Bas Steunebrink on Self-Reflective Programming

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Bas Steunebrink portraitBas Steunebrinkis a postdoctoral researcher at the Swiss AI lab IDSIA, as part ofProf. Schmidhuber’sgroup. He received his PhD in 2010 from Utrecht University, the Netherlands. Bas’s dissertation was on the subject of artificial emotions, which fits well in his continuing quest of finding practical and creative ways in which general intelligent agents can deal with time and resource constraints. A recentpaperon how such agents will naturally strive to be effective, efficient, and curious was awarded theKurzweil Prizefor Best AGI Idea at AGI’2013. Bas also has a great interest in anything related to self-reflection and meta-learning, and all “meta” stuff in general.

Luke Muehlhauser: One of your ongoing projects has been a Gödel machine (GM) implementation. Could you please explain (1) what a Gödel machine is, (2) why you’re motivated to work on that project, and (3) what your implementation of it does?


Bas Steunebrink: A GM is a program consisting of two parts running in parallel; let’s name them Solver and Searcher. Solver can be any routine that does something useful, such as solving task after task in some environment. Searcher is a routine that tries to find beneficial modifications to both Solver and Searcher, i.e., to any part of the GM’s software. So Searcher can inspect and modify any part of the Gödel Machine. The trick is that the initial setup of Searcher only allows Searcher to make such a self-modification if it has a proof that performing this self-modification is beneficial in the long run, according to an initially provided utility function. Since Solver and Searcher are running in parallel, you could say that a third component is necessary: a Scheduler. Of course Searcher also has read and write access to the Scheduler’s code.

Godel Machine: diagram of scheduler

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Probabilistic Metamathematics and the Definability of Truth

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On October 15th, Paul Christiano presented “Probabilistic metamathematics and the definability of truth” at Harvard University as part ofLogic at Harvard(detailshere). As explainedhere, Christiano came up with the idea for this approach, and it was developed further at a series of亚博体育苹果app官方下载 .

Video of the talk is now available:

视频偶尔模糊是由于相机problems, but is still clear enough to watch.

Hadi Esmaeilzadeh on Dark Silicon

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Hadi Esmaeilzadehrecently joined the School of Computer Science at the Georgia Institute of Technology as assistant professor. He is the first holder of the Catherine M. and James E. Allchin Early Career Professorship. Hadi directs theAlternative Computing Technologies (ACT) Lab, where he and his students are working on developing new technologies and cross-stack solutions to improve the performance and energy efficiency of computer systems for emerging applications. Hadi received his Ph.D. from the Department of Computer Science and Engineering at University of Washington. He has a Master’s degree in Computer Science from The University of Texas at Austin, and a Master’s degree in Electrical and Computer Engineering from University of Tehran. Hadi’s research has been recognized by threeCommunications of the ACMResearch Highlights and threeIEEE MicroTop Picks. Hadi’s work on dark silicon has also beenprofiledinNew York Times.

Luke Muehlhauser: Could you please explain for our readers what “dark silicon” is, and why it poses a threat to the historical exponential trend in computing performance growth?


Hadi Esmaeilzadeh: I would like to answer your question with a question. What is the difference between the computing industry and the commodity industries like the paper towel industry?

The main difference is that computing industry is an industry of new possibilities while the paper towel industry is an industry of replacement. You buy paper towels because you run out of them; but you buy new computing products because they get better.

And, it is not just the computers that are improving; it is the offered services and experiences that consistently improve. Can you even imagine running out of Microsoft Windows?

One of the primary drivers of this economic model is the exponential reduction in the cost of performing general-purpose computing. While in 1971, at the dawn of microprocessors, the price of 1 MIPS (Million Instruction Per Second) was roughly $5,000, it today is about 4¢.This is an exponential reduction in the cost of raw material for computing. This continuous and exponential reduction in cost has formed the basis of computing industry’s economy in the past four decades.

