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18	Citi GPS: Global Perspectives & Solutions	March 2018

Interview with Exponential View: Azeem Azhar

About Azeem Azhar

Azeem Azhar is Chief of the Exponential View technology newsletter. He is a strategist, product entrepreneur, and analyst. He also serves as a member of the editorial board of the Harvard Business Review. As an investor, he invests in tech startups (specifically in AI). He also founded Peer Index in 2010 (applied machine learning to large-scale social media graphs to make predictions about web users). Brandwatch acquired Peer Index in 2014.

Q: How is the geography of innovation changing? The U.S. has been a magnet for innovation in the past. Do you believe it is likely to stay this way?

The U.S. has been a magnet of FinTech and broader innovation. But three key themes will put pressure on this U.S. hegemony going forward: (1) U.S. domestic policy decisions are making it less appealing to geographically mobile entrepreneurs; (2) competing centers have become more appreciative of start-up innovation (e.g., security-related in Israel or crypto-related in Berlin); and (3) China is on the rise, with significant innovation in AI / FinTech as well as deployment of manpower and capital.

Q: Singapore has been pushing itself as a FinTech Hub – do you think Statesponsored FinTech innovation is an oxymoron?

I do not agree with that. In fact, Silicon Valley was initially state-sponsored as well – see, for example, the benefits from defense-related spending. Even elsewhere, most early innovation − such as canals, railways, etc. − was state sponsored. "The Entrepreneurial State" by Mariana Mazzucato argues that the private sector often invests only after the State has made the initial high-risk investments. Singapore has been very supportive of FinTech and Blockchain. And other financial centers, from London to Dubai, are following in promoting FinTech.

Q: Shifting from State-sponsored to decentralized tech, a popular theme for the past year has been cryptocurrencies. Are you a bull or a bear on crypto?

One definitely cannot deny that Bitcoin has a utility (and likewise Ethereum) – to this effect, there could always be a use-case market for these products. Today, there are dozens of pilots and proofs of concepts taking place all over the world that are showing positive results – this is surely good news. But I believe we are still in the Internet of 1993, wherein Yahoo and Netscape haven’t yet shown up. There is surely much more scope for expansion of crypto-technology. At the same time, I don’t see crypto disintermediating traditional currencies in the next couple of years.

Q: Switching to Artificial Intelligence, why has AI gained prominence recently? What are some of the use cases you are seeing of AI in finance?

AI includes a very broad class of technologies, but if we break it down into more granular techniques, some of the prominent ones are targeting (1) improving the interface (i.e., Natural Language processing); (2) speech generation, (3) data analysis; and (4) signal processing. The benefit of these techniques were shown in the lab four to five years ago, and today they are increasingly mature and ready for mainstream application and commercialization. I expect interfaces to have significant AI implementations in the years to come – these could be in the form of chat bots or AI handling document discovery, among others.

© 2018 Citigroup

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March 2018	Citi GPS: Global Perspectives & Solutions

Chapter A: Artificial Intelligence − the Finance Moment

“What stocks go up the most when a Category 3 hurricane hits Florida? Which stocks go up when Apple releases a new iPad?” Google-style, you can type these questions into Kensho’s platform and receive real-time answers based on machine learning. Kensho’s algorithm, originally called Warren after Warren Buffet, crunches millions of market data points to discover correlations and investment opportunities.

Daniel Nadler, founder of Kensho, notes: "Artificial intelligence is a misnomer. It's accelerated intelligence. It's about doing things, which historically you thought were impossible for a human being to do, at a blinding speed." (Forbes, March 6, 2018). “The coming era will be looked back upon as the ‘AI era,’ when AI became the defining competitive advantage for corporations, government agencies and investment professionals” (David Nadler, Kensho press release, February 2017).

AI is having a finance moment. Earlier this month, when Kensho was purchased by S&P Global for approximately $550 million, it was noted that “the biggest A.I. deal comes out of Wall Street (from a 158-year institution no less) and not Silicon Valley” (Forbes, March 6, 2018). In this chapter of our report, we take a deeper dive into some of the use cases in banking and finance today.

Evolution of AI – Why Now?

Advances in computing power, data volume, and connectivity are core components of the industrialization of AI, and together they are leading an explosion in AI applications, including in financial services.

High-Performance Computing, with the adoption of new algorithms and new computing tools, has improved the learning ability and usability of AI.

Increases in the number of devices and sensors connected to the Internet of Things are facilitating enormous amounts of data over the Internet.

Big Data generated by digitalized processes at economical prices are helping accumulate enormous amounts of data, which can then be processed to generate high-value insights with machine learning.

Figure 10. Trinity of Artificial Intelligence

High Performance

Computing

Analytics	Automation
	AI
Connec
Big	-tivity	Internet of
Data		Things

Source: Citi Research

© 2018 Citigroup

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20	Citi GPS: Global Perspectives & Solutions	March 2018

Automation and AI are sometimes used interchangeably, but there are, of course, some large differences. Automation is software that follows pre-programmed rules, allowing machines to perform repetitive monotonous tasks, freeing up time for humans to focus on tasks that require a personal touch.

AI, on the other hand, is designed to simulate human thinking by constantly seeking patterns, learning from experience, and providing responses based on situational awareness. Machine learning refers to a set of algorithms that enables it to recognize patterns from large datasets and then apply these findings to new data.

Deep learning is a sub-field of AI, where machine learning is based on a set of algorithms that attempt to model high levels of abstractions into usable data, mimicking the human brain. It requires the use of neural networks to learn from data and extend the ability of machines to react to nuances or the introduction of new data.

Figure 11. Some Terminologies and Methodologies

Artificial Intelligence Terms

Artificial Intelligence (AI)

Independent thinking and reasoning

Cognitive Computing (CC)

Situational / Environmental awareness

Machine Learning (ML)

Ability to learn over time

Distributed AI (DAI)

Distributed solutions

Methods of Learning

Machine Learning – Deep Learning

Artificial neural networks

Unsupervised

Filter / sorting data to look for patterns and trends

Supervised

Filtering / sorting data based on predetermined outcomes

Reinforced

Modification of algorithms based on feedback

Data Analytics

Analysis of statistical information

Enabling Technologies

Computer Vision

Sensors & Sensor Fusion

Dense High Speed Memory

Wireless Interfaces

Neural Networks

Energy Harvesting

Ensuing Technologies

Augmented Reality

Natural Language Processing

Event Prediction

Autonomous Machines

Security

Customized Experiences

Source: Citi Research

Artificial Intelligence has been discussed in academic circles for many decades. A 1956 workshop at Dartmouth College in the U.S. is credited with coining the term Artificial Intelligence. Alan Turing’s 1950 paper on “Computing machinery and intelligence” opened with the sentence “Can machines think?”

However, after the initial enthusiasm in academia and popular culture, as with many emergent technology hype cycles, AI entered a slump. The first “AI winter” in academic funding and popular interest began in the early 1970s and after a brief recovery was followed by a second “AI winter” in the late 1980s.

AI gained the limelight in the media and popular culture in 1996-97, after IBM's “Deep Blue” supercomputer beat world chess champion Garry Kasparov. But it is in the past few years that we have seen several mainstream applications of AI, driven by:

1.research reducing AI error rates to human levels;

2.GPU-increased calculation power; and

3.increased power and capacity to store data and reduce costs.

The way we use AI in our everyday life is rapidly changing — from static image recognition and tagging in social media platforms (Facebook, Google) to automated bots and complex natural language processing of voice commands (Siri, Google Now, Amazon Echo) to autonomous driving and fully functioning robots.

© 2018 Citigroup