The fix for this is for the AI to double-check all links before providing them to the user. I frequently ask ChatGPT to double check that references actually exist when it gives me them. It should be built in!
Gemini will lie to me when I ask it to cite things, either pull up relevant sources or just hallucinate them.
IDK how you people go through that experience more than a handful of times before you get pissed off and stop using these tools. I've wasted so much time because of believable lies from these bots.
Sorry, not even lies, just bullshit. The model has no conception of truth so it can't even lie. Just outputs bullshit that happens to be true sometimes.
I have found my self doing the same "citation needed" loop - but with ai this is a dangerous game as it will now double down on whatever it made up and go looking for citations to justify its answer.
Pre prompting to cite sources is obviously a better way of going about things.
It's bad when they indiscriminately crawl for training, and not ideal (but understandable) to use the Internet to communicate with them (and having online accounts associated with that etc.) rather than running them locally.
It's not bad when they use the Internet at generation time to verify the output.
I don't know for certain what you're referring to, but the "bulk downloads" of the Internet that AI companies are executing for training are the problem I've seen cited, and doesn't relate to LLMs checking their sources at query time.
I would distinguish between visual imagination and visuospatial reasoning.
For people like myself with aphantasia, there are often problems solving strategies that can help you when you can’t visualize. Like draw a picture.
And lots of problems don’t really require as much visual imagination as you would think. I’m pretty good at math, programming, and economics. Not top tier, but pretty good.
If there are problems out there that you struggle with compared to others, then that’s the universe telling you that you don’t have a comparative advantage in it. Do something else and hire the people who can more easily solve them if you need it.
It sounds like you have routed around your spatial visualization deficit, but that just proves the importance of alternate cognitive strategies rather than indicate that such an aptitude or deficit doesn’t ceteris paribus impact mathematical achievement.
I took some sort of IQ test when I was a kid and there was an entire section that was "if you rotate this object around that axis, it matches which of the followin g options". Try as I might, I can't picture this in my head (picturing anything other than a sphere or a cube is tough) but I found that I could look at the options and logically exclude them in a very tedious way by inspection.
It's one of the reasons I like computer graphics so much: the computer does the rotation for you! Stereo graphics (using the funny LCD glasses) was a true revelation to me, and learning how to rotate things using matrics was another.
You must hate those fancy new style captchas where you rotate the object. I’ve never considered the fairness and discriminatory aspect of captchas until now. I wonder if in the future eternal September will finally end as increasingly complex captchas act as a sort of poll test on posting.
Data science wasn't even a degree you could get 20 years ago. Twenty years ago if you were interested in what is now called data science, you were getting a degree with some kind of exposure to applied statistics. Economics is one of those disciplines (through econometrics).
No, I did stats as part of economics around then, and it's nothing like modern DS. It overlaps a fair bit, but in practice the classical stats student is bringing a knife to a gunfight.
The practice of working with huge datasets manipulated by computers is valuable enough that you need separate training in it.
I don't know what's in a modern stats degree though, I would assume they try to turn it into DS.
Data science is basically a marketing title given to what would have been a joint CS/statistics degree in the past. Maybe a double major, or maybe a major in one and an extensive minor in another. And it's mostly taught by people with a background in CS or statistics.
Like with most other academic fields, there is no clear separation between data science and neighboring fields. Its existence as a field tells more about the organization of undergraduate education in the average university than about the field itself.
The Finnish term for CS translates as "data processing science" or "information processing science". When I was undergrad ~25 years ago, people in the statistics department were arguing that it would have been a more appropriate name for statistics, but CS took it first. The data science perspective was already mainstream back then, as the people in statistics were concerned. But statistics education was still mostly about introductory classes of classical statistics offered to people in other fields.
No. Data science is different than statistics, because it is done on computers. It also uses machine learning algorithms instead of statistical algorithms. These advances, and the shedding of generations of restrictive cruft - frees data scientists to craft answers that their bosses want to hear - proving the superiority of data science over statistics.
yeah, we called that data mining, decision systems, and whatnot... mapreduce was as fresh and hot as the Paul Graham's essays book... folks were using Java over python, due to some open source library from around the globe...
essentially, provided you were at a right place in a right time, you could get a BSc in it
One of the difficulties with these models would be backtesting investment strategies. You always need to make sure that you are only using data that would have been available at the time to avoid look-ahead bias.
Linking blog articles that bury the lead behind paywall make it impossible to discuss anything.
However, at the core, US insurance system is the problem because it gets compounded by government trying to regulate such a system, so people do not die needlessly, but not destroy these profit seeking enterprises. So, what you end up with is a massive mess that leaves everybody cranky.
Up to the first or second chapter, depending on the book being used is more than sufficient to cover the foundational concepts. Sets, and Properties such as closure over given operations, and mathematical relabeling which is a function (f(x), the requirements for it (uniqueness of x, and projection onto) along with the tests for the presence of these, and common mathematic systems properties.
This naturally provides easily understood limitations of math systems which can be tested if there is a question, and allows recognition when they violate the properties that naturally lead to common mistakes, as well as providing a space where they can use numbers/geometry/reasoning at play.
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