66 research outputs found

    Research Priorities for Robust and Beneficial Artificial Intelligence

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    Success in the quest for artificial intelligence has the potential to bring unprecedented benefits to humanity, and it is therefore worthwhile to investigate how to maximize these benefits while avoiding potential pitfalls. This article gives numerous examples (which should by no means be construed as an exhaustive list) of such worthwhile research aimed at ensuring that AI remains robust and beneficial.Comment: This article gives examples of the type of research advocated by the open letter for robust & beneficial AI at http://futureoflife.org/ai-open-lette

    Author index volume 43 (1983)

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    Research Priorities for Robust and Beneficial Artificial Intelligence

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    Artificial intelligence (AI) research has explored a variety of problems and approaches since its inception, but for the last 20 years or so has been focused on the problems surrounding the construction of intelligent agents —systems that perceive and act in some environment. In this context, the criterion for intelligence is related to statistical and economic notions of rationality — colloquially, the ability to make good decisions, plans, or inferences. The adoption of probabilistic representations and statistical learning methods has led to a large degree of integration and cross-fertilization between AI, machine learning, statistics, control theory, neuroscience, and other fields. The establishment of shared theoretical frameworks, combined with the availability of data and processing power, has yielded remarkable suc- cesses in various component tasks such as speech recognition, image classification, autonomous vehicles, machine translation, legged locomotion, and question-answering systems

    Large Sets of t-Designs

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    We investigate the existence of large sets of t-designs. We introduce t-wise equivalence and (n, t)-partitionable sets. We propose a general approach to construct large sets of t-designs. Then, we consider large sets of a prescribed size n. We partition the set of all k-subsets of a v-set into several parts, each can be written as product of two trivial designs. Utilizing these partitions we develop some recursive methods to construct large sets of t-designs. Then, we direct our attention to the large sets of prime size. We prove two extension theorems for these large sets. These theorems are the only known recursive constructions for large sets which do not put any additional restriction on the parameters, and work for all t and k. One of them, has even a further advantage; it increase the strength of the large set by one, and it can be used recursively which makes it one of a kind. Then applying this theorem recursively, we construct large sets of t-designs for all t and some blocksizes k. Hartman conjectured that the necessary conditions for the existence of a large set of size two are also sufficient. We suggest a recursive approach to the Hartman conjecture, which reduces this conjecture to the case that the blocksize is a power of two, and the order is very small. Utilizing this approach, we prove the Hartman conjecture for t = 2. For t = 3, we prove that this conjecture is true for infinitely many k, and for the rest of them there are at most k/2 exceptions. In Chapter 4 we consider the case k = t + 1. We modify the recursive methods developed by Teirlinck, and then we construct some new infinite families of large sets of t-designs (for all t), some of them are the smallest known large sets. We also prove that if k = t + 1, then the Hartman conjecture is asymptotically correct.</p

    Applications of finite geometries to designs and codes

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    This dissertation concerns the intersection of three areas of discrete mathematics: finite geometries, design theory, and coding theory. The central theme is the power of finite geometry designs, which are constructed from the points and t-dimensional subspaces of a projective or affine geometry. We use these designs to construct and analyze combinatorial objects which inherit their best properties from these geometric structures. A central question in the study of finite geometry designs is Hamada’s conjecture, which proposes that finite geometry designs are the unique designs with minimum p-rank among all designs with the same parameters. In this dissertation, we will examine several questions related to Hamada’s conjecture, including the existence of counterexamples. We will also study the applicability of certain decoding methods to known counterexamples. We begin by constructing an infinite family of counterexamples to Hamada’s conjecture. These designs are the first infinite class of counterexamples for the affine case of Hamada’s conjecture. We further demonstrate how these designs, along with the projective polarity designs of Jungnickel and Tonchev, admit majority-logic decoding schemes. The codes obtained from these polarity designs attain error-correcting performance which is, in certain cases, equal to that of the finite geometry designs from which they are derived. This further demonstrates the highly geometric structure maintained by these designs. Finite geometries also help us construct several types of quantum error-correcting codes. We use relatives of finite geometry designs to construct infinite families of q-ary quantum stabilizer codes. We also construct entanglement-assisted quantum error-correcting codes (EAQECCs) which admit a particularly efficient and effective error-correcting scheme, while also providing the first general method for constructing these quantum codes with known parameters and desirable properties. Finite geometry designs are used to give exceptional examples of these codes

    What is Robotics: Why Do We Need It and How Can We Get It?

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    Robotics is an emerging synthetic science concerned with programming work. Robot technologies are quickly advancing beyond the insights of the existing science. More secure intellectual foundations will be required to achieve better, more reliable and safer capabilities as their penetration into society deepens. Presently missing foundations include the identification of fundamental physical limits, the development of new dynamical systems theory and the invention of physically grounded programming languages. The new discipline needs a departmental home in the universities which it can justify both intellectually and by its capacity to attract new diverse populations inspired by the age old human fascination with robots. For more information: Kod*la

    Intrinsically Evolvable Artificial Neural Networks

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    Dedicated hardware implementations of neural networks promise to provide faster, lower power operation when compared to software implementations executing on processors. Unfortunately, most custom hardware implementations do not support intrinsic training of these networks on-chip. The training is typically done using offline software simulations and the obtained network is synthesized and targeted to the hardware offline. The FPGA design presented here facilitates on-chip intrinsic training of artificial neural networks. Block-based neural networks (BbNN), the type of artificial neural networks implemented here, are grid-based networks neuron blocks. These networks are trained using genetic algorithms to simultaneously optimize the network structure and the internal synaptic parameters. The design supports online structure and parameter updates, and is an intrinsically evolvable BbNN platform supporting functional-level hardware evolution. Functional-level evolvable hardware (EHW) uses evolutionary algorithms to evolve interconnections and internal parameters of functional modules in reconfigurable computing systems such as FPGAs. Functional modules can be any hardware modules such as multipliers, adders, and trigonometric functions. In the implementation presented, the functional module is a neuron block. The designed platform is suitable for applications in dynamic environments, and can be adapted and retrained online. The online training capability has been demonstrated using a case study. A performance characterization model for RC implementations of BbNNs has also been presented
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