Will a Robot Take Your Job? It May Just Make Your Job Worse

The robot revolution is always allegedly just around the corner. In the utopian vision, technology emancipates human labor from repetitive, mundane tasks, freeing us to be more productive and take on more fulfilling work. In the dystopian vision, robots come for everyone’s jobs, put millions and millions of people out of work, and throw the economy into chaos.

Such a warning was at the crux of Andrew Yang’s ill-fated presidential campaign, helping propel his case for universal basic income that he argued would become necessary when automation left so many workers out. It’s the argument many corporate executives make whenever there’s a suggestion they might have to raise wages: $15 an hour will just mean machines taking your order at McDonald’s instead of people, they say. It’s an effective scare tactic for some workers.

But we often spend so much time talking about the potential for robots to take our jobs that we fail to look at how they are already changing them — sometimes for the better, but sometimes not. New technologies can give corporations tools for monitoring, managing, and motivating their workforces, sometimes in ways that are harmful. The technology itself might not be innately nefarious, but it makes it easier for companies to maintain tight control on workers and squeeze and exploit them to maximize profits.

“The basic incentives of the system have always been there: employers wanting to maximize the value they get out of their workers while minimizing the cost of labor, the incentive to want to control and monitor and surveil their workers,” said Brian Chen, staff attorney at the National Employment Law Project (NELP). “And if technology allows them to do that more cheaply or more efficiently, well then of course they’re going to use technology to do that.”

Tracking software for remote workers, which saw a bump in sales at the start of the pandemic, can follow every second of a person’s workday in front of the computer. Delivery companies can use motion sensors to track their drivers’ every move, measure extra seconds, and ding drivers for falling short.

Automation hasn’t replaced all the workers in warehouses, but it has made work more intense, even dangerous, and changed how tightly workers are managed. Gig workers can find themselves at the whims of an app’s black-box algorithm that lets workers flood the app to compete with each other at a frantic pace for pay so low that how lucrative any given trip or job is can depend on the tip, leaving workers reliant on the generosity of an anonymous stranger. Worse, gig work means they’re doing their jobs without many typical labor protections.

In these circumstances, the robots aren’t taking jobs, they’re making jobs worse. Companies are automating away autonomy and putting profit-maximizing strategies on digital overdrive, turning work into a space with fewer carrots and more sticks.

A robot boss can do a whole lot more watching

In recent years, Amazon has become the corporate poster child for automation in the name of efficiency — often at the expense of workers. There have been countless reports of unsustainable conditions and expectations at Amazon’s fulfillment centers. Its drivers reportedly have to consent to being watched by artificial intelligence, and warehouse workers who don’t move fast enough can be fired.

Demands are so high that there have been reports of people urinating in bottles to avoid taking a break. The robots aren’t just watching, they’re also picking up some of the work. Sometimes, it’s for the better, but in other cases, they may actually be making work more dangerous as more automation leads to more pressure on workers. One report found that worker injuries were more prevalent in Amazon warehouses with robots than warehouses without them.

“It would have been prohibitively expensive to employ enough managers to time each worker’s every move to a fraction of a second or ride along in every truck, but now it takes maybe one,” Dzieza wrote. “This is why the companies that most aggressively pursue these tactics all take on a similar form: a large pool of poorly paid, easily replaced, often part-time or contract workers at the bottom; a small group of highly paid workers who design the software that manages them at the top.”

A 2018 Gartner survey found that half of large companies were already using some type of nontraditional techniques to keep an eye on their workers, including analyzing their communications, gathering biometric data, and examining how workers are using their workspace. They anticipated that by 2020, 80 percent of large companies would be using such methods. Amid the pandemic, the trend picked up pace as businesses sought more ways to keep tabs on the new waves of workers working from home.

This has all sorts of implications for workers, who lose privacy and autonomy when they’re constantly being watched and directed by technology. Daron Acemoglu, an economist at MIT, warned that they’re also losing money. “Some of these new digital technologies are not simply replacing workers or creating new tasks or changing other aspects of productivity, but they’re actually monitoring people much more effectively, and that means rents are being shared very differently because of digital technologies,” he said.

He offered up a hypothetical example of a delivery driver who is asked to deliver a certain number of packages in a day. Decades ago, the company might pay the driver more to incentivize them to work a little faster or harder or put in some extra time. But now, they’re constantly being monitored so that the company knows exactly what they’re doing and is looking for ways to save time. Instead of getting a bonus for hitting certain metrics, they’re dinged for spending a few seconds too long here or there.

