61–70 of 84 posts
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Why are people benchmarking against 3.5? To me, the real race started with GPT-4. 3.5 and 4 are completely different beasts and that open models are catching up to 3.5 doesn't mean much. I've yet to see anything come close to 4.
Because 3.5 is a very useful model. Getting there with an open model already is pretty cool.
This article seems very corporate centric. Like, I am able to run a ChatGPT3-ish code LLM locally on a 2015 midrange laptop. Just like this: wget https://huggingface.co/TheBloke/deepseek-coder-6.7B-instruct-GGUF/resolve/main/deepseek-coder-6.7b-instruct.Q5_K_M.gguf git clone https://github.com/ggerganov/llama.cpp cd llama.cpp make ./main -ngl 32 -m ../deepseek-coder-6.7b-instruct.Q5_K_M.gguf --color -c 2048 --temp 0.…
I’m reminded of the classic Dropbox HN comment (https://news.ycombinator.com/item?id=8863): why use Dropbox when any Linux user can just use curlftpfs?
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They don't have to match the market leader, they just have to be "good enough". There are oodles of use-cases where sending your data to an outside provider is a complete no-go. In these cases OpenAI/Google/whoever-products aren't relevant competition.
I keep hearing this, but which company has this policy? The privacy policy of openAI's enterprise(or Azure's) is not a lot different than say AWS, which everyone uses.
Have you ever worked in any sector that has security policies?
Even if you haven't, perhaps spend 2 minutes using a search engine?
Here is a first page result for you: https://www.tomshardware.com/news/samsung-fab-workers-leak-c...
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I keep hearing this, but which company has this policy? The privacy policy of openAI's enterprise(or Azure's) is not a lot different than say AWS, which everyone uses.
"everyone"? What are you talking about? Have you ever worked in any sector that has security policies? Even if you haven't, perhaps spend 2 minutes using a search engine? Here is a first page result for you: https://www.tomshardware.com/news/samsung-fab-workers-leak-c...
Your own link shows Samsung using ChatGPT. Not sure what point you're trying to make with it.
AI has been gradually improving for decades, but this is the year we finally noticed. The big thing was huge progress in natural language understanding. 2023 was the year the Turing test was smashed. Seeing computers win games, drive cars, optimize systems, even design things wasn’t as subjectively impressive to most of us as being able to talk to them. This was the year we first saw AI that could sort of communicate…
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It’s because weights are where the magic lies, for the most part. Annnd nowadays tools like transformers can automatically select the architecture based on the name of the weights, so when interacting with LLMs, you generally just name the weights. Also MANY models are just using the llama or llama2 architecture.
> It’s because weights are where the magic lies, for the most part. What's that supposed to mean? Weights are not independent of the underlying architecture.
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Things are already changing for early adopters. Yesterday, I used a PDF GPT to review an 80+ page contract and GPT4 to explain specific parts of it in depth using layman's terms. I could have hired a lawyer like I have done in the past but that itself is a slow, laborious process and I often feel like my questions don't get thoroughly answered. I feel very confident of my understanding of the contract now. I also use…
When we use it to solve real problems, make some real progress on climate change, then it will be something else from hype and it will be the year of AI. When you as an individual use it to sort PDFs or something it’s really hype.
In fact, I would say thinking that AI is going to solve 'real' issues like that, is more 'hype' than the above commenter.
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Even on Windows?
It's probably a hell to run it on Windows. But again, for the audience here, that's not expected to be a large roadblock.
I think that it would also make sense to have a diagram that has open source achievements in Ai for 2023 (at least open weight & inference code). A lot of the announcements in the chart are behind an api so can’t be run locally. Just thinking off the top of my head, Segment Anything, Llama 1 and 2, Mistral, Stable diffusion XL, ControlNet, Whisper are all open source AI releases this year.
Something I find interesting is that in the last year or so, the talk around AI shifted from the model architecture to the trained model. People talk about Mistral 7B e.g., not transformer with rotary position embedding and gelu feed forward network (I don't know Mistral's architecture). Contrast this to a few years ago we'd talk about Resnet or Retina-net or whatever, not so much about the facebook pertain on Image-…
It’s because weights are where the magic lies, for the most part. Annnd nowadays tools like transformers can automatically select the architecture based on the name of the weights, so when interacting with LLMs, you generally just name the weights. Also MANY models are just using the llama or llama2 architecture.