Speaking at the Open Data Science Conference (ODSC) last week, I discussed where artificial intelligence is going, what it will automate, and what its impact will be on science, business, and jobs. While the impact from Eureqa has been overwhelmingly positive, many are warning about a darker future:
“With artificial intelligence we are summoning the demon. In all those stories where there’s the guy with the pentagram and the holy water, he’s sure he can control the demon [but it] doesn’t work out.” –Elon Musk
Elon Musk, in the above quote, is worried about a very specific area of AI research – the sentient autonomous AI and robotics research as popularized in movies.
In fact, the press has characterized Eureqa as a “Robot Scientist” as well, speculating advanced tasks like scientific inquiry may become automated by machines one day. However, Eureqa was born out of the challenge to accelerate and scale the complexity of problems that we can tackle and solve – not simply mimic human behavior.
The areas of AI focused on simply learning tasks and replicating human behavior (e.g. IBM Watson or Google AlphaGo) are much hazier. It’s not clear what type of impact this trajectory will have.
The research group OpenAI, founded to support “beneficial” AI research, signaled they are focused entirely on this type of AI last month. Their platform OpenAI Gym enables researchers to develop reinforcement learning algorithms. Reinforcement learning is a class of machine learning algorithms used for tasks like chat bots, video games, and robots. Interestingly, it doesn’t typically start with data or try to learn from an existing data set; it attempts to learn to control an agent (like a robot) based purely on a set of actions it can take and its current state.
The downside of reinforcement learning is that it is not immediately applicable or natural for most business problems that I observe today. That is, businesses are not clamouring for chat bots or interactive agents; they tend to have more data than they can analyze and are invested in putting it to work instead.
Of all areas of machine learning and AI, reinforcement learning may be the furthest out. But early research is producing some exciting results, for example learning to play videogames like Mario from trial and error.
It’s important to keep in mind, however, how far there is to go before sentient AI systems Musk and OpenAI are alluding to may arise. Last week the White House Science and Technology Office concluded that despite improvement in areas like machine vision and speech understanding, AI research is still far from matching the flexibility and learning capability of the human mind. That said, I’ll be rooting for OpenAI to keep this area of AI beneficial for us as it matures.