I am on a LitRPG site (RoyalRoad) and there are routinely AI Gen novels in the top 5 novels on Rising Stars on the site which is basically traffic and rating metrics for newish novels. People say they hate AI writing and downvote it when they know it’s AI but seem to read it just fine without noticing fairly often as well.
I think this mostly comes down to what work is productive for a person. People who are fascinated by research topics are likely to work on them and people who are enamoured with creating art will do so. But people who's productivity was largely being some cog in some machine will be all too happy to say good riddance.
It seems to me that recent changes have dramatically exacerbated this problem. Pangram is still claiming their extremely low false positive rate from before adding humanization detection and now very frequently detects actual human works as humanized AI.
There seems to be a huge backlash to existing works where AI was detected already at least in my art (creative writing). So I think we already have evidence consumers care. I don't think blockchaining is the solution for the general public, but I'd probably do exactly that if I was taking a university course these days since they tend to be hyper sensitive about AI and err on the side of the accuser so I want strong evidence my writing is mine. Which of course is ridiculously insufferable because I used to do a good portion of my homework in paper notebooks on the subway without stable internet.
LLMs still write middling quality prose. Aspects of it are good but they write in a very tropey manufactured style that isn't something we are fatigued of, it's something that's inherently flawed and disliked.
A lot of writing publications are so overwhelmed by submissions that they run an AI tool on everything and don't even read the ones that fail (for whatever threshold they think is best). Plus look at the recent backlash against the book where the author according to detectors used about three quarters AI gen. If you do your art for anyone but yourself you care a great deal about your work showing as human and being treated as having soul rather than testing as machine and being treated as witch hunt grade slop.
Pangram’s market claims are very different from independent validations of the platform. The 99.98% rate is on a toy dataset not resembling reality.
Even if 2/10000 is true that’s nowhere near accurate enough to make aggressive accusations that create anxiety at levels people need to medicate with potentially fatal consequences.
Neutrality is impossible. Left, right and centre are all relative terms that vary dramatically across cultures. A model that is slightly left in the U.S. is right wing in most of the world.
I think pop musicians are capable of doing greater works later, but the perception of pop works are so heavily influenced by the image/presentation of the artist that we view the works as lesser. I don't think there is something fundamentally different about pop music that leads to best works being earlier relative to other genres of music beyond that.
Elderly retirees seldom move. They’ve long paid off their mortgage and we keep property taxes extremely low so they are pretty much immune to the cost of housing. Once you put down roots in a community it’s hard to leave.
It's easier to define safeguards and the definition of inside information for stock markets than for prediction markets though. There is plenty of information that should ban someone from prediction markets that also wouldn't meet the definition of material non-public information.
OpenAI is a bet on LLMs replacing a large chunk of the labour force in whatever sector it’s best at replacing. It’s essentially looking to get companies to pay $5k-$10k a month to have coding agents replace the output of a single software engineer.
If the S-curve levels off below that level OpenAI will be an unsuccessful company.
Big tech likes this because there are a lot more face recognition technologies in the wild in real life and being able to connect all real life data to online data is quite valuable. It's also quite possibly the largest training set ever for face recognition if ids are stored and given how ids and images are sold across many companies it seems very high probability that some company will retain the data rather than delete after use.
I think more than ever programmers need jobs where performance matters and the naive way the AI does things doesn't cut it. When no one cares about things other than correctness your job turns into AI Slop. The good news right now is that AI tends to produce things that AI struggles to do well with so large scale projects often descend into crap. You can write a C-compiler for $20,000 with an explosive stack of agents, but that C-compiler isn't anywhere close to efficient or performant.
As model costs come down that $20,000 will become a viable number for doing entirely AI-generate coding. So more than ever you don't want to be doing work that the AI is good enough at. Either jobs where performance matters or being able to code the stack of agents needed to produce high quality code in an application context.
It’s far less important for ad-free content. They mainly want to connect your ad watching behaviour to an email and then have loyalty program data connected to the same email so that they can identify which ads convert vs not.
If it's actually in the service of rigor then it's not unnecessaryily unwelcoming. If it's only nominally in the service of rigor than maybe, but Mathematics absolutely needs extreme rigor.
This seems like the single worst way to measure this because by looking at chronological data you get all kinds of temporal effects. A better baseline would be to look at the difference between pre-cell phone ban scores and post cell-hone ban scores compared to other districts where the cell phone ban didn't occur.
I find it completely unremarkable that test scores went up post-COVID and feel it's very hard to tell what is causing what.
I feel like this is an inside view from the BPO community and the only part of AI they see is the part that affects BPO. But for most businesses AI strategy is not about AI for internal use but AI to either improve customer funnels or launch new products. Most of the companies I've talked to in the past year wanted a strategy for customer facing AI not internal AI.
Earning a 4.0 GPA in high school math while only knowing middle school math seems absolutely wild. Is that truly a side effect of COVID or are certain schools just going to be heavily penalized in admissions because their standards are far too low?
This program is an insignificant spec of spending compared to UBI though. UBI in most countries would be 50% of all government spending and welfare related spend is just nowhere close to that.
Psychological wellbeing contributes some amount of money though in systems that pay for healthcare (especially when that includes psychiatry). It's also to a degree one of the key things government spending is hoping to produce so if it is actually producing that it's a good use of funds.
I do agree the program needs to make it doable to get the funds in order to become an artist since otherwise it's exclusively for rich artists who can be an artist while waiting for eligibilty.
The article fails to cover the main point. It's not just about putting the south at the top of the map but doing the projection from the south instead of the north which makes the south appear bigger and the north appear smaller rather than vice versa. I think top vs bottom for good vs bad is weaker than the bigger is better piece they fail to even mention.
Ranking by Salary probably makes the most sense if you're going to cap the numbers you want the most productive people who contribute the most to the tax base.
People should be worried because right now AI is on an exponential growth trajectory and no-one knows when it will level off into an s-curve. AI is starting to get close to good enough. If it becomes twice as good in seven months then what?
I think the problem is do you want to give the AI access to prod. See the recent example where AI wiped a DB despite instructions not to (because AI sometimes does things more often when you tell it not to do something because the negative from not is not always reliably picked up)