The problem isn’t technology itself, it’s the managers and corporate structures behind it that look at workers as a cost to be cut instead of as a resource.

“A lot of this boom of Silicon Valley entrepreneurship where venture capital made it very easy for companies to create firms didn’t exactly prioritize the well-being of workers as one of their main considerations,” said Amy Bix, a historian at Iowa State University who focuses on technology. “A lot of what goes on in the structure of these corporations and the development of technology is invisible to most ordinary people, and it’s easy to take advantage of that.”

The future of Uber isn’t driverless cars, it’s drivers

Uber’s destiny was supposed to be driverless.

In 2016, former CEO Travis Kalanick told Bloomberg making an autonomous vehicle was “basically existential” for the company. After a deadly accident with an autonomous Uber vehicle in 2018, current chief executive Dara Khosrowshahi reiterated that the company remained “absolutely committed” to the self-driving cause. But in December 2020 and after investing $1 billion, Uber sold off its self-driving unit. A little over four months later, its main competitor, Lyft, followed suit. Uber says it’s still not giving up on autonomous technology, but the writing on the wall is clear that driverless cars aren’t core to Uber’s business model, at least in the near future.

“Five or 10 years from now, drivers are still going to be a big piece of the mix on a percentage basis [of Uber’s business], and on an absolute basis, they may be an even bigger piece than they are today even with autonomous in the mix because the business should get bigger as both segments get bigger,” said Chris Frank, director of corporate ratings at S&P Global. “In addition, drivers will need to handle more complex conditions like poorly marked roads or inclement weather.”

In other words, they’re going to need workers to make money — workers they would very much like not to classify as such.

Gig economy companies such as Uber, Lyft, and DoorDash are fighting tooth and nail to make sure the people they enlist to make deliveries or drive people around are not considered their employees. In California last year, such companies dumped $200 million into lobbying to pass Proposition 22, which lets app-based transportation and delivery companies classify their workers as independent contractors and therefore avoid paying for benefits such as sick leave, employer-provided health care, and unemployment. After it passed, a spokesman for the campaign for the ballot measure said it “represents the future of work in an increasingly technologically-driven economy.”

It’s a future of work that might not be pleasant for gig workers. In California, some workers say they’re not getting the benefits companies promised after Prop 22’s passage, such as health care stipends. Companies said that workers would make at least 120 percent of California’s minimum wage, but that’s contemplating the time they spend driving only. Before the ballot initiative was passed, research from the UC Berkeley Labor Center estimated that it would guarantee a minimum wage of just $5.64 per hour.

Companies say they’ve been clear with drivers about how to qualify for the health care stipend, which is available to drivers with more than 15 engaged hours a week (in other words, if you don’t have a job and are waiting around, it doesn’t count). In a statement to Vox, Geoff Vetter, a spokesperson for the Protect App-Based Drivers + Services Coalition, the lobbying group that championed Prop 22, said that 80 percent of drivers work fewer than 20 hours per week and most work less than 10 hours per week, and that many have health insurance through other jobs.

Gig companies have sometimes been cagey about how much their workers make, and they’re often changing their formulas. In 2017, Uber agreed to pay the Federal Trade Commission $20 million over charges that it misled prospective drivers about how much they could make with the app. The FTC found that Uber claimed some of its drivers made $90,000 in New York and $74,000 in San Francisco, when in reality their median incomes were actually $61,000 and $53,000, respectively. DoorDash caused controversy over a decision to pocket tips and use them to pay delivery workers, which it has since reversed.

Even though Uber is charging customers more for rides in the wake of the pandemic, that’s not directly being passed onto their drivers. According to the Washington Post, Uber changed the way it paid drivers in California soon after Prop 22 passed so that they were no longer paid a proportion of the cost of the ride but instead by time and distance, with different bonuses and incentives based on market and surge pricing. (This is how Uber does it in most states, but it had changed things up during the push to get Prop 22 passed.) Uber’s CEO pushed back on the Post story in a series of tweets, arguing that decoupling driver pay from customer fares had not hurt California drivers and that some are now getting a higher cut from their rides.

In light of a driver shortage, Uber recently announced what it’s billing as a $250 million “driver stimulus” that promises higher earnings to try to get drivers back onto the road. The company acknowledges this initiative is likely temporary once the supply-demand imbalance works itself out. Still, it’s hard not to notice how quickly Uber and Lyft have been able to corner most of the ride-hailing app market and exert control over their drivers and customers.

