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Frequently Asked Questions about AI


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FAQs About AI in General

What is AI?

  • 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."
  • AI Bites factsheets. From The Society for the Study of Artificial Intelligence and Simulation of Behaviour.
  • See 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)
  • AI FAQ Collection from Pamela McCorduck, author of Machines Who Think: 25th anniversary edition. Natick, MA: A K Peters, Ltd., 2004. Questions include:
sketch of puzzled person
  • 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?

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. 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.

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)

How Do I Find Old AI Programs?

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 excellent source of anecdotal information.


FAQs About Specific Topics within AI


FAQs About Courses and Schools

How Do I Start on 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 live in Australia. 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: Becausethe fields of robotics and artificial intelligence are so diverse, I thought that you would appreciate hearing from a few experts.graduation cap & diploma

The first three responses are from resources featured on the Resources for Students page and are part of the Scientific American Frontiers "Cool Careers in Science" web site (http://www.pbs.org/safarchive/5_cool/53_career.html):

1. Meet Manuela Veloso - With her students at Carnegie Mellon University, Manuela designs soccer-playing robots that have won international RoboCup competitions. How cool is that?!
>> excerpt
Q: If I'm a student thinking about a career designing and building robots, what can I do now to prepare?
A: Do well what you are interested in. Get a solid mathematical and engineering background. Biology and cognitive science are also very relevant for building robots. Get a broad view of what you think robots can be useful for.
2. Meet Maja Mataric - Maja is working on developing the next generation of intelligent robots! How cool is that?!
>> excerpt
Q: What educational background do you need to design robots?
A: Again, we need to be clear on what you mean by "designing robots." If you mean designing robot bodies, that requires knowledge of mechanical and electrical engineering, and knowing computer science also helps, but is not necessary. If you mean designing robot minds and behaviors, then you need a background in computer science, and electrical engineering also helps. Finally, if you are interested in animal-inspired robotics, it is very helpful to study biology, ethology (the study of animal behavior in nature) and cognitive science.
3. Meet Roger D. Quinn - He's teamed up with biologist Roy Ritzmann to design and build a robot that imitates the cockroach, an insect with superior locomotion. How cool is that?!
>> excerpt:
Q: If I'm a student thinking about a career designing and building robots, what can I do now to prepare?
A: Read, study and enjoy science and math. Tinker with mechanical and electronic devices. Learn how they operate and why.
4. The fourth response is one of the many interviews with Rodney Brooks (who, by the way, grew up in Adelaide, Australia) that appear on our Interviews & Oral Histories page.
Ask The Scientists: Almost Human
Is it possible to create a computer that mimics a human being? That's the goal of Rodney Brooks, who hopes his robot Cog will have the intelligence of a six-month-old human baby. Following the [Scientific American] Frontiers special Robots Alive!, Rodney answered viewers' questions in an Ask the Scientists panel. Here are viewers' answers and his answers:
>> excerpt
Q: I am interested in a career like yours, designing and building robots. What courses would I have to take in college? Do you have any other helpful information to help me get started in the field of robotics?
A: In high school (and college) make sure you take plenty of math -- it is the foundation for all good engineering. In college you could major in mechanical engineering, electrical engineering, aeronautics and astronautics, or in computer science. Robotics is very interdisciplinary and so except at a very few colleges there is not a major that is exactly fitted to robotics. While an undergraduate see if there are any robotics projects on your campus and see if there is any way to become an undergraduate research assistant on the project. Hands-on experience is the best way to learn about all the interdisciplinary aspects of robotics.
Whatever major you take, try to at least get the core courses in each of mechanical and electrical engineering, and in computer science. If majoring in mechanical or electrical engineering take some control theory courses. In computer science (or engineering) take courses in microprocessor control.
5. An Interview with Artificial Intelligence expert Ruth Aylett. The Science Teacher (the National Science Teachers Association's journal for high school science teachers). January 2003, page 52.
>> excerpt
Q: What educational background is needed to design robots?
A: Robots are composed of several systems working together: the controller is the robot's brain, which controls its movements; the body is the robot's physical appearance related to the job it performs; mobility, or how the robot moves, depends on the job it performs (for example, a robot uses propellers and rudders in the water, and legs or wheels on land); power is used to fuel the robot (electric solar cells are one example, such as the solar-powered robots described here); sensors provide signals to give robots a perceptual understanding of their environment so they can alter their behavior based on that information; and tools are unique to the task the robot performs. Just as robots are made of several systems, the field of AI requires a collaboration of many different disciplines to be successful. Engineering is clearly useful, but I know people who have a background in biology, psychology, physics, and computer science. What's most important is a willingness to learn a lot of new things from a variety of disciplines.
6. Georgia Tech's Ronald Arkin. (September 12, 2005). "Technology Research News Editor Eric Smalley carried out an email conversation with Georgia Institute of Technology professor Ronald C. Arkin in August of 2005 that covered the economics of labor, making robots as reliable as cars, getting robots to trust people, biorobotics, finding the boundaries of intimate relationships with robots, how much to let robots manipulate people, giving robots a conscience, robots as humane soldiers and The Butlerian Jihad."
Q: What's the most important piece of advice you can give to a college student who shows interest in science and technology?
A: Pay attention to basics. Defer gratification until you have mastered the fundamentals of mathematics, physics, and the other disciplines. Also pay attention to interdisciplinary studies - there's much to be learned by being a generalist. Also watch and learn from your more senior counterparts and find good role models.
  • 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

