marshallp

It's not simply the battery tech. It's all the extra interior stuff like display etc. It's the total package, like apple does. Plus, just like with apple and jobs, or virgin with branson, there's an extra brand factor developing with musk.

Apple didn't actually create completely new products, just polished versions, same as Tesla. The same with market share, they only want a small segment (the only exception to this rule are ipads).

Tesla are the Apple of cars. Elon Musk is the new Steve Jobs. The other car companies will have non-tech retards as ceo's and will always be playing catch-up. Tesla only has to fear techie's like google or apple itself entering the space.

Hackers can defeat these entrenched interests. Set up online websites that work through these problems in a structured manner (like a mechanical turk for anti-corruption).

They should simply have an upfront test drive fee. Book a test drive online.

reply to lutusp below because reply link takes too long:

You're just saying my definitions of things are wrong etc. etc. You just want to disagree with anything I say. My views are laid out clearly on the comments in this thread. I don't see what your views are (except to disagree with anything I say).

edit: replying to lutusp

You keep arguing that I'm just personal opinionating and that I'm wrong. I can't figure out what your views are - maybe I'm really dumb and you're really smart. You win.

Yeah, I totally agree, in non-english speaking countries it's completely justifiable (until human-level language translation comes around).

I'm not doing some post-modernism or whatever sneaky jab you're throwing in.

You're implying that you have some definition of algebra that is truer than mine. But that is all meaningless arguments over words anyway.

The real issue is that I'm arguing that the vast, vast majority of people only need to know that concepts can be "quantified" - "variabilized" or "symbolized". This is mostly so that they can interact with computers for the purposes of computer programming, or for creating tables of data to be fed into machine learning black boxes.

You've also think you know a truer definition of science than me. I think science today is about collecting data and having computers make predictions, increasingly use black box machine learning. I'm inferring that you think it's about theorizing from data and writing out simple equations. I have disagree with you on that (though you'll probably turn around with your post-modernist jabs).

Is it the responsibility of the taxpayer to fund the education for the leisure activity of the upper middle classes (speaking with foreigners or reading translated literature).

The education system that exists today (k12 + college) is a McReplica of what aristocratic people of earlier centuries considered fit for their children (who would never actually need to work). We're living the enviousness of the middle classes of a century ago (this same principle applies to the McMansions). It has no relation to what a more perfected reality could be.

With respect to google translate, I think we'll see a lot of improvements. I expect to see human level performance within 5 years (it's a "let the computers run long enough" + "enough data" problem).

I see where you're coming from, it's maybe useful to psychology practitioners. (I'll come across as a troll if I started airing my view that psychologists and psychiatrists are fraudsters so I'll stop there!).

I'll just have to disagree with you there. Your definition of algebra (beyond the naming of symbols, or variables in programming parlance) is not useful to most people. Where are they going to apply log or sqrt in everday life or even equations as you outlined (in my viewpoint, machine learning will automagically figure out equations or they will be apps on your smartphone).

I think people would be much better served implicitly dropping out than explicitly. People's identity has more to do with performance than innate ability.

Many people are mistakenly led to believe science is about symbolic equation rearrangement on paper (high school maths) + rote memorization and so choose arts instead. For many people, but not all, this is the reason why they don't pursue science careers. If they understood it was much more about teamwork and physical setting up and running equipment/software and thinking creatively, I'm certain we'd see different choices made.

I agree with you, I just feel like that is basically the understanding of variables (names of concepts) and not maths. So, you need to understand what measurements to take/can be taken, and then set up that system and feed it to the black box machine learner.

Now, there still need to be some people who understand how things work in detail, but this is much smaller than what is being taught (most k12 do not need to know maths or physics or chemistry etc). They do need practice in abstract thinkng and this would be much better done with programming (a playful, creative, logical, and useful activity).

Algebra (the art of rearranging equations) is not necessary anymore (you just need to know how to realize how things can be quantified - the naming of variables of computer programming). Arithmetic beyond use of calculator is useless.

Maths as traditionally taught is the leading cause of high school dropouts rates and probably a lot more damage than tahat (people who might have been scientists turning to arts instead).

Statistics was around then.

I consider science to be the art of making correct predictions. So you need a hypothesis (in the form of equations or computer programs) that can be tested on observations leading to correct predictions.

Extroversion/introversion might be scientifically rigorours by that definition (they might have done quantitative studies) but I'm not sure Jung did those, and even that it makes sense to create such a classificaton - people are a lot more complex and I don't really see the need for use of such classifications.

That's a problem I see with psychology, sociology, economics, and other "soft" fields. The quantification of things which are far more complex than the simplistic models created to the point that they are essentially meaningless. It's meaningless to quantify people into races or skin colors and that's about the level that those sciences are at.

Why do people need to know the under the hood stuff. As I mentioned in my other comment, we have the concept of division of labour.

Also, programming and machine learning will increasingly be most of what people's job description. In fact, in my extremest viewpoint, I think even programming is on it's way out and machine learning will the majority of people's work. Or put more simply, giving computers a few examples.

Already, with things like the Baxter robot from Rethink Robotics, and mechanical turk from amazon, we're seeing what the future of work will be like. The computers will just figure out things extrapolating from the choices we make.

That's missing the point entirely. I'm pretty sure you don't know the exact materials and the process for making the shoes you might be wearing. There are probably no shoe factories left in america. They same applies to the black box machine learning, very very few people need to know of what goes on under the hood. edit: I don't know if you changed your comment, but black boxes exist that can solve any problem (neural nets for example).

Well, I can't argue with the specifics of your problem since I don't know about it (but the general point that this doesn't justify the current maths system for 99% of people).