“When a new thing like this comes on, there’s huge new consumer benefits, and then over time they are the market, they have less competition except one another, probably they’re a cartel at this point. And then they start doing stuff that’s much nastier,” said David Autor, an economist at MIT.

One of the gig economy’s main selling points to workers is that it offers flexibility and the ability to work when they want. It’s certainly true that an Uber or Lyft driver has much more autonomy on the job than, say, an Amazon warehouse worker. “People drive with Lyft because they prefer the freedom and flexibility to work when, where, and for however long they want,” a Lyft spokesperson said in a statement to Vox.

“They can choose to accept a ride or not, enjoy unlimited upward earning potential, and can decide to take time off from driving whenever they want, for however long they want, without needing to ask a ‘boss’ — all things they can’t do at most traditional jobs.” The spokesperson also noted that most of its drivers work outside of Lyft.

But flexibility doesn’t mean gig companies have no control over their drivers and delivery people. They use all sorts of tricks and incentives to try to push workers in certain directions and manage them, essentially, by algorithm. Uber drivers report being bothered by the constant surveillance, the lack of transparency from the company, and the dehumanization of working with the app. The algorithm doesn’t want to know how your day is, it just wants you to work as efficiently as possible to maximize its profits.

Carlos Ramos, a former Lyft driver in San Diego, described the feeling of being manipulated by the app. He noticed the company must have needed morning drivers because of the incentives structures, but he also often wondered if he was being “punished” if he didn’t do something right.

“Sometimes, if you cancel a bunch of rides in a row or if you don’t take certain rides to certain things, you won’t get any rides. They’ve shadow turned you off,” he said. The secret deprioritization of a worker is something many Lyft and Uber drivers speculate happens. “You also have no way of knowing what’s going on behind there. They have this proprietary knowledge, they have this black box of trade secrets, and those are your secrets you’re telling them,” said Ramos, now an organizer with Gig Workers Rising.

Companies deny that they secretly shut off drivers. “It is in Lyft’s best interests for drivers to have as positive an experience as possible, so we communicate often and work directly with drivers to help them improve their earnings,” a Lyft spokesperson said. “We never ‘shadow ban’ drivers, and actively coach them when they are in danger of being deactivated.”

The future of innovation isn’t inevitable

We often talk about technology and innovation with a language of inevitability. It’s as though whenever wages go up, companies will of course replace workers with robots. Now that the country is turned on to online delivery, it can be made to seem like the grocery industry is on an unavoidable path to gig work. After all, that’s what happened with Albertsons. But that’s not really the case — there’s plenty of human agency in the technological innovation story.

“Technology of course doesn’t have to exploit workers, it doesn’t have to mean robots are coming for all of our jobs,” Chen said. “These are not inevitable outcomes, they are human decisions, and they are almost always made by people who are driven by a profit motive that tends to exploit the poor and working class historically.”

Chase Copridge, a longtime California worker who’s done the gamut of gig jobs — Instacart, DoorDash, Amazon Flex, Uber, and Lyft — is one of the people stuck in that position, the victim of corporate tendencies on technological overdrive. He described seeing delivery offers that pay as little as $2. He turns those jobs down, knowing that it’s not economically worth it for him. But there might be someone else out there who picks it up. “We’re people who desperately need to make ends meet, who are willing to take the bare minimum that these companies are giving out to us,” he said. “People need to understand that these companies thrive off of exploitation.”

Not all decisions around automation are ones that increase productivity or improve really anything except corporate profits. Self-checkout stations may reduce the need for cashiers, but are they really making the shopping experience faster or better? Next time you go to the grocery store and inevitably screw up scanning one of your own items and waiting several minutes for a worker to appear, you tell me.

Despite technological advancements, productivity growth has been on the decline in recent years. “This is the paradox of the last several decades, and especially since 2000, that we had enormous technological changes as we perceive it but measured productivity growth is quite weak,” Autor said. “One reason may be that we’re automating a lot of trivial stuff rather than important stuff. If you compare antibiotics and indoor plumbing and electrification and air travel and telecommunications to DoorDash and smartphones or self-checkout, it may just not be as consequential.”

Acemoglu said that when firms focus so much on automation and monitoring technologies, they might not explore other areas that could be more productive, such as creating new tasks or building out new industries. “Those are the things that I worry have fallen by the wayside in the last several years,” he said. “If your employer is really set on monitoring you really tightly, that biases things against new tasks because those are things that are not easier to monitor.”

