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 > <http://www.sciencedaily.com/releases/2014/09/140902151125.htm> > > Brent > > > > -------- Original Message -------- Subject: The Machine Intelligence > Research Institute BlogDate: Fri, 05 Sep 2014 12:07:00 +0000From: Machine > Intelligence Research Institute » Blog <[email protected]> > <[email protected]>To: [email protected] > > The Machine Intelligence Research Institute Blog > <http://intelligence.org/> > ------------------------------ > John Fox on AI safety > <http://intelligence.org/2014/09/04/john-fox/?utm_source=rss&utm_medium=rss&utm_campaign=john-fox> > Posted: 04 Sep 2014 12:00 PM PDT > [image: John Fox portrait] John Fox > <http://www.cossac.org/people/johnfox> 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 > <http://smile.amazon.com/Safe-Sound-Artificial-Intelligence-Applications/dp/0262062119/ref=nosim?tag=793775876-20>* > (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 > <http://smile.amazon.com/Safe-Sound-Artificial-Intelligence-Applications/dp/0262062119/ref=nosim?tag=793775876-20>*. > 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 > <http://intelligence.org/2014/09/04/john-fox/#footnote_0_11319> 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 > <http://www.tandfonline.com/doi/abs/10.1080/095281397146979> 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 > <http://intelligence.org/2014/09/04/john-fox/#footnote_1_11319> > 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 > <http://intelligence.org/2013/08/11/what-is-agi/>, 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 > <http://intelligence.org/2014/09/04/john-fox/#footnote_2_11319> 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 <http://intelligence.org/2014/09/04/john-fox/#footnote_3_11319> 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 > <http://intelligence.org/2014/09/04/john-fox/#footnote_4_11319> 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 <http://www.cossac.org/projects/red> > 2. Overconfidence in major disasters: > • D. Lucas. *Understanding the Human Factor in Disasters.* > <http://www.maneyonline.com/doi/abs/10.1179/isr.1992.17.2.185> > Interdisciplinary Science Reviews. Volume 17 Issue 2 (01 June 1992), pp. > 185-190. > • “Nuclear safety and security. > <http://en.wikipedia.org/wiki/Nuclear_safety_and_security> > Psychology of overconfidence: > • Overconfidence effect. > <http://en.wikipedia.org/wiki/Overconfidence_effect> > • C. Riordan. Three Ways Overconfidence Can Make a Fool of You > > <http://www.forbes.com/sites/forbesleadershipforum/2013/01/08/three-ways-overconfidence-can-make-a-fool-of-you/> > Forbes Leadership Forum. > Overconfidence in medicine: > • R. Hanson. Overconfidence Erases Doc Advantage. > <http://www.overcomingbias.com/2007/04/overconfidence_.html> > Overcoming Bias, 2007. > • E. Berner, M. Graber. Overconfidence as a Cause of Diagnostic Error > in Medicine. > <http://www.amjmed.com/article/S0002-9343%2808%2900040-5/abstract> 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. > > <http://www.houstonchronicle.com/news/health/article/Doctors-overconfident-study-finds-even-in-4766096.php> > 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. > <http://aisel.aisnet.org/icis2011/proceedings/generaltopics/4/> ICIS > 2011 Proceedings. Paper 4. December 6, 2011. > 3. Hazard and operability study > <http://en.wikipedia.org/wiki/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 <http://ieet.org/index.php/IEET/more/ford20140702>. Institute > for Ethics and Emerging Technologies, 2014. > > The post John Fox on AI safety > <http://intelligence.org/2014/09/04/john-fox/> appeared first on Machine > Intelligence Research Institute <http://intelligence.org/>. > Daniel Roy on probabilistic programming and AI > <http://intelligence.org/2014/09/04/daniel-roy/?utm_source=rss&utm_medium=rss&utm_campaign=daniel-roy> > Posted: 04 Sep 2014 08:03 AM PDT > [image: Daniel Roy portrait] Daniel Roy <http://danroy.org/> 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) > <http://danroy.org/papers/AckFreRoy-CompCondProb-preprint.pdf> 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 <http://en.wikipedia.org/wiki/Conditional_probability>, > 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 > <http://ppaml.galois.com/wiki/> 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 <http://danroy.org/papers/FreRoyTen-Turing.pdf> 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 > <http://arxiv.org/abs/1201.0488> 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 > <http://intelligence.org/2014/09/04/daniel-roy/> appeared first on Machine > Intelligence Research Institute <http://intelligence.org/>. > You are subscribed to email updates from Machine Intelligence Research > Institute » Blog <http://intelligence.org/> > To stop receiving these emails, you may unsubscribe now > <http://feedburner.google.com/fb/a/mailunsubscribe?k=HYpy2sNsssu2Xt-WF4TaHmHlEas> > .Email delivery powered by GoogleGoogle Inc., 20 West Kinzie, Chicago IL > USA 60610 > > > > > [image: image] > <http://www.sciencedaily.com/releases/2014/09/140902151125.htm> > > > > > > Extinctions during human era one thousand times more th... > <http://www.sciencedaily.com/releases/2014/09/140902151125.htm> > 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 > <http://www.sciencedaily.com/releases/2014/09/140902151125.htm> > 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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