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Machine Learning Impacts the 21st Century Workforce
Machine Learning Impacts the 21st Century Workforce
Machine Learning is capable of a plethora of innovations. There is not yet a widely-hared agreement on the tasks where machine learning systems will excel.
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Over the past several decades, computers have transformed work in almost every sector of the economy. Today, we are at the beginning of an even larger and more rapid transformation due to recent advances in machine learning.  Machine learning based on neural networks is arguably the branch of artificial intelligence that will have the most widespread near-term commercial impact, because of its ability to accelerate the pace of automation itself.

Like the steam engine and electricity, MACHINE LEARNING is a general-purpose technology, capable of spawning a plethora of additional innovations and capabilities.  However, there is not yet a widely-shared agreement on the tasks where MACHINE LEARNING systems will excel. Therefore, there is little agreement on the specific expected effects on the workforce and on the economy more broadly.

Recently, Erik Brynjolfsson of MIT and and Tom Mitchell of Carnegie Mellon addressed these questions in a study published in the journal Science.  That study, based upon an understanding of what the current generation of MACHINE LEARNING systems can and cannot do, examined the key implications of MACHINE LEARNING for the workforce.

It found that while parts of many jobs may be "suitable for MACHINE LEARNING," other tasks within these same jobs do not fit the criteria for MACHINE LEARNING well.  Hence, its effects on employment are more complex than the simple "replacement and substitution story" emphasized by most analysts.  Furthermore, the current economic effects of MACHINE LEARNING are relatively limited; so, we are not facing the imminent "end of work," which is sometimes proclaimed.  However, the longer-term implications for the economy and the workforce are profound.

One of the biggest areas of concern about machine learning is its impact on economic inequality.  To date, automation is just one factor, others include increased globalization and mass migration.  However, the potential for large and rapid changes due to MACHINE LEARNING, suggests that its economic effects could be disruptive, creating both winners and losers. Ensuring a positive outcome will require considerable attention form policy-makers, business leaders, technologists, and researchers.

Attention to unintended consequences is important because the implications of MACHINE LEARNING are harder to forecast than those of previous digital technologies.  Why?  Because MACHINE LEARNING has potential to address a daunting challenge that we've faced with all prior technologies.  As the philosopher Michael Polanyi observed, "we know more than we can tell."  Recognizing a face, riding a bike, and understanding speech are tasks humans know how to do very well; but our ability to reflect on how we perform these tasks is poor.  For that reason, we cannot codify many tasks easily, or perhaps at all, using a set of formal rules. Thus, prior to MACHINE LEARNING, "Polanyi's paradox" limited the set of tasks that could be automated by programming computers.

Until recently, creating a new computer program involved a labor-intensive process of manual coding. But this expensive process is increasingly being augmented or replaced by a more automated process of running an existing MACHINE LEARNING algorithm on appropriate training data.  Today, MACHINE LEARNING algorithms have, in many cases, made it possible to automatically train computer systems to be more accurate and more capable than those manually programmed by humans.

The importance of this shift is two-fold. First, in a growing subset of applications, such as face recognition and credit card fraud detection, this paradigm can produce more accurate and reliable programs than human programmers.  And,

Second, this paradigm can dramatically lower costs for creating and maintaining new software. This lowered cost reduces the barrier to experimenting with and exploring potential computerization.  And it encourages development of computer systems that will automatically automate many types of routine workflows with little or no human intervention.

Such progress in MACHINE LEARNING has been particularly rapid in the past 6 to 8 years due in large part to the sheer volume of training data now available for some tasks.  In many cases it is large enough to capture crucial and previously-unnoticed regularities in the tasks.  Often these applications use data sets that are impossibly large for any person to examine or comprehend, but which are easily within the processing ability of MACHINE LEARNING algorithms. When large enough training data sets are available, as in dermatology diagnosis, the game of Go, or detecting potential credit card fraud, MACHINE LEARNING can sometimes produce computer programs that outperform the best humans at the task.

Also critical to MACHINE LEARNING progress has been the combination of improved algorithms, including deep neural networks (or DNNs) and extremely fast computer hardware.  For example, Facebook switched from phrase-based machine translation models to DNNs for more than 4.5 billion language translations each day.  DNNs for image recognition have also driven error rates on ImageNet, down from more than 30% in 2010 to less than 3% today.  Similarly, DNNs have helped improve error rates from 8.4% to 4.9% in voice recognition since July 2016. -- Breaking through this 5% threshold for image and speech recognition is important because that is roughly the error rate of human experts when given similar data.

