Except that in industry no one gets tenure.

(Swiss)Germans are completely mad about food.

It’s their culture to complain about everything, except food. All they care about is that it’s as bland as possible and has big portions. If you manage that, they’ll give you five stars every time.

I spent 3 years living in Germany, and not only can you not get anything spicy for love nor money, they also don’t use herbs. It just blows my mind. They’re physically so close to France and Italy, but the food is so far away.

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Honestly, if you’re sharing office files you’re probably using office 365. This means everything is a web app first and therefore Linux compatible.

I tried using the desktop version of word on a Mac last week, and the latency was so bad on a shared document that I had to switch to the web app anyway.

Basically, if you just want to use Linux you’ll be fine. If instead you don’t want to use Microsoft, you’ll probably have lots of problems.

Microsoft have been brutally effective in getting their tentacles into academic institutes, and you’ll find that everything from email to logging into internal sites relies on an office 365 account.

No it doesn’t work.

But it’s better than not doing it.

People suspect who the author is but maybe you cited those papers because you’re afraid of getting the author to review them, or you’re a fan-boying grad student.

Yeah but they don’t use LLMs for this, they’ll use some other kind of machine learning mixed in a big pipeline of data processing. It makes it really hard to guess how much work it would take to fix. It might require retraining, might just require an easy patch of the rest of the pipeline.

My guess is that they’re just shitty jumpers and there’s nothing to fix anyway.

I’m at this point too. I think the next step is to just declare sock bankruptcy again and throw everything out and start over.

Yeah, but a combination of this approach, and adding all compiled file types including .pyc to .gitignore would fix it.

I’m not describing binary classification, I’m describing multiclass. “Group classification” isn’t really a thing. Yes, your ml system probably guesses what kind of plant it is and then looks up the ediblity of components.

The problem with this is how they will handle rare plants that aren’t in the dataset, or that are in the dataset but with insufficient data to be recognised.

Because multiclass assumes that it’s seen representative data on all possible outputs (e.g. plant types) it will tend to be dangerously confident on plant types it hasn’t seen before.

This is because it can rule out other classes. E.g. if you’re trying to classify as rose, tulip, or daisy and you get a bramble, your classifier is likely to be very certain it’s a rose because tulips and daisies don’t have thorns. So your softmax score is likely to show heavy confidence in rose even though it’s actually none of them.

This is exactly what can go wrong when you try to use the softmax/standard multiclass approach and come across an interesting rare mushroom or wild carrot. You don’t want it to guess which type of plant in the database it’s most like, even if this guess comes with scores, you want it to say that it genuinely doesn’t know and you shouldn’t eat it.

The key issue here is that ‘level of certainty’ doesn’t really mean what you would like it to.

You get back a number yes, but it can change according to what’s visible in the background, the angle that the plants at, how close is it to the camera, and how nice the camera is you’re using (professional photographers use expensive cameras and take shots of different things to everyone else).

Interpreting this score as “how safe is it to eat the plant” is a really bad idea. You will still eat the wrong plant. These scores can lead to very confident random guessing when you show it a plant it’s never seen before.

And no, softmax is a trick for making the scores all sum to one, so you get back a confidence for every possible thing the image could be of.

It’s worth saying that ml is in a very different position to most of academic publishing.

All of the serious journals are free to publish and fully open access and a significant amount of publication includes enough code that things are mostly replicable. GitHub has done wonders for our field. Also many tech companies use publications as an indication of prestige and go out of their way to publish stuff.

We’re still drowning in too many papers and 95% of everything is shit, but that’s every field really. Talking to musk on twitter is the not right place for a nuanced discussion about publication.

Work socks as well.

They’re socks that go with construction boots. Basically the same as hiking socks but cheaper.

Both. It’s like the saying “Governing a big country is like cooking small fish.” (With the explanation that if you keep poking it, it’ll disintegrate) also taught me how to cook fish as well as realpolitik.

The fish advice was most useful.

The term machine learning was coined in 1959 by Arthur Samuel, an IBM employee and pioneer in the field of computer gaming and artificial intelligence.[9][10] The synonym self-teaching computers was also used in this time period.[11][12]

https://en.m.wikipedia.org/wiki/Machine_learning

It wasn’t so much stolen as taken back.

It’s very true on a Mac. Almost every time you click the green button, it jumps to full screen and then you can’t drag another window on top of it.

It’s a pain in the arse because my workflow is to have a reading screen with documents and emails on, and a work screen with whatever I’m actually doing. But if outlook is full screen, you can’t drag any other windows on top of it.

Don’t know why the first guy was saying this is a Windows thing though. I only run onto it on macs.

Is buddy really a problem?

I’d be more worried that someone thinks I’m treating them like a dog, than a man.

If you don’t know about it, check out https://wordnet.princeton.edu/

It’s a great starting point for any English word hierarchy.

Dev jobs and data scientists often get a lot of leeway.

Very big tech companies tend to be more open to it. When I was at AWS their threat model was basically to treat every end user device as untrusted, which then meant that they didn’t rely on keeping laptops locked down for security.

More generally any personal data obtained from a third party. E.g. if you’re generating a credit score you might contact someone else with a record of financial transactions.

Ok but LinkedIn and Facebook don’t have a duty to broadcast what you want all over the internet.

They don’t need consent not to share personal information, only to share it.