Up to this point, most of AI's funding had gone to projects that used high-level symbols to represent
mental objects like plans, goals, beliefs, and known facts. In the 1980s, some researchers began to doubt
that this approach would be able to imitate all the processes of human cognition, especially perception,
robotics, learning and pattern recognition,[335] and began to look into "sub-symbolic" approaches.[336]
Rodney Brooks rejected "representation" in general and focussed directly on engineering machines that
move and survive.[x] Judea Pearl, Lofti Zadeh, and others developed methods that handled incomplete
and uncertain information by making reasonable guesses rather than precise logic.[86][341] But the most
important development was the revival of "connectionism", including neural network research, by
Geoffrey Hinton and others.[342] In 1990, Yann LeCun successfully showed that convolutional neural
networks can recognize handwritten digits, the first of many successful applications of neural
networks.[343]
AI gradually restored its reputation in the late 1990s and early 21st century by exploiting formal
mathematical methods and by finding specific solutions to specific problems. This "narrow" and "formal"
focus allowed researchers to produce verifiable results and collaborate with other fields (such as statistics,
economics and mathematics).[344] By 2000, solutions developed by AI researchers were being widely
used, although in the 1990s they were rarely described as "artificial intelligence" (a tendency known as
the AI effect).[345] However, several academic researchers became concerned that AI was no longer
pursuing its original goal of creating versatile, fully intelligent machines. Beginning around 2002, they
founded the subfield of artificial general intelligence (or "AGI"), which had several well-funded
institutions by the 2010s.[4]
Deep learning began to dominate industry benchmarks in 2012 and was adopted throughout the field.[11]
For many specific tasks, other methods were abandoned.[y] Deep learning's success was based on both
hardware improvements (faster computers,[347] graphics processing units, cloud computing[348]) and
access to large amounts of data[349] (including curated datasets,[348] such as ImageNet). Deep learning's
success led to an enormous increase in interest and funding in AI.[z] The amount of machine learning
research (measured by total publications) increased by 50% in the years 2015–2019.[306]
In 2016, issues of fairness and the misuse of technology were catapulted into center stage at machine
learning conferences, publications vastly increased, funding became available, and many researchers re-
focussed their careers on these issues. The alignment problem became a serious field of academic
study.[283]
In the late teens and early 2020s, AGI companies began to deliver programs that created enormous
interest. In 2015, AlphaGo, developed by DeepMind, beat the world champion Go player. The program
taught only the game's rules and developed a strategy by itself. GPT-3 is a large language model that was
released in 2020 by OpenAI and is capable of generating high-quality human-like text.[350] ChatGPT,
launched on November 30, 2022, became the fastest-growing consumer software application in history,
gaining over 100 million users in two months.[351] It marked what is widely regarded as AI's breakout
year, bringing it into the public consciousness.[352] These programs, and others, inspired an aggressive AI
boom, where large companies began investing billions of dollars in AI research. According to AI Impacts,
about $50 billion annually was invested in "AI" around 2022 in the U.S. alone and about 20% of the new