Category Archives: Artificial Intelligence

Why Computer Science Artificial Intelligence?

In this section you can learn and practice Computer Science Questions based on "Artificial Intelligence" and improve your skills in order to face the interview, competitive examination and various entrance test (CAT, GATE, GRE, MAT, Bank Exam, Railway Exam etc.) with full confidence.

IndiaBIX provides you lots of fully solved Computer Science (Artificial Intelligence) questions and answers with Explanation. Solved examples with detailed answer description, explanation are given and it would be easy to understand. All students, freshers can download Computer Science Artificial Intelligence quiz questions with answers as PDF files and eBooks.

Here you can find objective type Computer Science Artificial Intelligence questions and answers for interview and entrance examination. Multiple choice and true or false type questions are also provided.

You can easily solve all kind of Computer Science questions based on Artificial Intelligence by practicing the objective type exercises given below, also get shortcut methods to solve Computer Science Artificial Intelligence problems.

Continue reading here:

Artificial Intelligence - IndiaBIX

Stuart Russell was born in 1962 in Portsmouth, England. He received his B.A. with first-class honours in physics from Oxford University in 1982, and his Ph.D. in computer science from Stanford in 1986. He then joined the faculty of the University of California at Berkeley, where he is a professor of computer science, director of the Center for Intelligent Systems, and holder of the SmithZadeh Chair in Engineering. In 1990, he received the Presidential Young Investigator Award of the National Science Foundation, and in 1995 he was cowinner of the Computers and Thought Award. He was a 1996 Miller Professor of the University of California and was appointed to a Chancellors Professorship in 2000. In 1998, he gave the Forsythe Memorial Lectures at Stanford University. He is a Fellow and former Executive Council member of the American Association for Artificial Intelligence. He has published over 100 papers on a wide range of topics in artificial intelligence. His other books include The Use of Knowledge in Analogy and Induction and (with Eric Wefald) Do the Right Thing: Studies in Limited Rationality.

Peter Norvig is currently Director of Research at Google, Inc., and was the director responsible for the core Web search algorithms from 2002 to 2005. He is a Fellow of the American Association for Artificial Intelligence and the Association for Computing Machinery. Previously, he was head of the Computational Sciences Division at NASA Ames Research Center, where he oversaw NASAs research and development in artificial intelligence and robotics, and chief scientist at Junglee, where he helped develop one of the first Internet information extraction services. He received a B.S. in applied mathematics from Brown University and a Ph.D. in computer science from the University of California at Berkeley. He received the Distinguished Alumni and Engineering Innovation awards from Berkeley and the Exceptional Achievement Medal from NASA. He has been a professor at the University of Southern California and a research faculty member at Berkeley. His other books are Paradigms of AI Programming: Case Studies in Common Lisp and Verbmobil: A Translation System for Faceto-Face Dialog and Intelligent Help Systems for UNIX.

Go here to read the rest:

Artificial Intelligence: A Modern Approach - amazon.com

The following script is from Artificial Intelligence, which aired on Oct. 9, 2016. Charlie Rose is the correspondent. Nichole Marks, producer.

The search to improve and eventually perfect artificial intelligence is driving the research labs of some of the most advanced and best-known American corporations. They are investing billions of dollars and many of their best scientific minds in pursuit of that goal. All that money and manpower has begun to pay off.

In the past few years, artificial intelligence -- or A.I. -- has taken a big leap -- making important strides in areas like medicine and military technology. What was once in the realm of science fiction has become day-to-day reality. Youll find A.I. routinely in your smart phone, in your car, in your household appliances and it is on the verge of changing everything.

Play Video

On 60 Minutes Overtime, Charlie Rose explores the labs at Carnegie Mellon on the cutting edge of A.I. See robots learning to go where humans can'...

It was, for decades, primitive technology. But it now has abilities we never expected. It can learn through experience -- much the way humans do -- and it wont be long before machines, like their human creators, begin thinking for themselves, creatively. Independently with judgment -- sometimes better judgment than humans have.

The technology is so promising that IBM has staked its 105-year-old reputation on its version of artificial intelligence called Watson -- one of the most sophisticated computing systems ever built.

John Kelly, is the head of research at IBM and the godfather of Watson. He took us inside Watsons brain.

Charlie Rose: Oh, here we are.

John Kelly: Here we are.

Charlie Rose: You can feel the heat already.

John Kelly: You can feel the heat -- the 85,000 watts you can hear the blowers cooling it, but this is the hardware that the brains of Watson sat in.

Five years ago, IBM built this system made up of 90 servers and 15 terabytes of memory enough capacity to process all the books in the American Library of Congress. That was necessary because Watson is an avid reader -- able to consume the equivalent of a million books per second. Today, Watsons hardware is much smaller, but it is just as smart.

Play Video

What happens when Charlie Rose attempts to interview a robot named "Sophia" for his 60 Minutes report on artificial intelligence

Charlie Rose: Tell me about Watsons intelligence.

John Kelly: So it has no inherent intelligence as it starts. Its essentially a child. But as its given data and given outcomes, it learns, which is dramatically different than all computing systems in the past, which really learned nothing. And as it interacts with humans, it gets even smarter. And it never forgets.

[Announcer: This is Jeopardy!]

That helped Watson land a spot on one of the most challenging editions of the game show Jeopardy! in 2011.

[Announcer: An IBM computer system able to understand and analyze natural language Watson]

It took five years to teach Watson human language so it would be ready to compete against two of the shows best champions.

Play Video

Five years after beating humans on "Jeopardy!" an IBM technology known as Watson is becoming a tool for doctors treating cancer, the head of IBM ...

Because Watsons A.I. is only as intelligent as the data it ingests, Kellys team trained it on all of Wikipedia and thousands of newspapers and books. It worked by using machine-learning algorithms to find patterns in that massive amount of data and formed its own observations. When asked a question, Watson considered all the information and came up with an educated guess.

[Alex Trebek: Watson, what are you gonna wager?]

IBM gambled its reputation on Watson that night. It wasnt a sure bet.

[Watson: I will take a guess: What is Baghdad?]

[Alex Trebek: Even though you were only 32 percent sure of your response, you are correct.]

The wager paid off. For the first time, a computer system proved it could actually master human language and win a game show, but that wasnt IBMs endgame.

Charlie Rose: Man, thats a big day, isnt it?

John Kelly: Thats a big day

Charlie Rose: The day that you realize that, If we can do this

John Kelly: Thats right.

Charlie Rose: --the future is ours.

John Kelly: Thats right.

Charlie Rose: This is almost like youre watching something grow up. I mean, youve seen

John Kelly: It is.

Charlie Rose: --the birth, youve seen it pass the test. Youre watching adolescence.

John Kelly: Thats a great analogy. Actually, on that Jeopardy! game five years ago, I-- when we put that computer system on television, we let go of it. And I often feel as though I was putting my child on a school bus and I would no longer have control over it.

Charlie Rose: Cause it was reacting to something that it did not know what would it be?

John Kelly: It had no idea what questions it was going to get. It was totally self-contained. I couldnt touch it any longer. And its learned ever since. So fast-forward from that game show, five years later, were in cancer now.

Charlie Rose: Youre in cancer? Youve gone

John Kelly: Were-- yeah. To cancer

Charlie Rose: --from game show to cancer in five years?

John Kelly: --in five years. In five years.

Five years ago, Watson had just learned how to read and answer questions.

Now, its gone through medical school. IBM has enlisted 20 top-cancer institutes to tutor Watson in genomics and oncology. One of the places Watson is currently doing its residency is at the university of North Carolina at Chapel Hill. Dr. Ned Sharpless runs the cancer center here.

Charlie Rose: What did you know about artificial intelligence and Watson before IBM suggested it might make a contribution in medical care?

Ned Sharpless: I-- not much, actually. I had watched it play Jeopardy!

Charlie Rose: Yes.

Ned Sharpless: So I knew about that. And I was very skeptical. I was, like, oh, this what we need, the Jeopardy-playing computer. Thats gonna solve everything.

Charlie Rose: So what fed your skepticism?

Ned Sharpless: Cancers tough business. Theres a lot of false prophets and false promises. So Im skeptical of, sort of, almost any new idea in cancer. I just didnt really understand what it would do.

What Watsons A.I. technology could do is essentially what Dr. Sharpless and his team of experts do every week at this molecular tumor board meeting.

They come up with possible treatment options for cancer patients who already failed standard therapies. They try to do that by sorting through all of the latest medical journals and trial data, but it is nearly impossible to keep up.

Charlie Rose: To be on top of everything thats out there, all the trials that have taken place around the world, it seems like an incredible task

Ned Sharpless: Well, yeah, its r

Charlie Rose: --for any one university, only one facility to do.

