Chris Nelson

🚀 We've open-sourced TimesFM – a decoder-only foundation model for time-series forecasting 📈 Key Highlights: ✅ Large-Scale Training Data: Trained on a corpus of over 100 billion real-world time points, including Google Trends and Wikipedia pageviews. ✅ Zero-Shot Forecasting: Offers high-quality forecasts even on unseen datasets, without requiring fine-tuning or additional training. ✅ Outstanding Benchmark Performance: Achieved competitive results across a comprehensive suite of benchmarks. 🤗 Explore the model on Hugging Face and GitHub. I'm encouraged by the early feedback and look forward to seeing how #TimesFM will be used in real-world applications for forecasting and anomaly detection. 🙏 A special thanks to the talented research team behind this achievement: Abhimanyu Das Rajat Sen Yichen Zhou Weihao Kong

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Meta's Llama 3: A watershed moment for Open-Source LLMs? Exciting news in the tech world as Meta's launch of Llama 3 last week (https://lnkd.in/gk7VhheQ) is redefining the open source LLM landscape! 🚀   While 2023 saw remarkable strides with Llama 2, Falcon 180B, and Mistral 7B, Llama 3 is setting new standards with its unparalleled raw performance. The larger 70B version is now only second to OpenAI’s GPT-4 Turbo on the LMSYS leaderboard for English prompts (https://lnkd.in/gce2yKhy), and the smaller 8B model outperforms GPT-3.5, which powers the free version of ChatGPT. 🏆   Users are raving about Llama 3's faster inference compared to GPT, making it a game changer in the realm of super-fast image generation. Its integration with Meta products allows for real-time modifications with prompts, vastly enhancing user experience. Since it is open source, folks on X have even shown how incredible it is when paired with Groq!🌐   What's fascinating is that Llama 3 used ~75x more data (15T compared to ~200B) than the Chinchilla paper's benchmark (https://lnkd.in/gzv5H3FN), and Meta claims it still wasn't converging. This underscores the vast potential of data in enhancing models with sub-100B parameters. Yes, the training costs are higher, but the lower parameter size reduces inference costs, paving the way for running LLMs on local devices in the future! 💡   The only downside? A context length of just 8k when 128k is becoming the norm across top models. But hey, every innovation has room for improvement, right? And they have indicated that this is bound to increase in the coming months as well!   Kudos to Meta for pushing the boundaries of open-source development with Llama 3. Its impressive performance, combined with Meta's product integration, makes this a development worth watching! Do you think the 400B+ model (summer launch expected) would beat OpenAI's GPT 4 Turbo and maybe even be as good as GPT 5?🎉   #Meta #Llama3 #OpenSource #AI #LLM #TechInnovation #MachineLearning #GenAI #GPT4 #OpenAI

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We have been hiring a lot at ODIN AI lately and I've noticed a concerning trend during job interviews. It seems like some candidates are using AI assistants to cheat, which is frustrating and counterproductive. We've started to combat this by looking out for delays in responses and the inability to answer specific questions. Often, if you start a detailed train of thought, the candidate struggles to follow because the AI takes time to generate a response. This has become a reliable way to identify if someone is cheating. It's crucial to ensure that candidates are genuinely capable and not relying on AI to get through the interview process.

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