Introduction
The IBM product and project of
Watson will be presented here. Watson is
an extreme innovation that takes a large step towards making artificial
intelligence viable. Watson is more of a
long term engineering project which has gone through several iterations and
will continue to develop more fully in the future. Several IBM Watson products have been released
to the market, with applications of the artificial intelligence engine to
several knowledge domains to yield, essentially, expert systems in those
domains. Watson has expert system
products in oncology diagnosis, tax preparation, financial applications (credit
analysis), insurance coverage for medical procedures, and basic research, with
planned business applications, and more applications in healthcare, finance,
contact centers, government, and chemical industries. The reason Watson was chosen is because it is
an innovative project which has a long and grand future ahead of it.
General Description of Watson
Watson is a cognitive computing
product. You do not program Watson, you
use human language to interact with it.
It does human type reasoning or inferences using human language, in this case English (High, 2012). It understands English by using a deep
language processing set of algorithms.
It answers questions, analyzes text, and provides advice. It does English language understanding, it
conducts inferences and can derive new information from existing texts. It uses context to understand and doesn't
just operate literally on language. It
constantly improves its performance by using a machine learning algorithm to
modify the knowledge base as it searches for answers to questions or analyzes
novel texts. More on this will be said
in the next paper, where the general algorithm by which Watson works will be
presented.
In the next iteration of this paper
in Unit 5, the exact algorithm of how Watson answers a question will be
presented. Here just the general idea is
presented. Watson analyzes the
statement, decomposes it into its key parts and uses contextual information to
understand it. Once there is a deep
understanding of the question hypotheses as to the correct answer are
constructed and compared to information in the knowledge base, also known as
the corpus. Each possible answer is
evaluated and a confidence score is attached to it. These scores are ranked and the answer with
the highest score is presented along with its confidence number.
Scope and Features of Watson
The Watson Cognitive System has many
interesting features. First, it does
Deep Language Processing. Most language
understanding systems to date are shallow language processing. At the simplest level, search engines use
mere combinations of keywords to find relevant web pages. There isn't really much understanding here,
it's more of a word match, without any real understanding of the meaning of
sentences. Shallow language processing
uses specific well formed rules based upon keyword combinations to try to
understand language. It does not exhibit
general language understanding. It is
not adaptable. It is precise but not
accurate. Precision is literal
understanding. Non-literal aspects of
language are not understood, like metaphors, idioms, expression, etc...
Accuracy means understanding language when it is imprecise. Most human understanding is accurate but
imprecise, i.e., we understand what someone is saying regardless of the
particular form or syntax and to some extent exact words that are being
used. We understand things that are
non-literal, metaphoric, and expressive.
Deep language understanding attempts to model how humans actually
understand language. Deep language
understanding understands different sentences that have the same meaning, or
the same sentence that has different meanings in different contexts. It uses contextual information from a passage
or from the knowledge base to perform language understanding. It provides not a narrow but a broad and
adaptable understanding of language.
Like human language understanding
it is imprecise yet accurate.
A second feature that Watson has is
machine learning. I.e., in each
encounter where it answers a question, it stores the link between the question
and the answer in its knowledge base and hence becomes more skilled at
answering questions over time. In other
words, it learns, and by learning, it constantly improves its performance.
A third feature is that Watson has
an inference engine. I.e., it can infer
new information from existing texts by following chains of reasoning. Inference engines have been around for a
while. But natural language inference
engines are somewhat new. There are
close connections between Philosophy and Artificial Intelligence in
Computing. First of all, logic is a
subject taught in Philosophy departments.
