lisp where do we come from what are we where are we going peter norvig nasa ames research center n.
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Lisp : Where Do We Come From? What Are We? Where Are We Going? Peter Norvig NASA Ames Research Center. Paul Gauguin, 1898. The answer is  (and the Y combinator). John McCarthy Notation, Functional Programming, Statements as Expressions, Incremental Compilation MIT/Stanford/CMU/Xerox

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Paul Gauguin, 1898


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    1. Lisp:Where Do We Come From? What Are We? Where Are We Going?Peter NorvigNASA Ames Research Center Paul Gauguin, 1898

    2. The answer is(and the Y combinator)

    3. John McCarthy Notation, Functional Programming, Statements as Expressions, Incremental Compilation MIT/Stanford/CMU/Xerox Language of choice for many elite programmers Language of Artificial Intelligence Lisp Machines Tool of choice; A religion?; Commercialization Lucid/Franz/Harlequin/Digitool/... Many of benefits to stock HW (without religion) Where Do We Come From? (1958-1989: the first 30 years)

    4. Groups and Fields can be defined in terms of: Shared Community Shared Tools Shared Literature Ecological Niche What Are We?(1990s: the last ten years)

    5. But is the community healthy? Defensive stance (like Apple or Linux) Minority, with some zealots, past-looking Numbers holding steady(?) But the world is consolidating on C++/Java See http://www.norvig.com/Lisp-retro.html C++/Java way out in front Lisp comparable to Perl on Books, Usenet, URLs But Lisp far behind in Job postings We are a Shared Community

    6. Interactive RAD Garbage Collection Everything is an Object Higher-order Functions Emacs Lisp Machines Macros, Meta-Programming Custom Small Languages Generic Functions, Informal Interfaces Mostly Functional Programming We Have a Set of Shared Tools

    7. Six “Lasting Concepts” from tPoP: Simplicity and Clarity L/J=2 Generality L/J=1.5 Evolution L/J=3 Interfaces L/J=0.5 Automation (under-appreciated) L/J=10 (Programs writing other programs, tests, etc.) Notation (under-appreciated) L/J=5 (Little Languages) Our Tools are “Under-Appreciated” Amazon.com rank: 2,481Mentions of Lisp: a few

    8. We Have a Shared Literature • Books, Web • Good choices • Source Code • Lost LM code!! • Journals and Magazines • Not influential

    9. Artificial Intelligence Recent competition from C++, Java, MATLAB, etc. “Solving the hard problems” Very competitive niche RAD Lisp is losing the high ground: CAD, Education But … Lisp is the most adaptable language ever The key to survival is to find a new niche In 30 years Lisp will likely be ahead of C++/Java (but behind something else) We Inhabit an Ecological Niche Amazon.com rank: 690Mentions of Lisp: 0

    10. How to predict the future? Look at published theories What niche can we inhabit? Look at some new ones Where Are We Going?(the next 10 to 100 years)

    11. Worse Is Better(Gabriel 1990) • Simple is Better • First provide the right 50% capability • Users will grow it to 90% • Lisp: too “right thing,” CL too big, Scheme too small, large entry barrier (50% of population below median) • Prediction: Hard for Lisp to compete, even with “Win Big” strategy

    12. Crossing the Chasm(Moore 1991) • Useful is Better • Early adopters don’t predict mainstream adopters • Be useful in a vertical industry • Then move into the mainstream • Lisp as Tool: Too early-adopter • Prediction: Lisp can Help companies cross (Viaweb)

    13. Innovator’s Dilemma(Christensen 1997) • Cheaper is Better • Innovation moves up from the low end, often with new business model or manufacturing process • The best companies can lead tech development, and still miss out • Lisp Firms: overtaken by “inferior” C, etc.

