My experience, over 10 years building models with libraries using CUDA under the hood, this problem has nearly gone away in the past few years. Setting up CUDA on new machines and even getting multi GPU/nodes configuration working with NCCL and pytorch DDP, for example, is pretty slick. Have you experienced this recently?
yes, especially if you are trying to run various different projects you don't control
some will need specific versions of cuda
right now I masked cuda from upgrades in my system and I'm stuck on an old version to support some projects
I also had plenty of problems with gpu-operator to deploy on k8s: that helm chart is so buggy (or maybe just not great at handling some corner cases? no clue) I ended up swapping kubernetes distribution a few times (no chance to make it work on microk8s, on k3s it almost works) and eventually ended up installing drivers + runtime locally and then just exposing through containerd config
I was little confused after reading the article exactly how this would work. I checked out the actual regulations and found this formula and explanation:
> (Intermediary global revenue) × (Canadian share of global GDP [≈2%]) × (Contribution rate [4%])
Proxy for “Canadian revenue”
Intermediary global revenue
This figure refers to the annual global revenue of a digital news intermediary. It excludes other unrelated revenues from the company operating the intermediary.
Doesn't seem like a very fair or accurate way to implement it regardless of whether this is a good idea or not.
Didn't really seem that vituperative to me. Seemed like he's really frustrated with a bunch of events tangentially related to Jill, gets emotional and could have been more respectful. I think he probably owes her an offline apology but I don't think this a reason to discount this man entirely.
>4) The basic theory that got us to the current AI crop was defined decades ago and no new workable theories have been put forth that will move us closer to an AGI.
I guess it really depends on what you mean by "basic theory" but my view is that the framework that got us to our current crop of models (vision now too, not just LLMs) is much more recent, namely transformers circa 2017. If you're talking about artificial neural networks, in general, maybe. ANNs are really just another framework for a probabilistic model that is numerically optimized (albeit inspired by biological processes) so I don't know where to draw the line for what defines the basic theory...I hope you don't mean backprop either as the chain rule is pretty old too.
I'm confused, what is the proposed framework supposed to fix, or how is it better? Is the goal really to reduce achievement gaps by limiting the advancement of top students? Surely, that can't be the goal...that's crazy. Furthermore, that has the possibility to exacerbate the problem by forcing advanced students to augment their math education in the private sector, something only available to wealthier families.
Also, the shifted emphasis on data science stuff is a joke. The very courses they're talking about minimizing are the building blocks of data science and there's no shortcut.
Not sure, from other comments I gather the goal is to de-emphasize the idea that you need to be gifted to be good at math, which studies apparently have shown that this idea tend to discourage other students, often girls or racially disadvantaged boys, which do have the ability to succeed at math, to pursue math or be interested in it.
I don't think anything is changing for truly gifted students, they should still have the ability to be fast-tracked or options to take more advanced topics. It seems more that it's about focusing on students who don't see themselves as gifted or who aren't yet showing signs of it.
This is incorrect. Their explicit goal is to remove fast tracking and options to take more advanced topics for gifted students. The vision is for uniformly paced classes for all same-aged students.
> Does the draft Mathematics Framework eliminate middle school mathematics acceleration programs?
No. The draft Mathematics Framework does not eliminate middle school mathematics acceleration programs (including programs that offer Integrated Math 1 or Algebra 1 courses to grade eight students). The draft Mathematics Framework emphasizes the importance of following the sequenced progression of topics laid out in the Common Core State Standards for Mathematics (CCSSM) and considers the latest research on the impact of skipping grades or undermining the sequences progression. Additionally, the CA CCSSM are significantly more rigorous than those from previous grade eight content standards. They address the foundations of algebra and geometry by including content that was previously part of the Algebra I course, including but not limited to a more in-depth study of linear relationships and equations, a more formal treatment of functions, and the exploration of irrational numbers.
>I'm confused, what is the proposed framework supposed to fix, or how is it better? Is the goal really to reduce achievement gaps by limiting the advancement of top students?
Yes, that is the explicitly stated goal. Unfortunately a large number of ideologues believe that all people have the same inherent intelligence, and that disparities in outcome among children are due solely to racism and other forms of discrimination. That's the whole thrust of "equity". "Equity" means equal outcomes. It's proven too difficult for these people to bring up the performance of low achieving students, so they are left to bring down the performance of high achieving students and eliminate all objective metrics (like standardized testing) that can be used to assess student learning.
