Key AI Trends to Look For in 2024

When 2023 dawned, few observers predicted the impact AI would have on IT, business, and the world at large. Now that the dust has settled, it’s time to look forward to a new year and the trends that will define AI progress in 2024.

We queried 10 experts via email to find out how they expect AI to evolve over the next 12 months. Here are their insights.

1. LLMs lead to AGIs

The defining AI trend of 2024 will be the widespread integration of large language models (LLMs), like ChatGPT, progressing toward artificial general intelligence (AGI), predicts Avi Gruska, senior director, AI, at analytics’ technology firm Sisense. “This trend emphasizes the transformation in workforce dynamics, where AI enhances job roles by supporting core skills and creativity, especially in data analytics.

2. Stronger cyber defense; craftier attackers

AI is already providing a tremendous advantage for our cyber defenders, enabling them to improve capabilities, reduce toil, and better protect against threats, says Phil Venables, CISO of Google Cloud. “We expect these capabilities and benefits to surge in 2024, given that the defenders own the technology and thus can direct its development with specific use cases in mind,” he explains.

On the other hand, Venables expects that attackers will use generative AI and LLMs to personalize and slowly scale their destructive campaigns. “They will use anything they can to blur the line between benign and malicious AI applications, so defenders must act quicker and more efficiently in response.”

Related:Biden Signs Sweeping Executive Order Regulating Artificial Intelligence

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3. AI goes multi-modal

The most important AI trend of 2024 will be the emergence of multi-modal retrieval architectures and multi-modal inference taking center stage in AI products, predicts Rak Garg, a principal at Bain Capital Ventures.

Most 2023 AI products inspired by ChatGPT have been textual. “But users prefer more expressive software that meets them in every modality, from voice to video to audio to code and more,” Garg says. “If we can get these architectures to work at scale, which would require a specialized set of retrieval augmentation startups to innovate in multi-modal, we could unlock a new category of software that provides much more accurate and human results.”

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