text: 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 