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AI Trends to Watch For

By DeVry University

The information presented here is true and accurate as of the date of publication. DeVry’s programmatic offerings and their accreditations are subject to change. Please refer to the current academic catalog for details.


May 6, 2024

9 min read

AI Trends to Watch For

Among all the technologies introduced in the last few decades, artificial intelligence seems to have captured our awareness like no other. After gaining widespread adoption in the time since its emergence, generative AI (GenAI) is the technology driving many recent AI trends.

In this article, we’ll take a look at AI trends you may want to follow in 2024, including who the early adopters are, what changes AI is projected to undergo and the lingering regulatory, ethical and intellectual property implications of this data science technology.

The Use of AI

Although the concept was first thought up decades ago, AI burst onto the scene as an apparent overnight sensation in the last 2 or 3 years, thanks to the introduction of OpenAI’s ChatGPT chatbot. Prior to its launch and the tsunami of media coverage that it garnered, the concepts of artificial intelligence and machine learning had been somewhat nebulous ones.  

GenAI’s evolution has been compared to the computer’s, which came about more incrementally over time, transitioning from giant mainframe computers, to smaller versions used by businesses and researchers, to desktop devices, to the powerful and compact devices we use today.

The rate at which AI has been adopted has been far more rapid. Here are some of the insights gathered as part of IBM’s AI Global Adoption Index 2023:

  • Actively using AI: 42% of enterprise-scale organizations have AI actively in use in their companies.

  • Increasing investment: 59% of the organizations already working with AI intend to accelerate and increase their investment in it.

  • Top investment/deployment areas: Research and development (44%) and reskilling/workforce development (39%) are the top areas of AI investments at organizations that are exploring the technology.

  • Barriers to AI deployment: The biggest barriers preventing enterprises from implementing an AI strategy are limited AI skills and knowledge (33%), too much data complexity (25%) and ethical concerns (23%).

6 AI Trends to Watch Out For

There are several trends in AI in 2024 that are expected to stand out as the technology evolves, begging questions like: Will content generated by AI be trustworthy? How can enterprises using generative AI tools keep their data systems safe? As the rate of AI adoption and investment increases, will jobs be lost, or will the technology enable the existing workforce to do new things?

With those questions in mind, here are 6 AI trends to watch out for in 2024:

Multimodal AI

AI systems that can process multiple data input types such as text, images, audio and video are called multimodal (as opposed to systems that can only handle one type of input, which are unimodal.) New multimodal generative AI systems are emerging that can identify patterns between different types of data inputs, and produce results that are more intuitive, accurate, natural and informative.

Although it’s exciting, multimodal AI isn’t quite ready for widespread use. As the technology matures, however, it could make significant contributions in a variety of use cases, including:

  • Healthcare: Multimodal models could be used to develop new diagnostic tools that use data from scans, electronic health records and the results from genetic testing.

  • Customer experiences: Chatbots and virtual assistants could be improved by multimodal AI’s capabilities, processing a variety of inputs and creating more refined outputs.

  • Transportation: Self-driving vehicles could be improved by this technology and its ability to combine data from multiple sources, like cameras and radar.

  • Social media: Multimodal AI could be deployed to analyze social media data, including videos, text and images, to improve content moderation and identify trends.

Open Source AI

Open source software is programming with source code that can be inspected, modified and enhanced by anyone to manipulate how an application works. In an AI trend that’s already well under way, open-source technology is powering a rapidly increasing number of artificial intelligence models.  

AI insiders believe open source AI will surpass the offerings of Google and OpenAI because the open-source community has helped solve some major open problems and made it accessible to the general public.  

Some of the benefits of open source AI include:

  • Improved security: Whether data is managed on premises or in the cloud, by integrating open-source large language models (LLMs) into their own infrastructure companies can control their sensitive information and better safeguard data against security breaches.

  • Better transparency: Companies can benefit from the transparency an open-source LLM can deliver with regard to their working mechanism, data training and architecture. It can make the deficiencies and biases of the model and its specialized tasks easier to comprehend.

  • Reduced cost: With pricing based on hosting fees, open-source LLMs typically cost less than proprietary LLMs, which typically have pricing models based on margins and licensing fees paid to their developers.

  • Customization and support: Pretrained, open-source LLMs can be easily tuned and modified, allowing businesses to specialize models to specific datasets. They can also benefit from the no-cost support of a dedicated development community when they run into problems.

