Technology

Huge news for AMD fans and those who are hoping to see a real* open alternative to CUDA that isn't OpenCL!

*: Intel doesn't count, they still have to get their shit together in rendering things correctly with their GPUs.

We plan to expand ROCm support from the currently supported AMD RDNA 2 workstation GPUs: the Radeon Pro v620 and w6800 to select AMD RDNA 3 workstation and consumer GPUs. Formal support for RDNA 3-based GPUs on Linux is planned to begin rolling out this fall, starting with the 48GB Radeon PRO W7900 and the 24GB Radeon RX 7900 XTX, with additional cards and expanded capabilities to be released over time.

Although watching TV shows from the 1970s suggests otherwise, the era wasn't completely devoid of all things resembling modern communication systems. Sure, the 50Kbps modems that the ARPANET ran on were the size of refrigerators, and the widely used Bell 103 modems only transferred 300 bits per second. But long-distance digital communication was common enough, relative to the number of computers deployed. Terminals could also be hooked up to mainframe and minicomputers over relatively short distances with simple serial lines or with more complex multidrop systems. This was all well known; what was new in the '70s was the local area network (LAN). But how to connect all these machines?

Paper available here: https://www.computer.org/csdl/proceedings-article/sp/2023/933600a951/1NrbYmtLXB6

Transcription below

A BILL TO BE ENTITLED

AN ACT TO STUDY THE HOLDING OF BULLION AND VIRTUAL CURRENCY AND THEIR POTENTIAL BENEFITS AND WHETHER TO ESTABLISH A NORTH CAROLINA BULLION DEPOSITORY FOR SUCH ASSETS.

The General Assembly of North Carolina enacts: SECTION 1. The Department of State Treasurer shall conduct a study that examines (i) the process of acquiring, securely storing, insuring, and liquidating any investment metal bullion as defined in G.S. 105-164.13(69), such as gold, and virtual currency as defined in G.S. 53-208.42(20), such as Bitcoin, that may be held on behalf of the State, (ii) the expected impact of allocating a portion of the General Fund to investment metal bullion and virtual currency to hedge against inflation and systemic credit risks, reduce overall portfolio volatility, and increase portfolio returns over time, and (iii) the costs, benefits, and security of utilizing a privately managed depository or another state's depository or creating a State-administered depository in North Carolina to serve as the custodian, guardian, and administrator of certain investment metal bullion and virtual currency that may be transferred to or otherwise acquired by this State or an agency, a political subdivision, or another instrumentality of this State and to provide a repository for investors to use for such assets. The Department of State Treasurer shall 18 report on the results of the study, along with any legislative or other recommendations, to the 19 Joint Legislative Commission on Governmental Operations by January 1, 2024.

SECTION 2. There is appropriated from the General Fund to the Department of State Treasurer the nonrecurring sum of fifty thousand dollars ($50,000) for the 2023-2024 fiscal year to conduct the study required by this act.

SECTION 3. Section 2 of this act becomes effective July 1, 2023. The remainder of 24 this act is effective when it becomes law.

Rapid changes, fueled by AI, are impacting the large pockets of the internet, argues a new column. An excerpt:

In recent months, the signs and portents have been accumulating with increasing speed. Google is trying to kill the 10 blue links. Twitter is being abandoned to bots and blue ticks. There's the junkification of Amazon and the enshittification of TikTok. Layoffs are gutting online media. A job posting looking for an "AI editor" expects "output of 200 to 250 articles per week." ChatGPT is being used to generate whole spam sites. Etsy is flooded with "AI-generated junk."

Chatbots cite one another in a misinformation ouroboros. LinkedIn is using AI to stimulate tired users. Snapchat and Instagram hope bots will talk to you when your friends don't. Redditors are staging blackouts. Stack Overflow mods are on strike. The Internet Archive is fighting off data scrapers, and "AI is tearing Wikipedia apart." The old web is dying, and the new web struggles to be born. The web is always dying, of course; it's been dying for years, killed by apps that divert traffic from websites or algorithms that reward supposedly shortening attention spans. But in 2023, it's dying again -- and, as the litany above suggests, there's a new catalyst at play: AI.

Guess where? Unironically r/Save3rdPartyApps

The Reddit search for Lemmy also gives these privacy copy-pasta as top results when searching for Lemmy. I'm still betting that Reddit employees are involved in boosting these posts.

cross-posted from: https://lemmy.world/post/708817

Visit TheBloke's HuggingFace page to see all of the new models in their SuperHOT glory.

SuperHOT models are LLMs who's LoRAs have been adapted to support a context length of 8,000 tokens!

For reference, this is x4 times the default amount of many LLMs (i.e. 2048 tokens). Even some of the newer ones can only reach a context length of 4096 tokens, half the amount of these SuperHOT models!