What Courses Should I Take?

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 for Students page contains a lot of relevant background information.

Also, some of the questions already answered on the FAQs page seem relevant.

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

What Courses Should I Take?

Q: As far as classes go I have taken Calculus 1, Visual Basic Programming, Flash Programming, Econometrics, Statistics, and other quantitative coursework. I have never taken a course in advanced programming such as C, C++, Java and my understanding of programming right now is only at the Elementary level. I am trying to decide what would be the best use of the [remaining] time I will have....

Should I be signing up for Calculus 1 and 2 before I enter graduate school and take a course in more advanced programming such as Java or algorithms. I could take these at the Community College to refresh and expand what i have learned (And to save money).

A professor I have emailed told me that I should take English Composition or Technical writing in order to help improve my GRE score and English abilities. ... Is this a good plan of action? I feel kind of lost as to what I should be doing.

A: This is not a simple question to answer since every graduate school admissions committee has its own criteria for assessing prospective students, and each person presents a different array of skills and achievements.

It is notoriously difficult to predict success among students. Native talent and motivation are difficult to measure, but they are clearly important. You are probably a better judge of those than anyone else.

Years ago, the perceived success of students at one prestigious Computer Science graduate program was matched against the data originally collected on their applications. The single best predictor of success turned out to be the verbal GRE score -- perhaps, in part, because the variability of quantitative scores (and other mathematics-related measures) among CS students is low. Clear thinking and clear writing are probably correlated, and writing technical papers is an essential part of academic life. But there is also little doubt about the importance of mathematical concepts for the understanding of essential CS concepts and the design of sound programs.

Every AI faculty member has known excellent students who have been extremely "one-sided". Most admissions committees, however, will want to find exceptional strengths that compensate for any perceived one-sidedness.

A technical writing class sounds like it would be a good thing for you. Many graduate schools would presume you know calculus; almost all would need to know you are proficient in at least one programming language. You can look at the course requirements of most CS programs on their websites and gauge your own readiness pretty well.

Don't be discouraged by the hurdles. The satisfaction of working in AI outweighs the challenges of obtaining the skills.

B.Buchanan

How Do I Choose an Area of Specialization within AI?

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)

What Programming Languages Should I Know?

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.

How Important is Math for Work in AI?

