Ingredients of intelligence: From classic debates to an engineering roadmap

2017 ◽  
Vol 40 ◽  
Author(s):  
Brenden M. Lake ◽  
Tomer D. Ullman ◽  
Joshua B. Tenenbaum ◽  
Samuel J. Gershman
Keyword(s):  
Artificial Intelligence ◽  
Cognitive Science ◽  
Research Program ◽  
Symbolic Representations ◽  
Nature Versus Nurture ◽  
Long Run ◽  
Wide Range

AbstractWe were encouraged by the broad enthusiasm for building machines that learn and think in more human-like ways. Many commentators saw our set of key ingredients as helpful, but there was disagreement regarding the origin and structure of those ingredients. Our response covers three main dimensions of this disagreement: nature versus nurture, coherent theories versus theory fragments, and symbolic versus sub-symbolic representations. These dimensions align with classic debates in artificial intelligence and cognitive science, although, rather than embracing these debates, we emphasize ways of moving beyond them. Several commentators saw our set of key ingredients as incomplete and offered a wide range of additions. We agree that these additional ingredients are important in the long run and discuss prospects for incorporating them. Finally, we consider some of the ethical questions raised regarding the research program as a whole.

The Problem of Identifying and Modeling the Relationship between Monetary Factor and Inflation in the Russian Economy

2008 ◽  
pp. 61-76
Author(s):  
A. Porshakov ◽  
A. Ponomarenko
Keyword(s):  
Money Supply ◽  
Money Demand ◽  
Russian Economy ◽  
Long Run ◽  
Complex Approach ◽  
Wide Range ◽  
Long Time ◽  
The Relationship ◽  
Supply Side Factors

The role of monetary factor in generating inflationary processes in Russia has stimulated various debates in social and scientific circles for a relatively long time. The authors show that identification of the specificity of relationship between money and inflation requires a complex approach based on statistical modeling and involving a wide range of indicators relevant for the price changes in the economy. As a result a model of inflation for Russia implying the decomposition of inflation dynamics into demand-side and supply-side factors is suggested. The main conclusion drawn is that during the recent years the volume of inflationary pressures in the Russian economy has been determined by the deviation of money supply from money demand, rather than by money supply alone. At the same time, monetary factor has a long-run spread over time impact on inflation.

scholarly journals Representations, Affordances, and Interactive Systems

2021 ◽  
Vol 5 (5) ◽  
pp. 23
Author(s):  
Robert Rowe
Keyword(s):  
Artificial Intelligence ◽  
Cognitive Science ◽  
Data Structures ◽  
Digital Computer ◽  
Mental Representations ◽  
Interactive Systems ◽  
Algorithmic Composition ◽  
The World ◽  
History Of

The history of algorithmic composition using a digital computer has undergone many representations—data structures that encode some aspects of the outside world, or processes and entities within the program itself. Parallel histories in cognitive science and artificial intelligence have (of necessity) confronted their own notions of representations, including the ecological perception view of J.J. Gibson, who claims that mental representations are redundant to the affordances apparent in the world, its objects, and their relations. This review tracks these parallel histories and how the orientations and designs of multimodal interactive systems give rise to their own affordances: the representations and models used expose parameters and controls to a creator that determine how a system can be used and, thus, what it can mean.

Précis ofUnified theories of cognition

1992 ◽  
Vol 15 (3) ◽  
pp. 425-437 ◽  
Author(s):  
Allen Newell
Keyword(s):  
Cognitive Science ◽  
Full Range ◽  
Cognitive Architecture ◽  
Convincing Evidence ◽  
Syllogistic Reasoning ◽  
Human Cognition ◽  
Unified Theory ◽  
Theoretical Concepts ◽  
Wide Range ◽  
Unified Theories

