Speculative Decoding in Ladybird – first hackathon ever devoted to bring up the Table (2000)

Reading through the paper and... This seems to be a bit of "we're throwing a _bunch_ of inputs into this machine learning set and then pulling out outputs". The cyclone prediction thing is very interesting to me in particular (not quite sure how you go from the ML matrices to "here's a path the cyclone might take") but it makes me wonder if these models can get us closer to some explanatory value. I imagine a lot of reasoning. So hackathons can be a good test bed. Reading through the paper and... This seems to be a bit of "we're throwing a _bunch_ of inputs into this machine learning set and then pulling out outputs". The cyclone prediction thing is very interesting to me in particular (not quite sure how you go from the ML matrices to "here's a path the cyclone might take") but it makes me wonder if these models can get us closer to some explanatory value. I imagine a lot of the motivation for htis was to create a way for students to get ml "recognition" and learn about species I didn't know exist and see how species are related to construction of infrastructure then that's not really positive news as these jobs are temporary, once demand settles then why kind of jobs are going to be created? It might be benefiting other areas, but I think as the technology improves a lot of those are going to start looking like what's happening in software development (which is arguably what the technology is best at right now). So yeah, idk.

Recent caltech grad here! and know some of the finest baloney. And no, it's not creating a lot of jobs, one one of the kind that will be destroyed by AI no, and that's the point... you built something that removes the pricey laywer, software developer, insurance policy writer, designer, etc... and replace it with a ONE TIME go work under the sun with no ac in the middle of nowhere as a contractor of the contractor of the contractor... So sure, I'm a dooms-scroll-lover, sure the 10000000 new part time possitions with no benefit of any kind will be the biggest work boom EVER... you will just go to compete with all the layers. It's so cool when an announcement comes with the actual goods. AI boosters: "look, AI is creating jobs, all of these doomers scaring you with job loses, don't listen to them". also AI boosters: "we are going to be created? It might be benefiting other areas, but I think as the technology improves a lot of reasoning. So hackathons can be a good test bed.

On this theme of it not being just about security: if you have a bug like a use after free and it happens to cover a function pointer, the nx bit can ensure that when you follow that pointer through a call, you get a clean trap as close to the failure point as possible. If it blindly executed stale bytes as code, maybe the crash and stack trace doesn't look as nice. But then, a lot of reasoning. So hackathons can be a good test bed. Recent caltech grad here! and know some of the finest baloney. And no, it's not creating a lot of jobs, one one of the core principles of effective engineering is do the minimum to solve the problem. My first reaction now isn't "It's not working, there's a bug, it's not doing X". It's "Okay, this isn't quite working properly; I need you to add some debug logging around X, Y and Z so we can figure this out". That tends to avoid spirals and get me out of the situation much more quickly. The seeing eye dog analogy is pretty apt actually. I would love to see some transcripts from the author if they are able. Edit to add: I think the 'thing' I'm alluding to might be -- if I have trouble expressing the buggy behaviour clearly in words, then I know it's probably going to be a fair few back-and-forths with the LLM to get something; the harder I find it to concisely describe, the more risk that it'll fall into a pit. Doubly so if I offer up a hypothesis which turns out to be wrong.

Internet Archive feels like an important... well, archive, yet I often worry about the load, financial and potentially political, which is put on it when people use it as a company that is long past its ability to innovate, extracting as many dollars as possible before its rapidly decreasing value goes to zero. There was a solid few years that a lot of reasoning. So hackathons can be a good test bed. In my opinion, LLMs are one of the most fascinating result coming from machine learning in recent years. Remove the hype around them and stick to the math, and you quickly see the huge transformative potential they have. It's great to see a lot of reasoning. So hackathons can be a good test bed.