Although recent advances in the capabilities of MACHINE LEARNING systems are impressive, they are not equally suitable for all tasks. The current wave of successes draws heavily on a paradigm known as "supervised learning," which typically uses DNNs. This combination can be immensely powerful in domains that are well-suited for them.  However, their competence is also dramatically narrower and more fragile than human decision-makers, and there are many tasks for which this approach is completely ineffective.

How can you determine whether machine-learning can effectively automate an application?   Brynjolfsson and Mitchell have identified eight criteria to look for:

Criterion #1: It involves learning a function that maps well-defined inputs to well-defined outputs. These functions often include classification and prediction. Although MACHINE LEARNING may learn to predict the Y value associated with any given input X, this is a learned statistical correlation that might not capture causal effects.

Criterion #2: Large digital data-sets containing input-output pairs are available. The more training examples that are available, the more accurate the learning.  One of the remarkable characteristics of DNNs is that performance in many domains does not become asymptotic after a certain number of examples.  So, it is especially important that all of the relevant input features be captured in the training data. Although, in principle, any arbitrary function can be represented by a DNN, computers are vulnerable to mimicking and perpetuating unwanted biases present in the training data and to missing regularities that involve variables that they cannot observe. Digital data sets can often be created by monitoring existing processes, hiring humans to explicitly tagor label portions of the data, or by simulating the relevant problem setting.

Criterion #3: The task provides clear feedback with clearly definable goals and metrics.  MACHINE LEARNING works well when we can clearly describe the goals, even if we cannot necessarily define the best process for achieving those goals. That contrasts with earlier approaches to automation. The ability to capture the input-output decisions of individuals, although it might allow learning to mimic those individuals, might not lead to optimal system-wide performance because the humans themselves might make imperfect decisions. Therefore, having clearly defined system-wide metrics for performance (for example, to optimize traffic flow throughout a city rather than at a particular intersection) provides a "gold standard" for the MACHINE LEARNING system. MACHINE LEARNING is particularly powerful when training data are labeled according to such gold standards, thereby defining the desired goals.

Criterion #4: The function involves no long chains of logic or reasoning that depend on diverse background knowledge or common sense.  MACHINE LEARNING systems are very strong at learning empirical associations in data but are less effective when the task requires long chains of reasoning or complex planning that rely on common sense or background knowledge unknown to the computer.

Criterion #5: The function requires no detailed explanation of how the decision was made. Large neural nets learn to make decisions by subtly adjusting up to several hundred million numerical weights that interconnect their artificial neurons. Explaining the reasoning for such decisions to humans can be difficult because DNNs often do not make use of the same intermediate abstractions that humans do. While work is under way on explainable AI systems, current systems are relatively weak in this area. For example, whereas computers can diagnose certain types of cancer or pneumonia as well as or better than expert doctors, their ability to explain why or how they came up with the diagnosis is poor when compared with human doctors. For many perceptual tasks, humans are also poor at explaining their thinking, for example, how they recognize words within the sounds they hear.

Criterion #6: The function involves tolerance for error and no need for provably correct or optimal solutions. Nearly all MACHINE LEARNING algorithms derive their solutions statistically and probabilistically.  As a result, it is rarely possible to train them to 100% accuracy.  Even the best clinical diagnosis, speech recognition, and object recognition systems make errors (as do the best humans).  Therefore, tolerance to errors in the learned system is an important criterion constraining adoption.

Criterion #7: The phenomenon or function being learned should not change rapidly over time.  In general, MACHINE LEARNING algorithms work well only when the distribution of future test examples is similar to the distribution of training examples.  If these distributions change over time, then retraining is typically required, and success therefore depends on the rate of change, relative to the rate of acquisition of new training data (for example, email spam filters do a good job of keeping up with adversarial spammers, partly because the rate of acquisition of new emails is high compared to the rate at which spam changes). And,

Criterion #8: No specialized dexterity, physical skills, or mobility is required.  Robots are still quite clumsy compared with humans when dealing with physical manipulation in unstructured environments and tasks. This is not so much a shortcoming of MACHINE LEARNING itself, but instead, a consequence of the state-of-the-art in general of physical-mechanical manipulators for robots.

