No BiaS

The main objective of computer vision is to give machines the ability to see and interpret the world. This has proven a much more complex task than initially expected. We take for granted our innate ability to interpret and classify the world around us. We are attempting to do in decades what took evolution millions of years. In this episode Saurabh, Nikhil, and Melody discuss the emergence of computer vision as a discipline, the differences in the way that humans and computers “see” images,...

Did you know that not all bias in machine learning (ML) is bad? In fact, the concept of bias was first introduced into ML by Tom Mitchell in his 1980 paper, "The need for biases in learning generalizations.” He defines learning as the ability to generalize from past experience in order to deal with new situations that are related to this experience, but not identical to it. Applying what we’ve learned from past experiences to new situations is called an inductive leap and seems to only be...

When discussing machine learning development approaches, data scientists often need to ask themselves does this use case apply best for supervised or unsupervised learning? In this episode, we break down the strengths and weaknesses of each approach and discuss various use cases to which each one best applies. Melody explores the notion that supervised learning works much like our education system: there's a teacher "supervising" the learning process. Unsupervised learning, on the other...

Have you heard that “Data is the new oil”? It sounds cool, but what does it mean? Melody, Nikhil, and Saurabh tease out the ideas behind the metaphor and then discuss why Bernard Marr, a reporter for Forbes, wrote: “Data is not the new oil." They end up offering a different, and perhaps more fitting metaphor to describe what’s fueling the 4th industrial revolution: “AI is the new electricity.” https://content.alegion.com/podcast

Welcome to our first episode of No BiaS, where we discuss different perspectives on the emerging and ever-shifting terrain of artificial intelligence and machine learning. In future episodes we’ll dive deeper into the nuts and bolts of developing and training models, philosophical issues, and existential concerns. But since this is our first episode we decided to begin with the basics: AI versus ML. We offer definitions and historical background of how they have evolved over the past few...