A Framework for Evaluating Early-Stage Human - of Marcus Hutter

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Project to Build Programs that Understand Abstract This extended abstract outlines a project to build computer programs that understand. Understanding a domain is defined as the ability to rapidly produce computer programs to deal with new problems as they arise. This is achieved by building a CAD tool that collaborates with human designers who guide the system to construct code having certain properties. The code respects Occam's Razor, interacts with a domain simulation, and is informed by a number of mechanisms learned from introspection, the coding employed in biology, and analysis. Introduction This extended abstract outlines a project to build computer programs that understand. Definition 1: Understanding a domain is defined as the ability to rapidly produce programs to deal with new problems as they arise in the domain. This definition is proposed to model human understanding (which I hold to be of the domain of the natural world and extensions we have made into related domains such as mathematics), and also accurately describes biological evolution, which thus may be said to understand what it is doing. A program that can rapidly produce programs for new problems is rather unlike the standard kinds of programs that we usually see. They deal with contingencies that had been previously planned for. We will describe both a new programming method, and new style of program, in order to accomplish our task. Hypothesis 1: The property of understanding in thought and evolution arises through Occam's Razor. (Baum, 2004, 2007) By finding a concise genome that solves a vast number problems, evolution built a program comprising a hierarchic collection of modules that generalize that know how to rapidly produce programs to solve new problems. That genetic program also encodes inductive Copyright © 2008, The Second Conference on Artificial General Intelligence (agi09.org). All rights reserved. Eric B. Baum Baum Research Enterprises 41 Allison Road Princeton NJ 08540 ebaum@fastmail.fm 1 biases that construct a similar hierarchic collection of modules within your mind that rapidly produce programs to solve new problems. As a result of conciseness and the structure of the programs it requires, random mutations in the genome lead to meaningful programs a sizable fraction of the time. For example, a point mutation of a fly's genome may create a fly with an extra pair of wings instead of halteres, or a fly with an extra pair of legs instead of antennae (Kirschner and Gerhart 2005). These are recognizably meaningful in that they are functional in an interesting way. Evolution thus rapidly searches over meaningful outcomes looking for one that solves new problems 1 . That is, according to our definition, evolution understands. Another example within evolution is the coding of limbs (Kirschner and Gerhart 2005). If asked to code up a limb, human software engineers might attempt to separately specify the structure of the bones, the muscles, the nerves, and the blood vessels. If done that way, the program would be huge, like standard engineering specs for machines such as a jet aircraft, and it couldn't adapt to new uses, and it couldn't evolve. A favorable mutation in the bone structure would have to be matched by one in each of the other systems to survive. Proponents of Intelligent Design would have a point. Instead, biology is much more concisely coded. The bones grow out in a concisely and hierarchically specified way. There is no separate detailed specification of exactly how the muscles, nerves, or blood vessels grow. The muscle cells perform a search, and attach themselves to the bones, and grow so as to be functional in context. A muscle cell that is being useful expands (which is why rowers have big hearts). The nerves seek out muscles, and learn how to be functional. The vascular system grows out in a search toward cells that scream for oxygen. As a result of this extremely concise encoding, if you take up new exercises, your muscles adapt and grow in appropriate ways, and if a mutation changes the genetic coding of the 1 Language facilitates thinking in similar fashion. You can come up with incredibly concise formulations of big new ideas, just a sentence or several in natural language, that grow out into detailed executables much like concise specifications in the genome result in interacting searches that robustly construct a limb, or like RBP finds a high level plan and refines it through a series of searches.(Baum, 2008b)
one structure, everything else adapts to make a functional system, so mutations can easily explore meaningful possibilities. The same kind of conciseness is present in the genetic coding for your mind. Enough initial structure is provided, also called inductive bias, that a series of search programs can build a hierarchic series of modules to solve new problems. Previous attempts to produce understanding programs have mostly followed one of two paths. One path has been purely automatic methods, such as direct attempts to simulate evolution. This is hopeless because, while we may already have or may soon have computational resources comparable to those of the brain, we will never be able to compete with evolution which ran through some 10 44 creatures (Smith, 2006, footnote 95) (furthermore each creature