Category Archives: robotics

Video Friday on Steroids

Here is a list of the URL’s for all the YouTube and other videos shown on this blog from January 2014 through May of this year. I encourage you to shop this list, clicking on the links. There’s a lot of good stuff, including several  instructional videos on machine learning and other technical topics, a series on robotics, and several videos on climate and climate change.

January 2014

The Polar Vortex Explained in Two Minutes

NASA – Six Decades of a Warming Earth

“CHASING ICE” captures largest video calving of glacier

Machine Learning and Econometrics

Can Crime Prediction Software Stop Criminals?

Analytics 2013 – Day 1

The birth of a salesman

Economies Improve

Kaggle – Energy Applications for Machine Learning

2014 Outlook with Jan Hatzius

Nassim Taleb Lectures at the NSF

Vernon Smith – Experimental Markets



Forecast Pro – Quick Tour

February 2014

Stephen Wolfram’s Introduction to the Wolfram Language


Econometrics – Quantile Regression

Quantile Regression Example

Brooklyn Grange – A New York Growing Season

Getting in Shape for the Sport of Data Science

Machine Learning – Decision Trees

Machine Learning – Random Forests

Machine Learning – Random Forecasts Applications

Malcolm Gladwell on the 10,000 Hour Rule

Sornette Talk

Head of India Central Bank Interview

March 2014

David Stockman

Partial Least Squares Regression

April 2014

Thomas Piketty on Economic Inequality

Bonobo builds a fire and tastes marshmellows

Future Technology

May 2014

Ray Kurzweil: The Coming Singularity

Paul Root Wolpe: Kurzweil Critique

The Future of Robotics and Artificial Intelligence

Car Factory – KIA Sportage Assembly Line

10 Most Popular Applications for Robots

Predator Drones

The Future of Robotic Warfare

Bionic Kangaroo

Ping Pong Playing Robot

Baxter, the Industrial Robot


Automation, Robotics -Trends and Impacts

Trying to figure out the employment impacts of automation, computerization, and robotics is challenging, to say the least.

There are clear facts, such as the apparent permanent loss of jobs in US manufacturing since the early 1990’s.


But it would be short-sighted to conclude these jobs have been lost to increased use of computers and robots in production.

That’s because, for one thing, you might compare a chart like the above with statistics on Chinese manufacturing.


Now you can make a case – if you focus on the urban Chinese manufacturing employment – that these two charts are more or less mirror images of one another in recent years. That is urban manufacturing employment in China, according the US BLS, increased about 4 mllion 2002-2009, while US manufacturing employment dropped by about that amount over the same period.

Of course, there are other off-shore manufacturing sites of importance, such as the maquiladoras along the US border with Mexico.

But what brings robotics into focus for me is that significant automation and robotics are being installed in factories in China.

Terry Guo, head of Foxconn – the huge Chinese contract manufacturer making the I-phone and many other leading electronics products – has called for installation of a million industrial robots in Foxconn factories over the next few years.

In fact, Foxconn apparently is quietly partnering with Google to help bring its vision of robotics to life.

Decoupling of Productivity and Employment?

Erik Brynjolfsson at MIT is an expert on the productivity implications of information technology (IT).

About a year ago, the MIT Technology Review ran an article How Technology Is Destroying Jobs featuring the perspective developed recently by Brynjolfsson that there is increasingly a disconnect between productivity growth and jobs in the US.

The article featured two infographics – one of which I reproduce here.


There have been highly focused studies of the effects of computerization on specific industries.

Research published just before the recent economic crisis did an in-depth regarding automation or computerization in a “valve industry,” arriving at three, focused findings.

First, plants that adopt new IT-enhanced equipment also shift their business strategies by producing more customized valve products. Second, new IT investments improve the efficiency of all stages of the production process by reducing setup times, run times, and inspection times. The reductions in setup times are theoretically important because they make it less costly to switch production from one product to another and support the change in business strategy to more customized production. Third, adoption of new IT-enhanced capital equipment coincides with increases in the skill requirements of machine operators, notably technical and problem-solving skills, and with the adoption of new human resource practices to support these skills

This is the positive side of the picture.

No more drudgery on assembly lines with highly repetitive tasks. Factory workers are being upgraded to computer operatives.

More to follow.

Jobs and the Next Wave of Computerization

A duo of researchers from Oxford University (Frey and Osborne) made a splash with their analysis of employment and computerization in the US (English spelling). Their research, released September of last year, projects that –

47 percent of total US employment is in the high risk category, meaning that associated occupations are potentially automatable over some unspecified number of years, perhaps a decade or two..

Based on US Bureau of Labor Statistics (BLS) classifications from O*NET Online, their model predicts that most workers in transportation and logistics occupations, together with the bulk of office and administrative support workers, and labour in production occupations, are at risk.

This research deserves attention, if for no other reason than masterful discussions of the impact of technology on employment and many specific examples of new areas for computerization and automation.

