Even as concern and skepticism grows over U.S. AI startup OpenAI's buildout strategy and high spending commitments, Chinese open source AI providers are escalating their competition and one has even caught up to OpenAI's flagship, […]
Enterprise leaders grappling with the steep costs of deploying AI models could find a reprieve thanks to a new architecture design. While the capabilities of generative AI are attractive, their immense computational demands for both […]
In this tutorial, we explore how an agent can internalize planning, memory, and tool use within a single neural model rather than relying on external orchestration. We design a compact, model-native agent that learns to […]
The intelligence of AI models isn't what's blocking enterprise deployments. It's the inability to define and measure quality in the first place. That's where AI judges are now playing an increasingly important role. In AI […]
A new academic review suggests AI benchmarks are flawed, potentially leading an enterprise to make high-stakes decisions on “misleading” data. Enterprise leaders are committing budgets of eight or nine figures to generative AI programmes. These […]
One of the biggest challenges in real-world machine learning is that supervised models require labeled data—yet in many practical scenarios, the data you start with is almost always unlabeled. Manually annotating thousands of samples isn’t […]
A quiet revolution is reshaping enterprise data engineering. Python developers are building production data pipelines in minutes using tools that would have required entire specialized teams just months ago. The catalyst is dlt, an open-source […]
For more than three decades, modern CPUs have relied on speculative execution to keep pipelines full. When it emerged in the 1990s, speculation was hailed as a breakthrough — just as pipelining and superscalar execution […]
Recently, there has been a lot of hullabaloo about the idea that large reasoning models (LRM) are unable to think. This is mostly due to a research article published by Apple, "The Illusion of Thinking" […]
Researchers at Meta FAIR and the University of Edinburgh have developed a new technique that can predict the correctness of a large language model's (LLM) reasoning and even intervene to fix its mistakes. Called Circuit-based […]
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