individually interacted with the world and is thus expensive to simulate). To evaluate other automatic attempts, ask yourself what is enforcing Occam nature and keeping the solution constrained enough to generalize to new problems. An approach that can too easily add new knowledge without adequate constraint can (and will) simply memorize examples that it sees, rather than generalizing to new problems. In my view, this is a common flaw in AI/AGI approaches. A second approach has been to craft such a program by hand. I believe this is also hopeless for a variety of reasons. First, a wealth of experience in computational learning theory indicates that finding Occam representations in any interesting hypothesis space is NPhard. If finding Occam software is NPhard, it is no more likely to be susceptible to hand solution than other huge NPhard problems. Second, a wealth of experience with solving AGI by hand indicates it is hard. Winograd's SHRDLU, for example, seemed like a particularly well crafted project, yet a few years later he threw up his hands and wrote a book explaining why crafting an understanding program is impossible (Winograd and Flores 1987). Third, when its structure is examined (say by introspection and/or attempting to program it) the program of mind (and also the program of biological development) seems to contain a large number of ingeniously crafted modules. At the very least, figuring out each of these is a PhD dissertation level project, and some of them may be much harder. Evolution, which understands what it is doing, applied massive computational resources in designing them. And the Occam codings evolution found may inherently be hard to understand (Baum, 2004 chap 6). Finally, the problems are hard both at the inner level (figuring out ingenious, concise, fast modules to solve subproblems) and at outer levels of organizing the whole program. Humans are simply not competent to write computer programs to deal with hypercomplex domains in a robust way. Hand crafting has at times sought to benefit from introspection. A problem with this is that people do not have introspective access to the internals of the meaningful modules, which are hidden from introspection by information hiding. Consider, for example, when you 2 invoke Microsoft Word. You know why you are invoking it, and what you expect it to do, but you do not have much idea of its internal code. The same is true of meaningful internal modules within your mental program. However, we appeal (based on observation) to: Hypothesis 2: People do have introspective access to a meaninglevel (Baum 2007, 2004 chap 14), at which they call meaningful modules by name or other pointer. I use the term concept to denote a name (word) or other mental construction that you might think that has some meaning, i.e. that corresponds to a recognizable function in the world, and module to denote the computational procedure that would be summoned to compute a concept. Thus we may think of concepts as names that invoke modules. You can state in words why you call a given module, and you can give examples of concepts (for example, inputs and outputs of the associated module). This is incredibly powerful and detailed information, that we will use to get a huge jump on evolution. Note that the problem of finding an appropriate module is distinct from that of finding an appropriate concept, so this dichotomy factors the problem of producing code for new problems. For example, executing a certain module might create in a simulation model a corresponding goal condition (namely, realize a concept). Agents denote modules that recognize patterns and then may take other computational actions, as in, for example (Baum and Durdanovic, 2000, 2002) . In the present work, patterns are typically recognized in a simulation domain or annotated simulation domain or model, that is seeing particular annotations may be critical to the recognition of the pattern. The recognition will frequently involve taking a series of simulated actions on the model and then checking for a pattern or condition (as in (Baum and Durdanovic, 2000). Agents when activated will frequently post annotations on a model, which is how much of perception proceeds. The solution we propose, by which to construct understanding programs, is thus based on the following objectives and principles. (1) Humans can not write the detailed code. As much as possible it must be automated, but, the system must also support as much guidance as possible from humans. (2) The goal is to produce a program that will rapidly assemble programs to solve new problems. To that end, we have to ask what kinds of modules can be provided that can be flexibly assembled. We also have to ask what kinds of modules can be provided for guiding the assembly process, for example by finding high level plans that are then refined. We propose mechanisms for these tasks. Assembling programs for new tasks from primitive level instructions without a detailed plan is prohibitively expensive. But if a program can be divided into half a dozen tasks, and a program for each them assembled by combining a handful of meaningful modules, the search becomes manageable. Thus we need building systems and
Page 2 and 3: Ben Goertzel, Pascal Hitzler, Marcu
Page 4 and 5: Artificial General Intelligence Vol
Page 6: Preface Artificial General Intellig
Page 9 and 10: Organizing Committee Tsvi Achler U.
