Multimodal ai

Robots Don't Need More Compute. They Need This.

Robots Don't Need More Compute. They Need This.

Encord co-founders Eric and Ulrich discuss their $60M Series C, the company's origins before the AI hype, and their focus on building the essential data infrastructure for physical AI and robotics—the next frontier after LLMs.

Moonlake: Multimodal, Interactive, and Efficient World Models — with Fan-yun Sun and Chris Manning

Moonlake: Multimodal, Interactive, and Efficient World Models — with Fan-yun Sun and Chris Manning

Moonlake AI presents a distinctive approach to world modeling, prioritizing interactive, action-conditioned environments built on symbolic representations and game engines over purely pixel-based generative models. This method focuses on causal reasoning, long-term consistency, and programmable rendering (via their 'Reverie' diffusion model) to create dynamic, multiplayer worlds, positioning itself as a platform for training embodied AI and revolutionizing game development.

The AI Space Podcast-Live!: 2026 AI Market Outlook & Playbook | Panel Discussion + Q&A | Dallas, TX

The AI Space Podcast-Live!: 2026 AI Market Outlook & Playbook | Panel Discussion + Q&A | Dallas, TX

In this live podcast episode, a panel of AI founders and investors unpacks the '2026 AI Market Outlook & Playbook.' They share practical strategies for startups to achieve real-world traction, focusing on the shift from experimental AI to outcome-driven agentic systems. Key topics include building multimodal experiences, the importance of a proprietary 'validation layer,' mastering customer pain points for growth, and the ethical responsibility of building the next generation of AI.

AI year in review: Trends shaping 2026

AI year in review: Trends shaping 2026

In this special year-end episode, experts from the Mixture of Experts podcast review the biggest AI moments of 2025 and predict what's next for 2026. The discussion covers the rise of "super agents" and the battle for the user interface, open source's breakout year and its remaining challenges, the AI hardware supply crisis and the push for efficiency, and the future of modular, multimodal AI systems.

How Google’s Nano Banana Achieved Breakthrough Character Consistency

How Google’s Nano Banana Achieved Breakthrough Character Consistency

Nicole Brichtova and Hansa Srinivasan, the leads behind Google's Nano Banana image model, detail the technical breakthroughs in character consistency. They discuss how a focus on high-quality data, Gemini's multimodal architecture, and rigorous human evaluation enabled the model to realistically represent individuals from a single photo. The conversation covers the future of visual AI, moving beyond text prompts to specialized UIs, and the ultimate goal of a single, powerful model that can transform any modality into another, unlocking new applications in personalized education, professional design, and creative storytelling.

Google DeepMind Developers: How Nano Banana Was Made

Google DeepMind Developers: How Nano Banana Was Made

Google DeepMind's Oliver Wang and Nicole Brichtova discuss the creation of the Nano Banana image model, its viral success driven by character consistency, and the future of AI in creative work, from user interfaces and 2D/3D world models to the next frontier of video generation and high-fidelity reasoning.