Q: If you guys wouldn't mind. does math play a big role in all of your experiments?
A: According to the resources below, the answer to your question appears to be YES:

"Work in Artificial Intelligence has drawn heavily on that in mathematics in recent years, and a background in mathematics is increasingly important in research. Areas of AI that have particularly benefited from interaction with mathematics include machine learning, neural networks, and advanced logics. The flow has been two way. AI workers have contributed to the automated proof of mathematical theorems (some unproven by humans); to the development of new

forms of logic for fuzzy and temporal reasoning; and to the development of new randomised algorithms (eg genetic algorithms) that have attracted attention from the Mathematics community.

Specialities at Birmingham within AI include mathematical reasoning, evolutionary computing, neural networks, machine learning, and new logics. You will have the chance to study all of these, and to pursue one topic in depth for your final year project."
"The scope of Annals of Mathematics and Artificial Intelligence is intended to represent a wide range of topics of concern to scholars applying quantitative, combinatorial, logical, algebraic and algorithmic methods to Artificial Intelligence areas as diverse as decision support, automated deduction, reasoning, knowledge-based systems, machine learning, computer vision, robotics and planning.
The journal is aimed at: applied logicians, algorithms and complexity researchers, Artificial Intelligence theorists and applications specialists using mathematical methods."
For an idea as to the topics that are discussed, see the extended abstracts.
  • [added 1/04] Stuart Russell on the Future of Artificial Intelligence. Ubiquity; Volume 4, Issue 43 (December 24 - January 6, 2004). "Computer scientists use a lot of mathematics, but we're interested in computation. Mechanical engineers use lots of mathematics, but they're interested in mechanisms and design. And maybe sociologists and economists will use lots of AI models, but they'll still be interested in societies and economies."
  • [added 3/04] The Isaac Newton of logic - It was 150 years ago that George Boole published his classic The Laws of Thought, in which he outlined concepts that form the underpinnings of the modern high-speed computer. By Siobhan Roberts. The Globe and Mail (March 27, 2004; page F9). "It was 150 years ago that George Boole published his literary classic The Laws of Thought, wherein he devised a mathematical language for dealing with mental machinations of logic. It was a symbolic language of thought -- an algebra of logic (algebra is the branch of mathematics that uses letters and other general symbols to represent numbers and quantities in formulas and equations). In doing so, he provided the raw material needed for the design of the modern high-speed computer. His concepts, developed over the past century by other mathematicians but still known as 'Boolean algebra,' form the underpinnings of computer hardware, driving the circuits on computer chips. And, at a much higher level in the brain stem of computers, Boolean algebra operates the software of search engines such as Google. ... The most basic and tangible example is the machinations of Boolean searches, which operate on three logical operators: and, or, not. Algebra gets factored in to this logical equation when Boole designates a multiplication sign (x) to represent 'and,' an addition sign (+) to represent 'or,' and a subtraction sign (-) to represent 'not.'"
  • [added 6/04] Computer Science Major. From collegeboard.com Incorporated's Career Browser. "What the Major is Like. The major in computer science begins with a liberal education and study of the necessary mathematical tools, which include calculus, discrete math, and modern algebra. Students will learn about the design, development, and analysis of hardware; they will study the organization and processing of instructions and data to perform computations by hardware devices. ... Students will study artifical intelligence in software that exhibits intelligent behavior. These programs play games, solve puzzles, recognize speech, and recognize and act on visual images. Artificial intelligence is closely connected to robotics and cognitive psychology."
  • [added 10/04] The Age of Intelligent Machines, Chapter Three: Mathematical Roots. By Raymond Kurzweil. From Ray Kurzweil's book, The Age of Intelligent Machines, published in 1990. "The AI field was founded by mathematicians: John McCarthy, Alan Turing (1912-1954), Norbert Wiener (1894-1964), students of Alonzo Church, Claude Shannon, Marvin Minsky, and others. LISP, the primary language for academic research in artificial intelligence, was adapted from a mathematical notation designed by Stephen Kleene and Barkley Rosser, both students of Church. [fn] Mathematics has often been viewed as the ultimate formalization of our thinking process, at least of the rational side of it."
  • [added 1/06] Math Will Rock Your World. By Stephen Baker, with Bremen Leak. Business Week Magazine & BusinessWeek Online (January 23, 2006).
  • also see our Reasoning, Machine Learning, Vision, and Representation pages

What Are Good Schools for Studying AI?

