Tuesday, August 1, 2017

Pranab Chakraborty's blog post -- 3 Common Myths Around Machine Learning

This blog post Pranab Chakraborty's  blog post -- 3 Common Myths Around Machine Learning

The article leads off with a Bill Gates quote:

"A breakthrough in machine learning would be worth 10 Microsofts"

The article lays out the premise here:

"The resurrection of AI in recent years can be attributed to significant developments in machine learning systems, especially in one of its sub-field called – deep learning. Machine learning impartscomputers the ability to learn without being explicitly programmed”. Deep learning is a class of machine learning algorithms that use deep artificial neural networks with multiple hidden layers.
While evolution in machine learning drives the current AI boom, the hype has caused certain misconceptions around the capabilities of these systems. Some of these misconceptions have risen to the level of myths."

I won't spoil the punch line by listing all three here, but the author does make important distinctions between today's reinforcement learning and how a baby learns to walk.

"If we compare the learning process of a machine with that of a child, it becomes evident that machine learning is still in its infancy. For example, a baby doesn’t need to watch millions of other humans before it learns how to walk. She sets her own goal of walking, observes other humans around, intuitively creates her own learning strategy and refines that through trial and error until she succeeds. Without any outside intervention or guidance, a baby displays curiosity to learn and successfully walks, talks and understands others. Machines on the other hand requires guidance and support at each step of learning.

Moreover, a child easily combines inputs received through multiple sense organs to make the process of learning holistic and efficient. In one article, Dave Gershgorn indicates that “AI research has typically treated the ability to recognize images, identify noises, and understand text as three different problems, and built algorithms suited to each individual task.” Researchers from MIT and Google have published papers explaining the first steps on how a machine can be guided to synthesize and integrate inputs from multiple channels (sound, sight and text) to understand the world better."

I am excited about machine learning, but I am cautiously excited as I know at the end of the day it is still 1s and 0s running on hardware someplace and I remember the multiple AI winters going back to the AI Ambassadors in the 1980s at Sun Microsystems.