Ned Sharpless: Yeah, its essentially undoable. And understand we have, sort of, 8,000 new research papers published every day. You know, no one has time to read 8,000 papers a day. So we found that we were deciding on therapy based on information that was always, in some cases, 12, 24 months out-of-date.

However, its a task thats elementary for Watson.

Ned Sharpless: They taught Watson to read medical literature essentially in about a week.

Charlie Rose: Yeah.

Ned Sharpless: It was not very hard and then Watson read 25 million papers in about another week. And then, it also scanned the web for clinical trials open at other centers. And all of the sudden, we had this complete list that was, sort of, everything one needed to know.

Charlie Rose: Did this blow your mind?

Ned Sharpless: Oh, totally blew my mind.

Watson was proving itself to be a quick study. But, Dr. Sharpless needed further validation. He wanted to see if Watson could find the same genetic mutations that his team identified when they make treatment recommendations for cancer patients.

Ned Sharpless: We did an analysis of 1,000 patients, where the humans meeting in the Molecular Tumor Board-- doing the best that they could do, had made recommendations. So not at all a hypothetical exercise. These are real-world patients where we really conveyed information that could guide care. In 99 percent of those cases, Watson found the same the humans recommended. That was encouraging.

Charlie Rose: Did it encourage your confidence in Watson?

Ned Sharpless: Yeah, it was-- it was nice to see that-- well, it was also-- it encouraged my confidence in the humans, you know. Yeah. You know--

Charlie Rose: Yeah.

Ned Sharpless: But, the probably more exciting part about it is in 30 percent of patients Watson found something new. And so thats 300-plus people where Watson identified a treatment that a well-meaning, hard-working group of physicians hadnt found.

Charlie Rose: Because?

Ned Sharpless: The trial had opened two weeks earlier, a paper had come out in some journal no one had seen -- you know, a new therapy had become approved

Charlie Rose: 30 percent though?

Ned Sharpless: We were very-- that part was disconcerting. Because I thought it was gonna be 5 perc

Charlie Rose: Disconcerting that the Watson found

Ned Sharpless: Yeah.

Charlie Rose: --30 percent?

Ned Sharpless: Yeah. These were real, you know, things that, by our own definition, we wouldve considered actionable had we known about it at the time of the diagnosis.

Some cases -- like the case of Pam Sharpe -- got a second look to see if something had been missed.

Charlie Rose: When did they tell you about the Watson trial?

Pam Sharpe: He called me in January. He said that they had sent off my sequencing to be studied by-- at IBM by Watson. I said, like the

Charlie Rose: Your genomic sequencing?

Pam Sharpe: Right. I said, Like the computer on Jeopardy!? And he said, Yeah--

Charlie Rose: Yes. And whatd you think of that?

Pam Sharpe: Oh I thought, Wow, thats pretty cool.

Pam has metastatic bladder cancer and for eight years has tried and failed several therapies. At 66 years old, she was running out of options.

Charlie Rose: And at this time for you, Watson was the best thing out there cause youd tried everything else?

Pam Sharpe: Ive been on standard chemo. Ive been on a clinical trial. And the prescription chemo Im on isnt working either.

One of the ways doctors can tell whether a drug is working is to analyze scans of cancer tumors. Watson had to learn to do that too so IBMs John Kelly and his team taught the system how to see.

It can help diagnose diseases and catch things the doctors might miss.

John Kelly: And what Watson has done here, it has looked over tens of thousands of images, and it knows what normal looks like. And it knows what normal isnt. And it has identified where in this image are there anomalies that could be significant problems.

[Billy Kim: You know, you had CT scan yesterday. There does appear to be progression of the cancer.]

Pam Sharpes doctor, Billy Kim, arms himself with Watsons input to figure out her next steps.

[Billy Kim: I can show you the interface for Watson.]

Watson flagged a genetic mutation in Pams tumor that her doctors initially overlooked. It enabled them to put a new treatment option on the table.

Charlie Rose: What would you say Watson has done for you?

Pam Sharpe: It may have extended my life. And I dont know how much time Ive got. So by using this Watson, its maybe saved me some time that I wont-- wouldnt have had otherwise.

But, Pam sadly ran out of time. She died a few months after we met her from an infection never getting the opportunity to see what a Watson adjusted treatment could have done for her. Dr. Sharpless has now used Watson on more than 2,000 patients and is convinced doctors couldnt do the job alone. He has started using Watson as part of UNCs standard of care so it can help patients earlier than it reached Pam.

Charlie Rose: So what do you call Watson? A physicians assistant, a physicians tool, a physicians diagnostic mastermind?

See the article here:

Artificial intelligence positioned to be a game-changer - CBS ...

Sep. 7, 2016 When science fiction heroes communicate, they dont use landlines or cell phones. The caller simply appears in virtual form in the middle of the room; full sized and three dimensional. This vision ... read more Artificial Intelligence Could Improve Diagnostic Power of Lung Function Tests Sep. 4, 2016 Artificial intelligence could improve the interpretation of lung function tests for the diagnosis of long-term lung diseases, according to the findings of a new ... read more How AI Might Affect Urban Life in 2030 Sep. 1, 2016 A diverse panel of academic and industrial thinkers has looked ahead to 2030 to forecast how advances in artificial intelligence might affect life in a typical North American city, and to spur ... read more Aug. 31, 2016 You may not think of yourself in this way, but in some ways your body is just a host for hundreds of trillions of microbes (including bacteria) that colonize us in fairly unique combinations in our ... read more Aug. 15, 2016 In order to simplify program development, a recent project is developing technology that provides human operators with automated assistance. By removing the need for would-be programmers to learn ... read more Websites With History Can Be Just as Conversational as Chatting With a Person July 28, 2016 A website with search and interaction history can be just as engaging as chatting with an online human agent, or robot helper, according to ... read more July 25, 2016 It looks like a bicycle chain, but has just twelve segments about the size of a fist. In each segment there is a motor. This describes pretty much the robot developed by the four bachelor students in ... read more July 22, 2016 Researchers have used computer simulations and robotics to uncover a surprising insight into the mechanics of locomotion, namely that taming instability -- a factor that might be a disadvantage -- is ... read more July 21, 2016 How can we predict if a new haircut will look good without physically trying it? Or explore what missing children might look like if their appearance is changed? A new personalized image search ... read more July 21, 2016 A dielectric elastomer with a broad range of motion that requires relatively low voltage and no rigid components has now been created by scientists. This type of actuator could be used in everything ... read more July 20, 2016 Social robots can be used in the educational or health system, where they would support trainers and therapists in their work. The robots can be programmed to practice vocabulary with children or to ... read more Can Robots Recognize Faces Even Under Backlighting? July 19, 2016 Researchers have developed a novel technique to address the problem of vision-based face detection and recognition under normal and severe illumination conditions. This technique contributes to help ... read more July 18, 2016 Researchers have combined tissues from a sea slug with flexible 3-D printed components to build 'biohybrid' robots to manage different tasks than an animal or purely humanmade robot ... read more Robot Therapist Hits the Spot With Athletes July 18, 2016 Trials of a prototype robot for sports therapy have just begun in Singapore, to create a high quality and repeatable treatment routine to improve sports recovery, reducing reliance on trained ... read more July 13, 2016 A researcher has discovered how to control multiple robotic drones using the human brain. A controller wears a skull cap outfitted with 128 electrodes wired to a computer. The device records ... read more Advancing Self-Driving Car Design, Other Shared Human And Machine-Controlled Systems July 13, 2016 Computer scientists have described a new approach to managing the challenge of transferring control between a human and an autonomous ... read more July 7, 2016 Researchers have created a robotic mimic of a stingray that's powered and guided by light-sensitive rat heart cells. The work exhibits a new method for building bio-inspired robots by means of ... read more July 7, 2016 When early terrestrial animals began moving about on mud and sand 360 million years ago, the powerful tails they used as fish may have been more important than scientists previously realized. ... read more July 5, 2016 Recognizing early stages of esophageal cancer is difficult because it can easily be missed. Medical researchers have now been working to develop a method to enable a computer to scan esophagus images ... read more Study Exposes Major Flaw in Classic Artificial Intelligence Test July 5, 2016 A seriousproblem in the Turing test for computer intelligence is exposed in a new ... read more

