Top AI Trends in 2024

From task automation to deep learning, there is no denying that artificial intelligence (AI) remains at the forefront of the ongoing digital transformation, and will continue to play a crucial role in shaping our private and business lives of the future. In this article, Bocasay, our offshore IT agency based in Vietnam, explores the most important AI trends of 2024. 

Despite being in development for decades, for most people who aren’t IT professionals, 2023, was the year we witnessed the age of AI truly arriving to transform our everyday lives. From the launch of OpenAI’s GPT-4, all the way to Google’s AI-powered web searching, nothing would be the same again.

What does the world of AI look like today and where is it heading to? Before we examine the most dominant AI trends of 2024, here are the latest AI stats to consider:

♦ The global AI market was valued at approximately $208 billion. By 2030, that number is projected to reach nearly $2 trillion, according to Statista.

♦ According to McKinsey, AI has the potential to add a staggering $25.6 trillion to the world’s economy.

♦ Goldman Sachs reports that while two-thirds of jobs could be partially automated by AI, many of these jobs will only be complemented by AI and not replaced by it.

Major AI Trends in 2024

• Multimodal AI

Multimodal AI refers to artificial intelligence systems or models that can process and understand information from multiple modalities or sources of data simultaneously. These modalities may include text, images, videos, audio and sensor data.

In traditional AI systems, each modality is often processed separately, with different algorithms or models dedicated to handling specific types of data. However, multimodal AI seeks to integrate and analyze information from multiple modalities in a unified framework, enabling more comprehensive understanding and richer interactions with the environment.

Multimodal AI systems leverage advanced techniques such as deep learning, neural networks and multimodal fusion to combine information from diverse sources and extract meaningful insights. For example, a multimodal AI system might analyze both the text and images in a social media post to understand its content and context more comprehensively.

• Generative AI

Generative AI refers to a class of artificial intelligence techniques and models that are designed to generate new content, such as images, text, audio, or other data types, that is similar to, or inspired by existing examples.

Unlike traditional AI models that are typically used for classification, prediction, or pattern recognition tasks, generative AI models focus on creating new data instances rather than analyzing existing ones.

Generative AI models often rely on deep learning architectures, particularly generative adversarial networks (GANs) and variational autoencoders (VAEs), to learn the underlying distribution of a dataset and generate novel samples that mimic its characteristics. These models learn to capture the essential features and patterns present in the training data and use this knowledge to produce new, realistic-looking outputs.

• AI Skills in Demand

Currently, the demand for AI skills is outpacing the supply of AI talent. Highly-trained and talented professionals with AI skills will continue to be in high-demand in the near future. For businesses of all sizes, attracting and retaining workers with AI skills will become increasingly competitive.

Companies around the world are willing to pay premium salaries for workers with specialized AI skills. According to Glassdoor, the estimated total pay for an AI engineer in the U.S. is $141,379 per year, with the average salary standing at $115.812 per year.

For current and aspiring IT professionals of the future, acquiring solid AI knowledge and a wide range of AI skills will be a smart and lucrative career move, most likely resulting in job security and career advancement.  

• AI Automation

AI automation will only continue to dominate and transform virtually all industries and market sectors. It involves the use of artificial intelligence technologies to automate tasks, processes, or workflows that would typically require human intervention.

AI automation systems leverage algorithms, machine learning and other AI techniques in order to perform repetitive, rule-based, or data-intensive tasks with minimal or no human involvement.These systems aim to improve efficiency, accuracy, and scalability while reducing costs and freeing up human resources for more strategic or creative activities.

Industries and sectors radically transformed by AI automation include manufacturing, finance and banking, healthcare, retail and e-commerce, transportation and logistics, customer services, as well as marketing and advertising. 

• Natural Language Processing (NLP)

While not necessarily a new trend, natural language processing is a sector of AI that has witnessed groundbreaking advancements in recent years and will only continue to grow in the future. NLPis a field of AI and computational linguistics that focuses on the interaction between computers and human language.

NLP technologies power a wide range of applications and services, including virtual assistants, chatbots, machine translation systems, sentiment analysis tools and information retrieval systems. NLP enables computers to interact with humans in a more natural and intuitive way, facilitating communication and information processing across diverse domains and languages.

According to KBV Research, the global NLP market size is projected to reach $29.5 billion by 2025. The smart speaker market alone, an AI market segment relying heavily on NLP, is expected to be worth $34.24 billion by 2028.

• AI Ethics and Regulation

Just like any other revolutionary technology introduced throughout history, the fascinating world of AI could not exist without its very own set of risks and challenges. There is a growing and increasingly crucial demand for more AI regulation and ethical AI development.

The ethical use of AI will center on preventing biases, establishing fairness and transparency, privacy and security, as well as mitigating the potential effects of job displacements and other economic impacts. Addressing these risks and challenges requires collaboration among stakeholders, including researchers, policymakers, industry leaders and civil society organizations.

An example of what could go really wrong with unregulated AI, includes a healthcare algorithm that was less likely to recommend necessary medical treatment to black patients. By adopting a multi-disciplinary and multi-stakeholder approach, we can harness the benefits of AI while mitigating its potential risks and ensuring its responsible and ethical use.

Do you need a partner capable of producing high quality IT development for your company? At Bocasay, our dedicated teams of developers provide cutting edge software solutions for companies around the world. Get in touch to find out how we can help with your next project.