Were you able to process truly large datasets? I worked at a very excel company once but we kept running into row limit issues when scaling workloads and eventually migrated almost everything away from it. What industry were you in at the time?
626 karma · joined April 8, 2022
I would assume that cannabis use correlates with a few other important heart health variables and we would expect the odds ratio to be lower when accounting for those (alcohol doesn't have an OR more than 1.0, tobacco smoking ~1.5)
I'm sure that cannabis use is bad for cardio health but the reported odds ratio is very high. I personally do not use cannabis.
and it's open source so nothing stops a bigger producer of copying the exact technology with institutional funding and manufacturing expertise
It may be that spaceX is buying an operation that would realistically take 5 months and 100 million to copy in-house for 60B because the worry is that waiting 5 months might cost that much in some sort of lost opportunity. It also might be that in any negotiation SpaceX is viewed as incredibly cash-rich and so anything can be sold to them for inflated prices.
I really don't understand these companies valuations it seems like boardrooms everywhere are in a constant state of panic that they'll lose it all if they aren't growing a breakneck pace constantly.
but perhaps one individuals prompt feedback just isn't going to ever be enough I'm not sure how much you need (I know people working at big companies that have purchased in-house agents fine-tuned on internal documents etc.. and apparently these end up with bizarre behaviours not necessarily more helpful than the standard models)
I'd like to be able to essentially edit every response given by an agent and then finetune on the difference between what it produced and how I edited the text. Personally I would just remove a lot of the adjectives and try to distill the responses to core responses but I worry based on some of the work done by Owain Evans and other alignment researchers that this can sometimes push agents into tricky-to-predict tendancies.
Even with 3 weeks I'm just not the Fortran/C programmer to get that job done so I moved on to other things.
the tech is pretty good at helping identify simple bugs when they happen and to write short sections of code given very explicit instructions but yeah I have yet to see good examples of short one sentence ideas turned into a working product that looks better than anything that could be a UDemy tutorial app.
It's tough to answer because you want to hedge for both an AI enthused employer and an AI hesitant employer with limited information about who they are and how they personally use these products. I've been responding with a sort of long winded answer about how 'there is clearly a learning curve for how this technology fits into any process and how I always always always double double double check yadayadayada'
I'm probably using the chat/ask functionality on a daily basis for quick debugging / new technology learning questions but I have yet to really use the fully agent or computer-use products because I've had more bad results than good the few times I've tried them (re-factoring a big repo of decades old fortran+C code for modern compiler/OS some things started to work but ultimately I abandoned that effort).
I would hope there aren't too many large utility jurisdictions which would curtail citizen consumers in favour of industrial users in the event of a demand surge.
On a related note. It's worrying to me how quickly we've accepted that we're going to boost electricity consumption massively prior to achieving anything close to the carbon intensity reduction targets which would mitigate the worst of climate change effects. It's all driven by a market force which cannot be effectively regulated on a global scale for multinational tech firms who can shop around for the next data centre location with near total freedom. And with advances in over the top fibre networks etc... a tonne of AI demand can be met by a compute cluster on the other side of the world (especially during model training) so the externalities related to the computing infrastructure can theoretically be completely dumped somewhere far away from the paying customer.
The authors do include the claim that humans would immediately disregard this information and maybe some would and some wouldn't that could be debated and seemingly is being debated in this thread - but I think the thrust of the conclusion is the following:
"This work underscores the need for more robust defense mechanisms against adversarial perturbations, particularly, for models deployed in critical applications such as finance, law, and healthcare."
We need to move past the humans vs ai discourse it's getting tired. This is a paper about a pitfall LLMs currently have and should be addressed with further research if they are going to be mass deployed in society.
Andre Marziali - Physics of Racing https://www.youtube.com/watch?v=bYp2vvUgEqE
I'm at the beginning of my career and learning every day - I could do my job faster with an LLM assistant but I would lose out on an opportunity to acquire skills. I don't buy the argument that low-level critical thinking skills are obsolete and high level conceptual planning is all that anyone will need 10 years from now.
On a more sentimental level I personally feel that there is meaning in knowing things and knowing how to do things and I'm proud of what I know and what I know how to do.
Using LLM's doesn't look particularly hard and if I need to use one in the future I'll just pick whichever one is supposedly the newest and best but for now I'm content to toil away on my own.
Give them time...