Wednesday, September 7, 2016

Sunday, September 4, 2016

Thursday, September 1, 2016

Wednesday, August 31, 2016

Monday, August 15, 2016

Thursday, July 28, 2016

Monday, July 25, 2016

Friday, July 22, 2016

Thursday, July 21, 2016

Wednesday, July 20, 2016

Tuesday, July 19, 2016

Monday, July 18, 2016

Wednesday, July 13, 2016

Thursday, July 7, 2016

Tuesday, July 5, 2016

Monday, July 4, 2016

Thursday, June 30, 2016

Monday, June 27, 2016

Friday, June 24, 2016

Wednesday, June 22, 2016

Tuesday, June 21, 2016

Monday, June 20, 2016

Thursday, June 16, 2016

Friday, June 17, 2016

Tuesday, June 14, 2016

Friday, June 10, 2016

Thursday, June 9, 2016

Monday, June 6, 2016

Thursday, June 2, 2016

Wednesday, June 1, 2016

Tuesday, May 31, 2016

Wednesday, May 25, 2016

Thursday, May 19, 2016

Wednesday, May 18, 2016

Tuesday, May 17, 2016

Monday, May 16, 2016

Thursday, May 12, 2016

Wednesday, May 11, 2016

Monday, May 9, 2016

Saturday, May 7, 2016

Friday, May 6, 2016

Thursday, May 5, 2016

Wednesday, May 4, 2016

Tuesday, May 3, 2016

Monday, May 2, 2016

Wednesday, April 27, 2016

Tuesday, April 26, 2016

Thursday, April 21, 2016

Monday, April 18, 2016

Friday, April 15, 2016

Wednesday, April 13, 2016

Monday, April 11, 2016

Thursday, April 7, 2016

Wednesday, April 6, 2016

Tuesday, April 5, 2016

Monday, April 4, 2016

Friday, March 25, 2016

Thursday, March 24, 2016

Thursday, March 17, 2016

Monday, March 14, 2016

Friday, March 11, 2016

Thursday, March 10, 2016

Wednesday, March 9, 2016

Tuesday, March 8, 2016

Monday, March 7, 2016

Thursday, March 3, 2016

Wednesday, March 2, 2016

Tuesday, March 1, 2016

Monday, February 29, 2016

Friday, February 26, 2016

Thursday, February 25, 2016

Wednesday, February 24, 2016

Visit link:

Artificial Intelligence News -- ScienceDaily

Featured Celebrate our 5th birthday with a 55% discount! Enroll through this link by 7/10/16 & the discount will automatically apply.

Watch Video

Artificial Intelligence (AI) is a field that has a long history but is still constantly and actively growing and changing. In this course, youll learn the basics of modern AI as well as some of the representative applications of AI. Along the way, we also hope to excite you about the numerous applications and huge possibilities in the field of AI, which continues to expand human capability beyond our imagination.

Note: Parts of this course are featured in the Machine Learning Engineer Nanodegree and the Data Analyst Nanodegree programs. If you are interested in AI, be sure to check out those programs as well!

Artificial Intelligence (AI) technology is increasingly prevalent in our everyday lives. It has uses in a variety of industries from gaming, journalism/media, to finance, as well as in the state-of-the-art research fields from robotics, medical diagnosis, and quantum science. In this course youll learn the basics and applications of AI, including: machine learning, probabilistic reasoning, robotics, computer vision, and natural language processing.

Some of the topics in Introduction to Artificial Intelligence will build on probability theory and linear algebra. You should have understanding of probability theory comparable to that covered in our Intro to Statistics course.

See the Technology Requirements for using Udacity.

Peter Norvig is Director of Research at Google Inc. He is also a Fellow of the American Association for Artificial Intelligence and the Association for Computing Machinery. Norvig is co-author of the popular textbook Artificial Intelligence: A Modern Approach. Prior to joining Google he was the head of the Computation Sciences Division at NASA Ames Research Center.

Sebastian Thrun is a Research Professor of Computer Science at Stanford University, a Google Fellow, a member of the National Academy of Engineering and the German Academy of Sciences. Thrun is best known for his research in robotics and machine learning, specifically his work with self-driving cars.

This class is self paced. You can begin whenever you like and then follow your own pace. Its a good idea to set goals for yourself to make sure you stick with the course.

This class will always be available!

Take a look at the Class Summary, What Should I Know, and What Will I Learn sections above. If you want to know more, just enroll in the course and start exploring.

Yes! The point is for you to learn what YOU need (or want) to learn. If you already know something, feel free to skip ahead. If you ever find that youre confused, you can always go back and watch something that you skipped.

Its completely free! If youre feeling generous, we would love to have you contribute your thoughts, questions, and answers to the course discussion forum.

Collaboration is a great way to learn. You should do it! The key is to use collaboration as a way to enhance learning, not as a way of sharing answers without understanding them.

Udacity classes are a little different from traditional courses. We intersperse our video segments with interactive questions. There are many reasons for including these questions: to get you thinking, to check your understanding, for fun, etc... But really, they are there to help you learn. They are NOT there to evaluate your intelligence, so try not to let them stress you out.

Learn actively! You will retain more of what you learn if you take notes, draw diagrams, make notecards, and actively try to make sense of the material.

Nanodegree is a trademark of Udacity 20112016 Udacity, Inc.

See more here:

Intro to Artificial Intelligence Course and Training Online ...

Main TERM A

By Vangie Beal

Artificial intelligence is the branch of computer science concerned with making computers behave like humans. The term was coined in 1956 by John McCarthy at the Massachusetts Institute of Technology.

Artificial intelligence includes the following areas of specialization:

Currently, no computers exhibit full artificial intelligence (that is, are able to simulate human behavior). The greatest advances have occurred in the field of games playing. The best computer chess programs are now capable of beating humans. In May, 1997, an IBM super-computer called Deep Blue defeated world chess champion Gary Kasparov in a chess match.

In the area of robotics, computers are now widely used in assembly plants, but they are capable only of very limited tasks. Robots have great difficulty identifying objects based on appearance or feel, and they still move and handle objects clumsily.

Natural-language processing offers the greatest potential rewards because it would allow people to interact with computers without needing any specialized knowledge. You could simply walk up to a computer and talk to it. Unfortunately, programming computers to understand natural languages has proved to be more difficult than originally thought. Some rudimentary translation systems that translate from one human language to another are in existence, but they are not nearly as good as human translators. There are also voice recognition systems that can convert spoken sounds into written words, but they do not understand what they are writing; they simply take dictation. Even these systems are quite limited -- you must speak slowly and distinctly.

In the early 1980s, expert systems were believed to represent the future of artificial intelligence and of computers in general. To date, however, they have not lived up to expectations. Many expert systems help human experts in such fields as medicine and engineering, but they are very expensive to produce and are helpful only in special situations.

Today, the hottest area of artificial intelligence is neural networks, which are proving successful in a number of disciplines such as voice recognition and natural-language processing.

There are several programming languages that are known as AI languages because they are used almost exclusively for AI applications. The two most common are LISP and Prolog.

TECH RESOURCES FROM OUR PARTNERS

Stay up to date on the latest developments in Internet terminology with a free weekly newsletter from Webopedia. Join to subscribe now.

Go here to see the original:

What is Artificial Intelligence (AI)? Webopedia Definition

The phrase Artificial Intelligence was first coined by John McCarthy four decades ago. One representative definition is pivoted around comparing intelligent machines with human beings. Another definition is concerned with the performance of machines which historically have been judged to lie within the domain of intelligence.

Yet none of these definitions have been universally accepted, probably because the reference of the word intelligence which is an immeasurable quantity. A better definition of artificial intelligence, and probably the most accurate would be: An artificial system capable of planning and executing the right task at the right time rationally. Or far simpler: a machine that can act rationally.

With all this a common questions arises:

Does rational thinking and acting include all characteristics of an intelligent system?

If so, how does it represent behavioral intelligence such as learning, perception and planning?

If we think a little, a system capable of reasoning would be a successful planner. Moreover, a system can act rationally only after acquiring knowledge from the real world. So the property of perception is a perquisite of building up knowledge from the real world.

With all this we may conclude that a machine that lacks of perception cannot learn, therefore cannot acquire knowledge.

To understand the practical meaning or artificial intelligence we must illustrate some common problems. All problems that are dealt with artificial intelligence solutions use the common term state.

A state represents the status of a solution at a given step during the problem solving procedure. The solution of a problem is a collection of states. The problem solving procedure or algorithm applies an operator to a state to get the next state. Then, it applies another operator to the resulting state to derive a new state.

The process of applying operators to each state is continued until a desired goal is achieved.

Example : Consider a 4-puzzle problem, where in a 4-cell board there are 3 cells filled with digits and 1 blank cell. The initial state of the game represents a particular orientation of the digits in the cells and the final state to be achieved is another orientation supplied to the game player. The problem of the game is to reach from the given initial state to the goal (final) state, if possible, with a minimum of moves. Let the initial and the final state be as shown in figures 1(a) and (b) respectively.

We now define two operations, blank-up (BU) / blank-down (BD) and blank-left (BL) / blank-right (BR), and the state-space (tree) for the problem is presented below using these operators. The algorithm for the above kind of problems is straightforward. It consists of three steps, described by steps 1, 2(a) and 2(b) below.

