Talk:History of artificial intelligence/Archive 1

message: Under section Experimental AI research, the last sentance of the second paragraph reads: "However, it has become clear that contemporary methods using both broad approaches have severe limitations." This is unclear to me. I think that "either" is meant for "both", but before rewriting, this pre-bold author-respecting newbie thought i'd ask. If it's meant to be "both", i feel this should be explained in the article. Thx, "alyosha" 22:55, 15 December 2005 (UTC)
thread_title:  "both" or "either" 

message: This article omits what is probably the most important series of events in AI during the 80's and 90's.  The creation of the Japanese 5th Generation Project, whose aim was to produce computers "as smart as a man" programmed in Prolog, with their computing performance measured in LIPS (Logical Inferences per Second).  The US started a project to compete with this, headed by Navy Admiral Bobby Inman.  Both projects tanked horribly, and the AI community totally abandoned attempts to mimic human thought, and re-invented itself as researchers in expert systems and fuzzy logic.  Marvin Minsky is extremely critical of what is called AI today, with its robot wars, and Roomba, the intelligent vacuum cleaner.
Deep Blue was not an artifical intelligence exercise, but a demonstration that massively parallel computing with custom ASICs could do by brute force what heuristics and AI failed to accomplish - play chess better than a human.
I strongly suggest someone find a copy of "The Fifth Generation" by Edward A. Feigenbaum and read it.  The article currently reads as if Artificial Intelligence has been a rousing success, when in fact, research attempting to produce real artificial intelligence like HAL in 2001 spent billions of dollars, crashed and burned horribly, along with pretty much all of the AI movement.  What exists now has much lower expectations, and no longer has any resemblance to the AI of yesteryear.Hermitian 10:59, 10 January 2006 (UTC)
replies: text: I would support your creating a new subsection (possibly to be expanded into its own article) on AI failures and/or hype.  That is certainly a part of its history.  However, there is a reason this page seems enthusiastic; it lists only a subset of the succesful projects, and I think / hope people edit down the vain claims of researchers who pretend they have achieved more than they have.  The truth is that we have learned a lot about intelligence from trying to build it, so know why the early hopes were ludicrous, and have a better understanding of our own capacities as well now as a result.  And we are making progress, even if it is slower & steadier than we initially hoped.  I am sure you could find a lot of learned people from just a century ago that wouldn't have believed a machine could do mathematics or play chess, even though now we know recognizing faces is just as hard & wonderful.--Jaibe 17:18, 14 September 2006 (UTC)
thread_title:  5th Generation

message: Is this quote true: Douglas Hofstadter, in Gödel, Escher, Bach, pointed out that this moving of the goalposts effectively defines "intelligence" as "whatever humans can do that machines cannot".
:Can anybody refer me to the chapter/section? --moxon 20:15, 11 January 2006 (UTC)
thread_title:  Query quote 

message: An editor recently deleted all the date links on this page.  Personally, I always thought that date links are silly & wouldn't scale (so I don't mind!), but I just thought people interested in the page might want to weigh in on this change. Personally I'd say the page looks more professional now.  But I want to make sure we are following the norms. --Jaibe 17:10, 14 September 2006 (UTC)
thread_title:  linking dates 

message: I added a cleanup tag for the chronologic history, surely we can find some references for these events. Bugone 00:14, 9 March 2007 (UTC)
thread_title:  Cleanup Tag - References 

message: I'm not sure that the claim that AI has made "steady progress" can be justified. There have been at least two periods when AI seemed to falter: the so-called "AI Winters" of the late 70s and late 90s.
It seems to me a more neutral statement would be something like this: "Some believe that the quest for artificial intelligence has made steady progress since at least the 1950s1 while others claim that progress in A.I. been slow or non-existent2."  But I need to find the references. Dreyfus or Winograd might do for the second case, but they are kind of old now. For the first reference one could use Kurzweil or Moravec
The article should mention the discovery and resolution of major problems in the history of AI: Minsky & Paperts criticism of Perceptrons and how Hopfield and backpropagation resolved it.  The discovery of intractability () and how "scruffy" strategies attempt to address it. The discovery of the "common sense knowledge" problem and how expert systems and CYC use very different strategies to overcome it. The devastating effect of the Lighthill Report and the ALPAC report, the emergence of commercial success in the late 80s and then the most recent 'winter' of the 90s and where they are now at Google and elsewhere. It seems to me these are the central events and would capture some of the boom-and-bust nature of the history of AI. For reference, there is AI: The Tumultuous History of the Search for Artificial Intelligence by Crevier and I can look for a newer one. If I have time I'll look into it. CharlesGillingham 22:20, 10 June 2007 (UTC)
thread_title:  Steady progress? 

