Sampa Pal’s Post

🚀 Exciting News from Google! 🚀 Google has just launched Gemma 2, the latest iteration of its open large language model (LLM), available in two sizes: 9B and 27B parameters. Trained on a massive 13 trillion tokens, Gemma 2 is setting a new benchmark in AI performance. 🔍 Performance Highlights: Gemma 2 27B is making waves by closely approaching the performance of Meta's Llama 3 70B, despite having less than half the number of parameters. The 27B version of Gemma 2 is already making headlines for matching the performance of AI heavyweights like Meta’s Llama 3 70B, Anthropic’s Claude 3 Sonnet, and OpenAI's GPT-4 in initial evaluations at the LMSYS Chatbot Arena. 💡 Why It Matters: Gemma2 prompts us to question the current trajectory of AI development. Should we continue striving for larger models, or shift our focus towards refining and optimizing smaller models for greater efficiency and sustainability? This debate is crucial as we consider the future directions of AI technology—prioritizing advancements that enhance capabilities and improve practicality and accessibility. 🔗 Dive deeper into Gemma 2's features and capabilities https://lnkd.in/gnSnqmFj https://lnkd.in/g9ezfkju https://lnkd.in/gbVkuabv #AI #MachineLearning #TechNews #GoogleAI #Gemma2 #Innovation

#SORA aside, in two months, Google has improved Gemini in a massive way. Gemini 1.5 can handle a million tokens, and uses Mixture-of-Experts (MoE) architecture which is more efficient at training and serving and is what made Mistral AI as good as GPT3.5 #artificialintelligence #mixtureofexperts #nlp https://lnkd.in/dKN9bEKb

In the rapidly evolving field of multimodal AI, the competition among tech giants has intensified, with OpenAI's GPT-4o model emerging as the leader in the Multimodal Arena. Anthropic's Claude 3.5 Sonnet and Google's Gemini 1.5 Pro closely follow, highlighting the fierce drive among companies to dominate this cutting-edge technology space. A notable development is the performance of the open-source model LLaVA-v1.6-34B, which has achieved scores comparable to proprietary models like Claude 3 Haiku. This achievement suggests a potential democratization of advanced AI capabilities, potentially leveling the playing field for researchers and smaller companies that may lack the vast resources of major tech firms. The leaderboard assesses a diverse array of tasks, ranging from image captioning and mathematical problem-solving to document understanding and meme interpretation. This comprehensive evaluation aims to provide a holistic view of each model's ability to process visual information, reflecting the complex demands of real-world applications. As the field progresses, advancements in multimodal AI promise to revolutionize various industries, from healthcare and finance to entertainment and beyond, ushering in new possibilities for how AI interacts with and understands the world around us. https://lnkd.in/dxJA4qE2 #GPT4o #TechInnovation #OpenSourceAI #TechGiants #AIAdvancements

A step closer to human-level intelligence in AI

MEGALODON: Meta’s New AI Giant Revolutionizes Data Processing In the ever-evolving landscape of artificial intelligence (AI), the demand for efficient data processing capabilities continues to surge. Addressing this need, Meta has unveiled MEGALODON, a groundbreaking AI model poised to transcend the architectural limitations inherent in traditional Transformer models. This paradigm-shifting innovation represents a monumental leap forward in the realm of AI, offering unparalleled capabilities and ushering in a new era of computational efficiency and performance. https://lnkd.in/gPAj6GAD #MEGALODON #Meta #AI #artificialintelligence #dataprocessing #computationalcomplexity #innovation #technology #machinelearning #applications #integration #futuretech #techadvancements

Welcome Gemini 1.5! A huge breakthrough in terms of performance and utility, resulting from many architectural advances, like the integration of a transformer gating network with many specialist neural networks in a Mixture-of-Experts (MoE) architecture. Also, with its unprecedented 1M multimodal tokens context window, the number of use cases that can be covered is dramatically expanded. Make sure to watch the demo videos (with timestamps) to have an initial understanding of what this model can do. Great news! #llm #gemini #google #research #nlp #multimodal

Introducing Mixtral 8x7B: A Game-Changing AI Model We're thrilled to announce the release of Mixtral 8x7B, a groundbreaking sparse mixture of experts (SMoE) model from Mistral AI. This revolutionary model sets new benchmarks in performance and efficiency, outperforming Llama 2 70B while offering a 6x faster inference rate. Mixtral 8x7B is built on a unique architecture that leverages the strengths of multiple specialized experts. This approach allows the model to achieve exceptional accuracy while maintaining a competitive inference speed. Additionally, Mixtral 8x7B is licensed under the open and permissive Apache 2.0 license, making it freely available for research and development. This impressive model is poised to transform the AI landscape, empowering developers and researchers to build more powerful and efficient AI applications. We invite you to explore the full potential of Mixtral 8x7B and experience the future of AI. Read more about Mixtral 8x7B and its groundbreaking capabilities here: https://lnkd.in/evbknCrB

Meta's recent developments in AI highlight the company's strategic focus on multi-modal models. Chameleon, a state-of-the-art AI model, reportedly matches or exceeds the performance of leading models like GPT-4 and Google's Gemini. In my work with AI technologies, I find the early-fusion architecture utilised by Chameleon to be a significant advancement, enabling the integration of diverse data types from the outset. Traditional multi-modal models, which often rely on late-fusion techniques, can be limited by their segmented approach to data integration. As Meta continues to push for open-source solutions, the implications for the broader AI community are substantial. This approach could foster more collaborative innovation and accelerate advancements in the field. https://lnkd.in/gbTqJ2x7

The Battle of the Megatokens: Google & OpenAI Up the Ante in the Large Language Model Race The large language model space is heating up this week with exciting news from both Google and OpenAI! Google unveils Gemini 1.5: - Boasts a whopping 1 MILLION token context window - that's 30x larger than its predecessor! This opens doors for complex reasoning and handling of massive information sets. - Multimodal learning allows it to process information across different formats like text, code, and images. - Responsible development is emphasized with safety testing measures in place. OpenAI doubles down on GPT-4 Turbo: - Increases rate limits across the board, removing daily limits and potentially reaching 1.5 million tokens per minute! This grants users significantly faster access to GPT-4's power. - Focuses on technical aspects like tiers and usage, leaving specific capabilities to the user's imagination. So, who wins the megatokens war? Both advancements are impressive, but cater to different audiences: - Google AI: Researchers and developers seeking a powerful model for complex tasks and multimodal learning. - OpenAI: Users prioritizing speed and accessibility for GPT-4's capabilities. Ultimately, the "best" model depends on your specific needs. But one thing's clear: the large language model landscape is rapidly evolving, pushing the boundaries of what's possible with AI. P.S. What are your thoughts on these developments? Which model excites you more? Share your insights in the comments! #AI #MachineLearning #LLMs #GPT4 #GoogleAI #OpenAI #NLP #Gemini https://lnkd.in/gWfWydu7 https://lnkd.in/g2yDUHnH

The future of AI Models is to understand the world as it is. AI models should be able to gain knowledge about the world by passively observing it, similar to how humans learn. Or thats what a Yann LeCun's vision is, the Meta's Chief AI scientist.