Algorithm for solving state-space problems

Begin

It is clear that the main trick in solving problems by the state-space approach is to determine the set of operators and to use it at appropriate states of the problem.

Researchers in artificial intelligence have segregated the AI problems from the non-AI problems. Generally, problems, for which straightforward mathematical / logical algorithms are not readily available and which can be solved by intuitive approach only, are called AI problems.

The 4-puzzle problem, for instance, is an ideal AI Problem. There is no formal algorithm for its realization, i.e., given a starting and a goal state, one cannot say prior to execution of the tasks the sequence of steps required to get the goal from the starting state. Such problems are called the ideal AI problems.

The well known water-jug problem, the Traveling Salesperson Problem (TSP), and the n-Queen problem are typical examples of the classical AI problems.

Among the non-classical AI problems, the diagnosis problems and the pattern classification problem need special mention. For solving an AI problem, one may employ both artificial intelligence and non-AI algorithms. An obvious question is: what is an AI algorithm?

Formally speaking, an artificial intelligence algorithm generally means a non-conventional intuitive approach for problem solving. The key to artificial intelligence approach is intelligent search and matching. In an intelligent search problem / sub-problem, given a goal (or starting) state, one has to reach that state from one or more known starting (or goal) states.

For example, consider the 4-puzzle problem, where the goal state is known and one has to identify the moves for reaching the goal from a pre-defined starting state. Now, the less number of states one generates for reaching the goal, the better. That is the AI algorithm.

The question that then naturally arises is: how to control the generation of states?

This can be achieved by suitably designing control strategies, which would filter a few states only from a large number of legal states that could be generated from a given starting / intermediate state.

As an example, consider the problem of proving a trigonometric identity that children are used to doing during their schooldays. What would they do at the beginning? They would start with one side of the identity, and attempt to apply a number of formula there to find the possible resulting derivations.

But they wont really apply all the formula there. Rather, they identify the right candidate formula that fits there, such that the other side of the identity that seems to be closer in some sense (outlook). Ultimately, when the decision regarding the selection of the formula is over, they apply it to one side (say the L.H.S) of the identity and derive the new state.

Therefore, they continue the process and go on generating new intermediate states until the R.H.S (goal) is reached. But do they always select the right candidate formula at a given state? From our experience, we know the answer is not always. But what would we do if we find that after generation of a few states, the resulting expression seems to be far away from the R.H.S of the identity.

Perhaps we would prefer to move to some old state, which is more promising, i.e., closer to the R.H.S of the identity. The above line of thinking has been realized in many intelligent search problems of AI.

Some of these well-known search algorithms are:

a) Generate and Test Approach : This approach concerns the generation of the state-space from a known starting state (root) of the problem and continues expanding the reasoning space until the goal node or the terminal state is reached.

In fact after generating each and every state, the generated node is compared with the known goal state. When the goal is found, the algorithm terminates. In case there exist multiple paths leading to the goal, then the path having the smallest distance from the root is preferred. The basic strategy used in this search is only generation of states and their testing for goals but it does not allow filtering of states.

(b) Hill Climbing Approach : Under this approach, one has to first generate a starting state and measure the total cost for reaching the goal from the given starting state. Let this cost be f. While f = a predefined utility value and the goal is not reached, new nodes are generated as children of the current node. However, in case all neighborhood nodes (states) yield an identical value of f and the goal is not included in the set of these nodes, the search algorithm is trapped at a hillock or local extreme.

One way to overcome this problem is to select randomly a new starting state and then continue the above search process. While proving trigonometric identities, we often use Hill Climbing, perhaps unknowingly.

(c) Heuristic Search: Classically heuristics means rule of thumb. In heuristic search, we generally use one or more heuristic functions to determine the better candidate states among a set of legal states that could be generated from a known state.

The heuristic function, in other words, measures the fitness of the candidate states. The better the selection of the states, the fewer will be the number of intermediate states for reaching the goal.

However, the most difficult task in heuristic search problems is the selection of the heuristic functions. One has to select them intuitively, so that in most cases hopefully it would be able to prune the search space correctly.

(d) Means and Ends Analysis: This method of search attempts to reduce the gap between the current state and the goal state. One simple way to explore this method is to measure the distance between the current state and the goal, and then apply an operator to the current state, so that the distance between the resulting state and the goal is reduced. In many mathematical theorem- proving processes, we use Means and Ends Analysis.

The subject of artificial intelligence spans a wide horizon. It deals with various kinds of knowledge representation schemes, different techniques of intelligent search, various methods for resolving uncertainty of data and knowledge, different schemes for automated machine learning and many others.

Among the application areas of AI, we have Expert systems, Game-playing, and Theorem-proving, Natural language processing, Image recognition, Robotics and many others. The subject of artificial intelligence has been enriched with a wide discipline of knowledge from Philosophy, Psychology, Cognitive Science, Computer Science, Mathematics and Engineering. Thus has the figure shows, they have been referred to as the parent disciplines of AI. An at-a-glance look at fig. also reveals the subject area of AI and its application areas. Fig.: AI, its parent disciplines and application areas.

The subject of artificial intelligence was originated with game-playing and theorem-proving programs and was gradually enriched with theories from a number of parent disciplines. As a young discipline of science, the significance of the topics covered under the subject changes considerably with time. At present, the topics which we find significant and worthwhile to understand the subject are outlined below: FigA: Pronunciation learning of a child from his mother.

Learning Systems: Among the subject areas covered under artificial intelligence, learning systems needs special mention. The concept of learning is illustrated here with reference to a natural problem of learning of pronunciation by a child from his mother (vide figA). The hearing system of the child receives the pronunciation of the character A and the voice system attempts to imitate it. The difference of the mothers and the childs pronunciation, hereafter called the error signal, is received by the childs learning system auditory nerve, and an actuation signal is generated by the learning system through a motor nerve for adjustment of the pronunciation of the child. The adaptation of the childs voice system is continued until the amplitude of the error signal is insignificantly low. Each time the voice system passes through an adaptation cycle, the resulting tongue position of the child for speaking A is saved by the learning process. The learning problem discussed above is an example of the well-known parametric learning, where the adaptive learning process adjusts the parameters of the childs voice system autonomously to keep its response close enough to the sample training pattern. The artificial neural networks, which represent the electrical analogue of the biological nervous systems, are gaining importance for their increasing applications in supervised (parametric) learning problems. Besides this type, the other common learning methods, which we do unknowingly, are inductive and analogy-based learning. In inductive learning, the learner makes generalizations from examples. For instance, noting that cuckoo flies, parrot flies and sparrow flies, the learner generalizes that birds fly. On the other hand, in analogy-based learning, the learner, for example, learns the motion of electrons in an atom analogously from his knowledge of planetary motion in solar systems.

Knowledge Representation and Reasoning: In a reasoning problem, one has to reach a pre-defined goal state from one or more given initial states. So, the lesser the number of transitions for reaching the goal state, the higher the efficiency of the reasoning system. Increasing the efficiency of a reasoning system thus requires minimization of intermediate states, which indirectly calls for an organized and complete knowledge base. A complete and organized storehouse of knowledge needs minimum search to identify the appropriate knowledge at a given problem state and thus yields the right next state on the leading edge of the problem-solving process. Organization of knowledge, therefore, is of paramount importance in knowledge engineering. A variety of knowledge representation techniques are in use in Artificial Intelligence. Production rules, semantic nets, frames, filler and slots, and predicate logic are only a few to mention. The selection of a particular type of representational scheme of knowledge depends both on the nature of applications and the choice of users.

Planning: Another significant area of artificial intelligence is planning. The problems of reasoning and planning share many common issues, but have a basic difference that originates from their definitions. The reasoning problem is mainly concerned with the testing of the satisfiability of a goal from a given set of data and knowledge. The planning problem, on the other hand, deals with the determination of the methodology by which a successful goal can be achieved from the known initial states. Automated planning finds extensive applications in robotics and navigational problems, some of which will be discussed shortly.

Knowledge Acquisition: Acquisition (Elicitation) of knowledge is equally hard for machines as it is for human beings. It includes generation of new pieces of knowledge from given knowledge base, setting dynamic data structures for existing knowledge, learning knowledge from the environment and refinement of knowledge. Automated acquisition of knowledge by machine learning approach is an active area of current research in Artificial Intelligence. Intelligent Search: Search problems, which we generally encounter in Computer Science, are of a deterministic nature, i.e., the order of visiting the elements of the search space is known. For example, in depth first and breadth first search algorithms, one knows the sequence of visiting the nodes in a tree. However, search problems, which we will come across in AI, are non-deterministic and the order of visiting the elements in the search space is completely dependent on data sets. The diversity of the intelligent search algorithms will be discussed in detail later.

