Dreyfus on the “Fringe”: information processing, intelligent activity, and the future of thinking machines

For a more general review of Dreyfus’ work in philosophy of artificial intelligence with some attention, for example, to the issue of embodiment with which the present paper concludes, see Brey 2001.

“It’s like claiming that the first monkey that climbed a tree was making progress towards flight to the moon.” (cf. Dreyfus 2007, p. 1142).

Dreyfus seemed most disappointed that “neither Dennett nor anyone connected with the project has published an account of the failure and asked what mistaken assumptions underlay their absurd optimism” with Dennett pinning the stall-out on a “lack of graduate students” (2007, p. 1141).

Of the four, he holds pattern recognition to be most fundamental, noting that “resolution of the difficulties which have arrested development” in other fields “all presuppose success” in this one (1965, p. 14).

Later, he bundles these three forms of information processing into what he calls “perspicuous grouping”, the ability to perceive connections without explicitly counting them out, and to recognize individual examples as “typical” of a class without explicit comparison of every other given example (1965, p. 37–46).

As the chess player is “zeroing in” on opportune movements, he is effectively “unconsciously counting out” the various alternatives (1965, p. 53).

Structuring a problem “does not involve merely the given parts and their transformations” but “works in conjunction with the material that is structurally relevant but is selected from past experience.” (Dreyfus 1965, p. 29) This insight bears on prospective agency through backpropagation of perceived error as realized in contemporary dynamic systems models in an interesting way, noteworthy in terms of the neurorobotics research mentioned in the fourth section of this paper.

He identifies a similar loop arising in the context of gameplay, as the significance of any given piece is partly determined by any single game piece and partly by the position of that piece relative other pieces, with all of this only significant in the context of possible future moves as constrained by the structured goal conditions of the game. For a heuristic chess program, for example, to escape such circularity, the interdependence of such definitions requires that some be fixed, at which point its limits “become a matter of principle rather than simply of practice” (1965, p. 73).

For an interesting use of this example, see Williams et al. (1974).

Problems accessible to “associationistic” processes include term-replacement (“mechanical dictionary”), simple memory (associating symbols with patterns), conditioned response (as a means to producing these associations) and trial and error (maze navigation) type problems, all of which are accessible to traditional symbol-pushing AI in the form of decision trees or list searches. The second type is also fully accessible to traditional AI. “Simple formal” type problems are fully governed by explicit rules defining contexts simple enough for meanings to be made completely explicit independent of context, including games whose moves can be explicitly counted out, theorem proofs involving searches for optimal applications of rules under explicit constraints, and pattern recognition given salient features are defined beforehand. The third type, “complex formal” problems, are only partially accessible to traditional AI, including “uncomputable” games whose decision trees cannot be brute-force counted-out including chess and go which require an appreciation of global context to settle on which features are essential and which not before deciding on further moves, and pattern recognition wherein regularities must first be discovered before similarities are recognized. Finally, Dreyfus holds that the fourth type of problem is wholly outside of machine comprehension on principle, consisting in “non-formal” problems with solutions requiring context sensitivity and an ability to distinguish essential from nonessential aspects in patterns which are distorted, the translation of natural language into metaphorical contexts, and the ability to structure otherwise seemingly unstructured problems through what Dreyfus calls “perceptive guess” (see the section headed “Areas of Intelligent Activity Classified with Respect to the Possibility of Artificial Intelligence in Each” beginning on p. 75).

Alexander VonSchoenborn taught this lesson through the image of an ever-expanding sphere of light around an increasingly bright candle. The greater the light, the larger the unlit fringe.

By “resistor analogue”, Dreyfus is characterizing a machine that works as do human beings to infer best explanations or to abduce optimal solutions such as we see in contemporary machine learning.

Whereby AI becomes more intelligent than human beings, human beings are compelled to increasingly integrate themselves with AI to keep up with technological progress, and once bent to the solution of humanity’s most pressing problems, achieve a capacity to deliver humanity from them, cf. Kurzweil, 2005. For a technical argument as to why such an “explosion” of “superintelligence” in self-modifying AI is unlikely, see Benthall (2017).

It is interesting to contrast these efforts with a related assessment of the “real danger of artificial intelligence”, that increasing automation by way of semi-autonomous machines promises to exacerbate rather than abate an historically unprecedented degree of wealth inequality (cf. Lee 2017) which has proven historically to precede mass violence, social upheaval, and in the twentieth century World War, and of which “autonomous weapons” will surely now play a part.

Meanwhile, the simple-mindedness of such reasoning has not gone unrecognized by proponents of current research vectors. For example, late in 2015 the Information Technology and Innovation Foundation nominated the “loose coalition of scientists and luminaries who stirred fear and hysteria in 2015 by raising alarms that artificial intelligence (AI) could spell doom for humanity” for its annual Luddite Award, an honor that was finally awarded them in January of the next year (cf. Atkinson 2015; ITIF 2016).

Noting that the cognizance of the individual of its situation within a social collective is “emergent” in 2001, Sun remarked on research in “bridging” the two (an image also central to da Costa and Pereira 2015) that “It helps to unearth the social unconscious embedded into social institutions and lodged inside individual minds” with the “crucial step in establishing the micro–macro link … between individual agents and society” becoming “how we should understand and characterize the structures of the social unconscious and its ‘‘emergence’’, computationally or otherwise.” (Sun 2001, page 2).

This is the thesis driving the Bloom et al. (2017), for example.