In addition to AI systems focused on generating text and images, there are many other types of AI that are being rapidly developed and deployed.
One major area is code generation, where AI models can automatically generate computer code.
Conversational AI chatbots that can engage in contextualised dialogue are also increasingly common that can understand intent.
Further major advancements include:
Code generation refers to AI systems that can automatically generate source code in programming languages. Rather than writing code line-by-line, code generation AIs can suggest entire functions, classes, algorithms and more based on prompts from developers.
One of the most well-known examples is GitHub Copilot, developed through a partnership between GitHub and OpenAI. Copilot integrates directly into development environments and generates potential code completions in real-time as developers are typing. Drawing on vast training datasets of open-source code, Copilot has learned code patterns and conventions spanning many programming languages. As such, it can suggest whole lines and even full functions to developers to boost their productivity.
As with many emerging AI capabilities, code generation does raise some ethical concerns regarding data usage, code quality assurance, and potential impacts on software careers. Responsible development and application of these technologies will be an ongoing discussion. For computer science students especially, understanding code generation will be valuable given its rapid emergence in real-world software development workflows.
Conversational AI allows computers to understand common human speech and communicate through open-ended dialogues.
A key capability that underpins many conversational AI systems today is natural language processing (NLP). NLP algorithms allow computers to analyse textual or spoken information and interpret the meaning, emotions, intents behind human language. The system can then formulate relevant responses through text or synthesized speech.
Conversational AI has opened up new possibilities for system interfaces. Rather than relying on menus, buttons or rigidly structured inputs, users can now interact with services through intuitive voice or text conversations. Virtual assistants like Siri, Alexa and Google Assistant demonstrate the utilities of conversational AI in everyday home and work contexts. But chatbots are also transforming customer service, allowing smoother, more meaningful dialogues between businesses and consumers.
While there remain significant challenges in developing conversational AI that is truly intelligent and responsive, the pace of advancement in domains like natural language and speech processing suggests we will continue seeing rapid improvements. For university students thinking about careers powered by leading-edge technologies, focusing on fields fuelling conversational AI would be a forward-looking bet.