Beyond Artificial Intelligence: Investing in Deep Learning

The Deep Learning for Robotics summit June 28-29 could provide a sense of how this important technology is being utilized by major tech firms.

5 min read

Key Takeaways

  • Deep learning, a subset of machine learning, is an evolutionary step in artificial intelligence
  • Learn how deep learning seems to be impacting tech and other industries
  • Small startups and large tech firms represent two categories of potential deep learning investment opportunity

You’ve probably heard of artificial intelligence (AI), but the next wave of technological transformation might be “deep learning.” A meeting of the major players this week could provide investors with the latest news and trends about the concept.

Deep learning isn’t just one innovation in technological development. It aims to reboot the entire way we think about technology, and potentially could generate a huge market across multiple industries. Deep learning might help usher in the age of thinking machines, and many investors are watching how some major tech companies incorporate this evolutionary concept.

Exploring the intersection between Artificial Intelligence (AI) and the “real world” is the central theme of the Deep Learning for Robotics Summit scheduled to take place in San Francisco this Thursday and Friday. The summit will gather some of the most influential technologists, startups, and tech giants to discuss recent developments in deep learning.

The roster of attendees reads like a “who’s who” of large tech companies and potentially disruptive startups. Big names include Amazon (AMZN), Apple (AAPL), Capital One (COF), Cisco (CSCO) Facebook (FB), Alphabet (GOOG), Intel (INTC), Microsoft (MSFT), Twitter (TWTR), and many others.

“Deep learning has opportunities to solve challenges across all types of industries,” wrote Amit Karp, vice president at Bessemer Venture Partners, in a recent guest blog for VentureBeat.

Deep Learning: An Evolutionary Driver in Artificial Intelligence

To appreciate the potential scope and power of deep learning, let’s see how it evolved:

  • Artificial intelligence (AI) is the general field in which computers perform tasks requiring human-like intelligence.
  • Machine learning, a subset of AI, refers to algorithms that are capable of learning and developing solutions from big data with a limited degree of human supervision and guidance.
  • Deep learning, a subset of machine learning, is what happens when an algorithm becomes self-sufficient enough to learn and creatively devise solutions from data with little to no human supervision. 

Deep learning algorithms are designed as “artificial neural networks” that mirror the function of the human brain. And deep learning’s ability to learn and devise solutions in a nearly self-sufficient manner not only differentiates it from machine learning, but also gives it a significant technological advantage over other subsets of artificial intelligence.

In short, deep learning might herald the age of thinking machines. And that’s a reason why many tech companies are competitively seeking ways to harness its capabilities, incorporating it into their products and systems, and pushing it toward the share-driven community of open source development.

Deep Learning’s Impact On Tech and Other Industries

Here’s a brief look at how three major players are implementing deep learning now:

  • GOOG’s open source software library, TensorFlow, became a hub for machine learning and deep learning software development. It’s been used to help build efficacy within GOOG’s Voice Search tools;  to create a deep neural network model for identifying promising drug candidates (a project involving GOOG and Stanford University); and to contribute to the development of optimal AI-generated email responses for GOOG’s Gmail SmartReply.
  • AMZN created Amazon Web Services (AWS) to provide major corporations with software whose learning models are capable of generating accurate customer insights and predictions. Using complex pattern recognition in pictures, text, sounds, and other data, AWS is an example of a deep learning AI application geared toward marketing, one that may provide more accurate and predictive insights toward products that individual customers might desire. 
  • On the hardware side of things, NVIDIA (NVDA) has developed more intuitive GPUs, (Graphic Processing Units) that have been able to capitalize on the effects of parallel processing and GPU-accelerated AI neural networks.  This helps NVDA provide its services to clients like Tesla (TSLA), TensorFlow, and MSFT.

Ultimately, what might be at stake in deep learning is its potential to empower a large body of existing technologies, which in turn may affect a large number of new and emerging industries. 

Why Investors Should Notice 

In a report from 2017, Persistence Market Research estimated that by 2027, the deep learning market may generate up to $261 billion. Another research firm, Stratics MRC, forecasts revenue deep learning revenue of $72.1 billion by 2023.

Deep Learning Investment

Given the nascent state of this technology, there’s no one path for those who want to invest in deep learning. There are generally two categories of “players”: small startups, and large tech companies, many of which have increased their acquisitions of the smaller startups. TD Ameritrade provides resources that can help clients research opportunities in technology stocks and other industries.

According to tech research firm CB Insights, whose own platform is powered by machine-learning AI, acquisitions of AI-based startups jumped by 44% in 2017; a total acquisition of 115 companies compared with only 22 companies acquired in 2013.

Here’s a shortlist of AI and deep learning startup acquisitions by large tech firms:

  • GOOG: CleverSense (2012); DNNresearch (2013); DeepMind, Jetpac, and Emu (2014); Dark Blue Labs, Vision Factory, Granata, and Timeful (2015); Moodstocks (2016); and Halli Labs (2017); AIMatter and Banter (2018).
  • AAPL: Siri (2010); Novauris Technologies (2014); Perceptio and Vocal Q (2015); Emotient, tuplejump, and Turi (2016); SensoMotoric, Lattice, and RealFace (2017); Regaind,, and Pop Up Archive (2018).
  • FB: (2012); Mobile Technologies (2013); (2015); Masquerade Technologies (2016); Zurich Eye (2017; Ozio (2018).
  • AMZN: Evi Technologies (2013); Orbeus (2015); and (2017); Sqrrl (2018).

To learn more about the latest developments in artificial intelligence, machine learning, and deep learning, consider checking the Deep Learning Summit website. The information might give you a sense of just how massive an impact deep learning potentially has across a wide range of industries.

Research is an important component of any sound investing strategy. Learn more about the tools and resources TD Ameritrade provides to help you look for your next investment opportunity.

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