AI Horizon 2026: Strategic Outlook 📡 AI Horizon 2026 Strategic Intelligence Report Data verified: Jan 2026 v2.4.0 The Year of Reckoning As we pivot toward 2026, the AI narrative shifts from creation to execution. The industry is no longer defined by capabilities, but by sustainability, physical limits, and the ability to justify immense capital expenditure.
Fusing artificial intelligence and creativity is rapidly transforming how music is imagined and produced. Modern AI tools can now analyse musical data from around the world, learn patterns of melody and rhythm, and generate original compositions complete with lyrics. These do not simply mimic existing songs but instead recombine the learnt patterns to produce something
I recently explored the emerging practice of vibe coding—the concept of turning ideas into running software using nothing but natural language. It’s a shift in mindset from traditional programming toward conversational building. Less “code every line,” more “describe what you want.” To test this approach, I created an account on Firebase Studio, Google’s AI-powered development
Original Source AI Agents are rapidly evolving beyond simple chatbots to become autonomous entities capable of observing, planning, and acting with their environments. This shift is accelerating, and the first commercial agents are already generating meaningful revenue in various sectors. But how are these agents becoming more reliable, safe, and ready for enterprise use? A
Coalition for Health AI (CHAI) is a responsible AI (RAI) framework for healthcare. It is an important framework that has been created for ensuring AI and Gen AI solutions for healthcare encode trust and provides a structured approach to ensuring responsible and ethical development and deployment of AI in healthcare organizations and enterprises. In this
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In a previous post we explored the idea of using large language models (LLMs) to query structured datasets such as structured data files and databases. We introduced the Sketch library which integrates with Python and can be used in a Notebook to query large datasets. Sketch methods can be used for directly asking questions in
With the arrival of large language models (LLM), it has become quite commonplace to ask questions of unstructured long-form content which is present in documents in a multitude of formats. This can be done using commercial LLMs deployed via API endpoints (most famous being GPT by OpenAI) or recently by deploying open source LLMs (Llama2
The recent rise of open-source large language models (LLMs) is a welcome development for the field. Open LLMs have been around for some time now but with Facebook’s announcement of Llama 2 there has been lot of enthusiasm in the developer community. Llama 2 has three models of parameter size 7B, 30B, and 70B and