It matters what you automate, and not all automation is equally beneficial, not only to workers but also to customers, companies, and the broader economy.

Grappling with how to handle technological advancements and the ways they change people’s lives, including at work, is no easy task. While the robot revolution isn’t taking everyone’s jobs, automation is taking some of them, especially in areas such as manufacturing. And it’s just making work different: A machine may not eliminate a position entirely, but it may turn a more middle-skill job into a low-skill job, bringing lower pay with it. Package delivery jobs used to come with a union, benefits, and stable pay; with the rise of the gig economy, that’s declining. If and when self-driving trucks arrive, there will still be some low-quality jobs needed to complete tasks the robots can’t.

“The issue that we’ve faced in the US economy is that we’ve lost a lot of middle-skill jobs so people are being pushed down into lower categories,” Autor said. “Automation historically has tended to take the most dirty and dangerous and demeaning jobs and hand them over to machines, and that’s been great.

What’s happened in the last bunch of decades is that automation has affected the middle-skill jobs and left the hard, interesting, creative jobs and the hands-on jobs that require a lot of dexterity and flexibility but don’t require a lot of formal skills.”

But again, none of this is inevitable. Companies are able to leverage technology to get the most out of workers because workers often don’t have power to push back, enforce limits, or ask for more. Unionization has seen steep declines in recent decades. America’s labor laws and regulations are designed around full-time work, meaning gig companies don’t have to offer health insurance or help fund unemployment. But the laws could — and many would argue should — be modernized.

“The key thing is it’s not just technology, it’s a question of labor power, both collectively and individually,” Bix said. “There are a lot of possible outcomes, and in the end, technology is a human creation. It’s a product of social priorities and what gets developed and adopted.”

Maybe the robot apocalypse isn’t here yet. Or it is, and many of us aren’t quite recognizing it, in part because we got some of the story wrong. The problem isn’t really the robot, it’s what your boss wants the robot to do.

Source: Will a robot take your job? It may just make your job worse. – Vox

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Critics:

The history of robots has its origins in the ancient world. During the industrial revolution, humans developed the structural engineering capability to control electricity so that machines could be powered with small motors. In the early 20th century, the notion of a humanoid machine was developed.

The first uses of modern robots were in factories as industrial robots. These industrial robots were fixed machines capable of manufacturing tasks which allowed production with less human work. Digitally programmed industrial robots with artificial intelligence have been built since the 2000s.

Concepts of artificial servants and companions date at least as far back as the ancient legends of Cadmus, who is said to have sown dragon teeth that turned into soldiers and Pygmalion whose statue of Galatea came to life. Many ancient mythologies included artificial people, such as the talking mechanical handmaidens (Ancient Greek: Κουραι Χρυσεαι (Kourai Khryseai); “Golden Maidens”) built by the Greek god Hephaestus (Vulcan to the Romans) out of gold.

Reference:

6 Math Foundations to Start Learning Machine Learning

As a Data Scientist, machine learning is our arsenal to do our job. I am pretty sure in this modern times, everyone who is employed as a Data Scientist would use machine learning to analyze their data to produce valuable patterns. Although, why we need to learn math for machine learning? There is some argument I could give, this includes:

  • Math helps you select the correct machine learning algorithm. Understanding math gives you insight into how the model works, including choosing the right model parameter and the validation strategies.
  • Estimating how confident we are with the model result by producing the right confidence interval and uncertainty measurements needs an understanding of math.
  • The right model would consider many aspects such as metrics, training time, model complexity, number of parameters, and number of features which need math to understand all of these aspects.
  • You could develop a customized model that fits your own problem by knowing the machine learning model’s math.

The main problem is what math subject you need to understand machine learning? Math is a vast field, after all. That is why in this article, I want to outline the math subject you need for machine learning and a few important point to starting learning those subjects.

Machine Learning Math

We could learn many topics from the math subject, but if we want to focus on the math used in machine learning, we need to specify it. In this case, I like to use the necessary math references explained in the Machine Learning Math book by M. P. Deisenroth, A. A. Faisal, and C. S. Ong, 2021.

In their book, there are math foundations that are important for Machine Learning. The math subject is:

Image created by Author

Six math subjects become the foundation for machine learning. Each subject is intertwined to develop our machine learning model and reach the “best” model for generalizing the dataset.

