and patients. However proving drugs are safe is expensive and also >>runs the risk of revealing that your new wonder-drug isn’t even as effective >>as you claim! It’s the same with AI. >>I continue to be surprised how optimistic software developers are – they >>always seem to have supreme confidence that worst-case scenarios wont happen, >>or that if they do happen then their management is someone else’s >>responsibility. That kind of technical over-confidence has led to countless >>catastrophes in the past, and it amazes me that it persists. >>There is another piece to this, which concerns the roles and responsibilities >>of AI researchers. How many of us take the risks of AI seriously so that it >>forms a part of our day-to-day theoretical musings and influences our >>projects? MIRI has put one worst case scenario in front of us – the >>possibility that our creations might one day decide to obliterate us – but so >>far as I can tell the majority of working AI professionals either see safety >>issues as irrelevant to the pursuit of interesting scientific questions or, >>like the wider public, that the issues are just science fiction. >>I think experience in medical AI trying to articulate and cope with human >>risk and safety may have a couple of important lessons for the wider AI >>community. First we have a duty of care that professional scientists cannot >>responsibly ignore. Second, the AI business will probably need to be >>regulated, in much the same way as the pharmaceutical business is. If these >>propositions are correct then the AI research community would be wise to >>engage with and lead on discussions around safety issues if it wants to >>ensure that the regulatory framework that we get is to our liking! >>________________________________ >> >>Luke: Now you write, “That kind of technical over-confidence has led to >>countless catastrophes in the past…” What are some example “catastrophes” >>you’re thinking of? >>________________________________ >> >>John: >>Psychologists have known for years that human decision-making is flawed, even >>if amazingly creative sometimes, and overconfidence is an important source of >>error in routine settings. A large part of the motivation for applying AI in >>medicine comes from the knowledge that, in the words of the Institute of >>Medicine, “To err is human” and overconfidence is an established cause of >>clinical mistakes.2 >>Over-confidence and its many relatives (complacency, optimism, arrogance and >>the like) have a huge influence on our personal successes and failures, and >>our collective futures. The outcomes of the US and UK’s recent adventures >>around the world can be easily identified as consequences of overconfidence, >>and it seems to me that the polarized positions about global warming and >>planetary catastrophe are both expressions of overconfidence, just in >>opposite directions. >>________________________________ >> >>Luke: Looking much further out… if one day we can engineer AGIs, do you think >>we are likely to figure out how to make them safe? >>________________________________ >> >>John: History says that making any technology safe is not an easy business. >>It took quite a few boiler explosions before high-pressure steam engines got >>their iconic centrifugal governors. Ensuring that new medical treatments are >>safe as well as effective is famously difficult and expensive. I think we >>should assume that getting to the point where an AGI manufacturer could >>guarantee its products are safe will be a hard road, and it is possible that >>guarantees are not possible in principle. We are not even clear yet what it >>means to be “safe”, at least not in computational terms. >>It seems pretty obvious that entry level robotic products like the robots >>that carry out simple domestic chores or the “nursebots” that are being >>trialed for hospital use, have such a simple repertoire of behaviors that it >>should not be difficult to design their software controllers to operate >>safely in most conceivable circumstances. Standard safety engineering >>techniques like HAZOP3 are probably up to the job I think, and where software >>failures simply cannot be tolerated software engineering techniques like >>formal specification and model-checking are available. >>There is also quite a lot of optimism around more challenging robotic >>applications like autonomous vehicles and medical robotics. Moustris et al.4 >>say that autonomous surgical robots are emerging that can be used in various >>roles, automating important steps in complex operations like open-heart >>surgery for example, and they expect them to become standard in – and to >>revolutionize the practice of – surgery. However at this point it doesn’t >>seem to me that surgical robots with a significant cognitive repertoire are >>feasible and a