greazy

DAGs are used in scientific workflows like snakemake. I wonder if such pipeline engines are use in LLM space.

What's the deal with Amba eating in Iraq? It seems out of place as it's spicy source condiment.

Okay... There's a whole Wikipedia about this topic! https://en.wikipedia.org/wiki/Amba_(condiment)

Wow I have to tell my dad about this (he's going to not like this lol)

Plenty of research is performed by non PhDs.

The main difference is reproducibility and peer review process.

I think all viruses do to some extent. We refer to them as DIPs https://en.wikipedia.org/wiki/Defective_interfering_particle

List includes RSV, flu, sc2 and of course hiv

I'm not a big fan of puzzle games but these are great.

Are the clues hand coded?

There's plenty of viewers, I think even MS used to offer free MS Office readers.

We need interoperability between MSOffice and everything else. But were stuck with MSOffice because even the so called open format is not well documented.

MS Office is the last roadblock for Linux desktop. It's literally the last thing holding back many users from moving to anything else.

> EDIT: It's very funny, I've asked the same thing on Reddit and over there 14 out of 15 comments advised me to take the job offer and here on HN the first 3 out of 3 comments tell me to do the masters

Which subreddit did you post on? Try one that's similar Hacker News like maybe programming and I think you'd get similar adobe as HN.

Australia is limiting / banning non competes for certain workers

https://www.mlc.org.au/whats-changing-with-non-compete-claus...

Which distro are you running? Perchance did you run the shell script in alpine Linux (docker)?

I never considered the two are linked. Any research or modeling that shows this is true? Is it generalisable to other countries...

It appears the scientists or someone close to them have created a wiki https://en.wikipedia.org/wiki/SpudCell

I don't think I've ever seen researchers do PR like this directly. Interesting approach, will it become the norm?

I think you forgot to post a link?

Posit (previously RStudio) has a few tools to easy dependency problems such as rig.

Ive switched completely to conda handling my deps for R. Especially nicer on Linux systems as the packages are preinstalled.

Bioconductor (another R package repo) has stricter rules for submissions, such as minimum requirements for documentation.

CRAN should adopt the same set approach. Even widely used historical packages can be light on docs.

Clever joke. FYI Mousa is the Arabic pronunciation of Moses.

> There is an interesting third group emerging: People who acknowledge the quality problem, but think they can deal with it by applying more AI to the output.

Ah yes, the known unknowns.

The discussion reminds me of a talk Zizek gave in which he discusses the speech Rumsfeld gave regarding the evidence Iraq supplying weapons to terrorist[0].

Zezik argues the unknown knowns are far more interesting (and the reason why USA was losing in Iraq). While Rumsfeld focused on the unknown unknowns.

I've noticed that domain experts who implicitly know the the known unknowns of their field distrust LLMs because they can identify their shortcomings. Those subtle mistakes LLMs make. I argue this is why domain experts using LLMs get such a boost. They can identify and avoid pitfalls sometimes before they happen. But in other fields the same people are in awe of LLM capabilities precisely because the known unknowns are a mystery.

The Unknown Unknowns of LLMs are the IMO the most interesting. The so called emergent capabilities of the technology. The use of LLMs in others fields such as biology, eg in protein language models, is really cool.

Everyone focuses on replacement of people workers when I think opening new fields of work for humans should be the goal of LLMs by leveraging the tech to discover.

The other interesting caregory is unknown knows. But that's another topic for another time.

[0] https://en.wikipedia.org/wiki/There_are_unknown_unknowns

What is your ERM of choices? Opinion on EPIC?

Great article.

While the definition changes, the expertise shifts and with it the field. Computers eventually became statisticians and data scientists. Printers became graphic designers.

What I found most interesting is that when positions undergo such evolution (printer -> graphic designer), a number of skills which were previously different expertise altogether, combine to create a new field. In other words, a new multidisciplinary field is born.

I think a good example is data science, the field at it's core is applied statistics using modern techniques such as data management and computing [0].

The question is, what is the new evolution of a programmer? Lots of folks like to use the term "engineer", and previously I thought this was silly. But now with LLMs, maybe that is a good descriptor; software engineer.

[0] https://www.welcometothejungle.com/en/articles/story-origin-...

Agreed, the paper describing the methodology uses basic techniques, no mention of AI or LLM usage.

https://www.nature.com/articles/s41541-024-00950-9

Its bizarre and even frustrating to see basic bioinformatics methodology referred to as AI.

AI had become a catch all term for... Everything.

Notification requests add to decision fatigue, which can lead to bad things.

Its really telling the example of personal AI/AGI given was booking tickets to a show.

As someone who dreams up many types of businesses (yet rarely go through with any), thanks got sharing your blog.

Reading it now, great read!

Besides your point but I learned about Mondragon on hacker news!

https://news.ycombinator.com/item?id=41438060

How can you tell?

LibreOffice did a great job of transitioning to an alternative UX and went further to implement not just ribbons but different combinations classic menu with ribbons.

That's the answer IMO, yeah now there's two UX to maintain but it's a step forward.

The solution would seem obvious: the lecturer should fork the repo, students submit PR to the fork and if they are deemed worthy they're pushed further upstream.

Why are you comparing a machine to humans. They both clearly operate differently on a fundamental level.

Would therapy work on an LLM?

Automated drug discovery is already a thing. The proceeding steps are the issue, clinical trials, formulation, safety testing, etc, economies of scale etc.

True automation of scientific research requires true AI.

I'm not really sure why tech people keep suggesting scientists aren't incorporating the latest tech.

We are all about automation. The issue is funding.

Phylogenetics is amazing, given surviving members of a clade we can reconstruct the ancestors. Phylogeny techniques can use additional info, eg paleontological record.

Eg here's a nice paper discussing Bayesian phylo combined with rexord sampling. https://www.cambridge.org/core/journals/paleobiology/article...

I'm in an adjacent field so take it with a grain of salt.