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THE EXAMPLE OF ARTIFICIAL INTELLIGENCE IN MUSIC VIDEO
AI Problem Characteristics :-
1. Decomposable to smaller or easier problems
2. Solution steps can be ignored or undone
3. Predictable problem universe
4. Good solutions are obvious
5. Uses internally consistent knowledge base
6.Requires lots of knowledge or uses knowledge to constrain solutions
7. Requires periodic interaction between human and computer
1. Decomposable to smaller or easier problems
2. Solution steps can be ignored or undone
3. Predictable problem universe
4. Good solutions are obvious
5. Uses internally consistent knowledge base
6.Requires lots of knowledge or uses knowledge to constrain solutions
7. Requires periodic interaction between human and computer
Conclusion
Autobot is not a real A.I., it is totally hypothetical but it—and its features—can be valuable as a model for designing artificial intelligence. It has an adequate model of the world, made up of learned and tested information; it has goals which direct its behaviour and is able to create, modify, and improve these goals; it is capable of problem solving; it is capable of deducing new information; and it is capable of formulating strategies to achieve goals, and of adjusting those strategies as necessary. We can look at Autobot as a model for designing other A.I.s which can use the same basic architecture and design features to approach their own tasks and problems.
While it requires an investment in some infrastructure, and in creating the A.I. itself, using Autobot to control traffic networks is hugely more efficient than letting humans control traffic lights and maintenance. It just is not possible for a team of human controllers to adapt to changing circumstances, or micromanage things like case by case optimal red light lengths, as effectively as Autobot. This is true of to an A.I. in any position, it will require some investment of infrastructure to set it up with the necessary sensors to retrieve enough information to create an adequate model of the world, but the things it is capable of makes it more than worth the expenditure.
By studying realistically, and systematically designed A.I. like Autobot, instead of anthropomorphized A.I.s like HAL we can analyze and put to rest classic fears of rogue A.I.s hellbent on destruction. It is right for humans to fear that which they do not understand, but as its architects, humans will be able to understand an A.I. at least as well as they understand one another
There should be no worry about A.I. making humans redundant. Autobot (or any A.I)'s friendliness supergoal will cause it to value humanity and individual humans and their right to autonomy. An A.I. coordinating a city could do so in tandem with, not instead of, humans. It is possible for a team of people to control all of the traffic lights in a city, but people get bored and sick and quit, and they need breaks for lunch and cannot work continuously. A machine that controls the traffic signals can operate forever, never takes a day off, and never needs to be paid. Since it respects human autonomy, an A.I. with a friendliness supergoal will only take over the jobs delegated to it. It would be more efficient, and safe, to make Autobot directly responsible for piloting all of the cars (as seen in I, Robot) but humans are not currently willing to relinquish control of their cars and so Autobot seeks to fulfill its goals within this limitation, rather than challenging it. Humans can keep whatever jobs for themselves that they please but for this task, and many others, automation just makes more sense.
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IS ARTIFICIAL IS DENGEROUS WITHOUT EMOTION?????
The Artificial Intelligence in Education (AIED) theme within the Personalisation strand of TLRP TEL is concerned with exploring the ways in which the work conducted under TEL within and across projects can contribute to the (inter)discipline of AIED.
AIED draws together work from multiple disciplines, in particular: computer science, education and psychology to explore ways in which learning and teaching can benefit from technology that draws upon research in Artificial Intelligence (AI). The nature of the research conducted under the heading of AIED has developed and evolved over the past 25 years or so and the community is a broad church concerned with supporting the learning and teaching process in situ and in real time. Theoretically grounded research is supported by systematic empirical evaluation that informs further theory development. Increasing attention is paid to the affective and collaborative as well as the intellectual aspects of learning with very active research being conducted to investigate collaboration, metacognition, motivation, and emotions, as well as the more traditional areas of scaffolding and intelligent support; and the new possibilities afforded by data mining techniques.
Over the next year or so the work of the AIED theme will review the original AIED influences as cited by TEL project leaders and teams, through their funding applications and subsequent discussion. We will chart the themes within the AIED community and their changes in recent years in order to map potential links between the work of the TEL projects and the main foci of attention of the AIED community. In this way we will identify areas where the TEL projects have particular contributions to make and will support the communication of these contributions to the wider AIED community, in particular at the AIED conference in 2011.just CliCk
THE WORST VIDEO GAME ARTIFICIAL INTELLIGENCE
Artificial intelligence in medicine is a new research area that combines sophisticated representational and computing techniques with the insights of expert physicians to produce tools for improving health care.
Artificial Intelligence is the study of ideas which enable computers to do the things that make people seem intelligent ... The central goals of Artificial Intelligence are to make computers more useful and to understand the principles which make intelligence possible.
Medicine is a field in which technology is much needed. Our increasing expectations of the highest quality health care and the rapid growth of ever more detailed medical knowledge leave the physician without adequate time to devote to each case and struggling to keep up with the newest developments in his field. Due to lack of time, most medical decisions must be based on rapid judgments of the case relying on the physician's unaided memory. Only in rare situations can a literature search or other extended investigation be undertaken to assure the doctor (and the patient) that the latest knowledge is brought to bear on any particular case.
We view computers as an intellectual, deductive instrument, which can be integrated into the structure of the medical care system. The idea that these machines can replace the many traditional activities of the physician is probably. Advocators for artificial intelligence research envisions that physicians and the computer will engage in frequent dialogue, the computer continuously taking note of history, physical findings, laboratory data, and the like, alerting the physician to the most probable diagnoses and suggesting the appropriate, safest course of action
Expert or knowledge-based systems are the commonest type of AIM system in routine clinical use. They contain medical knowledge, usually about a very specifically defined task, and are able to reason with data from individual patients to come up with reasoned conclusions. Although there are many variations, the knowledge within an expert system is typically represented in the form of a set of rules.
