Re: Artificial Intelligence Technology
Kenneth Calimlim
Artificial intelligence (AI), in its broadest sense, is intelligence exhibited by machines, particularly computer systems. It is a field of research in computer science that develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals.[1] Such machines may be called AIs.
Some high-profile applications of AI include advanced web search engines (e.g., Google Search); recommendation systems (used by YouTube, Amazon, and Netflix); interacting via human speech (e.g., Google Assistant, Siri, and Alexa); autonomous vehicles (e.g., Waymo); generative and creative tools (e.g., ChatGPT, Apple Intelligence, and AI art); and superhuman play and analysis in strategy games (e.g., chess and Go).[2] However, many AI applications are not perceived as AI: "A lot of cutting edge AI has filtered into general applications, often without being called AI because once something becomes useful enough and common enough it's not labeled AI anymore."[3][4]
Alan Turing was the first person to conduct substantial research in the field that he called machine intelligence.[5] Artificial intelligence was founded as an academic discipline in 1956,[6] by those now considered the founding fathers of AI, John McCarthy, Marvin Minksy, Nathaniel Rochester, and Claude Shannon.[7][8] The field went through multiple cycles of optimism,[9][10] followed by periods of disappointment and loss of funding, known as AI winter.[11][12] Funding and interest vastly increased after 2012 when deep learning surpassed all previous AI techniques,[13] and after 2017 with the transformer architecture.[14] This led to the AI boom of the early 2020s, with companies, universities, and laboratories overwhelmingly based in the United States pioneering significant advances in artificial intelligence.[15]
The growing use of artificial intelligence in the 21st century is influencing a societal and economic shift towards increased automation, data-driven decision-making, and the integration of AI systems into various economic sectors and areas of life, impacting job markets, healthcare, government, industry, education, propaganda, and disinformation. This raises questions about the long-term effects, ethical implications, and risks of AI, prompting discussions about regulatory policies to ensure the safety and benefits of the technology.
To reach these goals, AI researchers have adapted and integrated a wide range of techniques, including search and mathematical optimization, formal logic, artificial neural networks, and methods based on statistics, operations research, and economics.[b] AI also draws upon psychology, linguistics, philosophy, neuroscience, and other fields.[17]
The general problem of simulating (or creating) intelligence has been broken into subproblems. These consist of particular traits or capabilities that researchers expect an intelligent system to display. The traits described below have received the most attention and cover the scope of AI research.[a]
Early researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical deductions.[18] By the late 1980s and 1990s, methods were developed for dealing with uncertain or incomplete information, employing concepts from probability and economics.[19]
Many of these algorithms are insufficient for solving large reasoning problems because they experience a "combinatorial explosion": They become exponentially slower as the problems grow.[20] Even humans rarely use the step-by-step deduction that early AI research could model. They solve most of their problems using fast, intuitive judgments.[21] Accurate and efficient reasoning is an unsolved problem.
Knowledge representation and knowledge engineering[22] allow AI programs to answer questions intelligently and make deductions about real-world facts. Formal knowledge representations are used in content-based indexing and retrieval,[23] scene interpretation,[24] clinical decision support,[25] knowledge discovery (mining "interesting" and actionable inferences from large databases),[26] and other areas.[27]
A knowledge base is a body of knowledge represented in a form that can be used by a program. An ontology is the set of objects, relations, concepts, and properties used by a particular domain of knowledge.[28] Knowledge bases need to represent things such as objects, properties, categories, and relations between objects;[29] situations, events, states, and time;[30] causes and effects;[31] knowledge about knowledge (what we know about what other people know);[32] default reasoning (things that humans assume are true until they are told differently and will remain true even when other facts are changing);[33] and many other aspects and domains of knowledge.
