But OpenAI has said they delete logs of outputs. If they're now saying that 120 million is too many to go through because more private data could be compromised, that means they haven't been deleting output logs.

Further, since the NYT and others are only looking for articles from their sites, their searches would be limited in nature. This would be no different than you using DDG and searching for how to make a toasted cheese sandwich. You would not get results for how to make a Tokamak reactor (at least we would hope so).

First, impact factor (not impact score) is used to compare journals, not individuals. Second, it is the average citations of every paper in a journal so one paper that gets a high citation rate through fraud will have minimal effect on the impact factor...and if you have so many fraudulent papers that it does then you are going to have a widepsread reputation in the field as as the journal if science-fiction at which point it will not matter what your imapct factor is, you'll be judged on that reputation.

Which is exactly how it should be because if all you have done is exactly reproduce someone else's work nobody has learnt anything new so the impact is close to zero. This is the sort of project you give to an undergrad, not something you would want to publish. Simply reproducing a previous work is a wasted opportunity to improve on it. Instead of aiming for replication, aim for improvement: increase the precision of the measurement or use an entirely different method that tests different assumptions. Replication usually happens naturally when people start to build on your result and improve it. This is why, despite as you point out there being no funding specifically for replication, we still catch scientific fraud.

You are NOT the one person who has trained AI in this discussion, I have been using it for the last 20+ years. I'm not afraid of AI at all but, unlike you, I am very much aware of its limitations because, also I suspect unlike you, I have actually trained and used AI systems. However, given that you failed to read that in my post I suspect you may be the one person in the discussion who can't read and understand English which is another reason to doubt your claims.

The regulations were so bad that they came up with a (lawful) workaround (changing the mounting position of the engines) to avoid triggering some of the worst of them, and then when that workaround changed things enough that too much regulation would be triggered, they came up with another lawful workaround, MCAS. And that caused the trouble. They couldn't do make anything new because the regulatory burden for new things was too great, so instead they piled workaround after workaround on the old thing.

And you can scoff at "cheapest" all you want, but if the result of Boeing doing something else is their customers have to retrain their pilots, then as someone else put it "they might as well go to Airbus".

Not exactly. Wine, you see, is not an emulator. It is just a compatibility layer. So it doesn't fully implement everything that windows does. If you want that, you would have to run a virtual machine (like using VirtualBox). So using wine does not equate to "you might as well be running windows".

But if you demand complete equivalence to windows for your every use case, and absolute perfection, then yes Linux is not for you.

But if you were motivated to use Linux, you might find that you can adapt and find workable solutions, setting your soul free from the tyranny of Microsoft (as I and many others have).

Oh and honorable mention goes to Apple, too. They are evil in their own ways, but differently-evil than Microsoft, so that might be good enough in some cases.

Maybe because obtaining a degree leaves most people in crippling debt, and the skill/knowledge benefits they get from it could have been gotten much more affordably through a self-study program at a library. There's also the problem that a lot of degrees aren't focused on giving you marketable skills, making them scams for people who are told to get a degree in what they love and then expect a fun life doing their passion all the time and getting paid.

The American education system is largely a scam, at this point, so I can see why people might have a vested interest in convincing employers to move away from it. It is POSSIBLE that some schools have good coursework that teaches students a lot. I am not saying they are all scams. But even the good ones cost a fortune, and more affordable means of acquiring an education are available.

Take control of your OS with this one weird trick.

(Microsoft hates this!)

OpenAI originally stated that the links would not be searchable by search engines. The share button was supposed to give you a link that you could give specifically to whomever you wanted to share with, with at least a needle-in-a-haystack's level of privacy.

Peer review is not the same as a fraud investigation. When we review papers we start from the assumption that the data in the paper was collected "honestly" i.e. that the researcher accurately reported to the best of their ability what they did and the data they collected. We then look at that data to ensure it looks consistent with what they did and that the method did not contain anything that might cause misleading data. Then we check that the conclusions in the paper are consistent with the data and analysis.

