perplexity UI using gradio built with sonnet 3.5
LLMS
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Aleph-Alpha Joins Open-Weight AI Community with Open Knowledge
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So great to welcome Aleph-Alpha in the OSS/open-weight AI community! Impressive open-knowledge approach https://t.co/f76SdM454v
— Thomas Wolf (@Thom_Wolf) 26 août 2024So great to welcome Aleph-Alpha in the OSS/open-weight AI community! Impressive open-knowledge approach
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China challenges Tesla Optimus, Grok-2 advances, new AI tools emerge
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Top stories in AI today: -China is coming for Tesla Optimus
-Grok-2 improves speed, accuracy, transparency
-How to use Ideogram for generating images
-AI learns to plan better without humans
-5 new AI tools & 4 new AI jobs Read more: http://
therundown.ai/p/2024-world-r
obot-conference
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Exploring Llama Recipes Repository for LLM Implementation
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Did you look at the llama-recipes repo?
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10 ChatGPT prompts to automate your copywriting tasks
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ChatGPT-4o is a GENIUS copywriter. But most people don't know how to use it. Here are 10 prompts that automate your copywriting tasks: (10/10 would bookmark )
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AI Transforms Software Development Workflow and Problem Solving
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Okay, I’m late to the game but: Developing with AI is a game changer. Years ago I used Twitter when I was stuck, now ChatGPT and friends answer pretty much all my questions. And so fast.
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Agentic LLMs and Iterative Conversation Progress
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I hope this will come later with agentic LLMs. Currently the LLMs are getting better at iterative continual conversations, which is a good step.
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Synthetic Data Enables Superhuman LLM Performance via RL Training
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Synthetic data for LLMS and RL/RLHF/DPO can both train superhuman performance, model permitting.
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Adversarial Attacks on LLMs and Malware Neural Networks
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Adversarial Attacks on LLMs and #Malware Neural Networks! #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #NLProc #Python #RStats #TensorFlow #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode https://
geni.us/Universal-Adv-
Attacks
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PEDAL: Hybrid Self-Ensembling Approach for LLM Performance
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9). PEDAL – uses a hybrid self-ensembling approach (based on diverse exemplars) to improve the overall performance of LLMs; specifically, it uses diverse exemplars to generate multiple candidate responses and then aggregates them using an LLM to generate a final response.