Growing startups and big tech companies like Meta have included open-source AI in their strategies. Examples of this AI trend include Meta’s Llama 2, the Technology Innovation Institute’s Falcon and Bloom from BigScience.

Why would tech players like Meta open source their products? One reason given by the company is that it democratizes access to the technology for those who wouldn’t otherwise have it. Another reason is the potential to attract customers and developers. In the case of OpenAI, the company built much of its brand initially on open sourcing the earlier versions of its GPT products.

Language Models

Along with open-source advancements, small language models may get the edge over LLMs for a few reasons relative to resource consumption and performance. OpenAI’s GPT-4 model is purported to have around 1.76 trillion parameters (the elements the model learns from the training data). But the massive LLMs that initially powered generative AI during its golden era may be giving way to small language models (SLMs) that don’t consume nearly this number of resources. It’s also been demonstrated that training smaller models on more data may also yield better performance than training larger models on less data.

Generative AI

No discussion of trends in AI would be complete without mentioning Generative AI. In recent years, the use of GenAI became widespread alongside considerable media coverage. Experimentation with its various tools became relatively common, and a broad cross-section of business leaders and the general public utilizing it, setting up high expectations for the technology in 2024. 

Research from 2023 indicates that although relatively few enterprises seem fully prepared for the widespread use of Generative AI and have yet to establish policies for its use, they’re taking precautions to address an important risk associated with it: inaccuracy. 

Nonetheless, if past results are any indication, organizations should continue to see the benefits of AI adoption. The report notes that reduced costs and enhanced revenues have been realized in areas like human resources, manufacturing, R&D and service operations, suggesting that businesses using AI tools to manage these areas where its potential has the most value.

According to the report, service operations seemed to be the only business function where most of the survey respondents said they expected to see a reduction in workforce due to the implementation of GenAI. But there is other research that suggests that while the emergence of GenAI has the potential to change the anatomy of work itself. Rather than replacing workforce roles, GenAI and other AI technologies could enhance the day-to-day experience of individual workers by automating activities that take up about 60-70% of their time, freeing them up to focus on other tasks. 

The big picture is that GenAI looks like it will become an increasingly accessible and versatile technology. Deloitte predicts that as enterprise software companies continue to integrate AI, it has the potential to give them a $10 billion boost by the end of 2024.

Shadow AI

In 2024, administrative leaders must take charge how AI is used in their workplaces in an effort to defend against practices like shadow AI

Shadow AI is when employees integrate AI tools, like ChatGPT, into their work systems without management’s knowledge. Even if it’s done to draft emails or write presentations, which are both innocuous on their own, the use of unauthorized AI models could expose their organizations to potential data security risks. 

First, employees could be feeding sensitive or proprietary information from PowerPoint decks or meeting chats into the AI model, and there is no guarantee where sensitive information like this might end up once it’s been fed into the tool. 

The second risk is that since these tools are being used outside of the awareness and oversight of cyber security management and IT staff, the company is not able to take the steps required to reduce risks. This is especially a concern if an employee is working remotely.

We may see companies moving toward policies that eliminate the use of shadow AI in 2024, including adoption of in-house GenAI tools like Llama2 and Falcon AI, which can be downloaded and used more securely, unlike the more public large language models used in ChatGPT.

Regulation in AI

Although we saved this one for last, a number of regulation, copyright and ethical concerns in areas like misinformation and exploitation of intellectual property will need to be more effectively addressed in the coming years. 

The enhanced multimodal capabilities we’ve discussed coupled with this technology’s low barrier to entry invite new ways for the technology to be abused, such as deepfakes.

In March 2024 the White House announced new guidelines for how federal agencies can and cannot use AI. This follows the October 2023 release of a wide-reaching Executive Order directing new standards for AI safety and security, protection of Americans’ privacy, consumer protections and the promotion of innovation and competition in the space.

The order followed efforts from the Spring of 2023, where the White House secured voluntary assurances from AI developers to adhere to certain trust and security guidelines in an attempt to promote ethical AI practices. 

One hotly contested issue that may be likely to remain in 2024 is the use of copyrighted material in AI model training and content generation. In 2023, the New York Times filed a lawsuit against OpenAI and Microsoft for copyright infringement. They claim the companies illegally copied what is likely to be millions of Times-owned works to train ChatGPT. This action seeks to limit the practice of content scraping from the internet without compensating the owners of the intellectual property. The outcome of this litigation has the potential to have a significant effect on any forthcoming AI regulation.

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