AI News, The race to build ‘‘big data machines’’ in financial investing

The race to build ‘‘big data machines’’ in financial investing

There is a palpable increase in machine intelligence across the touchpoints of our lives, driven by the proliferation of data feeding into intelligent algorithms capable of learning useful patterns and acting on them.

For example, they apply guidelines such as steering older people with limited means towards less risky investments, provide advice on diversification, implications of changing tax laws and regulation, and more.

In a class on systematic trading strategies that I have been teaching for over 10 years at New York University, most students admit that despite their diligence and experience, they typically do worse than the market, often citing emotion and complexity as reasons for their underperformance.

While one might uncharitably attribute this specific trade to luck, Druckenmiller’s subsequent record of zero losing years out of 30 makes it quite apparent that his prescience was exceptional in recognizing special economic situations with large upside with limited risk.

“Buffett’s alpha,” in contrast, which has been long lived, derives from a more systematic and definable strategy of buying cheap, safe, quality stocks and the use of leverage obtainable to him at favorable terms through his insurance businesses and other sources.

Figure 1 puts Buffett’s performance in perspective: The figure shows the information ratios (this is defined as the ratio of annualized average excess return over a benchmark (such as the S&P500) divided by the standard deviation of these returns.

To illustrate how this plays out in in investing, Figure 2 sketches out the investment landscape in terms of the holding periods of managers, and divides this into roughly three investment styles—less than a day, days to weeks, and months to years: On the left is the high frequency space, which is dominated by big data and fast machines.

Decisions are very frequent, in the hundreds or thousands per day, but only a small amount of risk can be allocated to each decision because the limited liquidity in the market severely constrains the size of each decision.

At the same time, the lack of data of sufficiently high frequency makes this a difficult terrain for machines, although it is possible that a “BuffettBot” that implements his core philosophy might well exist in the future.

In contrast to the “long only” types of strategies followed by most fund managers in the long-term space where it is difficult to beat the performance of the healthiest companies, managers can target other opportunities such as the “long/short” equities space where identifying relatively “unhealthy” companies is equally important.

In the mid-1990s I began to explore this space inside a Wall Street proprietary trading unit, using machine learning techniques as a tool to help discover actionable trading patterns in the data.

The machine, called AQT for “automated quant trading,” trades exchange-traded futures contracts and went live in August 2009 on Deustche Bank’s dbSelect platform, which contains roughly 100 futures trading programs that fall in the middle part of Figure 2 (performance is calculated on a daily basis for all programs on the platform and published monthly).

As a guiding principle, however, a robot should be considered seriously in situations where there is sufficient data from which it can learn, and the frequency of decisions favors unemotional systematic decision-making over human judgment that is often saddled by emotion and irrational bias.

However, it is important to stress that a machine is unlikely to recognize exceptional situations such as a budget crisis or acts of nature where humans must ultimately decide whether to take a robot offline or reduce its risk exposure temporarily.

Referring to the Bridgewater initiative, the CEO of a major recruitment firm remarked, “Machine learning is the new wave of investing for the next 20 years and the smart players are focusing on it.” The race to build “big data machines” in financial investing seems to be well under way.

Where machines could replace humans—and where they can’t (yet)

As automation technologies such as machine learning and robotics play an increasingly great role in everyday life, their potential effect on the workplace has, unsurprisingly, become a major focus of research and public concern.

Automation, now going beyond routine manufacturing activities, has the potential, as least with regard to its technical feasibility, to transform sectors such as healthcare and finance, which involve a substantial share of knowledge work.

Last year, we showed that currently demonstrated technologies could automate 45 percent of the activities people are paid to perform and that about 60 percent of all occupations could see 30 percent or more of their constituent activities automated, again with technologies available today.

In this article, we examine the technical feasibility, using currently demonstrated technologies, of automating three groups of occupational activities: those that are highly susceptible, less susceptible, and least susceptible to automation.

Toward the end of this article, we discuss how evolving technologies, such as natural-language generation, could change the outlook, as well as some implications for senior executives who lead increasingly automated enterprises.

In discussing automation, we refer to the potential that a given activity could be automated by adopting currently demonstrated technologies, that is to say, whether or not the automation of that activity is technically feasible.2 2.We define “currently demonstrated technologies”

Occupations in retailing, for example, involve activities such as collecting or processing data, interacting with customers, and setting up merchandise displays (which we classify as physical movement in a predictable environment).

The cost of labor and related supply-and-demand dynamics represent a third factor: if workers are in abundant supply and significantly less expensive than automation, this could be a decisive argument against it.

For example, the large-scale deployment of bar-code scanners and associated point-of-sale systems in the United States in the 1980s reduced labor costs per store by an estimated 4.5 percent and the cost of the groceries consumers bought by 1.4 percent.3 3.Emek Basker, “Change at the checkout: Tracing the impact of a process innovation,”

Almost one-fifth of the time spent in US workplaces involves performing physical activities or operating machinery in a predictable environment: workers carry out specific actions in well-known settings where changes are relatively easy to anticipate.

