Category Archives: T – work

The World’s First Self-Driving Semi-Truck Hits the Road

The truck in question is the Freightliner Inspiration, a teched-up version of the Daimler 18-wheeler sold around the world. And according to Daimler, which owns Mercedes-Benz, it will make long-haul road transportation safer, cheaper, and better for the planet.


Humans Don’t Want These Jobs

Another point in favor of giving robots control is the serious and worsening shortage of humans willing to take the wheel. The lack of qualified drivers has created a “capacity crisis,” according to an October 2014 report by the American Transportation Research Institute. The American Trucking Associations predicts the industry could be short 240,000 drivers by 2022. (There are roughly three million full-time drivers in the US.)


Killing the Human Driver

The way to handle that growth isn’t to convince more people to become long haul truckers. It’s to reduce, and eventually eliminate, the role of the human. Let the trucks drive themselves, and you can improve safety, meet increased demand, and save time and fuel.

The safety benefits of autonomous features are obvious. The machine doesn’t get tired, stressed, angry, or distracted. And because trucks spend the vast majority of their time on the highway, the tech doesn’t have to clear the toughest hurdle: handling complex urban environments with pedestrians, cyclists, and the like. If you can prove the vehicles are safer, you could make them bigger, and thus more efficient at transporting all the crap we buy on Amazon.


The end game is eliminating the need for human drivers, at least for highway driving. (An autonomous truck could exit the interstate near the end of its journey, park in a designated lot, and wait for a human to come drive it on surface streets to its destination.)

// Interesting comments

The reason for the driver shortage is partly due to pay and benefits. If you want a driver to be away from his/her family for weeks at a time you have to pay them enough to make it worth the loss of family time. Also partly due to unrealistic expectations for delivery times by dispatchers, which adds a lot of stress to a job that already has enough of that already. So yeah I can see where companies would love a driverless semi because it would eliminate them having to consider the human/personal considerations. I haul fuel locally so not much chance of this technology replacing me, but I hate to see more jobs lost.

There are 3.5 million truck drivers in US alone, not to mention countless other transportation related jobs. Those are mostly average to decent paying jobs. Think for a second about the far reaching consequences of elimination of these jobs and secondary jobs that are also related. Further we are looking at elimination most any human-related job in the next 25 years. Do you truly feel it is a good-progress? Is it humane and progressive to live in a world where less than one 0.1% of people enslaves the rest?

Ferguson or Baltimore is not a fluke…it’s not just about racial tension. It is a fabric of our society starting to tear. Where people feel powerless and disenfranchised, the only option to be heard thats left is often violence. Whats happening there is just a beginning of what is about to come next.

One thing that has always bothered me is they always say “A truck can’t stop as fast as as a car can”, and yet we accept that excuse for a ratio of tires, weight, and lives lost due to inadequate breaking. Everything has improved, but we have stopped making progress in trying to stop a loaded truck faster.

Imagine telling the public the truth. It’s too expensive to add tires to cut breaking distance, or haul lighter loads. (or use trains).


Ref: The World’s First Self-Driving Semi-Truck Hits the Road – Wired

Robots Venture Capital A Venture Capital Firm Just Named An Algorithm To Its Board Of Directors

A Hong Kong VC fund has just appointed an algorithm to its board.

Deep Knowledge Ventures, a firm that focuses on age-related disease drugs and regenerative medicine projects, says the program, called VITAL, can make investment recommendations about life sciences firms by poring over large amounts of data.

Just like other members of the board, the algorithm gets to vote on whether the firm makes an investment in a specific company or not. The program will be the sixth member of DKV’s board.


Ref: A Venture Capital Firm Just Named An Algorithm To Its Board Of Directors – BusinessInsider

In Hiring, Algorithms Beat Instinct

You know your company inside out. You know the requirements of the position you need to fill. And now that HR has finished its interviews and simulations, you know the applicants, too—maybe even better than their friends do. Your wise and experienced brain is ready to synthesize the data and choose the best candidate for the job.

Instead, you should step back from the process. If you simply crunch the applicants’ data and apply the resulting analysis to the job criteria, you’ll probably end up with a better hire.