Let’s dive deeper for each subject to know what they are.

Linear Algebra

What is Linear Algebra? This is a branch of mathematic that concerns the study of the vectors and certain rules to manipulate the vector. When we are formalizing intuitive concepts, the common approach is to construct a set of objects (symbols) and a set of rules to manipulate these objects. This is what we knew as algebra.

If we talk about Linear Algebra in machine learning, it is defined as the part of mathematics that uses vector space and matrices to represent linear equations.

When talking about vectors, people might flashback to their high school study regarding the vector with direction, just like the image below.

Geometric Vector (Image by Author)

This is a vector, but not the kind of vector discussed in the Linear Algebra for Machine Learning. Instead, it would be this image below we would talk about.

Vector 4×1 Matrix (Image by Author)

What we had above is also a Vector, but another kind of vector. You might be familiar with matrix form (the image below). The vector is a matrix with only 1 column, which is known as a column vector. In other words, we can think of a matrix as a group of column vectors or row vectors. In summary, vectors are special objects that can be added together and multiplied by scalars to produce another object of the same kind. We could have various objects called vectors.

Matrix (Image by Author)

Linear algebra itself s a systematic representation of data that computers can understand, and all the operations in linear algebra are systematic rules. That is why in modern time machine learning, Linear algebra is an important study.

An example of how linear algebra is used is in the linear equation. Linear algebra is a tool used in the Linear Equation because so many problems could be presented systematically in a Linear way. The typical Linear equation is presented in the form below.

Linear Equation (Image by Author)

To solve the linear equation problem above, we use Linear Algebra to present the linear equation in a systematical representation. This way, we could use the matrix characterization to look for the most optimal solution.

Linear Equation in Matrix Representation (Image by Author)

To summary the Linear Algebra subject, there are three terms you might want to learn more as a starting point within this subject:

  • Vector
  • Matrix
  • Linear Equation

Analytic Geometry (Coordinate Geometry)

Analytic geometry is a study in which we learn the data (point) position using an ordered pair of coordinates. This study is concerned with defining and representing geometrical shapes numerically and extracting numerical information from the shapes numerical definitions and representations. We project the data into the plane in a simpler term, and we receive numerical information from there.

Cartesian Coordinate (Image by Author)

Above is an example of how we acquired information from the data point by projecting the dataset into the plane. How we acquire the information from this representation is the heart of Analytical Geometry. To help you start learning this subject, here are some important terms you might need.

  • Distance Function

A distance function is a function that provides numerical information for the distance between the elements of a set. If the distance is zero, then elements are equivalent. Else, they are different from each other.

An example of the distance function is Euclidean Distance which calculates the linear distance between two data points.

Euclidean Distance Equation (Image by Author)
  • Inner Product

The inner product is a concept that introduces intuitive geometrical concepts, such as the length of a vector and the angle or distance between two vectors. It is often denoted as ⟨x,y⟩ (or occasionally (x,y) or ⟨x|y⟩).

Matrix Decomposition

Matrix Decomposition is a study that concerning the way to reducing a matrix into its constituent parts. Matrix Decomposition aims to simplify more complex matrix operations on the decomposed matrix rather than on its original matrix.

A common analogy for matrix decomposition is like factoring numbers, such as factoring 8 into 2 x 4. This is why matrix decomposition is synonymical to matrix factorization. There are many ways to decompose a matrix, so there is a range of different matrix decomposition techniques. An example is the LU Decomposition in the image below.

LU Decomposition (Image by Author)

Vector Calculus

Calculus is a mathematical study that concern with continuous change, which mainly consists of functions and limits. Vector calculus itself is concerned with the differentiation and integration of the vector fields. Vector Calculus is often called multivariate calculus, although it has a slightly different study case. Multivariate calculus deals with calculus application functions of the multiple independent variables.

There are a few important terms I feel people need to know when starting learning the Vector Calculus, they are:

  • Derivative and Differentiation

The derivative is a function of real numbers that measure the change of the function value (output value) concerning a change in its argument (input value). Differentiation is the action of computing a derivative.

Derivative Equation (Image by Author)
  • Partial Derivative

The partial derivative is a derivative function where several variables are calculated within the derivative function with respect to one of those variables could be varied, and the other variable are held constant (as opposed to the total derivative, in which all variables are allowed to vary).

