Greetings and happy Christmas Eve to all of you out there on the InterWebs that celebrate it! While it has been a few days since I have posted, I do have some great new AI video to share. This will also be my first attempt was embedding some content into the website.
In this video from IBM's Think Series, Martin Keen and Aaron Baughman discuss eight significant AI trends predicted for 2026:
- Multi-Agent Orchestration (0:32): Building on the rise of AI agents, 2026 will see the orchestration of multiple specialized agents working collaboratively. This involves a planner agent to decompose goals, worker agents for specific tasks like coding or API calls, and a critic agent to evaluate outputs, all coordinated by an orchestrator for cross-checking and problem-solving.
- Digital Labor Workforce (1:49): This trend focuses on autonomous digital workers capable of parsing tasks from multimodal input and executing workflows. These digital workers will integrate into existing systems and be enhanced by "human in the loop AI" for oversight, correction, and strategic guidance, effectively multiplying human capability.
- Physical AI (2:53): Moving beyond digital outputs like text and images, physical AI involves models that understand and interact with the real, 3D world. This includes models that perceive environments, reason about physics, and take physical action, such as robotics. The shift is from explicit rule programming to training models in simulation to understand physical behavior, leading to the commercial production of humanoid robots.
- Social Computing (4:47): This trend envisions a shared AI fabric where humans and AI agents operate seamlessly. Information flows between them, allowing for mutual understanding, intent recognition, and the ability to affect each other and their environment. This shared space fosters collaboration, context exchange, and effective understanding, leading to collective intelligence or "real-world swarm computing."
- Verifiable AI (5:51): With the EU AI Act becoming fully applicable by mid-2026, AI systems, especially high-risk ones, will need to be auditable and traceable. This includes requirements for technical documentation, transparency (e.g., labeling synthetic text), and data lineage to prove compliance with copyright and other regulations, setting a global template for AI governance.
- Quantum Utility Everywhere (7:13): By 2026, quantum computing is expected to reliably solve real-world problems more efficiently than classical methods. This involves quantum utility scale systems working alongside classical infrastructure to deliver practical value in everyday workflows, particularly in optimization, simulation, and decision-making, transforming quantum computing into a mainstream paradigm.
- Reasoning at the Edge (8:04): Smaller AI models (with fewer parameters) that can run on personal devices will learn to "think." This is achieved by distilling reasoning information from massive frontier models into these smaller models, enabling them to perform step-by-step reasoning locally. This eliminates latency to data centers, making reasoning models suitable for real-time and mission-critical applications where data never leaves the device.
- Amorphous Hybrid Computing (9:40): This future blends AI model topologies and cloud infrastructure into a fluid computing backbone. AI models are evolving beyond pure transformer designs to integrate other architectures like state-space models and new algorithms. Simultaneously, cloud computing is becoming more differentiated by combining various chip types (CPUs, GPUs, TPUs, QPUs, and neuromorphic chips) into a unified compute environment, automatically mapping model parts to optimal compute substrates for maximum performance and efficiency.
I hope to have some more time later this week to search for more useful information!