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user: fnands (* users last updated on 10/04/2024)
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created: 2021-12-17 17:22:45
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> Having killed the churches, we gotta figure out what actual services they provided to society and re-implement them somehow.

Well, I was with you on the first sentence, the rest not so much.

But ignoring that, I do believe there is some good that (some) churches do, and it's a shame to throw out the baby with the bathwater.


Yeah I was kinda wondering, what's their game plan here? Hope that people don't notice the price difference and DIY it? Might just be what someone else said, this is what Dell is not being paid to pre-install whatever

Eh, I would argue there is a massive difference between drug discovery and patient care, which was what Watson was aiming for.

Filtering out useful molecules from useless (or dangerous) ones is a nicely contained (if difficult) problem that ML is pretty good at solving.


It does. They used Chemistry42 which is Insilico's ML based molecule generation platform(?)[0]. Sure, they didn't train any models specifically for this case, but they used a protein structure predicted by one ML algorithm, then used another set of ML algorithms to find and rank molecules that might match the protein, and then tested the most likely ones in reality.

[0] https://arxiv.org/pdf/2101.09050.pdf


Cool when stuff like this works. Kinda like before they figured out you could cure syphilis with penicillin, they would infect the patient with malaria, which could be treated, which would then kill the syphilis.

Man, what a weird interview. Not that that's new for Elon, but it's like the interviewer went to the Werner Herzog school of interviewing

Things have changed a bit at least, but it's still very provider dependant.

Planet at least will let you pay per pixel for their PlanetScope data (3m res) and with a daily revisit time you are guaranteed at least a few hits per month in the growing season.


I think UP42 is trying this model. They have a catalogue where you can download archived data or task it. But yeah, right now their sources are a bit limited, and its' an open question whether or not they'll be able to sign up enough providers to make it work.

And they might be falling into the trap that the original article points out by trying to offer you processed data instead of being laser focused on having the best data sources and API.


At Fermilab they used a ferret (in a diaper!) to clean out the beam pipes: https://www.atlasobscura.com/articles/felicia-ferret-particl...

As the adage goes: "There are only two kinds of languages: the ones people complain about and the ones nobody uses".

The detectors on the LHC get upgraded regularly, between every run some part gets updated. Computing-wise as well, better computing resources are there.

The fundamental limitations isn't the detectors or computers, it's the energies you can reach with a collider of that size. Yes you can completely retool the LHC, which will probably allow you to do slightly better precision measurements, but won't strongly affect the energies you can probe.

To get to really high energies you need to build bigger. As a (former) particle physicist, I am also a bit sceptical if going bigger is better; the LHC was a calculated risk, with the goal of finding SUSY, which was a very plausible theory at the time.

The FCC seems to be a shot in the dark.

There is the argument that if we don't build a bigger one now we'll lose a lot of expertise, but that feels like a knee-jerk reaction from the community.


Interesting. Yeah, some of my running buddies report no appetite for about an hour after running.

I on the other hand am usually ravenous soon after exercise.

When I run more than 20 km I get the kind of bottomless hunger I used to have as a growing teenager, but I try to fill up with reasonably healthy stuff (sometimes it's fried chicken though).


My result: 45% Normal: "Pretty strange, in a sexy way."

Thanks, I guess? Got pretty close on age underestimated my BMI a little.


Definitely not.

Great to see them sending over an engineer for this one. Speaks to a kind of openness that's refreshing to see coming from Intel.

My worry would be maintenance. Yeah sure, you can get it for cheap, but will it take 6 months every time you need a replacement part?

Seems like a sweet gig

As soon as I read "whose family also summered in the same patch of northern pines" I knew where this story was going.

Basically read as: we started a failing company then used family connections to raise a fuckton of money to buy a successful company, and now we're successful.


That is kinda hilarious

GaaS - Goats as a Service

JFC that website makes me want to pluck out my own eyeballs.

Now that is some proper sci-fi weaponry. Questionable if practical, but so cool

Thanks for the input. I've been hearing about wakefield accelerators for a long time, but never got into the nitty gritty of it.

Is the beam quality a fundamental limiting factor, or is it solvable (at least theoretically).

It sees they're going for the get the energy up and solve the beam characteristics later approach


I was about to comment, if you like Fortran's syntax for numerical computation, you should try Julia. I feel it's basically the spiritual successor to Fortran, without all the historical baggage.

Tangentially, I also think the term data scientist has been so abused as to almost be meaningless at this point. When I was applying for jobs it could range from anything from "knows how to use MS Excel" to "Can train large language models at scale".

