Neats and scruffies
In the history of artificial intelligence, neat and scruffy are two contrasting approaches to artificial intelligence (AI) research. The distinction was made in the 1970s and was a subject of discussion until the mid-1980s.[1][2][3]
"Neats" use algorithms based on a single formal paradigms, such as
"Scruffies" use any number of different algorithms and methods to achieve intelligent behavior. Scruffies rely on incremental testing to verify their programs and scruffy programming requires large amounts of hand coding or knowledge engineering. Scruffies have argued that general intelligence can only be implemented by solving a large number of essentially unrelated problems, and that there is no silver bullet that will allow programs to develop general intelligence autonomously.
John Brockman compares the neat approach to physics, in that it uses simple mathematical models as its foundation. The scruffy approach is more like biology, where much of the work involves studying and categorizing diverse phenomena.[a]
Modern AI has elements of both scruffy and neat approaches. In the 1990s AI research applied mathematical rigor to their programs, as the neats did. [5][6] They also express the hope that there is a single paradigm (a "master algorithm") that will cause general intelligence and superintelligence to emerge.[7] But modern AI also resembles the scruffies:[8] modern machine learning applications require a great deal of hand-tuning and incremental testing; while the general algorithm is mathematically rigorous, accomplishing the specific goals of a particular application is not. Also, in the early 2000s, the field of software development embraced extreme programming, which is a modern version of the scruffy methodology -- try things and test them, without wasting time looking for more elegant or general solutions.
Origin in the 1970s
The distinction between neat and scruffy originated in the mid-1970s, by
The distinction was also partly geographical and cultural: "scruffy" attributes were exemplified by AI research at
Other AI laboratories (of which the largest were
The contrast between
In his 1983 presidential address to
Scruffy projects in the 1980s
The scruffy approach was applied to robotics by
Douglas Lenat's Cyc project was initiated in 1984 one of earliest and most ambitious projects to capture all of human knowledge in machine readable form, is "a determinedly scruffy enterprise".[14] The Cyc database contains millions of facts about all the complexities of the world, each of which must be entered one at a time, by knowledge engineers. Each of these entries is an ad hoc addition to the intelligence of the system. While there may be a "neat" solution to the problem of commonsense knowledge (such as machine learning algorithms with natural language processing that could study the text available over the internet), no such project has yet been successful.
The Society of Mind
In 1986 Marvin Minsky published The Society of Mind which advocated a view of intelligence and the mind as an interacting community of modules or agents that each handled different aspects of cognition, where some modules were specialized for very specific tasks (e.g. edge detection in the visual cortex) and other modules were specialized to manage communication and prioritization (e.g. planning and attention in the frontal lobes). Minsky presented this paradigm as a model of both biological human intelligence and as a blueprint for future work in AI.
This paradigm is explicitly "scruffy" in that it does not expect there to be a single algorithm that can be applied to all of the tasks involved in intelligent behavior.[15] Minsky wrote:
What magical trick makes us intelligent? The trick is that there is no trick. The power of intelligence stems from our vast diversity, not from any single, perfect principle.[16]
As of 1991, Minsky was still publishing papers evaluating the relative advantages of the neat versus scruffy approaches, e.g. “Logical Versus Analogical or Symbolic Versus Connectionist or Neat Versus Scruffy”.[17]
Modern AI as both neat and scruffy
New
However, by 2021, Russell and Norvig had changed their minds.[19] Deep learning networks and machine learning in general require extensive fine tuning -- they must be iteratively tested until they begin to show the desired behavior. This is a scruffy methodology.
Well-known examples
Neats
Scruffies
See also
Notes
- ^ a b John Brockman writes "Chomsky has always adopted the physicist's philosophy of science, which is that you have hypotheses you check out, and that you could be wrong. This is absolutely antithetical to the AI philosophy of science, which is much more like the way a biologist looks at the world. The biologist's philosophy of science says that human beings are what they are, you find what you find, you try to understand it, categorize it, name it, and organize it. If you build a model and it doesn't work quite right, you have to fix it. It's much more of a "discovery" view of the world."[4]
- ^ Winograd also became a critic of early approaches to AI as well, arguing that intelligent machines could not be built using formal symbols exclusively, but required embodied cognition.[9]
Citations
- ^ McCorduck 2004, pp. 421–424, 486–489.
- ^ a b Crevier 1993, p. 168.
- ^ Nilsson 1983, pp. 10–11.
- ^ Brockman 1996, Chapter 9: Information is Surprises.
- ^ Russell & Norvig 2021, p. 24.
- ^ a b McCorduck 2004, p. 487.
- ^ Domingos 2015.
- ^ Russell & Norvig 2021, p. 26.
- ^ Winograd & Flores 1986.
- ^ Crevier 1993, pp. 84−102.
- ^ Russell & Norvig 2021, p. 20.
- ^ Pentland and Fischler 1983, quoted in McCorduck 2004, pp. 421–424
- ^ McCorduck 2004, pp. 454–459.
- ^ McCorduck 2004, p. 489.
- ^ Crevier 1993, p. 254.
- ^ Minsky 1986, p. 308.
- ^ Lehnert 1994.
- ^ Russell & Norvig 2003, p. 25−26.
- ^ Russell & Norvig 2021, p. 23.
References
- Brockman, John (7 May 1996). Third Culture: Beyond the Scientific Revolution. Simon and Schuster. Retrieved 2 August 2021.
- ISBN 0-465-02997-3..
- ISBN 978-0465065707.
- Lehnert, Wendy C. (1 May 1994). "5: Cognition, Computers, and Car Bombs: How Yale Prepared Me for the 90's". In Schank, Robert; Langer, Ellen (eds.). Beliefs, Reasoning, and Decision Making: Psycho-Logic in Honor of Bob Abelson (First ed.). New York, NY: Taylor & Francis Group. p. 150. ISBN 9781134781621. Retrieved 2 August 2021.
- Minsky, Marvin (1986). The Society of Mind. New York: Simon & Schuster. ISBN 0-671-60740-5.
- ISBN 1-56881-205-1.
- ISBN 0-13-790395-2.
- LCCN 20190474.
- Winograd, Terry; Flores (1986). Understanding Computers and Cognition: A New Foundation for Design. Ablex Publ Corp.
Further reading
- Anderson, John R. (2005). "Human symbol manipulation within an integrated cognitive architecture". Cognitive Science. 29 (3): 313–341. PMID 21702777.
- Brooks, Rodney A. (2001-01-18). "The Relationship Between Matter and Life". Nature. 409 (6818): 409–411. S2CID 4430614.