In many parts of the U.S., robots have been replacing workers over the last few decades. But to what extent, really? Some technologists have forecast that automation will lead to a future without work, while other observers have been more skeptical about such scenarios.

Now a study co-authored by an MIT professor puts firm numbers on the trend, finding a very real impact — although one that falls well short of a robot takeover. The study also finds that in the U.S., the impact of robots varies widely by industry and region, and may play a notable role in exacerbating income inequality.

“We find fairly major negative employment effects,” MIT economist Daron Acemoglu says, although he notes that the impact of the trend can be overstated.

From 1990 to 2007, the study shows, adding one additional robot per 1,000 workers reduced the national employment-to-population ratio by about 0.2 percent, with some areas of the U.S. affected far more than others.

This means each additional robot added in manufacturing replaced about 3.3 workers nationally, on average.

That increased use of robots in the workplace also lowered wages by roughly 0.4 percent during the same time period.

“We find negative wage effects, that workers are losing in terms of real wages in more affected areas, because robots are pretty good at competing against them,” Acemoglu says.

The paper, “Robots and Jobs: Evidence from U.S. Labor Markets,” appears in advance online form in the Journal of Political Economy. The authors are Acemoglu and Pascual Restrepo PhD ’16, an assistant professor of economics at Boston University.

Displaced in Detroit
To conduct the study, Acemoglu and Restrepo used data on 19 industries, compiled by the International Federation of Robotics (IFR), a Frankfurt-based industry group that keeps detailed statistics on robot deployments worldwide. The scholars combined that with U.S.-based data on population, employment, business, and wages, from the U.S. Census Bureau, the Bureau of Economic Analysis, and the Bureau of Labor Statistics, among other sources.

The researchers also compared robot deployment in the U.S. to that of other countries, finding it lags behind that of Europe. From 1993 to 2007, U.S. firms actually did introduce almost exactly one new robot per 1,000 workers; in Europe, firms introduced 1.6 new robots per 1,000 workers.

“Even though the U.S. is a technologically very advanced economy, in terms of industrial robots’ production and usage and innovation, it’s behind many other advanced economies,” Acemoglu says.

In the U.S., four manufacturing industries account for 70 percent of robots: automakers (38 percent of robots in use), electronics (15 percent), the plastics and chemical industry (10 percent), and metals manufacturers (7 percent).

Across the U.S., the study analyzed the impact of robots in 722 commuting zones in the continental U.S. — essentially metropolitan areas — and found considerable geographic variation in how intensively robots are utilized.

Given industry trends in robot deployment, the area of the country most affected is the seat of the automobile industry. Michigan has the highest concentration of robots in the workplace, with employment in Detroit, Lansing, and Saginaw affected more than anywhere else in the country.

“Different industries have different footprints in different places in the U.S.,” Acemoglu observes. “The place where the robot issue is most apparent is Detroit. Whatever happens to automobile manufacturing has a much greater impact on the Detroit area [than elsewhere].”

In commuting zones where robots were added to the workforce, each robot replaces about 6.6 jobs locally, the researchers found. However, in a subtle twist, adding robots in manufacturing benefits people in other industries and other areas of the country — by lowering the cost of goods, among other things. These national economic benefits are the reason the researchers calculated that adding one robot replaces 3.3 jobs for the country as a whole.

The Inequality Issue
In conducting the study, Acemoglu and Restrepo went to considerable lengths to see if the employment trends in robot-heavy areas might have been caused by other factors, such as trade policy, but they found no complicating empirical effects.

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Though artificial intelligence (AI) has been a hot topic among technologists for quite some time, it’s only in recent years that semiconductor, software, and cloud capabilities have gotten to the point where companies are now deploying AI on a wider basis.

AI is so powerful because it can help companies on every part of the income statement. It can identify the best leads and better satisfy customers through recommendation engines, helping to boost revenue. AI can also help automate many back office tasks, saving companies on their selling, general, and administrative costs. And perhaps most exciting, AI can also help research and development departments find new solutions or correct flaws in highly technological manufacturing processes, benefiting both research and development as well as costs of goods sold.

February is shaping up to be a crucial month for these three AI leaders across cloud, memory hardware, and software-based analytics. Here’s what investors should watch.

Alphabet

Alphabet (NASDAQ:GOOG) (NASDAQ:GOOGL) reports its fourth-quarter earnings today, the last of the large-cap “FAANG” stocks to do so this earnings season.

Alphabet has two main issues that will heavily affect its earnings and outlook. Its main business of digital advertising saw a nice pickup last year; however, investors might be cautious on digital ads today after rival Facebook (NASDAQ:FB) disappointed in its revenue outlook last week. Facebook management said revenue would decelerate in the “low to mid-single digits” for the first quarter, because of “the maturity of our business, as well as the increasing impact from global privacy regulation and other ad targeting related headwinds.” 