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Russell and Norvig on Friendly AI

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russell-norvigAI: A Modern Approachis by far the dominant textbook in the field. It is used in 1200 universities, and is currently the22nd most-citedpublication in computer science. Its authors,Stuart RussellandPeter Norvig, devote significant space to AI dangers and Friendly AI in section 26.3, “The Ethics and Risks of Developing Artificial Intelligence.”

The first 5 risks they discuss are:

  • People might lose their jobs to automation.
  • People might have too much (or too little) leisure time.
  • People might lose their sense of being unique.
  • AI systems might be used toward undesirable ends.
  • The use of AI systems might result in a loss of accountability.

Each of those sections is one or two paragraphs long. The final subsection, “The Success of AI might mean the end of the human race,” is given 3.5pages. Here’s a snippet:

The question is whether an AI system poses a bigger risk than traditional software. We will look at three sources of risk. First, the AI system’s state estimation may be incorrect, causing it to do the wrong thing. For example… a missile defense system might erroneously detect an attack and launch a counterattack, leading to the death of billions…

Second, specifying the right utility function for an AI system to maximize is not so easy. For example, we might propose a utility function designed to minimize human suffering, expressed as an additive reward function over time… Given the way humans are, however, we’ll always find a way to suffer even in paradise; so the optimal decision for the AI system is to terminate the human race as soon as possible – no humans, no suffering…

Third, the AI system’s learning function may cause it to evolve into a system with unintended behavior. This scenario is the most serious, and is unique to AI systems, so we will cover it in more depth. I.J. Good wrote (1965),

让一个ultraintelligent机器被定义为一个马chine that can far surpass all the intellectual activities of any man however clever. Since the design of machines is one of these intellectual activities, an ultraintelligent machine could design even better machines; there would then be unquestionably be an “intelligence explosion,” and the intelligence of man would be left far behind. Thus the first ultraintelligent machine is the last invention that man need ever make, provided that the machine is docile enough to tell us how to keep it under control.

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Richard Posner on AI Dangers

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PosnerRichard Posneris a jurist, legal theorist, and economist. He is also the author of nearly 40 books, and is by far themost-cited legal scholar of the 20th century.

In 2004, Posner publishedCatastrophe: Risk and Response, in which he discusses risks fromAGIat some length. His analysis is interesting in part because it appears to be intellectually independent from the Bostrom-Yudkowsky tradition that dominates the topic today.

In fact, Posner does notappearto be aware of earlier work on the topic by I.J. Good (1970,1982), Ed Fredkin (1979), Roger Clarke (1993,1994), Daniel Weld & Oren Etzioni (1994), James Gips (1995), Blay Whitby (1996), Diana Gordon (2000), Chris Harper (2000), or Colin Allen (2000). He is not even aware of Hans Moravec (1990,1999), Bill Joy (2000), Nick Bostrom (1997;2003), or Eliezer Yudkowsky (2001). Basically, he seems to know only of Ray Kurzweil (1999).

Still, much of Posner’s analysis is consistent with the basic points of the Bostrom-Yudkowsky tradition:

[One class of catastrophic risks] consists of… scientific accidents, for example accidents involving particle accelerators, nanotechnology…, and artificial intelligence. Technology is the cause of these risks, and slowing down technology may therefore be the right response.

…也许有一天,也许有一天很快(十年s, not centuries, hence), be robots with human and [soon thereafter] more than human intelligence…

…Human beings may turn out to be the twenty-first century’s chimpanzees, and if so the robots may have as little use and regard for us as we do for our fellow, but nonhuman, primates…

…A robot’s potential destructiveness does not depend on its being conscious or able to engage in [e.g. emotional processing]… Unless carefully programmed, the robots might prove indiscriminately destructive and turn on their creators.