Q: What are the best undergraduate schools for me?
I have a student who loves linguistics, as well as robotics and looking into going into artificial intelligence. I wonder if anyone may suggest a list of universities which offers programs for both in the field of linguistics and A.I. Input is appreciated! A: Thanks for asking. We have a few lists of academic programs, among other resources for students, on our Resources for Students page.
As you would guess, there is no simple answer. E.g., research-oriented universities are exhilarating for some students and overwhelming for others. Strong undergraduate programs are probably more numerous than strong graduate programs in specific areas. If your student is female, then having a female faculty mentor can make a big difference. And so on. But the material we've collected should help. Let us know if we can do more.

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]

How Do I Prepare for a Career in AI?

Q: What would be a good direction to orient my career in AI?
A: 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.

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.


Projects and Reports

How Does Our Team Get Started on a Science Fair Project?

PLEASE CAN YOU HELP!!

My name is ___ and I teach at ___ High School in ___ Scotland. I am writing to inform you that our school held the first Science Fair in Scotland last June ___ and it was extremely successful. The Science & Technology Fayre has now been held every year during June and the event has been extremely successful.

The Fair has been set up so that pupils research areas of science and technology which they find of interest to them, build a model/devise an experiment to demonstrate the science involved and finally, through multimedia applications display and present to the public.

The Fair has many aims. We plan:

  • to get pupils enthused about science and technology.
  • to allow pupils with barriers to learning, to access science.
  • to give pupils the chance to be creative in their out look to science
  • to encourage citizenship and the ability to work in groups
  • to enhance their ICT capabilities by using music, video and photograph to capture their work
  • to encourage pupils to be enterprising and make links with industry and employers.

Our intention is that every pupil that comes to the school in S1 (12 year old pupils) will take part in this event (around 180 pupils per year) the school has great ties with the local press and any sponsorship that we receive would get the relevant advertising. ... I am now working on the 2011 Fair and I have one group that wants to build a human sized animatronics robot. I think that if we work on a head this would be enough. We are really interested in trying to get this robotics head with artificial intelligience. How can we do this? We are really struggling and any help you can provide would be greatly appreciated! ... May I take the opportunity to thank you again for your time and implore you to consider our request.

First of all, thanks to you and all the other creative teachers who devote enormous amounts of time on science fairs like yours.

Now to your specific question I have one group that wants to build a human sized animatronics robot. I think that if we work on a head this would be enough. We are really interested in trying to get this robotics head with artificial intelligience. How can we do this? We are really struggling and any help you can provide would be greatly appreciated!

As you already know, there are many aspects of intelligence and you need to define carefully which aspect(s) you want to demonstrate. You've already decided, quite appropriately, that creating a robot that moves or manipulates things in its world is more than you need to do. For example, learning is often taken to be an essential aspect of intelligent beings; so is communication in natural language. The ability to make a plan, or to revise a plan when things go wrong, is another important dimension of intelligence. Playing a game requires intelligence. So pick a dimension that seems fun and would make an engaging demonstration, but don't try to do everything all at once.

Part of picking one sort of behavior that requires intelligence is defining when you know the program has succeeded. Saying "hello world" in response to every question is not very intelligent, but it is a (very simple) question-answering program. Winning at Jeopardy against two former champions is definite, but would require more time than your students have.

You and your students will find resources on the AITopics site, www.aaai.org/aitopics. There is a lot of information here, but we hope that students can browse easily to find what they are looking for. There are also excellent AI scientists at the University of Edinburgh who may be able to help.

