Current deep learning algorithms and methods are nowhere near the holy grail of “Artificial General Intelligence (AGI).”
Current algorithms lean more towards narrow learning, meaning they are good at learning and solving specific types of problems under specific conditions. These algorithms take a humongous amount of data as compared to humans who can learn from relatively few learning encounters. The transfer process of these learnings from one problem domain to another domain is somewhat limited as well.
Recently, reinforcement learning (RL) has been gaining popularity compared to other deep learning techniques. The buzz around reinforcement learning started with the advent of AlphaGo by DeepMind. AlphaGo was built to play the very complex game of Go. The essence of RL is that it can train models through the interaction with the environment and learn and calibrate from their mistakes. Learning happens through a delayed and cumulative reward system where an agent deduces an action, which then acts on the environment to make a state change. The agent takes the next best action based on the optimized delayed reward. The system retains the learning and recalls the best action when a similar circumstance arises.
Read the full article by Visteon’s CIO, Raman Mehta, on DZone.