  • Gradient

The gradient is a word related to the derivative or the rate of change of a function; you might consider that gradient is a fancy word for derivative. The term gradient is typically used for functions with several inputs and a single output (scalar). The gradient has a direction to move from their current location, e.g., up, down, right, left.

Probability and Distribution

Probability is a study of uncertainty (loosely terms). The probability here can be thought of as a time where the event occurs or the degree of belief about an event’s occurrence. The probability distribution is a function that measures the probability of a particular outcome (or probability set of outcomes) that would occur associated with the random variable. The common probability distribution function is shown in the image below.

Normal Distribution Probability Function (Image by Author)

Probability theory and statistics are often associated with a similar thing, but they concern different aspects of uncertainty:

•In math, we define probability as a model of some process where random variables capture the underlying uncertainty, and we use the rules of probability to summarize what happens.

•In statistics, we try to figure out the underlying process observe of something that has happened and tries to explain the observations.

When we talk about machine learning, it is close to statistics because its goal is to construct a model that adequately represents the process that generated the data.

Optimization

In the learning objective, training a machine learning model is all about finding a good set of parameters. What we consider “good” is determined by the objective function or the probabilistic models. This is what optimization algorithms are for; given an objective function, we try to find the best value.

Commonly, objective functions in machine learning are trying to minimize the function. It means the best value is the minimum value. Intuitively, if we try to find the best value, it would like finding the valleys of the objective function where the gradients point us uphill. That is why we want to move downhill (opposite to the gradient) and hope to find the lowest (deepest) point. This is the concept of gradient descent.

Gradient Descent (Image by Author)

There are few terms as a starting point when learning optimization. They are:

  • Local Minima and Global Minima

The point at which a function best values takes the minimum value is called the global minima. However, when the goal is to minimize the function and solved it using optimization algorithms such as gradient descent, the function could have a minimum value at different points. Those several points which appear to be minima but are not the point where the function actually takes the minimum value are called local minima.

Local and Global Minima (Image by Author)
  • Unconstrained Optimization and Constrained Optimization

Unconstrained Optimization is an optimization function where we find a minimum of a function under the assumption that the parameters can take any possible value (no parameter limitation). Constrained Optimization simply limits the possible value by introducing a set of constraints.

Gradient descent is an Unconstrained optimization if there is no parameter limitation. If we set some limit, for example, x > 1, it is an unconstrained optimization.

Conclusion

Machine Learning is an everyday tool that Data scientists use to obtain the valuable pattern we need. Learning the math behind machine learning could provide you an edge in your work. There are many math subjects out there, but there are 6 subjects that matter the most when we are starting learning machine learning math, and that is:

  • Linear Algebra
  • Analytic Geometry
  • Matrix Decomposition
  • Vector Calculus
  • Probability and Distribution
  • Optimization

If you start learning math for machine learning, you could read my other article to avoid the study pitfall. I also provide the math material you might want to check out in that article.

 

By: Cornellius Yudha Wijaya

Source: 6 Math Foundations to Start Learning Machine Learning | by Cornellius Yudha Wijaya | Towards Data Science

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Critics:

Machine learning (ML) is the study of computer algorithms that improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as “training data“, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.

A subset of machine learning is closely related to computational statistics, which focuses on making predictions using computers; but not all machine learning is statistical learning. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a related field of study, focusing on exploratory data analysis through unsupervised learning. In its application across business problems, machine learning is also referred to as predictive analytics.

Machine learning approaches are traditionally divided into three broad categories, depending on the nature of the “signal” or “feedback” available to the learning system:

  • Supervised learning: The computer is presented with example inputs and their desired outputs, given by a “teacher”, and the goal is to learn a general rule that maps inputs to outputs.
  • Unsupervised learning: No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end (feature learning).
  • Reinforcement learning: A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle or playing a game against an opponent). As it navigates its problem space, the program is provided feedback that’s analogous to rewards, which it tries to maximize.

References

A Critical Piece Of The Machine Economy: The People

Over the shoulder view of young Asian businesswoman using AI assistant on smartphone

70% of GDP growth in the global economy between now and 2030 will be driven by the machines, according to PwC. This is a near $7 trillion dollar contribution to U.S. GDP based around the combined production from artificial intelligence, machine learning, robotics, and embedded devices. This is the rise of a new machine economy.

For those not familiar with the machine economy, it’s where the smart, connected, autonomous, and economically independent machines or devices carry out the necessary activities of production, distribution, and operations with little or no human intervention. The development of this economy is how Industry 4.0 becomes a reality.

