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Ce rapport intermédiaire du Shift Project examine les implications environnementales des technologies d’intelligence artificielle. L’étude analyse la consommation d’énergie, les émissions de carbone et les ressources nécessaires à l’entraînement et au déploiement des modèles d’IA. Le rapport formule des recommandations pour développer et utiliser l’IA en accord avec les objectifs de durabilité écologique et les principes de sobriété numérique.

Ce rapport étudie une composante clé des infrastructures du numérique, la filière centre de données, et la manière dont elle se construit en interaction avec l’intelligence artificielle, principal déterminant de ses dynamiques aujourd’hui. Celle-ci trace le contour de la manière dont le déploiement généralisé de l’IA infléchit ces dynamiques déjà insoutenables. Il éclaire les pistes à suivre pour réorienter vers la soutenabilité énergie-carbone nos choix technologiques, qui sont de véritables choix politiques, économiques et stratégiques.

This paper presents a comprehensive framework for developing interpretable AI systems that can explain their decisions to stakeholders. The research bridges the gap between theoretical approaches to AI interpretability and practical implementation challenges. The authors analyze various techniques for making AI systems more transparent and understandable, including feature attribution methods, counterfactual explanations, and human-centered design approaches. The study also addresses the crucial balance between model complexity and interpretability, offering guidelines for when and how to prioritize explainability in AI systems.

Chomsky and others have claimed that large language models (LLMs) are equally capable of learning languages that are possible and impossible for humans to learn. However, there is very little published experimental evidence to support such a claim. This study develops a set of synthetic impossible languages of differing complexity, each designed by systematically altering English data with unnatural word orders and grammar rules. These languages lie on an impossibility continuum: at one end are languages that are inherently impossible (random and irreversible shuffles of English words), and on the other, languages considered impossible in linguistics, particularly those with rules based on counting word positions. A wide range of evaluations assess the capacity of GPT-2 small models to learn these uncontroversially impossible languages, performed at various stages throughout training to compare the learning process for each language. The core finding is that GPT-2 struggles to learn impossible languages compared to English as a control, challenging Chomsky’s claim. The authors hope their approach opens a productive line of inquiry in which different LLM architectures are tested on a variety of impossible languages, as tools for cognitive and typological investigations.

Large language models display impressive capabilities. However, for the most part, the mechanisms by which they do so are unknown. The black-box nature of models is increasingly unsatisfactory as they advance in intelligence and are deployed in a growing number of applications. Our goal is to reverse engineer how these models work on the inside, so we may better understand them and assess their fitness for purpose. The challenges we face in understanding language models resemble those faced by biologists. Living organisms are complex systems which have been sculpted by billions of years of evolution. While the basic principles of evolution are straightforward, the biological mechanisms it produces are spectacularly intricate. Likewise, while language models are generated by simple, human-designed training algorithms, the mechanisms born of these algorithms appear to be quite complex.

This paper provides a comprehensive analysis of the environmental impact of AI systems throughout their lifecycle, from development to deployment and maintenance. The authors examine various strategies for reducing the carbon footprint of AI, including efficient model architectures, green computing practices, and renewable energy usage. The research also presents concrete recommendations for developing and deploying AI systems in an environmentally responsible manner.

This research investigates methods for developing environmentally sustainable natural language processing systems, focusing on reducing computational costs and energy consumption. The study analyzes various efficiency techniques specific to NLP tasks, including model compression, efficient attention mechanisms, and task-specific optimizations. The authors provide empirical evidence of energy savings and performance trade-offs across different NLP tasks and model architectures.