    14. Internet Time(eVeryone 1998) • First is Better • Rush to market, get market share • Lisp: not keeping up • Prediction: need for premier RAD tool; Lisp techniques could be part of solution

    15. Where is Lisp Winning? NASA Remote Agent (and various rovers) Yahoo Viaweb Shopping (Paul Graham) Ascent Datamining Tool Nichimen (nee Symbolics) Graphics Systems AT&T, Lucent, other 24x7 telecom switches NASA, Boeing, Airline planning/scheduling Reasoning Systems Y2K program analysis tools Prototyping by Wizards (many…)Answer: Where There Are Lisp Programmers

    16. “ The worst thing we can do is to stand still as a community, and that is what is happening.” - Richard Gabriel (in 1991, and since then?) “ Three strikes and you’re out” - Cordell Green (on Lisp+Unix+Category Theory) RAD development as good as Lisp And better for traditional GUIs Standardization falling far behind GUI, HTTP, TCP/IP, Threads, binariesAnswer: need a 10 or 100 breakthrough Where Is Lisp Losing?

    17. The High Ground in 10 or 100 improvements Look at three possibilities“We” means Lisp Community Not necessarily Lisp What Niche Can We Inhabit?

    18. Design Patterns are hot At least in bookstore; for actual production use??? Design Patterns are: Descriptions of what experienced designers know Hints/reminders for choosing classes and methods Higher-order abstractions for program organization To discuss, weigh and record design tradeoffs To avoid limitations of implementation language Lisp is the best host for first class patterns (1) First-class Design Patterns

    19. InvisibleSo much a part of language that you don’t notice(e.g. when class replaced struct in C++, no more “Encapsulated Class” pattern) InformalDesign pattern in prose; refer to by name, butMust be implemented from scratch for each use FormalImplement pattern itself within the languageInstantiate/call it for each useUsually implemented with macros First-class Design Patterns:Levels of Implementation

    20. 16 of 23 GOF patterns are either invisible or simpler: First-class types (6): Abstract-Factory, Flyweight, Factory-Method, State, Proxy, Chain-Of-Responsibility First-class functions (4.5): Command, Strategy, Template-Method, Visitor, Iterator Macros (1.5): Interpreter, Iterator Method Combination (2): Mediator, Observer Multimethods (1): Builder Modules (1): Facade Applying Lisp toDesign Patterns

    21. Constructing and DeconstructingPrograms into Patterns Construction Grammar’s What’s X doing Y? construction

    22. Not enough programmers to go around But many users, domain experts, critics Architect systems in which: Programmers specify initial architecture, specs Users say “good” or “bad” (reinforcement learning) Users can correct answers (supervised learning) Users can script new behavior, then improve Program learns from many users (statistics) Programs have ontology of user goals, situations (2) Adaptive, Teachable Systems

    23. Black Box: Input/Output spec. Can’t talk about performance Open Implementation: Performance tweaking, T Designer picks good tweaks Adaptive Software: Performance feedback, F Feedback informs tweaks I O T I O T I O F Adaptable, Teachable Systems:Interfaces vs. Performance

    24. Aspect-Oriented Programming Deal with one aspect at a time (performance, parallelism, replication, etc.) http://www.parc.xerox.com/aop Subject-Oriented Programming A subject is a collection of classes http://www.research.ibm.com/sop Intentional Programming Don’t bother with syntax; express intentions http://www.research.microsoft.com/research/ip/ Adaptable, Teachable Systems:Decomposing Into the Right Components

    25. Model Checking, other Formal Methods Formally prove properties of program Helps with concurrency, etc. Model-Based Reasoning Say what there is, not how to do things with it Separate reasoning engines for the what Adaptive to changes in environment, system (3) Self-verifying and Repairing Systems

    26. Lisp has always allowed us to imagine the abstractions we want, and then build a program with them (see On Lisp) Now it is time to imagine the program development and use system we really want, and then evolve all our programs starting with it Even if the resulting system is not in Lisp, its users will essentially be Lisp Programmers Conclusions