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ICYMI: The DoD Chief Information Officer posted FAQs about Cybersecurity Maturity Model Certification (CMMC), the so-called CMMC 2.0. Download this PDF as a handy reference to help with your questions, and reach out to Project Spectrum for an assessment and resources. #DoD #cybersecurity #CMMC #FAQ

Is it possible to detect if text has been generated by AI? Google DeepMind just published a paper in Nature saying yes. The article - Scalable watermarking for identifying large language model outputs - describes a pretty nifty method called SynthID-Text. It works by modifying the sampling process during text generation, which introduces a statistical signature without affecting the quality of the output. This watermark can then be reliably detected from the text alone. The downside? You need to have the watermarking key to be able to detect. In other words, only the model developer or accepted third parties can do that. Yet another reason why we need push forward on open source models. Google has apparently already deployed this at scale with Gemini. I’m not quite sure what the business reasoning is here. This makes me personally much less likely to use Gemini. But I appreciate the transparency. https://lnkd.in/e39phBkb

The industry is shifting toward smaller, more cost-effective models without significant performance loss. LLMs like Llama 3.1 405B and NVIDIA Nemotron-4 340B excel in many tasks but are resource-intensive. Check out the Pruning and Distillation technique, with an excellent Nvidia paper as an example. The paper presents a set of practical and effective structured compression best practices for LLMs that combine depth, width, attention, and MLP pruning with knowledge distillation-based retraining. Here's the link: https://lnkd.in/gKdV4Ahk

Wow! Meta dropped an open NotebookLM recipe: NotebookLlama 🔥 It uses L3.2 1B/ 3B for pre-processing the PDF, L3.1 70B for Transcript creation, L3.1 8B for re-writes and Parler TTS for Text to Speech ⚡ Step 1: Pre-process PDF: Use Llama-3.2-1B-Instruct to pre-process the PDF and save it in a .txt file. Step 2: Transcript Writer: Use Llama-3.1-70B-Instruct to write a podcast transcript from the text Step 3: Dramatic Re-Writer: Use Llama-3.1-8B-Instruct model to make the transcript more dramatic Step 4: Text-To-Speech Workflow: Use parler-tts/parler-tts-mini-v1 and bark/suno to generate a conversational podcast There's still some rough edges, but it already sounds pretty fire - Link to notebook in the comments ✨ Wow! Meta dropped an open NotebookLM recipe: NotebookLlama 🔥 It uses L3.2 1B/ 3B for pre-processing the PDF, L3.1 70B for Transcript creation, L3.1 8B for re-writes and Parler TTS for Text to Speech ⚡ Step 1: Pre-process PDF: Use Llama-3.2-1B-Instruct to pre-process the PDF and save it in a .txt file. Step 2: Transcript Writer: Use Llama-3.1-70B-Instruct to write a podcast transcript from the text Step 3: Dramatic Re-Writer: Use Llama-3.1-8B-Instruct model to make the transcript more dramatic Step 4: Text-To-Speech Workflow: Use parler-tts/parler-tts-mini-v1 and bark/suno to generate a conversational podcast There's still some rough edges, but it already sounds pretty fire - Link to notebook in the comments ✨

NIST announced it has selected 14 digital signature candidates to advance to the second round in the Post-Quantum Cryptography Standardization Process after over a year of evaluation. National Institute of Standards and Technology (NIST) https://lnkd.in/eccAzvSs

We have created a little guide on how to perform Flux.1 LoRA training with NF4 quantization. It's educational in nature so please go easy :D It's actually possible to do the training on a free-tier Colab Notebook, but it's incredibly slow. So, I tested it on a 24GB card, and things were fine. Guide: https://lnkd.in/gZT8URpK

If you're trying to find the best LLM for your use case, in straightforward terms, you should check out this guide on evaluation. It contains practical steps, tips, and tricks to get started with a reliable evaluation setup. You'll be able to answer simple questions like: which of these 5 million models is actually going to solve my use case? https://lnkd.in/eWTU7-7e

Data Scientists HAVE TO be good coders (unless they have this excuse) I’m kidding, there are no excuses for writing shitty code. 🔴 Some common bad coding practices: 1️⃣ Hardcoding variable values 2️⃣ Ignoring Modularization 3️⃣ Avoiding type annotations 4️⃣ Avoiding documenting the code 🔴 They make the code: 1️⃣ Hard to scale and test 2️⃣ Hard to collaborate on 3️⃣ Hard to maintain in production 🟢 Instead: 1️⃣ Configure code using a config file 2️⃣ Annotate and document the code 3️⃣ Split the code into functions. ☝️ One task - one function 🙏 Don't excuse yourself if: 1️⃣ You have hard deadlines 2️⃣ Work on the project alone 3️⃣ Think that DS should know only math It will hurt you later. Make sure you use the right practice! ♻️ Share with your network to show you value best coding practices. P.S. Do you use any useful framework for parsing the config files? Alex Razvant Paul Iusztin Raphaël Hoogvliets Maria Vechtomova Would you recommend some other simple and effective good coding practices for Data Scientists?

Today, the The White House released the National Security Memorandum on AI which acknowledges the importance of American AI leadership in supporting national security objectives.  We appreciate the Administration’s commitment to partnering with industry to promote and secure the foundational capabilities that power AI development. Our team has been dedicated to these initiatives since our founding and is proud to bring advanced AI capabilities to the USG. Learn more about the national security work taking place in our St. Louis AI Center here: https://lnkd.in/g4ux9z7h See the White House’s full announcement here: https://lnkd.in/efSbJjMr