Page 11 and 12: A formal framework for the symbol g
Page 13: Importing Space-time Concepts Into
Page 17 and 18: generalize is essential to understa
Page 19 and 20: First Application The above framewo
Page 21 and 22: problem solving, use of knowledge,
Page 23 and 24: Of particular note is the separatio
Page 25 and 26: Conclusions and Future Work While p
Page 27 and 28: Conceptual Spaces The conceptual ar
Page 29 and 30: perceptions and actions. The lingui
Page 31 and 32: means of the knowledge stored in th
Page 33 and 34: grams given some (syntactically res
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Page 47 and 48: Table 2 reviews the key capabilitie
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tuning may be done adaptively by te
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Of course, Harnad’s argument is n
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By exploring variations of Harnad
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Discussion The representational sys
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the cognitive map is less of a map
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models of cognition except in cases
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7(b), we can see that the straight
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eaction times, error rates, or exac
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comparing behavior with and without
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Doorenbos, R. B. 1994. Combining le
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egion, we leave the type of object
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extract features in support of reco
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with a minimum score of -1.0. The
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the NASA Human Error Modeling compa
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whether their models are capable of
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Incorporating Planning and Reasonin
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see an unclaimed ten-dollar bill al
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swering user queries) and the state
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Program Representation for General
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distribution P corresponding to the
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with all Ei replaced by the unbound
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Consciousness in Human and Machine:
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at the sensory receptors, is pre-pr
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The second choice is to take a deta
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Hebbian Constraint on the Resolutio
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The Case of Inputs that Change by T
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This route is relevant if the noise
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Our implemented Turing judge used a
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UnsupervisedSegmentationofAudioSpee
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FinallyweusedthediscreteFourierTran
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Parsing PCFG within a General Proba
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known in advance, although many imp
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Figure 2: Shows the underlying weig
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Self-Programming: Operationalizing
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expressivity. More over, self-progr
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existence of a distance function ov
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Bootstrap Dialog: A Conversational
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elation is typically a verb. In the
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node RDF proposition N1 N2 N3 N4 N5
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Analytical Inductive Programming as
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Problem domain: puttable(x) PRE: cl
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eq Hanoi(0, Src, Aux, Dst, S) = mov
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Human and Machine Understanding Of
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case orderings t correspond to the
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in (1) received a different denotat
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Abstract This paper analyzes the di
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Having grounded meaning: In an inte
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to experience” can be argued to b
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Abstract Case-by-case Problem Solvi
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First, since NARS is designed in th
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typical response is to find such an
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What Is Artificial General Intellig
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Finally, the facts that both seem t
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differ. NARS uses Narsese, the fair
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Integrating Action and Reasoning th
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states, with the condition that the
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action model. The system is able to
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Neuroscience and AI Share the Same
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Relevance Based Planning: Why Its a
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General Intelligence and Hypercompu
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To appear, AGI-09 1 Stimulus proces
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Distribution of Environments in For
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The Importance of Being Neural-Symb
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Improving the Believability of Non-
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Understanding the Brain’s Emergen
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Abstract The challenge of creating
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Importing Space-time Concepts Into
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HELEN: Using Brain Regions and Mech
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Holistic Intelligence: Transversal
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Achieving Artificial General Intell
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Achler, Tsvi . . . . . . . . . . .
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