Welcome to our biweekly selection of Free and Open machine learning news. Created using our own opinionated selection and summary algorithm. FOSS machine learning is crucial for everyone. Machine Learning is a complex technology. So keep it simple.
1 AI Democratization in the Era of GPT-3
On September 22nd, Microsoft announced that “Microsoft is teaming up with OpenAI to exclusively license GPT-3”. To me, AI democratization means making it possible for everyone to create artificial intelligence systems. For the purposes of this piece, I focus primarily on the “having access to powerful AI models” part of democratization since GPT-3 is such a pre-built AI model. Being able to use the algorithms and models, potentially without requiring advanced mathematical and computing science skills Democratization of AI means more people are able to conduct AI research and/or build AI-driven products and services. Others will still be able to access GPT-3 through the API.
2 Diversity in AI: The Invisible Men and Women
Some have made small gains in gender diversity. Two well-known AI corporate researchers — Facebook’s chief AI scientist, Yann LeCun, and Google’s co-lead of AI ethics, Timnit Gebru — expressed strongly divergent views about how to interpret the tool’s error. Since 2014, when the large tech companies began publishing annual diversity reports, few have made much ground in terms of ethnic diversity. Recognize that differences matter.** In machine learning, it’s not just sufficient to feed diverse data into a learning system. However, what the debate made obvious is that not all AI researchers have embraced concerns about diversity.
3 Launching the European AI Fund
The European AI Fund is hosted by the Network of European Foundations. At the same time, we want to bring in new civil society actors to the debate, especially those who haven’t worked on issues relating to AI yet, but whose domain of work is affected by AI. We’ve collaborated closely over the years with partners like European Digital Rights, Access Now Algorithm Watch and Digital Freedom Fund. Alternatively, we’ve seen what can go wrong when diverse voices like these aren’t part of important conversations: AI systems that discriminate, surveil, radicalize. Right now, we’re in the early stages of the next phase of computing: AI. First we had the desktop.
4 Tracking historical changes in trustworthiness using machine learning
Social trust is linked to a host of positive societal outcomes, including improved economicperformance, lower crime rates and more inclusive institutions. Yet, the origins of trustremain elusive, partly because social trust is difficult to document in time. Building on recentadvances in social cognition, we design an algorithm to automatically generate trustworthi-ness evaluations for the facial action units (smile, eye brows, etc.) of European portraits inlarge historical databases.
5 Adventures in hill climbing with AI
Let’s imagine for a moment that we are working on building a novel AI product. Our product will receive as input an image of a car crash and generate an output decision: whether the car is a total loss or not. One can imagine that this model could be used by insurance companies to make a quick assessment of car insurance claims they receive. They could use our product to quickly classify incoming claims as total losses or otherwise.
6 Collection of GANs Written in Pytorch
The core idea of a GANs is based on the “indirect” training through the discriminator, which itself is also being updated dynamically. GIT URL is: https://github.com/w86763777/pytorch-gan-collections
7 The Agent Web Model — Modelling web hacking for reinforcement learning
Website hacking is a frequent attack type used by malicious actors to obtain confidential information, modify the integrity of web pages or make websites unavailable. The tools used by attackers are becoming more and more automated and sophisticated, and malicious machine learning agents seems to be the next development in this line. In order to provide ethical hackers with similar tools, and to understand the impact and the limitations of artificial agents, we present in this paper a model that formalizes web hacking tasks for reinforcement learning agents.
8 OPFython: A Python-Inspired Optimum-Path Forest Classifier
Machine learning techniques have been paramount throughout the last years, being applied in a wide range of tasks, such as classification, object recognition, person identification, and image segmentation. Nevertheless, conventional classification algorithms, e.g., Logistic Regression, Decision Trees, and Bayesian classifiers, might lack complexity and diversity, not suitable when dealing with real-world data. A recent graph-inspired classifier, known as the Optimum-Path Forest, has proven to be a state-of-the-art technique, comparable to Support Vector Machines and even surpassing it in some tasks. This paper proposes a Python-based Optimum-Path Forest framework, denoted as OPFython, where all of its functions and classes are based upon the original C language implementation. Additionally, as OPFython is a Python-based library, it provides a more friendly environment and a faster prototyping workspace than the C language. Docs on https://opfython.readthedocs.io/en/latest/
9 The Future of Atoms: Artificial Intelligence for Nuclear Applications
The first ever IAEA meeting discussing the use of artificial intelligence (AI) for nuclear applications showcased the ways in which AI-based approaches in nuclear science can benefit human health, water resource management and nuclear fusion research.
The FOSS Machine Learning News Blog is a brief overview of open machine learning news from all over the world. Free and Open machine learning means that everyone must be able to develop, test and play and deploy machine learning solutions. Read and share the FOSS ML Guide! And remember:You are invited to join the Free and Open Machine Learning open collaboration project.