Java and AI. Is it possible to write artificial intelligence in Java?

We continue to talk about the use of Java in the hottest and trending niches of the IT market. We have already written about how Java is used in the Internet of Things , cloud services , big data and blockchain . Next up is another super interesting and important niche - artificial intelligence (AI). “Artificial intelligence will be the perfect version of Google. A perfect search engine that knows everything on the Internet, perfectly understands what you want and gives it to you. Right now we are still quite far from this. But we are working and are gradually getting closer to realizing this idea.” Larry Page, co-founder of Google.

Why is Java popular in AI development?

There are quite a few programming languages ​​used in the field of AI, but Java is undoubtedly one of the most popular. In particular, Java is used to create solutions for machine learning, neural networks, search algorithms, genetic programming and multi-robotic systems. Features such as object-orientation and scalability are essential for AI projects, making Java a perfect fit for them. Since today AI technologies are already actively used by business, Java capabilities are in great demand, allowing you to create a single version of the application that will work on several platforms. Another advantage of Java in AI programming is the ability to code different types of algorithms. And other strengths and advantages of Java, which we have already mentioned more than once, are in demand in the field of AI development. Among them are ease of creating and debugging code, simplified work with large-scale projects, simplified visualization, as well as the availability of Swing and SWT (Standard Widget Toolkit). These features allow you to create attractive, complex graphics and interfaces. “Artificial intelligence is developing at an incredible pace. Unless you have direct contact with teams of scientists working on AI, you have no idea how fast—progress is growing at a rate close to exponential.” Elon Musk, founder of SpaceX and Tesla Motors.

AI projects in Java

Java code is used by many projects related to one or another area of ​​AI. Here are some interesting examples.

• Mobile robots for Arctic Circle research

  Using the Java API, scientists have created a range of mobile robots that are used for research in the Arctic Circle - in Greenland and Antarctica. Of course, Java technologies are also used in the development of other mobile autonomous robots, but in the case of devices for exploring the Arctic Circle, there is especially a lot of open information on the Internet.

  Thus, the GUI (graphical user interface) for these robots is developed using Java Swing tools. Because Swing is easy to use and can interact directly with components, this platform was chosen to provide robot control and monitoring capabilities.

• JOONE Neural Engine

  JOONE Neural Engine (Java Object Oriented Neural Engine) is a popular multi-platform that allows you to create, train and test neural networks.

  Joone consists of a component architecture, that is, it is based on complementary components that can be expanded to build new algorithms and neural network architectures.

  Components are code modules that are attached to create an information flow. New components can be added or removed. In addition, Joone has a GUI application that allows you to graphically create and test any neural networks, as well as a distributed training environment that allows you to train neural networks on many remote machines.

• Robocode

  Robocode is a fairly old, well-known and still popular open source Java-based game that allows users to learn the principles of Java programming. It is also well suited for learning the basic principles of robotics and artificial intelligence.

  In Robocode, the player creates his own robot tank, programs its strategy and develops software intelligence, competing with the robots of other users. First created back in 2000, Robocode is still one of the most popular and famous games for programmers.

• WEKA Machine Learning Suite

  The Waikato Environment for Knowledge Analysis (WEKA) Machine Learning Suite is an open list of algorithms that are used to develop machine learning methods. All WEKA algorithms are tailored for machine learning and data mining.

  The WEKA Machine Learning Suite is now widely used in the business environment, providing companies with simplified data analysis and predictive analytics.

• Chatbot Alice

  Chatbot ALICE (Artificial Linguistic Internet Computer Entity), also known as Alicebot or simply Alice, is a program that can conduct a dialogue with a person in natural language. Modeled after the first conversational program, ELIZA, it uses a technique that heuristically matches the user's phrases with patterns in the knowledge base. ALICE is considered one of the best chatbots and has won the Loebner Prize three times (the Loebner Prize is awarded to the winner of the annual AI Loebner competition, in which programs compete to pass the Turing test).

