What Developers Need to Know About Generative AI
Jun 12, 2023
AI
Generative artificial intelligence (AI) is making waves, revolutionising the way we create content and code. As developers, it's crucial to stay ahead of the curve and understand the implications of this transformative technology. This article delves into the intricacies of generative AI, its applications, and how it's changing the landscape of software development.
Understanding Generative AI
At its core, generative AI refers to a category of AI models and tools designed to create new content, such as text, images, videos, music, or code. It employs a variety of techniques, including neural networks and deep learning algorithms, to identify patterns and generate new outcomes based on them.
As Damian Brady, a leading voice in the AI community, explains:
"Generative AI uses a variety of techniques—including neural networks and deep learning algorithms—to identify patterns and generate new outcomes based on them. Organisations and people, including software developers and engineers, are increasingly looking to generative AI tools to create content, code, images, and more."
Applications of Generative AI
Generative AI is not confined to a single framework, project, or application. Its potential applications are vast and varied, ranging from text generation to programming code generation.
Text Generation
Generative AI models can produce text that models human-like speech. For example, OpenAI’s application ChatGPT has been trained on thousands of texts, books, articles, and code repositories, and can respond with full answers to natural language prompts and questions.
Image Generation
Generative AI models can be used to create new images with natural language prompts. For instance, the AI image generator DALL-E can generate images based on specific text prompts.
Video Generation
Generative AI models, like Stable Diffusion, are creating new videos from existing videos by applying specified styles through a text prompt or image reference.
Programming Code Generation
Generative AI models can be used to help generate new programming code with natural language prompts, complete partially written code with suggestions, or even translate code from one programming language to another. GitHub Copilot, for example, uses OpenAI’s Codex model to offer code suggestions right from a developer’s editor.
Data Generation
Generative AI can create new data, also known as synthetic data, and augment existing data sets. This involves generating new samples from an existing dataset to increase the dataset’s size and improve machine learning models trained on it.
Language Translation
Generative AI combined with natural-language understanding (NLU) models can provide language translations on-the-fly, helping companies break language barriers and increase their scope of accessibility.
How Generative AI Works
Generative AI models work by using neural networks to identify patterns from large sets of data, then generate new and original data or content. Training these neural networks involves adjusting the weights or parameters of the connections between neurons to minimise the difference between predicted and desired outputs, which allows the network to learn from mistakes and make more accurate predictions based on the data.
The Impact of Generative AI on Developers
Generative AI is changing the way developers work. It empowers developers in two aspects: coding and enabling accessibility to developers with disabilities.
Coding
Generative AI coding tools can help automate some of the more repetitive tasks, like testing, as well as complete code or even generate brand new code. For example, GitHub Copilot uses generative AI to provide developers with code suggestions.
Accessibility
Generative AI has the potential to greatly impact and improve accessibility for folks with disabilities through a variety of modalities, such as speech-to-text transcription, text-to-speech audio generation, or assistive technologies.
As Brady explains, "Generative AI has the potential to greatly impact and improve accessibility for folks with disabilities through a variety of modalities, such as speech-to-text transcription, text-to-speech audio generation, or assistive technologies."
Challenges in Building Generative AI Models
While generative AI models are being used to power applications, there are two key challenges any organisation building or using one will face. Firstly, generative AI requires significant compute resources, powerful GPUs, and large amounts of memory. This type of hardware is costly, which in turn also creates a barrier to entry for a lot of individuals or organisations to build in-house solutions.
Secondly, training generative AI models to create accurate outputs also requires large amounts of high-quality data. If training data is biased or incomplete, the models may generate content that is inaccurate or not useful.
The Future of Software Development with Generative AI
Generative AI is bringing in a new mode of interaction and it allows developers to be more creative, feel empowered to tackle big problems, and model large, complex solutions in ways they couldn’t before.
"Generative AI is bringing in a new mode of interaction—and it doesn’t just alleviate the tedious parts of software development. Critically, it allows developers to be more creative, feel empowered to tackle big problems, and model large, complex solutions in ways they couldn’t before," Brady adds.
From increasing productivity and offering alternative solutions to helping developers build new skills—like learning a new language or framework, or even writing clear comments and documentation—there are so many reasons to be excited about the next wave of software development.
SUMMARY
In conclusion, generative AI is a transformative technology that is reshaping the landscape of software development. As developers, understanding its intricacies, applications, and implications is crucial to staying ahead of the curve and leveraging its potential to drive innovation and productivity.
Sources:
