The GenerIA Blog

Rethinking Tokenization: How SuperBPE Breaks the Space Barrier

It just took questioning an arbitrary assumption (the Einstein way) to bring tokenization closer to the reality and overcome a years-long limitation in one of the fundamental layers of the NLP stack.

From AI Agents To Agentic Systems: Understanding The Paradigm Shift

A shift is underway from predefined, automation-oriented "AI agents" to dynamic, context-sensitive "agentic systems". This evolution goes beyond a simple semantic change. It reflects a transformation in system design, operational logic and adaptive capacity.

Mapping AI risks: A Reference Base for Shared Governance

An international academic team proposes a unified directory of more than 700 risks associated with AI, particularly in business environments. This database aims to provide an overview and a common language to technical, regulatory and industrial actors confronted with these complex issues.

Regulating Frugal AI: Between Progress and Challenges...

Frugality is a radical shift in the way businesses and governments think about AI. But how do we regulate a technology that promises both performance and a sustainable environmental footprint? Let's take a look at how three major regions - Canada, Europe and the United States - are approaching the problem...

AFNOR SPEC 2314: Best Practices in Frugal AI

From project design to end-user acculturation, frugal AI is above all a matter of best practices. Numerous and complementary, these BPs are detailed in AFNOR SPEC 2314. Here is a thematic summary.

Frugal AI: A Gentle Introduction to the AFNOR SPEC 2314 Framework

Fostering innovation without hastening the attrition of natural resources. This is the rationale behind frugal artificial intelligence, whose definition, contours and practices AFNOR intends to normalize.

Telemetry, an essential component of the best AIs

Extensive telemetry brings a great deal to enterprise artificial intelligence. Performance, behavior, response biases, prompt injections... Everything that can be observed contributes to continuous optimization, thereby guaranteeing the full success of AI projects.

AI and environment (3/3): the systemic risks

Overloaded power grids, the return of fossil fuels, non-recycled electronic waste, skyrocketing social costs... Conventional AI's systemic and societal indicators are all red.

AI and environment (2/3): water, critical issue!

Artificial intelligence - at what cost to our water resources? Just like its carbon footprint, Conventional AI's consumption of cooling water is becoming a real ecological threat.

AI and environment (1/3): alarming numbers

Insatiable for energy and a major producer of CO2, conventional artificial intelligence looks more and more like an environmental dead end. Is there any hope of sustainability? Everywhere, the numbers suggest otherwise...