Kris T. Huang, MD, PhD, CTO

Based on the notion of biological neurons, deep neural networks (DNNs) loosely mimic the networked structure of a (very) simplified brain of sorts. DNNs have revolutionized and automated a number of tasks that were once considered next to intractable, yet we appear to be reaching a plateau as we bump up against the limitations of DNNs. From the opaque nature of neural network models, susceptibility to adversarial attacks, to large data requirements, there are a number of weaknesses uncovered by research that have been quietly reminding us that although pure connectionist models like DNNs mimic biological systems, they remain for the time being rough approximations.

Continue reading “Can An Old Neural Net Learn New Tricks?”

Kris T. Huang, MD, PhD, CTO

Deep learning is a tool. Machine perception is a potential resultant ability, the ability of a machine to interpret data in a manner similar to humans. Being (very) loosely patterned after biological systems, deep neural networks (DNNs) are able to accomplish certain tasks, like image classification or playing Go, with apparent human-like and at times even super-human skill. With performance like that, it is easy to believe (i.e., extrapolate), that its behavior is human-like, or perhaps in some way better.

Continue reading “If Deep Learning Were Human…”

Kris T. Huang, MD, PhD, CTO

Make no mistake, neural networks are powerful tools. This class of algorithms single-handedly brought about drastic and rapid advancement in tasks like classification, speech recognition, and natural language processing, bringing about the end of the second “AI winter” that lasted from the late 1980s until around the late 2000s.

Continue reading “Medicine, Deep Learning, and Black Boxes”