Logic Programming: For more than a century, mathematicians and logicians were used to designing various tools to represent logical statements by symbolic operators. One outgrowth of such attempts is propositional logic, which deals with a set of binary statements (propositions) connected by Boolean operators. The logic of propositions, which was gradually enriched to handle more complex situations of the real world, is called predicate logic. One classical variety of predicate logic-based programs is Logic Program. PROLOG, which is an abbreviation for PROgramming in LOGic, is a typical language that supports logic programs. Logic Programming has recently been identified as one of the prime area of research in AI. The ultimate aim of this research is to extend the PROLOG compiler to handle spatio-temporal models and support a parallel programming environment. Building architecture for PROLOG machines was a hot topic of the last decade.

Soft Computing: Soft computing, according to Prof. Zadeh, is an emerging approach to computing, which parallels the remarkable ability of the human mind to reason and learn in an environment of uncertainty and imprecision . It, in general, is a collection of computing tools and techniques, shared by closely related disciplines that include fuzzy logic, artificial neural nets, genetic algorithms, belief calculus, and some aspects of machine learning like inductive logic programming. These tools are used independently as well as jointly depending on the type of the domain of applications.

Management of Imprecision and Uncertainty: Data and knowledgebases in many typical AI problems, such as reasoning and planning, are often contaminated with various forms of incompleteness. The incompleteness of data, hereafter called imprecision, generally appears in the database for i) lack of appropriate data, and ii) poor authenticity level of the sources. The incompleteness of knowledge, often referred to as uncertainty, originates in the knowledge base due to lack of certainty of the pieces of knowledge Reasoning in the presence of imprecision of data and uncertainty of knowledge is a complex problem. Various tools and techniques have been devised for reasoning under incomplete data and knowledge. Some of these techniques employ i) stochastic ii) fuzzy and iii) belief network models. In a stochastic reasoning model, the system can have transition from one given state to a number of states, such that the sum of the probability of transition to the next states from the given state is strictly unity. In a fuzzy reasoning system, on the other hand, the sum of the membership value of transition from the given state to the next state may be greater than or equal to one. The belief network model updates the stochastic / fuzzy belief assigned to the facts embedded in the network until a condition of equilibrium is reached, following which there would be no more change in beliefs. Recently, fuzzy tools and techniques have been applied in a specialized belief network, called a fuzzy Petri net, for handling both imprecision of data and uncertainty of knowledge by a unified approach.

Almost every branch of science and engineering currently shares the tools and techniques available in the domain of artificial intelligence. However, for the sake of the convenience of the readers, we mention here a few typical applications, where AI plays a significant and decisive role in engineering automation. Expert Systems: In this example, we illustrate the reasoning process involved in an expert system for a weather forecasting problem with special emphasis to its architecture. An expert system consists of a knowledge base, database and an inference engine for interpreting the database using the knowledge supplied in the knowledge base. The reasoning process of a typical illustrative expert system is described in Fig. PR 1 in Fig. represents i-th production rule. The inference engine attempts to match the antecedent clauses (IF parts) of the rules with the data stored in the database. When all the antecedent clauses of a rule are available in the database, the rule is fired, resulting in new inferences. The resulting inferences are added to the database for activating subsequent firing of other rules. In order to keep limited data in the database, a few rules that contain an explicit consequent (THEN) clause to delete specific data from the databases are employed in the knowledge base. On firing of such rules, the unwanted data clauses as suggested by the rule are deleted from the database. Here PR1 fires as both of its antecedent clauses are present in the database. On firing of PR1, the consequent clause it-will-rain will be added to the database for subsequent firing of PR2. Fig. Illustrative architecture of an expert system.

Image Understanding and Computer Vision: A digital image can be regarded as a two-dimensional array of pixels containing gray levels corresponding to the intensity of the reflected illumination received by a video camera. For interpretation of a scene, its image should be passed through three basic processes: low, medium and high level vision . Fig.: Basic steps in scene interpretation.

The importance of low level vision is to pre-process the image by filtering from noise. The medium level vision system deals with enhancement of details and segmentation (i.e., partitioning the image into objects of interest ). The high level vision system includes three steps: recognition of the objects from the segmented image, labeling of the image and interpretation of the scene. Most of the AI tools and techniques are required in high level vision systems. Recognition of objects from its image can be carried out through a process of pattern classification, which at present is realized by supervised learning algorithms. The interpretation process, on the other hand, requires knowledge-based computation.

Speech and Natural Language Understanding: Understanding of speech and natural languages is basically two class ical problems. In speech analysis, the main probl em is to separate the syllables of a spoken word and determine features like ampli tude, and fundamental and harmonic frequencies of each syllable. The words then could be ident ified from the extracted features by pattern class ification techniques. Recently, artificial neural networks have been employed to class ify words from their features. The probl em of understanding natural languages like English, on the other hand, includes syntactic and semantic interpretation of the words in a sentence, and sentences in a paragraph. The syntactic steps are required to analyze the sentences by its grammar and are similar with the steps of compilation. The semantic analysis, which is performed following the syntactic analysis, determines the meaning of the sentences from the association of the words and that of a paragraph from the closeness of the sentences. A robot capable of understanding speech in a natural language will be of immense importance, for it could execute any task verbally communicated to it. The phonetic typewriter, which prints the words pronounced by a person, is another recent invention where speech understanding is employed in a commercial application.

Scheduling: In a scheduling problem, one has to plan the time schedule of a set of events to improve the time efficiency of the solution. For instance in a class-routine scheduling problem, the teachers are allocated to different classrooms at different time slots, and we want most classrooms to be occupied most of the time. In a flowshop scheduling problem, a set of jobs J1 and J2 (say) are to be allocated to a set of machines M1, M2 and M3. (say). We assume that each job requires some operations to be done on all these machines in a fixed order say, M1, M2 and M3. Now, what should be the schedule of the jobs (J1-J2) or (J2 -J1), so that the completion time of both the jobs, called the make-span, is minimized? Let the processing time of jobs J1 and J2 on machines M1, M2 and M3 be (5, 8, 7) and (8, 2, 3) respectively. The gantt charts in fig. (a) and (b) describe the make-spans for the schedule of jobs J1 J2 and J2 J1 respectively. It is clear from these figures that J1-J2 schedule requires less make-span and is thus preferred. Fig.: The Gantt charts for the flowshop scheduling problem with 2 jobs and 3 machines.

Flowshop scheduling problems are a NP complete problem and determination of optimal scheduling (for minimizing the make-span) thus requires an exponential order of time with respect to both machine-size and job-size. Finding a sub-optimal solution is thus preferred for such scheduling problems. Recently, artificial neural nets and genetic algorithms have been employed to solve this problem. The heuristic search, to be discussed shortly, has also been used for handling this problem.

Intelligent Control: In process control, the controller is designed from the known models of the process and the required control objective. When the dynamics of the plant is not completely known, the existing techniques for controller design no longer remain valid. Rule-based control is appropriate in such situations. In a rule-based control system, the controller is realized by a set of production rules intuitively set by an expert control engineer. The antecedent (premise) part of the rules in a rule-based system is searched against the dynamic response of the plant parameters. The rule whose antecedent part matches with the plant response is selected and fired. When more than one rule is firable, the controller resolves the conflict by a set of strategies. On the other hand, there exist situations when the antecedent part of no rules exactly matches with the plant responses. Such situations are handled with fuzzy logic, which is capable of matching the antecedent parts of rules partially/ approximately with the dynamic plant responses. Fuzzy control has been successfully used in many industrial plants. One typical application is the power control in a nuclear reactor. Besides design of the controller, the other issue in process control is to design a plant (process) estimator, which attempts to follow the response of the actual plant, when both the plant and the estimator are jointly excited by a common input signal. The fuzzy and artificial neural network-based learning techniques have recently been identified as new tools for plant estimation.

More:

Artificial Intelligence | Neuro AI

Watch Video

Artificial Intelligence (AI) is a field that has a long history but is still constantly and actively growing and changing. In this course, youll learn the basics of modern AI as well as some of the representative applications of AI. Along the way, we also hope to excite you about the numerous applications and huge possibilities in the field of AI, which continues to expand human capability beyond our imagination.

Note: Parts of this course are featured in the Machine Learning Engineer Nanodegree and the Data Analyst Nanodegree programs. If you are interested in AI, be sure to check out those programs as well!

Artificial Intelligence (AI) technology is increasingly prevalent in our everyday lives. It has uses in a variety of industries from gaming, journalism/media, to finance, as well as in the state-of-the-art research fields from robotics, medical diagnosis, and quantum science. In this course youll learn the basics and applications of AI, including: machine learning, probabilistic reasoning, robotics, computer vision, and natural language processing.

Some of the topics in Introduction to Artificial Intelligence will build on probability theory and linear algebra. You should have understanding of probability theory comparable to that covered in our Intro to Statistics course.

