From Toward Data Science.com (Sept. 11, 2021):
OpenAI was born to tackle the challenge of achieving artificial general intelligence (AGI) — an AI capable of doing anything a human can do.
Such a technology would change the world as we know it. It could benefit us all if used adequately but could become the most devastating weapon in the wrong hands. That’s why OpenAI took over this quest. To ensure it’d benefit everyone evenly: “Our goal is to advance digital intelligence in the way that is most likely to benefit humanity as a whole.”
However, the magnitude of this problem makes it arguably the single biggest scientific enterprise humanity has put its hands upon. Despite all the advances in computer science and artificial intelligence, no one knows how to solve it or when it’ll happen.
Some argue deep learning isn’t enough to achieve AGI. Stuart Russell, a computer science professor at Berkeley and AI pioneer, argues that “focusing on raw computing power misses the point entirely […] We don’t know how to make a machine really intelligent — even if it were the size of the universe.”
OpenAI, in contrast, is confident that large neural networks fed on large datasets and trained on huge computers are the best way towards AGI. Greg Brockman, OpenAI’s CTO, said in an interview for the Financial Times: “We think the most benefits will go to whoever has the biggest computer.”
And that’s what they did. They started training larger and larger models to awaken the hidden power within deep learning. The first non-subtle steps in this direction were the release of GPT and GPT-2. These large language models would set the groundwork for the star of the show: GPT-3. A language model 100 times larger than GPT-2, at 175 billion parameters.
GPT-3 was the largest neural network ever created at the time — and remains the largest dense neural net. Its language expertise and its innumerable capabilities were a surprise for most. And although some experts remained skeptical, large language models already felt strangely human. It was a huge leap forward for OpenAI researchers to reinforce their beliefs and convince us that AGI is a problem for deep learning. [read more]
Other related articles:
Posts
No comments:
Post a Comment