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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. On the other hand, some programs have attained the performance levels of human experts and professionals in executing certain specific tasks, so that artificial intelligence in this limited sense is found in applications as diverse as medical diagnosis, computer search engines, voice or handwriting recognition, and chatbots.
In the early 21st century faster processing power and larger datasets (“big data”) brought artificial intelligence out of computer science departments and into the wider world. Moore’s law, the observation that computing power doubled roughly every 18 months, continued to hold true. The stock responses of the early chatbot Eliza fit comfortably within 50 kilobytes; the language model at the heart of ChatGPT was trained on 45 terabytes of text.
The achievement of Deep Blue in beating world chess champion Garry Kasparov was surpassed by DeepMind’s AlphaGo, which mastered go, a much more complicated game than chess.
Machine learning has found applications in many fields beyond gaming and image classification. The pharmaceutical company Pfizer used the technique to quickly search millions of possible compounds in developing the COVID-19 treatment Paxlovid. Google uses machine learning to filter out spam from the inbox of Gmail users. Banks and credit card companies use historical data to train models to detect fraudulent transactions.
Machine learning and AI are foundational elements of autonomous vehicle systems. Vehicles are trained on complex data (e.g., the movement of other vehicles, road signs) with machine learning, which helps to improve the algorithms they operate under. AI enables vehicles’ systems to make decisions without needing specific instructions for each potential situation.
AI poses certain risks in terms of ethical and socioeconomic consequences. As more tasks become automated, especially in such industries as marketing and health care, many workers are poised to lose their jobs. Although AI may create some new jobs, these may require more technical skills than the jobs AI has replaced. Privacy is another aspect of AI that concerns experts. As AI often involves collecting and processing large amounts of data, there is the risk that this data will be accessed by the wrong people or organizations.
With generative AI, it is even possible to manipulate images and create fake profiles. AI can also be used to survey populations and track individuals in public spaces. Deepfakes are AI-generated media produced using two different deep-learning algorithms: one that creates the best possible replica of a real image or video and another that detects whether the replica is fake and, if it is, reports on the differences between it and the original.
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