Predictive analytics is transforming large data sets into actionable items
Technology companies are known for innovation, and it doesn’t take long for a revolutionary new technology to take hold and become a part of people’s daily lives. In my view, investors shouldn’t be threatened by technology. Rather, they should be skeptical of companies not utilizing technology to its fullest potential.
One common theme we find when considering the largest companies within the Nasdaq-100 Index is the early embrace of artificial intelligence (AI). Even the chief executive officer of Alphabet (the parent company of Google) acknowledged the importance of artificial intelligence in the company’s first quarter 2016 earnings call.1 While not all companies within the Nasdaq-100 Index have incorporated AI within their respective businesses, those that have are beginning to realize its importance.
The growing influence of artificial intelligence
What is artificial intelligence and how are companies implementing it? At its core, AI is the science of computers completing tasks that would require intelligence when performed by humans. Applying this definition, AI is synonymous with machine learning and predictive analytics. Although media excitement may have you believe that computers will take over the world in the not-so-distant future, the reality is that replacing human cognitive intellect has resulted in only modest gains in the five decades since AI was first introduced. Nonetheless, AI can be useful as an overlay that works to extend the intelligence of humans.
Much of the recent success companies have had in implementing AI is a result of large data sets — so-called “big data.” AI has helped transform massive amounts of data into actionable items. Whether it’s consumer purchasing history or driving logs from smart cars, data are used to “teach” computers to do tasks that, in the past, could only be performed using intrinsic intelligence.
Generally speaking, there are two types of AI, or machine learning:
- Supervised learning refers to humans directing a computer to solve a given problem.
- Unsupervised learning involves the use of computer algorithms to analyze data and present findings that a human can then use to solve problems.
Note that there is a difference between problems that computers can solve and the algorithms that actually solve them. For any given machine-learning problem, there are potentially many algorithms that can solve the problem.
The case of the autonomous cars: Overcoming the artificial intelligence fear factor
We are all aware of the amazing evolution of autonomous cars. Humans have been dreaming about self-driving cars for as long as automobiles have been in existence. The first experiments in autonomous cars began in the 1920s, and real trials took place as early as the 1950s. But without the power of big data, nothing succeeded. By the 1980s, the power of computers finally began to turn the dream into reality. Although they have certainly not been without issues, autonomous cars are legal in a growing number of states, and it may not be long before they are commonplace on every road.
How did we get here? The machine — in this case, the car — “learns” through a process called machine generalizes data in such a way that it can make intelligent driving decisions when it encounters new situations on the road. The process is surprisingly simple:
- A human driver takes the machine for a ride. During the ride, the machine observes the vehicle’s sensor readings and records each move the human driver makes. The machine then observes and records the outcome of those moves and classifies the quality of the driver’s decisions. Is the driver getting too close to the vehicle on the right? Is the driver getting too close to the sidelines? Is the driver too close to an object ahead? Are the machine’s sensors giving the driver warning signals?
- The machine records all observations and outcomes over time. The machine builds a database of information using every observation, every reading of the vehicle’s sensors and every decision by the driver.
- The machine generalizes the data to make intelligent decisions in new situations. Based on all past situations, the machine can now make highly intelligent decisions — even when it encounters new situations on the road that haven’t been previously observed.
- Over time the machine gets smarter. The more data that are available, the more inclusive the machine can be — essentially learning from itself.
Yet, as remarkable as autonomous vehicles are, the technology is actually quite simple. Why? Because there is a finite number of decisions a driver can make in any given situation. As a result, the car uses basic data-driven analysis to detect good or bad behavior and adapts accordingly, becoming “smart” in the process. By combining that simple process with big data, autonomous cars may be on the cusp of turning a century-old dream into reality.
Investors wishing to increase their exposure to companies implementing artificial intelligence may wish to consider PowerShares QQQ, an exchange-traded fund that tracks the Nasdaq-100 Index.
1 Source: Business Insider, April 2016
John Q. Frank, CFA
QQQ Equity Product Strategist
John Frank is the QQQ Equity Product Strategist representing the PowerShares family of exchange-traded funds (ETFs). In this role, Mr. Frank works on researching, developing product-specific strategies and creating thought leadership to position and promote the PowerShares QQQ.
Prior to joining PowerShares, Mr. Frank was an Assistant Portfolio Manager at RS Core Capital, a multi-asset class investment firm. In this role, his primary responsibilities included research, risk management and asset allocation with a focus on the equity and hedge portfolios. Before RS Core Capital, he spent six years at J.P. Morgan Asset Management advising institutional investors on asset/liability management, asset allocation and pension regulation and worked across the defined benefit, defined contribution, endowment and foundation segments. He began his career at General Electric in a leadership development program where he was placed within the GE Energy division.
Mr. Frank earned a BSE degree in industrial & operations engineering from the University of Michigan – Ann Arbor and an MBA with Honors from the University of Chicago Booth School of Business with concentrations in analytic finance, econometrics, and statistics. He is a CFA charterholder and a member of the CFA Society of Chicago as well as the Beta Gamma Sigma Society.
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Artificial intelligence: What is it, and why are companies adopting it? by Invesco
