2,864 karma · joined May 19, 2022
(Works okay, but not great)
Whenever I've opened watch duty, that's always the question I'm asking. How long might it take to reach [here/there]?
So engineers that like to iterate and explore are more likely to like LLMs.
Whereas engineers that like have a more rigid specific process are more likely to dislike LLMs.
If you dislike a situation you're in and you try and fix it by switching to a new situation, you'll generally bring with you some of the problems that created that prior situation.
If instead, you bit by bit improve the situation until you feel at peace with it, you'll then either no longer want to move to a new situation, or if you do want to move, you'll no longer bring with you the problems of the prior situation.
Applies to job changes, relationships, projects, goals. And, from OP, applies to architecting software projects.
https://news.ycombinator.com/item?id=39316653
https://news.ycombinator.com/item?id=39316653
https://news.ycombinator.com/item?id=37021837
https://news.ycombinator.com/item?id=36922114
Some good recs in the above threads. You'll be able to learn and thrive with those, IMO.
There's a ~3 month delay between o1's launch (Sep 12) and o3's launch (Dec 20). But, it's unclear when o1 and o3 each finished training.
Previously when Amazon lost or damaged items in their warehouses, they would reimburse sellers the full sales price. Starting March 2025, Amazon will only reimburse the manufacturing cost of lost or damaged items. Sellers have to either accept Amazon's estimated manufacturing cost or provide documentation of their actual manufacturing costs.
> Oh man :( We tried really hard to neither over- nor underclaim the results in our communication, but, predictably, some people drastically overclaimed them, and then based on that, others concluded that there was nothing to be seen here (see examples in thread). So, let me try again.
> Why our findings are concerning: We tell the model to very strongly pursue a goal. It then learns from the environment that this goal is misaligned with its developer’s goals and put it in an environment where scheming is an effective strategy to achieve its own goal. Current frontier models are capable of piecing all of this together and then showing scheming behavior.
> Models from before 2024 did not show this capability, and o1 is the only model that shows scheming behavior in all cases. Future models will just get better at this, so if they were misaligned, scheming could become a much more realistic problem.
> What we are not claiming: We don’t claim that these scenarios are realistic, we don’t claim that models do that in the real world, and we don’t claim that this could lead to catastrophic outcomes under current capabilities.
> I think the adequate response to these findings is “We should be slightly more concerned.”
> More concretely, arguments along the lines of “models just aren’t sufficiently capable of scheming yet” have to provide stronger evidence now or make a different argument for safety.
Key settings to adjust:
* Always use "Pro mode" searches
* Set the model to 3.5 sonnet in settings
Once you do those two things, you'll have a good time and after a week you'll dread going back to google.
> Within the next three years, robotics should be completely solved [wrong, unsolved 7 years later], AI should solve a long-standing unproven theorem [wrong, unsolved 7 years later], programming competitions should be won consistently by AIs [wrong, not true 7 years later, seems close though], and there should be convincing chatbots (though no one should pass the Turing test) [correct, GPT-3 was released by then, and I think with a good prompt it was a convincing chatbot]. In as little as four years, each overnight experiment will feasibly use so much compute capacity that there’s an actual chance of waking up to AGI [didn't happen], given the right algorithm — and figuring out the algorithm will actually happen within 2–4 further years of experimenting with this compute in a competitive multiagent simulation [didn't happen].
Being exceptionally smart in one field doesn't make you exceptionally smart at making predictions about that field. Like AI models, human intelligence often doesn't generalize very well.
* The company will not cancel any vested equity, regardless of whether employees sign separation agreements or non-disparagement agreements
* Former employees have been released from their non-disparagement obligations
* OpenAI sent messages to both former and current employees confirming that it "has not canceled, and will not cancel, any vested units"
https://www.theregister.com/2024/05/24/openai_contract_staff...
https://www.bloomberg.com/news/articles/2024-05-24/openai-re...
My notes:
Scaling is continuing. Amazon's 400k trainium2 chips, Meta's 2gw datacenter, OpenAI's multi-datacenter training.
