Unreasonable Ineffectiveness of Machine Learning in Computer Systems Research
1–10 of 105 posts
Re: Unreasonable Ineffectiveness of Machine Learning in Computer Systems Research
#2Branch predictions are an interesting use, although I'm wondering how expensive a misprediction really is.
But this:
"Another example is the use of regression techniques from machine learning to build models of program behavior. If I replace 64-bit arithmetic with 32-bit arithmetic in a program, how much does it change the output of the program and how much does it reduce energy consumption? For many programs, it is not feasible to build analytical models to answer these kinds of questions (among other things, the answers are usually dependent on the input values), but if you have a lot of training data, Xin Sui has shown that you can often use regression to build (non-linear) proxy functions to answer these kinds of questions fairly accurately."
I'm not sure whether I am fascinated or horrified.
Re: Unreasonable Ineffectiveness of Machine Learning in Computer Systems Research
#3> Driverless long-haul trucks are apparently just a few years away, and the main worry now is not so much the safety of these trucks but the specter of unemployment facing millions of people currently employed as truck drivers.
No, no they're not. We have some lane tracking in good weather etc., but we are still decades (or more) away from full level-5 autonomy that would make drivers behind the wheel unnecessary.
But it only goes to show that not even computer science experts are immune to marketing hype and well funded PR campaigns. :)
As for the unreasonable ineffectiveness - it's not just in systems research. ML can be very effective in some areas (especially when there is a ton of training data), but many areas of human endeavor are hard to model via function approximation techniques like those used in most of ML.
Re: Unreasonable Ineffectiveness of Machine Learning in Computer Systems Research
#4Cute title but the post didn't really address either reasonableness or effectiveness, but mostly claimed that the potential has not yet been realized. It's a pet peeve of mine to see these hackneyed joke titles referencing famous papers, "considered harmful" is another case in point. Let's just stick to descriptive titles.
Re: Unreasonable Ineffectiveness of Machine Learning in Computer Systems Research
#5To contrast this opinion, a promising research project from CMU that uses an RNN to manage a database based on "forecasted" workloads (I know it's not quite architecture, but still): http://pelotondb.io/
Re: Unreasonable Ineffectiveness of Machine Learning in Computer Systems Research
#6> Driverless long-haul trucks are apparently just a few years away, and the main worry now is not so much the safety of these trucks but the specter of unemployment facing millions of people currently employed as truck drivers. No, no they're not. We have some lane tracking in good weather etc., but we are still decades (or more) away from full level-5 autonomy that would make drivers behind the wheel unnecessary. Bu…
We are certainly not decades away.
It seems to be a classic human error: people always seem to overestimate how far we've come and how far we've to go... and also underestimate the rate of progress/change. That's why technologies like the iPhone seem to show up "out of the blue" and "change everything."
That said, people also seem to never take into account the politics. Even if the technology was ready tomorrow it's not clear that certain special interests and elected officials would allow driverless 40-ton trucks to roam free on the highways. There would probably be a lot of push-back -- for the kids of course. Transportation is the definition of a highly-regulated industry and it's not clear that technologists will be able to move in and disrupt it so easily.
Re: Unreasonable Ineffectiveness of Machine Learning in Computer Systems Research
#7Perhaps this paper provides an explanation?
https://arxiv.org/pdf/1608.08225.pdf
"The exceptional simplicity of physics-based functions hinges on properties such as symmetry, locality, compositionality and polynomial log-probability, and we explore how these properties translate into exceptionally simple neural networks approximating both natural phenomena such as images and abstract representations thereof such as drawings."
Re: Unreasonable Ineffectiveness of Machine Learning in Computer Systems Research
#8Cute title but the post didn't really address either reasonableness or effectiveness, but mostly claimed that the potential has not yet been realized. It's a pet peeve of mine to see these hackneyed joke titles referencing famous papers, "considered harmful" is another case in point. Let's just stick to descriptive titles.
It imitates an old paper title: "The Unreasonable Effectiveness of Mathematics in the Natural Sciences" by Eugene Wigner (1960)
Re: Unreasonable Ineffectiveness of Machine Learning in Computer Systems Research
#9> Driverless long-haul trucks are apparently just a few years away, and the main worry now is not so much the safety of these trucks but the specter of unemployment facing millions of people currently employed as truck drivers. No, no they're not. We have some lane tracking in good weather etc., but we are still decades (or more) away from full level-5 autonomy that would make drivers behind the wheel unnecessary. Bu…
> but we are still decades (or more) away from full level-5 autonomy that would make drivers behind the wheel unnecessary.
Do you have a citation for that? I often see this asserted, and I see many assertions the other way. Whenever I personally see self-driving cars in the wild, I'm astounded at their effectiveness on uncontrolled access roads. What makes you think controlled access roads will be much more difficult? Or to be more specific, why is industry consensus that we are decades (or more) away from this goal? Or is this just your personal opinion?
Re: Unreasonable Ineffectiveness of Machine Learning in Computer Systems Research
#10> Driverless long-haul trucks are apparently just a few years away, and the main worry now is not so much the safety of these trucks but the specter of unemployment facing millions of people currently employed as truck drivers. No, no they're not. We have some lane tracking in good weather etc., but we are still decades (or more) away from full level-5 autonomy that would make drivers behind the wheel unnecessary. Bu…
> but we are still decades (or more) away from full level-5 autonomy
Decades? Are you really sure? This assumption seems quite uninformed to me. The Grand Challenge was 12 years ago, from then we evolved from Level 1 to Level 3. Back then there was no data, computing power wasn't available as today, chips (&sensors) can be designed an built within a few months or even less these days for far less money. Machine Learning and Computer Vision made many advances in these 12 years (talent in these fields is now easier to acquire as more people can learn about it these days). We are at a completely different point these days. Level 5 is not the matter of decades, 5 years, 10 max. But not decades.