2-Min AI Newsletter #8

This edition of the 2 Minute AI Newsletter is all about research related to artificial intelligence (AI), machine learning and deep neural networks. We'll be sharing some of the latest and most interesting research news, so you can stay up-to-date on the latest advances in these exciting fields. As always, we'll be keeping it short and sweet, so you can get back to your busy day. Thanks for reading!

👨🏽 💻 Latest AI/ML Research Highlights 

✅ Check out  this open-source platform that enables research on privacy-preserving machine learning

• FedScale, a federated learning (FL) benchmarking suite with realistic datasets and a scalable runtime to enable reproducible FL research.
• FedScale datasets encompass a wide range of critical FL tasks, ranging from image classification and object detection to language modeling and speech recognition
• FedScale is open-sourced

✅  Google Researchers Open-Source the TensorFlow GNN (TF-GNN): A Scalable Python Library for Graph Neural Networks in TensorFlow

• TensorFlow GNN (TF-GNN) is a scalable library for Graph Neural Networks in TensorFlow
• This Python library enables GNN training and inference on graph-structured data by utilizing heterogeneous relational data to build GNN models.
• A group of scientists from Google Core ML, Google Research, and DeepMind open-sourced this TF-GNN library in their most recent article.

✅ Meta AI introduces an exploratory AI research concept called Make-A-Scene that demonstrates AI’s potential for empowering anyone to bring their imagination to life

• This multimodal generative AI method puts creative control in the hands of people who use it by allowing them to describe and illustrate their vision through both text descriptions and freeform sketches.
• Make-A-Scene uses a novel intermediate representation that captures the scene layout to enable nuanced sketches as input. It can also generate its own scene layout with text-only prompts if that’s what the creator chooses. 

✅  First-of-its-kind study shows algorithm can forecast the likelihood of firearm suicide using handgun purchasing data

• In 2020, almost 48,000 Americans died by suicide, of which more than 24,000 were firearm suicides. Firearms are by far the most lethal method of suicide. Access to firearms has been identified as a major risk factor for suicide and is a potential focus for suicide prevention.
• A new study from the Violence Prevention Research Program (VPRP) at UC Davis suggests machine learning, a type of artificial intelligence, may help identify handgun purchasers who are at high risk of suicide.
• It also identified individual and community characteristics that are predictive of firearm suicide. The study was published in JAMA Network Open.

🚀 Startup Featured For This Issue

✅  Meet ‘Tecton’, a California-Based Startup Providing an Enterprise-Ready Feature Store to Make Machine Learning Accessible to Every Company

• California-based Tecton is attempting to address this issue (need to reduce sources of bias) with an enterprise-ready feature platform that can deploy machine learning applications to production in minutes instead of months. 
• With the ability to construct and automate feature pipelines that generate feature values from batch, streaming, or real-time data, the company provides a comprehensive cloud-based feature platform for ML.
• This platform combines the feature store features of storing, sharing, and reuse.