What to Expect in the GenAI/LLMs in 2024
2023 has been a year of Generative AI and Large Language Models (LLMs). We’ve witnessed breakthroughs in capabilities, new players emerge, and ethical/safety concerns rise like a tide. AI is just getting warmed up, and while I’ve laid out some ideas for 2024, it’s anyone’s guess what the future holds. Buckle up because 2024 promises to be even more exhilarating and a bit bumpy.
1. Democratizing LLM Access: The Rise of the App Store Model
OpenAI’s pioneering initiatives with customGPT and GPTAssistant signal a significant move towards opening up LLM customization. Expect the emergence of robust LLM app stores, allowing developers to offer diverse models pre-trained on specific domains, tasks, and even stylistic preferences. This democratization will empower developers and businesses to leverage the power of LLMs without requiring extensive expertise.
2. Enterprise Adoption: Balancing Innovation with Risk Mitigation
While Proof-of-Concept (POC) projects have dominated the LLM landscape to date, 2024 will see a surge in enterprise adoption. However, this shift will necessitate a heightened focus on data security, privacy, and compliance. Expect stringent company policies and potential incidents to shape the discourse around responsible LLM deployment.
3. Hallucinations and Unpredictability: Navigating the Limits of LLM Capabilities
Despite their impressive capabilities, LLMs remain susceptible to biases, hallucinations, and unpredictable outputs. These limitations particularly concern high-stakes domains like healthcare, potentially delaying widespread adoption. Developers will prioritize low-risk use cases where some costs of inconsistent results are acceptable. And take a more cautious approach in sensitive applications.
4. Sticker Shock: Smaller, Thriftier LLMs
The computational cost of running LLMs at scale will be a significant barrier for many enterprises. This cost and expensive hardware requirement will fuel the development of smaller, more domain-specific models designed for specific tasks and running on low-powered hardware. Like Google launching Gemini Nano, current players might release a smaller version. But startups and open-source communities will be crucial in driving this trend, offering cost-effective alternatives to resource-intensive general-purpose LLMs.
5. The Multimodal Revolution: Beyond Text, LLMs Embrace Sensory Inputs
The future of LLMs lies in their ability to integrate information from multiple modalities, including images, audio, and even physical interactions. Expect significant advancements in multimodal learning and generation, enabling applications like interactive storytelling, personalized education, and AI-powered artistic creation.
6. The Global Garage: Micro Startups Drive Local Applications
The AI landscape is no longer confined to Silicon Valley. Expect a wave of innovative applications that leverage LLMs. Expect a surge of innovative use cases from small, two-three-person startups across the globe. Fueled by local knowledge and diverse perspectives, these teams will explore unique use cases and push the boundaries of LLM capabilities in unexpected ways.
7. Regulations Tighten the Reins: The Era of Responsible AI
The European Union’s recent AI regulation is just the beginning. Expect similar regulatory frameworks to emerge in the US and other countries throughout 2024. These regulations will focus on data governance, bias mitigation, transparency, and explainability, forcing developers and businesses to adapt their LLM development and deployment practices.
2024 promises to be a pivotal year for Generative AI and LLMs. It will be a year of innovation, adoption, and some bumps on the road. But one thing is sure: the future of AI is being shaped right now, and it’s a future where LLMs will play a central role. So, buckle up, hold on tight, and get ready for the ride.
