The Economist
EVERY January more than 10,000 economists meet for the annual conference of the American Economic Association (AEA). This year, the shindig was in balmy Chicago, a stone’s throw from its second-tallest building, the name TRUMP stamped in extra-large letters across its base. Most papers had been written months in advance; few sessions tackled the electoral earthquake in November. Yet there was no mistaking the renewed sense, following its failure to foresee the 2007-08 financial crisis, of an academic field in a crisis of its own. The election was seen as a defeat for liberalisation and globalisation, and hence for an economics profession that had championed them. If economists wish to remain relevant and useful, the modest hand-wringing at this year’s meetings will need to yield to much deeper self-reflection.
Their theories had always shown that globalisation would produce losers as well as winners. But too many economists worried that emphasising these costs might undermine support for liberal policies. A “circle the wagons” approach to criticism of globalisation weakened the case for mitigating policies that might have protected it from a Trumpian backlash. Perhaps the greatest omissions were the questions not asked at all. Most dismal scientists exclude politics from their models altogether. As Joseph Stiglitz, a Nobel laureate, put it on one star-studded AEA panel, economists need to pay attention not just to what is theoretically feasible but also to “what is likely to happen given how the political system works”......

It’s the politics, stupid
Many economists shy away from such questions, happy to treat politics, like physics, as something that is economically important but fundamentally the business of other fields. But when ignoring those fields makes economic-policy recommendations irrelevant, broadening the scope of inquiry within the profession becomes essential. Some justifiably worry that taking more account of politics could destroy what credibility economists have left as impartial, apolitical experts. Yet politics-free models are no insulation from political pressures—just ask a climate scientist—and nothing would boost economists’ reputations more than results which match, and even predict, critical outcomes.
Political and social institutions are much harder to model and quantify than commodity or labour markets. But a qualitative approach might actually be far more scientific than equations offering little guide to how the future will unfold. Donald Trump campaigned (and may well govern) by castigating the uselessness of experts. To prepare for a time when expertise comes back into fashion, economists should renew their commitment to generating knowledge that matters.

I often show this video at the start of my classes. BTW, Political Economy is at the root of the classical economics-  Smith (wrote The Theory of Moral Sentiments) and Ricardo and Malthus (whose adopted policies against the poor are a black mark on our science).

Noah Smith
Lots of economic policy debates end up going like this: First, one economist or policy wonk will propose a government intervention -- a minimum wage increase, a tax on sugar or subsidies for solar electricity. Another person, usually someone of a more free-market bent, will demand to know exactly which market failure justifies the intervention. A market failure, in the parlance of economics, means a situation in which free markets produce wasteful outcomes. If the advocate can’t produce a theory justifying the policy, the critic claims triumph. If the advocate can find a theory that seems to support the intervention, the critic will typically then criticize the assumptions of the theory. Since most econ theories are highly stylized and have questionable ability to fit the facts, this means that free marketers claim victory quite a lot.
The demand to demonstrate a market failure isn’t fair, because it puts too much burden of proof on advocates of intervention. But it’s often rhetorically effective, because of two sociological quirks. First, many people assumes that free markets are the natural state of things. The flow and bustle of the business world seems much like a jungle, while government action feels forced and artificial. Government interventions can seem a lot like medical procedures. And of course it makes sense for doctors to diagnose an ailment before they start prescribing treatments. The medical rule of first, do no harm is a good one because nature has had millions of years to turn human bodies into self-correcting systems. That principle also makes sense for human societies tampering with natural environments.
But economies are a little bit different from natural ecosystems or the human body. Where the latter is the result of evolution, economies are defined by systems of rules, made by human beings. In some cases, those systems appear to lead to a lot of wealth and prosperity -- the U.S., Japan and much of Europe, for instance. In other cases, as in many poor countries, markets are dysfunctional, inefficient and fail to produce growth. We can’t always know why.
Because the economy is to a large extent a human construct, there’s no reason not to believe that we should always be tinkering and trying to improve it. Think of the economy as somewhere between a jungle and a factory -- the latter is something that can almost always benefit from intentional improvement.
The second sociological quirk behind the show-me-the-market-failure argument is the econ profession’s lingering fetish for theory. There’s a shift underway from what labor economist David Card calls mathematical philosophy to a more data-focused discipline, but theory is still far more privileged and prized in economics than in many natural sciences. The insistence on citing a theory can be a sort of measuring contest.
In fact, economic theory suggests that real-world markets are probably a dense thicket of market failures -- asymmetric information, limited enforceability of contracts, incomplete markets, externalities, public goods and human irrationality. Often, one policy is needed to correct for the failings of another -- a phenomenon economists call the theory of the second-best. It can be politically easier to patch the system up than to overhaul it entirely.

