The Mila AI Policy Fellowship translates deep AI expertise into rigorous, public-interest policy. Read the newest publication Bridging the Expertise Gap: Knowledge Transfer Mechanisms for AI Regulation by Moritz von Knebel
This program supports AI startups at any time of the year. Benefit from cutting-edge resources and tailored support to accelerate your technology's development.
We use cookies to analyze the browsing and usage of our website and to personalize your experience. You can disable these technologies at any time, but this may limit certain functionalities of the site. Read our Privacy Policy for more information.
Setting cookies
You can enable and disable the types of cookies you wish to accept. However certain choices you make could affect the services offered on our sites (e.g. suggestions, personalised ads, etc.).
Essential cookies
These cookies are necessary for the operation of the site and cannot be deactivated. (Still active)
Analytics cookies
Do you accept the use of cookies to measure the audience of our sites?
Multimedia Player
Do you accept the use of cookies to display and allow you to watch the video content hosted by our partners (YouTube, etc.)?
Publications
Socially Assistive Robots for patients with Alzheimer's Disease: A scoping review.
Conscious states (states that there is something it is like to be in) seem both rich or full of detail, and ineffable or hard to fully descr… (see more)ibe or recall. The problem of ineffability, in particular, is a longstanding issue in philosophy that partly motivates the explanatory gap: the belief that consciousness cannot be reduced to underlying physical processes. Here, we provide an information theoretic dynamical systems perspective on the richness and ineffability of consciousness. In our framework, the richness of conscious experience corresponds to the amount of information in a conscious state and ineffability corresponds to the amount of information lost at different stages of processing. We describe how attractor dynamics in working memory would induce impoverished recollections of our original experiences, how the discrete symbolic nature of language is insufficient for describing the rich and high-dimensional structure of experiences, and how similarity in the cognitive function of two individuals relates to improved communicability of their experiences to each other. While our model may not settle all questions relating to the explanatory gap, it makes progress toward a fully physicalist explanation of the richness and ineffability of conscious experience: two important aspects that seem to be part of what makes qualitative character so puzzling.
Substitution of dietary monounsaturated fatty acids from olive oil for saturated fatty acids from lard increases low-density lipoprotein apolipoprotein B-100 fractional catabolic rate in subjects with dyslipidemia associated with insulin resistance: a randomized controlled trial
Louis-Charles Desjardins
Francis Brière
André J Tremblay
Maryka Rancourt-Bouchard
Jean-Philippe Drouin-Chartier
J. Corbeil
Valéry Lemelin
Amélie Charest
Ernst J Schaefer
Benoit Lamarche
Patrick Couture
2024-02-29
American Journal of Clinical Nutrition (published)
Substitution of dietary monounsaturated fatty acids from olive oil for saturated fatty acids from lard increases LDL apolipoprotein B-100 fractional catabolic rate in subjects with dyslipidemia associated with insulin resistance: a randomized controlled trial.
Louis-Charles Desjardins
Francis Brière
André J Tremblay
Maryka Rancourt-Bouchard
Jean-Philippe Drouin-Chartier
J. Corbeil
Valéry Lemelin
Amélie Charest
Ernst J Schaefer
Benoit Lamarche
Patrick Couture
2024-02-29
American Journal of Clinical Nutrition (published)
The « jingle-jangle fallacy » of empathy: Delineating affective, cognitive and motor components of empathy from behavioral synchrony using a virtual agent
The paper focuses on the role of the World Health Organization (WHO) in promoting a healthy world population as a generative and robust idea… (see more) within health policy. The WHO’s health credo transcends national boundaries to promote health globally. It is embedded in norms, values, and standards promulgated by the organization and contributes in shaping the health responses of national governments. Ideational robustness refers to the ability of the WHO to adapt its health credo to changing contexts and circumstances, thus promoting the legitimacy of an international health order. Disturbances, including the Covid-19 pandemic, test the credo’s robustness, forcing the WHO to constantly work at reframing ideas to adapt to political forces and competing logics that structure the field of international health. Empirically, the paper is based on an historical analysis of the evolution of the health credo of the WHO since its inception. Qualitative content analysis of secondary sources, such as policy documents, explores how ideational work performed by WHO leaders impacts on the organization’s position and legitimacy. Ideational robustness appears to be largely influenced by leadership vision, preexisting organizational structure, and the political economy of international health. Ideational robustness appears as a powerful yet insufficient ingredient of policy success.
Assessing the quality of summarizers poses significant challenges. In response, we propose a novel task-oriented evaluation approach that as… (see more)sesses summarizers based on their capacity to produce summaries that are useful for downstream tasks, while preserving task outcomes. We theoretically establish a direct relationship between the resulting error probability of these tasks and the mutual information between source texts and generated summaries. We introduce
The BigCode project, an open-scientific collaboration focused on the responsible development of Large Language Models for Code (Code LLMs), … (see more)introduces StarCoder2. In partnership with Software Heritage (SWH), we build The Stack v2 on top of the digital commons of their source code archive. Alongside the SWH repositories spanning 619 programming languages, we carefully select other high-quality data sources, such as GitHub pull requests, Kaggle notebooks, and code documentation. This results in a training set that is 4x larger than the first StarCoder dataset. We train StarCoder2 models with 3B, 7B, and 15B parameters on 3.3 to 4.3 trillion tokens and thoroughly evaluate them on a comprehensive set of Code LLM benchmarks. We find that our small model, StarCoder2-3B, outperforms other Code LLMs of similar size on most benchmarks, and also outperforms StarCoderBase-15B. Our large model, StarCoder2- 15B, significantly outperforms other models of comparable size. In addition, it matches or outperforms CodeLlama-34B, a model more than twice its size. Although DeepSeekCoder- 33B is the best-performing model at code completion for high-resource languages, we find that StarCoder2-15B outperforms it on math and code reasoning benchmarks, as well as several low-resource languages. We make the model weights available under an OpenRAIL license and ensure full transparency regarding the training data by releasing the SoftWare Heritage persistent IDentifiers (SWHIDs) of the source code data.