“AI Needs Specialization to Generalize”

Ameet Talwalkar is giving a talk with the above title and this abstract:

While modern AI holds great promise, the gap between its hype and practical impact remains substantial. This talk advocates for the importance of specialization to help bridge that gap–urging researchers to tailor problem formulations, modeling approaches, data collection, and evaluation methods to concrete downstream tasks. We begin by examining the limitations of existing domain-specific foundation models–for genomics, satellite imaging, and time series–that apply techniques from core AI domains such as vision and NLP with minimal specialization. We then present recent work from CMU and Datadog AI Research that advances specialized approaches across diverse tasks: solving partial differential equations, autonomously executing complex web tasks, and proactively detecting or predicting disruptions in production software systems. These efforts highlight the critical role of domain-aware design in moving beyond shiny demos and toward meaningful AI impact.

I don’t know anything about these particular application areas, but, speaking from my own experience, I agree with the title.

“Generalize” can be taken in two ways, and both of these need specialization.

The first sort of generalization is statistical: generalizing from sample to population, from treatment to control group, and from observed data to underlying constructs of interest. For these we need a mix of statistical and substantive models, with the statistical models supplying regularization (smoothness) and the substantive models setting up the generalization. For example, with MRP the statistical model is the multilevel regression and the substantive model helps us choose what variables to poststratify on, and similarly with causal and measurement models. The point is that specialization is necessary: without experience doing this in real problems, I think it would be difficult to generalize well, whether the application is pharmacology, political forecasting, or the mapping of environmental hazards.

The second sort of generalization is across problems: we develop a method in one application area and use it in another. These methods can be differential equation models, Gaussian processes, logistic regression, bootstrapping . . . also methods in experimental design, data collection, and statistical graphics. Sometimes these methods are developed on their own, Dirac-style, from first principles or as the logical consequence of other ideas, but often they arise out of particular application areas. This was the case with least-squares fitting, correlation, MRP, and all sorts of other methods–not to mention all the developments in AI that were motivated by particular applications. In that sense, we have indeed needed specialization to generalize. It is through solving domain-specific problems have we been able to create methods that are general enough to work in other domains.