Path: utzoo!utgpu!water!watmath!clyde!att!osu-cis!tut.cis.ohio-state.edu!bloom-beacon!uceng.UUCP!dmocsny From: dmocsny@uceng.UUCP (daniel mocsny) Newsgroups: comp.ai.digest Subject: Re: Grand Challenges Message-ID: <19880927032326.1.NICK@INTERLAKEN.LCS.MIT.EDU> Date: 27 Sep 88 03:23:00 GMT Sender: daemon@bloom-beacon.MIT.EDU Organization: The Internet Lines: 86 Approved: ailist@ai.ai.mit.edu ---- Forwarded Message Follows ---- Return-path: <@AI.AI.MIT.EDU:ailist-request@AI.AI.MIT.EDU> Received: from AI.AI.MIT.EDU by ZERMATT.LCS.MIT.EDU via CHAOS with SMTP id 196549; 24 Sep 88 06:48:11 EDT Received: from BLOOM-BEACON.MIT.EDU (TCP 2224000021) by AI.AI.MIT.EDU 24 Sep 88 06:55:41 EDT Received: by BLOOM-BEACON.MIT.EDU with sendmail-5.59/4.7 id; Sat, 24 Sep 88 06:25:14 EDT Received: from USENET by bloom-beacon.mit.edu with netnews for ailist@ai.ai.mit.edu (ailist@ai.ai.mit.edu) (contact usenet@bloom-beacon.mit.edu if you have questions) Date: 23 Sep 88 13:39:57 GMT From: ndcheg!uceng!dmocsny@iuvax.cs.indiana.edu (daniel mocsny) Organization: Univ. of Cincinnati, College of Engg. Subject: Re: Grand Challenges Message-Id: <266@uceng.UC.EDU> References: <123@feedme.UUCP> Sender: ailist-request@ai.ai.mit.edu To: ailist@ai.ai.mit.edu In article <123@feedme.UUCP>, doug@feedme.UUCP (Doug Salot) writes: [ goals for computer science ] > 2) Build a machine which can read a chapter of a physics text and > then answer the questions at the end. At least this one can be > done by some humans! > > While I'm sure some interesting results would come from attempting > such projects, these sorts of things could probably be done sooner > by tossing out ethical considerations and cloning humanoids. A machine that could digest a physics text and then answer questions about the material would be of atronomical value. Sure, humanoids can do this after a fashion, but they have at least three drawbacks: (1) Some are much better than others, and the really good ones are rare and thus expensive, (2) None are immortal or particularly speedy (which limits the amount of useful knowledge you can pack into one individual), (3) No matter how much the previous humanoids learn, the next one still has to start from scratch. We spend billions of dollars piling up research results. The result, which we call ``human knowledge,'' we inscribe on paper sheets and stack in libraries. ``Human knowledge'' is hardly monolithic. Instead we partition it arbitrarily and assign high-priced specialists to each piece. As a result, ``human knowledge'' is hardly available in any sort of general, meaningful sense. To find all the previous work relevant to a new problem is often quite an arduous task, especially when it spans several disciplines (as it does with increasing frequency). I submit that our failure to provide ourselves with transparent, simple access to human knowledge stands as one of the leading impediments to human progress. We can't provide such access with a system that dates back to the days of square-rigged ships. In my own field (chemical process design) we had a problem (synthesizing heat recovery networks in process plants) that occupied scores of researchers from 1970-1985. Lots of people tried all sorts of approaches and eventually (after who knows how many grants, etc.) someone spotted some important analogies with some problems from Operations Research work of the '50's. We did have to develop some additional theory, but we could have saved a decade or so with a machine that ``knew'' the literature. Another example of an industrially significant problem in my field is this: given a target molecule and a list of available precursors, along with whatever data you can scrape together on possible chemical reactions, find the best sequence of reactions to yield the target from the precursors. Chemists call this the design of chemical syntheses, and chemical engineers call it the reaction path synthesis problem. Since no general method exists to accurately predict the success of a chemical reaction, one must use experimental data. And the chemical literature contains references to literally millions of compounds and reactions, with more appearing every day. Researchers have constructed successful programs to solve these types of problems, but they suffer from a big drawback: no such program embodies enough knowledge of chemistry to be really useful. The programs have some elaborate methods to represent to represent reaction data, but these knowledge bases had to be hand-coded. Due to the chaos in the literature, no general method of compiling reaction data automatically has worked yet. Here we have an example of the literature containing information of enormous potential value, but it is effectively useless. If someone handed me a machine that could digest all (or at least large subsets) of the technical literature and then answer any question that was answerable from the literature, I could become a wealthy man in short order. I doubt that many of us can imagine how valuable such a device would be. I hope to live to see such a thing. Dan Mocsny