For years we've been making the case that artificial intelligence, and in particular the field of machine learning, is making rapid progress and is set to make a whole lot more progress. Along with this we have been standing up for the idea that the safety and ethics of artificial intelligence is an important topic we should all be thinking about very seriously. The potential benefits of artificial intelligence will be vast, but like any powerful technology these benefits will depend on this technology being applied with care.
While some researchers have been cheering us on since the start of DeepMind, others have been very skeptical. However, in recent years the climate for ambitious artificial intelligence research has much improved, no doubt due to a string of stunning successes in the field: not only have a number of long standing challenges finally fallen, but there is a growing sense among the community that the best is yet to come. We see this in our interactions with a wide range of researchers, and it can also be seen from the way in which media articles about artificial intelligence have changed in tone. If you hadn't already noticed, the "AI Winter" is over and the spring has begun.
As with many trends, some people have started to become a little bit too optimistic about the rate of progress, going as far as predicting that a solution to human level artificial intelligence might be just around the corner. It's not. Furthermore, with the very negative portrayals of futuristic artificial intelligence in Hollywood, it is perhaps not surprising that doomsday images are appearing with some frequency in the media. As Peter Norvig aptly put it, "The narrative has changed. It has switched from, 'Isn't it terrible that AI is a failure?' to 'Isn't it terrible that AI is a success?' "
As is usually the case, the reality is not so extreme. Yes, this is a wonderful time to be working in artificial intelligence, and like many people we think that this will continue for years to come. The world faces a set of increasingly complex, interdependent and urgent challenges that require ever more sophisticated responses. We'd like to think that successful work in artificial intelligence can contribute by augmenting our collective capacity to extract meaningful insight from data and by helping us to innovate new technologies and processes to address some of our toughest global challenges.
However, in order to realise this vision many difficult technical issues remain to be solved, some of which are long standing challenges that are well known in the field. While difficult, we think these problems can be overcome, but that it will take a generation of talented researchers equipped with plentiful computational resources and inspired by insights from machine learning and systems neuroscience. While this is likely to disappoint the most optimistic observers, it will give this community some time to come to grips with the many subtle safety and ethical questions that will arise. So let's enjoy this new sense of optimism, but let's not lose sight of how much hard work is left to do. As Turing once said: "We can only see a short distance ahead, but we can see plenty there that needs to be done."