TOP TRENDS TO FOCUS ON:

Reality check: more realistic expectations

Multimodal AISmall(er) language models and open source advancementsGPU shortages and cloud costs

Model optimization is getting more accessible

Customized local models and data pipelines

More powerful virtual agents

Regulation, copyright and ethical AI concerns

Shadow AI (and corporate AI policies)

2022 was the year that generative artificial intelligence (AI) exploded into the public consciousness, and 2023 was the year it began to take root in the business world. 2024 thus stands to be a pivotal year for the future of AI, as researchers and enterprises seek to establish how this evolutionary leap in technology can be most practically integrated into our everyday lives.

The evolution of generative AI has mirrored that of computers, albeit on a dramatically accelerated timeline. Massive, centrally operated mainframe computers from a few players gave way to smaller, more efficient machines accessible to enterprises and research institutions. In the decades that followed, incremental advances yielded home computers that hobbyists could tinker with. In time, powerful personal computers with intuitive no-code interfaces became ubiquitous

Generative AI has already reached its “hobbyist” phase—and as with computers, further progress aims to attain greater performance in smaller packages. 2023 saw an explosion of increasingly efficient foundation models with open licenses, beginning with the launch of Meta’s LlaMa family of large language models (LLMs) and followed by the likes of StableLM, Falcon, Mistral, and Llama 2. DeepFloyd and Stable Diffusion have achieved relative parity with leading proprietary models. Enhanced with fine-tuning techniques and datasets developed by the open source community, many open models can now outperform all but the most powerful closed-source models on most benchmarks, despite far smaller parameter counts.

As the pace of progress accelerates, the ever-expanding capabilities of state-of-the-art models will garner the most media attention. But the most impactful developments may be those focused on governance, middleware, training techniques and data pipelines that make generative AI more trustworthy, sustainable and accessible, for enterprises and end users alike.