No, absolutely the opposite. LLMs are terrible at things that require judgment and justifications, because they don't reason. They come up with something that sounds plausible.
That's not good enough when you're dealing with matters that can lead to civil or even criminal liability. Errors can be incredibly expensive to fix, if they can be fixed at all.
With a CPA or attorney, you at least have recourse if they screw up. You don't with LLMs.
You have the same problem that you have with legal LLMs; an LLM is incapable of providing legal or regulatory-involved advice, and anyone using an LLM for such purposes (even leaving aside hallucinations) forfeits any justifiable reliance defense. There's a role for LLMs, but no one with legal responsibility over reporting could or would possibly rely on an LLM for complex regulatory and rules analysis, not when there's the risk of your wardrobe being replaced with orange jumpsuits.
Yeah exactly. This is where an LLM could really shine. The trick though is consistency and that it’s often more on the basis of how the organization typically treats something and rationale to its applicability to GAAP. The creation and consistent adherence to internal standards and providing them and proving them to auditors is the key and LLMs would need infra to accomplish this.
I think F1 got significantly more popular in the past few years with Drive to Survive on Netflix, and then most recently with the F1 movie on Apple TV.
It’s a sports league with history and has been around for a while, but I think significant popular mindshare only happened in the last 5 years.
I think that heavily depends on regions. In Germany it peaked with Michael Schumacher. Later drivers like Vettel were successful, but didn't attract the same mainstream attention.
But in global terms F1 tried to grow it's reach to China and US. (Which then turned to "night time races" for their traditional European audience.
Are Amazon and Meta the ones losing out the most here, in terms of the companies building foundational models?
Probably more understandable for Meta, since they've been leaving the B2B space since Workplace has been sunset. Amazon losing out on this is pretty rough for AWS though.
Is Amazon trying to build a competitive foundation model? From what I can see AWS is instead focused on hosting and re-licensing Claude, Cohere, DeepSeek and others via Bedrock. And it's pretty likely that a large chunk of this $200M will anyways go to AWS. So I'd hardly call them a loser here.
Amazon has a number of foundation models under the name Amazon Nova, which they claimed were SOTA on release but I haven't heard much at all about them since.
Most of US government runs significant workloads on AWS now and that’s only increasing. They’ve cornered govt cloud infrastructure (with Azure, GCP, etc. very far behind) so not sure this matters in grand scheme of things.
Anecdotal based on industry experience, no citations.
My hot take - this isn’t that much different than English speakers not being able to write in cursive anymore. It’s just not something that’s as practiced as much now that we have digital input methods.
iOS development has been around for quite some time now. Most senior iOS and Cocoa developers probably started with Objective-C before slowly migrating codebases over to Swift.
#2 is a slippery slope if you don't do it properly.
You might look end up looking at lots of different slices of your data, and you might come to the conclusion, "Oh, it looks like France is statistically significant negative on our new signup flow changes".
It's important to make sure you have a hypothesis for the given slice before you start the experiment and not just hunt for outliers after the fact, or otherwise you're just p-hacking [1].
Fundamentally, you can't use the same data to both generate and validate/disprove a hypothesis.
Srgmenting and data dredging is fine provided you run a new test with fresh data to validate if there is a causal relationship in any correlations found.
I agree, as per example and point number one, if your goals was to increase conversions, you were successful. You can then go to the next step, slice the data up, and iterate on another change. If you fall into the box of over-analyzing you will probably find all sorts of irrelevant patterns.
Out of curiosity, what's the benefit of pharmaceuticals manufacturing in space?
Is there a benefit to manufacturing drugs in low gravity environments, or is it more of an experiment to see if it's feasible, in a future where more people might be living in space?
Perfect crystals. Also, the proof-of-concept drug was ritonavir, and it's nearly impossible to consistently grow large crystals of it on Earth. All of the labs that work with ritonavir are contaminated by a more stable form ("polymorph") that rapidly converts any ritonavir crystals into a less-useful form.
> All of the labs that work with ritonavir are contaminated by a more stable form ("polymorph") that rapidly converts any ritonavir crystals into a less-useful form.
More seriously: this was pretty much solved already through improved techniques. I'm generally of the opinion that if you have to send a molecule to space to crystallize it better, you should probably spend your money on other, more terrestrial approaches. I do credit Varda for doing this automatically, rather than on ISS, because launch costs for autonomous vehicles are much more affordable than human space flight.
It's actually still not solved, you can't grow large ritonavir crystals on Earth.
Ritonavir used as a drug works around the issue. It's produced as dispersed molten droplets inside a matrix of inactive material. Its heated above the melting temperature of ritonavir, so it prevents contamination.
I believe last I read about it, some drugs are formed as crystals, and being in 0g (or free fall if your pedantic) meant the formed crystals were much bigger or easier to actually form, can't remember which, the specific drug is related to HIV medications
I loved Legends of Runeterra, but I knew the writing was on the wall from the very start.
Bummer considering it was one of the best F2P online TCGs out there (and that was probably also the problem, it's such great value for the F2P playerbase and didn't go as hard on monetization other than cosmetics).
I don't think it's VC funding as much as being a public company, and being beholden to quarterly earnings. That's the cycle for most "revolutionary" tech companies that end up needing to keep revenue growth going each quarter.
For what it's worth, I don't think it's wrong or bad, but just the way corporations work.
The thing is it doesn't have to be that way. Well, Steve Jobs showed that it didn't have to be that way for Apple. He was able to command authority and weave a narrative that stakeholders could believe in, so bigger long-term outcomes could be pursued. Google was meant to be this kind of company too. So why did Larry and Sergey and other senior Googlers, who had the Steve Jobs example right there to follow, succumb to quarterly earnings servitude?
LLMs can help to handle the subjectivity in how GAAP is applied and provide justifications, which previous rules-based tax software could not before.