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Equity Perspectives

We believe the next revolution will be driven by better algorithms, advanced semiconductors, powerful networks, and cloud-based computing. 

 

In Brief:

  • One reason for the rise of growth stocks is the expectation for an exponential surge in use of artificial intelligence (A.I.), thanks to the confluence of Big Data, high-performance computing, and very clever algorithms. 
  • Massive investments in A.I.-related  technology have the potential to increase productivity, streamline operations, transform multiple industries, and boost the global economy.  
  • While examples can already be found in voice/image recognition, factory/process/warehouse automation, and biotech, deep learning (a subset of A.I.) also is being applied to cloud computing, car safety, virtual and augmented reality, and drones.
  • All these advances are just the first iteration of what will be a multi-decade evolution, as global tech giants, and China, vie for hegemony in various markets while building new ones.

 

If there is a microcosm of the transformational innovation driving growth stocks to new heights, it’s a company whose shares have jumped more than 1,577% over the last five years (as of November 30, 2017). Of course, there is no guarantee of such performance going forward, but similar to a number of other tech stocks, that company has benefited from the phenomenal deployment of artificial intelligence (A.I.), and its subsets of machine learning and deep learning. “A.I. is the new electricity,” said Andrew Ng, a well-known A.I. expert who worked for Google and Baidu. “It’s hard to think of an industry that won’t be transformed by it in the next several years.”    

Whatever analogy one chooses, the confluence of “Big Data,”1 high-performance computing, and ingenious algorithms has driven the further development of deep learning, which takes as its model the way neurons in the brain processes speech, written words, and images. That, in turn, has propelled a vast array of new applications (see Chart 1) and greater demand for hardware, software, and services that sustain them. (See Chart 2.)

As a result, machines and computers will be programmed to do some things as well as humans can. Initial iterations are in voice/image recognition and biotech, but we are beginning to see a tremendous amount of work being done with deep learning in the areas of cloud computing, virtual and augmented reality, drones, and automobiles. (See Table 1.)

A.I. has the potential to revolutionize the automotive industry and, more significantly, the automobile.  Among the trends driving A.I. automotive applications:

  • Driverless cars and trucks
  • Driver-assist features
  • Cloud-hosted intelligence (e.g., locating nearby gas stations, identifying restaurants, pre-ordering food, etc.)
  • The “Internet of Things”; analysts estimate that 250 million vehicles will be connected to the Internet by 2020. Cars equipped with a host of smart sensors could allow manufacturers to update a car’s firmware or remotely detect maintenance issues, for instance.
  • Enhanced connectivity (e.g., cars with their own WiFi hotspots)
  • Intelligent insurance-risk assessment (which could track driver alertness, near misses, and unsafe driving habits) 2

 

Chart 1. Deep Learning Is the Evolution of Artificial Intelligence
The convergence of three powerful technologies will drive more uses and greater chip demand 


Source: Lord Abbett. Provided for illustrative purposes only.

 

Chart 2. Artificial Intelligence Revenue, by Segment, 2015–25

Source: BofA Merrill Lynch Research estimates.

 

Table 1. The Applicability and Adoption of Deep Learning Continues to Expand
Multi-faceted innovation likely will encompass many industries 
  

Source: Machine Learning Landscape.

 

The Next Revolution
By now, anyone who has researched and/or ordered goods online knows that such efforts can trigger e-mails and social media ads. That’s because e-commerce leaders are adopting A.I. applications to power more accurate product recommendations and faster search results.

If you extrapolate the rapid development of A.I., the potential to transform various industries in an increasingly interconnected world cannot be understated. Just look at the smartphones with “personal assistants” that help users perform a number of tasks. With four to six billion transistors (and voice-recognition software), they’re as powerful as supercomputers were 15 years ago, and allow users to do things faster, better, cheaper—virtually any time or any place. One new model is equipped with facial recognition, which is a relatively primitive form of computer vision.

While this vision is already used in robotics, advanced manufacturing, packaging and distribution, more advanced versions of computer vision will have broad applications, from autonomous cars and trucks to factory surveillance. More advanced versions of smartphones (and their underlying operating systems) will have startling apps, such “augmented reality,” which will be capable of transporting users into new worlds or teaching them in three-dimensional lessons that transcend classrooms. 

