You can intuitively apply the word “frontier” to know that these are the biggest and best new systems that companies are pushing. Another way to describe frontier models is as “cutting-edge” AI systems that are broad in purpose, and overall frameworks for improving AI capabilities. When asked, ChatGPT gives us three criteria – massive data sets, compute resources, and sophisticated architectures. Here are some key characteristics of frontier models to help you flush out your vision of how these models work: First, there is multimodality, where frontier models are likely to support non-text inputs and outputs – things like image, video or audio. Otherwise, they can see and hear – not just read and write. Another major characteristic is zero-shot learning, where the system is more capable with less prompting. And then there’s that agent-like behavior that has people talking about the era of “agentic AI.” If you want to play “name that model” and get specific about what companies are moving this research forward, you could say that GPT 4o from OpenAI represents one such frontier model, with multi-modality and real-time inference. Or you could tout the capabilities of Gemini 1.5, which is also multimodal, with decent context. A team of experts analyzed what it takes to work in this part of the AI space and create these frontier models. The panel moderator, Peter Grabowski, introduced two related concepts for frontier models – quality versus sufficiency, and multimodality. Douwe Kiela, CEO of Contextual AI, pointed out that frontier models need a lot of resources, noting that “AI is a very resource-intensive endeavor.” “I see the cost versus quality as the frontier, and the models that actually just need to be trained on specific data, but actually the robustness of the model is there,” said Lisa Dolan, managing director of Link Ventures.