If you are looking for information about my upcoming Big Data course, which starts on April 2, 2018, it is in a different blog. Please go here to learn about the textbooks, and to see how the course worked last year.
This picture looks exciting, doesn’t it? But the vertical axis is not to scale. In fact the price changes are so small that they are barely visible. See the next figure. For 7 months a year, my prices will vary by only $.02/kWh over a week!
Our local utility, San Diego Gas & Electric, just sent us a notice that we will be switching to Time of Use (TOU) pricing. I have no objections, BUT:
- TOU was innovative in the 1980s. But for any house with a smart meter, which we all have now, it has been dominated by real-time related pricing for at least 20 years.
- The price differentials are negligible – 1 cent per kwh, or about 3 percent! In the winter almost nobody will adjust their usage, or even keep track of it. At least differences in the summer are substantially larger – as much as 30¢ per kwh.
Original question on Quora: Why does the Harvard Business School, Michael Porter, teach the essence of business strategy is the elimination of competition, by regulation if possible. Is this legal? Is this basically socialism or communism?
My response: Trying to pin this on Michael Porter is ridiculous. He says no such thing. Based on the way the question is phrased, I wonder if there is an ideological purpose in asking it.
But in any case, there is a serious issue behind the question, namely an increasing level of oligopoly (decreasing levels of competition) among companies in many US industries. See, for example, “Big Companies Are Getting a Chokehold on the Economy Even Goldman Sachs is worried that they’re stifling competition, holding down wages and weighing on growth.” or.
“America Has a Monopoly Problem—and It’s Huge”.
One theory about this trend is that it is partly due to growing power of corporations in Washington. That, in turn, may be traced partly to the increasing role of money in elections, largely as a result of the infamous Supreme Court “Citizens United” decision. For example, the way Trump’s massive tax cuts were put together without any hearings and in a VERY short period of time, and the amount of “goodies” for many industries in the resulting package, would never have happened with previous massive changes in taxes.
An effective strategy in some highly concentrated industries is to persuade the government to selectively regulate your industry, in ways that favor large and established companies. That is, all companies may experience higher costs because of a regulation, but if your company can respond more cheaply than anyone else, it is still a net win for you. An example is pharmaceuticals. For example pharma companies increasingly use the legal system, regulations, and side deals to keep generic drugs off the market for years after drug patents expire. The industry has also been very effective at keeping foreign competitors out – e.g. blocking imports by individual citizens from Canada.
(I buy one medication at $1 per pill from abroad, when it costs $30/pill at the local Rite-Aid. But it takes a lot of research and effort.)
Source: (32) Why does the Harvard Business School, Michael Porter, teach the essence of business strategy is the elimination of competition, by regulation if possible. Is this legal? Is this basically socialism or communism? – Quora
Here is an argument for allowing companies to maintain a lot of secrecy about how their data mining (AI) models work. The claime is that revealing information will put companies at a competitive disadvantage. Sorry, that is not enough of a reason. And it’s not actually true, as far as I can tell.
The first consideration when discussing transparency in AI should be data, the fuel that powers the algorithms. Because data is the foundation for all AI, it is valid to want to know where the data…
Here is my response.
Your questions are good ones. But you seem to think that explainability cannot be achieved except by giving away all the work that led to the AI system. That is a straw man. Take deep systems, for example. The IP includes:
1) The training set of data
2) The core architecture of the network (number of layers etc)
3) The training procedures over time, including all the testing and tuning that went on.
4) The resulting system (weights, filters, transforms, etc).
5) HIgher-level “explanations,” whatever those may be. (For me, these might be a reduced-form model that is approximately linear, and can be interpreted.)
Revealing even #4 would be somewhat useful to competitors, but not decisive. The original developers will be able to update and refine their model, while people with only #4 will not. The same for any of the other elements.
I suspect the main fear about revealing this, at least among for-profit companies, is that it opens them up to second-guessing . For example, what do you want to bet that the systems now being used to suggest recidivism have bugs? Someone with enough expertise and $ might be able to make intelligent guesses about bugs, although I don’t see how they could prove them.
Sure, such criticism would make companies more cautious, and cost them money. And big companies might be better able to hide behind layers of lawyers and obfuscation. But those hypothetical problems are quite a distance in the future. Society deserves to, and should, do more to figure out where these systems have problems. Let’s allow some experiments, and even some different laws in different jurisdictions, to go forward for a few years. To prevent this is just trusting the self-appointed experts to do what is in everyone else’s best interests. We know that works poorly!
It sounds like what we used to call a “bug” to me. I guess bugs are now promoted to “algorithm failures”.
Nearly half a million elderly women in the United Kingdom missed mammography exams because of a scheduling error caused by one incorrect computer algorithm, and several hundred of those women may have died early as a result. Last week, the U.K. Health Minister Jeremy Hunt announced that an independent inquiry had been launched to determine how a “computer algorithm failure” stretching back to 2009 caused some 450,000 patients in England between the ages of 68 to 71 to not be invited for their final breast cancer screenings.
The errant algorithm was in the National Health System’s (NHS) breast cancer screening scheduling software, and remained undiscovered for nine years.
“Tragically, there are likely to be some people in this group who would have been alive today if the failure had not happened,” Hunt went on to tell Parliament. He added that based on statistical modeling, the number who may have died prematurely as a result was estimated to be between 135 and 270 women.
There is a lot of concern about AI potentially causing massive unemployment. The question of whether “this time will be different” is still open. But another insidious effect is gaining speed: putting tools in the hands of large companies that make it more expensive and more oppressive to run into financial trouble. In essence, harder to live on the edges of “The System.”
- Cars with even one late payment can be spotted, and repossessed, faster. “Business has more than doubled since 2014….” This is during a period of ostensible economic growth.
- “Even with the rising deployment of remote engine cutoffs and GPS locators in cars, repo agencies remain dominant. … Agents are finding repos they never would have a few years ago.”
- “So much of America is just a heartbeat away from a repossession — even good people, decent people who aren’t deadbeats,” said Patrick Altes, a veteran agent in Daytona Beach, Fla. “It seems like a different environment than it’s ever been.”
- “The company’s goal is to capture every plate in Ohio and use that information to reveal patterns. A plate shot outside an apartment at 5 a.m. tells you that’s probably where the driver spends the night, no matter their listed home address. So when a repo order comes in for a car, the agent already knows where to look.”
- Source: The surprising return of the repo man – The Washington Post
A nice graphical illustration of what happened when NYC subway rules were changed in seemingly small ways. The time/distance buffers that used to exist between consecutive trains shrank, to the point that a small “blip” causes cascading effects in subsequent trains. TOM once more. (Thanks to Arpita Verghese.)