AbstractThe book presents the case that cognitive science should turn its attention to developing theories of human cognition that cover the full range of human perceptual, cognitive, and action phenomena. Cognitive science has now produced a massive number of high-quality regularities with many microtheories that reveal important mechanisms. The need for integration is pressing and will continue to increase. Equally important, cognitive science now has the theoretical concepts and tools to support serious attempts at unified theories. The argument is made entirely by presenting an exemplar unified theory of cognition both to show what a real unified theory would be like and to provide convincing evidence that such theories are feasible. The exemplar is SOAR, a cognitive architecture, which is realized as a software system. After a detailed discussion of the architecture and its properties, with its relation to the constraints on cognition in the real world and to existing ideas in cognitive science, SOAR is used as theory for a wide range of cognitive phenomena: immediate responses (stimulus-response compatibility and the Sternberg phenomena); discrete motor skills (transcription typing); memory and learning (episodic memory and the acquisition of skill through practice); problem solving (cryptarithmetic puzzles and syllogistic reasoning); language (sentence verification and taking instructions); and development (transitions in the balance beam task). The treatments vary in depth and adequacy, but they clearly reveal a single, highly specific, operational theory that works over the entire range of human cognition, SOAR is presented as an exemplar unified theory, not as the sole candidate. Cognitive science is not ready yet for a single theory – there must be multiple attempts. But cognitive science must begin to work toward such unified theories.

scholarly journals Corporate Governance of Artificial Intelligence in the Public Interest

Information ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 275
Author(s):  
Peter Cihon ◽  
Jonas Schuett ◽  
Seth D. Baum
Keyword(s):  
Artificial Intelligence ◽  
Corporate Governance ◽  
Law Enforcement ◽  
Nonprofit Organizations ◽  
Public Interest ◽  
Department Of Defense ◽  
The Public ◽  
Wide Range ◽  
The Media ◽  
The U.S

Corporations play a major role in artificial intelligence (AI) research, development, and deployment, with profound consequences for society. This paper surveys opportunities to improve how corporations govern their AI activities so as to better advance the public interest. The paper focuses on the roles of and opportunities for a wide range of actors inside the corporation—managers, workers, and investors—and outside the corporation—corporate partners and competitors, industry consortia, nonprofit organizations, the public, the media, and governments. Whereas prior work on multistakeholder AI governance has proposed dedicated institutions to bring together diverse actors and stakeholders, this paper explores the opportunities they have even in the absence of dedicated multistakeholder institutions. The paper illustrates these opportunities with many cases, including the participation of Google in the U.S. Department of Defense Project Maven; the publication of potentially harmful AI research by OpenAI, with input from the Partnership on AI; and the sale of facial recognition technology to law enforcement by corporations including Amazon, IBM, and Microsoft. These and other cases demonstrate the wide range of mechanisms to advance AI corporate governance in the public interest, especially when diverse actors work together.

The Research and Application of Artificial Intelligence in the Field of Intelligent Robot

2014 ◽  
Vol 898 ◽  
pp. 763-766
Author(s):  
Zhi Hao Li
Keyword(s):  
Artificial Intelligence ◽  
Environmental Changes ◽  
Vision System ◽  
Mineral Exploration ◽  
Intelligent Robot ◽  
Application Range ◽  
Intelligent Robots ◽  
External Data ◽  
Wide Range ◽  
Artificial Intelligence Application

The research and application of artificial intelligence has a very wide range in intelligent robot field. Intelligent robot can not only make use of artificial intelligence gain access to external data, information, (such as stereo vision system, face recognition and tracking, etc.), and then deal with it so as to exactly describe external environment, and complete a task independently, owing the ability of learning knowledge, but also have self-many kinds of artificial intelligence like judgment and decision making, processing capacity and so on. It can make corresponding decision according to environmental changes. Its application range is expanding. In deep sea exploration, star exploration, mineral exploration, heavy pollution, domestic service, entertainment clubs, health care and so on, the figure of intelligent robots artificial intelligence application can all be seen.

