This is the technology worth trillions of dollars huh
GitLab Enterprise somewhat recently added support for Amazon Q (based on claude) through an interface they call “GitLab Duo”. I needed to look up something in the GitLab docs, but thought I’d ask Duo/Q instead (the UI has this big button in the top left of every screen to bring up Duo to chat with Q):
(Paraphrasing…)
ME: How do I do X with Amazon Q in GitLab? Q: Open the Amazon Q menu in the GitLab UI and select the appropriate option.
ME: [:looks for the non-existant menu:] ME: Where in the UI do I find this menu?
Q: My last response was incorrect. There is no Amazon Q button in GitLab. In fact, there is no integration between GitLab and Amazon Q at all.
ME: [:facepalm:]
I don’t think this gets nearly enough visibility: https://www.academ-ai.info/
Papers in peer-reviewed journals with (extremely strong) evidence of AI shenanigans.
Stop using Google search, easy as that! I use duckduckgo and I have turned off AI prompts.
So this is the terminator consciousness so many people are scared will kill us all…
It ripped off this famous poem in the process:
With enough duct tape and chewed up bubble gum, surely this will lead to artificial general intelligence and the singularity! Any day now.
✅ Colorado
✅ Connedicut
✅ Delaware
❌ District of Columbia (on a technicality)
✅ Florida
But not
❌ I’aho
❌ Iniana
❌ Marylan
❌ Nevaa
❌ North Akota
❌ Rhoe Islan
❌ South Akota
Gosh tier comment.
You just described most of my post history.
Everyone knows it’s properly spelled “I, the ho” not Idaho. That’s why it didn’t make the list.
They took money away from cancer research programs to fund this.
After we pump another hundred trillion dollars and half the electricity generated globally into AI you’re going to feel pretty foolish for this comment.
Well as long as we still have enough money to buy weapons for that one particular filthy genocider country in the middle east, we’re fine.
only cancer patients benefit from cancer research, CEOs benefit from AI
Tbf cancer patients benefit from AI too tho a completely different type that’s not really related to LLM chatbot AI girlfriend technology used in these.
I would estimate that Google’s AI is helpful and correct about 7% of the time, for actual questions I’d like the answer to.
The only even mildly useful AI search results I have ever seen are on DuckDuckGo
Connecdicud.
Connedicut.
I wondered if this has been fixed. Not only has it not, the AI has added Nebraska.
You mean Connecdicud.
I would assume it uses a different random seed for every query. Probably fixed sometimes, not fixed other times.
What about Our Kansas? Cause according to Google Arkansas has one o in it. Refreshing the page changes the answer though.
Just checked, it sure does say that! AI spouting nonsense is nothing new, but it’s pretty ironic that a large language model can’t even parse what letters are in a word.
Well I mean it’s a statistics machine with a seed thrown in to get different results on different runs. So really, it models the structure of language, but not the meaning. Kinda useless.
i rather manually search for info
Well, for anyone who knows a bit about how LLMs work, it’s pretty obvious why LLMs struggle with identifying the letters in the words
Well go on…
Which is State contains 狄? They use a different alphabet, so understanding ours is ridiculous.
They don’t look at it letter by letter but in tokens, which are automatically generated separately based on occurrence. So while ‘z’ could be it’s own token, ‘ne’ or even ‘the’ could be treated as a single token vector. of course, ‘e’ would still be a separate token when it occurs in isolation. You could even have ‘le’ and ‘let’ as separate tokens, afaik. And each token is just a vector of numbers, like 300 or 1000 numbers that represent that token in a vector space. So ‘de’ and ‘e’ could be completely different and dissimilar vectors.
so ‘delaware’ could look to an llm more like de-la-w-are or similar.
of course you could train it to figure out letter counts based on those tokens with a lot of training data, though that could lower performance on other tasks and counting letters just isn’t that important, i guess, compared to other stuff
Of course, when the question asks “contains the letter _” you might think an intelligent algorithm would get off its tokens and do a little letter by letter analysis. Related: ChatGPT is really bad at chess, but there are plenty of algorithms that are super-human good at it.
Good read. Thank you
Con-ned-di-cut
Wouldn’t that only explain errors by omission? If you ask for a letter, let’s say D, it would omit words containing that same letter when in a token in conjunction with more letters, like Da, De, etc, but how would it return something where the letter D isn’t even in the word?
Well each token has a vector. So ‘co’ might be [0.8,0.3,0.7] just instead of 3 numbers it’s like 100-1000 long. And each token has a different such vector. Initially, those are just randomly generated. But the training algorithm is allowed to slowly modify them during training, pulling them this way and that, whichever way yields better results during training. So while for us, ‘th’ and ‘the’ are obviously related, for a model no such relation is given. It just sees random vectors and the training reorganizes them tho slowly have some structure. So who’s to say if for the model ‘d’, ‘da’ and ‘co’ are in the same general area (similar vectors) whereas ‘de’ could be in the opposite direction. Here’s an example of what this actually looks like. Tokens can be quite long, depending how common they are, here it’s ones related to disease-y terms ending up close together, as similar things tend to cluster at this step. You might have an place where it’s just common town name suffixes clustered close to each other.
and all of this is just what gets input into the llm, essentially a preprocessing step. So imagine someone gave you a picture like the above, but instead of each dot having some label, it just had a unique color. And then they give you lists of different colored dots and ask you what color the next dot should be. You need to figure out the rules yourself, come up with more and more intricate rules that are correct the most. That’s kinda what an LLM does. To it, ‘da’ and ‘de’ could be identical dots in the same location or completely differents
plus of course that’s before the llm not actually knowing what a letter or a word or counting is. But it does know that 5.6.1.5.4.3 is most likely followed by 7.7.2.9.7(simplilied representation), which when translating back, that maps to ‘there are 3 r’s in strawberry’. it’s actually quite amazing that they can get it halfway right given how they work, just based on ‘learning’ how text structure works.
but so in this example, us state-y tokens are probably close together, ‘d’ is somewhere else, the relation between ‘d’ and different state-y tokens is not at all clear, plus other tokens making up the full state names could be who knows where. And tien there’s whatever the model does on top of that with the data.
for a human it’s easy, just split by letters and count. For an llm it’s trying to correlate lots of different and somewhat unrelated things to their ‘d-ness’, so to speak
Thank you very much for taking your time to explain this. if you don’t mind, do you recommend some reference for further reading on how llms work internally?
For the byte pair encoding (how those tokens get created) i think https://bpemb.h-its.org/ does a good job at giving an overview. after that i’d say self attention from 2017 is the seminal work that all of this is based on, and the most crucial to understand. https://jtlicardo.com/blog/self-attention-mechanism does a good job of explaining it. And https://jalammar.github.io/illustrated-transformer/ is probably the best explanation of a transformer architecture (llms) out there. Transformers are made up of a lot of self attention.
it does help if you know how matrix multiplications work, and how the backpropagation algorithm is used to train these things. i don’t know of a good easy explanation off the top of my head but https://xnought.github.io/backprop-explainer/ looks quite good.
and that’s kinda it, you just make the transformers bigger, with more weight, pluck on a lot of engineering around them, like being able to run code and making it run more efficientls, exploit thousands of poor workers to fine tune it better with human feedback, and repeat that every 6-12 month for ever so it can stay up to date.
You could look up 3Blue1Brown’s explainers on YouTube, they are pretty good and shows a lot of visual examples. He has a lot of other videos on other areas of math.
Blows my mind people pay money for wrong answers.