Visionary leaders will implement new technologies and combine them with capital investments in ways that help them grow, expand, diversify, and actually improve lives. These machine economy leaders will operate in a new intelligent systems world in thousands of companies that will drive new economic models globally.

Sounds good so far, but all of that autonomous machinery isn’t going to build and operate itself.

Not enough people to do the work

While most people would agree that manufacturing is an important part of our economy, they aren’t recommending their children pursue that line of work. It’s expected that 4.6 million manufacturing jobs created between now and 2028 will go unfilled. Key drivers for this change include the fact that 10,000 baby boomers retire every day without people to replace them.

The workforce is quickly losing the second-largest age group, and millennials (the largest group) have so far not been attracted to manufacturing jobs at large. Instead they tend to be drawn toward technology, engineering, finance. The underlying issue may be one of perception, as the future of manufacturing will in fact include a much higher degree of technology, engineering, and finance in order to function.

Different skills are needed

Manufacturing jobs are changing. The number of purely manual, repetitive tasks are shrinking as technology advances to handle those jobs with robots and automation. Fifty percent of manufacturers have already adopted some form of automation, and now they need people with critical thinking, programming, and digital skills. Tomorrow’s jobs have titles such as Digital Twin Engineer, Robot Teaming Coordinator, Drone Data Coordinator, Smart Scheduler, Factory Manager, Safety Supervisor, and so on.

The shifts in productivity are happening so quickly, humans can’t keep up with them

An unskilled position can be filled relatively quickly as the prerequisite qualifications are limited. It typically takes months to fill a skilled position, and in most cases much longer for an individual to develop the requisite skills before they even think to apply. One alternative is to lower requirements in terms of education, skill, and experience in order to get someone new in the position, but then companies have to absorb the entire expense of training them.

Meanwhile there is increased pressure to utilize existing people’s and teams’ times and skills as much as possible, which can lead to burnout. This is a tenuous cycle that needs to be fortified by making sure our workforce has the skills training they need, when and where they need it.

In order to thrive in the machine economy, we need to invest significantly in people as well as in infrastructure. Focusing purely on infrastructure might lead to short-term and maybe mid-term profits, but ultimately it is not sustainable, and everyone loses. One can’t simply say, “We couldn’t fill the positions,” while there are people who need work.

Level-up our workforce

The human capacity to learn is basically limitless when individuals are motivated and have access to something to learn. There are several ways to tap into that capacity. First, we need to capture the knowledge and experience of the employees we have, so that those relevant skills can be passed on to the next wave of workers. We also need to ensure relevant training is available for people at every level of the company so that new people get up to speed and tenured employees don’t get left behind.

While some technologies need to be learned on the job, there is a level of foundational skill to understand in the machine economy, in addition to the technical and vocational skills required within a given field. An investment in, and possibly partnerships with, local schools could be a wise move for many companies. Lastly, while college is a great path for many people, it’s not the only form of higher education. Investments in vocational training and apprenticeship programs will be critical for our society to thrive in the machine economy.

Just as workers need to rethink and develop new skills, employers need to rethink and develop new ways of nurturing and attracting talent. To fully realize the promise of the machine economy, it is incumbent upon us to ensure people have access to the training and the tools they need in order to not only be successful but thrive. After all, what’s the point of all this technology if it doesn’t make life better for everyone?

PRESIDENT AND CEO

With more than 25 years of experience driving digital innovation and growth at technology companies, Kevin Dallas is responsible for all aspects of the Wind River business globally. He joined Wind River from Microsoft, where he most recently served as the corporate vice president for cloud and AI business development. At Microsoft, he led a team creating partnerships that enable the digital transformation of customers and partners across a range of industries including: connected/autonomous vehicles, industrial IoT, discrete manufacturing, retail, financial services, media and entertainment, and healthcare.

Prior to joining Microsoft in 1996, he held roles at NVIDIA Corporation and National Semiconductor (now Texas Instruments Inc.) in the U.S., Europe, and the Middle East in roles that included microprocessor design, systems engineering, product management, and end-to-end business leadership. He currently serves as a director on the board of Align Technology, Inc. He holds a B.S.c. degree in electrical and electronic engineering from Staffordshire University, Stoke-on-Trent, Staffordshire, England.

Source: A Critical Piece Of The Machine Economy: The People

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Critics:

Digital economy refers to an economy that is based on digital computing technologies, although we increasingly perceive this as conducting business through markets based on the internet and the World Wide Web. The digital economy is also referred to as the Internet Economy, New Economy, or Web Economy.