Shopping for Lower Sales Tax Rates

2021 ◽  
Vol 13 (3) ◽  
pp. 209-250
Author(s):  
Scott R. Baker ◽  
Stephanie Johnson ◽  
Lorenz Kueng
Keyword(s):  
Shopping Behavior ◽  
Sales Tax ◽  
Fixed Costs ◽  
Tax Rates ◽  
Inventory Problem ◽  
Sales Taxes ◽  
Long Run ◽  
Wide Range ◽  
Short Run ◽  
State And Local

Using comprehensive high-frequency state and local sales tax data, we show that shopping behavior responds strongly to changes in sales tax rates. Even though sales taxes are not observed in posted prices and have a wide range of rates and exemptions, consumers adjust in many dimensions. They stock up on storable goods before taxes rise and increase online and cross-border shopping in both the short and long run. The difference between short- and long-run spending responses has important implications for the efficacy of using sales taxes for countercyclical policy and for the design of an optimal tax framework. Interestingly, households adjust spending similarly for both taxable and tax-exempt goods. We embed an inventory problem into a continuous-time consumption-savings model and demonstrate that this behavior is optimal in the presence of shopping trip fixed costs. The model successfully matches estimated short-run and long-run tax elasticities. We provide additional evidence in favor of this new shopping complementarity mechanism. (JEL E21, E32, G51, H21, H25, H71)

APPLICATION OF ARTIFICIAL INTELLIGENCE TECHNIQUES IN MS. PAC-MAN GAME: A REVIEW

2015 ◽  
Vol 3 (1) ◽  
Author(s):  
Tse Guan Tan ◽  
Jason Teo
Keyword(s):  
Artificial Intelligence ◽  
Content Analysis ◽  
Video Games ◽  
Analysis Method ◽  
Engineering Economics ◽  
Game Ai ◽  
Content Analysis Method ◽  
Wide Range ◽  
Computer Based ◽  
Future Direction

AbstrakTeknik Kecerdasan Buatan (AI) berjaya digunakan dan diaplikasikan dalam pelbagai bidang, termasukpembuatan, kejuruteraan, ekonomi, perubatan dan ketenteraan. Kebelakangan ini, terdapat minat yangsemakin meningkat dalam Permainan Kecerdasan Buatan atau permainan AI. Permainan AI merujukkepada teknik yang diaplikasikan dalam permainan komputer dan video seperti pembelajaran, pathfinding,perancangan, dan lain-lain bagi mewujudkan tingkah laku pintar dan autonomi kepada karakter dalampermainan. Objektif utama kajian ini adalah untuk mengemukakan beberapa teknik yang biasa digunakandalam merekabentuk dan mengawal karakter berasaskan komputer untuk permainan Ms Pac-Man antaratahun 2005-2012. Ms Pac-Man adalah salah satu permainan yang digunakan dalam siri pertandinganpermainan diperingkat antarabangsa sebagai penanda aras untuk perbandingan pengawal autonomi.Kaedah analisis kandungan yang menyeluruh dijalankan secara ulasan dan sorotan literatur secara kritikal.Dapatan kajian menunjukkan bahawa, walaupun terdapat berbagai teknik, limitasi utama dalam kajianterdahulu untuk mewujudkan karakter permaianan Pac Man adalah kekurangan Generalization Capabilitydalam kepelbagaian karakter permainan. Hasil kajian ini akan dapat digunakan oleh penyelidik untukmeningkatkan keupayaan Generalization AI karakter permainan dalam Pasaran Permainan KecerdasanBuatan. Abstract Artificial Intelligence (AI) techniques are successfully used and applied in a wide range of areas, includingmanufacturing, engineering, economics, medicine and military. In recent years, there has been anincreasing interest in Game Artificial Intelligence or Game AI. Game AI refers to techniques applied incomputer and video games such as learning, pathfinding, planning, and many others for creating intelligentand autonomous behaviour to the characters in games. The main objective of this paper is to highlightseveral most common of the AI techniques for designing and controlling the computer-based charactersto play Ms. Pac-Man game between years 2005-2012. The Ms. Pac-Man is one of the games that used asbenchmark for comparison of autonomous controllers in a series of international Game AI competitions.An extensive content analysis method was conducted through critical review on previous literature relatedto the field. Findings highlight, although there was various and unique techniques available, the majorlimitation of previous studies for creating the Ms. Pac-Man game characters is a lack of generalizationcapability across different game characters. The findings could provide the future direction for researchersto improve the Generalization A.I capability of game characters in the Game Artificial Intelligence market.

