"Conclusions: This meta-analysis confirmed that skipping breakfast is associated with overweight/obesity, and skipping breakfast increases the risk of overweight/obesity. The results of cohort studies and cross-sectional studies are consistent. There is no significant difference in these results among different ages, gender, regions, and economic conditions."
People seem to be misunderstanding this paper. It doesn't claim that any previous papers have overestimated contamination. That would only happen if scientists didn't routinely use blanks as a comparison, which they do.
E.g. "A procedural filter blank was created during each sample batch and analysed alongside the samples, to enumerate potential contamination that could have been introduced during the extraction process."
I've worked in data extraction from documents for a decade and have developed algorithms in the space. I've developed a product using LLMs for this purpose too.
Do you have any reference for this claim, or are you guessing? It was reported that the algorithm was a Gradient Boosting Machine by investigators who gained access to the code.
I think because it's a relatively 'younger' field, there is a bit more need to know about the foundations in AI than in programming. You hit the perimeters a bit more often and need to do a bit of research to modify or create a model.
Whereas it's unlikely in most programming jobs you would need to do any research into programming language design.
Not at all. It's something I've seen in practice over many years. Neither skill set is 'better' than the other, just different.
There is a need for people who are able to build using available tools, but who don't have an interest in the theory or foundations of the field. It's a valuable mindset and nothing in my original comment suggested otherwise.
It's also pretty clear that many comments on this post divide into the two mindsets I've described.
Most comments here are in one of two camps: 1) you don't need to know any of this stuff, you can make AI systems without this knowledge, or 2) you need this foundational knowledge to really understand what's going on.
Both perspectives are correct. The field is bifurcating into two different skill sets: ML engineer and ML scientist (or researcher).
It's great to have both types on a team. The scientists will be too slow; the engineers will bound ahead trying out various APIs and open-source models. But when they hit a roadblock or need to adapt an algorithm many engineers will stumble. They need an R&D mindset that is quite alien to many of them.
The deeper problem here is that review sites don't work well for things as personal as books. I've read many books based on excellent reviews in amazon, and hated many of them. Most people don't have the same taste as me. Likewise many people hate the books I love, and give one star reviews to them.
This is to misunderstand Miles Davis. The point is that he treated the trumpet player with respect, as if he was taking to a trumpet player, not a child. He was engaging with a group of peers. The evidence for this is that he went on the hire the keyboard player, who was 16 years old, to play in his band a year later.