Keep in mind Facebook still grew revenue a solid 25% last quarter, so it’s not a huge deal to see some deceleration. I also don’t think Alphabet’s Google search engine would be quite as affected by privacy regulation, since customers voluntarily enter information they’re searching for. However, investors should keep a watch on Alphabet’s forward guidance and any commentary around privacy regulations affecting their core digital advertising business.

In addition, the next big growth driver for Alphabet could be its cloud computing division. Alphabet doesn’t disclose cloud revenue separately, as leader Amazon.com (NASDAQ:AMZN) does, but rather groups it in with its hardware and app store revenue in a category called “Google other.” Alphabet has also not disclosed specific cloud growth rates either, as does its other cloud rival, Microsoft (NASDAQ:MSFT). Nevertheless, management usually gives lots of qualitative commentary on the cloud, and it’s been investing heavily in that unit over the past 18 months under new cloud head Thomas Kurian. 

Both Microsoft and Amazon reported strong cloud results in their recent earnings reports, so investors should monitor management’s cloud commentary to see if Alphabet is falling behind these two leaders, or if a rising tide is lifting all boats.

Micron

Another leader in AI, though this time on the hardware side, is Micron Technology (NASDAQ:MU). Micron is a leader in both DRAM memory, NAND flash, and a new kind of memory component called 3D Xpoint, which is just being commercialized now.

Micron doesn’t report earnings this month; it has an off-quarter schedule and doesn’t report until late March. However, there are a number of interesting developments going on in the memory market in February. Most exciting, the memory market seems poised to begin a strong up-cycle, and Micron’s management called the bottom of the current cycle in late December.

The NAND flash market, where Micron earns about 30% of its revenue, is already in the early stages price increases after a huge crash in average selling prices over the past 18 months. Some analysts have even forecast a 40% rise in NAND flash pricing this year, which would significantly boost revenue and profit for all industry players if true.

Meanwhile, while Micron’s core DRAM market is lagging behind the NAND market in its upswing, the DRAM market is also showing signs of improvement. Memory market research firm DrameXchange, a division of Trendforce, recently revised upward its first-quarter forecast for DRAM pricing, from an initial projection of flattish average selling prices to a low-single-digit price increase. That could mean that the oversupply in the DRAM market is adjusting faster than some expected, which could lead to better projected results for 2020.

However, Micron is also very sensitive to macroeconomic conditions, and the recent coronavirus outbreak in China seems to have caused blind selling in all such component stocks — Micron included. While Micron would definitely be affected by a near-term drop-off in demand, it’s not showing up in results just yet. Storage and memory are crucial to AI applications, so long-term investors may wish to give all related stocks a look based on the current fear-induced sell-off.

Alteryx

Finally, big data analytics software company Alteryx (NYSE:AYX) reports earnings on Feb. 13. Alteryx makes an end-to-end software suite that allows both data scientists and average knowledge workers alike to create, organize, and share predictive analytics models across an enterprise.

Alteryx has been on my radar for a little while, and I’m sorry to say I’ve missed the stock’s ridiculous 35% surge in just the past month. What has investors so bullish? While there wasn’t any big news in January, investors may be refocusing on Alteryx’s long-term prospects after a bout of profit-taking in late 2019. Management believes Alteryx’s total addressable market for data analytics and spreadsheet users combined is around $73 billion, versus Alteryx’s trailing-12-month revenue of just $351 million. That leaves a huge amount of white space there for the taking, even if Alteryx shares that pie with competitors such as Tableau, which salesforce.com (NYSE:CRM) acquired in August.

However, Alteryx hasn’t shown much competitive pressure yet. Last quarter’s revenue accelerated 65%, beating the prior-year quarter’s revenue growth rate of 59%. That accelerating growth put the stock back up near its all-time highs, and it now trades at a quite lofty valuation multiple of 26 times sales. Admittedly, that’s pricey, even for the high-growth software-as-service sector.

For the upcoming earnings report, I’ll be watching closely to see if the company can beat its forecast for 44%-47% revenue growth. While that would mark a deceleration from last quarter, growth rates are bound to come down as the company’s revenue base gets bigger, and high-40s growth is hard to find in many large companies. Alteryx also has positive operating income, somewhat rare for a high-flying software company still investing heavily in growth.

With coronavirus fears hitting all tech stocks, investors should look for any type of pullback, perhaps after earnings, as a potential entry point in this winning AI-related software stock.

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Skin conditions are among the most common kind of ailment globally, just behind colds, fatigue, and headaches. In fact, it’s estimated that 25% of all treatments provided to patients around the world are for skin conditions and that up to 37% of patients seen in the clinic have at least one skin complaint.

