Why Deep Learning Matters and What’s Next for Artificial Intelligence

Big-Data-Analytics

By Matt Kiser, Product Manager at Algorithmia (@Matt_Kiser)

It’s almost impossible to escape the impact frontier technologies are having on everyday life.

At the core of this impact are the advancements of artificial intelligence, machine learning, and deep learning.

These change agents are ushering in a revolution that will fundamentally alter the way we live, work, and communicate akin to the industrial revolution – more specifically, AI is the new industrial revolution.

The most exciting and promising of these frontier technologies is the advancements happening in the deep learning space.

While still nascent, it’s deep learning percolating into your smartphone, driving advancements in healthcare, creating efficiencies in the power grid, improving agricultural yields, and helping us find solutions to climate change.

Just this year a handful of high-profile experiments came into the spotlight, including Microsoft Tay, Google’s DeepMind AlphaGo, and Facebook M and highlight the versatility of deep learning and the application of AI.

For instance, Google DeepMind has been used to master the game of Go, cut their data center energy bills by reducing power consumption by 15%, and even working with NHS to fight blindness.

“Deep Learning is an amazing tool that is helping numerous groups create exciting AI applications,” Andrew Ng says, Chief Scientist at Baidu and chairman/co-founder of Coursera. “It is helping us build self-driving cars, accurate speech recognition, computers that can understand images, and much more.”

These experiments all rely on a technique known as deep learning, which attempts to mimic the layers of neurons in the brain’s neocortex. This idea – to create an artificial neural network by simulating how the brain works – has been around since the 1950s in one form or another.

Deep learning is a subset of a subset of artificial intelligence, which encompasses most logic and rule-based systems designed to solve problems. Within AI, you have machine learning, which uses a suite of algorithms to go through data to make and improve the decision making process. And, within machine learning you come to deep learning, which can make sense of data using multiple layers of abstraction.

During the training process, a deep neural network learns to discover useful patterns in the digital representation of data, like sounds and images. In particular, this is why we’re seeing more advancements for image recognition, machine translation, and natural language processing come from deep learning.

One example of deep learning in the wild is how Facebook can automatically organize photos, identify faces, and suggest which friends to tag. Or, how Google can programmatically translate 103 languages with extreme accuracy.

Data, GPUs, and Why Deep Learning Matters

It’s been more than a half-century since the science behind deep learning was discovered, but why is it just now starting to transform the world?

The answer lies in two major shifts: an abundance of digital data and access to powerful GPUs.

Together, we are now capable of teaching computers to read, see, and hear simply by throwing enough data and compute at the problem.

There’s a special kind of irony reserved for all of these new breakthroughs that are really just the same breakthrough: deep neural networks.

The basic concept of deep learning reach back to the 1950s, but were largely ignored till the 1980s and 90s. What’s changed, however, is the context of abundant computation and data.

We now have access to, essentially, unlimited computational power thanks to Moore’s lawand the cloud. On the other side, we’re creating more image, video, audio, and text data everyday than before due to the proliferation of smartphones and cheap sensors.

“This is deep learning’s Cambrian explosion,” Frank Chen says, partner at the Andreessen Horowitz.

And it’s happening fast.

Four years ago, Google had just two deep learning projects. Today, the search giant is infusing deep learning into everything it touches: Search, Gmail, Maps, translation, YouTube, their self-driving cars, and more.

“We will move from mobile first to an AI first world,” Google’s CEO, Sundar Pichai said earlier this year.

What’s Next for Machine Intelligence

In a very real sense, we’re teaching machines to teach themselves.

“AI is the new electricity,” Ng says. “Just as 100 years ago electricity transformed industry after industry, AI will now do the same.”

Despite the breakthroughs, deep learning algorithms still they can’t reason the way humans do. That could change soon, though.

Yann LeCun, Director of AI Research at Facebook and Professor at NYU, says deep learning combined with reasoning and planning is one area of research making promising advances right now, he says. Solving this in the next five years isn’t out the realm of possibilities.

“To enable deep learning systems to reason, we need to modify them so that they don’t produce a single output, say the interpretation of an image, the translation of a sentence, etc., but can produce a whole set of alternative outputs. e.g the various ways a sentence can be translated,“ LeCun says.

Yet, despite plentiful data, and abundant computing power, deep learning is still very hard.

One bottleneck is the lack of developers trained to use these deep learning techniques. Machine learning is already a highly specialized domain, and those with the knowledge to train deep learning models and deploy them into production are even more select.

 

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