So this isn't groundbreaking results and the article itself is of questionable quality without sufficient detail as to why this is a newsworthy result. How is this the top rated article on hacker news? A more meaningful example would have been the paper that sets out a scalable and cost-effective route for closing the loop on LFP materials, while demonstrating that high-yield lithium recovery and environmental responsibility are not mutually exclusive: https://www.sciencedirect.com/science/article/pii/S092134492...
I actually like this idea - makes sense at face value - as long as they design the test in such a way that it aptly applies the knowledge instead of just learning for the sake of passing test like questions...
I get where you're coming from, was shocked when I left a relatively well organised corporate and did work at a relatively older company with a ton of legacy systems - when I asked what the strategy was they explained the structure to me - at the year end results they highlighted that they hit targets of cost cutting and saw this as an achievement, the whole narrative was around how its a tough economic environment (the presentation was literally all about things happening in the world - nothing about things they did/projects they delivered/value they added...) - they also had more project managers than engineers and wondered why projects kept missing deadlines- they hoped AI would solve their problems - but you can't get ROI in a space like that where your engineers are using AI to patch the ship to keep it afloat while the project managers think they're in an airplane and are trying to get it off the ground...
I've seen legitimately good outcomes with AI - a backlog has been cleared, features that were left on the cutting room floor have been pulled back in AND delivered all thanks to the use of AI coding tools. AI workflows have brought down processes from weeks of human processing to a couple of minutes with human oversight - and the revenue that it unlocks more than covers the AI bill. This is within a large corporate company - the "No such story exists for AI" feels overplayed. Sure, the wave of (quoting the article) "braindead executives, imbeciles and middle management hall monitors that don’t do any real work" might be bigger than with previous hype cycles because AI as a tool does enable pseudo-intellectualism, but the article overstates its case. I know, 1 counterpoint doesn't make a strong argument - but there's no reason the way we're applying this as a tool can't provide the same gains within other organisations - am I missing something/being delusional/huffing copium?
If you are eyeing the South African market - I can promise you granting credit here is waaaaayyy ahead of the US. There is a very solid credit bureau and a few of the banks are already on the "use AI to process docs" train. For rest of Africa - they're bigger on using cellphone data (see Optasia). If you want some insight into the market - happy to have a chat (email on profile)
Reflecting on my own experience - frequency of contact (if I see them once a year, can't really count them as close friends) How involved they are in my life - are they people I turn to when I'm facing a problem, do they turn to me when facing their own problems? Do we have frequent deep conversations - not just surface level discuss the weather, sports etc. but stuff that matter. Quantifying this - length of friendship (# of years), frequency of contact (annually, monthly, weekly etc.), level of trust (low, medium, high - can I trust my kids with them kind of trust), level of involvement (low, medium, high - what things do I feel comfortable sharing with them - suppose this is also level of trust?)
Yeah, he was quite vocal in his opinion that they would plateau earlier than they did and that little value would be derived from them because they're just stochastic parrots. Agree with him that they're probably not sufficient for AGI, but, at least in my experience, they're adding a lot of value and they're continuously performing better in a range of tasks that he wasn't expecting them to.
Was my thinking exactly - but also semantically equivalent is also only relevant when it needs to be factual, not necessarily for ALL outputs (if we're aiming for LLM's to present as "human" - or for interactions with LLMs to be natural conversational...). This excludes the world where LLMs act as agents - where you would of course always like the LLM to be factual and thus deterministic.
In a few years we've gone from gibberish (less poetic maybe, less polished and surprising, but none the less gibberish) - to legit conversational, and in my own opinion, well rounded answers. This is a great example of hard-core engineering - no matter what your opinion of the organisation and saltman is, they have built something amazing. I do hope they continue with their improvements, it's honestly the most useful tool in my arsenal since stackoverflow.
Which statistics in which study? Given the current system any sampling from college/university would be cherry picking vs general populous (unless you also sample general population with similar constraints to ensure a like for like comparison) so can't really be trusted.
Interesting/unfortunate/expected that GPT-5 isn't touted as AGI or some other outlandish claim. It's just improved reasoning etc. I know it's not the actual announcement and it's just a single page accidentally released, but it at least seems more grounded...? Have to wait and see what the actual announcement entails.
Extensive post on how neurosymbolic AI, the marriage between connectionist and neuro-symbolic approach to AI is potentially finally vindicated - opinion-piece by Gary Marcus
In America maybe, in south africa it's quite the opposite considering the government provides a lot more support for poor non-white folks than for white folks (specifically based om race)
The closest article I could find to "Smith, J. et al. (2023). "Advances in Probabilistic Forecasting." Journal of Forecasting" was from the April issue of Journal of Forecasting, but the article was titled: Advances in forecasting: An introduction in light of the debate on inflation forecasting and there was no Smith, J. that was involved in writing that article. Where do these numbers for improvements in the various industries come from? Somethings feels off with this readme.
So Minimax just "open-sourced" (I add it in "" because they have a custom license for its use and I've not read through that) but they have context length of 4-million tokens and it scored 100% on the needle in a haystack problem. It uses lightning attention - so still attention, just a variation? So this is potentially not as groundbreaking as the publishers of the paper hoped or am I missing something fundamental here? Can this scale better? Does it train more efficiently? The test-time inference is amazing - is that what sets this apart and not necessarily the long context capability? Will it hallucinate a lot less because it stores long-term memory more efficiently and thus won't make up facts but rather use what it has remembered in context?
FYI limited to to US only - they refer you to their sister project: https://darwinsark.org/ if you'd still like to contribute in a similar fashion but reside outside of USA.