The enormous case workload and a global shortage of dermatologists have forced sufferers to seek out general practitioners, who tend to be less accurate than specialists when it comes to identifying conditions. This trend motivated researchers at Google to investigate an AI system capable of spotting the most common dermatological disorders seen in primary care. In a paper (“A Deep Learning System for Differential Diagnosis of Skin Diseases“) and accompanying blog post, they report that it achieves accuracy across 26 skin conditions when presented with images and metadata about a patient case, and they claim that it’s on par with U.S. board-certified dermatologists.

“We developed a deep learning system (DLS) to address the most common skin conditions seen in primary care,” wrote Google software engineer Yuan Liu and Google Health technical program manager Dr. Peggy Bui. “This study highlights the potential of the DLS to augment the ability of general practitioners who did not have additional specialty training to accurately diagnose skin conditions.”

As Liu and Bui further explained, dermatologists don’t give just one diagnosis for any skin condition — instead, they generate a ranked list of possible diagnoses (a differential diagnoses) to be systematically narrowed by subsequent lab tests, imaging, procedures, and consultations. So too does the Google researchers’ system, which processes inputs that include one or more clinical images of the skin abnormality and up to 45 types of metadata (e.g., self-reported components of the medical history, such as age, sex, and symptoms).

The research team says it evaluated the model with 17,777 de-identified cases from 17 primary care clinics across two states. They bifurcated the corpus and used the portion of records dated between 2010 and 2017 to train the AI system, reserving the portion from 2017 to 2018 for evaluation. During training, the model leveraged over 50,000 differential diagnoses provided by over 40 dermatologists.

In a test of the system’s diagnostic accuracy, the researchers compiled diagnoses from three U.S. board-certified dermatologists. Just over 3,750 cases were aggregated to derive the ground truth labels, and the AI system’s ranked list of skin conditions achieved  71% and 93% top-1 and top-3 accuracies, respectively. Furthermore, when the system was compared against three categories of clinicians (dermatologists, primary care physicians, and nurse practitioners) on a subset of the validation data set, the team reports that its top three predictions demonstrated a top-3 diagnostic accuracy of 90%, or comparable to dermatologists (75%) and “substantially higher” than primary care physicians (60%) and nurse practitioners (55%).

Lastly, in order to evaluate potential bias toward skin type, the team tested the AI system’s performance based on the Fitzpatrick skin type, a scale that ranges from Type I (“pale white, always burns, never tans”) to Type VI (“darkest brown, never burns”). Focusing on skin types that represent at least 5% of the data, they found that the model’s performance was similar, with a top-1 accuracy ranging from 69% to 72%, and a top-3 accuracy from 91% to 94%.

The researchers credit the presence of metadata in the training corpus with the system’s overall accuracy and say the results suggest their approach might “help prompt clinicians … to consider possibilities” that weren’t originally in their differential diagnoses. However, they note that their training corpus was only taken from a one teledermatology service; that some Fitzpatrick skin types were too rare in their data set to allow meaningful training or analysis; and that their data set didn’t accurately detect some skin conditions, such as melanoma, due to a lack of available data samples.

“We believe these limitations can be addressed by including more cases of biopsy-proven skin cancers in the training and validation sets,” wrote Liu and Bui. “The success of deep learning to inform the differential diagnosis of skin disease is highly encouraging of such a tool’s potential to assist clinicians. For example, such a DLS could help triage cases to guide prioritization for clinical care or help non-dermatologists initiate dermatologic care more accurately and could potentially improve access [to care].”

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With every new trend comes a counter-trend. And so despite the current excitement over the wonders of artificial intelligence, one company is betting that human intelligence can still deliver solutions for businesses that AI cannot hope to match.

Article One Partners (AOP) is a crowdsourced network of over 42,000 researchers in 170 countries — 42% of whom have graduate degrees in a variety of science, technology, and engineering specialties. The firm got its start uncovering patent-busting prior art for defendants in high-stakes patent infringement suits, where it quickly earned a reputation for finding invalidating prior art in hidden corners of the globe that Google search could never reach — an unpublished Korean-language PhD dissertation, a rural Norwegian library, even in a New York City pawn shop. Their work often found that a “novel invention” wasn’t so novel after all.

But in recent years, AOP’s sleuths have begun to make a name for themselves as an all-purpose “human search engine” that can help businesses solve challenges that algorithm-based search engines cannot, especially in the development and marketing of innovative new products.

