An <a href="https://airhacks.fm">airhacks.fm</a> conversation with Dr. Zoran Sevarac (<a href="https://twitter.com/zsevarac">@zsevarac</a>) about:
<blockquote>Zoran previously on <a href="https://airhacks.fm:">airhacks.fm:</a> <a href="https://airhacks.fm/#episode_169">"#169 Deep Learning with Modern Java Code"</a>,
discussion about the latest updates and features in <a href="https://www.deepnetts.com/">DeepNetts</a>,
a full-stack <a href="https://www.java.com/en/">Java</a> AI platform,
University of Minnesota's drug testing application using DeepNetts,
Jefferson Lab's particle research using DeepNetts Community Edition,
including GPU support for faster inference using <a href="http://javagl.de/jcuda.org/">jcuda</a>,
TensorFlow compatibility, and simplified AI integration with <a href="https://jcp.org/en/jsr/detail?id=381">JSR-381</a>,
real-world applications of DeepNetts in drug testing and particle research,
challenges and considerations for using GPUs in <a href="https://en.wikipedia.org/wiki/Serverless_computing">serverless</a> environments,
the potential of Apple's M-series chips for machine learning,
exploring <a href="https://openjdk.org/projects/babylon/">Project Babylon</a> and <a href="https://openjdk.org/projects/babylon/">Code Reflection</a> in Java,
using <a href="https://openjdk.java.net/projects/panama/">Panama</a> and <a href="https://github.com/openjdk/jextract">jextract</a> for native library bindings,
the importance of having developer tools and an IDE for building AI models,
plans for integrating large language models into DeepNetts,
the advantages of a pure Java solution for AI in enterprise applications,
and the bright future of Java in the AI ecosystem,
Deep Nets 3.1.0 release with GPU support</blockquote>
<p> Dr. Zoran Sevarac on twitter: <a href="https://twitter.com/zsevarac">@zsevarac</a></p>
