I’ve been in so many meetings where the outcome is to plan another meeting, and include even more folks. Whichever team brings the most folks steers the decision in their favor, and thus manages hire more unnecessary employees for political will (which then increases the need for even more meetings).
The way out is creating a singular vision (eg leadership) and assigning teams goals they can work independently on towards that vision. It is to remove dependences between teams (and thus the need for them to communicate as much), not to increase communication or Jira tickets or Gantt charts or RACI matrices.
Seems equally valid to come out of this with the takeaway that code quality _does_ matter, because poor coding practices are what led to the leak.
Sure, the weights are where the real value lives, but if the quality is so lax they leak their whole codebase, maybe they are just lucky they didn’t leak customer data or the model weights? If that did happen, the entire business might evaporate overnight.
I agree AI could probably do a decent job on Kaggle problems. Of course, almost no DS job is building models with well-defined objectives and perfect data. The DS and MLE folks I work with mostly spend their time reframing ill-posed product requests into ML systems that can be maintained and improved with feedback loops.
A _huge_ part of a DS is saying "No" to bad ideas posed by non-experts. The issue with LLMs is all they ever say is "Yes" and "Wow, that's such a great idea!"
Classic 2024 company. Started out with a few genuinely cool products and a quirky, fun attitude. Got popular, decided to exploit that good will and now everything they do seems greedy and inauthentic (regardless of whether any specific product is or not). Maybe this is a great product? But after the OP Field pricing shenanigans and OP-Z quality issues... hard to give them a second chance.
That sounds right to me. I don't work in physics, but have many colleagues who have left it for that reason.
~The said, I think Dune still does seem useful. Hard to say whether the cost overruns are a lack of good management or if the experiment is just a quagmire, though. Guess I lean towards the scientists here that it's the former~
EDIT:
Doing some research it seems the Hyper K experiment might just be better, and funding might be better directed there than Fermilab.
I don't know all the details, but I think they are starting to do novel work with the accelerator to produce neutrinos in an experiment called Dune. So there actually is important stuff being done that isn't just superceded by Cern.
The larger scale models aren't necessarily built on the smaller models, they are independent and generally less specific. They are probably even inputs to the more specific models. So, like rising sea levels and increased temps (on average) is basically guaranteed, the _exact_ change for a particular place is more uncertain.
Neil deGrasse Tyson in his Cosmos show described it like a person walking a dog. We don't know where the dog will be exactly, but we can see the person with the leash is moving in one direction, and the dog isn't going to ever get too far from him (because of the leash).
Huh. I actually can understand devs not wanting to need permission to install libraries/versions, but with a pull-through cache there's no restrictions save for security vulnerabilities.
I think it actually winds up speeding up ci/cd docker builds, too.
Because of containers, my company now can roll out deployments using well defined CI/CD scripts, where we can control installations to force usage of pass-through caches (GCP artifact registry). So it actually has that data you're talking about, but instead of living in one person's head it's stored in a database and accessable to everyone via an API.
Agreed that the argument "overprotection leads to lack of free speech in academia" is tenuous.
That said, I do wonder if we all _are_ being protected from opposing views these days. Like, we come across the opposing views but usually in a filtered / characature form on Social Media/Fox News/MSNBC. It's actually kinda hard to find stuff without spin in my experience.
EDIT
My hypothesis is that's it's just cheaper to create speculative / opinion based journalism rather than real investigation. Since the former gets enough clicks, there's not a strong financial reason to create good journalism.
The article mentions Clarence Thomas has been courted (even bribed?) by the very people paying the lawyers trying to overturn Chevron. I think it's naive to believe the judges don't have policy preferences that are strongly reflected in their rulings... If that wasn't the case the GOP wouldn't have blocked nominations from Obama to get their preferred judges in.
If you had an efficient market, wouldn't a competitor charge just a little less than others who are using surge pricing? Isn't the whole efficient market thing about how eventually prices should reach the cost of production, which would not be affected by surge demand?
True, I suppose my disagreement is that I believe it doesn't go far enough to explain how big of a deal it is, and how there _aren't_ ways to deal with it without substantial, subjective intervention from the forecasters.
I've worked on weighting code for online polls, they literally rely on dozens of hand picked decisions to stay "reasonable". These decisions aren't factored into the error bars, making them appear smaller.
And as far as the fundamental style predictions, how can you use a single GDP number when Fox tells its viewers one number and MSNBC tells its viewers another?
This article does describe a faithful statistical effort, but to me it doesn't emphasize the risk of a "black swan" event enough.
This is great statistics, but it avoids the problem that moving to online polling has made it very difficult to get representative populations, so the data itself is biased in ways that cannot be counteracted by methods (only by assumptions, priors, etc). Which makes this misleading because it gives the forecast air of confidence that is unjustified.
I've worked as a data scientist for political campaigns (but no longer). The elephant in the room is that there are so many forces that make this election different from historical ones, from polls moving online away from phones (making them much much less reliable) to Trump's conviction to a completely unprecedented/fractured media landscape. Even if these forecasts were accurate, their usefulness for anyone other than people spending ad money is absolutely zero. Now that these forecast have so so much bigger error bars, I think public forecasts are actually harmful and just a scummy money grab from the media that posts them.
EDIT:
Why are they harmful? At best they make lots of people angry about something that hasn't happened. At worst they skew the election by making people not vote, or afterward serve to justify election fraud complaints ("my candidate wasn't supposed to lose based on forecasts, so fraud occured").
I think we all need to take a deep breath, accept we have little understanding what will happen, _vote_, and hope for a sane outcome.
Mostly agree, although I think "good" engineering culture is so incredibly rare that it feels strange to call it "good" and everything else "bad". It's more like "regular" engineering culture vs "exceptional" culture.
In my experience I think the larger any team gets the more likely the team is to hire "regular" engineers and the quality regresses to the mean. So it's only a small set of specialized teams with unusual luck and control over hiring that can become "exceptional".
This resonates with me, although I would go a step further to say the makeup/experience of the developers is a product of the competency of leadership. Most leaders wind up in their positions out of pure luck and chicanery. I can't tell you how many lay offs I've been through where the best people are let go and the worst are kept on and unintentionally sabotage the entire engineering team.
The way out is creating a singular vision (eg leadership) and assigning teams goals they can work independently on towards that vision. It is to remove dependences between teams (and thus the need for them to communicate as much), not to increase communication or Jira tickets or Gantt charts or RACI matrices.