Data Phoenix’s Post

Google DeepMind unveils Genie, a groundbreaking Generative AI capable of creating limitless, customizable virtual worlds from text, images, and sketches🔥 This model, trained unsupervised on internet videos and boasting 11 billion parameters, is a pioneering foundation world model. ______________ 🤳 Contact us if you made a great AI tool to be featured: https://lnkd.in/d5VZ-W8H #ai #innovation #generativeai

Guest Editorial: What Engineers Should Know About the Expanding Generative AI Toolkit You’ve heard of generative artificial intelligence, and odds are you’ve used it—but do you know how it works? https://lnkd.in/ePB4mmUm

Guest Editorial: What Engineers Should Know About the Expanding Generative AI Toolkit You’ve heard of generative artificial intelligence, and odds are you’ve used it—but do you know how it works? https://lnkd.in/ePB4mmUm

Guest Editorial: What Engineers Should Know About the Expanding Generative AI Toolkit You’ve heard of generative artificial intelligence, and odds are you’ve used it—but do you know how it works? https://lnkd.in/eUaCUtDi

Google DeepMind Founder Demis Hassabis Talks About Gemini 1.5 - A Revolutionary Multimodal AI Model Discover the game-changing features Google DeepMind founder Demis Hassabis talks about in Gemini 1.5 - what he describes as a revolutionary multimodal AI model that seamlessly handles text, image, code, and video inputs. With endless possibilities offered by its extensive context window, from fast-forwarding through lectures to storing entire code bases, this model is sure to be model that rivals many others. #ai #artificialintelligence #ainews #Technology #Business #AIhistory #AIarchive #history #shorts #short #demishassibis #google #deepmind ========== Digital independence for You and Your AI Triplets. https://ctrlv.ai

Putting Multimodal AI to the Test: Can GPT-4, Gemini, and Claude Solve the Google I/O 2024 Puzzle? Each new multimodal models from OpenAI, Google, and Anthropic has claimed to be best in class and beats the others on all multimodal benchmarks. We put ChatGPT-4, Gemini Ultra, and Claude3 Opus to the test on the Google I/O 2024 puzzle to find out! In this in-depth analysis, we: - Evaluate the current state-of-the-art in multimodal AI reasoning - Introduce the Google I/O 2024 puzzle as a novel benchmark task - Compare how GPT-4, Gemini, and Claude3 fare on understanding and solving progressively harder puzzles - Discuss key takeaways and what's needed for multimodal models to advance By the end, you'll have insights into the true multimodal reasoning capabilities of today's frontier AI models. Read more here: https://bit.ly/4cvnXZl

This is to share some key insights from a recent #NewMR webinar on AI in research trends: 1. 8 Tips for Using Microsoft Copilot by Ray Poynter [14 mins overview]. Ray shared hands-on tips for making the most of Microsoft Copilot. It's just amazing how much boring and preliminary manipulations Copilot can handle, allowing you to focus on deep analysis and insights generation. Clearly, Copilot has significant potential across the Microsoft ecosystem. https://lnkd.in/eWzpgEJT 2. Augmented Synthetic Data Capability by Livepanel [21 mins presentation]. A new ML-based solution that helps reach survey audiences that are typically hard to connect with and fill in missing data. It's not LLM-based, so it's free from hallucinations and bias. https://lnkd.in/eMaQ3CqN 3. Research trends in Luxury by Vesna Hajnšek [18 mins discussion]. Enjoyed catching up with a former colleague who shared thoughts on keeping emotional context alive in beauty research, even as AI tools grow. https://lnkd.in/eFZWUJmW Thank you once again to everyone involved! #MarketResearch #Insights #MicrosoftCopilot #NewMR #AI

📊 Why AI Explainability Matters: A Case Study with MNIST Are AI models with high accuracy always reliable? I did a simple yet revealing experiment with the MNIST dataset that explains the importance of XAI. This experiment underscores the critical need for explainability in AI development. As we build more complex models, understanding their decision-making processes becomes not just beneficial, but essential for creating reliable and trustworthy AI systems. Key findings: 1️⃣ A CNN achieving 99% accuracy on MNIST can be easily fooled by adding frames to digits. 2️⃣ The model misclassified all framed digits as '9', revealing a serious flaw. 3️⃣ Explainability techniques like Grad-CAM expose the model's focus on irrelevant features. For a deeper dive check out the Github repo: https://lnkd.in/eijPe3p8 #AIExplainability #MachineLearning #DataScience #ResponsibleAI #GradCam

Google’s DeepMind creates ‘Gecko’, a rigorous new standard for testing AI image generators Google DeepMind's Gecko benchmark reveals flaws in current text-to-image AI evaluation methods, introducing a rigorous new standard with 100,000+ human ratings.Read More https://ift.tt/OrUTFMn https://ift.tt/RIti6rY

I'm continually amazed by the capabilities of GenAI! Having spent considerable time exploring AWS BedRock, I can confidently say this isn't just a passing trend—it's a transformative technology that's here to stay. The potential to enhance operations across all customer bases, industries, and segments is truly remarkable. If you haven’t explored this yet, I highly recommend you do.