The mechanics of writing a program that exhibits some intelligence can be daunting. Here is where it helps a lot to start with a well-defined task, like learning to win at one specific game. It is extremely important to define the data structures that everyone on the team will use in their parts of the program. And it is important to make as much of the program as possible accessible to easy modification. For example, instead of using a number within a program to stand for a threshold for taking action, use a named variable whose value can be changed to make the program smarter.

It also helps to consider starting with a very simple version of the task and adding more and more capabilities incrementally.

I hope this helps. I'll be glad to answer additional questions, and I would be very interested in knowing how the project turns out.

best regards, Bruce Buchanan


Other FAQs Online

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.

Individual Topics

AI RELATED FAQs from the Internet FAQ Consortium. This site offers links and info about several collections of FAQs, including these:

  1. ai-faq/alife - comp.ai.alife Frequently Asked Questions (FAQ)
  2. ai-faq/expert/part1 - FAQ: Expert System Shells 1/1 [Monthly posting]
  3. ai-faq/general/part1 - REPOST: Artificial Intelligence FAQ: General Questions & Answers 1/6 [Monthly posting]
  4. ai-faq/general/part2 - Artificial Intelligence FAQ: Newsgroups and Mailing Lists 2/6 [Monthly posting]
  5. ai-faq/general/part3 - Artificial Intelligence FAQ: Associations and Journals 3/6 [Monthly posting]
  6. ai-faq/general/part4 - Artificial Intelligence FAQ: Bibliography 4/6 [Monthly posting]
  7. ai-faq/general/part5 - REPOST: Artificial Intelligence FAQ: AI Web Directories & Online Papers 5/6 [Monthly posting]
  8. ai-faq/general/part6 - Artificial Intelligence FAQ: Open Source AI Software 6/6 [Monthly posting]
  9. ai-faq/general/part7 - Artificial Intelligence FAQ: FTP Resources 7/7 [Monthly posting]
  10. ai-faq/genetic/part1 - FAQ: comp.ai.genetic part 1/6 (A Guide to Frequently Asked Questions)
  11. ai-faq/genetic/part2 - FAQ: comp.ai.genetic part 2/6 (A Guide to Frequently Asked Questions)
  12. ai-faq/genetic/part3 - FAQ: comp.ai.genetic part 3/6 (A Guide to Frequently Asked Questions)
  13. ai-faq/genetic/part4 - FAQ: comp.ai.genetic part 4/6 (A Guide to Frequently Asked Questions)
  14. ai-faq/genetic/part5 - FAQ: comp.ai.genetic part 5/6 (A Guide to Frequently Asked Questions)
  15. ai-faq/genetic/part6 - FAQ: comp.ai.genetic part 6/6 (A Guide to Frequently Asked Questions)
  16. ai-faq/neural-nets/part1 - comp.ai.neural-nets FAQ, Part 1 of 7: Introduction
  17. ai-faq/neural-nets/part2 - comp.ai.neural-nets FAQ, Part 2 of 7: Learning
  18. ai-faq/neural-nets/part3 - comp.ai.neural-nets FAQ, Part 3 of 7: Generalization
  19. ai-faq/neural-nets/part4 - comp.ai.neural-nets FAQ, Part 4 of 7: Books, data, etc.
  20. ai-faq/neural-nets/part5 - comp.ai.neural-nets FAQ, Part 5 of 7: Free software
  21. ai-faq/neural-nets/part6 - comp.ai.neural-nets FAQ, Part 6 of 7: Commercial software
  22. ai-faq/neural-nets/part7 - comp.ai.neural-nets FAQ, Part 7 of 7: Hardware
  23. air-industry/posting-guide - misc.transport.air-industry posting guidelines FAQ


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Q: Could you answer a few questions for a school report?

