Artificial intelligence

Artificial intelligence (AI) is the intelligence of machines and the branch of computer science which aims to create it. Major AI textbooks define the field as "the study and design of intelligent agents," where an intelligent agent is a system that perceives its environment and takes actions which maximize its chances of success.
John McCarthy, who coined the term in 1956, defines it as "the science and engineering of making intelligent machines."
The field was founded on the claim that a central property of human beings, intelligence—the sapience of Homo sapiens—can be so precisely described that it can be simulated by a machine.
This raises philosophical issues about the nature of the mind and limits of scientific hubris, issues which have been addressed by myth, fiction and philosophy since antiquity.
Artificial intelligence has been the subject of breathtaking optimism, has suffered stunning setbacks and, today, has become an essential part of the technology industry, providing the heavy lifting for many of the most difficult problems in computer science.
AI research is highly technical and specialized, so much so that some critics decry the "fragmentation" of the field. Subfields of AI are organized around particular problems, the application of particular tools and around longstanding theoretical differences of opinion. The central problems of AI include such traits as reasoning, knowledge, planning, learning, communication, perception and the ability to move and manipulate objects.
General intelligence (or "strong AI") is still a long term goal of (some) research.

Cybernetics and brain simulationMain articles:

Cybernetics and Computational neuroscience The human brain provides inspiration for artificial intelligence researchers, however there is no consensus on how closely it should be simulated.
In the 40s and 50s, a number of researchers explored the connection between neurology, information theory, and cybernetics. Some of them built machines that used electronic networks to exhibit rudimentary intelligence, such as W. Grey Walter's turtles and the Johns Hopkins Beast. Many of these researchers gathered for meetings of the Teleological Society at Princeton University and the Ratio Club in England.

Evaluating artificial intelligenceMain article:

Progress in artificial intelligence
How can one determine if an agent is intelligent? In 1950, Alan Turing proposed a general procedure to test the intelligence of an agent now known as the Turing test. This procedure allows almost all the major problems of artificial intelligence to be tested. However, it is a very difficult challenge and at present all agents fail.
Artificial intelligence can also be evaluated on specific problems such as small problems in chemistry, hand-writing recognition and game-playing. Such tests have been termed subject matter expert Turing tests. Smaller problems provide more achievable goals and there are an ever-increasing number of positive results.
The broad classes of outcome for an AI test are:optimal: it is not possible to perform betterstrong super-human: performs better than all humanssuper-human: performs better than most humanssub-human: performs worse than most humans
For example, performance at checkers (draughts) is optimal, performance at chess is super-human and nearing strong super-human, and performance at many everyday tasks performed by humans is sub-human.

expert system

An expert system is software that attempts to reproduce the performance of one or more human experts, most commonly in a specific problem domain, and is a traditional application and/or subfield of artificial intelligence. A wide variety of methods can be used to simulate the performance of the expert however common to most or all are
1) the creation of a so-called "knowledgebase" which uses some knowledge representation formalism to capture the Subject Matter Experts (SME) knowledge and
2) a process of gathering that knowledge from the SME and codifying it according to the formalism, which is called knowledge engineering.
Expert systems may or may not have learning components but a third common element is that once the system is developed it is proven by being placed in the same real world problem solving situation as the human SME, typically as an aid to human workers or a supplement to some information system.
As a premiere application of computing and artificial intelligence, the topic of expert systems has many points of contact with general systems theory, operations research, business process reengineering and various topics in applied mathematics and management science.

Expert system architecture
The following general points about expert systems and their architecture have been illustrated.

1. The sequence of steps taken to reach a conclusion is dynamically synthesized with each new case. It is not explicitly programmed when the system is built.

2. Expert systems can process multiple values for any problem parameter. This permits more than one line of reasoning to be pursued and the results of incomplete (not fully determined) reasoning to be presented.

3. Problem solving is accomplished by applying specific knowledge rather than specific technique. This is a key idea in expert systems technology. It reflects the belief that human experts do not process their knowledge differently from others, but they do possess different knowledge. With this philosophy, when one finds that their expert system does not produce the desired results, work begins to expand the knowledge base, not to re-program the procedures.

Expert systems versus problem-solving systems
The principal distinction between expert systems and traditional problem solving programs is the way in which the problem related expertise is coded. In traditional applications, problem expertise is encoded in both program and data structures. In the expert system approach all of the problem related expertise is encoded in data structures only; no problem-specific information is encoded in the program structure. This organization has several benefits.
An example may help contrast the traditional problem solving program with the expert system approach. The example is the problem of tax advice. In the traditional approach data structures describe the taxpayer and tax tables, and a program in which there are statements representing an expert tax consultant's knowledge, such as statements which relate information about the taxpayer to tax table choices. It is this representation of the tax expert's knowledge that is difficult for the tax expert to understand or modify.
In the expert system approach, the information about taxpayers and tax computations is again found in data structures, but now the knowledge describing the relationships between them is encoded in data structures as well. The programs of an expert system are independent of the problem domain (taxes) and serve to process the data structures without regard to the nature of the problem area they describe. For example, there are programs to acquire the described data values through user interaction, programs to represent and process special organizations of description, and programs to process the declarations that represent semantic relationships within the problem domain and an algorithm to control the processing sequence and focus.
The general architecture of an expert system involves two principal components: a problem dependent set of data declarations called the knowledge base or rule base, and a problem independent (although highly data structure dependent) program which is called the inference engine.

User interface
The function of the user interface is to present questions and information to the user and supply the user's responses to the inference engine.
Any values entered by the user must be received and interpreted by the user interface. Some responses are restricted to a set of possible legal answers, others are not. The user interface checks all responses to insure that they are of the correct data type. Any responses that are restricted to a legal set of answers are compared against these legal answers. Whenever the user enters an illegal answer, the user interface informs the user that his answer was invalid and prompts him to correct it.

