Important Terms and Ideas for Describing Artificial Intelligence

01There are several terms experts use to describe computer systems in the field of artificial intelligence.

02Recently, the French news agency, AFP, defined some of the common terms and ideas used in that field.

03Here is a version for English learners.

04The first term is artificial intelligence.

05When 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.

06AI's main quality or characteristic is taking in large amounts of data and then processing it using methods from statistics.

07AI involves using ideas from many fields, including computing, mathematics, languages, psychology, and others.

08Currently, the technology is being used heavily for investigating health issues, translating human languages, and predicting problems in machine tools and self-driving cars.

09But AI is affecting many fields of business and industry.

10AI is a second important term.

11A second important term is algorithm.

12An algorithm is important to all computer operations.

13It is a series of steps or instructions followed by a computer program to get a result.

14Algorithms can give rules for an AI's behavior, helping it to realize the objectives of computer program developers.

15Unlike a simple computer program, AI algorithms permit a computer system to learn for itself.

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.

22This means the system learns to put new data into specific groups based on a model.

23For example, the system could learn to identify spam in an email or other messaging programs.

24Unsupervised learning permits the system to independently discover new areas or ways of doing things.

25These discoveries in the available data might not have been immediately clear.

26An example would be letting an online store identify buying trends in sales data.

27Reinforcement learning adds a process of repeated trial and error.

28In this process, the system is rewarded based on its outcomes, causing it to learn and improve.

29One example might be a self-driving vehicle whose objective is to reach its destination as quickly as possible, but also safely.

30That requirement would lead it to learn to stop at red lights, although it requires additional time.

31Deep learning owes its name to its use of many layers of neural networks.

32Raw data is examined by each layer in turn at growing levels of abstraction.

33Jeffrey Hinton received the 2024 Nobel Peace Prize in Physics.

34Hinton is credited with developing deep learning.

35Hinton received the prize along with 1980s neural network developer John Hopfield.

36Francis Bach, head of France's Sierra Statistical Learning Laboratory, said this about deep learning.

37The more layers you have, the more complex behavior can become.

38And the more complex the behavior can be, the easier it is to learn a desired behavior efficiently.

39The method might help lead to scientific discoveries.

40We now turn to large language models, LLMs.

41These might be the most popular example of generative AI.

42Large language models power tools like OpenAI's ChatGPT or Google's Gemini.

43Such systems are able to write long papers, answer legal questions, or even produce a cake recipe based on their statistical models.

44But the technology is still new.

45LLMs can suffer from hallucinations, the creation of content that is false or incorrect.

46A final important term is Artificial General Intelligence, AGI.

47One of the big goals of the whole AI field.

48AGI suggests the unrealized dream of a machine able to reproduce all human processes of human thinking.

49People who push the idea include OpenAI chief Sam Altman and his competitors at Anthropic.

50They consider such a system to be within reach.

51The goal is to use large amounts of data and processing power to train LLMs that are increasingly powerful.

52But critics say that LLM technology has important limits, including its ability to reason.

53Maxime Amblar, computing professor at France's University of Lorraine, told AFP last year, LLMs do not work like human beings.

54Amblar added that humans, as flesh and blood intelligent beings, are sense-making machines, with different abilities from today's computer systems.

55I'm Ana Mateo.

56And I'm John Russell.