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How Deep Learning Reveals Data Insights

Michael Gschwind
Michael Gschwind, Chief Engineer, Machine and Deep Learning, IBM—Photo by Matt Carr


The average human brain has more than 80 billion neurons. The common fruit fly’s brain has about 250,000. Early artificial neural networks (ANNs)—computer systems inspired by the human brain—had even fewer neurons than a fruit fly.

“If all you can cough up is the intelligence of a fruit fly, you’re not going very far in evolutionary terms,” says Michael Gschwind, chief engineer, machine and deep learning, IBM.

Fortunately, computers have evolved since the late 1980s, and Gschwind has played a significant role in that progression. In the early ’90s, he invented one of the first neural accelerators: a hardware circuit that could think like an animal neuron and was small enough for several circuits to fit on a single chip. “We made great progress, but the machine learning was still too slow and the mental capacity of those early ANNs was limited,” he says.

“Companies that use Power Systems servers today are in a great position because AI is all about infusing big data with intelligence at a scale that is impractical to accomplish with human experts.
—Michael Gschwind, chief engineer, machine and deep learning, IBM

Two decades later, ANNs rebranded as deep learning—a key focus of IBM’s artificial intelligence (AI) and cognitive computing research—and are ready to become productive members of society.

IBM Systems Magazine sat down with Gschwind to get a better understanding of deep learning at IBM and what it means for IBM Power Systems* users.

IBM Systems Magazine (ISM): How do you describe deep learning to the layperson?
Michael Gschwind (MG):
Deep learning is a branch of machine learning that encompasses algorithms based on ANNs. It’s inspired by biological neural networks in the human brain, which has multiple layers of neurons.

Think of deep learning as the untrained brain of an infant who has the potential to grow up to be very intelligent. The neurons can’t solve a problem by themselves. However, they can learn to solve many problems by repeatedly feeding the network questions and answers, as you would explain the world to an infant.

ISM: Why is IBM focusing so much research in this area right now?
MG:
This is the future. Deep learning, machine learning and cognitive applications enable us to handle big data: millions of pictures, constant video streams, tons of information that humans can’t process or make sense of.

IBM Research has always pushed the boundaries of what’s possible in cognitive computing and AI. This includes milestones such as Deep Blue beating world chess champion Garry Kasparov, or IBM Watson* technology winning “Jeopardy!” Today’s programmable numerical accelerators that enable deep learning trace back to IBM’s cellular computing research from 20 years ago.

Eve Daniels is a Minneapolis-based freelance writer.


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