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Developmental Robotics, Optimal Artificial Curiosity, Creativity, Music, and the Fine Arts
, 2006
"... Even in absence of external reward, babies and scientists and others explore their world. Using some sort of adaptive predictive world model, they improve their ability to answer questions such as: what happens if I do this or that? They lose interest in both the predictable things and those predict ..."
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Cited by 67 (18 self)
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Even in absence of external reward, babies and scientists and others explore their world. Using some sort of adaptive predictive world model, they improve their ability to answer questions such as: what happens if I do this or that? They lose interest in both the predictable things and those predicted to remain unpredictable despite some effort. One can design curious robots that do the same. The author’s basic idea for doing so (1990, 1991): a reinforcement learning (RL) controller is rewarded for action sequences that improve the predictor. Here this idea is revisited in the context of recent results on optimal predictors and optimal RL machines. Several new variants of the basic principle are proposed. Finally it is pointed out how the fine arts can be formally understood as a consequence of the principle: given some subjective observer, great works of art and music yield observation histories exhibiting more novel, previously unknown compressibility / regularity / predictability (with respect to the observer’s particular learning algorithm) than lesser works, thus deepening the observer’s understanding of the world and what is possible in it.
The Singularity: A Philosophical Analysis
"... What happens when machines become more intelligent than humans? One view is that this event will be followed by an explosion to evergreater levels of intelligence, as each generation of machines creates more intelligent machines in turn. This intelligence explosion is now often known as the “singul ..."
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Cited by 37 (1 self)
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What happens when machines become more intelligent than humans? One view is that this event will be followed by an explosion to evergreater levels of intelligence, as each generation of machines creates more intelligent machines in turn. This intelligence explosion is now often known as the “singularity”.
Gödel machines: Fully selfreferential optimal universal selfimprovers
 Goertzel and C. Pennachin, Artificial General Intelligence
, 2006
"... Summary. We present the first class of mathematically rigorous, general, fully selfreferential, selfimproving, optimally efficient problem solvers. Inspired by Kurt Gödel’s celebrated selfreferential formulas (1931), such a problem solver rewrites any part of its own code as soon as it has found ..."
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Cited by 27 (13 self)
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Summary. We present the first class of mathematically rigorous, general, fully selfreferential, selfimproving, optimally efficient problem solvers. Inspired by Kurt Gödel’s celebrated selfreferential formulas (1931), such a problem solver rewrites any part of its own code as soon as it has found a proof that the rewrite is useful, where the problemdependent utility function and the hardware and the entire initial code are described by axioms encoded in an initial proof searcher which is also part of the initial code. The searcher systematically and efficiently tests computable proof techniques (programs whose outputs are proofs) until it finds a provably useful, computable selfrewrite. We show that such a selfrewrite is globally optimal—no local maxima!—since the code first had to prove that it is not useful to continue the proof search for alternative selfrewrites. Unlike previous nonselfreferential methods based on hardwired proof searchers, ours not only boasts an optimal order of complexity but can optimally reduce any slowdowns hidden by the O()notation, provided the utility of such speedups is provable at all. 1
New millennium AI and the convergence of history
 Challenges to Computational Intelligence
, 2007
"... Artificial Intelligence (AI) has recently become a real formal science: the new millennium brought the first mathematically sound, asymptotically optimal, universal problem solvers, providing a new, rigorous foundation for the previously largely heuristic field of General AI and embedded agents. At ..."
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Cited by 8 (4 self)
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Artificial Intelligence (AI) has recently become a real formal science: the new millennium brought the first mathematically sound, asymptotically optimal, universal problem solvers, providing a new, rigorous foundation for the previously largely heuristic field of General AI and embedded agents. At the same time there has been rapid progress in practical methods for learning true sequenceprocessing programs, as opposed to traditional methods limited to stationary pattern association. Here we will briefly review some of the new results, and speculate about future developments, pointing out that the time intervals between the most notable events in over 40,000 years or 2 9 lifetimes of human history have sped up exponentially, apparently converging to zero within the next few decades. Or is this impression just a byproduct of the way humans allocate memory space to past events? 1
Open problems in universal induction & intelligence
 Algorithms
, 2009
"... algorithms ..."
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A Family of Gödel Machine Implementations
"... Abstract. The Gödel Machine is a universal problem solver encoded as a completely selfreferential program capable of rewriting any part of itself, provided it can prove that the rewrite is useful according to some utility function, encoded within itself. Based on experience gained by constructing a ..."
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Cited by 3 (1 self)
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Abstract. The Gödel Machine is a universal problem solver encoded as a completely selfreferential program capable of rewriting any part of itself, provided it can prove that the rewrite is useful according to some utility function, encoded within itself. Based on experience gained by constructing a virtual machine capable of running the first Gödel Machine implementation written in selfreferential code, we discuss several important refinements of the original concept. We also show how different approaches to implementing the proof search leads to a family of possible Gödel Machine implementations. 1