Medicine has formed a rich test-bed for machine learning experiments in the past, allowing scientists to develop complex and powerful learning systems. While there has been much practical use of expert systems in routine clinical settings, at present machine learning systems still seem to be used in a more experimental way. There are, however, many situations in which they can make a significant contribution.
Artificial Intelligence is the study of ideas which enable computers to do the things that make people seem intelligent ... The central goals of Artificial Intelligence are to make computers more useful and to understand the principles which make intelligence possible.
Medicine is a field in which technology is much needed. Our increasing expectations of the highest quality health care and the rapid growth of ever more detailed medical knowledge leave the physician without adequate time to devote to each case and struggling to keep up with the newest developments in his field. Due to lack of time, most medical decisions must be based on rapid judgments of the case relying on the physician's unaided memory. Only in rare situations can a literature search or other extended investigation be undertaken to assure the doctor (and the patient) that the latest knowledge is brought to bear on any particular case.
We view computers as an intellectual, deductive instrument, which can be integrated into the structure of the medical care system. The idea that these machines can replace the many traditional activities of the physician is probably. Advocators for artificial intelligence research envisions that physicians and the computer will engage in frequent dialogue, the computer continuously taking note of history, physical findings, laboratory data, and the like, alerting the physician to the most probable diagnoses and suggesting the appropriate, safest course of action
Expert or knowledge-based systems are the commonest type of AIM system in routine clinical use. They contain medical knowledge, usually about a very specifically defined task, and are able to reason with data from individual patients to come up with reasoned conclusions. Although there are many variations, the knowledge within an expert system is typically represented in the form of a set of rules.
Medicine has formed a rich test-bed for machine learning experiments in the past, allowing scientists to develop complex and powerful learning systems. While there has been much practical use of expert systems in routine clinical settings, at present machine learning systems still seem to be used in a more experimental way. There are, however, many situations in which they can make a significant contribution.
The aim of Artificial Intelligence is to develop the machines to perform the tasks in a better way than the humans. Another aim of Artificial Intelligence is to understand the actions whether it occurs in humans, machines or animals. As a result, Artificial Intelligence is gaining Importance in science and engineering fields.
Artificial intelligence (AI) is arguably the most exciting field in robotics. It's certainly the most controversial: Everybody agrees that a robot can work in an assembly line, but there's no consensus on whether a robot can ever be intelligent.
Like the term "robot" itself, artificial intelligence is hard to define. Ultimate AI would be a recreation of the human thought process -- a man-made machine with our intellectual abilities. This would include the ability to learn just about anything, the ability to reason, the ability to use language and the ability to formulate original ideas. Roboticists are nowhere near achieving this level of artificial intelligence, but they have made a lot of progress with more limited AI. Today's AI machines can replicate some specific elements of intellectual ability.
Computers can already solve problems in limited realms. The basic idea of AI problem-solving is very simple, though its execution is complicated. First, the AI robot or computer gathers facts about a situation through sensors or human input. The computer compares this information to stored data and decides what the information signifies. The computer runs through various possible actions and predicts which action will be most successful based on the collected information. Of course, the computer can only solve problems it's programmed to solve -- it doesn't have any generalized analytical ability. Chess computers are one example of this sort of machine.
Some modern robots also have the ability to learn in a limited capacity. Learning robots recognize if a certain action (moving its legs in a certain way, for instance) achieved a desired result (navigating an obstacle). The robot stores this information and attempts the successful action the next time it encounters the same situation. Again, modern computers can only do this in very limited situations. They can't absorb any sort of information like a human can. Some robots can learn by mimicking human actions. In Japan, roboticists have taught a robot to dance by demonstrating the moves themselves.
Some robots can interact socially. Kismet, a robot at M.I.T's Artificial Intelligence Lab, recognizes human body language and voice inflection and responds appropriately. Kismet's creators are interested in how humans and babies interact, based only on tone of speech and visual cue. This low-level interaction could be the foundation of a human-like learning system.
Kismet and other humanoid robots at the M.I.T. AI Lab operate using an unconventional control structure. Instead of directing every action using a central computer, the robots control lower-level actions with lower-level computers. The program's director, Rodney Brooks, believes this is a more accurate model of human intelligence. We do most things automatically; we don't decide to do them at the highest level of consciousness.
The real challenge of AI is to understand how natural intelligence works. Developing AI isn't like building an artificial heart -- scientists don't have a simple, concrete model to work from. We do know that the braincontains billions and billions of neurons, and that we think and learn by establishing electrical connections between different neurons. But we don't know exactly how all of these connections add up to higher reasoning, or even low-level operations. The complex circuitry seems incomprehensible.
Because of this, AI research is largely theoretical. Scientists hypothesize on how and why we learn and think, and they experiment with their ideas using robots. Brooks and his team focus on humanoid robots because they feel that being able to experience the world like a human is essential to developing human-like intelligence. It also makes it easier for people to interact with the robots, which potentially makes it easier for the robot to learn.
Just as physical robotic design is a handy tool for understanding animal and human anatomy, AI research is useful for understanding how natural intelligence works. For some roboticists, this insight is the ultimate goal of designing robots. Others envision a world where we live side by side with intelligent machines and use a variety of lesser robots for manual labor, health care and communication. A number of robotics experts predict that robotic evolution will ultimately turn us into cyborgs -- humans integrated with machines. Conceivably, people in the future could load their minds into a sturdy robot and live for thousands of years!
In any case, robots will certainly play a larger role in our daily lives in the future. In the coming decades, robots will gradually move out of the industrial and scientific worlds and into daily life, in the same way that computers spread to the home in the 1980s.
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