• Smarter, leaner, continuous-learning models
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Google Research recently introduced a new machine-learning paradigm called Nested Learning — a method that breaks down learning into smaller, nested optimization tasks. This helps prevent “catastrophic forgetting” (where a model loses earlier learned abilities when trained on new tasks). Google Research
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Simultaneously, the push toward greater efficiency is evident: new techniques such as low-precision transformer training, sparse attention, and quantization are enabling powerful models to run with lower compute and energy demands. helloskillio.com+1
• Expanding multimodal AI and “embodied” intelligence
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The latest wave of models isn’t just about text: multimodal AI — capable of processing and generating across modalities like vision, language, even physical action — is accelerating. For instance, the family of models under Gemini (from Google) now includes a “Robotics” variant for vision-language-action tasks. Wikipedia+2Google AI+2
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In robotics, these advances matter: as per a recent market report, the “physical AI” sector — robotics and real-world intelligent systems — is projected to hit USD 49.7 billion by 2033, driven largely by new robotics-oriented compute platforms and vision-language-action models. GlobeNewswire+1
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