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◥ University. {q} PhD. {t} Themes. {t} AI. Artificial intelligence. Intelligence of machines or software Artificial intelligence (AI) is the intelligence of machines or software, as opposed to the intelligence of humans or animals. It is a field of study in computer science that develops and studies intelligent machines. Such machines may be called AIs. The various sub-fields of AI research are centered around particular goals and the use of particular tools.

The traditional goals of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception, and support for robotics. [a] General intelligence (the ability to complete any task performable by a human) is among the field's long-term goals.[11] To solve these problems, AI researchers have adapted and integrated a wide range of problem-solving techniques, including search and mathematical optimization, formal logic, artificial neural networks, and methods based on statistics, operations research, and economics. Goals Reasoning, problem-solving Learning Logic.

[C] AI

ↂ QuillBot. - AI for teachers - lesson planning and more! Magic ToDo - GoblinTools. DALL·E 2. Research Advancements Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, Mark Chen Engineering, Design, Product, and Prototyping Jeff Belgum, Dave Cummings, Jonathan Gordon, Chris Hallacy, Shawn Jain, Joanne Jang, Fraser Kelton, Vishal Kuo, Joel Lehman, Rachel Lim, Bianca Martin, Evan Morikawa, Rajeev Nayak, Glenn Powell, Krijn Rijshouwer, David Schnurr, Maddie Simens, Kenneth Stanley, Felipe Such, Chelsea Voss, Justin Jay Wang Comms, Policy, Legal, Ops, Safety, and Security Steven Adler, Lama Ahmad, Miles Brundage, Kevin Button, Che Chang, Fotis Chantzis, Derek Chen, Frances Choi, Steve Dowling, Elie Georges, Shino Jomoto, Aris Konstantinidis, Gretchen Krueger, Andrew Mayne, Pamela Mishkin, Bob Rotsted, Natalie Summers, Dave Willner, Hannah Wong Acknowledgments. | Personalized AI for every moment of your day. Data mining. Process of extracting and discovering patterns in large data sets Data mining is the process of extracting and discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.[1] Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal of extracting information (with intelligent methods) from a data set and transforming the information into a comprehensible structure for further use.[1][2][3][4] Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD.[5] Aside from the raw analysis step, it also involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.[1] Etymology[edit] Background[edit] The manual extraction of patterns from data has occurred for centuries.

Process[edit] Machine translation. Machine translation, sometimes referred to by the abbreviation MT (not to be confused with computer-aided translation, machine-aided human translation (MAHT) or interactive translation) is a sub-field of computational linguistics that investigates the use of software to translate text or speech from one natural language to another. On a basic level, MT performs simple substitution of words in one natural language for words in another, but that alone usually cannot produce a good translation of a text because recognition of whole phrases and their closest counterparts in the target language is needed. Solving this problem with corpus and statistical techniques is a rapidly growing field that is leading to better translations, handling differences in linguistic typology, translation of idioms, and the isolation of anomalies.[1] The progress and potential of machine translation have been debated much through its history.

History[edit] Translation process[edit] Approaches[edit] Rule-based[edit] Robotics. Robotics is the branch of mechanical engineering, electrical engineering and computer science that deals with the design, construction, operation, and application of robots,[1] as well as computer systems for their control, sensory feedback, and information processing. These technologies deal with automated machines that can take the place of humans in dangerous environments or manufacturing processes, or resemble humans in appearance, behavior, and/or cognition. Many of today's robots are inspired by nature contributing to the field of bio-inspired robotics. The concept of creating machines that can operate autonomously dates back to classical times, but research into the functionality and potential uses of robots did not grow substantially until the 20th century.[2] Throughout history, robotics has been often seen to mimic human behavior, and often manage tasks in a similar fashion.

Etymology[edit] History of robotics[edit] Robotic aspects[edit] Components[edit] Power source[edit] Machine vision. Early Automatix (now part of Microscan) machine vision system Autovision II from 1983 being demonstrated at a trade show. Camera on tripod is pointing down at a light table to produce backlit image shown on screen, which is then subjected to blob extraction.

