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For some time now, artificial intelligence that allows an image to be generated from a text input, has been more or less freely available. Well-known examples are OpenAI's DALL-E and Google's Imagen. Not too long ago, Stability.ai's DreamStudio.ai was released, which, unlike the other AIs, is completely open source.

For some time now, artificial intelligence that allows an image to be generated from a text input, has been more or less freely available. Well-known examples are OpenAI's DALL-E and Google's Imagen. Not too long ago, Stability.ai's DreamStudio.ai was released, which, unlike the other AIs, is completely open source.

This article describes use cases and tools that AI healthcare companies and research teams built to facilitate vaccine design, speed up trials, predict mutations, prioritize patients, and address vaccine hesitancy.

Java continuously introduces new, useful features. For instance, Java 8 introduced the Stream API, one of the biggest highlights of the past few years. But is aggregating data with the Stream API a panacea? In this article, I’d like to explore if there’s a better alternative for certain cases from a complexity perspective.

The decision to move Elasticsearch to proprietary licensing awakened a sleeping giant. The open source community rapidly flexed its muscle to ensure a true open source option for fast and scalable search and analytics—which many users depend on for ML projects—would continue to be available. The result is OpenSearch, a community-driven hard fork of Elasticsearch 7.10.2, built with Apache Lucene and available under the fully open source Apache 2.0 license.

Modularization is frequently discussed, but after some time, the speakers realize that they don’t mean the same thing. Over the last fifty years, computer science has given us a number of good explanations about what modularization is all about—but is that really enough to come to the same conclusions and arguments?

Your machine learning model is trained and finally running in production. But that was the easy part. Now, the real challenge is reliably running your machine learning system in production. For this, monitoring systems are essential. But while monitoring machine learning models, you must consider some challenges that go beyond traditional DevOps metrics.

With over 18 million monthly users, VS Code has become one of the most popular and fastest-growing text editors in the world. To learn more about why over 3.7 million of them find VS Code to be the perfect habitat for Python development and data science work, keep on reading!.

According to research from last year, 2020 had an impact on business globally, but NLP was a bright spot for technology investments. That momentum has carried through to this year, but even still, we are just at the tip of the iceberg in terms of what NLP has to offer.