However, at some point in time, computers will be taking over all human abilities (if you believe that AI is inevitbale). At what point will that occur? It's my strong suspicion that we have already reached that point and any evidence to the contrary is simply that people aren't trying automated methods (I believe that machine learning can solve any problem that humans can solve, and furthermore, this wouldn't require more computing power than we already have).

You don't need the teaching of abstract concepts. Why not just let students roam free with "programming", a set of api's, so they can roam with free with creativity. Why even teach arithmetic? Just given them the arithmetic computer functions and let them play around with it. As students build games or whatever else they'll discover the concepts that you might consider important themselves.

What use is the periodic table for almost anyone. Teaching the periodic table should be left for someone wishing to pursue research in chemistry, maybe as a 20 year old. The basic skills that chemists probably use today, like computer programming, would have been ingrained throughout k12.

Even if your argument is true, that maths is needed for that particular task, it doesn't justify teaching it to 99% of people. Also, the math you mention could have been taught as a set of computer programming functions (taught in a few hours) rather than how it's traditionally taught (as s semester long class or sequence of classes).

Black box machine learning might have given you better method than your whatever you're currently doing. And it would be done in a automated manner, with human effort just focusing on collecting data. I'm assuming whatever you're done is being done/has been done by many 1000s people as well. That's a tremendous waste of time. Even if the method you outline is better than what machine learnig would have found, it only needed to be done once (or a few times - marketplace) and exposed as an api.

Except the examples you've given like extroverts vs introverts are not scientifically rigorous and the terms already existed well before him all around the world in different cultures (outgoing vs shy/retiring etc).

There's a few problems with the article. The rules argument he makes could instead be made for the more useful field of computer programming.

Also, the reductionist paradigm is dead. It worked for finding laws of physics and some chemistry, but otherwise, in the most useful fields of today, like biology/materials/chemistry (high throughput methods), computer vision, search engines etc. the automated creation of hypotheses through machine learning is winning out.

Conrad Wolfram gives a talk about the need for math education reform http://www.youtube.com/watch?v=60OVlfAUPJg

He uses the term mathematics, but completely removing that term and replacing it with computer programming and machine learning should be done. They sound cooler and this step will remove all the baggage and ill-will mathematics has in society. That way, all mathematics teachers can be simply removed from high schools and colleges and replaced with computer programmers. Estonia is one of the few enlightened places on this subject with their introduction of programming as mandatory from year 1 in k12.

Almost the entire high school drop out rate can be attributed to maths teaching, and their is no solid empirical proof of it's utility over programming (the practice of "science" hasn't been applied to "maths" itself). http://en.wikipedia.org/wiki/Mathematical_anxiety

The same argument can be made for a number of other subjects, such as chemistry (why teach the periodic table?), languages (we have google translate and should taxpayers be funding this unproductive activity), and others (everything should be reexamined in light of search engines and instant information access).

The only useful subjects in k12 are probably

- computer programming, including machine learning/statistics

- physical education,

- communication (reading/writing/public speaking/socialization)

- general studies (trivia like history, geography, astronomy),

- personal finance

- physical crafts (3d printing, woodshop, cooking)

OK, but he's worded it wrong, it gives the impression of a snarkiness. Maybe he's saying that matlab users can't program well but are still intelligent/well paid (but that doesn't really make sense since numpy is equally easy and an intelligent/educated person wouldn't find programming hard). Anyway, maybe I misread it and you're right.

edit: great, I was getting points before, and then you come along with your italics.

His last paragraph is not clearly worded then. It comes of as thinly veiled insult to software developers.

Such a thing was already invented a long time ago and widely celebrated - SQL. It eliminated the need for loops, was designed for ease of use, had a lot of cool features added like ACID, took over the world and ran every kind of business and website. Sadly, because it was invented too long ago (1970s) many people thought of it as uncool and failed to realize it's absolute awesomeness.

So you're implying anyone who is a software developer is not educated or intelligent, nor has a large salary. I think a lot of people would beg to differ.

In reality, matlab only exists because of inertia. It's the same reason why microsoft windows is still around. There's no substance behind it.

edit: I thought you were agreeing with that slandering fool plinkplonk so I got a little out hand. Sorry jacquesm, i've edited it all out.

The evidence that is coming out from kaggle doesn't support the claim of teams with experienced specialized skill. Teams of a single student have beat out entire industry of companies (the essay score competition for example).

I don't think there's such a huge body of knowledge in "data science" as people claim. It's not astronomy or biology etc. As a "data scientist" the predictive ability of your model is all that counts. So the social skills and basic knowledge of stats + your progrmming skills/creativity is what's important (and the kaggle competitions have borne this out).

edit: Ok, ad hominem attack. And in that thread no one managed to making a convincing case for the utility of millions/billions of people learning maths as traditionally taught. You're probably a data scientist who wants to inflate your salary (there's my attack).

edit: LOL. you're a joke plinkponk, hahaha - being called out on your lies about your chosen profession being hard (so that you can feel special) really gets you doesn't it! http://www.urbandictionary.com/define.php?term=Wiener%20Didd...

The original gigaom article was about neophytes (people who only took coursera course) beating out people with much more experience. This has occurred many times on kaggle. All the preprocessing etc. you're talking is not a new thing, a lot of database developers have been doing that for decades (and that's in addition to my original claim that pretty much anyone with a science or engineering degree having those skills as well). As I said elsewhere, this data science is hard meme is just a ploy to inflate salaries (which every career does anyway, so there's nothing particularly sinister about it).

As practicing data scientists have pointed out in this thread, that is the least part of the job. And, really, how hard is it. As icelander points out, it comes down to plugging data into standard algo's.