But these theories are usually highly abstract. In order to make the math work, economists simplify their models so much that it can be hard to apply the theories to specific cases -- they end up being more like parables. That’s why when free-marketers demand to see a theory supporting a particular intervention in the real world, they’re making what usually amounts to an impossible demand.
So I propose we minimize our use of the show-me-the-market-failure argument. Sometimes there are policies that people have tried in the past, which seem to work even though it’s hard to tell exactly why. Public education is a great example. It seems to make economies more prosperous, and most economists support it, but no one can point to just why the free market doesn’t educate enough people on its own. Road-building is another -- there are essentially no countries with mostly private high-quality road systems, and economists struggle to explain why.
We know these government interventions work; figuring out why they work is a task for the future. Like the people who chewed tree bark to relieve pain long before the discovery of aspirin, or the engineers who used lithium-ion batteries without quite understanding the physics, sometimes it pays to go with evidence even before you have a theory in hand.

Noah Smith
In 2015, Forbes writer Adam Ozimek suggested that a “new liberal consensus” is forming in the economic-policy world. The data back him up. Many economics professors now tend to favor government intervention in the economy more than the general public. And the profession’s biggest public stars, from Paul Krugman to Thomas Piketty to Joseph Stiglitz, are now more likely to lean to the left than to the right. Meanwhile, I’ve tried to document the flood of new research showing that policies like public housing, welfare and public education spending are more beneficial than conservatives have recognized in decades past.
But there are not one, but two big trends in liberal economic thinking. One wants to modify the economic thinking of the past few decades, and the other wants to rip it up. I expect to see a lot of the economic debate in the coming years play out not between the left and right, but between these two strains of thought.
The research and people I’ve been writing about fit into what we might call the New Center-Left Consensus. This strain of thought is based on data and empiricism. Support for higher minimum wages, for example, has grown among economists because a large amount of careful empirical analysis has shown that minimum wage hikes don’t usually cause sizable immediate disruptions in local labor markets. These economists aren’t ignorant of the basic theory of labor supply and demand -- the kind that every undergrad econ student is forced to learn. They just realize that it might not be the right theory in this case.
The New Center-Left Consensus is attractive to academics and policy wonks. It draws on an eclectic mix of mainstream economic theory, empirical studies and historical experience. It refuses to assume, as many conservatives and libertarians do, that free markets are always the best unless there is a glaring case for government intervention. It’s more willing to entertain all kinds of ways that government can improve the economy, from welfare to infrastructure spending to regulation, but it also recognizes that these won’t always work. It embraces a philosophy of careful experimentation. Sometimes the new center-left is even in favor of deregulation -- for example, loosening zoning restrictions and reducing occupational licensing. It’s not ideologically opposed to the free market.

Noah Smith
For example, suppose the U.S. government is considering opening up trade with a large, poor country. The move would make most consumers a little better off -- they would have cheaper clothes and toys. But a few workers in the textile and toy-manufacturing industries would be devastated -- their careers would be down the drain, all their knowledge suddenly rendered obsolete. If they couldn’t retrain for new careers -- and let’s face it, how many 45-year-olds can retrain for new careers? -- they’d be stuck in low-wage, low-prestige service jobs, or dependent on government handouts.
Should economists recommend this policy, reasoning that the good of the many outweighs the good of the few? Or should they be opposed to hurting a few people a lot so the majority can reap a modest benefit?
For decades, economists have tried to sweep these hard questions under the rug. Uncomfortable with telling leaders and voters what’s right and wrong, they have focused on the objective, or positive side of their discipline -- finding the facts as best they can, and leaving hard moral (normative) decisions to the democratic process.

