Page 142 - Proceedings of the 2018 ITU Kaleidoscope

‎ 2018 ITU Kaleidoscope Academic Conference‎

research as its lens of enquiry, the issues that it poses and serves as a tool to study the mind and “strong AI” where the
the solutions that it suggests are also relevant to broader computer itself can be said to possess a mind. He focuses
pursuits of inclusiveness in AI-based systems. his criticisms on the latter by arguing that in order to
constitute strong AI a machine would need to satisfy the
2. DEFINING AI AND ITS “INTELLIGENCE” tests of consciousness and intentionality or causal powers
that are possessed by the human brain (Searle, 1980[8]).
John McCarty, one of the founders of this field, described Similar debates on the “intelligence” of AI have also
AI as the “the science and engineering of making emerged from other fields like psychology, economics,
intelligent machines” where intelligence refers to “the biology, neuro-science, engineering and linguistics (Russell
computational part of the ability to achieve goals in the and Norvig, 2010 [6]).
world” (McCarty, 2007[4]). Another suggestion is to look at
intelligence as a “quality that enables an entity to function Feminist epistemologist Alison Adam notes that these
appropriately and with foresight in its environment” popular criticisms of are lacking in two major respects.
(Nilsson, 2010[5]). Both these definitions, forwarded by First, they gauge the success or failure of AI based on
practitioners of AI, refer to intelligence in rather broad philosophical tests of ideal intelligence, which for Adam is
terms, as qualities which can be possessed by humans, less relevant than understanding how AI is actually being
animals and machines, albeit, at different levels. put to use. For her, the success of AI lies in its widespread
adoption in everyday life. Second, she notes that the
Russell and Norvig (2010)[6] present a classification of the traditional critiques of AI completely ignore how AI
available definitions of AI along two lines -- (i) based on systems reinforce existing power structures. AI research has
the function expected to be performed (thought processes/ failed to represent the knowledge of certain social groups,
reasoning of the machine versus the outcome/ behaviour such as women (Adam,2005[9]). This has worked to the
that it exhibits); or (ii) the metrics used for assessing the disadvantage of society as well as the field itself.
success of AI (human performance versus an ideal standard
of “rationality”). The Turing test, developed by British 3. GENDER OF AI DEVELOPERS AND THEIR
mathematician and cryptographer Alan Turing in 1950, ARTIFACTS
reflects a combination of the behavioral element and
human-like performance in the above classification. If upon While the contours of what constitutes intelligence in AI
the exchange of a series of questions with a person and has remained contested, a more operational understanding
machine, a human interrogator is unable to distinguish of AI has also emerged. As per some researchers, AI can
between the two, the Turing test would regard the machine simply be defined as “what AI researchers do” (Grosz et al,
to be an intelligent, thinking entity (Copeland, 2018[7]). 2016[10]). This approach clearly gives the practitioners in
this field immense power, not just in defining their own
Despite its continued relevance over the years, the Turing agenda but also the contours of the discipline that they
test has also come under attack for its attempt to define the represent. It therefore becomes pertinent to discuss who are
intelligence of machines by replicating human behaviour. these researchers and what is it that they do?
Russell and Norvig (2010)[6] point to this as a limitation by
saying, “Aeronautical engineering texts do not define the 3.1 Early choices in AI research
goal of their field as making machines that fly so exactly
like pigeons that they can fool even other pigeons”. Interestingly, even though we have seen significant
advances in AI applications in recent years, the fundamental
AI’s claims of building intelligence in machines have also elements of what constitutes AI research have not changed
faced strong philosophical criticisms. These criticisms stem very significantly. In 1955, the Dartmouth College proposal
from arguments about the lack of a mind, of consciousness identified the following as some of the components of the
and intentionality in machines, features which some AI problems that needed further research: programming a
philosophers regard as essential for establishing true computer to use a language (natural language processing);
intelligence. John Searle illustrated this through his famous self-improvement by machines (machine learning); and
Chinese room thought experiment. As per this, person who neuron nets (neural networks and deep learning)
does not know any Chinese can follow a set of rules on how (McCarthy et al, 1955[1]). The text in parenthesis reflects
to correlate Chinese symbols and produce a response to the currently in vogue terminology for these processes.
questions that may convince an outsider that the person is While these areas of research still remain relevant, newer
acting intelligently. Producing meaningful replies in sub-areas like computer vision and robotics have also been
Chinese would however not mean that the person has any added along the way (Grosz, 2016[10]).
actual understanding of the language.
This leads us to ask – on what basis did AI researchers
In making the claim that similar behavior by a computer decide that certain elements of intelligence (versus others)
programme cannot be equated with intelligence, Searle were worth replicating in machines? In 1950, Alan Turing
draws a distinction between “weak AI”, where the computer admitted that he did not know the right answer. He

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