| Frequently Asked Questions |
 |
Some FAQs from the AI Topics' collection:
Q: Can
you please help me. I am doing a report about AI for school and I don't
know where to begin.
A: We sure can! See our special page: Doing
a School Report About AI: Tips & Suggestions
Q:
I am planning on entering a career in robotics and artificial intelligence
and was unsure if my course selection for university next year is suitable.
A: Go to the response.
- also see our Resources for Students page
Q: If
you guys wouldn't mind. does math play a big role in all of your experiments?
A: Go to the response.
- also see our Reasoning, Machine Learning, Vision, and Representation pages
Q:
[addressed to ASK-AN-EXPERT] I'm not sure this question
will be interesting enough to qualify, but I've been having trouble getting
this information, so here goes:I graduated from __ University last year
with a degree in honors theoretical math, minors in computer science and
physics. I had only a 3.3 GPA, but that comes with medical excuses. I've
since taken several graduate computer science courses and done well, as
well as the graduate logic sequence in our math department from __ (who
is well known in his field, as I understand it), and I've managed to earn
at least a couple good faculty reccomendations that way. I am doing an
internship, helping with research on machine learning image ehancement
algorithms for the __ , at __ at the moment, and I've been part of a Machine
Learning seminar at __ for the past few quarters. I'll be taking the GRE
this summer (I was a national merit scholar, I *hope* I'll do fairly well
on that), and the computer science gre this fall. I'll be applying this
fall/winter to graduate school. I just want to find a few graduate schools
with decently interesting Machine Learning/AI programs (I really like
decision making, game theory... something in between being really applied
and really abstract).... that I might actually be able to get into (I'm
pretty sure CMU is out of the question). Do such even exist? Where should
I look? I've asked around at __, but none of the faculty seem to pay much
attention to other schools' graduate programs...... and I really don't
know where to go next to get advice on this (most ranking systems fall
short of providing this detailed information).Thanks for your time...
[C: 7/29/04]
A: It sounds like you have a strong interest in machine
learning and AI, and I certainly encourage you to follow this interest
- it's a great area! If you're looking into graduate programs, there are
many that have strong machine learning research groups. I'd suggest you
look through the recent AAAI, ICML, and NIPS conference proceedings and
see for yourself which universities the research papers are coming from
-- that's probably the single best way to find out who's doing what.
Hope that's helpful. By the way, graduate admissions committees often
look more at your recommendation letters and GRE scores than your undergrad
grades. Good luck with your applications to grad school! [T: 8/2/04]
- also see the graduate school resources on our Resources for Students page
Q:
How do I prepare for a job in AI?
A: There are many types of jobs and careers involving AI but two
of the usual dichotomies are: academic vs industrial jobs, and research
vs application jobs In all cases, a solid preparation in the tools of
the trade is recommended. These tools include: programming languages,
algorithm design, operating systems, data structures, logic & mathematics,
probability theory & statistics, and the specialized topics covered in
AI courses. These areas are covered in standard
courses in most undergraduate and graduate Computer Science programs,
but other majors may include many of these courses as well. Some people
emphasize the cognitive science aspects of AI, for which cognitive psychology,
neurobiology, and philosophy courses are also relevant. Specialized subject
areas, such as computational biology, legal reasoning, medical informatics,
image understanding, mobile robots, and instructional systems, will also
require specialized training in areas outside of AI. Applications-oriented
work in either an academic lab or an industrial setting usually involves
considerable programming. For programming to include AI, one needs a thorough
knowledge of AI techniques for problem solving and knowledge representation.
Research jobs, except for implementation tasks that are well-defined,
generally require advanced training in AI beyond a BS or BA degree. PhD
training is recommended for anyone wanting to make AI research his or
her career, and is necessary to compete for academic jobs. Understanding
intelligence requires more than an ability to write programs.
- also see our Resources for Students page
Q: I am applying
for a Masters course in AI, but in the interim I would like to get a head
start in AI programming. Can you suggest which programming language(s)
I should learn ?
A: A good understanding of computer science is important for AI,
as well as facility with at least one programming language and operating
system. Generally, undergraduate classes in algorithms and data structures
provide a good introduction to fundamental concepts. Programming for AI
has traditionally been done in LISP or Prolog, but any language can be
used. An object-oriented language like C++ is a reasonable alternative
to LISP or Prolog.
- also see our Systems & Languages page
Q:
After strolling around on your website the other day, I came across a
frequently asked questions page on Machine Learning. One of the questions
was, are the computers today powerful enough for Artificial Intelligence?
And the answer was, I believe the computers of 30 years ago were powerful
enough if only we knew how to program them. which leads me to my question.
In current research, is AI research being programmed on top of exisiting
operating systems, and basically are programs running on top of other
programs? I've started work on a kernel, which IS the AI program, not
a program on top of a kernel. Considering I dont know anything about AI,
I've come up with the theory that, in order for AI to work cleanly, it
must have DIRECT access to the hardware, not have to make system calls
to access hardware and memory and so forth. But I dont want to start working
unless I know for sure that the research currently being conducted isnt
already based on this.