In some ways, that was an admirable impulse. But it’s getting harder and harder to maintain that just-the-facts attitude, because society seems to be facing increasingly tough choices about who should receive the fruits of prosperity. As productivity growth slows and inequality becomes more severe, questions about how the economic pie should be divided have come to dominate our national discourse.

Vox
Albert Einstein was one of the most important physicists of all time. His scientific predictions have withstood 100 years of scientific challenges. His thinking fundamentally changed the way we understand the universe. Yet people are more likely to be convinced Einstein wasn’t a great physicist than to change their minds on topics like immigration or the death penalty.
It has nothing to do with a person’s intelligence (or the quality of information on Einstein or immigration policy). It’s due to the fact that we’re simply more open to changing our minds on nonpolitical topics. Scientists have been keen to figure out why — because if they can, it may open the door to the hardest challenge in politics right now: changing minds.

Psychologists have been circling around a possible reason political beliefs are so stubborn: Partisan identities get tied up in our personal identities. Which would mean that an attack on our strongly held beliefs is an attack on the self. And the brain is built to protect the self.

But these results are an intriguing step: The brain processes politically charged information (or information about strongly held beliefs) differently (and perhaps with more emotion) than it processes more mundane facts. It can help explain why attempts to correct misinformation can backfire completely, leaving people more convinced of their convictions.
The results also jibe with some of Kaplan and Harris’s past work on religious beliefs. “When we compared evaluating religious statements to nonreligious statements, we [found] some of the same brain regions that are active in the current study,” Kaplan said. Which makes sense, because religious beliefs also factor into our identities.
What the new study definitely doesn’t show is that “political beliefs are hardwired,” Kaplan says. We can change our minds. Reflecting on his work and his own experience, Kaplan says a good way to make facts matter is to remind people that who they are and what they believe are two separate things.

​On March 7, 2007, the late Steve Jobs sent an email to Eric Schmidt, who was at the time Google’s CEO and still a member of Apple’s board of directors: “Eric, I would be very pleased if your recruiting department would stop doing this. Thanks, Steve.”1)
 
“This” was a cold call from a Google recruiter to an Apple engineer trying to convince the engineer to move to Google. The next day, Schmidt sent the following email to Google’s HR department: “I believe we have a policy of no recruiting from Apple and this is a direct inbound request. Can you get this stopped and let me know why this is happening? I will need to send a response back to Apple quickly so please let me know as soon as you can. Thanks, Eric.” Shortly thereafter the recruiter was fired.
 
This exchange between two of the most powerful people in the tech world—and definitely in Silicon Valley—was one of the pieces of evidence in a 2010 antitrust lawsuit by the DOJ against Adobe, Apple, Google, Intel, Intuit, Lucasfilm, and Pixar (US v. Adobe Systems Inc., et al.). The DOJ claimed that the defendants entered into an illegal “no cold call” agreement, thus limiting their respective employees’ career options.
 
The DOJ used harsh words in its complaint: “Defendants’ concerted behavior both reduced their ability to compete for employees and disrupted the normal price-setting mechanisms that apply in the labor setting. These no cold call agreements are facially anticompetitive because they eliminated a significant form of competition to attract high tech employees, and, overall, substantially diminished competition to the detriment of the affected employees who were likely deprived of competitively important information and access to better job opportunities.”2)
 
The DOJ and the defendant companies proposed a settlement on the same day that the suit was filed. Although the DOJ was not timid in the way it described the defendants’ conduct—and although it was definitely a high-profile case that was not just well-covered by the press but also followed closely by corporate executives—the outcome was only moderately impactful: in the settlement that was approved by the court, the companies agreed to broader limitations on the recruitment practices for a period of five years. The settlement included no compensation for the employees. However, a class action resulted in Adobe, Apple, Google, and Intel paying $415 million, Pixar and Lucasfilm paying $9 million, and Intuit paying $11 million.

