Dean Mai

I specialize in identifying disruptive, core technologies and strategic technology trends in early-stage startups, research universities, government sponsored laboratories and commercial companies.

In my current role, I lead sourcing of strategic technology investment opportunities and managing Dyson’s diligence and outreach processes, specifically in the U.S., Israel and China.

I write here (sporadically) on the convergence of science and engineering, with broader adopted interests in novel disruptive technologies, cognitive psychology, human-computer interaction (HCI), philosophy, linguistics and artificial intelligence (AI).

All tagged federated learning

Dec 13
Dec 13 Specialization and Modularity in AI Architecture with Multi-Agent Systems

artificial intelligence, software

Unstructured data encompasses a wide array of information types that do not conform to predefined data models or organized in traditional relational databases. This includes text documents, emails, social media posts, images, audio files, videos, and sensor data. The inherent lack of structure makes this data difficult to process using conventional methods, yet it often contains valuable insights that can drive innovation, improve decision-making, and enhance customer experiences.

Nov 26
Nov 26 Adopting Function-as-a-Service (FaaS) for AI workflows

artificial intelligence, software

Unstructured data encompasses a wide array of information types that do not conform to predefined data models or organized in traditional relational databases. This includes text documents, emails, social media posts, images, audio files, videos, and sensor data. The inherent lack of structure makes this data difficult to process using conventional methods, yet it often contains valuable insights that can drive innovation, improve decision-making, and enhance customer experiences.

Jun 29
Jun 29 Architectural Paradigms for Scalable Unstructured Data Processing in Enterprise

artificial intelligence

Unstructured data encompasses a wide array of information types that do not conform to predefined data models or organized in traditional relational databases. This includes text documents, emails, social media posts, images, audio files, videos, and sensor data. The inherent lack of structure makes this data difficult to process using conventional methods, yet it often contains valuable insights that can drive innovation, improve decision-making, and enhance customer experiences.

Jan 23
Jan 23 Federated machine learning as a distributed architecture for real-world implementations

artificial intelligence

Present performance of machine learning systems—optimization of parameters, weights, biases—at least in part relies on large volumes of training data which, as any other competitive asset, is dispersed, distributed, or maintained by various R&D and business data owners, rather than being stored by a single central entity. Collaboratively training a machine learning (ML) model on such distributed data—federated learning, or FL—can result in a more accurate and robust model than any participant could train in isolation.