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How machine learning transforms software development

How machine learning transforms software development

The worldwide market for artificial intelligence (AI) is expanding at an astonishing pace, forecasted to hit $1.8 trillion by 2030, with an average growth rate of 32.9% from 2022. Key developments such as chatbots, generating images, and AI for smartphones are driving this rapid growth. Several important trends are starting to take shape, which will influence the future of AI and its practical effects on the world.

Large language models, AI on the go, self-learning AI, and AI in chip creation are four sectors that are expected to make significant strides and come together to support the next level of AI features. With proficient software developers using these cutting-edge technologies wisely, AI is set to play an even more crucial role in our tools and applications in 2024 and the years that follow.

Growth in Large Language Models

A significant trend is the creation of increasingly bigger language models, such as GPT-3. These models can comprehend and produce text that resembles human language, and they are used in various applications, including chatbots. By the end of the year, we are expected to witness models that are 10 to 100 times bigger than GPT-3, boasting more than a trillion parameters.

This advancement will allow them to become more engaging, informed, and inventive. These large models will drive applications across the spectrum, from search to customer support to the creation of content. Nonetheless, issues related to bias, the spread of misinformation, and the expense of computation continue to pose obstacles.

On-Device AI

A growing trend is the expansion of AI capabilities built directly into devices. Instead of transferring information to remote servers, these models will be executed on-site in gadgets such as smartphones, vehicles, and Internet of Things (IoT) devices. This approach minimises delay, enhances data security, and permits AI to function without an internet connection.

Major corporations such as Google, Apple, and Tesla are currently developing in-device AI capabilities. As specialised AI chips and methods like model compression progress, there will be an increase in tasks performed locally on devices instead of in the cloud. This shift will support the development of new technologies like self-driving cars, augmented reality/virtual reality, and intelligent personal assistants.

Automated Machine Learning (AutoML)

Automated machine learning, or AutoML, is designed to simplify the use of artificial intelligence for those without advanced technical backgrounds. These tools are expected to evolve further, enabling individuals with basic knowledge of data science to easily create, refine, and implement models. AutoML can handle mundane and time-intensive activities such as preparing data, choosing models, adjusting parameters, and enhancing models.

This approach to making AI more accessible could broaden its use in both business and academia. Among the primary applications of AutoML are predicting equipment failures, reducing customer attrition, tailoring marketing efforts, and analysing medical images. Nonetheless, there are obstacles to overcome. Regulations will be necessary to ensure that novices do not misuse AutoML without grasping the nuances of data biases or the objectives of their projects.

The issue of explainability persists, as AutoML might produce models that are not easily interpretable. The development of tools for managing computing resources and facilitating teamwork within large organisations is still in progress. Although AutoML will not eliminate the role of data scientists, it can handle the mundane aspects of their jobs, freeing them to concentrate on tasks that offer greater value. The strategic use of AutoML, while considering its ethical and technical constraints, will be crucial.

The Role of Software Developers

To unlock the full potential of these trends, it’s essential to have proficient software developers. This includes system software for controlling AI on devices and interacting with hardware, frameworks for creating and implementing large language models, AutoML tools for automating machine learning processes, and AI-powered toolchains for designing chips.

These tools will need to be coded into functional products. Following best practices in software engineering, such as version control, testing, and continuous integration, will be key to ensuring the dependability and security of these AI systems as they become larger and more complex. Therefore, as AI technology expands its capabilities, human software developers continue to play a vital role in converting these technologies into tangible benefits for the real world.


This year and going forward, AI and ML will play an even more crucial role in our tools and applications. As models expand, AI will be integrated into devices, automation will rise, and new fields such as chip design will develop. This presents an exciting but also significant responsibility for software developers to guide AI’s advancement towards societal improvement. Through careful leadership and teamwork, the future appears promising for AI that is beneficial and accessible to all.

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