scholarly journals Computational Ion Channel Research: from the Application of Artificial Intelligence to Molecular Dynamics Simulations.

2020 ◽  
Vol 55 (S3) ◽  
pp. 14-45
Keyword(s):  
Artificial Intelligence ◽  
Molecular Dynamics ◽  
Ion Channels ◽  
Ion Channel ◽  
Computational Methods ◽  
Homology Modelling ◽  
Channel Structure ◽  
Learning Approaches ◽  
Qsar Analysis ◽  
Wide Range

Although ion channels are crucial in many physiological processes and constitute an important class of drug targets, much is still unclear about their function and possible malfunctions that lead to diseases. In recent years, computational methods have evolved into important and invaluable approaches for studying ion channels and their functions. This is mainly due to their demanding mechanism of action where a static picture of an ion channel structure is often insufficient to fully understand the underlying mechanism. Therefore, the use of computational methods is as important as chemical-biological based experimental methods for a better understanding of ion channels. This review provides an overview on a variety of computational methods and software specific to the field of ion-channels. Artificial intelligence (or more precisely machine learning) approaches are applied for the sequence-based prediction of ion channel family, or topology of the transmembrane region. In case sufficient data on ion channel modulators is available, these methods can also be applied for quantitative structureactivity relationship (QSAR) analysis. Molecular dynamics (MD) simulations combined with computational molecular design methods such as docking can be used for analysing the function of ion channels including ion conductance, different conformational states, binding sites and ligand interactions, and the influence of mutations on their function. In the absence of a three-dimensional protein structure, homology modelling can be applied to create a model of your ion channel structure of interest. Besides highlighting a wide range of successful applications, we will also provide a basic introduction to the most important computational methods and discuss best practices to get a rough idea of possible applications and risks.

scholarly journals Retrosynthetic Accessibility Score (RAscore) - Rapid Machine Learned Synthesizability Classification from AI Driven Retrosynthetic Planning

2020 ◽  
Author(s):  
Amol Thakkar ◽  
Veronika Chadimova ◽  
Esben Jannik Bjerrum ◽  
Ola Engkvist ◽  
Jean-Louis Reymond
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Virtual Screening ◽  
Generative Models ◽  
Synthetic Route ◽  
Synthetic Accessibility ◽  
Wide Range ◽  
Computer Aided ◽  
Synthesis Planning ◽  
Retrosynthetic Analysis

<p>Computer aided synthesis planning (CASP) is part of a suite of artificial intelligence (AI) based tools that are able to propose synthesis to a wide range of compounds. However, at present they are too slow to be used to screen the synthetic feasibility of millions of generated or enumerated compounds before identification of potential bioactivity by virtual screening (VS) workflows. Herein we report a machine learning (ML) based method capable of classifying whether a synthetic route can be identified for a particular compound or not by the CASP tool AiZynthFinder. The resulting ML models return a retrosynthetic accessibility score (RAscore) of any molecule of interest, and computes 4,500 times faster than retrosynthetic analysis performed by the underlying CASP tool. The RAscore should be useful for the pre-screening millions of virtual molecules from enumerated databases or generative models for synthetic accessibility and produce higher quality databases for virtual screening of biological activity. </p>

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