Emerging Tech Trends 2019: NLP and VR Show Promise

It is clear that artificial intelligence (AI) and emerging technologies could fundamentally change the landscape of the future. In line with this, 2019 brought movement across sectors such as AI, virtual reality (VR) and finance, to name just a few.

Not only did a wide variety of emerging technologies make advancements, but applications branched out heavily as well. For example, AI applications were incredibly diverse in 2019.

“Machine learning (ML), computer vision and natural language processing (NLP) technologies are the most sought-after areas of AI that experienced tremendous growth in 2019,” Dr. Alex Bogdon, chief scientific officer at Castle Ridge Asset Management, told the Investing News Network (INN).

This trend was paired with a number of key breakthroughs in AI, including Microsoft’s (NASDAQ:MSFT) investment in Graphcore, an AI chip maker.

“Microsoft, which put its own money into Graphcore last December as part of a US$200 million funding round, has just published benchmarks that suggest that Graphcore’s intelligence processing unit matches or exceeds the performance of the top AI chips from Nvidia (NASDAQ:NVDA), Advanced Micro Devices (NASDAQ:AMD) and Google (NASDAQ:GOOG) using algorithms written for those rival platforms.”

Graphcore’s hardware also exceeds the performance of all other AI-focused processors, Bogdon said. “This is huge news; it may change the landscape of AI development forever.”

As mentioned, advancements also took place in VR. According to Technavio, the global location-based VR market will increase at a compound annual growth rate of 36 percent between 2019 and 2023.

Read on for more key takeaways from experts on emerging tech trends for 2019.

Emerging tech trends 2019: Virtual reality

Over 2019, the VR market revealed steady signs of growth. According to Allied Market Research, the VR industry will reach US$571.2 billion by 2023, driven by growth in segments such as mobile and gaming. However, gaps in user experience design continue to be challenges for the industry.

“Up to 2018, the VR industry, specially for location-based (VR), was seen as a prototype, a test,” Daniel Japiassu, CEO of YDX Innovation (TSXV:YDX), told INN. “Operators and family entertainment center owners were still seeing it as something cool to have, without really understanding where it was going.”

YDX Innovation is a VR company that has worked with large companies like Ericsson (NASDAQ:ERIC), Shell (NYSE:RDS.A) and Disney (NYSE:DIS).

Japiassu explained how location-based VR — a division of VR that provides dynamic and unique experiences of a given environment — is showing practical signs of improvement.

“Now (location-based VR) has numbers to show where it works, where it doesn’t and ways to lower the risk and have a better return on income,” said Japiassu.

Emerging tech trends 2019: Robotics

Robotics continued to integrate into a number of sectors throughout the year, including agriculture, construction and industry.

“Agriculture is in the middle of a technical transformation,” Wilson Acton, executive vice president of corporate affairs at Whipcord, said at the iTech Conference in Vancouver, British Columbia, in November.

Currently, for example, a John Deere (NYSE:DE) tractor acquires 7,000 data points each minute. Acton projects that in five years, farming equipment will become autonomous.

When it comes to retail, Amazon (NASDAQ:AMZN) launched its first delivery robot, Scout, in August. Scout is designed for “last-mile” delivery of packages. However, Scout is not without its drawbacks, such as difficulties navigating complex environments such as sidewalks.

“You’ve seen a lot of startups working on this, but I think there’s always been this question of whether it’s going to make economic sense,” Brian Gerke of Open Robotics said. “It’s tough to beat the capabilities of a person who goes around doing that last-mile delivery.”

Emerging tech trends 2019: NLP and investment management

Pivoting over to AI and finance, three key areas of AI are utilized predominantly right now: NLP, risk modeling and alternative data.

“NLP is one of the fastest-growing applications of AI in financial asset management,” said Bogdon. “Chatbots, virtual assistants and robo advisors are becoming a norm within the banking sector.”

These NLP applications are also getting smarter, he added.

“Faster, more accurate interpretations, inference engines (and) self-evolving NLP models are just a few areas of the cutting-edge AI trends that have seen rapid development,” said Bogdon.

According to Statista, the virtual digital assistant market is projected to reach US$15 billion by 2021.

Emerging tech trends 2019: Sentiment analysis

Another area of NLP that is being applied in finance is sentiment analysis. Sentiment analysis aims to identify affective states of given data points in NLP.

“In most instances, sentiment at most … is going to drive 20 percent of the value of a stock,” Rick Roche, managing director at Little Harbor Advisors, told INN. “I think sentiment can be very valuable, but it’s not going to explain all the behavior of the stock price.”

