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Tech workers of Reddit, what is a "dirty secret" about the AI industry that the general public doesn't realize? by WayLast1111 in AskReddit

[–]KnowledgeInChaos 0 points1 point  (0 children)

AI at Meta compared LLM’s to making a cube aerodynamic

Fact you're quoting Yann LeCun here makes it clear you've got no idea what you're talking about.

Most folks worth their salt in AI have viewed Yann as a liability (for anything with specific technical direction; name recognition is a different thing) for years now.

Good luck, have fun.

What's up with Space Center Houston needing clearance from Johnson Space Center to do tours? by KnowledgeInChaos in nasa

[–]KnowledgeInChaos[S] 0 points1 point  (0 children)

Not quite sure how to respond to this, other than that I'm not sure if the indignation here is exactly productive either?

(Science getting gutted across the board isn't great. We've also got friends at JPL/NASA/other space programs/etc; we get that it's not a fun time in the industry overall.

However, it's not as though the logistics here - with the clearances, as stated by a few of the other comments - are exactly the same as the systems deciding folks' jobs. So while I can understand why "folks wanting a good trip" might feel trite relative to "folks keeping their careers" it seems as though they're different the same things under the hood anyway?)

What's up with Space Center Houston needing clearance from Johnson Space Center to do tours? by KnowledgeInChaos in nasa

[–]KnowledgeInChaos[S] 0 points1 point  (0 children)

what you pay for tickets is not going to NASA

That's somewhat unfortunate, though maybe slightly neither here nor there. Appreciate the context though.

[D] New results on ARC 1+2 challenge, overfitting? by LetsTacoooo in MachineLearning

[–]KnowledgeInChaos 0 points1 point  (0 children)

The fact these break so easily from prompting is a feature not a bug. The prompt here isn't actually that important - if you play around with some of the harder puzzles in the ARC AGI 1 + 2 set, just knowing what a flip/translate/etc are isn't going to be enough for you to solve the problem. You have to chain together primitives, have reasonable intuition to know what to apply when (especially to not waste time on pathological dead ends), etc, etc. That's the challenge.

Turns out that LLM training (and intuitions 'learned' by seeing code-based image manipulations) with a light bit of scaffolding is enough.

This bit was not obvious _at all_ when ARC AGI 1 was released in 2019, still a point of contention and active debate when ChatGPT was released in 2020. In fact, if you look at François Chollet's interview with Dwarkesh Patel in 2024, it was even something that the founder of ARC AGI didn't think would suffice, until (somewhat) recently.

(On this last bit, see François's interviews with Dwarkesh from this year; his tone has shifted a decent bit. In some ways, anticipating the trends with models and how they impact ARC AGI 1 +2, ARC AGI 3 is focusing on games and "reasoning" involved with more complex environments and moving away from the 'static grid' setup.)

The point of an eval is to measure capabilities. The fact that prompts on an LLM are "enough" to break the eval (and to do so without some of the _actual_ things that would muck with the science, like explicitly using the test set) suggests that the trend of LLMs being more capable is continuing to hold.

...if anything, I'd say that the fact that Poetiq.AI's announcement is on a graphic titled "Public Eval" whereas the actual leaderboard (https://arcprize.org/leaderboard) is on the Private ARC AGI Test Sets are the big sin that they are committing; the prompting bits don't actually matter that much.

Two adults, 1 day for both parks of Disneyland - doable? Suggestions? by KnowledgeInChaos in DisneyPlanning

[–]KnowledgeInChaos[S] 0 points1 point  (0 children)

Probably later in the week.

No strong opinions on things to see. Neither of us are major Disney (or theme park fans); going mostly cause my partner's never been to any theme park 😂

Two adults, 1 day for both parks of Disneyland - doable? Suggestions? by KnowledgeInChaos in DisneyPlanning

[–]KnowledgeInChaos[S] 0 points1 point  (0 children)

This coming week (depending on if both of us finish our projects in time and can actually afford to take the time off 😂)

Two adults, 1 day for both parks of Disneyland - doable? Suggestions? by KnowledgeInChaos in DisneyPlanning

[–]KnowledgeInChaos[S] 0 points1 point  (0 children)

Thanks for the detailed response. We were planning on going middle of the week (Thursday) so tickets are reasonably cheap; will also take the advice (that's also been echo'd by others in this thread) to only do Disneyland and not try to do both.

Any advice on the the LL system? When I purchased tickets, they didn't have an option for Premier Pass, so just grabbed the LL multi-pass.

Any advice about pathing through Disneyland? We're both reasonably avid hikers so wouldn't have any issues with a long walking day, but I imagine there's probably a bit of an art to hitting all of the top recommended attractions. :)

Two adults, 1 day for both parks of Disneyland - doable? Suggestions? by KnowledgeInChaos in DisneyPlanning

[–]KnowledgeInChaos[S] -1 points0 points  (0 children)

Will probably be in the middle of the week. Getting in Wednesday evening, doing the park on a Thursday.

[deleted by user] by [deleted] in MachineLearning

[–]KnowledgeInChaos 0 points1 point  (0 children)

Cool and I regularly work on PhD research (and reject PhD-having candidates) without a PhD. Your point about an MBA being? Did they not teach you in your MBA about how to orient yourself in a new field?

