A new study by Shanghai Jiao Tong University and SII Generative AI Research Lab (GAIR) shows that training large language models (LLMs) for complex, autonomous tasks does not require massive datasets.

From fine-tuning open source models to building agentic frameworks on top of them, the open source world is ripe with projects that support AI development.

Companies hate to admit it, but the road to production-level AI deployment is littered with proof of concepts (PoCs) that go nowhere, or failed projects that never deliver on their goals. In certain ...

The path to AI success starts with a single, well-chosen use case: one that is bold enough to inspire, urgent enough to ...

We are living through one of those rare moments when an entire industry cycle is being reimagined. Like the internet revolution of the 1990s, artificial intelligence is fundamentally reshaping how ...

Overview: Generative AI is rapidly becoming one of the most valuable skill domains across industries, reshaping how professionals build products, create content ...

The artificial intelligence (AI) systems most companies are building today are flying blind. Here's why context engineering is the missing piece and the race is already on. The promise of enterprise ...

A new study from MIT's NANDA initiative has found that 95% of generative AI pilots fail to deliver measurable ROI for companies – a failure rate rooted not in flawed models but in poor integration and ...

Warning: This graphic requires JavaScript. Please enable JavaScript for the best experience. Imagine you are redesigning your living space. You could hire an interior ...

To make cloud or AI projects successful and complete them on time, you need a clear understanding of business goals and technology capabilities, and that understanding needs to be kept current and ...

A recent MIT report featured in Fortune magazine revealed that ninety-five percent of generative-AI pilot projects are failing, resulting in investors pulling back and misrepresenting this as a ...