1. What kinds of applications is artificial intelligence currently in?
A: There are literally thousands of different applications of AI in every area of industry, science, medicine, finance, defense, and government. Computers are everywhere, and no matter what they are doing it makes sense to think of software to help them work smarter, not harder. See our Applications page.
2. What limitations are there currently on further development of artificial intelligence? (Problems that currently are trying to be solved)
A: Two big problems, among others:
LEARNING -- Every computer program should be able to learn from its mistakes and from the preferences & behavior of its users.
REPRESENTING KNOWLEDGE – AI programs can pretty easily store and use factual information. Much of what we know, though, is information about how things work, how we can do things, what mechanisms are involved – and all this has to be integrated with specific facts. Also, the kind of common sense information that children learn in their first five years is difficult to represent and use effectively.
3. What attempts are being made to solve some of these problems?
A: Many smart people around the world are working on these problems, with funding from government and private sources. Problems that we can identify and define precisely are much more likely to be solved than the problems we are not even aware of.
4. Do you believe that one day robots will be able to work and live like humans?
A: They certainly will be able to work as well or better than humans. In specialized areas, computers have been shown to outperform the very best humans. Chess is the example everyone thinks of. It won’t be necessary for any single robot to be better than humans at every task, however – after all, we rarely find humans who outperform peers in many different tasks.
There may be no advantage in making robots live like humans, as in the movie “AI”; it’s not even clear we will want them to.
5. Will we one day be living among robots, if so do you think people could handle it?
A: Think about the Roomba vacuum cleaner. People already live in the same households with these specialized robots, and welcome their help. A similar commercially-available device washes floors. Put those two tasks into one robot, then begin adding other things that would be helpful – sweeping the porch, answering the phone, running to the store for eggs, whatever. Some people probably would rather do these things themselves, some may prefer paying a human. But many people would have no problem having more help with routine duties, some might even welcome having a device they could have a good conversation with.
6. Could artificial intelligence and robots with AI be one day smarter than humans, is this possible?
A: “Smarter” means different things to different people. Computers without AI are already much faster and more accurate with arithmetic, which 300 years ago was considered to be a skill that required great intelligence. (Incidentally, people with that skill were called “computers”). Computers now can store and retrieve far more facts than humans, also a skill that people with superb memories used to sell. AI programs can now solve numerous specific problems better than people who are routinely paid to solve them. The Turbo-Tax program, for example, knows as much or more about filling out income tax forms than many accountants. Until programs can learn continuously, though, and improve their knowledge & skills by interacting with the world, most of us would say they are not as smart as three-year olds.
7.Do you think this could happen? Do you think they could dominate humans? Should we be afraid?
A: Yes, it probably will happen. I’m not sure about the domination part, not even sure what it means. Does a cockroach feel dominated by humans? We’re probably smarter, but they seem to survive all our attempts to eradicate them. Instead of being afraid, it makes more sense to think about what are the worst sorts of things that could happen and then design safeguards so they don’t. We have much more to fear from fellow humans who practice genocide and those who knowingly sacrifice the health of our species, and the overall health of the planet, for corporate profits. We need computers with more intelligence than we have ourselves to help us think through the complex problems we humans have created.
8. What is the most human-like case of AI have you seen or heard of?
A: The Japanese are building robots that look like people and interact with people in our world. (Look up “Aibo” on YouTube.) Their cognitive skills are not great -- that is, they’re not very smart. But then again, a lot of people are a few bricks shy of a load too and they are very human-like.
9. What is the coolest case of AI?
A: Tough to pick just one.
NASA’s planetary rovers are exploring unknown terrain entirely autonomously and autonomous vehicles can drive long distances on unpaved roads on their own.
The translating telephone is pretty cool, too, although not as close to daily use as other applications. With it you could carry on your half of a conversation in English with someone who knows no English and whatever they said in their own language you would hear in English. It needs to have more than ability to translate one sentence at a time from one language to another; it needs a sense of dialogue & social customs, knowledge of the world, and expectations about what people believe.
And then anything else you like to share with me would be awesome.
AI is contributing to our understanding of one of the big questions of all time – what is the nature of intelligence? What can be more satisfying than to work on this?

Bruce Buchanan

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