Introduction to AI in Java

Software developers at all levels of experience have embraced AI as part of their workflows. AI-related tools and techniques can significantly enhance our ability to develop high-performing Java applications. There are two aspects for us to consider. First, we can use AI tools to help us develop, test, and enhance our code. Secondly, we can incorporate AI into our applications to introduce innovation and enhance functionality.

In this section, we will look at AI’s relevance to high-performing Java applications, current trends in AI for Java, and future directions for AI.

AI’s relevance to high-performance Java applications

The power of AI technologies is undeniable. With ML, a subset of AI, we can train models to make advanced predictions, analyze data, and automate complex tasks. Java developers can leverage these capabilities to improve their development, testing, and maintenance of Java projects.

Here are four key methods by which we can leverage AI as part of our Java development and system support efforts:

• Automated monitoring: We can use AI to enhance legacy monitoring tools. This can lead to automated anomaly, bug, and bottleneck detection. The output is alerts that let us stay informed of our system’s performance and provide targeted refactoring and optimization. The use of automated monitoring with AI can minimize downtime and safeguard the user experience.
• Performance optimization: AI can analyze large data sets quickly, which can be leveraged to help us identify bottlenecks. Moreover, AI can suggest optimizations based on its own analysis. Taking this a step further, we can use ML models to predict which parts of our code are most likely to cause issues. This lets us be proactive instead of reactive with our optimizations.
• Predicative analytics: AI and ML models are increasingly being used in various industries to make predictions based on dataset and trend analysis. For Java developers, this can take the form of forecasting future system loads and performance issues. This allows us to make informed decisions about infrastructure, scaling, and resource allocation.
• Predictive maintenance: Employing predictive maintenance models can help us accurately anticipate future failures, both in hardware and software. This allows us to plan, take preventative maintenance actions, prevent performance degradation, and improve the overall efficiency of our systems.

Now that we have a sense of how we can leverage AI for high-performance Java applications, let’s look at some current trends related to AI in Java.

Current trends

There are four AI-related trends that are shaping how Java developers integrate AI and harness its power:

• Cloud services: Each of the major cloud service providers (Amazon Web Services (AWS), Google Cloud, and Microsoft Azure) has AI services that can be integrated with Java applications. The models vary among service providers and generally provide pre-built models and Application Programming Interfaces (APIs) for complex tasks such as image recognition, predictive analytics, and Natural Language Processing (NLP).
• Edge AI: The concept of edge computing was realized with the wide adoption of cloud services. The concept is simply deploying systems and data close to the user to increase response times and reduce network latency. Edge AI, an extension of edge computing, involves deploying AI models on edge devices.
• Libraries and frameworks: There is an increasing number of AI-specific libraries and frameworks becoming available to Java developers. The goal of these libraries and frameworks is to simplify our implementation of AI models in Java applications. Notable libraries and frameworks worth researching include the following:
  • Apache Spark MLlib
  • Deep Java Library (DJL)
  • TensorFlow for Java
• Transparency: As our use of AI increases, the need to document how AI decisions are made are understandable. Explainable AI (XAI) calls for AI decisions and processes to be transparent.

The trends that we have described are currently seen in the industry. The next section reveals what we might see in the future.

Future directions

The efficiencies and capabilities we can currently gain from the use of AI tools and technologies are impressive. It is exciting to consider which future directions these tools and technologies might take and how we might be able to leverage them to enhance our ability to create and maintain high-performing Java applications. Here are four future trends:

• AI-driven development tools: As AI tools and techniques become mature, they are likely to be built into our integrated development environments (IDEs). AI-specific development tools, a new breed of IDEs, could emerge for our use in the next couple of years.
• AI for cybersecurity: Cybersecurity’s importance increases as new AI tools and technologies are released. AI can be used to detect and even respond to cybersecurity threats to our systems. This capability is likely to increase in the coming years.
• Hybrid models: It is common for similar technologies to be introduced independently and then later combined to form a hybrid model. For example, augmented reality and virtual reality were introduced separately and later formed a hybrid model referred to as mixed reality. This is like with AI, ML, deep learning, and other related systems.
• Quantum computing: Quantum computing is a field that is ripe for the use of AI. The power of quantum computing’s computing power married with the intelligence of AI stands to revolutionize AI and how we use it.

AI tools and technologies can enhance our high-performance Java toolkits. By understanding and leveraging AI technologies, we can create applications that are not only efficient and scalable but also intelligent and adaptive. In the next section, we will take a specific look at AI for performance optimization.