human surgeon will be in the loop for the foreseeable future. >>________________________________ >> >>Luke: So what might artificial intelligence learn from natural intelligence? >>________________________________ >> >>As a cognitive scientist working in medicine my interests are co-extensive >>with those of scientists working on AGIs. Medicine is such a vast domain that >>practicing it safely requires the ability to deal with countless clinical >>scenarios and interactions and even when working in a single specialist >>subfield requires substantial knowledge from other subfields. So much so that >>it is now well known that even very experienced humans with a large clinical >>repertoire are subject to significant levels of error.5 An artificial >>intelligence that could be helpful across medicine will require great >>versatility, and this will require a general understanding of medical >>expertise and a range of cognitive capabilities like reasoning, >>decision-making, planning, communication, reflection, learning and so forth. >>If human experts are not safe is it well possible to ensure that an AGI, >>however sophisticated, will be? I think that it is pretty clear that the >>range of techniques currently available for assuring system safety will be >>useful in making specialist AI systems reliable and minimizing the likelihood >>of errors in situations that their human designers can anticipate. However, >>AI systems with general intelligence will be expected to address scenarios >>and hazards that are beyond us to solve currently and often beyond designers >>even to anticipate. I am optimistic but at the moment I don’t see any >>convincing reason to believe that we have the techniques that would be >>sufficient to guarantee that a clinical super-intelligence is safe, let alone >>an AGI that might be deployed in many domains. >> >>________________________________ >> >>Luke: Thanks, John! >>________________________________ >> >> 1. Rigorously Engineered Decisions >> 2. Overconfidence in major disasters: >>• D. Lucas. Understanding the Human Factor in Disasters. Interdisciplinary >>Science Reviews. Volume 17 Issue 2 (01 June 1992), pp. 185-190. >>• “Nuclear safety and security. >>Psychology of overconfidence: >>• Overconfidence effect. >>• C. Riordan. Three Ways Overconfidence Can Make a Fool of You Forbes >>Leadership Forum. >>Overconfidence in medicine: >>• R. Hanson. Overconfidence Erases Doc Advantage. Overcoming Bias, 2007. >>• E. Berner, M. Graber. Overconfidence as a Cause of Diagnostic Error in >>Medicine. The American Journal of Medicine. Volume 121, Issue 5, Supplement, >>Pages S2–S23, May 2008. >>• T. Ackerman. Doctors overconfident, study finds, even in hardest cases. >>Houston Chronicle, 2013. >>General technology example: >>• J. Vetter, A. Benlian, T. Hess. Overconfidence in IT Investment Decisions: >>Why Knowledge can be a Boon and Bane at the same Time. ICIS 2011 Proceedings. >>Paper 4. December 6, 2011. >> 3. Hazard and operability study >> 4. Int J Med Robotics Comput Assist Surg 2011; 7: 375–39 >> 5. A. Ford. Domestic Robotics – Leave it to Roll-Oh, our Fun loving >> Retrobot. Institute for Ethics and Emerging Technologies, 2014. >>The post John Fox on AI safety appeared first on Machine Intelligence >>Research Institute. >>Daniel Roy on probabilistic programming and AI >>Posted: 04 Sep 2014 08:03 AM PDT >> Daniel Roy is an Assistant Professor of Statistics at the University of >> Toronto. Roy earned an S.B. and M.Eng. in Electrical Engineering and >> Computer Science, and a Ph.D. in Computer Science, from MIT. His >> dissertation on probabilistic programming received the department’s George M >> Sprowls Thesis Award. Subsequently, he held a Newton International >> Fellowship of the Royal Society, hosted by the Machine Learning Group at the >> University of Cambridge, and then held a Research Fellowship at Emmanuel >> College. Roy’s research focuses on theoretical questions that mix computer >> science, statistics, and probability. >>Luke Muehlhauser: The abstract of Ackerman, Freer, and Roy (2010) begins: >>As inductive inference and machine learning methods in computer science see >>continued success, researchers are aiming to describe even more complex >>probabilistic models and inference algorithms. What are the limits of >>mechanizing probabilistic inference? We investigate the computability of >>conditional probability… and show that there are computable joint >>distributions with noncomputable conditional distributions, ruling out the >>prospect of general inference algorithms. >>In what sense does your result (with Ackerman & Freer) rule out the prospect >>of general inference algorithms? >>________________________________ >> >>Daniel Roy: First, it’s important to highlight that when we say >>“probabilistic inference” we are referring