message: I am preparing a complete rewrite of this article. It's at User:CharlesGillingham/History of AI. Once it's finished, I will move most of the information on this page to a new "Timeline of artificial intelligence" page, archive this talk page, and replace the entire article. I should be done before the end of this week. CharlesGillingham 21:53, 22 July 2007 (UTC)
thread_title:  Rewrite in Progress 

message: I found five references to Ray Kurzweil, who did not contribute anything to Artificial Intelligence! Apparently inserted by Kurzweil himself (or perhaps by somebody in his company's PR  department).  In the field of AI he certainly is not regarded as an influential researcher, although he writes a lot about the future of AI, elaborating on big ideas introduced by others, such as the technological singularity popularized by Vernor Vinge 20 years ago. I suggest to remove those Kurzweil references, and focus on people who really had an impact on AI history. Quiname (talk) 20:42, 14 April 2011 (UTC)
replies: text: Kurzweil is used in the text only as an example of current optimism. I think this is appropriate and I can't think of anyone more optimistic or more popular. Kurzweil's Singularity is also used to cite a few things (such as the "AI effect"). This is appropriate, I think, because the book is a popular introduction to AI, and popular introductions (i.e. WP:SECONDARY sources) are good sources for an article such as this, which is partly about AI, but also partly about what people say about AI. ---- CharlesGillingham (talk) 22:56, 14 April 2011 (UTC)
thread_title:  All this Kurzweil Self-Promotion 

message: The tone (WP:TONE, WP:PEACOCK) of this article's introduction is too triumphant and embellished. I don't know enough about the topic to improve it but it could undoubtedly use improving. Exercisephys (talk) 16:45, 25 May 2013 (UTC)
thread_title:  [[WP:TONE|Tone]] of the Introduction 

message: The article should perhaps mention the Turing tradition which is perhaps less well known on the other side of the pond.  The Turing tradition is an approach to machine learning based on "(i) the use of logic and (ii) close attention to practical problems".   It is a common theme in the work of Alan Turing, Donald Michie, Ehud Shapiro, Ross Quinlan, Stephen Muggleton.
Pgr94 (talk) 08:43, 16 August 2008 (UTC)
replies: text: The school of thought is certainly influential in the development of Inductive Logic Programming and probably also Abductive Logic Programming. Pgr94 (talk) 09:01, 16 August 2008 (UTC)
replies: text: If I'm reading this right, this is a tradition (1) popular in England (2) based on logic (3) focussed on machine learning, right? Do you think it's fair to throw this in with other logical approaches to AI in England, i.e. Robert Kowalski at Edinburgh University, etc.? ---- CharlesGillingham (talk) 00:47, 17 August 2008 (UTC)
replies: text: I agree this should be covered. I've had to dig for more material about England, Europe and Japan.  Just one sentence under "Logic" should do it. This may also belong in AI#Traditional symbolic AI#Logical AI and  probably in machine learning#History (if someone gets around to writing a History section for that article). The key question is, how influential was it?  I'd like to find a second source. ---- CharlesGillingham (talk) 01:01, 17 August 2008 (UTC)
replies: text: Schools of thought don't respect national boundaries, so "popular in England" is not how I'd describe it.  ILP is significant on an international level (although hasn't received much attention in the US for some reason).  Michie was a colleague of Turing; Muggleton (principle player in ILP) was his student.  The influence of Turing is clearly there.  Gillies named it, but perhaps the few refs suggest that the name is not that notable  Pgr94 (talk) 11:08, 18 August 2008 (UTC)

text: A small point perhaps but the UK is not England and Edinburgh is in Scotland, also part of the UK. 17:06, 7 March 2015 (UTC)
thread_title:  Turing tradition 

message: Consider developing AI for space exploration. AI would have the ability to make decisions on flight path, speed and modifications of vehicle to enhance speed, data transmission to point of origin, data processing and storage among other goals. It could have constant communication with a terra-based AI system for transmission of knowledge and reporting.
Would be best possible method for humans to explore at least the local cluster of stars at 0.5 to 0.9 light speed travel.  
unsigned comment added by 70.43.17.173 (talk) 20:47, 29 October 2015 (UTC)
thread_title:  AI used for long duration Space Exploration  

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thread_title:  External links modified 

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thread_title:  External links modified 

message: Looks like promotion to me.  — Preceding unsigned comment added by Callmesolis (talk • contribs) 22:12, 9 February 2019 (UTC)
thread_title:  Do we really need Pamela McCorduck in the 1st paragraph? Or at all? 