Advantages and disadvantages:-
Advantages:Provides consistent answers for repetitive decisions, processes and tasksHolds and maintains significant levels of informationEncourages organizations to clarify the logic of their decision-makingNever "forgets" to ask a question, as a human mightCan work round the clockCan be used by the user more frequentlyA multi-user expert system can serve more users at a time
Disadvantages:Lacks common sense needed in some decision makingCannot make creative responses as human expert would in unusual circumstancesDomain experts not always able to explain their logic and reasoningErrors may occur in the knowledge base, and lead to wrong decisionsCannot adapt to changing environments, unless knowledge base is changed

cloud computing

Cloud computing is a style of computing in which dynamically scalable and often virtualised resources are provided as a service over the Internet. Users need not have knowledge of, expertise in, or control over the technology infrastructure "in the cloud" that supports them.

The concept incorporates infrastructure as a service (IaaS), platform as a service (PaaS) and software as a service (SaaS) as well as Web 2.0 and other recent (ca. 2007–2009) technology trends that have the common theme of reliance on the Internet for satisfying the computing needs of the users. Examples of SaaS vendors include Salesforce.com and Google Apps which provide common business applications online that are accessed from a web browser, while the software and data are stored on the servers.
The term cloud is used as a metaphor for the Internet, based on how the Internet is depicted in computer network diagrams, and is an abstraction for the complex infrastructure it conceals.

Characteristics
As customers generally do not own the infrastructure, they merely access or rent, they can avoid capital expenditure and consume resources as a service, paying instead for what they use. Many cloud-computing offerings have adopted the utility computing model, which is analogous to how traditional utilities like electricity are consumed, while others are billed on a subscription basis. Sharing "perishable and intangible" computing power among multiple tenants can improve utilization rates, as servers are not left idle, which can reduce costs significantly while increasing the speed of application development. A side effect of this approach is that "computer capacity rises dramatically" as customers do not have to engineer for peak loads. Adoption has been enabled by "increased high-speed bandwidth" which makes it possible to receive the same response times from centralized infrastructure at other sites.

Companies:-
Cloud Service Providers (CSPs) including Amazon, Microsoft , Google , Sun and Yahoo exemplify the use of cloud computing. It is being adopted by individual users through large enterprises including General Electric, L'Oréal, and Procter & Gamble

Key characteristics

Cost is greatly reduced and capital expenditure is converted to operational expenditure. This lowers barriers to entry, as infrastructure is typically provided by a third-party and does not need to be purchased for one-time or infrequent intensive computing tasks. Pricing on a utility computing basis is fine-grained with usage-based options and minimal or no IT skills are required for implementation.Device and location independence enable users to access systems using a web browser regardless of their location or what device they are using, e.g., PC, mobile. As infrastructure is off-site (typically provided by a third-party) and accessed via the Internet the users can connect from anywhere.Multi-tenancy enables sharing of resources and costs among a large pool of users, allowing for: Centralization of infrastructure in areas with lower costs (such as real estate, electricity, etc.)Peak-load capacity increases (users need not engineer for highest possible load-levels)Utilisation and efficiency improvements for systems that are often only 10-20% utilised.Reliability improves through the use of multiple redundant sites, which makes it suitable for business continuity and disaster recovery. Nonetheless, most major cloud computing services have suffered outages and IT and business managers are able to do little when they are affected.Scalability via dynamic ("on-demand") provisioning of resources on a fine-grained, self-service basis near real-time, without users having to engineer for peak loads. Performance is monitored and consistent and loosely-coupled architectures are constructed using web services as the system interface.Security typically improves due to centralization of data,increased security-focused resources, etc., but raises concerns about loss of control over certain sensitive data. Security is often as good as or better than traditional systems, in part because providers are able to devote resources to solving security issues that many customers cannot afford. Providers typically log accesses, but accessing the audit logs themselves can be difficult or impossible.Sustainability comes about through improved resource utilisation, more efficient systems, and carbon neutrality. Nonetheless, computers and associated infrastructure are major consumers of energy

green technology

Study Blends Coal with Solar Power
study hybrid coal and solar power generation to understand how they can complement one another to smooth out (via smart control strategies) power generation to efficiently meet electricity demand through the day. ...... "The control strategy of a typical coal-fired plant with supplemental solar power will require agility to keep everything running on an even keel. " ...

Time for Biofuel Education?
University of Wisconsin-Madison national survey shows that Americans are aware of biofuels and want to learn more. ... ... "The national survey showed that 67 percent of respondents were interested in learning more about renewable biofuels. On the positive side, a majority of respondents perceive some clear benefits of biofuels, with 66 percent agreeing that using them can help the United States reduce reliance on foreign oil. " ...

Energy Efficient Green Roof
The George K. Brushaber Commons building, on the campus of Bethel University, opens as a student common service area and has been designed and built with a large green roof and other sustainable features. ...... "On Brushaber Commons’ highest level is a 6,500 square foot green roof, one of the largest of its kind in the Twin Cities. Benefiting both energy efficiency and ecology, the roof has vegetation planted over a waterproofing membrane --- a design that serves as insulation as well as a storm retention system. " ...

Honda's 2010 INSiGHT Hybrid Sedan
Honda launched the first electric car with the Insight and it was available during the 2000 - 2006 model years. Honda is introducing it's new version for 2010.
" ... 2010 Honda Insight has made its debut on America's streets. ... hybrid technology ... affordable price and 41 mpg* combined fuel economy. ... increased the green factor with a sophisticated new system called Eco Assist™"
"*40 city/43 hwy/41 combined mpg. Based on 2010 EPA mileage estimates, reflecting new EPA fuel-economy methods beginning with 2008 models."