The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. Let the OSS Enterprise newsletter guide your open source journey! In June, OpenAI teamed up with GitHub to launch Copilot, a service that provides suggestions for whole lines of code inside development environments like Microsoft Visual Studio. Powered by an AI model called Codex -- which OpenAI later exposed through an API -- Copilot can translate natural language into code across more than a dozen programming languages, interpreting commands in plain English and executing them. Now, a community effort is underway to create an open source, freely available alternative to Copilot and OpenAI's Codex model.

As machine learning (ML) becomes more effective and widespread it is becoming more prevalent in systems with real-life impact, from loan recommendations to job application decisions. With the growing usage comes the risk of bias – biased training data could lead to biased ML algorithms, which in turn could perpetuate discrimination and bias in society. In a new paper from Google, researchers propose a novel technique to train machine learning algorithms fairly even with a biased dataset. At the heart of the technique is the idea that a biased dataset can be perceived as an unbiased dataset which has gone through manipulation by a biased agent. Using this framework, the biased dataset is re-weighted to fit the (theoretical) unbiased dataset, and only then fed into a machine learning algorithm as training data.

What can stage magic reveal about cognitive biases? Quite a lot, as it turns out. Researchers at the Institute of Neuroscience in Spain, Teatro Encantado in Madrid, and University Pompeu Fabra in Barcelona sought to apply AI and machine learning to quantify a professional magician's skills in "naturalistic conditions." They say that their trained system -- which was designed to follow coins as a magician made them appear and disappear -- not only served as a tracking tool but as an "artificial spectator" that could infer their location, paving the way for experiments in a subfield they describe as "artificial illusionism." "Magic is not the violation of the natural order of things, but the command of cognitive processes," wrote the researchers.

As machine learning (ML) becomes more effective and widespread it is becoming more prevalent in systems with real-life impact, from loan recommendations to job application decisions. With the growing usage comes the risk of bias -- biased training data could lead to biased ML algorithms, which in turn could perpetuate discrimination and bias in society. In a new paper from Google, researchers propose a novel technique to train machine learning algorithms fairly even with a biased dataset. At the heart of the technique is the idea that a biased dataset can be perceived as an unbiased dataset which has gone through manipulation by a biased agent. Using this framework, the biased dataset is re-weighted to fit the (theoretical) unbiased dataset, and only then fed into a machine learning algorithm as training data.