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Comment Re:Progressivism (Score 2) 258

*sigh* The core concept of progressivism is what most of us want - policy based on our current best understanding of how the natural and social worlds work. The fact that it's been used to promote questionable policies in the past shows its flexibility: as we learn more, those policies are abandoned. The alternative, blindly holding on to policies that have been proven not to work (supply side economics on the right, Marxism on the left) just shows... what? That adherents are too proud to admit mistakes and evolve?

It's true that in modern American/Western politics the term has a slightly different connotation, but to pretend that the idea of using data and knowledge to find good policies is new (which the OP claimed - millennials are the first generation to use data!) is silly. Smart people for centuries have been trying this approach and it's never caught on with the general public.

The fact that my original post got modded "funny" shows just how hard it is to get people to think seriously about this approach.


Comment Re:Not going to happen (Score 4, Insightful) 247

That's not exactly correct... the Google cars have incredibly precise maps of the roads they're on, not just the route, but maps of the actual surface of the road (e.g., where the potholes are). That level of detail available to the onboard computers is pretty much the same as having sensors on the road. It requires an incredible amount of prep work. Of course, map updates could be handled by sensors on other cars constantly providing real time information. It's a cool approach, but only practical when you have that level of detail available.

Google, et al, are showing very controlled research projects. Even though they're testing in the real world, they're still highly controlled experiments.

Sure, many of the problems are resolvable using this approach, but what we don't know is what new problems will evolve once there are more than a handful of self driving cars on the road. More research will help identify these, but anyone who's done real science or engineering knows that what works at small scale rarely scales as you would hope/expect.


Comment Re: the story abridged: (Score 3, Interesting) 80

Um, you shouldn't be one, either.

Let's do the math: drop tens of millions of dollars of your investor's money to be the beat out the other institutional investors to a company with a cool prototype built from Kickstarter funds. Realize a few months into it that the tech, while kick ass, isn't quite ready for prime time and won't have an available market of 10M users for at least a few years. Do some more math and realize they'll run out of money before then and have to take on additional VC money, possibly in a down round that will affect your position. Call one of your successful investments with money to burn, ask them to buy you out of your investment and give everyone a good return. Repeat with the next cool tech. Claim you have the unique ability to spot unicorns. Raise more money for your fund.

_That's_ how you think like a modern VC.

Of course, this is just a variation on the IPO scams of the first boom. In this case, the few successful companies (Facebook, Google, etc) replace the role of the public in providing quick returns on questionable investments. The public foots the bill indirectly: some IPO money and shares are used for the buyouts and ad revenue provides the rest of it.


Comment Re:Replace one bad method with another? (Score 1) 157

That's pretty much my point: most people can't even get it right with simple statistical models. I advocate sticking with the simple mathematical models and dumping the "turn-key", simple statistical models that keep getting people in trouble. p value .05, publish!

Those that truly grok the statistics behind the more complex models can still use them, but the bar for acceptance by the community needs to be much higher. (which goes into fixing peer review and down that whole rabbit hole...)


Comment Replace one bad method with another? (Score 2) 157

To illustrate, the summary could easily be restated this way:

"...field of [data science] frequently uses [machine learning] in an unhealthy way. Many [data scientists] don't use [machine learning] as a tool to describe reality, but rather as an abstract foundation for whatever theory they've come up with."

Replacing "math" with "machine learning" isn't going to make a difference if the practitioners don't understand how to use it properly. Machine learning models are much more subtle and complex than simple mathematical models and very easy to misuse. To use them properly, you really need a much stronger understanding of the math behind them than most people have.

See the entire field of psychology and most GWAS studies for an example of where over reliance on (simple) models can get you into a lot of trouble.


Comment Re:I would hardly call R obscure. (Score 1) 429

That reminds me of a Java "class" I took in the late 90s. The "instructor" kept talking about how great "jay vac" was and how it made Java run faster. Yeah, that was javac he was referring too ("java see"). Took all of us programmers half the day to figure that one out. I still call it jayvac when I want to mess with people.


Comment Re:Comparison? (Score 1) 257

Replication and reproducibility are not the same. Simply getting the source code and re-running the results is just replicating the study. It doesn't tell you anything about how reproducible the results are.

