Publications associées à Collective Memory Through Computer Memories: Retracing and Interpreting the Archive of the Stanford Artificial Intelligence Laboratory

Publications associées (21)

Collaborative AI Agents in the Era of Large Language Models

Developing agents that can reliably act on our behalf is central to artificial intelligence (AI). These agents must seamlessly interact with tools, like search engines and databases, and collaborate. In this thesis, we study the abstractions, methods, and ...

EPFL2025

Unlocking high-performance photocapacitors for edge computing in low-light environments

Michael Graetzel, Sandy Sanchez Alonso

Driving continuous, low-power artificial intelligence (AI) in the Internet of Things (IoT) requires reliable energy harvesting and storage under indoor or low-light conditions, where batteries face constraints such as finite lifetimes and increased environ ...

2025

Towards more Practical Threat Models in Artificial Intelligence Security

Alexandre Massoud Alahi, Kathrin Grosse

Recent works have identified a gap between research and practice in artificial intelligence security: threats studied in academia do not always reflect the practical use and security risks of AI. For example, while models are often studied in isolation, th ...

Usenix Association2024

AI-Generated Inventions: Implications for the Patent System

Gaétan Jean A de Rassenfosse

This symposium Article discusses issues raised for patent processes and policy created by inventions generated by artificial intelligence (“AI”). The Article begins by examining the normative desirability of allowing patents on AI-generated inventions. Whi ...

2024

Data-driven models in fusion exhaust: AI methods and perspectives

Yoeri Renier Johannes Poels

A review is given on the highlights of a scatter-shot approach of developing machine-learning methods and artificial neural networks based fast predictors for the application to fusion exhaust. The aim is to enable and facilitate optimized and improved mod ...

IOP Publishing Ltd2024

Trustworthy and Efficient LLMs Meet Databases

Kyoung-Min Kim, Anastasia Ailamaki

In the rapidly evolving AI era with large language models (LLMs) at the core, making LLMs more trustworthy and efficient, especially in output generation (inference), has gained significant attention. This is to reduce plausible but faulty LLM outputs (a.k ...

2024

A journey toward generalizable trajectory forecasting models

Mohammadhossein Bahari

Autonomous driving is a revolutionary technology that has seen considerable advancements through the adoption of deep learning solutions. One of the major challenges in this field is the interaction with other road users. This interaction necessitates a "t ...

EPFL2024

Hyperdimensional computing with holographic and adaptive encoder

Alejandro Hernández Cano

Introduction: Brain-inspired computing has become an emerging field, where a growing number of works focus on developing algorithms that bring machine learning closer to human brains at the functional level. As one of the promising directions, Hyperdimensi ...

2024

Live-cell imaging powered by computation

Suliana Manley

The proliferation of microscopy methods for live-cell imaging offers many new possibilities for users but can also be challenging to navigate. The prevailing challenge in live-cell fluorescence microscopy is capturing intra-cellular dynamics while preservi ...

Nature Portfolio2024

A Conceptual Framework for Integrating Conversational Agents in Digital Education

Juan Carlos Farah

The presence of conversational agents (or chatbots) in educational contexts has been steadily increasing over the past few years. Recent surveys have shown widespread interest in the use of chatbots in education, both for research and practice. Although th ...

EPFL2023

Towards Robust Monitoring of the Laser Powder Bed Fusion Process based on Acoustic Emission combined with Machine Learning Solutions

Rita Drissi Daoudi

Laser Powder Bed Fusion (LPBF) is an Additive Manufacturing (AM) process consolidating parts layer by layer, from a metallic powder bed. It allows no limitation in terms of geometry and is therefore of particular interest to various industries. Metallic LP ...

EPFL2023

Bit-Line Computing for CNN Accelerators Co-Design in Edge AI Inference

David Atienza Alonso, Giovanni Ansaloni, Alexandre Sébastien Julien Levisse, Flavio Ponzina, Marco Antonio Rios

By supporting the access of multiple memory words at the same time, Bit-line Computing (BC) architectures allow the parallel execution of bit-wise operations in-memory. At the array periphery, arithmetic operations are then derived with little additional o ...

2023

Monotonicity Reasoning in the Age of Neural Foundation Models

Zeming Chen

The recent advance of large language models (LLMs) demonstrates that these large-scale foundation models achieve remarkable capabilities across a wide range of language tasks and domains. The success of the statistical learning approach challenges our unde ...

Springer2023

Supporting Therapists’ Assessment in Parent-Mediated Training through Autonomous Data Collection

Daniel Carnieto Tozadore

Parental Training is a methodology where therapists teach caregivers how to train their kids in specific behaviors practicing. It can be used in verbal behavior acquisition for children with autism mediated by the parents. In this paper, we are presenting ...

2022

From Human-Designed Convolutional Neural Networks Towards Robust Neural Architecture Search

Kaicheng Yu

Artificial intelligence has been an ultimate design goal since the inception of computers decades ago. Among the many attempts towards general artificial intelligence, modern machine learning successfully tackles many complex problems thanks to the progres ...

EPFL2021

Parallel convolutional processing using an integrated photonic tensor core

Xin Fu, Tobias Kippenberg, Junqiu Liu, Maxim Karpov, Arslan Sajid Raja, Anton Lukashchuk

With the proliferation of ultrahigh-speed mobile networks and internet-connected devices, along with the rise of artificial intelligence (AI)(1), the world is generating exponentially increasing amounts of data that need to be processed in a fast and effic ...

NATURE RESEARCH2021

Self-Supervised Prototypical Transfer Learning for Few-Shot Classification

Matthias Grossglauser, Arnout Jan J Devos

Recent advances in transfer learning and few-shot learning largely rely on annotated data related to the goal task during (pre-)training. However, collecting sufficiently similar and annotated data is often infeasible. Building on advances in self-supervis ...

2020

Latest advances in aging research and drug discovery

Andrea Ablasser

An increasing aging population poses a significant challenge to societies worldwide. A better understanding of the molecular, cellular, organ, tissue, physiological, psychological, and even sociological changes that occur with aging is needed in order to t ...

IMPACT JOURNALS LLC2019

Artificial Intelligence in Musculoskeletal Imaging: Review of Current Literature, Challenges, and Trends

Tobias Kober, Jonas Richiardi

Artificial intelligence (AI) has gained major attention with a rapid increase in the number of published articles, mostly recently. This review provides a general understanding of how AI can or will be useful to the musculoskeletal radiologist. After a bri ...

THIEME MEDICAL PUBL INC2019

CP-CHARM: Segmentation-Free Image Classification Made Accessible

Virginie Sophie Uhlmann

Background: Automated classification using machine learning often relies on features derived from segmenting individual objects, which can be difficult to automate. WND-CHARM is a previously developed classification algorithm in which features are computed ...

2016