See the Technology Requirements for using Udacity.

Peter Norvig is Director of Research at Google Inc. He is also a Fellow of the American Association for Artificial Intelligence and the Association for Computing Machinery. Norvig is co-author of the popular textbook Artificial Intelligence: A Modern Approach. Prior to joining Google he was the head of the Computation Sciences Division at NASA Ames Research Center.

Sebastian Thrun is a Research Professor of Computer Science at Stanford University, a Google Fellow, a member of the National Academy of Engineering and the German Academy of Sciences. Thrun is best known for his research in robotics and machine learning, specifically his work with self-driving cars.

This class is self paced. You can begin whenever you like and then follow your own pace. Its a good idea to set goals for yourself to make sure you stick with the course.

This class will always be available!

Take a look at the Class Summary, What Should I Know, and What Will I Learn sections above. If you want to know more, just enroll in the course and start exploring.

Yes! The point is for you to learn what YOU need (or want) to learn. If you already know something, feel free to skip ahead. If you ever find that youre confused, you can always go back and watch something that you skipped.

Its completely free! If youre feeling generous, we would love to have you contribute your thoughts, questions, and answers to the course discussion forum.

Collaboration is a great way to learn. You should do it! The key is to use collaboration as a way to enhance learning, not as a way of sharing answers without understanding them.

Udacity classes are a little different from traditional courses. We intersperse our video segments with interactive questions. There are many reasons for including these questions: to get you thinking, to check your understanding, for fun, etc... But really, they are there to help you learn. They are NOT there to evaluate your intelligence, so try not to let them stress you out.

Learn actively! You will retain more of what you learn if you take notes, draw diagrams, make notecards, and actively try to make sense of the material.

Nanodegree is a trademark of Udacity 20112016 Udacity, Inc.

Continue reading here:

Intro to Artificial Intelligence Course and Training ...

Latest Articles

The titan of consumer technology has a conundrum: Can it create consumer delight in technology without snooping on its customers?

By QUENTIN HARDY

Apple still seems to view online services as add-ons to its devices, not as products or platforms that rise above the equipment.

By FARHAD MANJOO

Virtual travel assistant services designed to understand conversational language are expected to change the way travel is planned.

By JANE L. LEVERE

At an event sponsored by the Office of Science and Technology Policy, experts explored questions about systems that would make decisions without human input.

By JOHN MARKOFF

Googles Home device puts it in the race to become the go-to company for A.I. along with several rivals.

By PUI-WING TAM

A virtual assistant designed to compete with the Echo from Amazon and other artificial intelligence devices coming from Microsoft, Apple and Facebook.

By DAVID STREITFELD

Smartphone apps offer a hint of the possible uses for emerging artificial intelligence technology.

By KIT EATON

Apple has Siri, Amazon has its Echo, and now Google will introduce its virtual agent, Google Home.

By DAVID STREITFELD

The Pentagon is trying to build bridges with Silicon Valley as it looks to build a new generation of smart weapons.

By QUENTIN HARDY

Fund-Raisers Pitch for Trump at Hedge Fund Conference | Online Art Auctioneers to Merge

Secretary of Defense Ashton B. Carter takes his bridge-building message to Silicon Valley despite skepticism among some in the tech community.

By JOHN MARKOFF

A submersible robot in humanoid form, developed at Stanford University, completed its first dive in April, recovering a 17th-century vase.

By JAMES GORMAN

Mr. Cohen, an abstract painter, developed Aaron, a software program that learned to create art in a manner similar to freehand drawing.

By WILLIAM GRIMES

Regulators and others question whether robo-advisers, which assemble investment portfolios online, can grasp clients situations the way humans can.

By TARA SIEGEL BERNARD

The two-foot-tall robot Xianer dwells in a Buddhist temple, dispensing wisdom about religion and life.

By DIDI KIRSTEN TATLOW

Silicon Valley has fallen in love with A.I. assistants, but so far theyre hardly impressive. Is it the industrys fault, or is it ours?

By JENNA WORTHAM

The company reported a 21 percent decline in first-quarter earnings for the first quarter, though operating earnings per share were above estimates.

By STEVE LOHR

Wall Street Veterans Bet on Low-Income Homebuyers | Jose Cuervo Said to Be Preparing for I.P.O.

These automated software critters are growing in popularity, especially now that theyre doing more than pretending to be a human in a call center.

By JIM KERSTETTER

Features being added to Googles calendar will let users program in their aspirations for times when they dont have work or meetings scheduled.

By QUENTIN HARDY

The titan of consumer technology has a conundrum: Can it create consumer delight in technology without snooping on its customers?

By QUENTIN HARDY

Apple still seems to view online services as add-ons to its devices, not as products or platforms that rise above the equipment.

By FARHAD MANJOO

Virtual travel assistant services designed to understand conversational language are expected to change the way travel is planned.

By JANE L. LEVERE

At an event sponsored by the Office of Science and Technology Policy, experts explored questions about systems that would make decisions without human input.

By JOHN MARKOFF

Googles Home device puts it in the race to become the go-to company for A.I. along with several rivals.

By PUI-WING TAM

A virtual assistant designed to compete with the Echo from Amazon and other artificial intelligence devices coming from Microsoft, Apple and Facebook.

By DAVID STREITFELD

Smartphone apps offer a hint of the possible uses for emerging artificial intelligence technology.

By KIT EATON

Apple has Siri, Amazon has its Echo, and now Google will introduce its virtual agent, Google Home.

By DAVID STREITFELD

The Pentagon is trying to build bridges with Silicon Valley as it looks to build a new generation of smart weapons.

By QUENTIN HARDY

Fund-Raisers Pitch for Trump at Hedge Fund Conference | Online Art Auctioneers to Merge

Secretary of Defense Ashton B. Carter takes his bridge-building message to Silicon Valley despite skepticism among some in the tech community.

By JOHN MARKOFF

A submersible robot in humanoid form, developed at Stanford University, completed its first dive in April, recovering a 17th-century vase.

By JAMES GORMAN

Mr. Cohen, an abstract painter, developed Aaron, a software program that learned to create art in a manner similar to freehand drawing.

By WILLIAM GRIMES

Regulators and others question whether robo-advisers, which assemble investment portfolios online, can grasp clients situations the way humans can.

By TARA SIEGEL BERNARD

The two-foot-tall robot Xianer dwells in a Buddhist temple, dispensing wisdom about religion and life.

By DIDI KIRSTEN
TATLOW

Silicon Valley has fallen in love with A.I. assistants, but so far theyre hardly impressive. Is it the industrys fault, or is it ours?

By JENNA WORTHAM

The company reported a 21 percent decline in first-quarter earnings for the first quarter, though operating earnings per share were above estimates.

By STEVE LOHR

Wall Street Veterans Bet on Low-Income Homebuyers | Jose Cuervo Said to Be Preparing for I.P.O.

These automated software critters are growing in popularity, especially now that theyre doing more than pretending to be a human in a call center.

By JIM KERSTETTER

Features being added to Googles calendar will let users program in their aspirations for times when they dont have work or meetings scheduled.

By QUENTIN HARDY

Read more here:

Artificial Intelligence - The New York Times

A.I. Artificial Intelligence, also known as A.I., is a 2001 American science fiction drama film directed by Steven Spielberg. The screenplay by Spielberg was based on a screen story by Ian Watson and the 1969 short story Super-Toys Last All Summer Long by Brian Aldiss. The film was produced by Kathleen Kennedy, Spielberg and Bonnie Curtis. It stars Haley Joel Osment, Jude Law, Frances O'Connor, Brendan Gleeson and William Hurt. Set in a futuristic post-climate change society, A.I. tells the story of David (Osment), a childlike android uniquely programmed with the ability to love.

Development of A.I. originally began with producer-director Stanley Kubrick in the early 1970s. Kubrick hired a series of writers until the mid-1990s, including Brian Aldiss, Bob Shaw, Ian Watson, and Sara Maitland. The film languished in protracted development for years, partly because Kubrick felt computer-generated imagery was not advanced enough to create the David character, whom he believed no child actor would convincingly portray. In 1995, Kubrick handed A.I. to Spielberg, but the film did not gain momentum until Kubrick's death in 1999. Spielberg remained close to Watson's film treatment for the screenplay. The film was greeted with generally positive reviews from critics, grossed approximately $235 million, and was nominated for two Academy Awards at the 74th Academy Awards for Best Visual Effects and Best Original Score (by John Williams). The film is dedicated to Stanley Kubrick.

In the late 21st century, global warming has flooded the coastlines, wiping out coastal cities (such as Amsterdam, Venice, and New York City) and drastically reducing the human population. There is a new class of robots called Mecha, advanced humanoids capable of emulating thoughts and emotions.