Any Reply Appreciated,
J.R.
A: As you've noted, AI research is largely based on existing
hardware and operating systems. Since the mechanisms for achieving intelligent
behavior are not at all well understood, researchers need experimental
environments that are easy to work in. They (we) assume that once some
of the mechanisms are worked out at a conceptual level it will be possible
to optimize them by mapping them into hardware or systems capabilities.
An example from the early history of AI is McCarthy's mapping the powerful
idea of linked lists into the Lisp language, and then people at Xerox
and TI building special-purpose Lisp machines with those constructs in
the hardware. It gained speed, but the conceptual advances do not seem
to me to be that great. Another example is Danny Hillis' construction
of the Connection Machine, with the concepts of neural networks mapped
into massively parallel machines. Both machines provided nifty platforms
that ran AI programs faster, but they did not seem to solve conceptual
problems.
The main advantage of avoiding system calls would seem to be speed. We're
more hobbled by lack of ideas than slowness of operation, I believe. There
may well be AI researchers who disagree, but I don't know who they are.
good question.
B.Buchanan (10/6/03)
- also see our Systems & Languages page
Q: I am a University
Student at ___ . I am part of an honors seminar that will debate whether
or not AI is a threat, or could become a threat to mankind and why.
A: See this response by Patrick
J. Hayes.
- also see our Ethical & Social Implications and SciFi pages, Student Mail, letter #2, and this letter in our Mailbox
Q:
Do you know where I can find information about "old" AI programs, systems,
and projects ?
A: Stanford Medical Informatics offers brief descriptions and related
readings for a number of Historical
Projects such as DENDRAL (1965-83), MYCIN (1972-80), TEIRESIAS (1974-77),
CENTAUR (1977-80), Contract Nets (1976-79), and QUIST (1978-81). Another
good source for this type of information is IEEE's
Annals of the History of Computing, and The Babbage Institute's Software
History Dictionary Project. And don't forget to check out oral histories,
such as those in the The Babbage Institute's oral
history collection, for they are an excellect source of anecdotal
information.
- also see our History page
Q: Where can I find information about
commercially available products that incorporate AI ?
A: A good place to start is with our AI
at your service page. There are also web sites such as the Pacific
Northwest National Laboratory's "links to Commercial Software Tools for
implementing Classical Artificial Intelligence" that offer information
about AI products. And be sure to check out AI
in the news for exciting news about AI products and much more!
>>> PLEASE READ OUR DISCLAIMER OF IMPLIED
ENDORSEMENT AND/OR AFFILIATION as well as the other related notices.
ALSO SEE:
- FAQs about the AI Topics web site (which is where you'll find THE ANSWER FAQ)
- Interviews & Oral Histories (because it's not uncommon for questions such as "How do you define AI?" and "Where do you think AI will be in 10 years?" to come up in the course of the interview)
FAQs Online: Collections & Individual Topics
Artificial Intelligence FAQ's. Easy access to the collection of FAQs that moved from CMU to UCLA. Maintained by Amit Dubey and Ric Crabbe; written by Ric Crabbe, Amit Dubey, and Mark Kantrowitz.
- Part 1: General Questions & Answers
- Part 2: Newsgroups & Mailing Lists
- Part3: Associations & Journals
- Part 4: Bibliography
- Part 5: AI Web Directories & Online Papers
- Part 6: Open Source AI Software
AI FAQ Index. Maintained by the Internet FAQ Consortium, this sites offers links and info about several collections of FAQs. You can also access their extensive list of other FAQs by following the "By Category" link at the bottom of their page.
AI FAQ Collection from Pamela McCorduck, author of Machines Who Think: 25th anniversary edition. Natick, MA: A K Peters, Ltd., 2004. Questions include:
- How long has the human race dreamed about thinking machines?
- What does it mean that a machine beat Garry Kasparov, the world's chess champion?
- Artificial intelligence - is it real?
- What so-called smart computers do -- is that really thinking?
- But doesn't that mean our own machines will replace us?
- Shouldn't we just say no to intelligent machines? Aren't the risks too scary?
- What's ahead as AI succeeds even more?
How does artificial intelligence work...? By Jack Valko. Special to ABCNEWS.com.
Natural Language Processing/Computational Linguistics FAQs
Robotics FAQs. NASA responds to questions from Students & Educators.
Robot FAQs and more Robot FAQs
What is AI ? Professor John McCarthy "get[s] many email inquiries about what artificial intelligence is all about. This is [his] first attempt at answering them on a layman's level or beginning student's level."
Also try entering "faq" and a subject of your choice in our search engine.
|
If you have been unable to find the answer to your AI-related question, we invite you to ask us. While we can't promise a response to every question, we will try to forward meritorious inquiries to one of our experts.
Because the question and its answer may be of interest to others, we may want to post it somewhere on this site. For this and other reasons, we ask that you please read our notices and disclaimers. To submit your question for consideration, click here and please indicate that it is a question for ASK-AN-EXPERT.
We look forward to hearing from you.