Opus 3.5 training succeeded. But it's a more profitable decision to use it to train Sonnet 3.5 and serve that instead. Large models are now teachers, not necessarily end products. Too expensive to serve to end users vs what they'll pay, but great for improving smaller models that are cheaper and faster to serve.
Orion (GPT-5) is being used for training data generation and in verifier/reward models. They say it's not economical to serve to end users until Blackwell chips (B200).
Models that can explore reasoning chains get smarter on certain kinds of problems. [My note, not from article: Math, science, law, programming. R&D, law and programming are perhaps the industries that are willing to pay more for higher reliability.]
Scaling with "berry training" - monte carlo tree search generating thousands of different answer trajectories, then uses functional verifiers to get rid of the ones that didn't arrive at the correct answer.
Big focus is on making inference cheaper and faster. [My note: If you want to work in AI, I imagine any research on LLM inference cost and speed will be highly valuable.]
Step 1 - get them to sign up for AI image tools.
* Midjourney is best for quick images
* Playground AI is good if they need to modify images but the quality doesn't need to be perfect
* Leonardo AI (now owned by Canva) is a good full suite
* Photoshop AI feature is best if they already work in photoshop
Then show them how to use these tools! That might require you signing up for these tools first and learning yourself.
Step 2 - For learning about how AI image generators work here's my video list.
1) AltexSoft - has a low viewcount but it's a great overview - https://www.youtube.com/watch?v=Rke0V_VkF3c
2) Jay Alammar - it's technical but also visual and he explains it well - https://www.youtube.com/watch?v=MXmacOUJUaw
3) Gonkee - again, technical, but visual, great - https://www.youtube.com/watch?v=sFztPP9qPRc
Workflow example: good for seeing the workflow of SD as of May 2023 - https://www.youtube.com/watch?v=K0ldxCh3cnI
Too technical for what you're looking for: Computerphile, Ari Seff, Jia-Bin Huang
Step 3 - For assuaging their fears about becoming obsolete - I think the following is a great podcast episode. https://80000hours.org/podcast/episodes/michael-webb-ai-jobs...
But their fears might be valid. A test is perhaps: if their boss spent a few days learning to use AI image generators, would they still need them? For some artists the answer would be no, for many the answer would be yes. It'll change over time as the tools get better, but that's a pretty good proxy. If they're doing things that require more iteration, interacting with users and humans and the physical world, nuanced judgement, in-person work, safer. If they're doing things that are contract based, no iteration, get a request and deliver a result, much less safe.
Who led the adoption of GPT-1/2 there - a developer, you, a VP, someone else?
https://news.ycombinator.com/item?id=36195527 and
Hacker's Guide to LLMs by Jeremy from Fast.ai - https://www.youtube.com/watch?v=jkrNMKz9pWU
State of GPT by Karpathy - https://www.youtube.com/watch?v=bZQun8Y4L2A
LLMs by 3b1b - https://www.youtube.com/watch?v=LPZh9BOjkQs
Visualizing transformers by 3b1b - https://www.youtube.com/watch?v=KJtZARuO3JY
How ChatGPT trained - https://www.youtube.com/watch?v=VPRSBzXzavo
AI in a nutshell - https://www.youtube.com/watch?v=2IK3DFHRFfw
How Carlini uses LLMs - https://nicholas.carlini.com/writing/2024/how-i-use-ai.html
For staying updated:
X/Twitter & Bluesky. Go and follow people that work at OpenAI, Anthropic, Google DeepMind, and xAI.
Podcasts: No Priors, Generally Intelligent, Dwarkesh Patel, Sequoia's "Training Data"
A Kobo device? Colorsoft? Scribe? Paperwhite?
I think it'd popular more easily if:
rename it, and give it a new tagline
eg Opencast or something. and tagline: Open-source alternative to NotebookLM's podcast feature
His framing of intelligence is one thing. The people who disagree with him are framing intelligence a different way.
End of story.
I wish that all the energy went towards substantive disagreements rather than disagreements that are mostly (not entirely) rooted in semantics and definitions.
Perplexity is my favorite narrow ai agent tool, kind of like an ai agent as an alternative to google search