Personalized AI systems, from recommendation systems to chatbots, are a prevalent method for distributing content to users based on their learned preferences. However, there is growing concern about the adverse effects of these systems, including their potential tendency to expose users to sensitive or harmful material, negatively impacting overall well-being. To address this concern quantitatively, it is necessary to create datasets with relevant sensitivity labels for content, enabling researchers to evaluate personalized systems beyond mere engagement metrics. To this end, we introduce two novel datasets that include a taxonomy of sensitivity labels alongside user-content ratings: one that integrates MovieLens rating data with content warnings from the Does the Dog Die? community ratings website, and another that combines fan-fiction interaction data and user-generated warnings from Archive of Our Own.

Cultural evolution is driven by how we choose what to consume and share with others. A common belief is that the cultural artifacts that succeed are ones that balance novelty and conventionality. This balance theory suggests that people prefer works that are familiar, but not so familiar as to be boring; novel, but not so novel as to violate the expectations of their genre. We test this idea using a large dataset of fanfiction. We apply a multiple regression model and a generalized additive model to examine how the recognition a work receives varies with its novelty, estimated through a Latent Dirichlet Allocation topic model, in the context of existing works. We find the opposite pattern of what the balance theory predicts$\unicode{x2014}$overall success decline almost monotonically with novelty and exhibits a U-shaped, instead of an inverse U-shaped, curve. This puzzle is resolved by teasing out two competing forces: sameness attracts the mass whereas novelty provides enjoyment. Taken together, even though the balance theory holds in terms of expressed enjoyment, the overall success can show the opposite pattern due to the dominant role of sameness to attract the audience. Under these two forces, cultural evolution may have to work against inertia$\unicode{x2014}$the appetite for consuming the familiar$\unicode{x2014}$and may resemble a punctuated equilibrium, marked by occasional leaps.

Does your PC really need to search for aliens? How about pitching in your resources to help make AI art, instead? A new community effort, Stable Horde, allows you to donate your PC's extra GPU cycles to create AI art and use your donated time to create AI art in just a fraction of the time instead. Stable Horde is a grass-roots effort where you can donate your PC's idle time to help others create fabulous AI art -- or you can use the "horde" of PCs to create your own AI art, too. Stable Horde is similar to both SETI@Home (which went into "hibernation" in 2020) or Folding@Home.

I think this a step in the right direction. We may not be perfect and make all the right decisions all the time but it does some there's enough people with their head screwed on right. This is a crucial area for R&D. I am more excited about the computing part than the AI part. You need computing clusters to run FEM codes and FDC sims and I suppose PAF has realized this (it was about time they did). That being said many interesting applications for AI, but not in the way most people think (read autonomous fighter jets).

Michelangelo can deploy multiple models in the same serving container, which allows for safe transitions from old to new model versions and side-by-side A/B testing of models. The original incarnation of Michelangelo did not support deep learning's need to train on GPUs, but that the team addressed that omission in the meantime. The current platform uses Spark's ML pipeline serialization but with an additional interface for online serving that adds a single-example (online) scoring method that is both lightweight and capable of handling tight SLAs, for instance, for fraud detection and prevention. It does so by bypassing the overhead of Spark SQL's Catalyst optimizer. Noteworthy is that both Google and Uber built in-house protocol buffer parsers and representations for serving, avoiding bottlenecks present in the default implementation. Airbnb established their own ML infrastructure team in 2016/2017 for similar reasons. First, they only had a few models in production, but building each model could take up to three months. Second, there was no consistency among models. And third, there were large differences between online and offline predictions.

D2iQ has added a curated distribution of Kubeflow, open source software that makes it easier to deploy workflows that incorporate machine learning algorithms on a Kubernetes cluster, as an extension to its existing portfolio of automation tools. Jie Yu, chief architect for D2iQ, says KUDO for Kubeflow will make it easier for IT teams to deploy workloads that include frameworks such as Spark and Horovod on Kubernetes clusters. At the core of KUDO for Kubeflow is Kommander, a role-based tool that provides centralized management, governance and visibility into disparate Kubernetes regardless of where they are running. IT organizations that are building and deploying artificial intelligence (AI) applications based on machine learning algorithms have embraced containers to simplify building and managing all the elements of what otherwise would be a massive monolithic application that would be too unwieldy to build, update and deploy. Kubernetes, meanwhile, has become the de facto default standard for orchestrating containers.

D2iQ has added a curated distribution of Kubeflow, open source software that makes it easier to deploy workflows that incorporate machine learning algorithms on a Kubernetes cluster, as an extension to its existing portfolio of automation tools. Jie Yu, chief architect for D2iQ, says KUDO for Kubeflow will make it easier for IT teams to deploy workloads that include frameworks such as Spark and Horovod on Kubernetes clusters. At the core of KUDO for Kubeflow is Kommander, a role-based tool that provides centralized management, governance and visibility into disparate Kubernetes regardless of where they are running. IT organizations that are building and deploying artificial intelligence (AI) applications based on machine learning algorithms have embraced containers to simplify building and managing all the elements of what otherwise would be a massive monolithic application that would be too unwieldy to build, update and deploy. Kubernetes, meanwhile, has become the de facto default standard for orchestrating containers.

AI Daily Roundup starts today! We are covering the top updates from around the world. The updates will feature state-of-the-art capabilities in artificial intelligence, Machine Learning, Robotic Process Automation, Fintech and human-system interactions. We will cover the role of AI Daily Roundup and their application in various industries and daily lives. In Bangalore, India, 10th grader Rahul Jaikrishna developed Cyber Detective – an artificial intelligence-based model that detects cyber bullying with an accuracy of up to 80%.

I was honoured to be part of a team of International experts who recently published UNESCO report on exploring the use of Artificial Intelligence to support teachers and teacher development: Discussion report from the International Task Force on Teachers for Education 2030's Mobile Learning Week 2019 Strategy Lab. In an effort to better understand the potential role of Artificial Intelligence (AI) in education, UNESCO Mobile Learning Week 2019 was convened to focus on the role of AI and sustainable development. As a part of this flagship event, a diverse gathering of over 40 international experts in teacher education participated in a strategy lab to explore the potential use of AI to support teachers and teacher development. Please download our report through the link below.