Important Terms and Ideas for Describing Artificial Intelligence
2025-02-13
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1There are several terms experts use to describe computer systems in the field of artificial intelligence.
2Recently, the French News Agency (AFP) defined some of the common terms and ideas used in that field.
3Here is a version for English learners:
4The first term is "artificial intelligence."
5When asked what artificial intelligence is, the AI-powered ChatGPT system says that the term means "the simulation of human intelligence in machines that are programmed to think, learn and make decisions".
6AI's main quality or characteristic is taking in large amounts of data and then processing it using methods from statistics.
7AI involves using ideas from many fields including computing, mathematics, languages, psychology, and others.
8Currently, the technology is being used heavily for investigating health issues, translating human languages, and predicting problems in machine tools and self-driving cars.
9But AI is affecting many fields of business and industry.
10A second important term is "algorithm."
11An algorithm is important to all computer operations. It is a series of steps or instructions followed by a computer program to get a result.
12Algorithms can give rules for an AI's behavior, helping it to realize the objectives of computer program developers.
13Unlike a simple computer program, AI algorithms permit a computer system to "learn" for itself.
14A third important term is "machine learning."
15Machine learning is one method that researchers have used in their efforts to produce artificial intelligence.
16Machine learning lets computers learn from data without being directly programmed on what results to produce.
17In recent years, the field of neural networks has given important results.
18In a neural network, connections between some nodes are strengthened and others weakened as the system learns and makes changes.
19Learning can be "supervised."
20This means the system learns to put new data into specific groups based on a model.
21For example, the system could learn to identify spam in an email or other messaging programs.
22"Unsupervised" learning permits the system to independently discover new areas or ways of doing things.
23These discoveries in the available data might not have been immediately clear.
24An example would be letting an online store identify buying trends in sales data.
25"Reinforcement" learning adds a process of repeated trial-and-error.
26In this process, the system is rewarded based on its outcomes, causing it to learn and improve.
27One example might be a self-driving vehicle whose objective is to reach its destination as quickly as possible but also safely.
28That requirement would lead it to learn to stop at red lights although it requires additional time.
29Deep learning owes its name to its use of many layers of neural networks.
30Raw data is examined by each layer in turn at growing levels of abstraction.
31Geoffrey Hinton received the 2024 Nobel Prize in Physics.
32Hinton is credited with developing deep learning.
33Hinton received the prize along with 1980s neural-network developer John Hopfield.
34Francis Bach, head of France's SIERRA statistical learning laboratory, said this about deep learning: "The more layers you have, the more complex behavior can become, and the more complex the behavior can be, the easier it is to learn a desired behavior efficiently."
35The method might help lead to scientific discoveries.
36We now turn to large language models (LLMs).
37These might be the most popular example of generative AI. Large language models power tools like OpenAI's ChatGPT or Google's Gemini.
38Such systems are able to write long papers, answer legal questions or even produce a cake recipe based on their statistical models.
39But the technology is still new. LLM's can suffer from "hallucinations"- the creation of content that is false or incorrect.
40A final important term is artificial general intelligence (AGI) - one the big goals of the whole AI field.
41AGI suggests the unrealized dream of a machine able to reproduce all human processes of human thinking.
42People who push the idea include OpenAI chief Sam Altman and his competitors at Anthropic.
43They consider such a system to be within reach.
44The goal is to use large amounts of data and processing power to train LLMs that are increasingly powerful.
45But critics say that LLM technology has important limits, including its ability to reason.
46Maxime Amblard, computing professor at France's University of Lorraine, told AFP last year, "LLMs do not work like human beings."
47Amblard added that humans, as flesh-and-blood -intelligent beings, are "sense-making machines" with different abilities from today's computer systems.
48I'm Anna Matteo. And I'm John Russell.