Through the adaptation and adoption of currently available technologies, we estimate the technical feasibility of automating such activities at 78 percent, the highest of our seven top-level categories (Exhibit 2).

Since predictable physical activities figure prominently in sectors such as manufacturing, food service and accommodations, and retailing, these are the most susceptible to automation based on technical considerations alone.

Within manufacturing, 90 percent of what welders, cutters, solderers, and brazers do, for example, has the technical potential for automation, but for customer-service representatives that feasibility is below 30 percent.

A service sector occupies the top spot: accommodations and food service, where almost half of all labor time involves predictable physical activities and the operation of machinery—including preparing, cooking, or serving food;

We calculate that 47 percent of a retail salesperson’s activities have the technical potential to be automated—far less than the 86 percent possible for the sector’s bookkeepers, accountants, and auditing clerks.

The heat map in Exhibit 3 highlights the wide variation in how automation could play out, both in individual sectors and for different types of activities within them.4 4.For a deeper look across all sectors in the US economy, please see the data representations from McKinsey on automation and US jobs, on

Long ago, many companies automated activities such as administering procurement, processing payrolls, calculating material-resource needs, generating invoices, and using bar codes to track flows of materials.

Examples include operating a crane on a construction site, providing medical care as a first responder, collecting trash in public areas, setting up classroom materials and equipment, and making beds in hotel rooms.

Already, some activities in less predictable settings in farming and construction (such as evaluating the quality of crops, measuring materials, or translating blueprints into work requirements) are more susceptible to automation.

The hardest activities to automate with currently available technologies are those that involve managing and developing people (9 percent automation potential) or that apply expertise to decision making, planning, or creative work (18 percent).

For now, computers do an excellent job with very well-defined activities, such as optimizing trucking routes, but humans still need to determine the proper goals, interpret results, or provide commonsense checks for solutions.

Overall, healthcare has a technical potential for automation of about 36 percent, but the potential is lower for health professionals whose daily activities require expertise and direct contact with patients.

One of the biggest technological breakthroughs would come if machines were to develop an understanding of natural language on par with median human performance—that is, if computers gained the ability to recognize the concepts in everyday communication between people.

The actual level will reflect the interplay of the technical potential, the benefits and costs (or the business case), the supply-and-demand dynamics of labor, and various regulatory and social factors related to acceptability.

E-commerce players, for example, compete with traditional retailers by using both physical automation (such as robots in warehouses) and the automation of knowledge work (including algorithms that alert shoppers to items they may want to buy).

The greater challenges are the workforce and organizational changes that leaders will have to put in place as automation upends entire business processes, as well as the culture of organizations, which must learn to view automation as a reliable productivity lever.

Understanding the activities that are most susceptible to automation from a technical perspective could provide a unique opportunity to rethink how workers engage with their jobs and how digital labor platforms can better connect individuals, teams, and projects.6 6.See Aaron De Smet, Susan Lund, and William Schaninger, “Organizing for the future,”

McKinsey Quarterly, January 2016., It could also inspire top managers to think about how many of their own activities could be better and more efficiently executed by machines, freeing up executive time to focus on the core competencies that no robot or algorithm can replace—as yet.

Humans vs. Robots: Who Should Dominate Space Exploration?

The most recent footprints on the moon are 40 years old, and the next artificial mark on the lunar surface will probably be made by a robot's wheels rather than human soles.

In past decades, rovers, landers, and orbiters have visited the moon, asteroids and comets, every planet in the solar system and many of their moons as well.

More than 2,000 papers have been published over the last four decades using data collected during the manned Apollo missions, and the rate of new papers is still rising.

They are more mobile than current robot explorers: The Apollo 17 astronauts covered more than 22 miles in three days, a distance that has taken the Mars Opportunity rover eight years to match.

The Apollo program was incredibly expensive – about $175 billion in today's money – though it was not solely a scientific mission.

The total amount spent on science over the Apollo missions, Crawford estimates, comes to about $2.09 billion in today's dollars, making it comparable to or even cheaper than the recent $2.5 billion Mars Science Laboratory.

If space exploration continues to focus on sending robots to other planets, "we will learn less about the solar system in the next 100 years than we will if we engage in an ambitious program of human exploration,"

moons Phobos and Deimos and order remote-controlled robots to drive long distances over the planet's surface, set up geologic instruments, and collect samples for analysis.

He estimates this could greatly reduce costs because roughly half the price tag of a manned mission is spent on getting people down and back up the deep gravity well of a planet.

Crawford agrees such a plan would be a step beyond simply sending a robot, though perhaps less efficient than putting people on a planet's surface.

Opinion: These 3 stocks are smart bets on the artificial intelligence revolution

“Artificial intelligence” is a misunderstood term, thanks in part to dystopian views of the technology across pop culture — from the iconic Terminator to Cylons in Battlestar Galactica to HAL 9000 in 2001: A Space Odyssey.

In reality, most scientists working on artificial intelligence aren’t trying to simulate true human intelligence at all.

