On finding the right tool, hiring or outsourcing annotators, and ML-assisted annotation — As you may know, data science teams spend about 80% of their time creating and managing training data. The usual issues are often related to poor in-house tooling, labeling re-work, finding the needed data, and the difficulties associated with collaborating and iterating on distributed teams’ data. Frequent workflow changes, large-volume…

Short guide for deploying ML models faster and creating a new competitive advantage — After different AI projects, I realized how quickly building and deploying efficient Machine Learning (ML) models can become a source of competitive advantage. …

Commercial applications, Brain augmentation and Future of brain-computer interfaces — Imagine having the feeling of touching an object miles away from where you are. This scenario is one of the possible applications of a bidirectional brain-computer interface. As the name suggests, a brain-computer interface (BCI) connects a computer to a brain, either by reading out brain signals or writing in…

Active algorithms, hybrid BCI, Scalability, Bandwith, and Future of non-invasive BCI headsets — Brain-Computer Interfaces are poised to become the next trendy consumer device. As such, most consumer electronics companies have started to integrate them into their roadmap in some way or another. Some companies have already developed and released consumer wireless EEG-based BCI with interesting non-medical applications. …

How to improve the scalability of Swarm robotics applications, Collective AI algorithms and hardware limits — Swarm robotics is a promising recent researching area inspired by swarm intelligence and robotics. The objective is to control a large number of simple robots to solve complex tasks. Despite the growing number of research papers, scalable applications are still far away, and new business models still need to be…

New Business applications combined with Brain-computer interface and Generative Adversarial networks — Despite significant progress in Brain-Computer Interface (BCI), many issues remain associated with collecting Electroencephalography (EEG) signals in real-world environments. This situation makes it difficult for BCIs to become a scalable device. Brain-computer interface has always been facing severe data-related issues such as lack of sufficient data, lengthy calibration time and…

Differences with Reinforcement Learning and non-exhaustive list of use cases — A growing number of AI projects rely on learning a mapping between observations and actions. For strategic and technical reasons, learning from demonstrations will play a crucial role in developing several use cases (robots, video games, self-driving vehicles). In my latest project, I had the chance to gain a solid…

Introduction to Autonomous Economic Agent, Machine Learning and New Business Models in the Machine to Machine economy (M2M) — The upcoming Machine to Machine (M2M) era will radically transform the way companies and assets are managed. As a consequence, a majority of existing business models will become obsolete. Based on this assessment, I was given the task to build an M2M strategy for my client as well as a…

How Machine Learning combined with Brain-computer interface can create new revenue streams — Recently, I had the chance to be part of a task force in charge of creating new business models related to Brain-computer Interfaces. The goal was to help a large tech firm understand how they could ethically leverage brain data. With the rise of non-invasive brain-computer interfaces (BCIs), development of…