Machine vision (MV) is the technology and methods used to provide imaging-based automatic inspection and analysis for such applications as automatic inspection, process control, and robot guidance in industry.[1][2] The scope of MV is broad.[2][3][4] MV is related to, though distinct from, computer vision.[2] Applications[edit] The primary uses for machine vision are automatic inspection and industrial robot guidance.[5] Common machine vision applications include quality assurance, sorting, material handling, robot guidance, and optical gauging.[4] Methods[edit] Imaging[edit] Image processing[edit] After an image is acquired, it is processed.[19] Machine vision image processing methods include[further explanation needed] Outputs[edit] Artificial intelligence. AI research is highly technical and specialized, and is deeply divided into subfields that often fail to communicate with each other.[5] Some of the division is due to social and cultural factors: subfields have grown up around particular institutions and the work of individual researchers.

AI research is also divided by several technical issues. Some subfields focus on the solution of specific problems. Others focus on one of several possible approaches or on the use of a particular tool or towards the accomplishment of particular applications. The central problems (or goals) of AI research include reasoning, knowledge, planning, learning, natural language processing (communication), perception and the ability to move and manipulate objects.[6] General intelligence is still among the field's long-term goals.[7] Currently popular approaches include statistical methods, computational intelligence and traditional symbolic AI.

History[edit] Research[edit] Goals[edit] Planning[edit] Logic-based. Turing test. The "standard interpretation" of the Turing Test, in which player C, the interrogator, is tasked with trying to determine which player - A or B - is a computer and which is a human. The interrogator is limited to using the responses to written questions in order to make the determination. Image adapted from Saygin, 2000. The test was introduced by Alan Turing in his 1950 paper "Computing Machinery and Intelligence," which opens with the words: "I propose to consider the question, 'Can machines think? '" Because "thinking" is difficult to define, Turing chooses to "replace the question by another, which is closely related to it and is expressed in relatively unambiguous words.

" Turing's new question is: "Are there imaginable digital computers which would do well in the imitation game? "[4] This question, Turing believed, is one that can actually be answered. In the remainder of the paper, he argued against all the major objections to the proposition that "machines can think".[5] Research Question. Programming language. The earliest programming languages preceded the invention of the digital computer and were used to direct the behavior of machines such as Jacquard looms and player pianos.[1] Thousands of different programming languages have been created, mainly in the computer field, and many more still are being created every year. Many programming languages require computation to be specified in an imperative form (i.e., as a sequence of operations to perform), while other languages utilize other forms of program specification such as the declarative form (i.e. the desired result is specified, not how to achieve it).

Definitions[edit] A programming language is a notation for writing programs, which are specifications of a computation or algorithm.[2] Some, but not all, authors restrict the term "programming language" to those languages that can express all possible algorithms.[2][3] Traits often considered important for what constitutes a programming language include: Function and target Abstractions. Hypothesis. Psychological level. In finance, psychological level, is a price level in technical analysis that significantly affects the price of an underlying security, commodity or a derivative. Typically, the number is something that is "easy to remember," such as a rounded-off number. When a specific security, commodity, or derivative reaches such a price, financial market participants (traders, market makers, brokers, investors, etc.) tend to act on their positions (buy, sell or hold). Examples[edit] Dow Jones Industrial Average Index - $14,000.00 - the all-time high psychological thousandth level as of 11/9/2007.

Also known as "Dow 14,000"Crude Oil (light, sweet) - $100.00/barrel References[edit] As used by finance analysts and business reporters: External links[edit] Psychological barriers in gold prices? Human behavior. Human behavior is experienced throughout an individual’s entire lifetime. It includes the way they act based on different factors such as genetics, social norms, core faith, and attitude. Behaviour is impacted by certain traits each individual has. The traits vary from person to person and can produce different actions or behaviour from each person. Social norms also impact behaviour.