They are simply trying to create practical machines capable of analyzing data and making decisions to achieve a goal.

Case in point —

CRM, +0.00%

  has a valuable artificial intelligence application called Einstein that it provides to clients.

This AI engine helps marketing and sales teams by suggesting which customers are the most valuable, and which products they are most likely to buy.

Not only is that a far-less sinister example of AI, it’s also exemplary of how businesses can use this technology to create serious profits.

Salesforce stock, for example, is up 40% year-to-date compared with less than 15% for the broader S&P 500

SPX, -0.10%


In fact, the most practical applications of artificial intelligence are side-by-side with Big Data and cloud-computing applications that many investors are already familiar with.

Think of artificial intelligence as just the natural next step now that we’ve created all this data — something has to make sense of it.

For example, retailers have been trying for years to harness the predictive power of your shopping habits in order to put offers in front of you.

Case-in-point: A now-infamous story about

 investing in how to predict when a customer was (or soon would become) pregnant.

While fears of the robot apocalypse may never completely disappear from pop culture, the business case for AI is clear in this age of information.

The only question is who will provide the artificial intelligence engines of the future, and which companies and investors will profit.

If you’re interested in playing this emerging-tech trend, here are three AI plays to consider:

  made a splash a few years ago as it seemed to be diving into deep machine learning with the acquisition of DNNresearch, DeepMind Technologies, and JetPac among others.

The flurry of acquisitions in 2013 and 2014 made waves at the time, and in the near term were seen as incrementally improving areas of Google’s internet business, such as improving search or providing better bidding on ad rates.

But the tech giant hasn’t taken its eye off the ball in the intervening years, and overlooking its long-term commitment to AI would be a mistake.

Just like it has cemented its role in the smartphone ecosystem with its Android operating system, Google is pushing hard to share its open-source TensorFlow machine learning software with developers and companies of all sizes While many companies like

are using AI internally to improve customer experience or to create products like voice assistant Alexa, Google has opened up the gates and is welcoming the world into its AI ecosystem.

We’ve seen this blueprint before, where Google was happy to allow a community of smart, driven experts to help it build Android to be a world leader in mobile software.

You could do worse than bet they would do the same thing with their artificial intelligence platform.

Sure, there’s no material profits yet.

But if AI becomes the next big Google platform, running the systems in homes and cars the way Android runs tablets and phones, Alphabet will surely find a way to capitalize on that in the years ahead.

IBM The opposite of Google’s approach is the proprietary Watson system created by International Business Machines Corp.

Many Americans are most familiar with Watson for its trivia skills displayed on television show “Jeopardy.” But aside from quirky PR stunts, the supercomputer has found a role performing much more practical tasks in recent years.

Since 2013, for example, Watson has been in use at Memorial Sloan-Kettering Cancer Center in New York to help oncologists make the best decisions based on mountains of medical records and real-life diagnoses.

And last January a Japanese insurance firm became so reliant on Watson’s actuarial skills that it laid off a few dozen human employees.

IBM has married a powerful machine learning interface with its existing enterprise tech operation, selling Watson’s AI under the “software-as-a-service model” that has been so profitable for cloud computing firms in recent years.

It’s a natural iteration for IBM’s business — and a necessary one, too, as the struggling technology giant sees persistent revenue headwinds and increasingly is looking to both the cloud and artificial intelligence results to boost performance.

The company just reported its 22nd consecutive quarter of revenue declines, though it did beat on profits thanks in part to 20% growth in its cloud division.

When you marry the strategic imperatives of cloud and AI with the existing scale and reach of IBM, it’s hard to imagine that the company will not be a serious play in AI for years to come.

Furthermore, a 10-year partnership with MIT launched this year will all but ensure a generation of eager engineers come into the American workforce with ready skills to deploy Watson at their workplaces.

This is not as sexy or as grandiose as Google’s plan to democratize AI and spread it around the world.

But for investors, the appeal is IBM’s bright line between this emerging technology and near-term profit potential.

Robotics and AI ETF If you’re unwilling to pick a winner in the race for artificial intelligence applications, I don’t blame you.

Emerging technologies are not just hard to fully understand, but they are tumultuous businesses where upstarts can come out of nowhere and leaders can fall from grace.

  comes in.

This unique and diversified ETF invests in companies “that potentially stand to benefit from increased adoption and utilization of robotics and artificial intelligence.” Because this spans all applications, it makes for an intriguing portfolio.

Top holdings now include Nvidia Corp.

  for its leading Drive PX platform that can power self-driving cars, Japanese “smart factory” supplier Omron Corp.

  and medical robotics company Cyberdyne

The most interesting thing about these holdings is that they aren’t nebulous plays on some general AI theme and the hope of machine learning on a grand scale.

Most are profiting now with targeted business models that marry automation and AI to produce real-world results.

For this strategy the ETF charges a rather modest 0.68% expense ratio, or $68 annually on $10,000 invested.

That seems a small price to pay for a diversified and thoughtful basket of potential AI winners.

Humans Need Not Apply

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