Personally I went for ML Engineering. My company at some point hired people as data scientists (some of my more senior colleagues still have the title, despite doing the same work I do), but started hiring people as ML engineers, i.e. people who can do half-decent SW engineering and also do ML. Just a filtering thing I guess.

I have a suspicion the term will start to fall out of fashion as things become more specialised.


There is a bit of a joke that a data scientist is someone who can do better stats then the average SWE and can write better code than the average statistician. Both of those are relatively low bars to clear though

Honestly, I can't tell you how many jobs ads I saw where I was wondering: "What would they expect me to bring to the table here?"

Some companies just don't have the data, or heck even the need, for data scientist yet try and hire them anyway.

Give smart people a fundamentally ill-posed problem and they won't get anywhere anyway.


Harsh, but funnier than how I phrased it.

Yeah I feel a lot of companies could do with running their problems past a consultant first.

Also, w.r.t hiring in cases like these, I think often the experienced candidates can smell that this won't be a good gig so don't apply, while the less experienced (or desperate) ones apply. This means the workers get stuck with an intractable problem, and the company gets stuck with workers who are too inexperienced to know better.


Data lake? More like data pond

- Improve my online presence, mostly through some blog posts.

- See if I can squeeze a publication or two out of the stuff we're doing at work.

- Make some more meaningful open source contributions


Considering something like torchgeo (https://github.com/microsoft/torchgeo), or maybe some geospatial library written in Julia.

> but the problem I have is that everything seems to just work and I don't run into many issues to fix.

May I point you at linux audio? ;-) Getting any type of DAW working in linux without having to do some crazy rewiring with jack would be a godsend.


If Elizabeth Holmes is going to jail then SBF probably is too.

As far as I understand they were making pretty good bank with Alameda doing crypto arbitrage. Could probably have been set for life just doing that.

Human psychology is funny. Not content with being stinking rich in their 20's, they embarked on a massive fraud to get even richer.

To give them the benefit of the doubt, I'm not sure they set out to commit fraud, just make money off suckers, but when the temptation presented itself they couldn't resist it seems.


Really liking the watercolour aesthetic!

Agreed. I am just fascinated by the fact that they made an few million easily, and instead of rolling that into something productive they went for fraud.

Definitely true (not that should stop anyone, little projects like these are a great way to get exposed to other languages).

From my personal experience of doing AoC in Julia this year, my Julia code started out smelling a lot like Python, but managed to adopt a bit more of the Julian way of doing things as I went along (not done yet though...)


> 1. We will learn that one or more traditional banks have taken on cryptocurrency exposure and will need to be bailed out to prevent another financial crisis.

Don't know if this will pan out, but if it does it will probably be Deutsche Bank right?


Also a strong contender. Likely the DB/CS double whammy

If you don't mind ~3m resolution you could pay Planet labs for a roughly once a day (weather dependent) Google Earth.

However, large parts of google earth uses aerial (i.e. non satellite) imagery.

So yeah, way of.

Also, if you do a quick back of the napkin math, it's a lotta data.


Any recommendation for CO2 sensors?

How?

Wild.

Very cool to see someone come back out of "industry" and to crack a problem. On a side note: the name looked familiar to me, and I realized I had spent a decent amount of time re-implementing his most cited (ML) paper a few years back.

Yeah, I think there's a lot of hype around generative models, but in my mind only a few use cases that will really stick.

Stock photos/illustrations is imho the most likely industry that these models will change.


Today? No. Tomorrow? Maybe.

I think it will be an expensive luxury for another while, but commonplace in a decade or so.


I guess this speaks to the way that people like binary thinking.

so "ML is cool!" -> "AI will take over all the jobs"

"Quantum computing is cool" -> "Quantum computers will replace all other computers"

In most of these cases these new tools will take over some amount of whatever came before, but it's rarely as simple as completer replacement.

Nice article, btw.


Ugh, worked with Keras/tf last week for the first time in a few years. It's so hard going back to tf after working in PyTorch.

It just feels so clunky, and progress was so slow.


I really like your pricing model (i.e. use forever + 1 year of support).

Oh, reminds me a of a time some students lost a Polonium source in the physics lab while I was a Masters student. The HoD had them crawling around the building for days with a Geiger counter, but they never found it.

Luckily it was a small source and Polonium has a relatively short half-life, but the department had to go through a whole song and dance of declaring an incident.

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