1There are several terms experts use to describe computer systems in the field of artificial intelligence. 2Recently, the French News Agency (AFP) defined some of the common terms and ideas used in that field. 3Here is a version for English learners: 4Artificial intelligence 5The first term is "artificial intelligence." 6When asked what artificial intelligence is, the AI-powered ChatGPT system says that the term means "the simulation of human intelligence in machines that are programmed to think, learn and make decisions". 7AI's main quality or characteristic is taking in large amounts of data and then processing it using methods from statistics. 8AI involves using ideas from many fields including computing, mathematics, languages, psychology, and others. 9Currently, the technology is being used heavily for investigating health issues, translating human languages, and predicting problems in machine tools and self-driving cars. But AI is affecting many fields of business and industry. 10Algorithm 11A second important term is "algorithm." 12An algorithm is important to all computer operations. It is a series of steps or instructions followed by a computer program to get a result. 13Algorithms can give rules for an AI's behavior, helping it to realize the objectives of computer program developers. 14Unlike a simple computer program, AI algorithms permit a computer system to "learn" for itself. 15Machine learning 16A third important term is "machine learning." 17Machine learning is one method that researchers have used in their efforts to produce artificial intelligence. 18Machine learning lets computers learn from data without being directly programmed on what results to produce. 19In recent years, the field of neural networks has given important results. 20In a neural network, connections between some nodes are strengthened and others weakened as the system learns and makes changes. 21Learning can be "supervised." This means the system learns to put new data into specific groups based on a model. For example, the system could learn to identify spam in an email or other messaging programs. 22"Unsupervised" learning permits the system to independently discover new areas or ways of doing things. These discoveries in the available data might not have been immediately clear. 23An example would be letting an online store identify buying trends in sales data. 24"Reinforcement" learning adds a process of repeated trial-and-error. In this process, the system is rewarded based on its outcomes, causing it to learn and improve. 25One example might be a self-driving vehicle whose objective is to reach its destination as quickly as possible but also safely. That requirement would lead it to learn to stop at red lights although it requires additional time. 26Deep learning 27Deep learning owes its name to its use of many layers of neural networks. 28Raw data is examined by each layer in turn at growing levels of abstraction. 29Geoffrey Hinton received the 2024 Nobel Prize in Physics. Hinton is credited with developing deep learning. Hinton received the prize along with 1980s neural-network developer John Hopfield. 30Francis Bach, head of France's SIERRA statistical learning laboratory, said this about deep learning: "The more layers you have, the more complex behavior can become, and the more complex the behavior can be, the easier it is to learn a desired behavior efficiently." 31The method might help lead to scientific discoveries. 32Language models 33We now turn to large language models (LLMs). 34These might be the most popular example of generative AI. Large language models power tools like OpenAI's ChatGPT or Google's Gemini. 35Such systems are able to write long papers, answer legal questions or even produce a cake recipe based on their statistical models. 36But the technology is still new. LLM's can suffer from "hallucinations"- the creation of content that is false or incorrect. 37Artificial general intelligence 38A final important term is artificial general intelligence (AGI) - one the big goals of the whole AI field. 39AGI suggests the unrealized dream of a machine able to reproduce all human processes of human thinking. 40People who push the idea include OpenAI chief Sam Altman and his competitors at Anthropic. They consider such a system to be within reach. 41The goal is to use large amounts of data and processing power to train LLMs that are increasingly powerful. 42But critics say that LLM technology has important limits, including its ability to reason. 43Maxime Amblard, computing professor at France's University of Lorraine, told AFP last year, "LLMs do not work like human beings." 44Amblard added that humans, as flesh-and-blood -intelligent beings, are "sense-making machines" with different abilities from today's computer systems. 45I'm Anna Matteo. And I'm John Russell. 46Pierre Celerier reported on this story for Agence France-Presse. John Russell adapted it for VOA Learning 47_____________________________________________________ 48Words in This Story 49simulation - n. the representation of the functioning of one system or process by means of the functioning of another system 50statistics -n. pl. (science) the field of processing numerical information to describe processes and things 51neural -adj. related to the brain or nerves 52node - n. a point at which smaller parts begin or center 53spam - n. unsolicited messages (such as an email) that often have a commercial purpose 54trend - n. a line or direction of movement or change 55abstraction -n. the formation of ideas 56efficiently - adv. with success, competence, or a suitable effect