To be reproducible, someone should be able to use similar methods and get the same results. If a result is completely dependent on a specific build of the software, it's not robust enough to be considered reproducible.

Publications should require a concise written description of the method and solution that is complete enough that a competent practitioner could reproduce the results using whatever appropriate tools they want.

I'm dismayed that in CS that the academic community is putting so much emphasis on replication and not enough on robust reproducibility.


Comment Simple, Analog Please! (Score 2) 417

I have two cars: a '96 Jeep Cherokee and an '01 M Coupe. You know what I love about both of them? The climate control system is analog. I user a slider or knob, feel the resistance, and know the temperature will adjust. The radios are simple: a few preset (physical) buttons, a volume knob, and a tuner knob. Sure, bluetooth would be nice, but I have a cigarette lighter dongle that works just fine over FM for streaming music and taking calls (I actually still have the cigarette lighters for both cars, too).

My wife, OTOH, has had a stream of cars with electronic climate control, complex infotainment systems, and all sorts of other bells and whistles. You know why we got rid of the last one? The climate control system kept thinking it was 20F outside and adjusted the heat accordingly. This in the summer in Texas. The automaker, despite repeated visits every summer, couldn't resolve the issue.

Oh, and navigation? For the few times I don't know where I'm going (really, it's scary how people rely on nav systems for drives they do every day), a quick glance at Google maps on my phone orients me (usually before I get in the car).

I'll allow some local microprocessor control for drivability and performance, but when it comes to the creature comforts and extras, I want them simple and functional. I want my car to talk to me through the engine, not the speakers.


Comment Re:Have you ever been to a grocery store? (Score 5, Informative) 259

Milk and meat are around the periphery because their display cases are connected to (or close to) the bulk cold storage in the stores. It's part of preserving the "cold chain" of ensuring that products that need constant refrigeration throughout the supply chain actually get constant refrigeration.

Most of the marketing text written about grocery store layouts was developed after the layouts were already in use. Most of the layout quirks are the way they are for more practical and mundane reasons. Layout as a conspiracy makes a great story, but in general, it's just that. Yes, impulse aisles are exceptions as are some other elements in the store, but for the most part, the practicalities of storing and presenting large amounts of food determine the layout.


Comment It helps to define the cloud first... (Score 2) 154

One thing that gets me in discussions across organizations is how poorly the "cloud" is defined. IT often has a slightly different definition of the cloud than senior management than end users than tech support (and so on). Are we moving email to the cloud, setting up a collection of virtual servers to run our custom apps on, using Salesforce, creating a hybrid solution for redundancy? Even in those situations, the motivations and concerns are often different.

Then there's the accounting aspect. Is the shift simply to move IT from CapEx to OpEx? Does the IT staff understand the difference? Has management worked out a 3 year forecast to make sure the financials actually work out?

When making these decisions, all major stakeholders need to be involved and represented. You need to look at it from different perspectives and make sure everyone understands those perspectives. Only then can you really make an informed decision. Yes, that's much more difficult than simply believing the sales guy, but for something as important as IT infrastructure, it's what you should do.

I work on the laboratory informatics/gene sequencing side of the world and these conversations are becoming more common. To help give scientists some perspective, I've putting together some blog posts that introduce all the different angles:

Yes, it's a bit of shameless self-promotion, but it's also relevant to the discussion (and I don't want to just cut-and-paste it here :) ).


Comment Re:Solution: Embrace an actual free market (Score 1) 250

Taking it a step further, the core platform could be a general "market" engine that simply provides a method for posting bids and asks. Immediately on top of it could be a meta-data engine that allows market-specific parameters to be attached to the requests that refine them (e.g., location, car size, etc). Developers could then leverage this to provide vertically oriented apps for different types of end users (UberX, UberBlack, UberPool, et) that filter the bids/asks based on that specific market's needs. You could also add in some basic rules at that level as well (since markets only exist in terms of the rules that set them up).

This would also open up the possibility of developing completely new markets. Want a local chef who can come over and cook dinner for 4 kids? We can make a market for that! Want someone to paint your house? Market!

It'd make for a really interesting experiment, if nothing else....


You will lose an important tape file.