Consider, then, that the incarnations we are seeing now are collectively just the first iteration of what will be a multi-decade evolution. Fiber optic cable helped drive the Internet revolution. The next revolution will be very different, driven by advanced computing semiconductors, powerful networking, and cloud-based computing.

A case in point is the way A.I. is being deployed in medicine, where diagnostics, treatment, and logistics collide daily with regulatory hurdles, cost pressures, and structural change. According to one tech company, the average hospital generates 50 petabytes of data annually, through medical images, clinical charts, and sensors, as well as operational and financial sources. Yet, less than 3% of that data is actionable, tagged, or analyzed.  

All of which helps explain a major industrial company’s recently announced plan to extend sophisticated A.I. to its 500,000 imaging devices globally and to accelerate the speed at which healthcare data can be processed—from real-time medical condition assessment to point-of-care interventions to predictive analytics for clinical decision-making. For patients, the aim is to drive lower radiation doses, faster exam times, and higher-quality medical imaging.

This initiative was one of six new A.I.-based imaging technologies that made their debut at the Radiological Society of North America’s conference in late November.

A.I. techniques also are being applied to drug discovery, partly driven by the emergence of powerful new algorithms, but also by cost-effective new ways of sequencing whole genomes, the entire readout of a person’s DNA. Brendan Frey, a professor at the University of Toronto, who specializes in both machine learning and genomic medicine, foresees a massive shakeup of the pharmaceutical sector.  “In five years or so, the pharmaceutical companies that are going to be successful are going to have a culture of using these A.I. tools,” he said.4

Put another way, deep learning should continue to drive personalized medicine by much faster decoding of the genomes of various diseases. The potential to modulate such organisms is phenomenal. Whether the regulatory system keeps up with such burgeoning innovation, however, is another matter.

The Rise of the Machines
While labor rates in Asia generally are lower than they are in the United States and Europe, the fastest adoption of industrial robots in recent years has been in Asia, led by China; but the region has a long way to catch up with the dominance of the United States in A.I. (See Chart 3.)

According to the Wuhzen Institute (a China-based think tank), the United States has more A.I. companies than the rest of the world combined. How long that lasts, though, is open to debate. 

China already is the world’s largest e-commerce market, and is well on its way to becoming a “cashless society,” in which consumers scan quick-response codes using smartphones for certain transactions without the need of their wallets. To help boost and modernize its economy further, Chinese leader Xi Jinping recently unveiled a plan to build a domestic A.I. industry worth some $150 billion in the next few years.5

Xi’s stated goal is to foster new growth drivers from sectors, including mid- and high-level consumption, innovation, green economy, shared economy, modern supply chain, and human resource services.  But with A.I. playing a critical role in national defense and cybersecurity, some pundits suggest that the A.I. revolution has the potential to shift power globally. 

“China’s military and commercial ambitions pose the first credible threat to United States technological supremacy since the Soviet Union,” said Adam Segal, director of the Digital and Cyberspace Policy Program at the Council on Foreign Relations. “China’s advantage is its strategic focus and funding to match A.I.’s extraordinary opportunity.”6

Looking Ahead
In India, the government’s digital push is helping startups and small businesses move much faster in order to reach the market, and A.I. should help millions of users to leapfrog desktops and come online by using mobile phones.7

In the United States, MIT’s Computer Science and Artificial Intelligence Laboratory and Harvard’s Wyss Institute have created a super strong, affordable artificial muscle that could be used to create soft robots capable of lifting up to 1,000 times their weight.8  Law enforcement has the capability to search surveillance videos at high speeds for faces, words, objects, tattoos, and other targets. Educators can use A.I. to customize content, track diagnostics, and automate routine chores like grading tests.

How big of a game changer will A.I. be? A recent report by PwC estimates A.I. could contribute up to $15.7 trillion to the global economy by 2030, more than the current output of China and India combined. Of this, $6.6 trillion likely will come from increased productivity and $9.1 trillion likely will come from consumption-side effects.