For example, I did not know,

Oncologists at Memorial Sloan-Kettering Cancer Center are, for example, using IBM’s Watson computer to provide chronic care and cancer treatment diagnostics. Knowledge from 600,000 medical evidence reports, 1.5 million patient records and clinical trials, and two million pages of text from medical journals, are used for benchmarking and pattern recognition purposes. This allows the computer to compare each patient’s individual symptoms, genetics, family and medication history, etc., to diagnose and develop a treatment plan with the highest probability of success..

There are also specifics of computerized condition monitoring and novelty detection -substituting for closed-circuit TV operators, workers examining equipment defects, and clinical staff in intensive care units.

A followup Atlantic Monthly article – What Jobs Will the Robots Take? – writes,

We might be on the edge of a breakthrough moment in robotics and artificial intelligence. Although the past 30 years have hollowed out the middle, high- and low-skill jobs have actually increased, as if protected from the invading armies of robots by their own moats. Higher-skill workers have been protected by a kind of social-intelligence moat. Computers are historically good at executing routines, but they’re bad at finding patterns, communicating with people, and making decisions, which is what managers are paid to do. This is why some people think managers are, for the moment, one of the largest categories immune to the rushing wave of AI.

Meanwhile, lower-skill workers have been protected by the Moravec moat. Hans Moravec was a futurist who pointed out that machine technology mimicked a savant infant: Machines could do long math equations instantly and beat anybody in chess, but they can’t answer a simple question or walk up a flight of stairs. As a result, menial work done by people without much education (like home health care workers, or fast-food attendants) have been spared, too.

What Frey and Osborne at Oxford suggest is an inflection point, where machine learning (ML) and what they call mobile robotics (MR) have advanced to the point where new areas for applications will open up – including a lot of menial, service tasks that were not sufficiently routinized for the first wave.

In addition, artificial intelligence (AI) and Big Data algorithms are prying open up areas formerly dominated by intellectual workers.

The Atlantic Monthly article cited above has an interesting graphic –

jobsautomationSo at the top of this chart are the jobs which are at 100 percent risk of being automated, while at the bottom are jobs which probably will never be automated (although I do think counseling can be done to a certain degree by AI applications).

The Final Frontier

This blog focuses on many of the relevant techniques in machine learning – basically unsupervised learning of patterns – which in the future will change everything.

Driverless cars are the wow example, of course.

Bottlenecks to moving further up the curve of computerization are highlighted in the following table from the Oxford U report.


As far as dexterity and flexibility goes, Baxter shows great promise, as the following YouTube from his innovators illustrates.

There also are some wonderful examples of apparent creativity by computers or automatic systems, which I plan to detail in a future post.

Frey and Osborn, reflecting on their research in a 2014 discussion, conclude

So, if a computer can drive better than you, respond to requests as well as you and track down information better than you, what tasks will be left for labour? Our research suggests that human social intelligence and creativity are the domains were labour will still have a comparative advantage. Not least, because these are domains where computers complement our abilities rather than substitute for them. This is because creativity and social intelligence is embedded in human values, meaning that computers would not only have to become better, but also increasingly human, to substitute for labour performing such work.

Our findings thus imply that as technology races ahead, low-skill workers will need to reallocate to tasks that are non-susceptible to computerisation – i.e., tasks requiring creative and social intelligence. For workers to win the race, however, they will have to acquire creative and social skills. Development strategies thus ought to leverage the complementarity between computer capital and creativity by helping workers transition into new work, involving working with computers and creative and social ways.

Specifically, we recommend investing in transferable computer-related skills that are not particular to specific businesses or industries. Examples of such skills are computer programming and statistical modeling. These skills are used in a wide range of industries and occupations, spanning from the financial sector, to business services and ICT.

Implications For Business Forecasting

People specializing in forecasting for enterprise level business have some responsibility to “get ahead of the curve” – conceptually, at least.

Not everybody feels comfortable doing this, I realize.

However, I’m coming to the realization that these discussions of how many jobs are susceptible to “automation” or whatever you want to call it (not to mention jobs at risk for “offshoring”) – these discussions are really kind of the canary in the coal mine.

Something is definitely going on here.

But what are the metrics? Can you backdate the analysis Frey and Osborne offer, for example, to account for the coupling of productivity growth and slower employment gains since the last recession?

Getting a handle on this dynamic in the US, Europe, and even China has huge implications for marketing, and, indeed, social control.

Machine Learning and Next Week

Here is a nice list of machine learning algorithms. Remember, too, that they come in two or three flavors – supervised, unsupervised, semi-supervised, and reinforcement learning.


An objective of mine is to cover each of these techniques with an example or two, with special reference to their relevance to forecasting.

I got this list, incidentally, from an interesting Australian blog Machine Learning Mastery.

The Coming Week

Aligned with this marvelous list, I’ve decided to focus on robotics for a few blog posts coming up.

This is definitely exploratory, but recently I heard a presentation by an economist from the National Association of Manufacturers (NAM) on manufacturing productivity, among other topics. Apparently, robotics is definitely happening on the shop floor – especially in the automobile industry, but also in semiconductors and electronics assembly.

And, as mankind pushes the envelope, drilling for oil in deeper and deeper areas offshore and handling more and more radioactive and toxic material, the need for significant robotic assistance is definitely growing.

I’m looking for indices and how to construct them – how to guage the line between merely automatic and what we might more properly call robotic.