Calgary Herald

​Another bright spot has been the results of new fluids Crescent Point has been using to help extract oil from the Viewfield Bakken formation in southeastern Saskatchewan. In tests last year, the fluids improved initial 90-day oil production rates by about 50 per cent.Saxberg did not divulge details about the fluids because they are "proprietary."
"Early results are pretty impressive," he said. "It's pretty exciting stuff in this environment and I think the low-cost environment allows us to do this experimentation and we probably put $50 million of our budget across all of our fields toward that technology."

​Only a matter of time before this diffuses to rest of industry.

 

Udacity
https://www.udacity.com/courses/web-development
http://www.charlierose.com/watch/60666797


Machine Learning and other resources (Thanks Murray):

Stanford
https://www.coursera.org/learn/machine-learning

The course is taking into consideration that a lot of the students have very little background in math and statistics, so some of the material will seem too easy for you. With that said, the value is in seeing how to develop the mechanisms to proceed from a conceptual understanding of a least-squares regression (for the beginner example) and produce your beta coefficients that STATA or R would be giving you...Or, how do you actually tell a computer to minimize your MSE, etc.

Once you access the material you'll see that you can move ahead as fast as you want, so it is self paced.

I think you'll find a lot of the content super interesting and I think you'll quickly see ways of how to put the material into practice for yourself.

Here is an overview of the curriculum:

Course
WEEK 1
Introduction
Linear Regression with One Variable
Linear Algebra Review
WEEK 2
Linear Regression with Multiple Variables
Octave Tutorial
WEEK 3
Logistic Regression
Regularization
WEEK 4
Neural Networks: Representation
WEEK 5
Neural Networks: Learning
WEEK 6
Advice for Applying Machine Learning
Machine Learning System Design
WEEK 7
Support Vector Machines
WEEK 8
Unsupervised Learning
Dimensionality Reduction
WEEK 9
Anomaly Detection
Recommender Systems
WEEK 10
Large Scale Machine Learning
WEEK 11
Application Example: Photo OCR

I've been humm'ing and haa'ing about Udacity since I stumbled across it a few weeks ago. I will probably do the Machine Learning Udacity courses because it combines high-level thinking, programming, and [math and statistics] education. For the forseeable future, I think there will be a higher demand for guys (and girls) who can cover those bases. I mean, I've done a lot of reading and came across a group of guys at mlwave.com who can seemingly solve a wide array of problems from predictingwhich Grandmaster played x chess-game to a fascinating (but incomplete) tutorial on using timeseries analysis to predict neuronal firing patterns in a zebrafish embryo. Tell your students this is why they should pay attention in Econometrics!!

Here is a list of the better resources/things I've found:

Stanford Machine Learning - In the community this is considered your square-one for Machine Learning.
Udacity's Nanodegree - For redundancy: can't leave this out for obvious reasons.
Codecademy.com - This is the site I used to get a feel for the syntax of Python
University of Washington - Machine Learning Specialization - I am in a couple of courses from this specialization. Apparently they have a pretty good reputation and are respected. I am trying to get away from UofW because I personally don't like the way their assignments are setue because they use certain code libraries that you have to pay for once you're done the course. I don't see it as valuable in the long run to use things that aren't open-source.
*Kaggle.com - Popular website for budding data scientists, programmers, math-heads, etc., to try their hand at competitions to solve some cutting-edge data problems. I'm currently using Kaggle tutorials to teach myself.
Data Science iPython Notebooks - Amazing resource for code examples and tutorials on some data science and Machine Learning stuff including Kaggle and Tensorflow materials.
Tensorflow.org - In late 2015, Google open-sourced their Machine Learning and AI library. A lot of the better Kaggle tutorials seem to be using them now and their capabilities are pretty mind-blowing.
A Tour of Machine Learning Algorithms - Good Summary of different Machine Learning Algorithms.
Livecoding.tv - A website with streaming and archived programmers doing their thing. Haven't watched many but I think I should.

P.S. Can't send an Email like this without a plug for Watson.