7 Emerging AI Technologies to Watch in 2024

Companies worldwide are keen on AI adoption. According to estimates, this market is expected to grow at a CAGR of 28%, becoming a trillion-dollar market in the next decade. Did you know AI has existed since the early 1940s?

However, it was not as popular as today until ChatGPT was rolled out. Today, one out of five marketers is using it somehow. Copilot programs are also becoming more prevalent among professionals. AI-generated music and videos are flooding the social platforms right now. Amid all the noise, AI technology is evolving very fast.

But to really get a sense of what's coming, we need to look at current developments and new AI technologies. By knowing these current trends and recent developments in AI, we can better predict what's ahead. 

AI adoption has many technical limitations. It is not easy to integrate it with traditional technology. Developing AI models comes with hefty price tags. Large language models are still exclusive to biggies only.

Generative AI is now transitioning from the hype stage to becoming a value-driven technology with real-world applications. Basically, artificial intelligence combines various technologies. To understand what’s happening in the AI development world, it is crucial to know emerging AI technologies that can guide the course of AI itself.

However, in the field of artificial intelligence, there are many emerging technologies that have the potential to change the direction and scope of AI.

Because of these developments, there could be several transformations in AI development. Some of these emerging technologies are surfacing due to the growing concerns associated with AI, and collectively, these technologies can make AI tech more powerful. 

In this article, we'll list some of the top emerging technologies in artificial intelligence  and explore why these technologies are currently getting so much attention.

If we talk about the most popular AI technology of today, whether you're a user or a business, both are likely to be most familiar with generative AI technology. In fact, this is the field of AI that's currently attracting the most attention.

These developments are reshaping industries and creating opportunities for transformative solutions across various sectors.

What are these new emerging AI technologies? We'll explore them in this blog. We'll know how AI technologies shape the current artificial intelligence world. So, let's take a look at the top emerging technologies in artificial intelligence.

Top Emerging AI Technologies [In 2024]

1. Generative AI

In just the last two years, generative AI has become one of the most popular AI technologies, especially because of its widespread use in customer-facing applications like chatbots and conversational tools.

According to reports, over 80% of companies are either already using AI in some way or exploring it. Many businesses are delving into advanced use cases, like AI-driven business strategies and custom AI models to tackle specific problems.

If we look at "Gartner's Hype Cycle for Emerging Technologies in 2024," GenAI has stated that it provided real value to businesses, moving from the hype stage. Businesses are testing it across different operations to drive value. It generates different types of content, including audio, text, video, etc. 

Generative AI has surged in popularity, transforming how we interact with technology. From chatbots that craft personalized responses to tools that generate art and music, its applications are expanding rapidly.

Major tech companies are investing heavily in AI, fueling widespread adoption across industries. This growing fascination reflects both the impressive capabilities of the technology and the increasing demand for innovative solutions in everyday life.

2. Explainable AI (XAI)

AI can do incredible things that humans can't, but often, we don't really know how these systems make their decisions.

In the blink of AI, AI models can produce results, but how? This is still a mystery with deep learning models. XAI, which stands for explainable AI, focuses on explaining decisions or output delivered by AI models to humans

In simple words, it explains how! How an AI model makes decisions. That's why it is called Explainable artificial intelligence. XAI address is a field of AI that addresses this mystery, enabling AI programs to explain their decision-making process.

Black Box in Deep Learning

Over the last few years, it has gotten more attention as businesses are more concerned about understanding what goes behind the doors that make an AI program draws conclusions. Explainable AI is considered a key driver in increasing AI adoption in the upcoming years, shaping the future of generative AI.

By understanding how a particular model produces output, models can be made to deliver more accurate and precise outputs. The transparency in AI models becomes more crucial when they are put in a regulated industry.  

3. Edge AI

Within the edge computing ecosystem, data is processed by nearby computers rather than processing on centralized cloud servers. This apporach (or edge computing) is highly beneficial in making real-time applicaiton more reliable and cost-effective.

When AI is deployed at the edge, it means that machine learning models and algorithms run on local devices—like smartphones, IoT devices, and sensors—rather than in a data center located miles away from you. It is obvious that processing data locally reduces latency, another advantage of edge computing. 

It also addresses privacy concerns raised in AI development, as data is sent to external sources for processing in case a cloud platform is being used. With edge computing, data processing is really fast as compared to cloud computing.