Peer review is there to prevent a researcher from fooling themselves (and others) by making claims that are not warranted by data or missing some subtle effect that could explain what is going on without the need for new science etc. It cannot check that the data are real although sometimes it can catch fraudulent data if that data are not consistent with well-known and established science. However, fakers are usually smart enough to be able to mske the data look consistent enough so it can be very hard to spot that in peer review. Ultimately it will get spotted as others try to reproduce results, faill and then start to look in much, much more detail at previous claimed results but peer review can't spot things at that level of detail.

Impact factor generally is a credibility factor or at least I do not know of any journal in my field where there is a low-credibility journal with a high impact factor, although there are some specialist journals - e.g. instrumentation - which are highly credible but with a low impact factor. Generally speaking though anyone in the field worth their salt will know which the good journals are and where a paper is published generally does have a large impact on how we regard its quality.

I do not see a good way for a "credibility factor" to be calculated in an objective manner that would not have significant negative repurcussions e.g. counting the number of retractions would be bad since it would encourage journals never to retract papers. Similarly even the best intitutes can hire rogue researchers - or more commonly have bad grad students or postdocs - and enouraging journals to accept anything from any researcher in a "respected" instistute to boost their credibility would be bad too. Also papers in many fields cannot and do not have a single "primary" author.

That is utterly wrong. As a scientist who has sat on several grant review boards there are no politicians involved at all in deciding who gets funding. The politicians set the size of the pot we have to give out but grant applications undergo rigorous, multi-stage peer evaluation. Even in the US where a single expert program officer has a lot of control over a grant program (or at least they used to) peer evaluation was still critical to the process. The only exception to this are "mega-projects" where the cost is so significant that it merits a line-item in the national budget and then yes, politicians obviously have to be involved but this is not where the vast majority of research funding comes from and at that point they are listening to the views of multiple experts and weighing in the national and political interests, not counting papers.

When grants are peer reviewed nobody just looks at the number of papers if the appilcants and goes "oh wow that guy published X papers lets give him everything he asked for!". Instead we look at the quality and impact of that work as well as what they are actually proposing. Different people weight these things differently - I tend to weigh the proposal more, others weigh past pulication record higher and both are very valid. However, in evaluating publications we use things like venue of publication (how many are in top journals for the field?) and citations (h-index) - although even then you have be to careful since that depends a lot on the field. Rate is a consideration but large numbers of papers in dodgy journals will count for nothing, indeed they would be detrimental since those reviewing it would be asking what he person is up to and how can they not know that the journals they are publishing in are trash.

If you think you need a Harvard degree, let alone a Harvard MBA to work for Google, your view of the world is quite off.

They did learn but like any good follow-on they added a twist. Instead of the computer driven insane by its programmer, in the MS version it will be the user driven insane by the computer.

It will not be the chatter that kills this but the input bandwidth. Even if you assume it would allow you to set up some "verbal macros" to execute when a single word is spoken I can still click mouse buttons faster than I can speak words. The same goes for output bandwidth but even more so - it is much, much faster to see diagrams, buttons and read text then it is to listen to the computer speak information.

I can see this being useful in limited applications - such as in-car systems where a verbal inface and lowing bandiwicth would be a huge benefit. However, I cannot see it replacing a regular desktop/laptop OS.

I've trained many machine learning models as well, from BDTs through to GNNs but never an LLM - although arguably not entirely from scratch (except for an early BDT) since we used existing libraries to implement a lot of ML functionality and once setup we just provided the training data. If you really have trained an LLM "from scratch" as you claim then surely you must be aware of how inaccurate they can be? I mean even the "professional grade" ones like Gemini and ChatGPT get things wrong, omit details and make utterly illogical inferences from time to time.

I'd agree with the OP that you do not know AI - even if you are capable of building an ML model from scratch (I presume using a suitable toolkit so not realy from scratch) you clearly do not understand the reliabilty of its output or are incapable of seeing how that might be a serious problem when advising someone with mental health issues which raises questions about exactly how much you understand of what you might be doing.

The new law seems to be well written. All it does is ensure that a medical professional has approved the use of the system. It's the same type of protections we have for drugs, we do not just let companies release any drug they like before it has undergone testing and a panel of medical experts agrees that it is both safe and effective and even then they do not always get it right! How is it stupid to have similar protections for computer software used to treat mental health problems? It does not prevent you from using software in this way all it requires is that an expert has said that it is safe and effective.