…Kurzweil is probably correct that “once a computer achieves a human level of intelligence, it will necessarily roar past it”…

One major point of divergence seems to be that Posner worries about a scenario in which AGIs become self-aware, re-evaluate their goals, and decide not to be “bossed around by a dumber species” anymore. In contrast, Bostrom and Yudkowsky think AGIs will be dangerous not because they will “rebel” against humans, but because (roughly) using all available resources — including those on which human life depends — is a convergent instrumental goal for almost any set of final goals a powerful AGI might possess. (See e.g.Bostrom 2012.)

Ben Goertzel on AGI as a Field

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Ben Goertzel portraitDr. Ben Goertzel is Chief Scientist of financial prediction firmAidyia Holdings; Chairman of AI software companyNovamente LLCand bioinformatics companyBiomind LLC; Chairman of theArtificial General Intelligence Society and theOpenCog Foundation; Vice Chairman of futurist nonprofitHumanity+; Scientific Advisor of biopharma firmGenescient Corp.; Advisor to theSingularity UniversityandMIRI; Research Professor in the Fujian Key Lab for Brain-Like Intelligent Systems at Xiamen University, China; and general Chair of theArtificial General Intelligence conferenceseries. His research work encompasses artificial general intelligence, natural language processing, cognitive science, data mining, machine learning, computational finance, bioinformatics, virtual worlds and gaming and other areas. He has published a dozen scientific books, 100+ technical papers, and numerous journalistic articles. Before entering the software industry he served as a university faculty in several departments of mathematics, computer science and cognitive science, in the US, Australia and New Zealand. He has three children and too many pets, and in his spare time enjoys creating avant-garde fiction and music, and exploring the outdoors.

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MIRI’s October Newsletter

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Greetings from the Executive Director

Dear friends,

The big news this month is that Paul Christiano and Eliezer Yudkowsky are giving talks at Harvard and MIT about the work coming out of MIRI’s workshops, on Oct. 15th and 17th, respectively (details below).

Meanwhile we’ve been planning future workshops and preparing future publications. Our experienced document production team is also helping to prepareNick Bostrom‘sSuperintelligencebook for publication. It’s a very good book, and should be released by Oxford University Press in mid-2014.

By popular demand, MIRI research fellow Eliezer Yudkowsky now has a few “Yudkowskyisms” available on t-shirts, atRational Attire. Thanks to Katie Hartman and Michael Keenan for setting this up.

Cheers,

Luke Muehlhauser
Executive Director

Upcoming Talks at Harvard and MIT

If you live near Boston, you’ll want to come see Eliezer Yudkowsky give a talk about MIRI’s research program in the spectacular Stata building on the MIT campus, onOctober 17th.

His talk is titledRecursion in rational agents: Foundations for self-modifying AI. There will also be a party the next day in MIT’s Building 6, with Yudkowsky in attendance.

Two days earlier, Paul Christiano will give a technical talk to a smaller audience about on of the key results from MIRI’s research workshops thus far. This talk is titledProbabilistic metamathematics and the definability of truth.

For more details on both talks, see the blog posthere.

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Mathematical Proofs Improve But Don’t Guarantee Security, Safety, and Friendliness

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encryptionIn 1979, Michael Rabinprovedthat his encryption system could be inverted — so as to decrypt the encrypted message — only if an attacker could factorn. And since this factoring task iscomputationally hardfor any sufficiently largen, Rabin’s encryption scheme was said to be “provably secure” so long as one used a sufficiently largen.

Since then, creating encryption algorithms with this kind of “provable security” has been a major goal of cryptography,1and new encryption algorithms that meet these criteria are sometimes marketed as “provably secure.”

Unfortunately, the term “provable security” can be misleading,2for several reasons3.

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  1. An encryption system is said to be provably secure if its security requirements are stated formally, and proven to be satisfied by the system, as was the case with Rabin’s system. SeeWikipedia.
  2. Security reductions can still be useful (Damgård 2007). My point is just that term “provable security” can be misleading, especially to non-experts.
  3. For more details, and some additional problems with the term “provable security,” see Koblitz & Menezes’Another Lookwebsite and its linked articles, especiallyKoblitz & Menezes (2010).