Humans are very good at specifying what’s needed for a position and eliciting information from candidates—but they’re very bad at weighing the results. Our analysis of 17 studies of applicant evaluations shows that a simple equation outperforms human decisions by at least 25%. The effect holds in any situation with a large number of candidates, regardless of whether the job is on the front line, in middle management, or (yes) in the C-suite.


Ref: In Hiring, Algorithms Beat Instinct – Harvard Business Review

Algorithms <-> Taylorism

By breaking down every job into a sequence of small, discrete steps and then testing different ways of performing each one, Taylor created a set of precise instructions—an “algorithm,” we might say today—for how each worker should work. Midvale’s employees grumbled about the strict new regime, claiming that it turned them into little more than automatons, but the factory’s productivity soared.

More than a hundred years after the invention of the steam engine, the Industrial Revolution had at last found its philosophy and its philosopher. Taylor’s tight industrial choreography—his “system,” as he liked to call it—was embraced by manufacturers throughout the country and, in time, around the world. Seeking maximum speed, maximum efficiency, and maximum output, factory owners used time-and-motion studies to organize their work and configure the jobs of their workers. The goal, as Taylor defined it in his celebrated 1911 treatise, The Principles of Scientific Management, was to identify and adopt, for every job, the “one best method” of work and thereby to effect “the gradual substitution of science for rule of thumb throughout the mechanic arts.” Once his system was applied to all acts of manual labor, Taylor assured his followers, it would bring about a restructuring not only of industry but of society, creating a utopia of perfect efficiency. “In the past the man has been first,” he declared; “in the future the system must be first.”

Taylor’s system is still very much with us; it remains the ethic of industrial manufacturing. And now, thanks to the growing power that computer engineers and software coders wield over our intellectual lives, Taylor’s ethic is beginning to govern the realm of the mind as well. The Internet is a machine designed for the efficient and automated collection, transmission, and manipulation of information, and its legions of programmers are intent on finding the “one best method”—the perfect algorithm—to carry out every mental movement of what we’ve come to describe as “knowledge work.”


Ref: Is Google Making Us Stupid? – The Atlantic

Google’s Algorithms Outsmart its Human Employees

Google’s “deep learning” clusters of computers churn through massives chunks of data looking for patterns–and it seems they’ve gotten good at it. So good, in fact, that Google announced at the Machine Learning Conference in San Francisco that its deep learning clusters have learned to recognize objects on their own.
Traditionally, computers have been great at transporting data, but terrible at understanding what’s contained therein. The goal of movements like the semantic web have been to build webpages that understand what kind of content they’re serving, but advances have been slow in coming. Whereas common pigeons have the conceptual ability to tell the difference between a tree and a shrub, even the most expensive supercomputers today would struggle.

Google software engineer Quoc V. Le said at the conference that he realized the deep learning clusters had made a breakthrough when they were able to recognize discrete workplace objects, such as distinguishing two different brands of paper shredders. Interestingly, Lee didn’t train the machines this way–the software had figured that out on its own.


To be clear, Google is not afraid that this will blow up into a fully sentient computer system–but it’s a stepping stone toward Google’s goal to get its computers solving menial problems in order to free up human engineers, who currently spend innumerable hours programming data processing solutions. Google’s using its deep learning clusters to better auto-recognize things in images, Android’s voice recognition, and Google Translate, among others.


Ref: How Google’s “Deep Learning” Is Outsmarting Its Human Employees – FastCompany
Ref: If this doesn’t terrify you… Google’s computers OUTWIT their humans – TheRegister

Luddite Legacy


This is the disturbing thought that, sluggish business cycles aside, America’s current employment woes stem from a precipitous and permanent change caused by not too little technological progress, but too much. The evidence is irrefutable that computerised automation, networks and artificial intelligence (AI)—including machine-learning, language-translation, and speech- and pattern-recognition software—are beginning to render many jobs simply obsolete.

