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ARTIFICIAL INTELLIGENCE AND NEUROCOGNITION2018/2019
Lecture 1: Introduction to Artificial Intelligence and Neurocognition
Explain the difference between weak AI and strong AI. What are their assumptions and
what does that mean for AI?
The terms strong and weak don't actually refer to processing, or optimization power, or
any interpretation leading to "strong AI" being stronger than "weak AI". It holds
conveniently in practice, but the terms come from elsewhere. In 1980, John Searle
coined the following statements:
AI hypothesis, strong form: an AI system can think and have a mind (in the
philosophical definition of the term);
AI hypothesis, weak form: an AI system can only act like it thinks and has a mind.
So strong AI is a shortcut for an AI systems that verifies the strong AI hypothesis.
Similarly, for the weak form. The terms have then evolved: strong AI refers to AI that
performs as well as humans (who have minds), weak AI refers to AI that doesn't.
The problem with these definitions is that they're fuzzy. For example, AlphaGo is an
example of weak AI, but is "strong" by Go-playing standards. A hypothetical AI
replicating a human baby would be a strong AI, while being "weak" at most tasks.
Explain the difference between symbolic AI and connectionist AI.
If one looks at the history of AI, the research field is divided into two camps – Symbolic
& Non-symbolic AI that followed different path towards building an intelligent system.
Symbolists firmly believed in developing an intelligent system based on rules and
knowledge and whose actions were interpretable while the non-symbolic approach
strived to build a computational system inspired by the human brain.
The traditional symbolic approach, introduced by Newell & Simon in 1976 describes
AI as the development of models using symbolic manipulation. In AI applications,
computers process symbols rather than numbers or letters. In the Symbolic approach,
AI applications process strings of characters that represent real-world entities or
concepts. Symbols can be arranged in structures such as lists, hierarchies, or networks
and these structures show how symbols relate to each other. An early body of work in
AI is purely focused on symbolic approaches with Symbolists pegged as the “prime
movers of the field”.
A research paper from University of Missouri-Columbia cites the computation in these
models is based on explicit representations that contain symbols put together in a
, ARTIFICIAL INTELLIGENCE AND NEUROCOGNITION
2018/2019
specific way and aggregate information. In this approach, a physical symbol system
comprises of a set of entities, known as symbols which are physical patterns. Search and
representation played a central role in the development of symbolic AI.
This approach, also known as the traditional AI spawned a lot of research in Cognitive
Sciences and led to significant advances in the understanding of cognition.
Non-symbolic AI systems do not manipulate a symbolic representation to find
solutions to problems. Instead, they perform calculations according to some principles
that have demonstrated to be able to solve problems. Without exactly understanding
how to arrive at the solution. Examples of Non-symbolic AI include genetic algorithms,
neural networks and deep learning. The origins of non-symbolic AI come from the
attempt to mimic a human brain and its complex network of interconnected neurons.
Non-symbolic AI is also known as “Connectionist AI” and the current applications are
based on this approach – from Google’s automatic transition system (that looks for
patterns), IBM’s Watson, Facebook’s face recognition algorithm to self-driving car
technology.
What was Turing’s imitation game about and how does this relate to intelligent systems?
Alan Turing, in a 1951 paper, proposed a test called "The Imitation Game" that might
finally settle the issue of machine intelligence. The first version of the game he explained
involved no computer intelligence whatsoever. Imagine three rooms, each connected
via computer screen and keyboard to the others. In one room sits a man, in the second
a woman, and in the third sits a person - call him or her the "judge". The judge's job is
to decide which of the two people talking to him through the computer is the man. The
man will attempt to help the judge, offering whatever evidence he can (the computer
terminals are used so that physical clues cannot be used) to prove his man-hood. The
woman's job is to trick the judge, so she will attempt to deceive him, and counteract her
opponent's claims, in hopes that the judge will erroneously identify her as the male.
