Less (Data) Is More: Why Small Data Holds the Key to the Future of Artificial Intelligence
It addresses the problem of overreliance on big data in AI for researchers and practitioners, advocating for a shift towards more human-centric and privacy-focused approaches, which is incremental as it builds on existing critiques.
The paper argues that small data, rather than big data, is crucial for AI's future by highlighting limitations of deep learning in areas like NLP and emphasizing privacy and human collaboration.
The claims that big data holds the key to enterprise successes and that Artificial Intelligence is going to replace humanity have become increasingly more popular over the past few years, both in academia and in the industry. However, while these claims may indeed capture some truth, they have also been massively oversold, or so we contend here. The goal of this paper is two-fold. First, we provide a qualified defence of the value of less data within the context of AI. This is done by carefully reviewing two distinct problems for big data driven AI, namely a) the limited track record of Deep Learning in key areas such as Natural Language Processing, b) the regulatory and business significance of being able to learn from few data points. Second, we briefly sketch what we refer to as a case of AI with humans and for humans, namely an AI paradigm whereby the systems we build are privacy-oriented and focused on human-machine collaboration, not competition. Combining our claims above, we conclude that when seen through the lens of cognitively inspired AI, the bright future of the discipline is about less data, not more, and more humans, not fewer.