According to a report from Tractica, accelerated adoption of NLP is being driven by demand for increased efficiency and by enterprise firms tapping into unstructured data.

“Increasing demand for sentiment analysis and management of huge volumes of contracts drove NLP applications to new highs this year,” explained Bogdon.

For example, Roche discussed how NLP firm Prattle used NLP techniques to analyze American Century’s earnings conference calls.

“What they’re looking for is deception — where the people are not being fully transparent or being misleading — during an earnings conference call,” said Roche.

The core attributes that Prattle looks for during conference calls are related to the nuances within deception. “They’re trying to detect perception by omission, the failure to disclose certain details, putting a positive spin on something, obfuscation and blame.”

Other firms, on the other hand, are taking different approaches to NLP.

Bloomberg, along with Thomson Reuters (NYSE:TRI), has been doing sentiment analysis since 2009.

“Bloomberg assumes that you have a long position, and in the absence of any other information, would this news you’re seeing — that they’re trying to measure investor sentiment — cause you to be either positive, negative or neutral (on a company),” said Roche.

Bloomberg describes this process as assigning a sentiment score to text rather than outcome. From here, data is fed through a support vector machine that then classifies the data.

Emerging tech trends 2019: AI risk modeling

Of the portfolio managers polled recently by the CFA Institute, 10 percent said they use AI or ML in their investment processes. But Roche believes that the number is even less than that.

The most common use of AI was in “predicting asset price direction or finding signals from noisy data,” which 69 percent of respondents pointed to. Following behind was “arriving at buy or sell decisions based on macro, fundamentals, or market input variables using classification” at 15 percent.

“The field of financial risk modeling has been a natural domain of application for ML/AI techniques,” Bogdon said to INN. “By capitalizing on many known statistical models, such as logistic regression, discriminant analysis, classification trees, etc., AI systems can further improve the risk modeling accuracy, speed and adaptability.”

Bogdon noted that AI models used in investment management continue to advance. In light of this, different areas are more well suited to AI.

Credit risk scoring for credit cards and mortgages, for example, is more suited to AI technologies, said Bogdon. By contrast, low-default credit portfolios for highly rated counterparties such as sovereigns, financials and investment-grade corporates, are less applicable because they are less data intensive.

But of course the advantages of AI are not without their pain points. Two of these challenges are historical anomalies and blind alleys.

“You have a number of these factors that don’t have historical precedence. I think that, in some way, could explain why these really advanced statistical models have not been able to keep up with things like the S&P 500 (INDEXSP:.INX),” said Roche.

The unprecedented US$17 trillion in negative-yielding debt in 2019 is one phenomenon that Roche discussed. Another was the idea of AI producing ineffective results, also known as blind alleys.

“Marcos Lopez de Prado, chief investment officer at True Positive Technologies, says that ML can find patterns even where they don’t exist,” said Roche. “So how does that affect someone building a ML-driven model? You could be led into lots of blind alleys.”

Emerging trends 2019: Alternative data

Alternative data is defined as non-company fundamentals, and it figured prominently into AI and investment management in 2019.

“It includes weather forecasting, shipping data, satellite imaging. The satellite imaging could be either on oil tankers, oil storage facilities or parking lots,” said Roche. He noted that Eagle Alpha — based in Dublin, Ireland — has over 1,000 unique data sets.

“Alternative data is typically used to augment existing investment and risk models,” Bogdon said. “Web scraping, which is searching websites for key information such as sale prices or inventory counts disclosed on public retail websites to determine brand or company performance, still seems to be the most used source of alternative data.”

Roche added that alternative data has been used in particular among hedge fund models.

“There has been an immense focus and importance placed on alternative data this year on AI-based methods of incorporating this data into existing models,” said Bogdon.

Emerging tech trends 2019: Investor takeaway

As AI gains traction, there are naturally a number of opportunities to invest in this field. The Eureka AI Hedge Fund Index, for example, contains 29 equally weighted constituents. The index includes hedge funds that apply ML to their trading processes.

Similarly, Roche noted that he would invest in a number of pioneering AI investment firms.

“I would overweight the Renaissance Technologies Medallion Fund. I would overweight Two Sigma, Bridgewater Associates and DE Shaw,” said Roche.

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Securities Disclosure: I, Dorothy Neufeld, hold no direct investment interest in any company mentioned in this article. 

Editorial Disclosure: YDX Innovation is a client of the Investing News Network. This article is not paid for content.

The Investing News Network does not guarantee the accuracy or thoroughness of the information reported in the interviews it conducts. The opinions expressed in these interviews do not reflect the opinions of the Investing News Network and do not constitute investment advice. All readers are encouraged to perform their own due diligence.