I know some are mourning the loss of the eink, but as someone who tried to make the LP2 work for a few months, I think this higher price point and better display and build will ultimately serve them better. The LP2 was too underpowered, felt cheap, and frankly unreliable (alarms straight-up not working were a big part of me ditching it). 5G, NFC, a camera, a good screen, etc. all make this a much more appealing product. I don't think the increased capabilities will cause "app creep" and negate the original intent of the project.
First thing I notice is that Finnish is part of a completely different language family from the other Nordic languages and English (Uralic vs. Indo-European). I wonder to what extent this affects the effectiveness of their low-resource training. Finnish is highly agglutinative, adding prefixes and suffixes to modify a root. My (amateur) take is that the tokenization and attention patterns may differ a lot? Would love to see more educated people than I discuss this.
>> to what extent this affects the effectiveness of
The correct use of those words demonstrates that you are either not an AI, all of them being trained on so much bad language, or are an AI from a more perfect future.
Finnish is not so different dispute having different lineages. Even if we talk about morphology, sometimes it's simply that e.g. prepositions are affixed to the end of a word big whoop. There are many dimensions to language vairation. Finnish has a long history of contact with Scandi languages and a lot of borrowed words and logic. It would be good to have Estonian and possibly Baltic languages too.
ETA: It is differentof course just perhaps not as much as people sometimes try to say. You can definitely ruffle some feathers with this one given the uniqueness of Finnish is pretty central to Finnish nationalism.
As someone growing up in relatively close contact with Finnish, I can assure you that there's no real common ground between Scandinavian languages (Swedish, Danish, Norwegian) and Finnish. There are loan words, but they are few and far between and in any case does not make for any mutual understanding. I've been so much to Finland that I would really like to learn to at least understand the language, instead of relying of memorized names of foodstuff and the like. Just have to tackle Japanese first.. (and I consider that one an easier operation)
We do have one small language, the Kven language(https://en.wikipedia.org/wiki/Kven_language), which is a sort of "Finnish structure, but with lots of borrowed Norwegian words. But for all intents and purposes, it very much sounds like Finnish.
It basically sounds like the language a Finnish person that has lived their whole life in Norway, and then starts to mix words because they forgot the Finnish words.
But that's about it. I know there are some other dialects, too, but these are all very small-scale languages that are either extinct, or will be extinct in some decades.
Much easier for Finns to learn Swedish, that for Swedes to learn Finnish, IMO. I speak Norwegian and Finnish (lived in Finland when I was young)
Loan words from Scandi are more common than you think. Eg Hei is a common greeting, tykätä is a common verb. For nouns there is even a whole paradigm for loans, a large number of which are Scandi. They are not necessarily easy to recognise since they undergo sound changes eg plaasteri becomes laasteri
Loan words, yes, but that has very little to do with the grammar and structure of the language. "Jag tycker om dig" [sv] translates to "Tykkään sinusta" [fi], which isn't anywhere near the Scandic.
Oh well if I made a spelling mistake that obviously invalidates my whole point. Thank you for teaching me that Finnish is a very special language -- just like the Finns -- such an amazing an unique people ;)
Indeed, while not cannabis related, I visited the ER once due to a panic attack (which I did't know at the time). My combination of high heart rate, mild abdominal pain, and a low grade fever triggered their sepsis protocol. This constellation of symptoms caused me to spiral more and eventually I was discharged with nothing more than some chill pills and constipation.
Does anyone have any recommendations for a decent crash course on using vector DBs in conjuction with LLMs? I wanna do some experimentation with getting a model to comment on the similarity of data vectors etc. and I don't really know where to start.
If you want to experiment with vector stores, you can do that locally with something like faiss which has good multiplatform support and sufficient tutorials: https://github.com/facebookresearch/faiss
Doing full retrieval-augmented generation (RAG) and getting LLMs to interpret the results has more steps but you get a lot of flexibility, and despite what AI influencers say there's no standard best-practice. When you query a vector DB you get the most similar texts back (or an index integer in the case of faiss), you then feed those result to an LLM like a normal prompt, which can be optimized with prompt engineering.
The codifer for the RAG workflow is LangChain, but their demo is substantially more complex and harder-to-use than even a homegrown implementation: https://minimaxir.com/2023/07/langchain-problem/
Also, if what you look up has no semantic meaning like parts number you might be better off with an inverted index in addition to ANN lookups. Especially if the embedding model has been trained on a dataset that is not similar to what you use it for. That's a common situation right now with embedding models based on LLMs.
I recommend pgvector, it's simple and well featured with good example code. Once you have a dataset of vectors loaded in, the next step is called rag / retrieval augmented generation
