heise+ | Transparenz in der KI: Wie Entscheidungen verständlich werden
Ein neues Verfahren entlockt leistungsfähigen KIs ihre komplexen Kriterien zur Bildeinordnung. Damit werden deren Entscheidungen viel besser nachvollziehbar.
People who work in AI and libraries/archives/museums, we need your help! 👋🏻
A few of us maintain an "awesome-ai4lam" 🕶️ list at https://github.com/AI4LAM/awesome-ai4lam and we need your help finding more things to add. Please tell us what we missed!
You can just reply to this toot, or open an issue/ticket in the GitHub repo, or email me, or whatever is easiest for you.
Wow, TIL that #eigen, the powerful #cpp library for all sorts of numerical computing started as a #KDE project to support visualization. Eigen is now a building block in various #machinelearning efforts.
The magic of #opensource development is that pieces evolve, recombine and are re-used in novel contexts, almost like a biological process.
"In Florida, where it is against the law to transport undocumented immigrants, students mention that their peers with undocumented family members are at risk if they have relatives in the car with them on campus. The automated license plate reader cameras can capture not only the license plate itself but the entire car and who is in it."
Fake Intelligence is where we try to simulate intelligence by feeding huge amounts of dubious information to algorithms we don’t fully understand to create approximations of human behaviour where the safeguards that moderate the real thing provided by family, community, culture, personal responsibility, reputation, and ethics are replaced by norms that satisfy the profit motive of corporate entities.
> “Apple joins AI fray with release of model framework”—The Verge
Given that four years ago I managed to write a 500-page book on all the tools Apple had made and all the things you could do with AI on Apple Platforms (and had to cut content to even fit that), these headlines about MLX feel a bit unkind to all the efforts made up until now… 🤔
Whoever would have predicted that the AI tech to predict/detect problems would itself become a problem?
Some #AI image detecting tools are labeling real #photographs from the #Israel-#Hamas war as fake, creating what an expert calls a "second level of disinformation"
Please DM me if you have a formal computer science background (AI and machine learning but other areas desired, too) and would be interested in volunteering for an open source journal currently seeking new editors. Thanks.
He talks about how he takes modern functional programing techniques from all walks, so not just monads, but reproducible builds (e.g. Nix, although Nix is not yet used), and building these very complex data processing pipelines. He talks about how at Cambridge he has to often sit down with scientists to discuss with them how they gather and process data and produce visualizations.
He then takes the code they have written, often in languages like R and Python, and translates the stateless, functional essence of it into OCaml, and then takes the references to the datasets (often hard-coded URLs) and turns them into proper data sources. The OCaml is annotated with symbols that allow for automatic generation of GUIs.
The data sources are incredibly diverse. Many of them come from scientific experiments that have been ongoing for decades, many of the sources come from multiple generations of measuring devices, where older devices give lower-accuracy information and newer sources give higher accuracy. He also talks about the importance of security for some data sources, e.g. the location of critically endangered animals that would almost certainly be poached if photographs of these animals leaked to the public, what with how easy it is to localize nowadays.
He also inspires computer scientists to use their talents to start talking with activists, and possibly even policy makers, directly to learn what their needs are and see how you can apply yout own skills.
WIlliam Byrd was in the audience and during the Q&A session informed the audience of a workshop related to this kind of intersection of technology and activism at the DECLMed workshop ("Declarative Programming for Biology and Medicine") colocated with ICFP2023, so please check that out as well.
🚀 ESA Internship Alert (2)! 🌠 Exciting opportunities in data science and machine learning at ESAC for 2024.
🤖 Contribute to shaping the future of science operations through NLP, create ML-ready datasets for ESA Sky or craft Jupyter notebooks for the ESA Mars missions on the ESA Datalabs.
I am looking for a new position in computational biology/bioinformatics preferably in #academia in the #USA.
I am an experienced Computational Biologist with eight years of post-PhD experience in applying computational methods across diverse domains, from molecular modeling to #highdimensional and #machinelearning based single-cell flow/mass cytometry and serum proteomics data analysis.
Dimenticavo di segnalare che è aperto un bando (scade il 20 novembre, quindi spargete la voce) per un assegno di #ricerca di tipo “Grant” sul tema «Sviluppo e applicazione di tecniche di #machineLearning per l’analisi
integrata di DataCube ottenuti da immagini satellitari» all'interno del progetto PIANETA DINAMICO — SAFARI. Aiutateci a costruire un algoritmo per identificare lo stato di un vulcano con tecniche di monitoraggio satellitare
Harvard's metaLab has launched https://aipedagogy.org, a resource chock full of tasty assignments by trailblazers of generative AI in the classroom. (My own "AI Sandwich" is also on the menu.)
I've already stolen Juliana Castro's "Illustrate a Hoax" for my own class!