Earlier this year, for example, a small manufacturer based in Europe needed to develop a pipe system that could move highly-volatile and abrasive hydrocarbons like solvents and metal cleaning agents safely over long distances. Hydrocarbons tend to destroy everything they touch — park your car in a puddle of gasoline and your tires will swell and eventually rot. So the company needed to invent a new type of material for the pipe works that would be resistant to organic chemical reactions from the liquid passing through it at varying pressures — and yet still be deformable (i.e., able to swell up to twice its width but then reform to its original shape).

A well-formulated search engine string could certainly point to materials already developed, and research already published. But to find a truly novel yet cost-effective solution, the company felt it needed human insight and expertise in multiple scientific and engineering disciplines. So it retained the British-based innovation consultancy The Moon on a Stick, which in turn called upon AOP for help.

According to The Moon on a Stick’s managing partner Sean Warren, the results were impressive. “AOP’s research crowd came back with 142 possible solutions or compositions that would enable the pipes to withstand the volatile hydrocarbon material and perform as needed,’ Warren noted.  “I was quite surprised by the depth and relevance of the technical approaches they uncovered, some of which the client had never even imagined.”

These included a novel approach using nanotechnology, as well as some little-known new research underway at U.S., European, and Asian universities.

AOP also works with large enterprises, even those with vast internal resources like the telecom giant AT&T and the $30 billion technology giant Philips, the latter of which initially retained AOP to assist with its patent function. But as Brian Hinman, the firm’s retiring chief intellectual property officer, explained, the relationship soon expanded. “We now use AOP to identify manufacturing and distribution channels for certain goods, as well as to explore new trends in particular technology domains.”

One new tech area where Philips was considering expanding its R&D effort was Visible Light Communications (VLC), which uses a band of visual light between 400 and 800 THz to send data such as ads to in-store consumers (or potentially, instant replay video to spectators in a football stadium). Philips deployed AOP experts to start digging for everything they could find — products, companies making products, and new cutting-edge research in VLC — that would help the company make a business decision on whether, and how, to invest in VLC or not.

This is where the distinction between algorithms and human judgment becomes crucial. A search engine query can quickly tell you a lot about VLC, its history, a few of the major players, and some published research in the field. But to make a business decision about whether to invest tens of millions of dollars in developing and marketing VLC products, Philips needed the experi8ence, insight, and business judgment of human experts who could assess the size and scope of the market opportunity as well as the best “white space” innovation areas for the firm.

Bet-the-company decisions like that should not be left to an algorithm, said Philips’s Hinman. “AOP produced actionable intelligence that enabled us to make informed decisions regarding innovation focus, invention generation, and potential acquisitions.”

To be sure, the robust AI systems now being designed and implemented do more than simply answer search queries. They can also manage systems, conduct operations, and take action. But fundamentally — at least so far — these are differences mostly of degree, not kind.

In any event, for challenges that quite literally require boots on the ground, even the most advanced AI system won’t be able to compete with a network of human sleuths. AOP’s CEO Peter Vanderheyden offered one example:

“We were engaged by a global licensing organization for one of the world’s biggest consumer products,” he recalled. “They asked us to find out where unlicensed devices were being sold around the world. Now, Google could point to all kinds of articles about counterfeit products in China or in India. It could also give you estimates of the losses due to counterfeit product sales. But that only tells this licensing organization what they already know, right?

“So we asked our researchers to go out and actually knock on doors,” he continued. “We had them go into their local stores, in whatever country they were located, and take six pictures of every box containing a device that featured this advertised consumer technology — one photo of each side of the box. The goal was to see if the package displayed the proper license label.”

To no one’s surprise, AOP sleuths produced photos of quite a few unlicensed products around the world. “And mind you, this was unbiased, third party, time-stamped evidence,” he added. “Very admissible in court. Which you better believe this licensing organization made sure to mention when it contacted those unlicensed vendors.”

Vanderheyden claimed that AOP’s work helped the licensing organization collect millions of dollars in new licensing revenues, though he declined to be more specific. “We also helped identify ways to improve their licensing control practices to reduce problems,” he added.

AOP’s latest bet on human intelligence was the launch last month of a new TalentSource service, offering qualified expert technologists from its crowd on a contract basis to companies. The aim here is to fill a growing need within companies for expertise in new or adjacent technologies outside their core R&D competence that industry convergence is increasingly forcing them to contend with. TalentSource enables these firms to bring in the talent needed to explore these new technological areas without having to invest yet in hiring full-time staff.

What’s unique about TalentSource compared to traditional technology consulting firms in the industry? AOP’s Vanderheyden claims it’s the depth of its bench of subject matter experts — again, 42,000 experts, almost half of whom have advanced degrees — as well as the flexible on-demand nature of their availability.

Whatever happens with Article One Partners and its various ventures in HI (human intelligence), it does seem clear that despite the enormous promise of AI,  there will always be some challenges that require human judgment, expertise, and insight to deal with effectively.

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