Research proposal This essay has been submitted by a student. This is not an example of the work written by our professional essay writers. Currently, ontology is a process which allows the machines to understand domains and concepts which define the relationship between artificial intelligence and its application in the management of cyber crimes and other internal problems which affect the operations of an organization In Yager, et. The combination of the ontology as well as the artificial intelligence can give a leeway in the determination of causes of cyber security, their associations and understand the level in which the model can handle cyber security problems In Yager, et.
This insight, that digital computers can simulate any process of formal reasoning, is known as the Church—Turing thesis. Herbert Simon predicted, "machines will be capable, within twenty years, of doing any work a man can do". Marvin Minsky agreed, writing, "within a generation Progress slowed and inin response to the criticism of Sir James Lighthill  and ongoing pressure from the US Congress to fund more productive projects, both the U.
The next few years would later be called an " AI winter ",  a period when obtaining funding for AI projects was difficult. In the early s, AI research was revived by the commercial success of expert systems a form of AI program that simulated the knowledge and analytical skills of human experts.
Bythe market for AI had reached over a billion dollars. S and British governments to restore funding for academic research. Clark also presents factual data indicating that error rates in image processing tasks have fallen significantly since Over the time America and China has collected and attracted the core information that contributed to development of Artificial Intelligence ranging from facial recognition to driver-less cars.
Basics A typical AI perceives its environment and takes actions that maximize its chance of successfully achieving its goals.
Goals can be explicitly defined, or can be induced. If the AI is programmed for " reinforcement learning ", goals can be implicitly induced by rewarding some types of behavior and punishing others. An algorithm is a set of unambiguous instructions that a mechanical computer can execute.
A simple example of an algorithm is the following recipe for optimal play at tic-tac-toe: Otherwise, if a move "forks" to create two threats at once, play that move. Otherwise, take the center square if it is free. Otherwise, if your opponent has played in a corner, take the opposite corner.
Otherwise, take an empty corner if one exists. Otherwise, take any empty square.
Many AI algorithms are capable of learning from data; they can enhance themselves by learning new heuristics strategies, or "rules of thumb", that have worked well in the pastor can themselves write other algorithms. Some of the "learners" described below, including Bayesian networks, decision trees, and nearest-neighbor, could theoretically, if given infinite data, time, and memory, learn to approximate any functionincluding whatever combination of mathematical functions would best describe the entire world.
These learners could therefore, in theory, derive all possible knowledge, by considering every possible hypothesis and matching it against the data. In practice, it is almost never possible to consider every possibility, because of the phenomenon of " combinatorial explosion ", where the amount of time needed to solve a problem grows exponentially.
Much of AI research involves figuring out how to identify and avoid considering broad swaths of possibilities that are unlikely to be fruitful. A second, more general, approach is Bayesian inference: The third major approach, extremely popular in routine business AI applications, are analogizers such as SVM and nearest-neighbor: These four main approaches can overlap with each other and with evolutionary systems; for example, neural nets can learn to make inferences, to generalize, and to make analogies.
Some systems implicitly or explicitly use multiple of these approaches, alongside many other AI and non-AI algorithms;  the best approach is often different depending on the problem. Learning algorithms work on the basis that strategies, algorithms, and inferences that worked well in the past are likely to continue working well in the future.
These inferences can be obvious, such as "since the sun rose every morning for the last 10, days, it will probably rise tomorrow morning as well". The simplest theory that explains the data is the likeliest. Therefore, to be successful, a learner must be designed such that it prefers simpler theories to complex theories, except in cases where the complex theory is proven substantially better.
Settling on a bad, overly complex theory gerrymandered to fit all the past training data is known as overfitting. Many systems attempt to reduce overfitting by rewarding a theory in accordance with how well it fits the data, but penalizing the theory in accordance with how complex the theory is.
A toy example is that an image classifier trained only on pictures of brown horses and black cats might conclude that all brown patches are likely to be horses.
Faintly superimposing such a pattern on a legitimate image results in an "adversarial" image that the system misclassifies. This enables even young children to easily make inferences like "If I roll this pen off a table, it will fall on the floor".
Humans also have a powerful mechanism of " folk psychology " that helps them to interpret natural-language sentences such as "The city councilmen refused the demonstrators a permit because they advocated violence".
A generic AI has difficulty inferring whether the councilmen or the demonstrators are the ones alleged to be advocating violence. For example, existing self-driving cars cannot reason about the location nor the intentions of pedestrians in the exact way that humans do, and instead must use non-human modes of reasoning to avoid accidents.
The general problem of simulating or creating intelligence has been broken down into sub-problems. These consist of particular traits or capabilities that researchers expect an intelligent system to display.
The traits described below have received the most attention. They solve most of their problems using fast, intuitive judgements.Artificial Intelligence Artificial intelligence is the use of computers to capture human brains in limited domains.
This is a result of computer revolution whereby systems developed behave intellectually, reason rationally and have the ability to effectively interpret the environment in real time. Essay Artificial Intelligence ABSTRACT Current neural network technology is the most progressive of the artificial intelligence systems today.
Applications of neural networks have made the transition from laboratory curiosities to large, successful commercial applications. Writing research papers has become inevitable while in college. This is because, in each module that you study, you are expected to do a research to .
Post: [FoR&AI] The Origins of “Artificial Intelligence” April 27, — Essays [FoR&AI] The Origins of “Artificial Intelligence”. How advanced artificial intelligence relates to global risk as both a potential catastrophe and a potential solution.
Contains considerable background material in cognitive sciences, and conveys much of my most recent views on intelligence, AI, . An executive guide to artificial intelligence, from machine learning and general AI to neural networks.