Fifth Generation Computer Systems: Difference between revisions

Revision as of 00:46, 26 May 2023

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The FGCS Project did not meet with commercial success for reasons similar to the [[Lisp machine]] companies and [[Thinking Machines Corporation|Thinking Machines]]. The highly parallel computer architecture was eventually surpassed in speed by less specialized hardware (for example, Sun workstations and [[Intel]] [[x86]] machines).

A primary problem was the choice of concurrent logic programming as the bridge between the parallel computer architecture and the use of logic as a [[knowledge representation]] and problem solving language for AI applications. This never happened cleanly; a number of languages were developed, all with their own limitations. In particular, the committed choice feature of [[concurrent constraint logic programming]] interfered with the logical semantics of the languages.<ref>Carl Hewitt. [https://arxiv.org/abs/0904.3036 Inconsistency Robustness in Logic Programming] ArXiv 2009.</ref> The project found that the promisesbenefits of [[logic programming]] were largely negated by the use of committed choice.{{Citation needed|date=August 2011}}

Another problem was that existing CPU performance quickly pushed throughovercame the barriers that experts perceivedanticipated in the 1980s, and the value of parallel computing dropped to the point where it was for some time used only in niche situations. Although a number of [[workstation]]s of increasing capacity were designed and built over the project's lifespan, they generally found themselves soon outperformed by "off the shelf" units available commercially.

The project also failed to maintainincorporate continuousoutside growthinnovations. During its lifespan, [[GUI]]s became mainstream in computers; the [[internet]] enabled locally stored databases to become distributed; and even simple research projects provided better real-world results in data mining.{{Citation needed|date=September 2008}}

The FGCS workstations had no appeal in a market where general purpose systems could now replace and outperform them. This is parallel to the Lisp machine market, where rule-based systems such as [[CLIPS]] could run on general-purpose computers, making expensive Lisp machines unnecessary.<ref name='HendlerEditorial'>{{cite journal|last=Hendler|first=James|title=Avoiding Another AI Winter|journal=IEEE Intelligent Systems|date=1 March 2008|volume=23|issue=2|pages=2–4|doi=10.1109/MIS.2008.20|s2cid=35914860|url=http://csdl2.computer.org/comp/mags/ex/2008/02/mex2008020002.pdf|url-status=dead|archive-url=https://web.archive.org/web/20120212012656/http://csdl2.computer.org/comp/mags/ex/2008/02/mex2008020002.pdf|archive-date=12 February 2012}}</ref>

=== Ahead of its time ===

In summary, the Fifth-Generation project was revolutionary, and accomplished some basic research that anticipated future research directions. Many papers and patents were published. MITI established a committee which assessed the performance of the FGCS Project as having made major contributions in computing, in particular eliminating bottlenecks in parallel processing software and the realization of intelligent

interactive processing based on large knowledge bases. However, the committee was strongly biased to producejustify athe positive outcomeproject, so this overstates the actual results.<ref name=Odagiri>{{Cite journal|last1=Odagiri|first1=Hiroyuki|last2=Nakamura|first2=Yoshiaki|last3=Shibuya|first3=Minorul|date=1997|title=Research consortia as a vehicle for basic research: The case of a fifth generation computer project in Japan|url=https://linkinghub.elsevier.com/retrieve/pii/S0048733397000085|journal=Research Policy|language=en|volume=26|issue=2|pages=191–207|doi=10.1016/S0048-7333(97)00008-5}}</ref>

Many of the themes seen in the Fifth-Generation project are now being re-interpreted in current technologies., Inas the earlyhardware 21stlimitations century,forseen many flavors of [[parallel computing]] began to proliferate, including [[multi-core]] architectures atin the low-end1980s andwere [[massivelyfinally parallel|massivelyreached parallel processing]] atin the high end2000s. When [[clock speed]]s of CPUs began to move into the 3–5&nbsp;GHz range, [[CPU power dissipation]] and other problems became more important. The ability of [[Private industry |industry]] to produce ever-faster single CPU systems (linked to [[Moore's Law]] about the periodic doubling of transistor counts) began to be threatened.

In the early 21st century, many flavors of [[parallel computing]] began to proliferate, including [[multi-core]] architectures at the low-end and [[massively parallel|massively parallel processing]] at the high end. Ordinary consumer machines and [[game console]]s began to have parallel processors like the [[Intel Core]], [[AMD K10]], and [[Cell (microprocessor)|Cell]]. [[Graphics card]] companies like Nvidia and AMD began introducing large parallel systems like [[CUDA]] and [[OpenCL]]. On another line of development, the [[Web Ontology Language]] (OWL) employs several layers of logic-based knowledge representation systems.{{cn|date=November 2022}}

It appears, however, that these new technologies do not cite FGCS research. It is not clear if FGCS was leveraged to facilitate these developments in any significant way. No significant impact of FGCS on the computing industry has been demonstrated.{{cn|date=November 2022}}