It is highly beneficial for developing real-time applications such as autonomous vehicles and industrial automation tools. As generative continues to grow in IoT, Edge AI will become more important. Devices such as smart cameras, home assistants, and industrial sensors will leverage it to handle complex tasks locally.

Edge AI is expected to see growing adoption across industries like healthcare, manufacturing, retail, and automotive. Till 2033, the global Edge AI market will grow at a CAGR of 24.1%, reaching USD 163 billion from USD 19 billion in 2023. 

4. Quantum Machine Learning

Quantum computing, which involves concepts from quantum mechanics to perform computations beyond the capabilities of classical computers, is also applied in machine learning, enabling AI models to solve complex problems at breakneck speed.

Quantum computing is continuously evolving, and it has been explored in AI development and machine learning. Quantum ML is growing at speed in both academia and industry.

Popular companies like Google, IBM, and Microsoft are doing significant research in quantum algorithms that outperform classic ones. Though this tech is in its early stages, the exploratory applications of QML are being developed to take the AI revolution the next step further.

5. Large Language Models (LLMs)

Large language models are one of the most popular AI innovations of today. These models are being trained with millions and billions of parameters, which are the heart of today's content generation models. 

However, LLMs are also developed by combining different AI technologies in specific ways. They combine different technologies, enabling them to process millions and billions of parameters. They are being developed using modern AI technologies such as GANs, transformers, NLP, and evergreen deep learning techniques, particularly neural networks with numerous layers (hence the term "large").

LLMs handle a variety of tasks, from text generation to graphic design, with great speed and efficiency. For example, today's image generators can create realistic images in just a few seconds, which an expert designer may take many hours to design.

The race for dominance in the LLM space is heating up among major tech industry players. Companies like OpenAI, Google, Microsoft, and others are heavily invested in advancing their models' capabilities, efficiency, and applications.

LLMs are being integrated into various applications, from virtual assistants and customer service bots to content creation tools and language translation services. 

Many businesses are adopting LLMs to enhance their products and services. Companies are using these models to automate processes, improve customer interactions, and generate high-quality content.

6. Federated Learning

Federated AI is a way to train artificial intelligence models without sending sensitive data from individual devices or organizations. With this approach, different devices are used to train a model. Here, the model is traded by local devices. What is unique about this approach is that the devices used in this process only share the model updates with a central server.

A central server combines these updates to improve a global model. This global model is then sent back to the devices, which continue to refine it locally. It’s great for maintaining privacy while still improving AI across many sources.

Federated learning is emerging because it enhances privacy. In this method, data is not kept in a shared or centralized directory. Also, there is not need to transfer large data, which cuts costs and minimizes security risks.

Additionally, it allows AI models to be trained on diverse data sources without compromising privacy. As concerns about data protection grow, federated learning offers a practical solution for developing advanced AI while respecting user confidentiality.

7. Computer Vision

Computer vision is being used in many fields today, a powerful technology working behind the scenes. It powers generative AI, multimodal AI, edge computing, and 3D computer vision. These technologies are pushing artificial intelligence to new heights. Computer vision has also proved helpful in deciphering deepfakes and computer-generated content. In the future, we can expect advancements and increased usage in this area.

8. NLP

NLP stands for natural language processing. This is what enables AI models to understand our natural conversations with them. This technology works behind popular AI systems to enable them to interact with humans intelligently and learn and comprehend natural or human-spoken languages.

Today's major AI tools leverage NLP to converse with users and transcribe the conversation.

The development of popular AI tools such as GPT models and BERT has significantly improved NLP capabilities. Furthermore, this technology is widely used across various industries. Today, we see many use cases for NLP, including chatbots, virtual assistants, content generators, and language translators.

9. AIoPS

AIoPS refers to a process that combines AI in IT operations, enabling businesses to manage IT operations more efficiently than ever. This practice helps professionals analyze data, automate operations, and detect issues using machine learning algorithms and other traditional technologies.

As rising complexities within the IT ecosystem and ever-growing data present several challenges, decision-makers are shifting to incorporate emerging AI technologies into their operations.

AIoPS is becoming more popular in the IT ecosystem as organizations increasingly leverage technologies in operations. As a result, AI use in IT operations is seen as an effective solution to streamline operations.

While AIoPS is still a relatively new field, its popularity is expected to continue to grow in the coming years as organizations recognize the value it can bring to their IT operations.