What does any of this have to do with machine intelligence? Turing then proposed a
modification of the game, in which instead of a man and a woman as contestants, there
was a human, of either gender, and a computer at the other terminal. Now the judge's
job is to decide which of the contestants is human, and which the machine. Turing
proposed that if, under these conditions, a judge were less than 50% accurate, that is, if
a judge is as likely to pick either human or computer, then the computer must be a
passable simulation of a human being and hence, intelligent.
, ARTIFICIAL INTELLIGENCE AND NEUROCOGNITION
2018/2019
How does Searle’s Chinese room argument relate to Turing’s imitation game? What
conclusions would Searle draw out of a computer passing the imitation game?
(Searle: Strong AI vs. Weak AI, Chinese Room Argument: Strong AI is false because main
feature of (only) human mind = understanding of meaning)
Chinese Room Argument Explanation: https://www.youtube.com/watch?v=18SXA-
G2peY
Searle proposes that research in Artificial Intelligence has advanced so that there is a
program that behaves as if it understands Chinese and that it does as well as any native
speaker of Chinese and therefore indistinguishable from them in this ability that then
passed the Test of Turing to speak Chinese.
According to Searle, a machine can look very intelligent, but it does not represent real
intelligence or understanding.
Explain the criticism on symbolic AI. Give arguments against and for.
Symbolic AI refers to the fact that all steps are based on symbolic human readable
representations of the problem that use logic and search to solve problem.
Key advantage of Symbolic AI is that the reasoning process can be easily understood – a
Symbolic AI program can easily explain why a certain conclusion is reached and what the
reasoning steps had been.
+ It can handle noise very well.
A key disadvantage of Symbolic AI is that for learning process – the rules and knowledge
has to be hand coded which is a hard problem.
+ It is unclear how processes like pattern recognition would work in a purely symbolic
way.
+ It doesn't seem necessary for many behaviors.
What are the similarities between connectionist AI architectures and the human brain?
The central connectionist principle is that mental phenomena can be described by
interconnected networks of simple and often uniform units. The form of the connections
and the units can vary from model to model. For example, units in the network could
represent neurons and the connections could represent synapses, as in the human
brain.
Connectionist models are believed to be a step in the direction toward capturing the
intrinsic properties of the biological substrate of intelligence, in that they have been
, ARTIFICIAL INTELLIGENCE AND NEUROCOGNITION
2018/2019
inspired by biological neural networks and seem to be closer in form to biological
processes. They are capable of dealing with incomplete, approximate, and
inconsistent information as well as generalization.
What does it mean for human memory to be content-addressable?
Content-addressable memory (CAM) is a special type of computer memory used in
certain very-high-speed searching applications. It is also known as associative memory
or associative storage and compares input search data (tag) against a table of stored
data, and returns the address of matching data (or in the case of associative memory,
the matching data).
"The content of the memory serves as its address." When presented with a fragment of
a memory, we can reconstruct the rest, because it points us in the direction of the
memory. And this seems to be true for all memories, regardless of whether they were
acquired by the method of loci or the less elaborate and self-conscious methods the rest
of us use. In technical circles this faculty is called content-addressable memory. It
borrows the concept of an address from the inscription metaphor. We have versions of
content-addressing that work in certain special-purpose computers, and simplistic
models of how it might work in the brain. Such techniques can, for example, reconstruct
a stored image when presented with just a part of it, or with a distorted version. These
models count as progress, but they are at best the tip of the content-addressable iceberg
that is human memory.
Content-addressable memory might sound a bit counterintuitive when you first
encounter it. What if I don't have any of the content of the memory? Here's an example.
If you are trying to remember the capital of Ethiopia, you might not initially have even
a fragment of the answer. Even knowing that the capital starts with ‘A’ doesn’t
necessarily help much. So how can content-addressable memory help explain how we
recall the answer? The answer involves expanding our notion of what an individual
memory is. The memory being reconstructed here is not just the name 'Addis Ababa'.
Rather, it is an 'associative whole' that includes the words ‘Ethiopia’, ‘capital’, and
‘Addis Ababa’. So when you consider parts of this whole, you have already found your
way to the part of your memory palace where ‘Addis Ababa’ is likely to be located.
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