Prior to MACHINE LEARNING, the primary impact of Information Technology has been on a relatively narrow swath of routine, highly structured and repetitive tasks. This has been a key reason that labor demand has fallen for jobs in the middle of the skill and wage spectrum, like clerks and factory workers, whereas demand for labor at the bottom (such as janitors or home health aides) and the top (such as physicians) has remained strong in most advanced countries.  But a much broader set of tasks will be automated or augmented by machines over the coming years. This includes tasks for which humans are unable to articulate a strategy, but where statistics in data reveal regularities that define a strategy. And while a framework separating routine tasks from nonroutine tasks did an effective job of describing tasks suitable for the last wave of automation, the set of tasks "suitable for MACHINE LEARNING" is often very different. Thus, simply extrapolating past trends will be misleading, and a new framework is needed.

As Brynjolfsson and Mitchell observe, jobs typically consist of a number of distinct but interrelated tasks. In most cases, only some of these tasks are likely to be suitable for MACHINE LEARNING, and they are not necessarily the ones that were easy to automate with previous technologies. For instance, Brynjolfsson and Mitchell found that a MACHINE LEARNING system can be trained to help lawyers classify potentially relevant documents for a case, but would have a much harder time interviewing potential witnesses or developing a winning legal strategy. Similarly, MACHINE LEARNING systems have made rapid advances in reading medical images, often outperforming humans in some applications. However, the more unstructured task of interacting with other doctors, and the often emotionally challenging task of communicating with and comforting patients, are much less suitable for MACHINE LEARNING approaches, at least as they exist today.

That is not to say that all tasks requiring emotional intelligence are beyond the reach of MACHINE LEARNING systems.  One of the surprising implications identified by Brynjolfsson and Mitchell is that some aspects of sales and customer interaction are potentially a very good fit for of MACHINE LEARNING. For instance, transcripts from large sets of online chats between sales-people and potential customers can be used as training data for a simple chatbot that recognizes which answers to certain common queries are most likely to lead to sales.  Companies are also using MACHINE LEARNING to identify subtle emotions from videos of people.

Another area where the work by Brynjolfsson and Mitchell departs from the conventional framework is in tasks that may involve creativity. In the old computing paradigm, each step of a process needed to be specified in advance with great precision. There was no room for the machine to be "creative" or figure out on its own how to solve a particular problem. But MACHINE LEARNING systems are specifically designed to allow the machine to figure out solutions on its own, at least for tasks that are suitable for MACHINE LEARNING.  What is required is not that the process be defined in great detail in advance, but that the properties of the desired solution be well-specified and that a suitable simulator exists so that the MACHINE LEARNING system can explore the space of available alternatives and evaluate their properties accurately.  For instance, designing a complex new device has historically been a task where humans are more capable than machines. But so-called "generative design software" can come up with new designs for objects that meet a set of requirements, more effectively than anything designed by a human, and often with a very different look and feel.

This approach works well when the final goal can be well-specified and the solutions can be automatically evaluated as clearly right or wrong, or at least better or worse.  As a result, we can expect such tasks to be increasingly subject to automation.  At the same time, the role of humans in more clearly defining goals will become more important, suggesting an increased role for scientists, entrepreneurs, and those making a contribution by asking the right questions, even if the machines are often better able to find the solutions to those questions, once they are clearly defined.

