Artificial Intelligence

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Goldman Sachs Global Investment Research 3

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November 14, 2016 Profiles in Innovation

We profile the ecosystem of public and private companies enabling the AI revolution on pp. 83 to 89.

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reduces the need for labor input into high value added types of production. Those cost saving innovations in the business sector are things statisticians are probably better set up to capture than increases in variety and availability of apps for the iPhone, for example. To the extent Artificial Intelligence has a broad based impact on cost structures in the business sector, I’m reasonably confident that it would be picked up by statisticians and would show up in the overall productivity numbers.”

Premium technology. The value of speed in AI and machine learning has the potential to reverse the trend towards cheaper commodity hardware in building data centers and networks. We believe this could drive substantial shifts in market share in hardware, software, and services spending. For example, an AWS workload running on a “standard” datacenter compute instance costs as little as $0.0065/hour compared to $0.900 for a GPU instance optimized for AI.

Competitive Advantage. We see the potential for AI and machine learning to reshuffle the competitive order across every industry. Management teams that fail to invest in and leverage these technologies risk being passed by competitors that benefit from the strategic intelligence, productivity gains, and capital efficiencies they create. In the vignettes starting on page 41 we examine how these competitive advantages are developing in Healthcare, Energy, Retail, Finance and Agriculture.

New Company Creation. We have identified over 150 private companies in the AI and ML space founded over the last 10 years (Exhibits 69-75). While we believe that much of the value in AI will accrue to large companies with the resources, data, and ability to invest, we expect that venture capitalists, entrepreneurs and technologists will continue to drive the creation of new companies that will, in turn, drive substantial innovation and value creation through, at the very least, M&A, though we certainly wouldn’t dismiss the potential for a “Google or Facebook of AI” to emerge.

In the following pages we delve into AI the technology, its history, the ecosystem being created around machine learning, applications for these technologies across industries and the companies that are leading the way.

What is AI?

AI is the science and engineering of making intelligent machines and computer programs capable of learning and problem solving in ways that normally require human intelligence. Classically, these include natural language processing and translation, visual perception and pattern recognition, and decision making, but the number and complexity of applications is rapidly expanding.

In this report, we will focus most of our analysis on machine learning, a branch of AI, and deep learning, a branch of machine learning. We highlight two key points:

- Simplistically, machine learning is algorithms that learn from examples and experience (i.e., data sets) rather than relying on hard-coded and predefined rules. In other words, rather than a developer telling a program how to distinguish between an apple and an orange, an algorithm is fed data (“trained”) and learns on its own how to distinguish between an apple and an orange.

- Major advances in deep learning are one of the driving forces behind the current AI inflection point. Deep learning is a sub-set of machine learning. In most traditional machine learning approaches, features (i.e., the inputs or attributes that may be predictive) are designed by humans. Feature engineering is a bottleneck and requires significant expertise. In unsupervised deep learning, the important features are not predefined by humans, but learned and created by the algorithm.

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Goldman Sachs Global Investment Research 4

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November 14, 2016 Profiles in Innovation

To be clear, we’re not yet focusing on the kind of True, Strong, or General Artificial Intelligence that is meant to replicate independent human intelligence, and that is often the AI in popular culture. While there have been certain potential breakthroughs there, like Google DeepMind’s AlphaGo system, which not only defeated a Go world champion, but did so using moves no human ever had before, we focus on the more immediately economically tangible areas of development in artificial intelligence.

Why is AI development accelerating now?

Major leaps in deep learning capabilities have been one of the catalysts behind the AI inflection currently underway. Neural networks, the underlying technology framework behind deep learning, have been around for decades, but three things have changed over the last five to ten years:

- Data. There has been massive growth in the amount of unstructured data being created by the increasingly ubiquitous connected devices, machines, and systems globally. Neural networks become more effective the more data that they have, meaning that as the amount of data increases the number of problems that machine learning can solve using that data increases. Mobile, IoT, and maturation of low cost data storage and processing technologies (often in the cloud) has created massive growth in the number, size, and structure of available data sets. For example, Tesla has aggregated 780mn miles of driving data to date, and adding another million miles every ten hours through its connected cars, while Jasper (acquired by Cisco for $1.4bn in Feb. 2016) has a platform powering machine to machine communication for multiple automobile manufacturers and telco companies. Verizon made a similar investment in August when it announced it would acquire Fleetmatics, which connects remote sensors on vehicles to cloud software via increasingly fast wireless networks. The rollout of 5G will only accelerate the rate at which data can be generated and transmitted. Annual data generation is expected to reach 44 zettabytes (trillions of GB) by 2020, according to IDC’s Digital Universe report, a CAGR of 141%