For some time now, Google’s uber-secretive and appropriately titled “X Laboratory” research center (responsible for such cutting-edge innovations as Google Glasses) has been quietly creating one of the largest artificial intelligences in existence. After linking together over 16,000 computer processors to create a neural network with over 1 billion connections, researchers at the X Lab exposed their new creation to 10 Million digital images found in YouTube videos (sounds like my kids).
How did Google’s artificial brain do? It performed far better than any previous effort, roughly doubling its accuracy in recognizing objects in a list of 20,000 items. Perhaps most significantly, Google’s AI brain taught itself to recognize a cat without any guidance from the research team.
While this may seem like a silly exercise, it actually represents a profound application of “big data” that may have lasting and unforeseen repercussions.
The experiment is but one example of the many possibilities unleashed by such large scale software simulations, known as “deep learning models.” These simulations tap into the power of massive computing data centers to build programs that can mimic higher-level brain functions such as vision and perception, speech recognition, and language translation. In fact, just last year Microsoft scientists presented research showing how such systems could be utilized to understand human speech.¹
The key point is that Google’s researchers didn’t give their machine any help finding the features of a cat. “We never told it during the training, “This is a cat,”” noted Dr. Dean, one of the project researchers, “it basically invented the concept of a cat.”¹
To do this, the scientists were able to mimic what naturally takes place in the brain’s visual cortex. “A loose and frankly awful analogy is that our numerical parameters correspond to synapses,” said Dr. Ng, another member of the Google team. “It is worth noting that our network is still tiny compared to the human visual cortex, which is a million times larger in terms of the number of neurons and synapses,” the researchers wrote.¹
The experiment reflected an additional profound conclusion: even though (at present) our biological brains may be more advanced, Google’s research shows that existing machine learning algorithms vastly improve as they are given access to large pools of data.¹…