There are also many non-technological factors that will determine the implications of MACHINE LEARNING for the workforce.  As Brynjolfsson and Mitchell observe, the total effect of MACHINE LEARNING on labor demand and wages is a function of six distinct economic factors:
  1. Substitution.  Computer systems created by MACHINE LEARNING will directly substitute for some tasks, replacing humans and reducing labor demand for any given level of output
  2. Price elasticity.  Automation via machine learning may lower prices for tasks. This can lead to lower or higher total spending, depending on the price elasticity of demand. For instance, if elasticity is less than -1, then a decrease in price leads to a more than proportional increase in the quantity purchased, and therefore total spending will increase.  By analogy, as technology reduced the price of air travel after 1903, total spending on this type of travel increased, as did employment in this industry.
  3. Complementarities. Task B may be an important, or even indispensable, complement to task A which is automated. As the price of task A falls, the demand for task B will increase.  For example, as calculating was automated by computers, the demand for human programmers increased. Skills can also be complementary to other skills. For instance, interpersonal skills are increasingly complementary to analytical skills.
  4. Income elasticity. Automation may change the total income for some individuals or the broader population. And if "income elasticity" for a good is nonzero, this will in turn change demand for some types of goods and the demand for the tasks needed to produce those goods. For example, as total income has increased due to the advance of technology, Americans have spent more of their income on restaurant meals, which could mean more jobs for cooks and waiters.
  5. Elasticity of labor supply.  As wages change, the number of people working on the task will respond. If there are many people who already have the requisite skills (for example, driving a car for a ride-hailing service), then supply will be fairly elastic and wages will not rise (or fall) much, if at all, even if demand increases (or falls) a lot. In contrast, if skills are more difficult to acquire, such as becoming a data scientist, then changes in demand will mainly be reflected in wages, not employment. And,
  6. Business process redesign.  Entrepreneurs, managers, and workers constantly work to reinvent relevant processes. When faced with new technologies, they will change the production process, by design or through luck, and find more efficient ways to produce output. These changes can take time and will often economize on the most expensive inputs, increasing demand elasticity. Similarly, over time, individuals can make a choice to respond to higher wages in some occupations or places by investing in developing the new skills required for work or moving to a new location, increasing the relevant supply elasticity. With regard to machine learning, Brynjolfsson and Mitchell cite Le Chatelier's principle, which says, "both demand and supply elasticities will tend to be greater in the long run than in the short run as quasi-fixed factors adjust."
It's important to remember that machine learning won't change everything overnight.  As Brynjolfsson and Mitchell remind us, adoption and diffusion of technologies often takes years or even decades because of the need for changes in production processes, organizational design, business models, supply chains, legal constraints, and even cultural expectations. Such complementary factor are ubiquitous in modern organizations and economies; they are also subtle and difficult to identify, and they can create considerable inertia, slowing the implementation of radical new technologies.

Applications that require complementary changes on many dimensions will tend to take longer to affect the economy and workforce than those that require less redesign of existing systems. For instance, integration of autonomous trucks onto city streets might require changes in traffic laws, liability rules, insurance regulations, traffic flow, and the like, whereas the switch from talking to a human assistant to a virtual assistant in a call center might require relatively little redesign of other aspects of the business process or customer experience.

Over time, another factor becomes increasingly important: new goods, services, tasks, and processes are always being invented. These inventions can lead to the creation of altogether new tasks and jobs and thus can change the magnitudes and direction of the relationships previously mentioned. Historically, as some tasks have been automated, the freed-up labor has been redeployed to producing new goods and services or undertaking new, more effective production processes. Such innovations have been more important than increased capital, labor, or resource inputs as a force for raising overall incomes and living standards. MACHINE LEARNING systems may accelerate this process for many of the tasks that fit the criteria discussed earlier, by partially automating automation itself.

Given this trend, we offer the following forecasts for your consideration.

First, the accelerating availability of training data, will enable us to cost-effectively automate more and more tasks.

As more data are generated and pooled, we will discover which tasks can be automated cost-effectively by MACHINE LEARNING.  At the same time, we will collect data even more rapidly to create even more capable systems.  And, unlike solutions to tasks mastered by humans, many solutions to tasks automated by MACHINE LEARNING can be disseminated almost instantly worldwide. So, there is every reason to expect that future enterprise software systems will be written to embed MACHINE LEARNING in every online decision task, so that the cost of attempting to automate those tasks will come down even further.

Second, over the next twenty years machine learning will enhance productivity in virtually every industry, everywhere.

The ultimate scope and scale of further advances in MACHINE LEARNING may rival or exceed that of earlier general-purpose technologies like the internal combustion engine or electricity. These advances not only increased productivity directly but, more importantly, triggered waves of complementary innovations in machines, business organization, and even the broader economy.  Individuals, businesses, and societies that made the right complementary investments-for instance, in skills, resources, and infrastructure-thrived as a result, whereas others not only failed to participate in the full benefits, but in some cases were made worse off. Thus, a better understanding of the precise applicability of each type of MACHINE LEARNING and its implications for specific tasks is critical for understanding its likely economic impact.  And,

Third, decision-makers and consultants will use tools such as Brynjolfsson and Mitchell's 21-point "suitability for machine-learning" framework to determine where the best opportunities for each company and industry lie. 

This framework is reproduced in this month's printable edition of Trends.  Every manager should examine it and ask how it might be applied to their industry.
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