Artificial Intelligence

November 14, 2016        Profiles in Innovation

Contents

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Contributing Authors: Heath P. Terry, CFA, Jesse Hulsing, Robert D. Boroujerdi, Jan Hatzius, Piyush Mubayi, Mark Grant, Daniel Powell, Waqar Syed, Adam Hotchkiss, Komal Makkar, Yena Jeon, Toshiya Hari, Heather Bellini, CFA, Simona Jankowski, CFA, Matthew J. Fassler, Terence Flynn, PhD, Jerry Revich, CFA, Salveen Richter, CFA, Rob Joyce, Charles Long

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This is the seventh report in our Profiles in Innovation series analyzing emerging technologies that are creating profit pools and disrupting old ones. Access previous reports in the series below or visit our portal for more, including a video summary of this report.

• Virtual and Augmented Reality
• Drones
• Factory of the Future
• Blockchain
• Precision Farming
• Advanced Materials

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Goldman Sachs Global Investment Research        2

November 14, 2016        Profiles in Innovation

Portfolio Manager’s summary

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See profiles of 5 real-world use cases for AI on pp. 41 to 81.

We interview GS Chief Economist Jan Hatzius about the impact AI/machine learning could have on lagging US productivity growth on p. 18.

Artificial Intelligence (AI) is the apex technology of the information age. The leap from computing built on the foundation of humans telling computers how to act, to computing built on the foundation of computers learning how to act has significant implications for every industry. While this moment in time may be viewed as the latest cycle of promise and disappointment before the next AI Winter (Exhibit 8), these investments and new technologies will at the very least leave us with the tangible economic benefit to productivity of machine learning.

In the meantime, AI, bots, and self-driving cars have risen to the forefront of popular culture and even political discourse. However, our research over the last year leads us to believe that this is not a false start, but an inflection point. As we shall explore in this report, the reasons for the inflection range from the obvious (more and faster compute and an explosion of more data) to the more nuanced (significant strides in deep learning, specialized hardware, and the rise of open source).

One of the more exciting aspects of the AI inflection is that “real-world” use cases abound. While deep-learning enabled advances in computer vision and such technologies as natural language processing are dramatically improving the quality of Apple’s Siri, Amazon’s Alexa, and Google’s photo recognition, AI is not just “tech for tech”. Where large data sets are combined with powerful enough technology, value is being created and competitive advantage is being gained.

For example, in healthcare, image recognition technology can improve the accuracy of cancer diagnosis. In agriculture, farmers and seed producers can utilize deep learning techniques to improve crop yields. In pharmaceuticals, deep learning is used to improve drug discovery. In energy, exploration effectiveness is being improved and equipment availability is being increased. In financial services, costs are being lowered and returns increased by opening up new data sets to faster analysis than previously possible. AI is in the very early stages of use case discovery, and as the necessary technology is democratized through cloud based services we believe a wave of innovation will follow, creating new winners and losers in every industry.

The broad applicability of AI also leads us to the conclusion that it is a needle-moving technology for the global economy and a driver behind improving productivity and ending the period of stagnant productivity growth in the US. Leveraging the research of Chief GS economist Jan Hatzius, we frame the current stagnation in capital deepening and its associated impact on US productivity. We believe that AI technology driven improvements to productivity could, similar to the 1990's, drive corporates to invest in more capital and labor intensive projects, accelerating growth, improving profitability, and expanding equity valuations.

Implications

While we see artificial intelligence impacting every corporation, industry, and segment of the economy in time, there are four implications for investors that we see as among the most notable.

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Productivity. AI and machine learning (ML) has the potential to set off a cycle of productivity growth that benefits economic growth, corporate profitability, returns on capital, and asset valuations. According to GS Chief Economist Jan Hatzius “In principle, it [AI] does seem like something that could be potentially captured better in the statistics than the last wave of innovation to the extent that artificial intelligence reduces costs and