Disappointing such basics of vaccine and disease spread theory have to be explained on HN, and you explained it very well.
The US is under such an enormous disinformation campaign. I don't know how we can immunize people against the BS they are assaulted with everyday. Too many people benefit from that gullibility and ignorance.
What that page doesn't tell you is the precedence courts have set for those tests is so high that nearly no one qualifies for them, and thus it is the case that the loans are virtually impossible to discharge even if it means losing your home and all your possessions.
Anyone know what the refresh rate for these displays are, at least with the stock firmware? Reading the datasheets didn't help, though maybe I didn't know what to look for.
Those mechanisms don't work. Unless the driver was a hit and run or DUI, even fatal crashes almost never result in any charges if the driver claimed they "never saw them", especially so against pedestrians.
Large vehicles are killing people, even with sober drivers and nobody is being held responsible. This is well investigated at this point. Only recently are we finally getting some traction on the awareness front of how bad it is getting.
LLMs lack context, and I found the more information I provided the better. At some point it was better to just talk to the LLM like I would anyone else. For that matter, LLMs were trained on human speech anyway. It isn't like it was trained on if-else blocks like an Alexa speaker that tries to string together recognized tokens into a pre-configured execution flow.
And finally, LLMs also lack the emotional or human context for why I am doing the specific thing I am doing. Otherwise it will revert to the mode/mean in everything it does. This is obvious, btw: LLMs are generative but they are trained on and largely produce median results if given median inputs. To get results that are "outside the mean/median/average/mode", you need to provide it sufficient context, tokens and input to guide it towards a path that generates higher quality output.
Once you stop approaching LLMs like a machine, and view them more like pseudo-random walks across the compressed set of human written knowledge, it is a little clearer (or at least was to me) how to better write to them.
Cool, the price of solar right now is $30-$85 / MWh, and that range is dependent on whether you got storage included in the bill or not.
And that price will only get cheaper, as both the US and China continue ramping up production.
Nuclear? It would need to reduce its costs by 70% to get where solar is now. And then do it again to be competitive with where solar+storage will be in 10 years.
If they actually believe this stuff is so dangerous, they should shut down their company, and use their fortune to buy up/shut down all the others.
But of course they don't actually believe that. It is just marketing hype.
You can't be out there selling doomsday devices, saying "maybe we should slow down development of bigger doomsday devices", while *still selling doomsday devices". That is just blatant hypocrisy. Eating cake and having it, too. Oxymoron.
No, they don't actually believe this. Not in a meaningful capacity.
(FWIW, I don't believe it, either. I think we should continue developing the technology).
Yes, which is nothing compared to the destruction wrought by oil companies, steel manufacturing, meat production, concrete, energy, and transport.
Which have actually destroyed our environment for over a century. With only a slight bend towards slowing.
And I assure you. If you wanna argue "those are useful", not all of those things are useful, as useful as they could be, as efficient as they could be, or could be replaced today if the will was there with better options.
Data centers are a boogeyman and only cared about by Americans and some Europeans. The other 6 billion people in the world really dont think they are bad, nor have such strong feelings towards AI.
And I bet they feel a lot more animosity towards the military industrial complex and the oil empire financing it destroying our planet.
Bizarre to say that. When I have it perform work on a bespoke code base on a niche videogame, in a less commonly used language, is that still "regurgitating stuff"?
No, it is impossible for it to have seen this combination of things.
It routinely produces, suggests, and correctly implements novel things that had not existed.
You can see this yourself by learning how LLMs work, or anecdotally using these tools.
That sounds more like the extractive setup of corporations like IBM, where return/specialist must be maximized against the number of clients that can be juggled simultaneously.
Whereas in larger technology firms, yes that happens to some degree, but only with the highest level specialists such as PEs, fellows, etc.
They are already so wildly profitable and valuable by the simple nature of computing itself: it scales second only to money with regards to compounding effects. Once you have software someone can use, it scales near infinitely to more users, thus value is extracted from the work of a single specialist for all time. No need to complicate it with trying to abstract work juniors can do (and fail) and then have seniors correct. What would they be doing in non-extractive firms?
The first premise is reasonable. The second premise is so outlandish as to require dozens of other assumptions, optimistic outcomes for launch, and pessimistic views on the cost of earthbound systems.
I care about the environment and I think we can keep earthbound systems, and also reduce their impact. Making assumptions about the feasibility of launch and the economic absurdity of orbital compute, but not affording the same assumptions for what could be done for earthbound systems, is confusing?
And no, orbital compute is absolutely not far lower than earthbound in co2 cost. Because it doesn't exist at any scale. All orbital compute is solely dedicated to switching where it is best served. If you were to spitball numbers, are we even willing to assume orbital matches earthbound in compute total, dollar cost, uptime, or any beneficial metric?
The only metric I see is just slinging silicon into space.
This becomes clear to anyone that wants to do marginally complex work. Developing pipelines that combine pre-processing flows, semantic targeting, and minimal contextual calls to an LLM API gets you powerful automated steps. Combined with separate validation steps, LLMs go from toys to useful.
The second one is almost always used by Healthcare companies in my experience, in a backwards attempt at HIPPA compliance.
They want to make sure they called the right person. Except they know everyone hates getting called like this, so they take "who is this?" as affirmation and then proceed to tell you their company and the call.
The biggest irony with telling a recruiter they'll be replaced, is how much easier a data scientist is to replace with LLMs. With their sycophantic nature, execs will eat up whatever "data" the LLMs make up, too.
People are shamed for using LLMs at all. So they use them privately, hide them, or disguise their use.
They are definitely being used for public projects. But people are afraid of backlash. Look at some of the comments here.
Hell, Reddit is extraordinarily against LLMs such that even neutral takes are down voted. Mostly by younger generations that aren't in the workforce.
Then you have all the regular people against AI generally.
Ironically there is a conspiracy here. But in the opposite direction.