Java and AI? - Inside Java Newscast #72

Summary

The video script challenges the notion that AI in Java is inferior, arguing that Java is well-positioned for AI's future with ongoing projects like Valhalla, Panama, and Babylon. It categorizes AI development into model development, model execution, and AI as a feature in products. Java's strengths in software development, coupled with emerging features, make it a formidable contender for AI integration and execution, despite not leading in AI-centric product development or model creation.

Takeaways

• 😀 The speaker initially accepted the common opinion that AI in Java is inferior without much knowledge of the subject.
• 🤔 The speaker, being a Java enthusiast, was annoyed by the negative view of AI in Java and anticipated improvements with upcoming Java projects like Valhalla, Panama, and Babylon.
• 🔮 The video aims to challenge the myopic view that AI in Java is currently inadequate, suggesting that Java is well-positioned for the future of AI.
• 📈 AI development is categorized into three areas: developing a machine learning model, executing a model, and integrating AI as a feature or product.
• 🛠️ Java is already strong in many development aspects, making it a suitable candidate for projects that include AI-based features.
• 📚 Java offers several libraries and runtime options for executing machine learning models, such as TornadoVM, ONNX Runtime, DJL, Tribuo, and LangChain4j.
• 🚀 Upcoming Java projects like Valhalla, Panama, and Babylon are expected to enhance Java's capabilities in AI, especially in executing models with improved performance.
• 💡 Valhalla's value types and limited operator overloading, Panama's Foreign Function and Memory (FFM) API and vector APIs, and Babylon's code reflection are highlighted as potential game-changers.
• 🤖 The speaker argues that Java's ecosystem for executing ML models is already robust and will only get stronger with the integration of native code and pure Java implementations.
• 🏆 While Java may not be the best for developing machine learning models currently, its strengths in other areas make it competitive for AI integration in existing projects.
• 🔄 The future of AI development may lean towards integration into other applications rather than AI-centric products, positioning Java favorably for this trend.

Q & A

What is the common misconception about AI in Java that the speaker initially accepted?

The common misconception is that AI in Java is bad, a view that the speaker grudgingly accepted without initially knowing much about the subject.

What upcoming Java projects does the speaker believe will significantly improve AI capabilities in Java?

The speaker mentions Valhalla, Panama, and Babylon as upcoming Java projects that are expected to make substantial progress in enhancing AI capabilities in Java.

How does the speaker categorize AI development?

The speaker categorizes AI development into three areas: developing a machine learning model, executing a machine learning model, and integrating a machine learning model as a feature into larger, often pre-existing products.

Why might Java be a preferable choice for integrating AI as a feature into an existing project?

Java might be preferable because it avoids the complexity of creating a new service in a different language and incorporating it via a REST API or as foreign code. Java's strong ecosystem for other development aspects makes it a strong candidate for integrating AI features.

What are some of the Java libraries and runtimes mentioned for executing machine learning models?

Some of the Java libraries and runtimes mentioned are TornadoVM, ONNX Runtime, DJL, Tribuo, and LangChain4j.

What is Project Valhalla aiming to achieve in the context of AI and Java?

Project Valhalla aims to provide the capability to define types that 'code like a class, work like an int', which is relevant for AI as it could allow the use of primitives like half-floats and enable writing performant code without sacrificing good design and maintainability.

What is the significance of Panama's vector API for AI in Java?

Panama's vector API can dramatically speed up CPU-based computations, which is beneficial for executing machine learning models efficiently in Java.

What is the primary goal of Project Babylon in relation to AI and Java?

Project Babylon's goal is to allow Java code to parse other Java code and derive new code that can be executed by a GPU, which is directly aimed at improving AI capabilities in Java by enabling GPU-accelerated computation.

Why does the speaker believe that Java may not need to be the best for just running an ML model?

The speaker believes that Java may not need to be the best for just running an ML model because the larger portion of AI-related development work will be its integration into other projects, where Java is already competitive and will become stronger with the advancements in Java projects like Valhalla, Panama, and Babylon.

What challenges does Java face in the area of developing machine learning models?

Java faces challenges such as the need for a type system that can easily handle heterogeneous data, operator overloading, ease of use with mathematical functions, libraries designed for large data set analysis, and good visualization tools. Additionally, Java's explicit static typing and checked exceptions can be seen as a downside for those who value simplicity over robustness in the early stages of model development.

What is the speaker's view on the future of AI in Java, considering the ongoing and upcoming Java projects?

The speaker is optimistic about the future of AI in Java, stating that with the advancements in projects like Valhalla, Panama, and Babylon, Java will not only become more competitive but also strengthen its position in the coming years, especially for integrating AI as a feature into other applications.