Why has the title of this post been changed, when HN rules are to use the title of the article? The rename is less engaging and misses the point completely. Moderators did this or?
I've started using SAD-lamps for this exact reason. I've used it every day from october-april for 2 years and it has benefitted me a lot. I don't feel as bad through winter anymore.
It's quite literally kaamosmasennus as we Finns call it.
Merely losing the sun behind cloud cover for an extended period of time seems to have an effect if not the quite gradual but rapid shortening of available daylight. When the weather reports start including how gray it is today, you know it's the season again.
Increasing your vitamin D3/D6 dosage and being exposed to bright blue light daily helps most people to get through the winter.
Vitamin D has helped me as well as many of my immigrant friends tremendously when we moved from South Asia to Canada. Been taking 5000 IU for 15 years.
Oh weird. I use Dia now and then and noticed my ability to upgrade vanished, but I still have access to all the AI features and the morning brief things. I just assumed the Atlassian acquisition made it free for everyone. Is that not the case? Do people here actually see a upgrade prompt?
/EDIT: I looked into this by... well, asking Dia:
> That fits what I described. You aren't on a plan because you haven't had to pick one yet. People who were using Dia before paid plans launched on August 17 kept the AI features for free, with nothing to choose and no card on file. Dia says it will email those users at least 30 days before that changes.
>
> So nothing's wrong. It just isn't permanently free either. It's free for now because you joined early. When the notice email comes, you'll have about a month to decide:
So I'm grandfathered "for now", but the Daily brief and AI features are so low value to me, I just assumed they're free for everyone because of how little they do. I never use the built-in AI in Dia and get annoyed when a query that I wanted to go to a search engine gets answered by the assistant instead.
I really hate this company and how they operate. Key example, they sold a «lifetime» plan for Day One that was quite expensive. I used it a lot and went for it.
After they saturated the market for lifetime buyers, they started launching new features not included for lifetime buyers. Annoying.
Then they started pushing advertising for upgrading. Very annoying.
Then they started taking away features lifetime plan users had already. Infuriating.
Then they made it so that support was only available for users on a subscription plan. My complaints weren’t even answered. Unforgivable.
Average and median is not the same. Listed speed and actual speed is not the same (as mentioned in the article).
Also, about the size of the countries. The underlying model scales, that’s the point. Unless you were talking about political willingness to back large commitments. That’s a another question.
As a Norwegian this sounds like a mistake. Who will use this LLM? Where? For what? The underlying data could be made more easily searchable and digestible for agents in general if the goal is better knowledge of Norwegian culture.
That said, they are quite limited in what they are allowed to share of in-copyright works, and nb.no is a fantastic resource as it is (though you'll need a Norwegian IP address for too much of it - it's one of th main reasons I maintain a VPN) - if they are allowed to make it accessible there, it'd be great.
But they also have vast amounts of out-of-copyright data that I hope they'd make more easily accessible...
Why would the gap grow? There is no more training data to acquire, frontier model are training on the entire internet. Everything from now on is just fine-tuning.
Your statement assumes training data is the only thing that matters for the big players, while not considering it limiting for the small Norwegian model. That’s a fallacy.
Exactly, if there's one thing transformers are good at it's translation. One I've found particularly nice: any question ChatGPT can answer in English it can answer in French. I'm assuming Norwegian too. So there's no point.
The point is that norway willl have its own LLM. And will not have dependencies to another state or private company. The goal is not to be the best model. But to have a model that include more Norwegian data then other LLM and that it's not screwed against other sources.
But what does that give you? If the model is far less capable? What will it do for you with that Norwegian data, that a better model could not do with better search or context?
A model has many dimensions. You can't have them on one scale from good to bad. The model will most likely be poor at coding. But will give better answers about Norwegian cultur. I assume the tone of voice will be (by default) much closer to how Norwegians talk and write then what we current see from model from the US. They seem to be a bit to much.. Norwegian people are a bit more down to earth
Yes transformers are great at translation as that is their purpose.
LLMs are not great at preserving cultural uniqueness and diversity. Take how “delve” has reentered the lexicon because the human assessors for pre training dialect of English uses “delve” a lot.
There is a lot of benefits to training specifically for a unique culture with unique norms to preserve the culture as we increasingly rely on LLMs.
There is a lot more to it than literal translations. Even if an american can talk Norwegian it doesn't mean that they get the cultural context right.
"Oh yeah after you drive 2 hours home from work your wife and kid will greet you with some delicious pie" doesnt work so well even if its in Norwegian.
Both Claude and ChatGPT can translate into minor dialects of Norwegian they will have seen very few works in because very few printed works exist in them.
E.g. I've tested both my local spoken dialect, which is rarely written, and a sociolect used by a 1970's Maoist group consiting of a few hundred people, where most of the printed material consists of novels from a couple of ex-members that became authors.
In the latter case, it claimed to not know, but was able to get a good match from just a description.
I also just had it ape Norwegian orthography from the 1910's by having it look up the rules and translate a text it had first translated from English to modern Norwegian, and it did just fine.
They will have seem some work in these dialects, but mostly it transfer really well to know related languages (English, Dutch, German, Swedish, Danish, roughly form a continuum from least in common to most in common with modern Norwegian; they all share vocabulary and significant parts of grammar with Norwegian), and then a relatively limited exposure to Norwegian itself is sufficient to do fairly well.
They're also really good at "style transfer" of text in the form of tweaking orthography, word order, and minor grammar changes from descriptions and examples.
(incidentally, the latter is one way of getting an LLM to sound a lot less like an LLM)
This is all true, but I assumed the original posters were talking about cultural knowledge, not linguistic correspondences.
To do translation well you still need cultural knowledge. (E.g. the particular modes of specific kinds of legalese, or slang and the nuances of social class, etc)
I think it's not that this knowledge isn't present in the model somewhere, but probably more that it gets killed by instruction tuning for US corporate values.
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