Why the conversion of legacy applications is not a matter of linguistic translation but of semantic reconstruction, and how we addressed the issue at Scriba.
The largest qualitative study ever conducted: Anthropic interviews half the world to understand real hopes, fears, and expectations about AI.
At Algoretico, we have developed a framework that does something no Transformer does today: it develops a cognitive personality that evolves with experience.
Artificial intelligence approached with scientific rigor, engineering precision, and human depth.
The Global AI Report by NTT DATA reveals a clear picture: experimentation gives way to the creation of measurable value.
Continuous Latent Reasoning: 16x-128x Semantic Compression and End-to-End Optimization for Next-Generation RAG Systems
Today, the pre-built management system is no longer just an operational limit, but a cognitive constraint that prevents the company from understanding, governing, and evolving its real complexity.
A technical analysis of why the self-attention architecture makes modern LLMs much more than mere "stochastic parrots."
Draghi's warning about European stagnation concerns not only the number of artificial intelligence models developed but also the very structure of the economy and how we use (or do not use) AI in production processes.
Why the real productive revolution does not come from LLMs.
Exploring the critical importance of private AI infrastructure for organizations requiring absolute control, performance, and intellectual property ownership.
How orchestrated AI agents are transforming complex problem-solving through coordinated autonomy and specialized capabilities.
Most organizations don't have labeled datasets. They have processes, constraints, and domain expertise. Here's how to build AI systems that learn from structure, not just examples.
Beyond pattern matching and statistical correlation—exploring what distinguishes true intelligence from sophisticated computation, and why the question matters for how we build AI.
Why my students implement backpropagation by hand, build neural networks from NumPy, and learn to architect systems instead of calling APIs.
Why industrial automation demands AI that runs on-premise, operates without internet connectivity, and makes millisecond decisions in environments where downtime costs millions.
When AI systems trained on AI-generated content degrade over time, losing diversity and capability. Understanding the mechanics of model collapse and architectural solutions that preserve knowledge.
Building Retrieval-Augmented Generation systems that actually understand your organization's knowledge, not just find semantically similar text snippets.
Creativity doesn't emerge from unlimited freedom—it emerges from intelligent navigation of constraints. What this means for building AI systems that generate novel solutions.
The loss landscape of deep networks is high-dimensional, non-convex, and full of local minima. Yet gradient descent finds good solutions anyway. Understanding why reveals fundamental insights about deep learning.