Due to the inherently conformist nature of human society in general, humans are pressurised into following certain rules and display certain behaviours in society, which conditions the way people behave. Different behaviours are deemed to be either acceptable or unacceptable in different societies and cultures. Factors[edit] Genetics[edit] Human behavior can be impacted by many factors, including genetics and behavioural genetics. Social norms[edit] Social norms, the often-unspoken rules of a group, shape not just our behaviours but also our attitudes. Core faith and culture[edit] Attitude[edit] See also[edit] Neural network. An artificial neural network is an interconnected group of nodes, akin to the vast network of neurons in a brain.

Here, each circular node represents an artificial neuron and an arrow represents a connection from the output of one neuron to the input of another. For example, a neural network for handwriting recognition is defined by a set of input neurons which may be activated by the pixels of an input image. After being weighted and transformed by a function (determined by the network's designer), the activations of these neurons are then passed on to other neurons. This process is repeated until finally, an output neuron is activated. Like other machine learning methods - systems that learn from data - neural networks have been used to solve a wide variety of tasks that are hard to solve using ordinary rule-based programming, including computer vision and speech recognition. Background[edit] There is no single formal definition of what an artificial neural network is.

History[edit] and. Activation function. In computational networks, the activation function of a node defines the output of that node given an input or set of inputs. A standard computer chip circuit can be seen as a digital network of activation functions that can be "ON" (1) or "OFF" (0), depending on input. This is similar to the behavior of the linear perceptron in neural networks. However, it is the nonlinear activation function that allows such networks to compute nontrivial problems using only a small number of nodes. Functions[edit] In biologically inspired neural networks, the activation function is usually an abstraction representing the rate of action potential firing in the cell.

. , where is the Heaviside step function. A line of positive slope may also be used to reflect the increase in firing rate that occurs as input current increases. Is the slope. All problems mentioned above can be handled by using a normalizable sigmoid activation function. . , where the hyperbolic tangent function can also be any sigmoid. Where. Free variables and bound variables. A bound variable is a variable that was previously free, but has been bound to a specific value or set of values. For example, the variable x becomes a bound variable when we write: 'For all x, (x + 1)2 = x2 + 2x + 1.' or 'There exists x such that x2 = 2.' In either of these propositions, it does not matter logically whether we use x or some other letter. Examples[edit] Before stating a precise definition of free variable and bound variable, the following are some examples that perhaps make these two concepts clearer than the definition would: In the expression n is a free variable and k is a bound variable; consequently the value of this expression depends on the value of n, but there is nothing called k on which it could depend. y is a free variable and x is a bound variable; consequently the value of this expression depends on the value of y, but there is nothing called x on which it could depend.

Variable-binding operators[edit] The following are variable-binding operators. For sums or where. CABot3. Steve Furber. Stephen Byram "Steve" Furber CBE, FRS, FREng (born 1953) is the ICL Professor of Computer Engineering at the School of Computer Science at the University of Manchester[61] and is probably best known for his work at Acorn Computers, where he was one of the designers of the BBC Micro and the ARM 32-bit RISC microprocessor.[3][62][63][57][64][65][66] Education[edit] Furber was educated at Manchester Grammar School and represented the UK in the International Mathematical Olympiad in Hungary in 1970 and won a bronze medal.[67] He went on to study the Cambridge Mathematical Tripos at St John's College, Cambridge, receiving a Bachelor of Arts degree in mathematics in 1974.

In 1978, he was appointed the Rolls-Royce Research Fellow in Aerodynamics at Emmanuel College, Cambridge and was awarded a PhD in 1980 on the fluid dynamics of the Weis-Fogh principle.[68][69] Acorn Computers, BBC Micro and ARM[edit] Research[edit] Awards and honours[edit] Furber's nomination for the Royal Society reads: Spiking neural network. Neural processing for individual categories of objects. Simulation modeling. Loebner Prize. Allen Newell. Carnegie Mellon University Libraries. Digital Collections. Herbert A. Simon.


☢️ Cognitive

Overturning Statements. Jerry Fodor. Expert system. European Laboratory for Learning and Intelligent Systems.