to the problem of computing >>conditional probabilities, while highlighting the role of conditioning in >>Bayesian statistical analysis. >>Bayesian inference centers around so-called posterior distributions. From a >>subjectivist standpoint, the posterior represents one’s updated beliefs after >>seeing (i.e., conditioning on) the data. Mathematically, a posterior >>distribution is simply a conditional distribution (and every conditional >>distribution can be interpreted as a posterior distribution in some >>statistical model), and so our study of the computability of conditioning >>also bears on the problem of computing posterior distributions, which is >>arguably one of the core computational problems in Bayesian analyses. >>Second, it’s important to clarify what we mean by “general inference”. In >>machine learning and artificial intelligence (AI), there is a long tradition >>of defining formal languages in which one can specify probabilistic models >>over a collection of variables. Defining distributions can be difficult, but >>these languages can make it much more straightforward. >>The goal is then to design algorithms that can use these representations to >>support important operations, like computing conditional distributions. >>Bayesian networks can be thought of as such a language: You specify a >>distribution over a collection of variables by specifying a graph over these >>variables, which breaks down the entire distribution into “local” conditional >>distributions corresponding with each node, which are themselves often >>represented as tables of probabilities (at least in the case where all >>variables take on only a finite set of values). Together, the graph and the >>local conditional distributions determine a unique distribution over all the >>variables. >>An inference algorithms that support the entire class of all finite, >>discrete, Bayesian networks might be called general, but as a class of >>distributions, those having finite, discrete Bayesian networks is a rather >>small one. >>In this work, we are interested in the prospect of algorithms that work on >>very large classes of distributions. Namely, we are considering the class of >>samplable distributions, i.e., the class of distributions for which there >>exists a probabilistic program that can generate a sample using, e.g., >>uniformly distributed random numbers or independent coin flips as a source of >>randomness. The class of samplable distributions is a natural one: indeed it >>is equivalent to the class of computable distributions, i.e., those for which >>we can devise algorithms to compute lower bounds on probabilities from >>descriptions of open sets. The class of samplable distributions is also >>equivalent to the class of distributions for which we can compute >>expectations from descriptions of bounded continuous functions. >>The class of samplable distributions is, in a sense, the richest class you >>might hope to deal with. The question we asked was: is there an algorithm >>that, given a samplable distribution on two variables X and Y, represented by >>a program that samples values for both variables, can compute the conditional >>distribution of, say, Y given X=x, for almost all values for X? When X takes >>values in a finite, discrete set, e.g., when X is binary valued, there is a >>general algorithm, although it is inefficient. But when X is continuous, >>e.g., when it can take on every value in the unit interval [0,1], then >>problems can arise. In particular, there exists a distribution on a pair of >>numbers in [0,1] from which one can generate perfect samples, but for which >>it is impossible to compute conditional probabilities for one of the >>variables given the other. As one might expect, the proof reduces the halting >>problem to that of conditioning a specially crafted distribution. >>This pathological distribution rules out the possibility of a general >>algorithm for conditioning (equivalently, for probabilistic inference). The >>paper ends by giving some further conditions that, when present, allow one to >>devise general inference algorithms. Those familiar with computing >>conditional distributions for finite-dimensional statistical models will not >>be surprised that conditions necessary for Bayes’ theorem are one example. >> >>________________________________ >> >>Luke: In your dissertation (and perhaps elsewhere) you express a particular >>interest in the relevance of probabilistic programming to AI, including the >>original aim of AI to build machines which rival the general intelligence of >>a human. How would you describe the relevance of probabilistic programming to >>the long-term dream of AI? >>________________________________ >> >>Daniel: If you look at early probabilistic programming systems, they were >>built by AI researchers: De