David (Haley Joel Osment), a prototype model created by Cybertronics of New Jersey, is designed to resemble a human child and to display love for its human owners. They test their creation with one of their employees, Henry Swinton (Sam Robards), and his wife Monica (Frances O'Connor). The Swintons' son, Martin (Jake Thomas), had been placed in suspended animation until a cure could be found for his rare disease. Initially frightened of David, Monica eventually warms up enough to him to activate his imprinting protocol, which irreversibly causes David to have an enduring childlike love for her. He is also befriended by Teddy (Jack Angel), a robotic teddy bear, who takes it upon himself to care for David's well-being.

A cure is found for Martin and he is brought home; as he recovers, it becomes clear he does not want a sibling and soon makes moves to cause issues for David. First, he attempts to make Teddy choose whom he likes more. He then makes David promise to do something and in return Martin will tell Monica that he loves his new "brother", making her love him more. The promise David makes is to go to Monica in the middle of the night and cut off a lock of her hair. This upsets the parents, particularly Henry, who fears that the scissors are a weapon, and warns Monica that a robot programmed to love may also be able to hate.

At a pool party, one of Martin's friends unintentionally activates David's self-protection programming by poking him with a knife. David grabs Martin, apparently for protection, but they both fall into the pool. David sinks to the bottom while still clinging to Martin. Martin is saved from drowning, but Henry mistakes David's fear during the pool incident as hate for Martin.

Henry persuades Monica to return David to Cybertronics, where he will be destroyed. However, Monica cannot bring herself to do this and, instead, tearfully abandons David in the forest (with Teddy) to hide as an unregistered Mecha.

David is captured for an anti-Mecha "Flesh Fair", an event where obsolete and unlicensed Mecha are destroyed in front of cheering crowds. David is nearly killed, but the crowd is swayed by his fear (since Mecha do not plea for their lives) into believing he is human and he escapes with Gigolo Joe (Jude Law), a male prostitute Mecha on the run after being framed for murder.

The two set out to find the Blue Fairy, who David remembers from the story The Adventures of Pinocchio. He is convinced that the Blue Fairy will transform him into a human boy, allowing Monica to love him and take him home.

Joe and David make their way to Rouge City, a Las Vegas-esque resort. Information from a holographic answer engine called "Dr. Know" (Robin Williams) eventually leads them to the top of Rockefeller Center in the flooded ruins of Manhattan. There, David meets an identical copy of himself and, believing he is not special, becomes filled with anger and destroys the copy Mecha. David then meets his human creator, Professor Allen Hobby (William Hurt), who excitedly tells David that finding him was a test, which has demonstrated the reality of his love and desire. However, David learns that he is the namesake and image of Professor Hobby's deceased son and that many copies of David, along with female versions, are already being manufactured.

Sadly realizing that he is not unique, a disheartened David attempts to commit suicide by falling from a ledge into the ocean, but Joe rescues him with their stolen amphibicopter. David tells Joe he saw the Blue Fairy underwater and wants to go down to her. At that moment, Joe is captured by the authorities with the use of an electromagnet, but he sets the amphibicopter on submerge. David and Teddy take it to the fairy, which turns out to be a statue from a submerged attraction at Coney Island. Teddy and David become trapped when the Wonder Wheel falls on their vehicle. Believing the Blue Fairy to be real, David asks to be turned into a real boy, repeating his wish without an end, until the ocean freezes in another ice age and his internal power source drains away.

Two thousand years later, humans are extinct and Manhattan is buried under several hundred feet of glacial ice. The now highly advanced Mecha have evolved into an intelligent, silicon-based form. On their project to study humans believing it was the key to understanding the meaning of existence they find David and Teddy and discover they are original Mecha who knew living humans, making the pair very special and unique.

David is revived and walks to the frozen Blue Fairy statue, which cracks and collapses as he touches it. Having downloaded and comprehended his memories, the advanced Mecha use these to reconstruct the Swinton home and explain to David via an interactive image of the Blue Fairy (Meryl Streep) that it is impossible to make him human. However, at David's insistence, they recreate Monica from DNA in the lock of her hair, which Teddy had saved. One of the Mecha warns David that the clone can live for only a single day and that the process cannot be repeated. The next morning, David is reunited with Monica and spends the happiest day of his life with her and Teddy. Monica tells David that she loves him and has always loved him as she drifts to sleep for the last time. David lies down next to her, closes his eyes and goes "to that place where dreams are born." Teddy climbs onto the bed and watches as David and Monica lie peacefully together.

Kubrick began development on an adaptation of Super-Toys Last All Summer Long in the early 1970s, hiring the short story's author, Brian Aldiss, to write a film treatment. In 1985, Kubrick brought longtime friend Steven Spielberg on board to produce the film,[5] along with Jan Harlan. Warner Bros. agreed to co-finance A.I. and cover distribution duties.[6] The
film labored in development hell, and Aldiss was fired by Kubrick over creative differences in 1989.[7]Bob Shaw served as writer very briefly, leaving after six weeks because of Kubrick's demanding work schedule, and Ian Watson was hired as the new writer in March 1990. Aldiss later remarked, "Not only did the bastard fire me, he hired my enemy [Watson] instead." Kubrick handed Watson The Adventures of Pinocchio for inspiration, calling A.I. "a picaresque robot version of Pinocchio".[6][8]

Three weeks later Watson gave Kubrick his first story treatment, and concluded his work on A.I. in May 1991 with another treatment, at 90 pages. Gigolo Joe was originally conceived as a GI Mecha, but Watson suggested changing him to a male prostitute. Kubrick joked, "I guess we lost the kiddie market."[6] In the meantime, Kubrick dropped A.I. to work on a film adaptation of Wartime Lies, feeling computer animation was not advanced enough to create the David character. However, after the release of Spielberg's Jurassic Park (with its innovative use of computer-generated imagery), it was announced in November 1993 that production would begin in 1994.[9]Dennis Muren and Ned Gorman, who worked on Jurassic Park, became visual effects supervisors,[7] but Kubrick was displeased with their previsualization, and with the expense of hiring Industrial Light & Magic.[10]

Stanley [Kubrick] showed Steven [Spielberg] 650 drawings which he had, and the script and the story, everything. Stanley said, "Look, why don't you direct it and I'll produce it." Steven was almost in shock.

In early 1994, the film was in pre-production with Christopher "Fangorn" Baker as concept artist, and Sara Maitland assisting on the story, which gave it "a feminist fairy-tale focus".[6] Maitland said that Kubrick never referred to the film as A.I., but as Pinocchio.[10]Chris Cunningham became the new visual effects supervisor. Some of his unproduced work for A.I. can be seen on the DVD, The Work of Director Chris Cunningham.[12] Aside from considering computer animation, Kubrick also had Joseph Mazzello do a screen test for the lead role.[10] Cunningham helped assemble a series of "little robot-type humans" for the David character. "We tried to construct a little boy with a movable rubber face to see whether we could make it look appealing," producer Jan Harlan reflected. "But it was a total failure, it looked awful." Hans Moravec was brought in as a technical consultant.[10] Meanwhile, Kubrick and Harlan thought A.I. would be closer to Steven Spielberg's sensibilities as director.[13][14] Kubrick handed the position to Spielberg in 1995, but Spielberg chose to direct other projects, and convinced Kubrick to remain as director.[11][15] The film was put on hold due to Kubrick's commitment to Eyes Wide Shut (1999).[16] After the filmmaker's death in March 1999, Harlan and Christiane Kubrick approached Spielberg to take over the director's position.[17][18] By November 1999, Spielberg was writing the screenplay based on Watson's 90-page story treatment. It was his first solo screenplay credit since Close Encounters of the Third Kind (1977).[19] Spielberg remained close to Watson's treatment, but removed various sex scenes with Gigolo Joe. Pre-production was briefly halted during February 2000, because Spielberg pondered directing other projects, which were Harry Potter and the Philosopher's Stone, Minority Report and Memoirs of a Geisha.[16][20] The following month Spielberg announced that A.I. would be his next project, with Minority Report as a follow-up.[21] When he decided to fast track A.I., Spielberg brought Chris Baker back as concept artist.[15]

The original start date was July 10, 2000,[14] but filming was delayed until August.[22] Aside from a couple of weeks shooting on location in Oxbow Regional Park in Oregon, A.I. was shot entirely using sound stages at Warner Bros. Studios and the Spruce Goose Dome in Long Beach, south LA.[23] The Swinton house was constructed on Stage 16, while Stage 20 was used for Rouge City and other sets.[24][25] Spielberg copied Kubrick's obsessively secretive approach to filmmaking by refusing to give the complete script to cast and crew, banning press from the set, and making actors sign confidentiality agreements. Social robotics expert Cynthia Breazeal served as technical consultant during production.[14][26] Haley Joel Osment and Jude Law applied prosthetic makeup daily in an attempt to look shinier and robotic.[3] Costume designer Bob Ringwood (Batman, Troy) studied pedestrians on the Las Vegas Strip for his influence on the Rouge City extras.[27] Spielberg found post-production on A.I. difficult because he was simultaneously preparing to shoot Minority Report.[28]

The film's soundtrack was released by Warner Bros. Records in 2001. The original score was composed by John Williams and featured singers Lara Fabian on two songs and Josh Groban on one. The film's score also had a limited release as an official "For your consideration Academy Promo", as well as a complete score issue by La-La Land Records in 2015. The band Ministry appears in the film playing the song "What About Us?" (but the song does not appear on the official soundtrack album).

