FAQs

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Frequently Asked Questions

Some FAQs from the AI Topics collection

GETTING STARTED WITH A REPORT

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

WHAT COURSES SHOULD I TAKE?

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:
  • Cynthia Breazeal's answer to the question: "I'm just starting my B.S. in Computer Science. What educational path should I take to get into social robotics and AI [artificial intelligence]?" From the Ask the Expert portion of NOVA scienceNOW's Profile of Cynthia Breazeal (November 2006).
  • our Resources for Students page

Q: I am 14 years old and live in England. I have to choose my options soon, and am interested in a career in Artificial Intelligence. As these GCSE options will affect my future career choices, I was hoping you could provide me with the necessary information so that I can choose the right subjects. ...

A: Thanks for your interest. We are very happy to help.

You might start by perusing the AITopics information portal. The Resources page contains a lot of background information that would seem to be relevant: http://www.aaai.org/AITopics/pmwiki/pmwiki.php/AITopics/StudentResources

Also, some of the questions already answered on the FAQ page seem relevant: http://www.aaai.org/AITopics/pmwiki/pmwiki.php/AITopics/FAQs

To answer your question more directly, I believe the subjects that will give you the best preparation are mathematics and science. Ordinarily, you would not begin to specialize in AI until university, and only then after taking several courses in computer science, which mostly assume a good working knowledge of mathematics. You will want to be very familiar with at least one programming language and one operating system before you start specializing.

One thing to consider is why you would like to make computers smarter. Smart computers can be used, for example, to help people stay healthy, to make transportation safer, to provide more relevant information from the web, to crete household robots, to explore outer space, to assist scientists with theory formation, and to make businesses more efficient & profitable. If there is a particular kind of application that you feel interests you a lot, then you will want to be sure you take courses in that area as well as in computer science.

I hope this helps. Good luck with your studies, and be sure you enjoy them. B.Buchanan

Q: I am a B.Tech Bioinformatics graduate. I want to pursue career in Artificial Intelligence. I even did final semester project on Neural networks in order to gain knowledge about the field. I am in processof applying to universities for Masters. But i can not decide my specialization in Artificial Intelligence. Its such a vast field. I want to master AI such that i can apply it to various fields. Kindly suggest me the specialization that is having great future scope.

  Please help me with this. I have been referring  many papers and magazines but can not decide. 
   Thanking you for your precious time. 

A: thank you for your inquiry. As you know, there are many areas in AI, as in every other discipline. The areas you will probably make the most contributions to are very likely to be the ones that are most fun for you personally, the ones that capture your attention and match the skills and experience you have already acquired.

Ask yourself where you believe computers need to be much smarter than they are now and which of those areas seem particularly important or interesting to you. Then put as much energy as you can into making it happen.

For example, making better use of all the information and data on the web, making automobiles safer, creating substitutes for care-givers that can help older people, discovering new scientific theories or medical interventions from accumulated data. All these are areas where there is considerable activity now and in the future, but there are more.

Good luck with your studies. Learn as much as you can about the foundations of AI and Computer Science because you will be able to build on them for the next decades, while the details of hardware, languages and formats will change more rapidly.

B.Buchanan (2/11/10)

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. Any programming language can be used, but an interpreted language generally makes program development easier. Programming for AI has traditionally been done in LISP or Prolog, but any language with strong symbolic-processing features can be used. An object-oriented language like C++ is a reasonable alternative to LISP or Prolog. Python and Java are also used. In developing and testing new ideas, which can take weeks, months or years, the speed of implementation is far more important than the run-time speed of the program, which is usually measured in seconds or minutes.

• also see the answer to this question on the AI FAQ page and general discussions on our Systems & Languages 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

WHICH GRADUATE SCHOOLS?

Q: Graduate school question 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

WHAT CAN COMPUTERS DO?

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!
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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, and Student Mail, letter #2.

ARCHIVES / HISTORY

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: that's where you'll find resources such as the timeline of AI with many classic programs named, and Heather Knight's Early Artificial Intelligence Projects - A Student Perspective.
  • AAAI's Classic Books in AI collection for the full text of Rule-Based Expert Systems --The MYCIN Experiments of the Stanford Heuristic Programming Project. Bruce G. Buchanan and Edward H. Shortliffe, editors (1984). Reading, MA: Addison-Wesley.

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

Collections:

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 Bites factsheets. From The Society for the Study of Artificial Intelligence and Simulation of Behaviour.

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?

Individual Topics:

• Artificial Life FAQs
• Constraint-Based Reasoning FAQs (Constraint Programming/Satisfaction)
• Fuzzy Logic FAQs
• Genetic Algorithms FAQs
• How does artificial intelligence work...? By Jack Valko. Special to ABCNEWS.com.
• Medical Informatics FAQs
• Natural Language Processing/Computational Linguistics FAQs
• Neural Network FAQs
• Qualitative Reasoning FAQs
• Real-Time FAQs
• Robot Building Mini-FAQs
• Robotics FAQs. NASA responds to questions from Students & Educators.
• Robot FAQs
• Speech 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."
• What is Computer Science?

Also try entering "faq" and a subject of your choice in our search engine.

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