Increasingly, the digital economy is intertwined with the traditional economy, making a clear delineation harder. It results from billions of everyday online connections among people, businesses, devices, data, and processes. It is based on the interconnectedness of people, organizations, and machines that results from the Internet, mobile technology and the internet of things (IoT).

Digital economy is underpinned by the spread of Information and Communication Technologies (ICT) across all business sectors to enhance its productivity.Digital transformation of the economy is undermining conventional notions about how businesses are structured, how consumers obtain services, informations and goods and how states need to adapt to these new regulatory challenges.

Intensification of the global competition for human resources

Digital platforms rely on ‘deep learning‘ to scale up their algorithm’s capacity. The human-powered content labeling industry is constantly growing as companies seek to harness data for AI training. These practices have raised concerns concerning the low-income revenue and health-related issues of these independent workers. For instance, digital companies such as Facebook or YouTube use ‘content monitor’-contractors who work as outside monitors hired by a professional services company subcontractor- to monitor social media to remove any inappropriate content.

Thus, the job consists of watching and listening to disturbing posts that can be violent or sexual. In January 2020, through its subcontractor services society, Facebook and YouTube have asked the ‘content moderators’ to sign a PTSD (Posttraumatic Stress Disorder) disclosure after alleged cases of mental disorders witnessed on workers.

See also

References

AI Innovators: This Researcher Uses Deep Learning To Prevent Future Natural Disasters – Lisa Lahde

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Meet Damian Borth, chair in the Artificial Intelligence & Machine Learning department at the University of St. Gallen (HSG) in Switzerland, and past director of the Deep Learning Competence Center at the German Research Center for Artificial Intelligence (DFKI). He is also a founding co-director of Sociovestix Labs, a social enterprise in the area of financial data science. Damian’s background is in research where he focuses on large-­scale multimedia opinion mining applying machine learning and in particular deep learning to mine insights (trends, sentiment) from online media streams. Damian talks about his realization in deep learning and shares why integrating his work with deep learning is an important part to help prevent future natural disasters……..

Read more: https://www.forbes.com/sites/nvidia/2018/09/19/ai-innovators-this-researcher-uses-deep-learning-to-prevent-future-natural-disasters/#be6f7b16cd16

 

 

 

 

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Microlearning Best Practices Creating A Lesson – Isha Sood

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Microlearning definitely does not involve cramming all the material you used to deliver in 15 minutes into 5 – that is a strategy bound to lead to failure. Some re-engineering of content to match a targeted approach on the achievement of one key outcome must happen and will put most of our skills as communicators to the test. Microlearning development involves two key stages……

Read more: https://elearningindustry.com/microlearning-best-practices-creating-lesson

 

 

 

Your kindly Donations would be so effective in order to fulfill our future research and endeavors – Thank you

How Will Artificial Intelligence Change The Legal System – Christian Haigh

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How will AI, machine learning, and big data affect the legal system as technology improves? originally appeared on Quora: the place to gain and share knowledge, empowering people to learn from others and better understand the world.

Answer by Christian Haigh, Co-founder, Legalist, on Quora:

For a long time, lawyers believed they couldn’t be replaced by machines.

It’s true: the legal industry over the past decade has amassed a graveyard of failed attempts to innovate and few large exits. It’s also true that legal arguments can be highly case-specific and not necessarily conducive to automation.

But asking whether individual lawyers can be entirely replaced by machines isn’t asking the right question. Rather, can one lawyer, augmented by machines, perform the same work that five lawyers used to do?

Easily. It’s already happening.

When Curtis, our General Counsel started his career, he and other associates at his law firm would physically go to the offices of the defendant and take evidence for discovery. When he started his law firm, he owned his own servers. E-discovery did not exist. The cloud was not widely used. You needed teams of associates just to go to the law library and do research.

As a business, you needed a lawyer just to draft incorporation documents.

Change rarely comes in the forms that we would expect. Companies like LegalZoom provide free legal resources. Axiom provides remote lawyers on demand. At Legalist, our engineers supplement our business team and allow us to punch above our weight compared to every other litigation funding company in the industry. That’s because of our technology.

This question originally appeared on Quora – the place to gain and share knowledge, empowering people to learn from others and better understand the world. You can follow Quora on Twitter, Facebook, and Google+. More questions:

Quora: The best answer to any question.

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