>Suppose [a person] had a basket full of apples and, being worried that some of the apples were rotten, wanted to take out the rotten ones to prevent the rot spreading. How would he proceed? Would he not begin by tipping the whole lot out of the basket? And would not the next step be to cast his eye over each apple in turn, and pick up and put back in the basket only those he saw to be sound, leaving the others?
-Descartes
Science is done by clearly and logically addressing doubt. Sweeping doubt under the rug and showing prejudice in which evidence is presented is antithetical to the impetus of science (a disimpassioned search for unwavering truth). I'm surprised this is published in nature.
The issue this article is talking about is you can very quickly generate hundreds of bad studies and outright lies that take decades to discredit. And even after all that effort, those studies will STILL be cited not because of the validity, but the narrative.
For example, vaccines an autism. We have so many high quality studies proving with as much certainty as you can in medicine that vaccines do not cause autism. Yet that's a claim that hasn't died off yet (And Mr. Wakefield's fraud study with the initial lie is STILL cited as if there were some sort of conspiracy to cover it up).
So what's the solution? It's easier to quickly lie than it is to experiment and prove. It's easier to falsify data than it is to prove data was falsified.
What other choice is there but to lean on consensus and reputation?
Well, one strategy would be to preemptively rebut common misrepresentations and meaningless critiques, I do agree scientists could benefit from this technique. And if it's just misinformation with no merit, use Hitchen's Razor.
However, established science has been wrong before about things there was a consensus on. We should investigate evidence that casts doubt on consensus if there is some merit, even if it is painstaking. It's one of the less sexy and tedious aspects of science, nevertheless important.
> However, established science has been wrong before about things there was a consensus on.
As time goes on, this is something that only becomes more rare. How many established scientific consensus's proven wrong can you think of in the past 30 years?
That is the nature of science. We aren't going to discover that, all along the world was actually flat. Similarly, we aren't going to discover that global warming isn't real or evolution doesn't exist.
Some areas of science have unbelievable amounts of evidence of support. Yet you often find those facts to be challenged the most when the come in conflict with profits (the fossil fuel industry).
"Well, one strategy would be to preemptively rebut common misrepresentations and meaningless critiques..."
The reply: "What's that got to do with anything I said? I'm saying <a rephrased version of one or more of the misrepresentations>." If they reply with a different misrepresentation, it becomes the Gish Gallop; they will eventually cycle back around to one of your rebutted misrepresentations, but by then everyone will have forgotten your rebuttal.
"And if it's just misinformation with no merit, use Hitchen's Razor."
The response: "See, they're not even responding to our evidence! They're silencing dissent!"
"We should investigate evidence that casts doubt on consensus if there is some merit, even if it is painstaking."
Which is one of the points of this article: while you are reconsidering the evidence for thermodynamics, you are not addressing the problem. They've won. This is why it took thirty years after the original Surgeon General's report to begin to address cigarette smoking. This is why humanity has done essentially nothing about climate change.
Established science has been wrong before, but when facing an immediate problem the current consensus is probably where you want to put your money.
These have piqued a lot of people's interest around me. My friend recently quit a steady job to go work at Modal https://livemodal.com
I have wondered about the necessity of a crane though, seems like a pretty big limiting factor.
I think this is a bit misleading. The metric is "problems" per 100 cars. That could mean the car's transmission gave out or it could mean there is a software bug or paint issue that is causing the driver problems. In the case of Tesla it could be the case a lot of problems are due to software bugs that can be corrected over the air seamlessly for free. I think classifying each problem into categories with associated weights would help to make the metric more meaningful
What? Just saying a minor software bug (possibly Spotify not integrating correctly) is not the same as a transmission or brake failure. Also, in Tesla's case the minor bugs can be fixed relatively quickly and for free. I don't know what you're talking about being used to software bugs.
Thanks a lot. The big impact vs specialized impact does frame things well. I almost see the PhD as a necessary condition for my career path; akin to an MD for a doctor. There are no laws governing the work like an MD. However, when I look at the people who are in the positions I want, less than 5% have masters, the rest are all PhDs. I'm in my late 20s by the way.
