Ateeshay Jain’s Post

🚀 Google I/O 2024 was a blast! The #Gemini family of models just got even more powerful with the introduction of Gemini 1.5 Flash and the expanded capabilities of Gemini 1.5 Pro. 🤯 Gemini 1.5 Pro's 1 million token context window is a game-changer, allowing for deeper analysis and understanding across vast amounts of information. This is a major leap forward in #AI language models! ⚡ Gemini 1.5 Flash is the lightweight, speedy sibling designed for tasks where low latency and cost-efficiency are key. It's perfect for real-time applications and high-volume use cases. 💡 The potential applications are endless: from smarter chatbots and virtual assistants to enhanced content creation and data analysis tools. The future of AI is here, and it's incredibly exciting! #GoogleIO #Gemini15Pro #Gemini15Flash #AI #Innovation #Google

If you're in the data and analytics space, this is worth paying close attention to. The business intelligence potential of #GenAI, as Jesse points out, is groundbreaking. Excited to see how this evolves.

I had an amazing time chatting with customers and colleagues about data and AI at #GoogleCloudNext. It was inspiring to hear about exciting use cases and examples of how #GenAI is already having a positive impact on businesses across various segments and industries. One of the most remarkable announcements was the expanded context window of Gemini 1.5 Pro. With 1 million tokens, Gemini 1.5 Pro can process a staggering amount of information, including text, images, and videos, allowing us to perform tasks and streamline processes more efficiently. One noteworthy example mentioned is a university professor who was able to extract relevant information from a 3,000-page document in a single shot. This is a significant improvement compared to other models like Claude’s Opus (5x), GPT-4 (7x), and Gemini 1.0 (30x). To put this progress into perspective, it took roughly 25 years for the USB drive to be released after the floppy disk, representing an 8x increase in storage capacity. In contrast, Gemini 1.5 Pro boasts a 30x increase in input/output tokens from version 1.0 to 1.5. In the world of analytics, we are now feeding models with a greater volume of information and retrieving more intricate outputs, which will yield unprecedented business intelligence. Can’t wait to see what comes next! 

At Google I/O 2024, Google unveiled two new additions to its Gemini AI model series: Gemini 1.5 Pro and Gemini 1.5 Flash. Gemini 1.5 Pro boasts the longest context window among existing foundational models, with the ability to process multiple large documents (up to 1,500 pages), summarize 100 emails, handle an hour of video content, or analyze codebases with over 30,000 lines. That's mind blowing! The model currently supports 35 languages and offers a 2 million token window, which measures context and indicates the amount of information the model can process at once. Gemini 1.5 Flash is a lightweight AI model optimized for speed and efficiency, designed for high-volume, high-frequency tasks at scale. It excels at summarization, chat applications, image and video captioning, data extraction from long documents and tables, and more. Both Gemini 1.5 Pro and 1.5 Flash are available in public preview with a 1 million token context window on Google AI Studio and Vertex AI. The unveiling of these new models comes at a time when generative AI is becoming increasingly essential for tech companies, particularly for Google, as it offers consumers more advanced capabilities. The announcement followed OpenAI reveal of its newest AI model, GPT-4o, on Monday. https://lnkd.in/druyHJDE #gemma #openai #google #llm #gpt4o #geminipro #geminiflash

Move over OpenAI, Google's Gemini just raised the bar for AI models with a massive 2 million token context window (currently in private preview). This leapfrogs current industry standards (for reference, GPT-4 Turbo/GPT-4o offer 128K token context length, Mistral 7b 128k, Claude 3 200K) and unlocks new possibilities: - 𝐈𝐧𝐝𝐮𝐬𝐭𝐫𝐲 𝐈𝐦𝐩𝐚𝐜𝐭: We're talking about processing 3,000-page PDFs, 2 hours of video content, or 60,000 lines of code – all in one go. This could drastically reduce the need for Retrieval Augmented Generation (RAG) in certain applications. - 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐬: one that stood out was Context Caching, which allows you to send parts of your prompt, including large files, to the model only once and store it. This will play a role when you work with recurring queries against large document sets, chatbots with extensive system instructions, or analysing a big code repository for bug-fixing - 𝐅𝐮𝐧 𝐟𝐚𝐜𝐭: they even successfully tested up to 10 million tokens context in their research studies. #GoogleIO #GenAI #LLMs

Finally an open-source LLM from Google after T5 series. Gemma. These are lightweight state-of-the-art models that use same tech and methodology behind Gemini models. 2B and 7B models are available in pre-tained and instruction tuned variants. For more details refer this blog: https://lnkd.in/dWdPZ62G #llms #genai #generatieveai #googleai

Google’s new version of Gemini can handle far bigger amounts of data Google DeepMind has upgraded its artificial-intelligence model, Gemini, with the launch of Gemini 1.5 Pro, a mid-tier version that rivals the performance of its predecessor, Gemini 1.0 Ultra, but with reduced computing power requirements. This new version can handle significantly larger data inputs, up to 128,000 tokens as standard, and a select group of developers can test inputs up to 1 million tokens. This capability allows for the processing of extensive amounts of video, audio, or text, showcasing its advanced functionality through demonstrations, including accurately identifying moments from the Apollo moon landing transcript and analyzing silent films. Gemini 1.5 Pro has outperformed previous versions in benchmarks and utilizes a mixture-of-experts architecture for efficient computing. Despite its impressive demonstrations, experts caution against overestimating AI capabilities, highlighting the potential limitations and the brittleness of AI models. The release is currently limited to select developers and business customers, with no specified date for a broader release. https://lnkd.in/eFFf-JcN

Exciting news from Google! 🚀 They've just introduced Gemma, a cutting-edge family of open models derived from the powerful Gemini models. 🔍 Key Features: - Model Variants: Choose from 2B and 7B parameter versions, each in Instruction and base formats. - Commercial Use: Fully authorized for commercial applications, unlocking new business possibilities. - Context Window: Enjoy an 8192 token context window for comprehensive understanding. - Performance: The 7B model surpasses competitors like Mistral AI 7B and LLaMa 2 in Human Eval and MMLU tests, scoring an impressive 64.56 on MMLU. 🌐 Innovations in Gemma's Architecture: - Multi-Query Attention and RoPE: Elevates attention mechanisms and positional encoding for more effective learning. - GeGLU Activations: Dynamic model responses with ReLU replacement. - Normalization Techniques: Improved stability with RMSNorm across layers. Access Gemma now on platforms like Hugging Face, Kaggle, and Vertex AI. > [Official Page Here] https://lnkd.in/e2vp35tu #Google #Gemma #AI #MachineLearning #Innovation

🌟 Google DeepMind Unveils #Gemini 1.5: Next-Gen AI with Breakthrough Long-Context Capability (1 M tokens) Key Developments: 🚀 Gemini 1.5 Pro: Performs on par with Gemini 1.0 Ultra while being more efficient. 🔍 1 Million Token Context Window: Processes significantly more information for richer understanding.  👨💻 Limited Preview Available: Sign up to experiment in AI Studio and Vertex AI. Highlights: 💡 Enhanced Efficiency: Mixture of Experts (MoE) architecture reduces training and serving costs. 🌐 Greater Context: Understands vast information like hour-long videos and codebases.  🔍 Multimodal Reasoning: Analyzes and reasons across text, code, images, audio, and video.  💻 Problem-Solving Prowess: Tackles complex code tasks with longer context windows.  🛡️ Ethical and Safe: Extensive testing ensures responsible deployment. #Google #AI #Gemini1.5 #MachineLearning #VertexAI #Innovation #FutureTech

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