Gretel’s Post

Exciting results from recent tests of Gretel Navigator, our agent-based, compound AI synthesizer: 🔥 Surpassed human expert-generated data in 73.6% of cases 🔥 Outperformed GPT-4 by 25.6% in comparative tests 🔥 Crushed GPT-3.5-turbo by 97.3% 🔥 Beat Llama3-70b by 48.1% Huge potential for synthetic data enhancing AI model training, particularly in domains with limited data availability. Full report and code: https://lnkd.in/eg2tSFes #SyntheticData #AI #LLM

Exciting Developments in AI from T-Mobile’s Capital Markets Day! Sam Altman, CEO of OpenAI, recently discussed the new #o1model and its advanced reasoning capabilities, which mark a significant leap forward in AI development. He described #o1model as the first AI system to demonstrate this level of reasoning, comparing its current stage to the early development of GPT-2, indicating that much more is to come, with a GPT-4-level equivalent on the horizon. Altman also discussed five levels of AI development, positioning o1 at level 2 (reasoners), and hinted that level 3, which involves fully capable AI agents, could arrive relatively soon due to faster iterations. This could mean huge advancements in AI over the next few months! Check out the full discussion here (from 51:00 to 1:06:00): YouTube link in comments section. #ArtificialIntelligence #AI #OpenAI #SamAltman #o1Model #MachineLearning #FutureOfAI

👉Formal Interaction Model (FIM): A Mathematics-based Machine Learning Model that Formalizes How AI and Users Shape One Another #machine_learning #AI #هوش_مصنوعی #یادگیری_عمیق #ماشین_لرنینگ 📚 Read more 👇👇 https://lnkd.in/efiiQJx4

"Robots" lead "Robots". Will this be the future? In a recent essay titled "WEAK-TO-STRONG GENERALIZATION: ELICITING STRONG CAPABILITIES WITH WEAK SUPERVISION" published by OpenAI, researchers explore the prospect of employing a smaller LLM to supervise a larger LLM. They present a case utilizing a model akin to GPT-2 to harness a significant portion of GPT-4's capabilities, achieving performance levels nearly on par with GPT -3.5. This implies a considerable feasibility of weak-to-strong generalization, suggesting a promising avenue for advancements in AI capabilities. #openai #ai #whatsnewaboutAI #gpt4 #gpt3.5 Link to the Essay: https://lnkd.in/eERR__jX

RIG vs. RAG: A Key Difference in AI Response Generation Unlike RAG (Retrieval-Augmented Generation), which performs retrieval once before generating an answer, RIG (Retrieval-Integrated Generation) takes it a step further! 🌟 RIG adapts in real-time while generating responses, allowing the model to iteratively refine its output as it retrieves new information on the fly. This makes the process more dynamic and ensures the answers are as accurate and up-to-date as possible. #AI #MachineLearning #RAGvsRIG #ArtificialIntelligence #TechInnovation #DataScience #DL

📢 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐔𝐧𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐟𝐨𝐫 𝐈𝐦𝐚𝐠𝐞-𝐭𝐨-𝐈𝐦𝐚𝐠𝐞 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐌𝐨𝐝𝐞𝐥𝐬 💡 Machine unlearning has emerged as a new paradigm to deliberately forget data samples from a given model in order to adhere to stringent regulations. However, existing machine unlearning methods have been primarily focused on classification models. ✅ This paper provides a unifying framework of machine unlearning for image-to-image generative models via a computationally-efficient algorithm. 👉 Paper https://lnkd.in/dvypEZ6C #machinelearning #ai #genai ♻️ 𝑰𝒇 𝒚𝒐𝒖 𝒇𝒐𝒖𝒏𝒅 𝒕𝒉𝒊𝒔 𝒉𝒆𝒍𝒑𝒇𝒖𝒍, 𝒌𝒊𝒏𝒅𝒍𝒚 𝒓𝒆𝒑𝒐𝒔𝒕 ♻️

🔍 Open Set Recognition I want to share a paper that has sparked my interest in this particular topic. Nowadays, it's very common to see multimodal models like GPT-4 or Gemini that can analyze images and "understand whatever is in them." This makes me wonder if they incorporate OSR (Open Set Recognition) models or if they are simply trained with massive labeled data at scale. The fascinating aspect of OSR models is that they are designed for real-world applications where, in addition to detecting the object, the classifier can generalize to an arbitrary set of object classes at test time, even classes of objects that were unknown during the model's training. This leads us to applications like Zero-shot Classification. Paper: https://lnkd.in/eNNsGMRZ 🚀 We are entering an era where models can adapt more easily to their environment without needing numerous training iterations. #OpenSetRecognition #ArtificialIntelligence #AI #MultimodalModels #AIResearch

𝗛𝗼𝘄 𝘁𝗼 𝗗𝗲𝘁𝗲𝗰𝘁 𝗣𝗼𝗶𝘀𝗼𝗻𝗲𝗱 𝗗𝗮𝘁𝗮 𝗶𝗻 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗗𝗮𝘁𝗮𝘀𝗲𝘁𝘀 https://lnkd.in/gZEtpVZK It's critical to detect poisoned data in machine learning datasets. It entails locating unusual data that may distort the performance of the model. More dependable results are produced by this procedure, which guarantees the integrity and correctness of machine learning models. #DetectPoisonedDataInMachineLearningDatasets #DetectPoisonedData #MachineLearningDatasets #MachineLearning #DataScience #AI #AINews #AnalyticsInsight #AnalyticsInsightMagazine

🤖 Llama 3.1 vs GPT-4 vs Mixtral 8x22B vs Claude 3.5: Which is the Best LLM Model? 🔍 Meta’s open-source Llama 3.1 takes on private giants like GPT-4, Mixtral 8x22B, and Claude 3.5 Sonnet. 💥 This article dives into the differences between these top-tier LLMs, comparing performance, features, and capabilities to determine the ultimate AI champion! 🏆✨ 🔍 Key Comparisons: Performance Metrics 📊 Features and Capabilities 💡 Accessibility and Flexibility 🌐 Find out which LLM stands out in the AI landscape! 🌟🤖 See here - https://lnkd.in/gFdhYT-S #AI #LLM #Llama3 #GPT4 #Mixtral8x22B #Claude3.5 #TechComparison #AIInnovation #MachineLearning #TechNews

Who is working on probabilistic machine learning often struggles in finding the right metrics for very custom tasks. Besides and beyond what you could read in literature (from ELBO to CRPS), there is also a more subtle misunderstanding going on, because in this realm you are not only trying to optimise your metrics for accuracy, but for calibration too, and that could lead to seemingly paradoxical results and trade offs. What if the most accurate model can't be the more calibrated and viceversa? 🤔 #ai #machinelearning #datascience https://lnkd.in/dbaWgHR2