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artificial intelligence (AI), the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience. Since their development in the 1940s, digital computers have been programmed to carry out very complex tasks—such as discovering proofs for mathematical theorems or playing chess—with great proficiency. Despite continuing advances in computer processing speed and memory capacity, there are as yet no programs that can match full human flexibility over wider domains or in tasks requiring much everyday knowledge. |
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In other words AI is not intelligent, it mimics intelligence. - - "ChatGPT can make mistakes" - - quote from chatgp.com Often AI will produce wrong and misleading answers, see https://tech.co/news/list-ai-failures-mistakes-errors Abercorn Corporation uses AI to provide efficient designs and outputs that would not normally be produced using standard software, we always check to ensure the results are correct. |
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the use or study of computer systems or machines that have some of the qualities that the human brain has, such as the ability to interpret and produce language in a way that seems human, recognize or create images, solve problems, and learn from data supplied to them https://www.oed.com/dictionary/artificial-intelligence_n?tl=true The capacity of computers or other machines to exhibit or simulate intelligent behaviour; the field of study concerned with this. In later use also: software used to perform tasks or produce output previously thought to require human intelligence, esp. by using machine learning to extrapolate from large collections of data. |
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ChatGPT is trained on large amounts of data that enable it to make predictions about how to string words together. https://www.nasa.gov/what-is-artificial-intelligence/ AI tools used at NASA sometimes use machine learning, which uses data and algorithms to train computers to make classifications, generate predictions, or uncover similarities or trends across large datasets. https://www.iso.org/artificial-intelligence/what-is-ai#toc1 The definition of artificial intelligence goes beyond simple automation – it’s the ability of machines to think, learn and adapt. No longer confined to routine tasks, AI now tackles complex challenges once exclusive to human intelligence. It understands language, detects patterns, makes decisions, and even predicts future outcomes with uncanny accuracy. |
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One of the biggest risks over the use of AI is that it may (will?) give incorrect responses. At the time of writing, Generative AI produces text by calculating the probability that it will be relevant to the prompt a user has submitted. As a result, the responses produced by generative AI tools tend to reflect consensus understandings, including any biases and inaccuracies that inform those positions. Because generative AI predicts words based on probabilities, they mostly produce oversimplified or generic outputs. They are best at responding to prompts to summarise information and solve problems whose answers are already known https://www.bbc.com/news/business-64576225 A Google advert designed to show off its new AI bot, showed it answering a query incorrectly. Shares in parent company Alphabet lost $100bn (£82bn) off the firm's market value. The bot, known as Bard, was asked about what to tell a nine-year-old about discoveries from the James Webb Space Telescope. Its response was that the telescope was the first to take pictures of a planet outside the earth's solar system, when in fact that milestone was claimed by the European Very Large Telescope in 2004 . |
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Artificial intelligence (AI) is technology that enables computers and machines to simulate human learning, comprehension, problem solving, decision making, creativity and autonomy. Generative AI Generative AI, sometimes called "gen AI", refers to deep learning models that can create complex original content such as long-form text, high-quality images, realistic video or audio and more in response to a user’s prompt or request. At a high level, generative models encode a simplified representation of their training data, and then draw from that representation to create new work that’s similar, but not identical, to the original data. Generative models have been used for years in statistics to analyze numerical data. But over the last decade, they evolved to analyze and generate more complex data types. |
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