Raedt, Koller, McAllester, Muggleton, Pfeffer, >>Poole, Sato, to name a few. The Church language, which was introduced in >>joint work with Bonawitz, Mansinghka, Goodman, and Tenenbaum while I was a >>graduate student at MIT, was conceived inside a cognitive science laboratory, >>foremost to give us a language rich enough to express the range of models >>that people were inventing all around us. So, for me, there’s always been a >>deep connection. On the other hand, the machine learning community as a whole >>is somewhat allergic to AI and so the pitch to that community has more often >>been pragmatic: these systems may someday allow experts to conceive, >>prototype, and deploy much larger probabilistic systems, and at the same >>time, empower a much larger community of nonexperts to use probabilistic >>modeling techniques to understand their data. This is the basis for the DARPA PPAML program, which is funding 8 or so teams to engineer scalable systems over the next 4 years. >>From an AI perspective, probabilistic programs are an extremely general >>representation of knowledge, and one that identifies uncertainty with >>stochastic computation. Freer, Tenenbaum, and I recently wrote a book chapter >>for the Turing centennial that uses a classical medical diagnosis example to >>showcase the flexibility of probabilistic programs and a general QUERY >>operator for performing probabilistic conditioning. Admittedly, the book >>chapter ignores the computational complexity of the QUERY operator, and any >>serious proposal towards AI cannot do this indefinitely. Understanding when >>we can hope to efficiently update our knowledge in light of new observations >>is a rich source of research questions, both applied and theoretical, >>spanning not only AI and machine learning, but also statistics, probability, >>physics, theoretical computer science, etc. >>________________________________ >> >>Luke: Is it fair to think of QUERY as a “toy model” that we can work with in >>concrete ways to gain more general insights into certain parts of the >>long-term AI research agenda, even though QUERY is unlikely to be directly >>implemented in advanced AI systems? (E.g. that’s how I think of AIXI.) >>________________________________ >> >>Daniel: I would hesitate to call QUERY a toy model. Conditional probability >>is a difficult concept to master, but, for those adept at reasoning about the >>execution of programs, QUERY demystifies the concept considerably. QUERY is >>an important conceptual model of probabilistic conditioning. >>That said, the simple guess-and-check algorithm we present in our Turing >>article runs in time inversely proportional to the probability of the >>event/data on which one is conditioning. In most statistical settings, the >>probability of a data set decays exponentially towards 0 as a function of the >>number of data points, and so guess-and-check is only useful for reasoning >>with toy data sets in these settings. It should come as no surprise to hear >>that state-of-the-art probabilistic programming systems work nothing like >>this. >>On the other hand, QUERY, whether implemented in a rudimentary fashion or >>not, can be used to represent and reason probabilistically about arbitrary >>computational processes, whether they are models of the arrival time of spam, >>the spread of disease through networks, or the light hitting our retinas. >>Computer scientists, especially those who might have had a narrow view of the >>purview of probability and statistics, will see a much greater overlap >>between these fields and their own once they understand QUERY. >>To those familiar with AIXI, the difference is hopefully clear: QUERY >>performs probabilistic reasoning in a model given as input. AIXI, on the >>other hand, is itself a “universal” model that, although not computable, >>would likely predict (hyper)intelligent behavior, were we (counterfactually) >>able to perform the requisite probabilistic inferences (and feed it enough >>data). Hutter gives an algorithm implementing an approximation to AIXI, but >>its computational complexity still scales exponentially in space. AIXI is >>fascinating in many ways: If we ignore computational realities, we get a >>complete proposal for AI. On the other hand, AIXI and its approximations take >>maximal advantage of this computational leeway and are, therefore, ultimately >>unsatisfying. For me, AIXI and related ideas highlight that AI must be as >>much a study of the particular as it of the universal. Which potentially >>unverifiable, but useful, assumptions will enable us to efficiently represent, update, and act upon knowledge under uncertainty? >>________________________________ >> >>Luke: You write that “AI must be as much a study of the particular as it is >>of the universal.” Naturally, most AI scientists are working on the >>particular, the near term, the applied. In your view, what are some other >>examples of work on the universal, in AI? Schmidhuber’s Gödel machine comes >>to mind, and also some work that is as likely to be done in a logic or formal >>philosophy department as a computer science department — e.g. perhaps work on >>logical priors — but I’d love to hear what kinds of work you’re thinking of. >>________________________________ >> >>Daniel: I wouldn’t equate any two of the particular, near-term, or applied. >>By the word particular, I am referring to, e.g., the way that our environment >>affects, but is also affected by, our minds, especially through society. More >>concretely, both the physical spaces in which most of us spend our days and >>the mental concepts we regularly use to think about our daily activities are >>products of the human mind. But more importantly, these physical and mental >>spaces are necessarily ones that are easily navigated by our minds. The >>coevolution by which this interaction plays out is not well studied in the >>context of AI. And to the extent that this cycle dominates, we would expect a >>universal AI to be truly alien. On the other hand, exploiting the constraints >>of human constructs may allow us to build more effective AIs. >>As for the universal, I have an interest in the way that noise can render >>idealized operations computable or even efficiently computable. In our work >>on the computability of conditioning that came up earlier in the discussion, >>we show that adding sufficiently smooth independent noise to a random >>variable allows us to perform conditioning in situations where we would not >>have been able to otherwise. There are examples of this idea elsewhere. For >>example, Braverman, Grigo, and Rojas study noise and intractability in >>dynamical systems. Specifically, they show that computing the invariant >>measure characterizing the long-term statistical behavior of dynamical >>systems is not possible. The road block is the computational power of the >>dynamical system itself. The addition of a small amount of noise to the >>dynamics, however, decreases the computational power of the dynamical system, >>and suffices to make the invariant measure computable. In a world subject to noise (or, at least, well modeled as such), it seems that many theoretical obstructions melt away. >>________________________________ >> >>Luke: Thanks, Daniel! >>The post Daniel Roy on probabilistic programming and AI appeared first on >>Machine Intelligence Research Institute. >>You are subscribed to email updates from Machine Intelligence Research >>Institute » Blog >>To stop receiving these emails, you may unsubscribe now. Email delivery >>powered by Google >>Google Inc., 20 West Kinzie, Chicago IL USA 60610 >> >> >> >> >>Extinctions during human era one thousand times more th... >>The gravity of the world's current extinction rate becomes clearer upon >>knowing what it was before people came along. A new estimate finds that >>species d... >>View on www.sciencedaily.com Preview by Yahoo >> >> -- >>You received this message because you are subscribed to the Google Groups >>"Everything List" group. >>To unsubscribe from this group and stop receiving emails from it, send an >>email to [email protected]. >>To post to this group, send email to [email protected]. >>Visit this group at http://groups.google.com/group/everything-list. >>For more options, visit https://groups.google.com/d/optout. >> >> >> -- >>You received this message because you are subscribed to the Google Groups >>"Everything List" group. >>To unsubscribe from this group and stop receiving emails from it, send an >>email to [email protected]. >>To post to this group, send email to [email protected]. >>Visit this group at http://groups.google.com/group/everything-list. >>For more options, visit https://groups.google.com/d/optout. >> > -- >You received this message because you are subscribed to the Google Groups >"Everything List" group. >To unsubscribe from this group and stop receiving emails from it, send an >email to [email protected]. >To post to this group, send email to [email protected]. >Visit this group at http://groups.google.com/group/everything-list. >For more options, visit https://groups.google.com/d/optout. > > > -- >You received this message because you are subscribed to the Google Groups >"Everything List" group. >To unsubscribe from this group and stop receiving emails from it, send an >email to [email protected]. >To post to this group, send email to [email protected]. >Visit this group at http://groups.google.com/group/everything-list. >For more options, visit https://groups.google.com/d/optout. > -- You received this message because you are subscribed to the Google Groups "Everything List" group. 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