message: One sentence struck me in this article, regarding the vanishing gradient problem. Overcoming this problem was central to the advance of Deep Learning, and Long short-term memory models were the first and still are one of the most widely used models that resolved this critical issue. So the current text "There have been many methods developed to approach this problem, such as Long short-term memory units." seems to play down the important role of these types of networks, while being vague in not listing any other of the "many methods".
Unless someone wants to suggest some other prominent solutions to the vanishing gradient problem that I am not aware of (more modern ones, perhaps?) I would propose revising this section to clarify that LSTMs were a key breakthrough in the field, precisely because they were the first solution to the vanishing gradient problem, and still to date constitute a prevalent network type in modern deep learning research and applications.  — Preceding unsigned comment added by 62.178.202.229 (talk) 14:19, 13 July 2019 (UTC)
thread_title:  Vague "weasel" language? 

message: I plan on restructuring the "precursors" section by subdividing it into two sub-sections: "Mythical, Fictional, and Speculative" and "Theoretical and Technological." The bulk of what is currently in the precursors section will go under the second heading. I plan on expanding upon the first heading significantly. (Not more than ~500 words.) HieronymusBot (talk) 18:17, 15 March 2020 (UTC)
thread_title:  Expansion  / Reorganization of "Precursors" 

message: Ada Lovelace is quoted: "might compose elaborate and scientific pieces of music of any degree of complexity or extent", however in the linked source (https://johnrhudson.me.uk/computing/Menabrea_Sketch.pdf) this is only found in the section "notes by the translator".  — Preceding unsigned comment added by Cashney (talk • contribs) 11:48, 9 August 2020 (UTC)
thread_title:  Wrong attribution of ada lovelace quotation 

message: Every once in a while, during the past 15 years, someone comes through a deletes all references the terms "neat" and "scruffy". So let's talk about it. 
I've put the terms back into the article because:
(a) "neats vs. scruffies" is genuine, real history: people talked about this (a lot) in the late 70s and 80s. See AAAI conferences and talks and so on. Part of writing history is describing the world as it was, as it described itself. They described themselves this way, so we need to give the reader an insight into how they thought at the time. 
(b) Russell and Norvig's "victory of the neats" quip provides a nice through-line for the article, and emphasizes the fact that each generation has viewed the field differently as it has evolved. This is also good history writing, and there is simply is no more reliable source on AI than Russell and Norvig.
On the other side, by the late eighties, most people were sick of hearing about it, and sick of the ridiculous ways that people would try to weave the distinction into badly thought out "general theories" of AI or cognition. I remember Robert Wilensky telling me in 1987: "Never read anything that mentions neats vs scruffies or procedural vs. declarative". In short: we realized it was a stupid distinction years later, but that doesn't mean it isn't historically relevant. 
If your disagree, please, let me know what the actual problem is. ----CharlesGillingham (talk) 16:44, 5 July 2020 (UTC)
replies: text:  "Neats and scruffies" was not as prevalent in the community as this WIKI entry makes it out to be. It was limited to a small group of admittedly combative researchers, who did not typically describe themselves that way--but did describe their processes in that manner. Utilizing a process does not make one a particular type of researcher or developer, and thus limits the application of the term. The term doesn't show up in any relevant reporting of the day (aka, Freedman, Newquist, Levy, any computer journals). It can be referenced, perhaps to another entry, but to use that term as a linchpin for this entry is completely disproportionate to its use then and now. I would like to see an actual set of citations from the era describing the developers in actual terms such as "XXX is a scruffy" or "XXX and the team of neats." Thanks. TrainTracking1 (talk) 20:31, 2 April 2021 (UTC)
replies: text:  TrainTracking1, here is a reference: Minsky, Marvin L. "Logical versus analogical or symbolic versus connectionist or neat versus scruffy." AI magazine 12.2 (1991): 34-34. . --Hectorpal (talk) 02:13, 1 August 2021 (UTC)
replies: text:  Weighing in. Completely against the isolated use of "neats vs scruffies." It's what the real world calls "inside baseball"--as far as that goes--in that it has no relevance to the greater discussion, and is not at all relative to the understanding of the history of AI. You might has well start talking about McCarthyists vs Minskyites if you want to get into conflict. And by 1991, the terms were already out of whatever fashion they were in. Perhaps it's time for someone to start a "neats vs scruffies" page, which should include the use of the term in other disciplines, as it apparently is not limited to AI.Andreldritch (talk) 22:37, 1 August 2021 (UTC)
thread_title:  Neats vs scruffies 

message: In 2022, Google is told to Havel developed an AI that is 158 million times faster than the world's fasterst supercomputer (source: Medium.com)  — Preceding unsigned comment added by 151.38.135.105 (talk) 20:39, 22 August 2022 (UTC)
thread_title: Google, 2022