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

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.

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.

Positive Outcomes
Many positive outcomes in our society can result with the use of artificial intelligence.  Increased production and indirectly lowered costs have already been witnessed in factories and production lines.  Jobs better suited for computers have decreased errors and increased efficiency.  One example of this is with detecting credit card fraud.  American Express has developed an "Authorization Assistant" that uses artificial intelligence to determine whether a purchase is out of character for a card member.  This system is more accurate than when done by a human and it saves time.  This and many infinite other possibilities exist for using artificial intelligence to increase efficiency.
Artificial intelligence is also being pursued to replace humans in dangerous situations.  Not only can they withstand radioactive elements but they also work better in places where there is confined space and little oxygen to breathe.  This replacement will eliminate unwarranted deaths due to potential accidents and unsafe conditions.
Another important area that artificial intelligence is projected to improve concerns the lives of the elderly.  Because of the demand for adults to be fully involved in their work, the care for the elderly at home has diminished.  Now the numbers needing nursing care has risen.  The desire for these individuals to be independent can no longer be met and the elderly will have to live in a nursing home.  Artificially intelligent robots are an attempt to rectify this problem.  If a spouse passes away, the widowed spouse, while perhaps not fully independent, may no longer have to seek help in a nursing home.  Now a robot can oversee the individual and help with tasks too difficult for the person on their own.  As a whole, society will begin to change.  Menial tasks done by humans will no longer need attention and time can be spent doing more constructive things.  The systems run by artificial intelligence will be more accurate then ever, thereby increasing the level of trust in making certain decisions.  Lives can be lived more fully.  Perhaps years from now people will look back, much like individuals today look back at progress, and ponder how unnecessarily difficult their lives used to be.


Negative Outcomes
Along with any progress in technology are negative outcomes as well.  Because computers are more capable of producing accurate results, they will potentially replace humans in jobs that are better suited for them.  This could mean that the workplace will no longer be man's domain.  Unemployment rates could go up.  Humans could soon lose their ground as dominant creature.  Most drastic of possibilities is complete destruction of the human race.  If artificial intelligence at the level of Moravec's Fourth Generation Robots is created, these machines will have a "mind" of their own and could potentially annihilate humanity.
At a more basic level, the use of artificial intelligence in everyday tasks might produce laziness on the part of humans.  Mentality might become; "if the computer can do it why should I waste my time trying it myself?"  Humans have an extraordinary ability to think, analyze, and use judgment.  If artificial intelligence is used for interpreting, then the human mind and its capabilities might go to waste.

game playing

You can buy machines that can play master level chess for a few hundred dollars. There is some AI in them, but they play well against people mainly through brute force computation--looking at hundreds of thousands of positions. To beat a world champion by brute force and known reliable heuristics requires being able to look at 200 million positions per second.

speech recognition

In the 1990s, computer speech recognition reached a practical level for limited purposes. Thus United Airlines has replaced its keyboard tree for flight information by a system using speech recognition of flight numbers and city names. It is quite convenient. On the the other hand, while it is possible to instruct some computers using speech, most users have gone back to the keyboard and the mouse as still more convenient.

understanding natural language

Just getting a sequence of words into a computer is not enough. Parsing sentences is not enough either. The computer has to be provided with an understanding of the domain the text is about, and this is presently possible only for very limited domains.

computer vision

The world is composed of three-dimensional objects, but the inputs to the human eye and computers' TV cameras are two dimensional. Some useful programs can work solely in two dimensions, but full computer vision requires partial three-dimensional information that is not just a set of two-dimensional views. At present there are only limited ways of representing three-dimensional information directly, and they are not as good as what humans evidently use.

expert systems

A ``knowledge engineer'' interviews experts in a certain domain and tries to embody their knowledge in a computer program for carrying out some task. How well this works depends on whether the intellectual mechanisms required for the task are within the present state of AI. When this turned out not to be so, there were many disappointing results. One of the first expert systems was MYCIN in 1974, which diagnosed bacterial infections of the blood and suggested treatments. It did better than medical students or practicing doctors, provided its limitations were observed. Namely, its ontology included bacteria, symptoms, and treatments and did not include patients, doctors, hospitals, death, recovery, and events occurring in time. Its interactions depended on a single patient being considered. Since the experts consulted by the knowledge engineers knew about patients, doctors, death, recovery, etc., it is clear that the knowledge engineers forced what the experts told them into a predetermined framework. In the present state of AI, this has to be true. The usefulness of current expert systems depends on their users having common sense.

heuristic classification

One of the most feasible kinds of expert system given the present knowledge of AI is to put some information in one of a fixed set of categories using several sources of information. An example is advising whether to accept a proposed credit card purchase. Information is available about the owner of the credit card, his record of payment and also about the item he is buying and about the establishment from which he is buying it (e.g., about whether there have been previous credit card frauds at this establishment).

Although the computer provided the technology necessary for AI, it was not until the early 1950's that the link between human intelligence and machines was really observed. Norbert Wiener was one of the first Americans to make observations on the principle of feedback theory feedback theory. The most familiar example of feedback theory is the thermostat: It controls the temperature of an environment by gathering the actual temperature of the house, comparing it to the desired temperature, and responding by turning the heat up or down. What was so important about his research into feedback loops was that Wiener theorized that all intelligent behavior was the result of feedback mechanisms. Mechanisms that could possibly be simulated by machines. This discovery influenced much of early development of AI.
In late 1955, Newell and Simon developed The Logic Theorist, considered by many to be the first AI program. The program, representing each problem as a tree model, would attempt to solve it by selecting the branch that would most likely result in the correct conclusion. The impact that the logic theorist made on both the public and the field of AI has made it a crucial stepping stone in developing the AI field.