Results


Moore's Law may be out of steam, but the power of artificial intelligence is accelerating

#artificialintelligence

A paper from Google's researchers says they simultaneously used as many as 800 of the powerful and expensive graphics processors that have been crucial to the recent uptick in the power of machine learning (see "10 Breakthrough Technologies 2013: Deep Learning"). Feeding data into deep learning software to train it for a particular task is much more resource intensive than running the system afterwards, but that still takes significant oomph. Intel has slowed the pace at which it introduces generations of new chips with smaller, denser transistors (see "Moore's Law Is Dead. It also motivates the startups--and giants such as Google--creating new chips customized to power machine learning (see "Google Reveals a Powerful New AI Chip and Supercomputer").


Moore's Law may be out of steam, but the power of artificial intelligence is accelerating

#artificialintelligence

A paper from Google's researchers says they simultaneously used as many as 800 of the powerful and expensive graphics processors that have been crucial to the recent uptick in the power of machine learning (see "10 Breakthrough Technologies 2013: Deep Learning"). Feeding data into deep learning software to train it for a particular task is much more resource intensive than running the system afterwards, but that still takes significant oomph. Intel has slowed the pace at which it introduces generations of new chips with smaller, denser transistors (see "Moore's Law Is Dead. It also motivates the startups--and giants such as Google--creating new chips customized to power machine learning (see "Google Reveals a Powerful New AI Chip and Supercomputer").


Report 77-25.pdf

Classics (Collection 2)

Atm 11177 Stanford Heuristic Programming Project August 1977 Memo HPP-77-25 Computer Science Department Report No. STAN-CS-77-62I THE ART OF ARTIFICIAL INTELLIGENCE: 1. THEMES AND CASE STUDIES OF KNOWLEDGE ENGINEERING by E. A. Feigenbaum COMPUTER SCIENCE DEPARTMENT School of Humanities and Sciences STANFORD UNIVERSITY THE ART OF ARTIFICIAL INTELLIGENCE: I. Themes and Case Studies of Knowledge Engineering STAN-CS-77-621 Heuristic Programming Project Memo 77-25 Edward A. Feigenbaum Department of Computer Science Stanford University Stanford, California ABSTRACT The knowledge engineer practices the art of bringing the principles and tools of Al research to bear on difficult applications problems requiring experts' knowledge for their solution. The technical issues of acquiring this knowledge, representing it, and using it appropriately to construct and explain lines-of-reasoning, are important problems in the design of knowledge-based systems. Various systems that have achieved expert level performance in scientific and medical inference illuminates the art of knowledge engineering and its parent science, Artificial Intelligence. The views and conclusions in this document are those of the author and should not be interpreted as necessarily representing the official policies, either express or implied, of the Defense Advanced Research Projects Agency of the United States Government. This research has received support from the lollowing agencies: Defense Advanced Research Projects Agency, DAHC 15-73-C-0435; National Institutes of Health, 5R24-RR00612, RR-00785; National Science Foundation, MCS 76-11649, DCR 74-23461; The Bureau of Health Sciences Research and Evaluation, HS-01544.