“We call it artificial intelligence, but in reality this technology improves and complements our intelligence. Therefore, instead of artificial intelligence, I would call it augmented intelligence.” Ginni Rometty, CEO of IBM

AI libraries in Java

It must be said that today almost any savvy Java programmer with basic knowledge of traditional Java algorithms and concepts can implement solutions based on certain technologies from the field of AI. To do this, there are a whole lot of open Java libraries that will help you implement many fairly complex functions. It should only be noted that AI in general is a fairly broad field, and today we will talk about Java libraries in the most popular AI niches, such as natural language processing, machine learning, neural networks, etc.

Expert systems

In AI, expert systems are computer systems that emulate human decision-making abilities.

• Apache Jena

  Apache Jena is an open Java framework for building semantic web and linked data applications based on the RDF model. On the official Apache Jena website you can find a detailed guide to using this framework with a short description of the RDF specification.

• PowerLoom

  PowerLoom (PowerLoom Knowledge Representation and Reasoning System) is a platform for creating intelligent, knowledge-based applications. PowerLoom uses a knowledge representation language (a variant of KIF), which is based on a natural deduction mechanism. This mechanism is able to create a logical chain to obtain conclusions based on the facts and rules specified in the knowledge base.

• D3web

  D3web is an open source semantic reasoning engine for developing, testing, and applying factual knowledge to a specific situation. D3web includes a range of complex algorithms. On the official website you can find a brief introduction to the platform with many examples of its use and documentation.

“I think artificial intelligence is almost a humanities discipline. In fact, it is nothing more than an attempt to understand human intelligence and human consciousness.” Sebastian Thrun, computer science expert and head of Kitty Hawk Corporation.

Neural networks

• Neuroph

  Neuroph is an open source library for creating neural networks and using machine learning, written in Java.

  Neuroph users can create neural networks through the provided GUI or using Java code. There is documentation on the Neuroph website that also explains in detail what a neural network actually is and how it works.

• Deeplearning4j

  Deeplearning4j is a deep learning library for the JVM that also provides an API for creating neural networks. Deeplearning4j provides a wealth of tutorials and theory to deepen your knowledge of deep learning and neural networks .

Natural Language Processing

• Apache OpenNLP

  Apache OpenNLP is a machine learning-based natural language text processing toolkit. There is documentation on the site with information on how to use this library.

• Stanford CoreNLP

  Stanford CoreNLP is one of the most popular natural language processing frameworks and includes many tools for performing tasks in this area.

“A year of work in the field of artificial intelligence is enough to make a person believe in God.” Alan Perlis is a computer science expert and lecturer at Purdue University, Carnegie Mellon University and Yale University.

Machine learning

• Java Machine Learning Library (Java-ML)

  Java-ML is an open Java framework that contains a whole set of different machine learning algorithms for programmers. On the project website you can find official documentation with tutorials on the possibilities of using Java-ML with code examples.

• Weka

  Weka is a collection of machine learning algorithms that can be applied directly to a dataset through a provided GUI or called via an API.

• RapidMiner

  RapidMiner is a data mining platform that provides various machine learning algorithms via GUI and Java API. On the Internet you can find a lot of manuals and other materials about using RapidMiner.

• Genetic algorithms

  A genetic algorithm is a heuristic search algorithm used to solve optimization and modeling problems by randomly selecting, combining, and varying desired parameters using mechanisms similar to natural selection in nature.

• Jenetics

  Jenetics is an advanced library of genetic and evolutionary algorithms, genetic programming, and multi-objective optimization written in modern Java. On the project website you can find documentation and tutorials for new users.

• ECJ 23

  ECJ 23 is a Java-based research framework with strong support for genetic algorithms. Developed at the ECLab at George Mason University in the USA.

• Watchmaker Framework

  The Watchmaker Framework is a framework designed to implement platform-independent genetic and evolutionary algorithms in Java. On the project website you can find information about the framework with detailed documentation and examples of its use.

Instead of an epilogue

As mentioned above, AI is a very broad field, and there are a huge number of frameworks and systems that are quite simple to use and can be useful in a wide variety of applications and for solving different types of problems. Today, the scope of AI is growing very quickly, which means companies need more and more qualified specialists in AI technologies. If the segments of AI application listed above seemed interesting to you, it is better not to put off getting acquainted with them - this knowledge and skills are almost guaranteed to come in handy.