sangfroid_bio

Depends on whether you are looking for effectiveness vs efficiency. YOLO et al. are optimised for general feature extraction. They are ridiculously good if you need to build something quickly. If you are looking for the most efficient way e.g. lowest processing power per image then use a network that is optimised for the particular feature you are trying to extract.

A real life Flowers for Algernon :(.

I have mentioned this before, this is a supply problem. If you do not gatekeep medical degrees with irrelevant non-academic barriers (volunteering, portfolios, admission letters) and mandate medical schools budgets to keep up with new demand, then there would be plenty of doctors and diversity of thought. Let the MCAT standardized test be the only signal for admissions and force the acceptance of medical accreditation based solely on technical competence. Medicine has a scale problem and nobody is trying to tackle it. The American Medical Association recently pulled their collaboration with Khan Academy to further keep up the barriers. At the end of the day, regardless of the complaints of big companies trying to seek "cheaper workers", software engineering as a field is still much more meritocratic than others. Attempts at unionisation and accreditation instead of leetcoding (kicking the ladder hmm?) has all gone absolutely nowhere. Be vigilant about attempts to gatekeep. For too long MBA and public policy institutions spewed the drivel that non-free-access accreditation is the end-all be-all, of the idea that if you do not genuflect before various admissions committees of expensive professional degree schools you do not have the right to take the accreditation exam, that you are a threat to public safety. Imagine where the world would be if the average admissions rate for computer science was 7%.

At that time software's AT&T style winner-take-all and network effect lock-in was still not that obvious. The PC was not yet the golden standard for desktop hardware and words like minicomputers and mainframes were not uncommon terms.

Organic chemistry and neuroscience are complex. FWIW soursop has also been linked to causing Parkinson's like syndromes due to neuro toxicity.

This is a bog standard modern NLP problem, I love how all the comments here are on how impossible it is using traditional heuristics and quoting various pop neuroscience authors and anecdotes. You only need a sufficiently large language model (and fine-tuned on short location strings) and run Named Entity Recognition on the input. Tricky? Yes. But there is only a finite number of prefix names in the English language and should automated solutions fail, you can always fallback to using a natural language form. A large neural network based solution running in the cloud would be able to get to 95+% accuracy. It would not be perfect but is good enough for most use cases.

Does JAMA network handle the journal translations or are they done by the authors?

So an inversed Dyson bladeless fan.

I think WebAssembly+low-level DOM API would be a good compromise for the lack of JavaScript.

Was the firmware at that time complex enough that you can attempt an OTA brick? Are radios from 1984 mostly analogue?

Which Bossware SaaS has such sophisticated NLP?

Looking forward to differentiable programming in Fortran.

It is great until your charity gets acquihired by a big think tank/bigger charity/international aid group and most doctors/charity operators are not known for their talent at scaling software.