The AI Community On Kbin

A breaking down of how 6 of the most important EdTech companies are thinking about AI:

• Duolingo
• Powerschool
• Coursera
• Docebo
• Instructure
• Nerdy

In a VentureBeat Q&A, Princeton University's Arvind Narayanan and Sayash Kapoor, authors of the upcoming "AI Snake Oil," discuss AI hype.

Siggraph 2023, Nvidia improves on their previous research into controllable, natural movement learnt from unlabelled data. Code and paper available.

Estimates show that without significant interventions, AI models could consume more energy than the entire human workforce by 2025, considerably impacting global carbon reduction goals

Sam takes us on a journey of how A.I. can create an image using its collection of pictures and artwork and construct something we perceive as unique.

cross-posted from: https://lemmy.ml/post/2811405

"We view this moment of hype around generative AI as dangerous. There is a pack mentality in rushing to invest in these tools, while overlooking the fact that they threaten workers and impact consumers by creating lesser quality products and allowing more erroneous outputs. For example, earlier this year America’s National Eating Disorders Association fired helpline workers and attempted to replace them with a chatbot. The bot was then shut down after its responses actively encouraged disordered eating behaviors. "

"We are about to train models that are 10 times larger than the cutting edge GPT-4 and then 100 times larger than GPT-4. That’s what things look like over the next 18 months."

Title

Meta’s latest large language model (LLM), Llama 2, “may not be suitable to use in other languages.”

cross-posted from: https://lemmy.world/post/1894070

Welcome to the Llama-2 FOSAI & LLM Roundup Series!

(Summer 2023 Edition)

Hello everyone!

The wave of innovation I mentioned in our Llama-2 announcement is already on its way. The first tsunami of base models and configurations are being released as you read this post.

That being said, I'd like to take a moment to shoutout TheBloke, who is rapidly converting many of these models for the greater good of FOSS & FOSAI.

You can support TheBloke here.

• https://ko-fi.com/TheBlokeAI

Below you will find all of the latest Llama-2 models that are FOSAI friendly. This means they are commercially available, ready to use, and open for development. I will be continuing this series exclusively for Llama models. I have a feeling it will continue being a popular choice for quite some time. I will consider giving other foundational models a similar series if they garner enough support and consideration. For now, enjoy this new herd of Llamas!

All that you need to get started is capable hardware and a few moments setting up your inference platform (selected from any of your preferred software choices in the Lemmy Crash Course for Free Open-Source AI or FOSAI Nexus resource, which is also shared at the bottom of this post).

Keep reading to learn more about the exciting new models coming out of Llama-2!

8-bit System Requirements

Model	VRAM Used	Minimum Total VRAM	Card Examples	RAM/Swap to Load*
LLaMA-7B	9.2GB	10GB	3060 12GB, 3080 10GB	24 GB
LLaMA-13B	16.3GB	20GB	3090, 3090 Ti, 4090	32 GB
LLaMA-30B	36GB	40GB	A6000 48GB, A100 40GB	64 GB
LLaMA-65B	74GB	80GB	A100 80GB	128 GB

4-bit System Requirements

Model	Minimum Total VRAM	Card Examples	RAM/Swap to Load*
LLaMA-7B	6GB	GTX 1660, 2060, AMD 5700 XT, RTX 3050, 3060	6 GB
LLaMA-13B	10GB	AMD 6900 XT, RTX 2060 12GB, 3060 12GB, 3080, A2000	12 GB
LLaMA-30B	20GB	RTX 3080 20GB, A4500, A5000, 3090, 4090, 6000, Tesla V100	32 GB
LLaMA-65B	40GB	A100 40GB, 2x3090, 2x4090, A40, RTX A6000, 8000	64 GB

*System RAM (not VRAM), is utilized to initially load a model. You can use swap space if you do not have enough RAM to support your LLM.

The Bloke

One of the most popular and consistent developers releasing consumer-friendly versions of LLMs. These active conversions of trending models allow for many of us to run these GPTQ or GGML variants at home on our own PCs and hardware.

70B

• TheBloke/Llama-2-70B-chat-GPTQ

• TheBloke/Llama-2-70B-Chat-fp16

• TheBloke/Llama-2-70B-GPTQ

• TheBloke/Llama-2-70B-fp16

13B

• TheBloke/Llama-2-13B-chat-GPTQ

• TheBloke/Llama-2-13B-chat-GGML

• TheBloke/Llama-2-13B-GPTQ

• TheBloke/Llama-2-13B-GGML

• TheBloke/Llama-2-13B-fp16

7B

• TheBloke/Llama-2-7B-GPTQ

• TheBloke/Llama-2-7B-GGML)

• TheBloke/Llama-2-7B-fp16

• TheBloke/Llama-2-7B-fp16

• TheBloke/Llama-2-7b-Chat-GPTQ

LLongMA

LLongMA-2, a suite of Llama-2 models, trained at 8k context length using linear positional interpolation scaling.

13B

• conceptofmind/LLongMA-2-13b

7B

• conceptofmind/LLongMA-2-7b

Also available from The Bloke in GPTQ and GGML formats:

7B

• TheBloke/LLongMA-2-7B-GPTQ

• TheBloke/LLongMA-2-7B-GGML

Puffin

The first commercially available language model released by Nous Research! Available in 13B parameters.

13B

• NousResearch/Redmond-Puffin-13B-GGML

• NousResearch/Redmond-Puffin-13B

Also available from The Bloke in GPTQ and GGML formats:

13B

• TheBloke/Redmond-Puffin-13B-GPTQ

• TheBloke/Redmond-Puffin-13B-GGML

Other Models

Leaving a section here for 'other' LLMs or fine tunings derivative of Llama-2 models.

7B

• georgesung/llama2_7b_chat_uncensored

Getting Started w/ FOSAI!

Have no idea where to begin with AI/LLMs? Try starting here with UnderstandGPT to learn the basics of LLMs before visiting our Lemmy Crash Course for Free Open-Source AI

If you're looking to explore more resources, see our FOSAI Nexus for a list of all the major FOSS/FOSAI in the space.

If you're looking to jump right in, visit some of the links below and stick to models that are <13B in parameter (unless you have the power and hardware to spare).

FOSAI Resources

Fediverse / FOSAI

• The Internet is Healing
• FOSAI Welcome Message
• FOSAI Crash Course
• FOSAI Nexus Resource Hub

LLM Leaderboards

• HF Open LLM Leaderboard
• LMSYS Chatbot Arena

LLM Search Tools

• LLM Explorer
• Open LLMs

GL, HF!

If you found anything about this post interesting - consider subscribing to [email protected] where I do my best to keep you in the know about the most important updates in free open-source artificial intelligence.

I will try to continue doing this series season by season, making this a living post for the rest of this summer. If I have missed a noteworthy model, don't hesitate to let me know in the comments so I can keep this resource up-to-date.

Thank you for reading! I hope you find what you're looking for. Be sure to subscribe and bookmark the main post if you want a quick one-stop shop for all of the new Llama-2 models that will be emerging the rest of this summer!

The Google engineering fellow who recently resigned was key to the development of generative AI and chatbots; he now believes he underestimated the existential threat they pose, and once AI can create its own goals, humans won't be needed.

Douglas Hofstadter, the Pulitzer Prize–winning author of Gödel, Escher, Bach, reflects on how he got interested in the mind and consciousness, how he came to write Gödel, Escher, Bach, and why he is terrified by the current state of AI.

I am sure many of you have heard by now about the OpenAI data leak. Here is one article on it, but there are many others of you search. https://www.databreaches.net/ftc-investigates-openai-over-data-leak-and-chatgpts-inaccuracy/

When I learned of this data breach from a company who is committed to security and containment of AI, it does not bode well for the future. Data breaches are not really exclusive to OpenAI as they have become very common place across all organizations despite how much effort they put into prevention. ASI is going to get out and there is nothing we can really do about it if we are honest with ourselves. Try for sure, but prepare for an ASI breach as it is not an if, it is a when.

We tested five services that claim to detect what is real and what isn’t.

Archive link

From games & chat bots to ChatGPT: exploring AI's modern usage and evolution.

Subscribe for good luck - Most Popular videos - https://www.youtube.com/watch?v=72qmtr41iWU&list=UULP3MF3KCtKQkCudSvNpk3BOg #chatgpt , #ai , #art

This paper discusses ways in which automation of industrial processes may expand rather than eliminate problems with the human operator. ...

What components are needed for building learning algorithms that leverage the structure and properties of graphs?

Monthly traffic and unique visitors were down in June, the first sign of decline since it launched in November.

Generative AI, a technology that can create human-like responses to user prompts, is being tested by the US military for the first time. The Pentagon is running an eight-week exercise with five large-language model (LLM) platforms, such as Scale AI’s Donovan, that are trained on huge amounts of internet data. The LLMs can help the military complete tasks faster, plan responses to crises, and generate new options that they have never considered before. However, the military also faces challenges and risks in using generative AI, such as bias, hacking, and data quality. The military is working with tech security companies to evaluate and mitigate these issues. The article is written by Jeff Stone and Margi Murphy, who can be reached at their email addresses.

Chatbot said it was ‘impressed’ when Jaswant Singh Chail told it he was ‘an assassin’ before he broke into Windsor Castle, court hears

Scaling sequence length has become a critical demand in the era of large language models. However, existing methods struggle with either computational complexity or model expressivity, rendering the maximum sequence length restricted. In this work, we introduce LongNet, a Transformer variant that can scale sequence length to more than 1 billion tokens, without sacrificing the performance on shorter sequences. Specifically, we propose dilated attention, which expands the attentive field exponentially as the distance grows. LongNet has significant advantages: 1) it has a linear computation complexity and a logarithm dependency between tokens; 2) it can be served as a distributed trainer for extremely long sequences; 3) its dilated attention is a drop-in replacement for standard attention, which can be seamlessly integrated with the existing Transformer-based optimization. Experiments results demonstrate that LongNet yields strong performance on both long-sequence modeling and general language tasks. Our work opens up new possibilities for modeling very long sequences, e.g., treating a whole corpus or even the entire Internet as a sequence.