Lisp machines are general-purpose computers designed to efficiently run Lisp as their main software and programming language, usually via hardware support. They are an example of a high-level language computer architecture, and in a sense, they were the first commercial single-user workstations. Despite being modest in number (perhaps 7,000 units total as of 1988[1]), Lisp machines commercially pioneered many now-commonplace technologies, including effective garbage collection, laser printing, windowing systems, computer mice, high-resolution bit-mapped raster graphics, computer graphic rendering, and networking innovations such as Chaosnet.[citation The operating systems were written in Lisp Machine Lisp, Interlisp (Xerox), and later partly in Common Lisp. Artificial intelligence (AI) computer programs of the 1960s and 1970s intrinsically required what was then considered a huge amount of computer power, as measured in processor time and memory space.

In the history of artificial intelligence, an AI winter is a period of reduced funding and interest in artificial intelligence research.[1] The term was coined by analogy to the idea of a nuclear winter.[2] The field has experienced several hype cycles, followed by disappointment and criticism, followed by funding cuts, followed by renewed interest years or decades later. The term first appeared in 1984 as the topic of a public debate at the annual meeting of AAAI (then called the "American Association of Artificial Intelligence"). It is a chain reaction that begins with pessimism in the AI community, followed by pessimism in the press, followed by a severe cutback in funding, followed by the end of serious research.[2] At the meeting, Roger Schank and Marvin Minsky--two leading AI researchers who had survived the "winter" of the 1970s--warned the business community that enthusiasm for AI had spiraled out of control in the 1980s and that disappointment would certainly follow. Three years later, the billion-dollar AI industry began to collapse.[2] Hype is common in many emerging technologies, such as the railway mania or the dot-com bubble. The AI winter is primarily a collapse in the perception of AI by government bureaucrats and venture capitalists.

Since Alan Turing first posed the question "can machines think?" in his seminal paper in 1950, "Computing Machinery and Intelligence", Artificial Intelligence (AI) has failed to deliver on its promise. That is, Artificial General Intelligence. There have, however, been incredible advances in the field, including Deep Blue beating the world's best chess player, the birth of autonomous vehicles, and Google's DeepMind beating the world's best AlphaGo player. The current achievements represent the culmination of research and development that occurred over more than 65 years. Importantly, during this period there were two well documented AI Winters that almost completely debunked the promise of AI.

The annual conference of the American Association for Artificial Intelligence (AAAI) is the premier U.S. gathering for artificial intelligence (AI) theoreticians and practitioners. Regardless of the exact figure, I believe that the majority of large U.S. organizations are currently preparing to put AI technology into operation or have already done so. IBM Unveils Its Al Plans As if to testify that AI is now legitimate technology in corporate America, IBM chose AAAI-86 to unfurl its dedication to AI. In the keynote address, Herb Schorr, IBM's AI czar, presented IBM's recently organized program for AI activities. Schorr leads IBM's AI Project Office, a new type of IBM organization that will permeate all IBM activities and organizations related to AI. Schorr's organization currently consists of 12 people with full responsibility within IBM for (1) developing AI products for internal and external use, (2) marketing AI products outside IBM, and (3) applying AI tools and technology within IBM.

At the recent AAAI conference at Stanford, it became apparent that many new AI research centers are being established around the country in industrial and governmental settings and in universities that have not paid much attention to Al in the past. What does an AI researcher want from his computing facility? What will make him most productive? In fact, the needs of the Al researcher are not very different from the needs of any other researcher in computer science, except that facility-related problems seem to become acute in AI a few years sooner than they are felt in other research areas The considerations are roughly as follows: 0 AI programs tend to be very large because they contain, in one form or another, a lot of knowledge. It follows, then, that any machine used for AI must provide a large virtual address space in order to insulate the researcher from having to think about how to chop up his task into smaller tidbits and overlays.

The annual conference of the Association for the Advancement of Artificial Intelligence (AAAI) is the premier U.S. gathering for artificial intelligence (AI) theoreticians and practitioners. On the commercial side, AAAI is the only event with a comprehensive exhibition that includes most significant U.S. vendors of AI products and services. In 1986 some 5100 people attended AAAI- a very good showing considering that the 1987 International Joint Conference on Artificial Intelligence (IJCAI) drew about the same number of people even with its substantial international support. The commercial exhibits at AAAI-86 (110 exhibitors; 80,000 square feet) gave us opportunity to take a snapshot of an industry in transition. What I saw was a dramatic increase in the commercialization of AI technology and a decrease in the mystique, smoke, and hype. A preliminary tour of the AAAI-86 exhibits indicated that participants could expect substantial changes from the situation at IJCAI-85.

GTE Laboratories is the central corporate research and development facility for the sixty subsidiaries of the worldwide GTE corporation. Located in the Massachusetts Route 128 high technology area, the five laboratories that comprise GTE Laboratories generate the ideas, products, systems, and services that provide technical leadership for GTE. The two laboratories which conduct artificial intelligence research are the Computer Science Laboratory (CSL) and the Fundamental Research Laboratory (FRL). Artificial Intelligence projects within the CSL are directed towards the research techniques used in expert systems, and their application to GTE products and services. AI projects within FRL have longer-term AI research goals.

At the recent AAAI conference at Stanford, it became apparent that many new AI research centers are being established around the country in industrial and governmental settings and in universities that have not paid much attention to AI in the past. At the same time, many of the established AI centers are in the process of converting from older facilities, primarily based on Decsystem-10 and Decsystem-20 machines, to a variety of newer options. At present, unfortunately, there is no simple answer to the question of what machines, operating systems, and languages a new or upgrading AI facility should use, and this situation has led to a great deal of confusion and anxiety on the part of those researchers and administrators who are faced with making this choice. In this article I will survey the major alternatives available at present and those that are clearly visible on the horizon, and I will try to indicate the advantages and disadvantages of each for AI work. This is mostly information that we have gathered at CMU in the course of planning for our own future computing needs, but the opinions expressed are my own.