AI Alignment

At least since the arrival of ChatGPT, many people have become fearful that we are losing control over technology and that we can no longer anticipate the consequences they may have. AI Alignment deals with this problem and the technical approaches to solve it.

Google Bard: The Answer to ChatGPT?

With the release of the AI ChatGPT at the end of November 2022, OpenAI made big waves that don’t seem to be dying down. For a long time, not just in the tech bubble, people waited for the giant Google to answer. Now here it is: Google introduced its conversational AI, Bard. We take a look at the announcement, the technology, and speculate a bit about Google’s apparent hesitation.

Three Key Considerations When Implementing AI

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.

Scalable Programming

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.

Take Control of ML Projects

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.

Why are we doing this anyway?

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?

Keeping an Eye on AI

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.

Behind the Tracks