fast.ai link actually points to http://book.realworldhaskell.org/
What gives?
Edit: having read to the end, there's a mention of Haskell book, so just an editing error then.
Machine Learning: The Great Stagnation
221–227 of 227 posts
Re: Machine Learning: The Great Stagnation
#222Earlier quoted context omitted.
How has the experience been getting a PhD after so long out of school? I'm quickly approaching 14 years in industry but still haven't ruled out a PhD.
I just started my PhD in AI (specifically NLP). Also took a non-traditional route (creative writing major -> 10 years in industry -> phd). It's interesting so far. Research feels very open ended compared to industry. While I was in industry (AI fintech startup), even though goals were rapidly changing, I had a good idea of what problems to work on and how to gauge progress. In contrast, research is far more undefined…
from "creative writing" to AI (industry or academia) sounds like such a wide gap, I did not think such a jump would be possible... congrats!
Re: Machine Learning: The Great Stagnation
#223> We’ve gamified and standardized the process so much that it’s starting to resemble case studies at consulting interviews. This is precisely true, as someone who has passed both screens for competitive jobs. Cracking the coding interview Case in Point Live coding Do 3-digit multiplication in your head (eg 347 * 469) Sorting algorithms M&A Evaluation Frameworks I could go on... You just memorize a bunch of crap that'…
>> We’ve gamified and standardized the process so much that it’s starting to resemble case studies at consulting interviews.
Love the way this is put.. it is so true :(
Re: Machine Learning: The Great Stagnation
#224I still feel like much of AI is a plot to dumb down the modern economy. We want our business people to be just as effective as our quants; we want nothing to require real intellectual labor. The idea that you traditionally have these programmers who spout mumbo-jumo all day, cost a lot of money, and seem to always be planning stuff behind your back is threatening, and all the more so because you are utterly dependent…
This seems fair to me. The executive view of ML is "can you do me a magic?" And as this article's "Graduate Student Descent" bit makes clear, the worker response is often to semi-randomly perturb code, show some graphs, and say, "Is this a magic?" For me, most software development is about finding something boring and laborious. We get a computer to do the work so humans can level up and work on something requiring a…
>Especially people in the managerial caste, as the reigning dogma there is that management is a universal skill.
This would be funny if it were not also so true and sad... management as a skill (and it is a skill, it is not IMHO something that can be taught, especially in business schools!) is such a rarity.
Re: Machine Learning: The Great Stagnation
#225I kind of agree with the author's major sentiment: that ML research is stuck in a rut with incremental improvement. However, the longer the article goes on, the less and less I agree with any of their statements. They start of criticizing the incremental improvers. They advocate later that if "stack more layers" beats a method, the method isn't good while completely ignoring anything other than the standard SOTA metr…
> and there are some great AD packages, but in no way is everything automatically differentiable (even with the nice packages), nor is that a design goal. I work on the AD infastructure for Julia. That absolutely is a design goal. Certainly we are not there yet; we still have a long way to go. But that is where we want to go to. With the cavet that thigns that are not mathematically defined to have derivatives (e.g.…
Sorry, I guess I was unclear.
It's definitely a goal for the AD ecosystem, but, as far as I'm aware, AD is not a design goal of Julia itself. That was the point I was trying to make.
Re: Machine Learning: The Great Stagnation
#226Earlier quoted context omitted.
> and there are some great AD packages, but in no way is everything automatically differentiable (even with the nice packages), nor is that a design goal. I work on the AD infastructure for Julia. That absolutely is a design goal. Certainly we are not there yet; we still have a long way to go. But that is where we want to go to. With the cavet that thigns that are not mathematically defined to have derivatives (e.g.…
Sorry, I guess I was unclear. It's definitely a goal for the AD ecosystem, but, as far as I'm aware, AD is not a design goal of Julia itself. That was the point I was trying to make.
Perhaps not originally, but now it is certainly a language design goal that the entire compilation pipeline can be parameterized and modified to allow for a huge variety of custom transforms. AD is a subset of the transformations they are endeavouring to support.
E.g. see https://github.com/JuliaLang/julia/pull/33955
Generally speaking, if there is any sort of code transformation pass that's needed for AD but not supported, you can expect people to be working to support either that specific transformation, or a generalization of the transformation. This has been a theme in the language development for years now.
Re: Machine Learning: The Great Stagnation
#227Earlier quoted context omitted.
Sorry, I guess I was unclear. It's definitely a goal for the AD ecosystem, but, as far as I'm aware, AD is not a design goal of Julia itself. That was the point I was trying to make.
Perhaps not originally, but now it is certainly a language design goal that the entire compilation pipeline can be parameterized and modified to allow for a huge variety of custom transforms. AD is a subset of the transformations they are endeavouring to support. E.g. see https://github.com/JuliaLang/julia/pull/33955 Generally speaking, if there is any sort of code transformation pass that's needed for AD but not sup…
That’s really interesting. Thanks for sharing.