By Matthew Griffin, Futurist
One of the most significant Artificial Intelligence (AI) milestones in history was quietly ushered into being this summer. I am, of course, am speaking about the quest for Artificial General Intelligence (AGI), probably the most sought after goal in the entire field of computer science. With the introduction of the Impala architecture, DeepMind, the company behind AlphaGo and the self-learning AlphaZero, now has AGI firmly in its sights, and while many people predicted the first AGI’s would emerge in or around 2035 we know know that date should be 2018. A staggering 18 years early – even if Impala is, by all interpretations, a basic first generation AGI.
Firstly let me define AGI, since it’s been used by different people to mean lots of different things, including the latest, and also revolutionary breakthrough for “General AI” which was realised earlier this year. Unlike today’s so called narrow AI’s that can only learn one thing very well AGI is a single intelligence, or algorithm, that can learn multiple tasks and exhibits “positive memory transfer” when doing so, sometimes called meta-learning. During meta-learning, the acquisition of one skill helps the learner to pick up another new skill faster, just as we ourselves do when we’re learning, because it applies some of its previous “know-how” to the new task. In other words, one learns how to learn — and can generalise that to acquiring new skills, the way humans do. This has been the holy grail of AI for a long time.
As it currently exists, AI shows little ability to transfer learning towards new tasks. Typically, it must be trained anew every time from scratch, although even the way AI’s learn is changing as new more powerful AI’s being to figure out how to evolve and self-learn, like the ones from OpenAI and Baidu, which achieved the “Zero shot learning” goal, which both hit those milestones last year. For instance, the same neural network that makes recommendations to you for a Netflix show cannot use that learning to suddenly start making meaningful grocery recommendations. Even these single-instance “narrow” AIs can be impressive though, such as IBM Watson or Google’s self-driving car tech. However, these aren’t nearly so much so an artificial general intelligence, which could conceivably unlock the kind of recursive self-improvement variously referred to as the “intelligence explosion” or “Singularity” which many estimate will happen in the mid 2040’s.
Those who thought that the development of the first AGI’s would be sometime in the far and distant future would now be wise to think again. To be sure, DeepMind has made inroads into AGI before when they released the world’s first breakthrough blueprint for an AGI architecture in March last year, as well as their work on Psychlab and Differentiable Neural Computers. However, Impala is their largest and most successful effort to date, showcasing a single algorithm that can learn 30 different challenging tasks requiring various aspects of learning, memory, and navigation.
But enough preamble, let’s look under the hood and see what makes Impala tick. First, Impala’s based on reinforcement learning, an AI technique that has its origins in behaviorism. It parallels the way humans build up an intuition-based skill, such as learning to walk or riding a bicycle. Reinforcement learning has already been used for some amazing achievements, such as endowing an AI with emotions, see the video below, and learning complex games like Go and Poker, like the Liberatus AI did recently when it whipped the world’s top poker players.
Read the source article in Fanatical Futurist.