When we think about the possibilities for A.I to drive innovation, spur consumption, and address the world’s problems, the prospects are likely limited only by the imagination of human beings. What we’re seeing now is just sliver of the exciting long-term potential.

 

Chart 3. With A.I. Advances Have Come a Proliferation of Industrial Robots 
Estimated annual shipments worldwide, by region and units (in 000s), 2007–16

Source: IFR World Robotics.

 

1 Gartner, a leading technology research and advisory company, defines Big Data as “high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.”
2 Source: Ignites Inc.
3
GE and NVIDIA Join Forces to Accelerate Artificial Intelligence Adoption in Healthcare,” nvidia.com, November 26, 2017.
4
Will Knight, “An AI-Driven Genomics Company Is Turning to Drugs,” MIT Technology Review, May 3, 2017.
5Xie Yu and Meng Jing, “China Aims to Outspend the World in Artificial Intelligence, and Xi Jinping Just Green Lit the Plan,” South China Morning Post, October 18, 2017.
6Adam Segal, "China’s Artificial Intelligence Strategy Poses a Credible Threat to U.S. Tech Leadership," cfr.org, December 4, 2017.
7Ananya Bhattacharya, “Neither Google nor Facebook: An Israeli Startup Is Bringing India’s Small Businesses Online,” Quartz India, December 3, 2017.
8Darrell Etherington, “MIT and Harvard Create Cheap Artificial Muscles with Super Strength,” techcrunch.com, November 28, 2017.

9“Sizing the Prize: What’s the Real Value of AI for Your Business, and How Can You Capitalize?” PwC, June 2017.

Artificial Intelligence (also known as  Machine Intelligence) is intelligence exhibited by machines. It is often defined as when a machine uses cutting-edge techniques to competently perform or mimic "cognitive" functions that we intuitively associate with human minds, such as "learning" and "problem solving.”

Machine learning is a subfield of computer science that evolved from the study of pattern recognition and computational learning theory in AI.

Deep learning (using a variant of a deep neural network) is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using multiple processing layers. (“Deep” = many layers.)

The information provided is not directed at any investor or category of investors and is provided solely as general information about Lord Abbett’s products and services and to otherwise provide general investment education.  None of the information provided should be regarded as a suggestion to engage in or refrain from any investment-related course of action as neither Lord Abbett nor its affiliates are undertaking to provide impartial investment advice, act as an impartial adviser, or give advice in a fiduciary capacity.   If you are an individual retirement investor, contact your financial advisor or other fiduciary about whether any given investment idea, strategy, product or service may be appropriate for your circumstances.

This material is provided for general and educational purposes only. It is not intended as an offer or solicitation for the purchase or sale of any financial instrument, or any Lord Abbett product or strategy Assumptions, opinions and estimates and references to specific financial market trends are provided for illustrative purposes only  and are not intended to be, and should not be interpreted as, recommendations or investment advice. The examples provided are hypothetical, are for illustrative purposes only, and are not indicative of any particular investor situation.

This commentary may contain assumptions that are “forward-looking statements,” which are based on certain assumptions of future events. Actual events are difficult to predict and may differ from those assumed. There can be no assurance that forward-looking statements will materialize or that actual returns or results will not be materially different from those described here.

Statements concerning financial market trends are based on current market conditions, which will fluctuate. Projections are based on current market conditions and are subject to change without notice. Projections should not be considered a guarantee.

All investments involve risk, including possible loss of principal. No investing strategy can overcome all market volatility or guarantee future results.

Past performance is not a guarantee or a reliable indicator of future results.

The opinions provided in this posting contains the current opinions of the author are as of the date of publication, are subject to change based on subsequent developments, and may not reflect the views of the firm as a whole. This commentary is not intended to be relied upon as a forecast, research, or investment advice regarding a particular investment or the markets in general. Nor is it intended to predict or depict performance of any investment. This commentary is prepared based on information Lord Abbett deems reliable; however, Lord Abbett does not warrant the accuracy and completeness of the information. Consult a financial advisor on the strategy best for you.

Not FDIC-Insured. May lose value. Not guaranteed by any bank. Copyright © 2017 Lord, Abbett & Co. LLC. All rights reserved.  

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