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Summary of the text “Symbolism vs. Connectionism: A Closing Gap in Artificial Intelligence” by Wang (2017). About the differences between symbolic and connectionist AI.
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7 - Symbolism vs. Connectionism: A Closing Gap in Artificial Intelligence -Wang (2017)
Dit paper bespreekt symbolisme en connectionisme:
• Wat zijn symbolische en connectionistische AI?
• Wat zijn de voordelen van symbolische AI? EN van connectionistische AI?
• Hoe beargumenteert de auteur dat het onderscheid tussen symbolische en
connectionistische AI steeds verder vervaagt of zelfs verdwijnt?
- AI: “find how to make machines use language, form abstractions and concepts, solve kinds of
problems now reserved for humans, and improve themselves.” (1956)
→ focused on the symbolic capacities
- central problem of AI: how knowledge is represented, encoded, and processed
- main debate: the dichotomy (contrast) of symbolic and connectionist paradigms
- AGI: artificial general intelligence; human level AI that is capable of completing a wide range
of tasks in an appropriate fashion
- symbolic AI was conceived in the attempt to explicitly represent human knowledge in facts,
rules, and other declarative, symbolic forms
- symbol: a pattern that stands for other things (target can be object, symbol or relation)
- the nature of human language is the organization of signs
→ individual signs have limited ability to convey meanings unless embodied in a sign system
- by abstracting symbols from lower levels to higher levels (physical, cognitive, social,
narrative), we form abstract concepts and find universal meanings
- physical symbol system hypothesis (PSSH): the PSS has the necessary and sufficient
means for general intelligent action, it is the only way toward AGI
- physical symbol system (PSS): a physical computing device for symbol manipulation, which
consists of discrete symbols
- symbols: form expressions, or symbol structures through some sort of physical connections
- physical structures often work as internal representations of the environments and also contain
a set of processes that “operate on expressions to produce other expressions”
→ PSSH implies that the existence of symbolic-level computing in a system is independent of the
physical substrate it operates on
- representation: the mapping from one sign system to another (semiotic morphism)
- knowledge representation: to represent information about the world in a system in a way the
system can employ to store and retrieve old information, infer new knowledge, and perform
complex functions (main problem in AI)
, - symbolic approaches represent knowledge in a highly structured fashion
- the basic units of symbolic representation are symbolic atoms, specific words or concepts
→ this representation paradigm is also called localist as opposed to distributed representation in
connectionist models
- existential graph (EG): symbolic in the sense that it uses individual nodes and arcs to represent
different concepts and their relationships, it captures the aggregate structures of knowledge
- semantic networks: use graphic notations to represent individual objects and categories of
objects
→ the notations include nodes that are connected by labeled links, which represent relations
among objects
- symbolic paradigm is criticized for many reasons, for example: many symbolic structures need
manual coding
→ it is believed that an essential component of intelligence is a physical body that interacts with
the environment through perceptions and behaviors, “grounding” the symbols to the world and
giving them meanings (purely manipulating symbols misses that)
→ intelligence requires non-symbolic processing (but the relationship between symbolic and
non-symbolic prodess is supplement instead of replacement)
→ computers will never be the same as brains (but they both involve computation processes,
both brains and computers are essentially physical symbol systems that can give rise to
intelligence)
- representation-transformation: focuses on information process (instead of computers)
→ Boolean dream: problems explored in symbolic paradigm are too simple from a neural science
point of view, unable to provide rich insight for the computational organization of the brain
- rules can always lead from true statements to other true statements and see thinking
as the manipulations of propositions
→ symbolic models are only succesful at coarse levels, unable to account for the detailed
structure of cognition
- connectionist models refer to bio-inspired networks composed of a large number of
homogenous units and weighted connections among them, analogous to neurons and synapses
in the brain
→ the strengths of the connections reflect how closely the units are linked and can be
strengthened or weakened dynamically by new training data
→ the main task of connectionist paradigm is to tune the weights until the optimum is reached
through techniques like gradient descent
- the signals in the neural nets in brains can be modeled by logic expressions , exhibiting digital
properties
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