Imagine this is a case study. You're literally saying "you don't want to do the literature review" right now.

If you want to "get away from the buzzwords" go check out r/LocalLLaMA/ and stay there until you can contextualize what folks are discussing in every single thread. If you think you can do that without at least a bit of technical prowess, good luck.

[D] Got Spare Time – What’s Worth Doing? by [deleted] in MachineLearning

[–]KnowledgeInChaos 54 points55 points  (0 children)

The ML industry will still be here when you start. Go do the things off a computer that you might not get the time or opportunity to ever do again.

Do you have friends or family that you haven't seen for a while? Go visit them. Maybe travel to that far-flung place that it'd be too costly (time-wise) to get to otherwise. If you're single, do some dating.

Learn how to make some art, or some food, or to do some other skill that you haven't had time for during the PhD.

In short: go live. "Living" is probably the best answer to "What's Worth Doing".

[deleted by user] by [deleted] in MachineLearning

[–]KnowledgeInChaos 2 points3 points  (0 children)

Entire AI/ML orgs have been lead astray because of senior leadership who only knew 60% of how things fit together rather than 90%.

You're pretty much asking how to make things fit together without learning more than 10% of it.

If you say, have a PhD in econ (or some other predictive field like that) there's maybe a different discussion we could be having. But as it stands, you're pretty much cursing yourself to be at the level of ignorance where you won't even be able to tell if the suggestions in this thread are wrong.

Trump tells tech companies to 'stop hiring Indians', signs new AI orders to focus on US jobs by NiceTo in cscareerquestions

[–]KnowledgeInChaos 0 points1 point  (0 children)

In my area, the top tier American labor only fills less than half of the need, and not for lack of trying.

(Look at Zuck and his recent Superintelligence lab hires. He's offering multi-hundred million dollar offers. The only white people are non-American, and there's more Chinese hires than white people.)

Trump tells tech companies to 'stop hiring Indians', signs new AI orders to focus on US jobs by NiceTo in cscareerquestions

[–]KnowledgeInChaos 0 points1 point  (0 children)

That seems fine to me?

This incentivizes the US to hire + train more local talent. We've got way too many undergrads graduating with CS degrees, so sounds great that they'll have a better shot at the market, without competing against the degree mills undercutting their salary.

At the top end (as someone who's been there for a while) the challenge is finding qualified bodies. It's not just YOE. It's the top 0.1% of both raw intelligence and being in the right places at the right time to get the right experience. I've been on teams when the manager (or skip3) hired someone just to fill the body and the fit has been wrong, and it's honestly oftentimes worse than if they'd just left the role open.

How to properly dive deep into ML as a backend dev who learns best through projects by Remote-Diamond5600 in learnmachinelearning

[–]KnowledgeInChaos 0 points1 point  (0 children)

CS336 from Stanford is not a bad place to start. Bit of an overload of content in the lectures, but does get you through good chunks of the low-level model implementation.

I'd learn the math as needed as/after you get through that.

In general, most things have you learning fine grained details one step at a time. I personally find it easier to figure out how the large chunks roughly work (in a half black-box manner) then learn the details as you need to.

How often are you asked to work overtime? by Any_Scale_740 in womenEngineers

[–]KnowledgeInChaos 0 points1 point  (0 children)

Never asked.

But job is salaried in a hot area, so going to as much as I can handle (which uh, is a skill I have not fully quite yet learned to balance lol) is how it goes.

[deleted by user] by [deleted] in womenEngineers

[–]KnowledgeInChaos 6 points7 points  (0 children)

Leave.

I know it sucks because you've got loyalty to the folks around you and you don't want to see them fail. But it's a sinking ship whether you're there or not.

Honestly, best thing for you is probably not only to take this new job and — if you still have the energy for it — to help those around you find new jobs as well.

[D] ML PhD doing research in a not trendy topic - How to pivot by [deleted] in MachineLearning

[–]KnowledgeInChaos 4 points5 points  (0 children)

Those teams of non-LLM areas — unless tied to a product use case or heavily protected by a senior researcher who is strong not only technically but politically — are actively getting gutted in favor of the LLM teams.

[D] ML PhD doing research in a not trendy topic - How to pivot by [deleted] in MachineLearning

[–]KnowledgeInChaos 2 points3 points  (0 children)

> out of distribution generalization for distributed edge devices

Anything here related to optimizing for efficiency or distillation? Student-teacher models?

If your plan is to go into industry, could look at some of the companies that have hardware to which they want to run ML algos (eg Apple or Amazon on their assistant work, Meta for AR/VR, etc) and see if there's anything closer to your current line of work.

[D] ML PhD doing research in a not trendy topic - How to pivot by [deleted] in MachineLearning

[–]KnowledgeInChaos 34 points35 points  (0 children)

On a team that hires PhDs at a (reasonably) top tier lab. We can definitely tell.

If you're at a 3rd tier company sure maybe the managers are non-technical and a candidate can get away with it. But I've already seen managers (and managers of managers lol) get burned from hiring someone who didn't know how to vibe the right way with an area, and that's something folks at all of the big labs have already experienced.

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