Among the most difficult problems in knowledge representation are the breadth of commonsense knowledge (the set of atomic facts that the average person knows is enormous);[34] and the sub-symbolic form of most commonsense knowledge (much of what people know is not represented as "facts" or "statements" that they could express verbally).[21] There is also the difficulty of knowledge acquisition, the problem of obtaining knowledge for AI applications.[c]
In classical planning, the agent knows exactly what the effect of any action will be.[40] In most real-world problems, however, the agent may not be certain about the situation they are in (it is "unknown" or "unobservable") and it may not know for certain what will happen after each possible action (it is not "deterministic"). It must choose an action by making a probabilistic guess and then reassess the situation to see if the action worked.[41]
In some problems, the agent's preferences may be uncertain, especially if there are other agents or humans involved. These can be learned (e.g., with inverse reinforcement learning), or the agent can seek information to improve its preferences.[42] Information value theory can be used to weigh the value of exploratory or experimental actions.[43] The space of possible future actions and situations is typically intractably large, so the agents must take actions and evaluate situations while being uncertain of what the outcome will be.
A Markov decision process has a transition model that describes the probability that a particular action will change the state in a particular way and a reward function that supplies the utility of each state and the cost of each action. A policy associates a decision with each possible state. The policy could be calculated (e.g., by iteration), be heuristic, or it can be learned.[44]
There are several kinds of machine learning. Unsupervised learning analyzes a stream of data and finds patterns and makes predictions without any other guidance.[49] Supervised learning requires a human to label the input data first, and comes in two main varieties: classification (where the program must learn to predict what category the input belongs in) and regression (where the program must deduce a numeric function based on numeric input).[50]
In reinforcement learning, the agent is rewarded for good responses and punished for bad ones. The agent learns to choose responses that are classified as "good".[51] Transfer learning is when the knowledge gained from one problem is applied to a new problem.[52] Deep learning is a type of machine learning that runs inputs through biologically inspired artificial neural networks for all of these types of learning.[53]
Natural language processing (NLP)[55] allows programs to read, write and communicate in human languages such as English. Specific problems include speech recognition, speech synthesis, machine translation, information extraction, information retrieval and question answering.[56]
Early work, based on Noam Chomsky's generative grammar and semantic networks, had difficulty with word-sense disambiguation[f] unless restricted to small domains called "micro-worlds" (due to the common sense knowledge problem[34]). Margaret Masterman believed that it was meaning and not grammar that was the key to understanding languages, and that thesauri and not dictionaries should be the basis of computational language structure.
Modern deep learning techniques for NLP include word embedding (representing words, typically as vectors encoding their meaning),[57] transformers (a deep learning architecture using an attention mechanism),[58] and others.[59] In 2019, generative pre-trained transformer (or "GPT") language models began to generate coherent text,[60][61] and by 2023 these models were able to get human-level scores on the bar exam, SAT test, GRE test, and many other real-world applications.[62]
Machine perception is the ability to use input from sensors (such as cameras, microphones, wireless signals, active lidar, sonar, radar, and tactile sensors) to deduce aspects of the world. Computer vision is the ability to analyze visual input.[63]
However, this tends to give nave users an unrealistic conception of the intelligence of existing computer agents.[71] Moderate successes related to affective computing include textual sentiment analysis and, more recently, multimodal sentiment analysis, wherein AI classifies the affects displayed by a videotaped subject.[72]
State space search searches through a tree of possible states to try to find a goal state.[74] For example, planning algorithms search through trees of goals and subgoals, attempting to find a path to a target goal, a process called means-ends analysis.[75]
Simple exhaustive searches[76] are rarely sufficient for most real-world problems: the search space (the number of places to search) quickly grows to astronomical numbers. The result is a search that is too slow or never completes.[20] "Heuristics" or "rules of thumb" can help prioritize choices that are more likely to reach a goal.[77]
Gradient descent is a type of local search that optimizes a set of numerical parameters by incrementally adjusting them to minimize a loss function. Variants of gradient descent are commonly used to train neural networks.[80]
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