Steven Atkinson

@Sdatkinson
19 Followers
76 Following
30 Posts
Statistics, machine learning, physics, engineering, scientific computing, music, woodworking.
Show threadSteven AtkinsonDec 23, 2022
@twiecki @remilouf @sethaxen @larryshamalama Ah, ok. *Much* less surprising then! 😅
Steven AtkinsonDec 23, 2022
Cédric ArchambeauDec 20, 2022
Today, we open sourced Fortuna (https://github.com/awslabs/fortuna) a library for uncertainty quantification.
Deep neural networks are often overconfident and do not know what they don’t know. Quantifying the uncertainty in the predictions they make will help deploy deep learning more responsibly and more safely.
#responsibleAI #ConformalPrediction #BayesianInference #UncertaintyQuantification #deeplearning #opensource
GitHub - awslabs/fortuna: A Library for Uncertainty Quantification.

A Library for Uncertainty Quantification. Contribute to awslabs/fortuna development by creating an account on GitHub.

GitHub
Show threadSteven AtkinsonDec 23, 2022

@twiecki @remilouf @sethaxen @larryshamalama

Hmm, perhaps a typo? Uncertainty seems _highest_ at the decision boundary in the example and _decreases_ as you move away from it.

Show threadSteven AtkinsonDec 22, 2022
@twiecki @remilouf @sethaxen @larryshamalama Thanks! I'll check this out.
Show threadSteven AtkinsonDec 22, 2022
@twiecki @remilouf @sethaxen @larryshamalama under what conditions does this happen? Interested to learn about this if you can point me at papers?
Steven AtkinsonDec 19, 2022
Patrick KidgerDec 19, 2022

Announcing the #ICLR2023 workshop on "Physics for Machine Learning"🔥

Send us: equivariant NNs, Lie algebra approaches, neural ODEs, fluid/molecular/particle/multi-scale physics - etc!

Site: https://physics4ml.github.io

OpenReview: https://openreview.net/group?id=ICLR.cc/2023/Workshop/Physics4ML

Deadline 3rd Feb!⚡️

#machinelearning #deeplearning

1/2

Overview

Physics4ML

ICLR 2023 Workshop on Physics for Machine Learning
Steven AtkinsonDec 5, 2022

I suspect that the distribution of chats people are having in private with #ChatGPT is quite different when they're not making content to share on the internet.

Can anyone point me to #HCI research on what people do when given a chatbot like this?

I'm curious what picture is emerging from the data from the >1M users #OpenAI has so far. Reflecting on my impressions, I have a few guesses.

Steven AtkinsonNov 26, 2022
Dan SinkerNov 26, 2022
*whispers* most people don't care about decentralization and governance models, they just want someplace to hang out that's not a hassle.
Steven AtkinsonNov 26, 2022
Kathy BaxterNov 24, 2022

The Algorithmic Imprint

In this paper, we introduce the notion of the "algorithmic imprint" to illustrate how merely removing an algorithm does not necessarily undo or mitigate its consequences.

@upol @Riedl @jakemetcalf

#responsibleAI
https://arxiv.org/abs/2206.03275v1

The Algorithmic Imprint

When algorithmic harms emerge, a reasonable response is to stop using the algorithm to resolve concerns related to fairness, accountability, transparency, and ethics (FATE). However, just because an algorithm is removed does not imply its FATE-related issues cease to exist. In this paper, we introduce the notion of the "algorithmic imprint" to illustrate how merely removing an algorithm does not necessarily undo or mitigate its consequences. We operationalize this concept and its implications through the 2020 events surrounding the algorithmic grading of the General Certificate of Education (GCE) Advanced (A) Level exams, an internationally recognized UK-based high school diploma exam administered in over 160 countries. While the algorithmic standardization was ultimately removed due to global protests, we show how the removal failed to undo the algorithmic imprint on the sociotechnical infrastructures that shape students', teachers', and parents' lives. These events provide a rare chance to analyze the state of the world both with and without algorithmic mediation. We situate our case study in Bangladesh to illustrate how algorithms made in the Global North disproportionately impact stakeholders in the Global South. Chronicling more than a year-long community engagement consisting of 47 inter-views, we present the first coherent timeline of "what" happened in Bangladesh, contextualizing "why" and "how" they happened through the lenses of the algorithmic imprint and situated algorithmic fairness. Analyzing these events, we highlight how the contours of the algorithmic imprints can be inferred at the infrastructural, social, and individual levels. We share conceptual and practical implications around how imprint-awareness can (a) broaden the boundaries of how we think about algorithmic impact, (b) inform how we design algorithms, and (c) guide us in AI governance.

arXiv.org
Steven AtkinsonNov 21, 2022
Molly WhiteNov 21, 2022
lorna shore goes so hard, damn