Collaborating Authors


Heyday lands $6M to build a knowledge base from the services you already use – TechCrunch


Ever spend much too long trying -- and failing -- to rediscover articles you've partially read? This reporter's been there, and it seems I'm not the only one. According to 2021 Carnegie Mellon study on browser tab usage, many participants admitted to feeling overwhelmed by the amount of tabs they kept open but were compelled not to close them out of fear of missing out on valuable information. Samiur Rahman is familiar with the feeling -- so much so that he co-created a product, Heyday, to alleviate it. Launched in 2021, Heyday is designed to automatically save web pages and pull in content from cloud apps, resurfacing the content alongside search engine results and curating it into a knowledge base. Investors include Spark Capital, which led a $6.5 million seed round in the company that closed today.

Modular Design Patterns for Hybrid Learning and Reasoning Systems: a taxonomy, patterns and use cases Artificial Intelligence

The unification of statistical (data-driven) and symbolic (knowledge-driven) methods is widely recognised as one of the key challenges of modern AI. Recent years have seen large number of publications on such hybrid neuro-symbolic AI systems. That rapidly growing literature is highly diverse and mostly empirical, and is lacking a unifying view of the large variety of these hybrid systems. In this paper we analyse a large body of recent literature and we propose a set of modular design patterns for such hybrid, neuro-symbolic systems. We are able to describe the architecture of a very large number of hybrid systems by composing only a small set of elementary patterns as building blocks. The main contributions of this paper are: 1) a taxonomically organised vocabulary to describe both processes and data structures used in hybrid systems; 2) a set of 15+ design patterns for hybrid AI systems, organised in a set of elementary patterns and a set of compositional patterns; 3) an application of these design patterns in two realistic use-cases for hybrid AI systems. Our patterns reveal similarities between systems that were not recognised until now. Finally, our design patterns extend and refine Kautz' earlier attempt at categorising neuro-symbolic architectures.