Claims

Deep Mind was founded in 2010 with the goal of solving the 'problem of intelligence' with the intention of using that solution to 'solve everything else'—a maximally ambitious research program grounded in a specific theory of intelligence.

The next major advance in AI came from a shift from training networks to recognize patterns to training networks to predict patterns, beginning with Gerald Tesauro's network in 1992 that learned to play Backgammon by predicting probability of winning from board positions rather than using human-designed rules.

In 2016, unsupervised learning was an unsolved problem in machine learning with no clear insight into how to achieve it, and the breakthrough came when AI achieved nature's third layer of learning: language.

The discovery of sentiment neurons led to the GPT series, which learned to understand language itself and discovered it all on its own; OpenAI saw the implications and wondered what would happen with much larger models, leading them to bet everything on this approach.

Joshua Bengio took Hinton's basic network architecture from the 1980s and successfully applied it to natural language about 10 years later, showing that the same approach could scale to real natural language when made much larger

Hinton developed backpropagation with colleagues in the 1980s, and it is fundamental to all modern deep learning