This is unlike the job destruction and creation that has taken place continuously since the beginning of the Industrial Revolution, as machines gradually replaced the muscle-power of human labourers and horses. Today, automation is having an impact not just on routine work, but on cognitive and even creative tasks as well. A tipping point seems to have been reached, at which AI-based automation threatens to supplant the brain-power of large swathes of middle-income employees.


Radiologists, who can earn over $300,000 a year in America, after 13 years of college education and internship, are among the first to feel the heat. It is not just that the task of scanning tumour slides and X-ray pictures is being outsourced to Indian laboratories, where the job is done for a tenth of the cost. The real threat is that the latest automated pattern-recognition software can do much of the work for less than a hundredth of it.


Ref: Difference Engine: Luddite legacy – The Economist

Google Search Terms Can Predict the Stock Market

A new study published today in Scientific Reports by a team of British researchers, though, harnesses Google Trends data to produce investing strategies in a more nuanced way. Instead of looking at the frequency that the names of stocks or companies were searched, they analyzed a broad range of 98 commonly used words—everything from “unemployment” to “marriage” to “car” to “water”—and simulated investing strategies based on week-by-week changes in the frequencies of each of these words as search terms by American internet users.


The strategy was relatively straightforward: The system tracked whether a word such as “debt” increased in search frequency or decreased in search frequency from one week to the next. If the term was suddenly searched much less frequently, the investment simulation bought all the stocks of the Dow on the first Monday afterward, then sold all the stocks one week later, essentially betting that the overall market would rise in value.


During the period of time studied (2004-2011), making investment choices based on a few of these words in particular would have yielded overall profits several times higher than a conservative investment strategy of simply buying and holding the stocks of the Dow for the entire time. For example, basing a strategy solely on the search frequency of the word “debt,” which turned out to be the single most profitable term in the study, would have generated a profit of 326% over the seven years studied—compared to a profit of just 16% if you owned all the stocks of the Dow for the whole period.


Ref: Google Search Terms Can Predict the Stock Market – Smithsonian
Ref: Quantifying Trading Behavior in Financial Markets Using Google Trends – Nature

Google Can Identify Which of its 20,000 Employees are Most Likely to Quit

The Internet search giant recently began crunching data from employee reviews and promotion and pay histories in a mathematical formula Google says can identify which of its 20,000 employees are most likely to quit.

The move is one of a series Google has made to prevent its most promising engineers, designers and sales executives from leaving at a time when its once-powerful draws — a start-up atmosphere and soaring stock price — have been diluted by its growing size. The data crunching supplements more traditional measures like employee training and leadership meetings to evaluate talent.

Google’s algorithm helps the company “get inside people’s heads even before they know they might leave,” said Laszlo Bock, who runs human resources for the company.


Ref: Google Searches for Staffing Answers – The Wall Street Journal

Business Turns to Ants and Algorithms in Search for Profit

Foraging ants are just one example. When finding food, they lay down pheromones to mark the route to and from their nest. If something disrupts the route, the next-best alternative is quickly found. It is, therefore, what is known as a “self-healing” route.

George Danner, director of UK analytics firm Torus Business Web, says an algorithm based on ant foraging is perfect for helping companies find the optimum route for getting their products from A to B.

He has worked with a US energy major to do just this, helping it ship oil across the Gulf of Mexico more efficiently


By using this approach, Mr Danner devised a process designed to help the UK criminal justice system process cases faster, by grouping together similar kinds of offences. To a mathematician, the judiciary is just like any other factory, with complex moving parts that interact on many different levels.


“As we sit here in 2012, we are almost unlimited in what we can do. The only limitations to solving the world’s problems are ignorance and apathy.”


Ref: Business Turns to Ants and Algorithms in Search for Profit – BBC News

Algorithm that Design Structures Better than Engineers


You are watching an optimisation algorithm come up with the best design completely automatically. The outcome is greatest stiffness shape possible for a given amount of material. And amazingly it’s a nuanced truss that isn’t far removed from the look of most motorway bridges. That’s pretty reassuring, actually.

This sample 2D image was made with ToPy – open source Python ‘Topology Optimisation’ code.


Ref: Algorithms that design structures better than engineers – Jordan Burgess