DeepMind co-founder Demis Hassabis on the current state of AI:
The problem is that these challenges are so complex that even the world’s top scientists, clinicians and engineers can struggle to master all the intricacies necessary to make the breakthroughs required. It has been said that Leonardo da Vinci was perhaps the last person to have lived who understood the entire breadth of knowledge of their age. Since then we’ve had to specialise, and today it takes a lifetime to completely master even a single field such as astrophysics or quantum mechanics.
Today, working on AI has become very fashionable. However, the term AI can mean myriad things depending on the context. The approach we take at DeepMind, the company I co-founded, focuses on notions of learning and generality, with the aim of developing the kind of AI we need for science. If we want computers to discover new knowledge, then we must give them the ability to truly learn for themselves.
We believe that in the next few years scientists and researchers using similar approaches will generate insights in a multitude of areas, from superconductor material design to drug discovery. In many ways I see AI as analogous to the Hubble telescope — a scientific tool that allows us to see farther and better understand the universe around us.
It is in this collaboration between people and algorithms that incredible scientific progress lies over the next few decades. I believe that AI will become a kind of meta-solution for scientists to deploy, enhancing our daily lives and allowing us all to work more quickly and effectively. If we can deploy these tools broadly and fairly, fostering an environment in which everyone can participate in and benefit from them, we have the opportunity to enrich and advance humanity as a whole.
And so much more; I would definitely read the entire thing. The high-level notion of AI as a tool for good is obviously controversial – and the subsequent debate is a huge part of the TED conference this year.