Intelligence artificielle générale

Résumé

vignette|Image générée en juin 2022 par le modèle de génération d'images DALL-E-mini, à partir de la consigne « Intelligence artificielle ». Une intelligence artificielle générale (IAG) est une intelligence artificielle capable d'effectuer ou d'apprendre pratiquement n'importe quelle tâche cognitive propre aux humains ou autres animaux. La création d'intelligences artificielles générales est un des principaux objectifs de certaines entreprises comme OpenAI, DeepMind et Anthropic. C'est aussi un thème majeur de la science-fiction et de la futurologie. Même si GPT-4 a été décrit comme ayant des « étincelles d'intelligence artificielle générale », il n'existe pas en 2023 d'IA consensuellement considérée comme générale. Bien que l'intelligence artificielle générale puisse être très utile dans de nombreux domaines, des études en cours évaluent les risques potentiels de cette technologie en voie de développement. Notamment les risques de désinformation et de chômage de masse. Selon des exp

Source officielle

https://fr.wikipedia.org/wiki/Intelligence_artificielle_g%C3%A9n%C3%A9rale

À propos de ce résultat

Cette page est générée automatiquement et peut contenir des informations qui ne sont pas correctes, complètes, à jour ou pertinentes par rapport à votre recherche. Il en va de même pour toutes les autres pages de ce site. Veillez à vérifier les informations auprès des sources officielles de l'EPFL.

Publications associées (72)

Publications associées

Chargement

Personnes associées (4)

Personnes associées

Chargement

Unités associées (7)

Unités associées

Chargement

Concepts associés (36)

Concepts associés

Chargement

Cours associés (20)

Software agents are widely used to control physical, economic and financial processes. The course presents practical methods for implementing software agents and multi-agent systems, supported by programming exercises, and the theoretical underpinnings including computational game theory.

Computer Vision aims at modeling the world from digital images acquired using video or infrared cameras, and other imaging sensors. We will focus on images acquired using digital cameras. We will introduce basic processing techniques and discuss their field of applicability.

This course covers optical remote sensing from satellites and airborne platforms. The different systems are presented. The students will acquire skills in image processing and machine/deep learning to extract end-products, such as land cover or risk maps, from the images.

Afficher plus

Cours associés

Chargement

Séances de cours associées (13)

Séances de cours associées

Chargement

Technology Strategy in Dynamic Environments: A Computational Analysis of the Automation of Routines, the Organization of AI, and the Evaluation of Technology Risk

In this dissertation, I develop theory and evidence to argue that new technologies are central to how firms organize to create and capture value. I use computational methods such as reinforcement learning and probabilistic topic modeling to investigate three topics: the automation of routines, the organization of artificial intelligence (AI), and the evaluation of technology risk. Overall, I argue that new technologies are not a panacea for the firm but require deliberate strategic planning to manage the potential downsides of myopic automation, AI interdependencies, and the disclosure of technology risks.In the first essay, I argue that while automation can increase productivity by reducing the costs of coordinating individuals, the automation of routines can also incur an indirect opportunity cost due to slow adaptation to environmental change. I develop a reinforcement learning simulation to model the impact of automation on the returns from the division of labor in dynamic environments and to show how automation incurs opportunity costs through lost learning and slow adaptation. Moreover, automation can be suboptimal when it brings about myopic behavior, i.e. high returns from the division of labor in the short term, but negative returns in the long term. Given the simulation results, I argue that firms need dynamic routines to simultaneously balance learning and automation. I open-source the simulation platform as OrgSim-RL on GitHub.In the second essay, I argue that a data-driven culture - what I define as a Data Clan - can help to coordinate complex interdependencies between AI components within a firm. I analyze in-depth semi-structured interview data with a hierarchical stochastic block model (hSBM) and hand-coding to find that managers focus primarily on building a strong culture and establishing high-quality data assets when allocating resources to AI initiatives. Given the results, I inductively develop implications for theory and argue that the emergence of a Data Clan can be a governance mechanism to reduce coordination frictions and build a competitive advantage in the age of AI.In the third essay, I argue that investors require a higher initial return to take on more technology risk disclosure during an IPO. I quantify the magnitude of disclosed risk and the risk disclosure topics based on a latent Dirichlet allocation (LDA) topic model of IPO prospectus text and find a return-for-risk association between text-based technology risk disclosure and underpricing. The study also finds evidence that owning granted patents is associated with a lower return-for-risk association, suggesting that intellectual property allows the disclosure of risk without losing the competitive advantage. I open-source the code for quantifying risk disclosure as RiskyData-LDA on GitHub.In summary, this dissertation develops theory and finds evidence across three essays to argue that leveraging new technologies requires deliberate strategic planning to manage potential downsides of new technologies, such as the opportunity costs of automation, coordination costs, and costs associated with raising capital. The results suggest three mitigating solutions: dynamic routines to balance learning and automation, a Data Clan to improve coordination, and disclosure through patents to reduce underpricing.

EPFL2022

Chargement

Experimental indoors localization using distance measurements obtained from visual data

Yuan Yao

In recent years, the emergence of artificial intelligence increases the demand of automatic and robust localization outdoors and indoors. While GPS provides enough accuracy in most outdoor cases, there is still a lack of robust and efficient indoor localization systems available on the market. In this report, an experimental framework for indoor localization is developed and tested. To operate an automatic robot owned by LCAV laboratory, two different operation modes have been successfully implemented, including controlling a robot in real-time or with extra input containing a list of commands. Also, a new visual fiducial system have been developed, and is able to capture the locations of Apriltags inside a room accurately. Multidimensional scaling (MDS) and Squared range least square (SRLS) algorithms containing distance information will also be introduced and the final localization result is within 2% error of tolerance compared with the ground truth results measured by a laser meter.

2017

Chargement

Many real-life optimization problems involve multiple entities, or agents (individuals, companies...), with their own private constraints and preferences, communicating with each other in order to find a solution that maximizes the overall public satisfaction. In Artificial Intelligence, the field of Distributed Constraint Optimization (DCOP) has been addressing such multi-agent optimization problems, through distributed message-passing algorithms such as the DPOP algorithm. However, the research in DCOP has been neglecting the privacy of the information exchanged by the agents during the computing of the solution, which is critical to many real-life problems. While agents are willing to cooperate with each other to produce an optimal solution, they are most often reluctant to reveal their private constraints and preferences to other, which hinders this cooperation. The goal of this project was to implement, test, and evaluate a secured version of DPOP, P-DPOP. P-DPOP provides strong agent privacy and topology privacy by randomization, constraints privacy and limited decision privacy by obfuscation. The algorithm was implemented in Java, as part of the open-source FRODO platform for DCOP.

2010

Chargement

Technology Strategy in Dynamic Environments: A Computational Analysis of the Automation of Routines, the Organization of AI, and the Evaluation of Technology Risk

EPFL2022