Even if you study symbolic mathematical logic, you do not take the
subject in the Math department, you take it in the Philosophy department. The reason logic is a part of Philosophy and
not Math is because logic underlies all mathematics and all language (natural
and artificial, i.e., human language and programming languages). It applies to and underlies both. When things are general, i.e., are cross-departmental,
they are philosophical. When I was at
Berkeley, in order to get my Philosophy degree I had to take a year of symbolic
mathematical logic (Fregean Quantificational Predicate Calculus). One way that an inference engine works is by
syllogisms (invented by Aristotle). A
syllogism is a simple line of reasoning that allows one to proceed from
premises to a conclusion. A simple
example is "Socrates is a man," "all men are mortal," (the
two premises) therefore (the conclusion)
"Socrates is mortal." Stated
symbolically, S is M, All M are m, therefore S is m. In symbolic form the syllogism is universal,
i.e., it is always true as long as one preserves the logical form. Therefore, a carp is a fish, all fish have
gills, therefore a carp has gills. This
is the same syllogism or logic. The
premises are based upon evidence. But
the reasoning is unassailable. If it is
true that all fish have gills and a carp is a fish, then by the logical form of
the syllogism, it is also true that carp have gills. You can see how syllogisms can be used to
create chains or lines of reasoning, to take a set of premises and derive
conclusions. All and Some are
existential quantifiers (hence Quantificational predicate calculus). There are many logic laws of inference that
Watson would use: modus ponens, modus
tollens, hypothetical syllogism, disjunctive syllogism, constructive dilemma,
absorption, simplification, conjunction, addition, De Morgan's theorem,
material equivalence, and many others.
There are also logical fallacies which an inference engine would avoid, but
a human may not. This is how Watson does
computerized inference or has reasoning algorithms based upon these logical
inference laws.
As an aside, computers would not
exist without logic. Computers would not
exist with Aristotle who invented logic.
Computers are logical machines both in terms of their hardware and
software. Hardware is based upon Boolean
logic; in fact, for every circuit there is a Boolean logic equation. Software is based upon logic. Anyone who programs knows that programming is
all logic (not mathematics, logic).
Programs can be written that do mathematical tasks as well as linguistic
tasks. Since logic underlies both
language and math, computer use logic, particularly binary logic. Programming languages have keywords and syntax. If one makes a syntactical error in
programming a program doesn't compile.
But one can create a perfectly syntactically correct program which
compiles and yet doesn't work right, doesn't do what it's supposed to do, i.e.,
there are logic errors. Also programming
languages have logic features, like "if ... then ... else" and
"or" and "and" and "not," etc... Computers wouldn't exist without logic and
artificial intelligence in particular wouldn't exist without logic and
Philosophy.
There are many cognitive
capabilities that stem from the ability of a program to infer. These include among others the ability of the
program (Watson) to infer new knowledge by running inference algorithms on its
existing and growing knowledge base.
Watson's Current Limitations
There are several limitations to the
current Watson project and products.
First, it only speaks English. It
is a Natural Language Processing and Understanding System, but many of its
features are based upon English specific idioms, expression, and oddities. It is perfectly possible to adapt this NLP
technology to other languages, this just hasn't been done yet. The goal was to completely master English
language understanding first.
Watson has many other current
limitations to a full-fledged cognitive system.
These capabilities will be added at some later date. These include the ability to sense and
perceive, the ability to have a dialog or do planning, the ability to have
foresight, or do knowledge extrapolation and the creation of new
knowledge. These capabilities will be
discussed in the next paper on Watson, which will be described as areas of
future research.
Why Watson is Needed: Its Purpose
Greatly needed are computing systems
that can talk to us, that can understand human language and answer questions
and carry out tasks stated in English.
This is true in many domains. And
as Watson can be adapted to answer questions and provide advice in many expert
domains, it can be of great help to doctors doing diagnoses, to insurers or
bankers doing credit analysis or valuation, to people doing their taxes, and to
people doing many other complex tasks that need questions answered or advice.
Greatly needed are computer systems
that can reason and can deduce new information and therefore make discoveries
by inferring from current expert knowledge bases.
References
High, R.
(2012). The era of cognitive systems: An inside look at IBM Watson and how it
works. IBM Corporation, Redbooks.
No comments:
Post a Comment