Warner Bros. used an alternate reality game titled The Beast to promote the film. Over forty websites were created by Atomic Pictures in New York City (kept online at Cloudmakers.org) including the website for Cybertronics Corp. There were to be a series of video games for the Xbox video game console that followed the storyline of The Beast, but they went undeveloped. To avoid audiences mistaking A.I. for a family film, no action figures were created, although Hasbro released a talking Teddy following the film's release in June 2001.[14]

In November 2000, during production, a video-only webcam (dubbed the "Bagel Cam") was placed in the craft services truck on the film's set at the Queen Mary Dome in Long Beach, California. Steven Spielberg, producer Kathleen Kennedy and various other production personnel visited the camera and interacted with fans over the course of three days.[29][30]

A.I. had its premiere at the Venice Film Festival in 2001.[31]

The film opened in 3,242 theaters in the United States on June 29, 2001, earning $29,352,630 during its opening weekend. A.I went on to gross $78.62 million in US totals as well as $157.31 million in foreign countries, coming to a worldwide total of $235.93 million.[32]

The film received generally positive reviews. Based on 190 reviews collected by Rotten Tomatoes, 73% of the critics gave the film positive notices with a score of 6.6 out of 10. The website described the critical consensus perceiving the film as "a curious, not always seamless, amalgamation of Kubrick's chilly bleakness and Spielberg's warm-hearted optimism. [The film] is, in a word, fascinating."[33] By comparison, Metacritic collected an average score of 65, based on 32 reviews, which is considered favorable.[34]

Producer Jan Harlan stated that Kubrick "would have applauded" the final film, while Kubrick's widow Christiane also enjoyed A.I.[35] Brian Aldiss admired the film as well: "I thought what an inventive, intriguing, ingenious, involving film this was. There are flaws in it and I suppose I might have a personal quibble but it's so long since I wrote it." Of the film's ending, he wondered how it might have been had Kubrick directed the film: "That is one of the 'ifs' of film history - at least the ending indicates Spielberg adding some sugar to Kubrick's wine. The actual ending is overly sympathetic and moreover rather overt
ly engineered by a plot device that does not really bear credence. But it's a brilliant piece of film and of course it's a phenomenon because it contains the energies and talents of two brilliant filmmakers."[36]Richard Corliss heavily praised Spielberg's direction, as well as the cast and visual effects.[37]Roger Ebert awarded the film 3 out of 4 stars, saying that it was "Audacious, technically masterful, challenging, sometimes moving [and] ceaselessly watchable. [But] the movie's conclusion is too facile and sentimental, given what has gone before. It has mastered the artificial, but not the intelligence."[38] On July 8, 2011, Ebert reviewed A.I. again when he added it to his "Great Movies" pantheon.[39]Leonard Maltin gives the film a not-so-positive review in his Movie Guide, giving it two stars out of four, writing: "[The] intriguing story draws us in, thanks in part to Osment's exceptional performance, but takes several wrong turns; ultimately, it just doesn't work. Spielberg rewrote the adaptation Stanley Kubrick commissioned of the Brian Aldiss short story 'Super Toys Last All Summer Long'; [the] result is a curious and uncomfortable hybrid of Kubrick and Spielberg sensibilities." However, he calls John Williams' music score "striking". Jonathan Rosenbaum compared A.I. to Solaris (1972), and praised both "Kubrick for proposing that Spielberg direct the project and Spielberg for doing his utmost to respect Kubrick's intentions while making it a profoundly personal work."[40] Film critic Armond White, of the New York Press, praised the film noting that "each part of Davids journey through carnal and sexual universes into the final eschatological devastation becomes as profoundly philosophical and contemplative as anything by cinemas most thoughtful, speculative artists Borzage, Ozu, Demy, Tarkovsky."[41] Filmmaker Billy Wilder hailed A.I. as "the most underrated film of the past few years."[42] When British filmmaker Ken Russell saw the film, he wept during the ending.[43]

Mick LaSalle gave a largely negative review. "A.I. exhibits all its creators' bad traits and none of the good. So we end up with the structureless, meandering, slow-motion endlessness of Kubrick combined with the fuzzy, cuddly mindlessness of Spielberg." Dubbing it Spielberg's "first boring movie", LaSalle also believed the robots at the end of the film were aliens, and compared Gigolo Joe to the "useless" Jar Jar Binks, yet praised Robin Williams for his portrayal of a futuristic Albert Einstein.[44][not in citation given]Peter Travers gave a mixed review, concluding "Spielberg cannot live up to Kubrick's darker side of the future." But he still put the film on his top ten list that year for best movies.[45] David Denby in The New Yorker criticized A.I. for not adhering closely to his concept of the Pinocchio character. Spielberg responded to some of the criticisms of the film, stating that many of the "so called sentimental" elements of A.I., including the ending, were in fact Kubrick's and the darker elements were his own.[46] However, Sara Maitland, who worked on the project with Kubrick in the 1990s, claimed that one of the reasons Kubrick never started production on A.I. was because he had a hard time making the ending work.[47]James Berardinelli found the film "consistently involving, with moments of near-brilliance, but far from a masterpiece. In fact, as the long-awaited 'collaboration' of Kubrick and Spielberg, it ranks as something of a disappointment." Of the film's highly debated finale, he claimed, "There is no doubt that the concluding 30 minutes are all Spielberg; the outstanding question is where Kubrick's vision left off and Spielberg's began."[48]

Screenwriter Ian Watson has speculated, "Worldwide, A.I. was very successful (and the 4th highest earner of the year) but it didn't do quite so well in America, because the film, so I'm told, was too poetical and intellectual in general for American tastes. Plus, quite a few critics in America misunderstood the film, thinking for instance that the Giacometti-style beings in the final 20 minutes were aliens (whereas they were robots of the future who had evolved themselves from the robots in the earlier part of the film) and also thinking that the final 20 minutes were a sentimental addition by Spielberg, whereas those scenes were exactly what I wrote for Stanley and exactly what he wanted, filmed faithfully by Spielberg."[49]

In 2002, Spielberg told film critic Joe Leydon that "People pretend to think they know Stanley Kubrick, and think they know me, when most of them don't know either of us". "And what's really funny about that is, all the parts of A.I. that people assume were Stanley's were mine. And all the parts of A.I. that people accuse me of sweetening and softening and sentimentalizing were all Stanley's. The teddy bear was Stanley's. The whole last 20 minutes of the movie was completely Stanley's. The whole first 35, 40 minutes of the film all the stuff in the house was word for word, from Stanley's screenplay. This was Stanley's vision." "Eighty percent of the critics got it all mixed up. But I could see why. Because, obviously, I've done a lot of movies where people have cried and have been sentimental. And I've been accused of sentimentalizing hard-core material. But in fact it was Stanley who did the sweetest parts of A.I., not me. I'm the guy who did the dark center of the movie, with the Flesh Fair and everything else. That's why he wanted me to make the movie in the first place. He said, 'This is much closer to your sensibilities than my own.'"[50]

Upon rewatching the film many years after its release, BBC film critic Mark Kermode apologized to Spielberg in an interview in January 2013 for "getting it wrong" on the film when he first viewed it in 2001. He now believes the film to be Spielberg's "enduring masterpiece".[51]

Visual effects supervisors Dennis Muren, Stan Winston, Michael Lantieri and Scott Farrar were nominated for the Academy Award for Best Visual Effects, while John Williams was nominated for Best Original Music Score.[52] Steven Spielberg, Jude Law and Williams received nominations at the 59th Golden Globe Awards.[53] The visual effects department was once again nominated at the 55th British Academy Film Awards.[54]A.I. was successful at the Saturn Awards. Spielberg (for his screenplay), the visual effects department, Williams and Haley Joel Osment (Performance by a Younger Actor) won in their respective categories. The film also won Best Science Fiction Film and for its DVD release. Frances O'Connor and Spielberg (as director) were also nominated.[55]

American Film Institute lists

Excerpt from:

A.I. Artificial Intelligence - Wikipedia, the free ...