We return to the question of terminology that we started this post with. Our feeling is that the term "artificial intelligence" has been used in so many ways that it is now confusing. People use AI to refer to all three approaches described above, plus others, and therefore has become almost meaningless. The term "machine learning" is a more narrowly defined term for machines that learn from data, including simple neural models such as ANNs and Deep Learning. We use the term "machine intelligence" to refer to machines that learn but are aligned with the Biological Neural Network approach. Although there still is much work ahead of us, we believe the Biological Neural Network approach is the fastest and most direct path to truly intelligent machines. This blog entry was modified on Thu Mar 24 2016 to clarify the timing of neural network research.
One of the difficulties when it comes to creating visual recognition systems for an AI is to program what the human brain does effortlessly. Specifically, when a person enters an unfamiliar area, it's easy to recognize and categorize what's there. Our brains are designed to automatically take it in at a glance, make inferences based on prior knowledge and see it from a different angle or recreate it in our heads. The team at Google's DeepMind are working on a neural network that can do similar things. The problem is that to be able to train an AI to make these sort of inferences, researchers have to input tremendous amounts of carefully labeled data.
At its first AI Developer Conference, Intel announced the Nervana NNP-L1000, which is the first neural network processor (NNP) to come out of the Nervana acquisition. The chip will prioritize memory bandwidth and compute utilization over theoretical peak performance. Initially, Intel started competing with Nvidia in the machine learning (ML) chip market with its Xeon Phi architecture, which used tens of Atom cores to "accelerate" ML tasks. However, Intel must have realized that Phi alone wasn't going to allow it to catch up to Nvidia, which seems to make significant leaps in performance every year. As such, the company began looking for other options, which led it to buy Altera for its field programmable gate arrays (FPGAs), Movidius for its embedded vision processor, MobilEye for its self-driving chip, and Nervana for its specialized neural network processor.