diff --git a/posts/symbol-grounding.org b/posts/symbol-grounding.org deleted file mode 100644 index 9f5432b..0000000 --- a/posts/symbol-grounding.org +++ /dev/null @@ -1,108 +0,0 @@ ---- -title: "Reading Notes: \"The Symbol Grounding Problem\", Stevan Harnad" -date: 2020-02-02 ---- - -cite:harnad1990_symbol_groun_probl [[https://eprints.soton.ac.uk/250382/1/symgro.pdf][(PDF version)]] defined the /symbol -grounding problem/, which is one of the most influential issues in -natural language problems since the 1980s. The issue is to determine -how a formal language system, consisting in simple symbols, can be -imbued with any /meaning/. - -From the abstract: -#+begin_quote -How can the semantic interpretation of a formal symbol system be made -/intrinsic/ to the system, rather than just parasitic on the meanings -in our heads? How can the meanings of the meaningless symbol tokens, -manipulated solely on the basis of their (arbitrary) shapes, be -grounded in anything but other meaningless symbols? -#+end_quote - -In this landmark paper, Harnad makes the issue explicit, in its -context of cognitivism and competing theories of mind and -intelligence. He then proposes an original solution based on a -combination of symbolic and connectionist properties. The problem -itself is still highly relevant to today's NLP advances, where the -issue of extracting /meaning/ is still not solved. - -* What is the symbol grounding problem? - -** Context: cognitivism, symbolism, connectionism - -/Behaviourism/ was the dominant framework of experimental psychology in -the first half of the 20th century. It grounded psychology firmly in -an empirical setting, arguing that mental events are not observable, -and that only external behaviour can be studied -citep:miller2003_cognit_revol. - -In the 1950s, new theories, in particular Chomsky's theories in -linguistics, started to question this approach and highlighted its -limitations. /Cognitivism/ arose as a way to take into account -internal mental states. It allowed scientists to make hypotheses about -unobservable phenomenons, provided they made predictions testable in -an experimental setting. - -Harnad defines a /symbol system/ as a set of arbitrary token with -explicit rules (also in the form of tokens or strings of tokens) to -combine them. Note that the set of rules should be explicit and not -defined as posteriori, because nearly every phenomenon can be -interpreted as following a set of rules. - -An additional (and most relevant for us) property of symbol systems is -that they are /semantically interpretable/: we can associate a meaning -in a systematic fashion to every token or string of tokens. - -This exposes /symbolism/, i.e. the view that cognition is a symbolic -system. The alternative view, /connectionism/, has its root in -biological models of the brain, and posits that the network of -connections in the brain is what defines cognition, without any formal -symbol system. - -#+begin_quote -According to connectionism, cognition is not symbol manipulation but -dynamic patterns of activity in a multilayered network of nodes or -units with weighted positive and negative -interconnections. citep:harnad1990_symbol_groun_probl -#+end_quote - -One common criticism of connectionism is that it does not meet the -compositionality criterion. Moreover, we cannot give a semantic -interpretation of connectionist patterns in a systematic way as we can -in symbolic systems. This issue was recently raised again by Gary -Marcus in his recent book /Rebooting AI/ -citep:marcus2019_reboot_ai. Human cognition makes extensive use of -internal representations. Chomsky's theories on the existence of a -"universal grammar" is a good example of such internal structure for -linguistics. These cognitive representations seem to be highly -structured (as demonstrated by the work of Kahneman and Tversky -citep:kahneman2011_think_fast_slow), and compositional. (See also his -[[https://thegradient.pub/an-epidemic-of-ai-misinformation/][recent article]] in /The Gradient/.) - -#+CAPTION: Connectionism versus symbol systems (Taken from cite:harnad1990_symbol_groun_probl.) -|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| *Strengths of connectionism:* | -| 1) Nonsymbolic Function: As long as it does not aspire to be a symbol system, a connectionist network has the advantage of not being subject to the symbol grounding problem. | -| 2) Generality: Connectionism applies the same small family of algorithms to many problems, whereas symbolism, being a methodology rather than an algorithm, relies on endless problem-specific symbolic rules. | -| 3) "Neurosimilitude": Connectionist architecture seems more brain-like than a Turing machine or a digital computer. | -| 4) Pattern Learning: Connectionist networks are especially suited to the learning of patterns from data. | -|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| *Weaknesses if connectionism:* | -| 1) Nonsymbolic Function: Connectionist networks, because they are not symbol systems, do not have the systematic semantic properties that many cognitive phenomena appear to have. | -| 2) Generality: Not every problem amounts to pattern learning. Some cognitive tasks may call for problem-specific rules, symbol manipulation, and standard computation. | -| 3) "Neurosimilitude": Connectionism's brain-likeness may be superficial and may (like toy models) camoflauge deeper performance limitations. | -|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| *Strengths of symbol systems:* | -| 1) Symbolic Function: Symbols have the computing power of Turing Machines and the systematic properties of a formal syntax that is semantically interpretable. | -| 2) Generality: All computable functions (including all cognitive functions) are equivalent to a computational state in a Turing Machine. | -| 3) Practical Successes: Symbol systems' ability to generate intelligent behavior is demonstrated by the successes of Artificial Intelligence. | -|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| *Weaknesses of symbol systems:* | -| 1) Symbolic Function: Symbol systems are subject to the symbol grounding problem. | -| 2) Generality: Turing power is too general. The solutions to AI's many toy problems do not give rise to common principles of cognition but to a vast variety of ad hoc symbolic strategies. | -|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| - -** Exposing the issue: thought experiments - -* What the human mind does, and what AIs could do - -* References