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Generative AI is transforming industries worldwide and  tech is no exception. Ceiba examines the advances in automated code generation and how businesses in search of both digital products and talented software developers can benefit.

Generative AI is transforming industries across the globe

  1. How are machine learning models evolving?
  2. How does a source code generator work?
  3. What are the latest trends in code generation?
  4. How can software developers utilize these advances?
  5. How will AI generated code impact your business?

 

Generative AI is the term on everyone’s lips with multiple industries considering how best to incorporate the most evolved artificial intelligence applications into their business models.

The tech industry is no exception, especially as the latest machine learning incarnations have taught themselves to produce functional code for software development.

In the following article, Ceiba considers the massive advances in AI and explains how automated code generation will impact companies’ software development aspirations, as well as how talented developers can utilize the latest AI systems to their advantage.

How are machine learning models evolving?

Generative AI has advanced so much in recent years that we now take its content generation skills for granted, including AI’s ability to produce emails, cartoons, poems, essays, and computer code to a human standard without the need for direct human input.

This has generated huge excitement with open-source programs such as text generator ChatGPT and art generator DALL-E gaining traction worldwide, and industries from finance, transport, and healthcare to retail, marketing, and cybersecurity scrambling to incorporate machine learning in their fields.

Lesser-publicized uses for generative AI include intricate drug design, which has helped reduce pharmaceutical companies’ costs and timeframes, as well as AI-generated designs for new synthetic materials based on their physical properties, which is transforming the automotive, defense, aerospace, electronics and energy industries.

The creative industries are undergoing a revolution too, with some experts suggesting audiences will enjoy 90% AI-generated movies by 2030.

How does a source code generator work?

Two standout examples of generative AI are Generative Adversarial Networks (GANs) and Large Language Models (LLMs).

GANs pit two neural networks against one another in order to generate content.

One neural network generates false content and the other learns to tell the difference between that and genuine content. This feedback process, coupled with competition, teaches the second machine to generate increasingly valuable content.

LLMs generate text, such as poems, essays, customer services’ correspondence, books, articles, and product descriptions by analyzing vast amounts of text.

They use the knowledge they gather to generate new content that is similar in style or tone to the inputs. This new content can, however, extend to various topics or be produced in other languages, making LLMs a much sought-after content creation tool.

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machine learning models

What are the latest trends in code generation?

Code generation for software development is just one of the uses now being explored thanks to rapid advances in machine learning and generative AI.

CLIP-R is one example. It is based on the Clip architecture for text and image processing and generates source code by responding to natural language prompts.

Codex, on the other hand, is an AI model developed by OpenAI, the developer behind ChatGPT and DALL-E. This natural language processing tool generates code in a variety of programming languages, and can be integrated into other software development tools too.

AlphaCode is another example, and was developed by DeepMind for use in competitive programming challenges, whereas GitHub Copilot, as the name suggests, works in conjunction with developers – suggesting excerpts of code to programmers as they write. GitHub Copilot was trained to do this by ingesting massive amounts of reliable code.

One final example is CodeGen, which was developed by Salesforce. CodeGen can generate code based on natural language descriptions of software requirements. 

How can software developers utilize these advances?

Generative AI is far from perfect and even though it can now create human-standard content without direct human intervention – that content still requires human oversight.

AI generated code is unlikely to ever fully replace talented software developers, but it will help such developers to innovate and improve their efficiency.

CLIP-R has been shown to be effective in a wide range of programming tasks and brings benefits for efficiency, while GitHub Copilot can increase the speed and reliability of code-writing, and even help automate repetitive coding tasks.

AlphaCode has been shown to help developers solve complex coding challenges, at speed, while CodeGen can also help programmers automate the code-writing process and transform their software development processes and goals.

How will AI generated code impact your business?

The ongoing evolution of artificial intelligence and machine learning brings numerous benefits for business and, in terms of code generation, will help companies secure the digital products they desire.

As AI generated code continues to automate and ease the burden of the most basic and repetitive aspects of coding, talented developers, such as those at Ceiba, will have even more freedom to innovate and fine-tune their software development skills.

Ceiba’s talented developers utilize state-of-the-art AI tools for programmers. To find out how we create digital products that will revolutionize your business, contact us today.

 

You may also be interested in: 

Prompt engineering is key to the AI and machine learning revolution

More than just geeks: The key skills needed for a software developer

 

Sources: 

https://www.gartner.com/en/articles/beyond-chatgpt-the-future-of-generative-ai-for-enterprises

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