Here are a few that were released if you couldn't view his HuggingFace:

New GPTQ Models from TheBloke

• airoboros (13B)
• CAMEL (13B)
• Chronos (13B)
• Guanaco (13B & 33B)
• Manticore (13B)
• Minotaur (13B)
• Nous Hermes (13B)
• Pygmalion (13B)
• Samantha (13B & 33B)
• Snoozy (13B)
• Tulu (13B & 33B)
• Vicuna (13B & 33B)
• WizardLM (13B)

We owe a tremendous thank you to TheBloke, who has enabled many of us in the community to interact with versions of Manticore, Nous Hermes, WizardLM and others running the remarkable 8k context length from SuperHOT.

Many of these are 13B models, which should be compatible with consumer grade GPUs. Try using Exllama or Oobabooga for testing out these new formats.

Shoutout to Kaikendev for the creation of SuperHOT. You can learn more about their work here.

If you enjoyed reading this, please consider subscribing to /c/FOSAI where I do my best to keep you in the know with the latest and greatest advancements regarding free open-source artificial intelligence.

https://web.archive.org/web/20230627195308/https://www.redhat.com/en/blog/red-hats-commitment-open-source-response-gitcentosorg-changes

cross-posted from: https://lemmy.world/post/699214

Hello everyone!

I'd like to share with you another new resource: NLP Cloud - a provider and platform aimed to help you streamline AI/LLM deployments for your business or project.

If you're considering a startup in AI, this is a valuable read and resource. Support these developers by visiting their site and checking out the platform for yourself.

NLP Cloud (Natural Language Processing Cloud)

GPT-3, GPT-4, ChatGPT, GPT-J, and generative models in general, are very powerful AI models. We're showing you here how to effectively use these models thanks to few-shot learning, also known as prompt engineering. Few-shot learning is like training/fine-tuning an AI model, by simply giving a couple of examples in your prompt.

GPT-3, GPT-4, And ChatGPT

GPT-3, GPT-4, and ChatGPT, released by OpenAI, are the most powerful AI model ever released for text understanding and text generation.

GPT-3 was trained on 175 billion parameters, which makes it extremely versatile and able to understanding pretty much anything! We do not know the number of parameters in GPT-4 but results are even more impressive.

You can do all sorts of things with these generative models like chatbots, content creation, entity extraction, classification, summarization, and much more. But it takes some practice and using them correctly might require a bit of work.

GPT-J, GPT-NeoX, And Dolphin

GPT-NeoX and GPT-J are both open-source Natural Language Processing models, created by, a collective of researchers working to open source AI (see EleutherAI's website).

GPT-J has 6 billion parameters and GPT-NeoX has 20 billion parameters, which makes them the most advanced open-source Natural Language Processing models as of this writing. They are direct alternatives to OpenAI's proprietary GPT-3 Curie.

These models are very versatile. They can be used for almost any Natural Language Processing use case: text generation, sentiment analysis, classification, machine translation,... and much more (see below). However using them effectively sometimes takes practice. Their response time (latency) might also be longer than more standard Natural Language Processing models.

GPT-J and GPT-NeoX are both available on the NLP Cloud API. On NLP Cloud you can also use Dolphin, an in-house advanced generative model that competes with ChatGPT, GPT-3, and even GPT-4. Below, we're showing you examples obtained using the GPT-J endpoint of NLP Cloud on GPU, with the Python client. If you want to copy paste the examples, please don't forget to add your own API token. In order to install the Python client, first run the following: pip install nlpcloud.

Few-Shot Learning

Few-shot learning is about helping a machine learning model make predictions thanks to only a couple of examples. No need to train a new model here: models à la GPT-3 and GPT-4 are so big that they can easily adapt to many contexts without being re-trained.

Giving only a few examples to the model does help it dramatically increase its accuracy.

In Natural Language Processing, the idea is to pass these examples along with your text input. See the examples below!

Also note that, if few-shot learning is not enough, you can also fine-tune GPT-3 on OpenAI's website and GPT-J and Dolphin on NLP Cloud so the models are perfectly tailored to your use case.

You can easily test few-shot learning on the NLP Cloud Playground, in the text generation section. Click here to try text generation on the Playground. Then simply use one of the examples showed below in this article and see for yourself.

If you use a model that understands natural human instructions like ChatGPT or ChatDolphin, you might not always have to use few-shot learning, but it is alway interesting to apply few-shot learning when possible in order to get the most advanced results. If you do not want to use few-shot learning, read our dedicated guide about how to use ChatGPT and ChatDolphin with simple instructions: see the article here.

In my opinion, I think this is a big highlight of this service:

Data Privacy And Security

NLP Cloud is HIPAA / GDPR / CCPA compliant, and working on the SOC 2 certification. We cannot see your data, we do not store your data, and we do not use your data to train our own AI models.

You can read the full page and article here. If you're still interested, consider checking out this other amazing resource detailing how to utilize chat-gpt alternatives.