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AI Consultant @Philips | I write about AI and BCI https://www.linkedin.com/in/agonfalonieri9/

Commercial applications, Brain augmentation and Future of brain-computer interfaces

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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 brain signals. As classical brain-computer interface systems move beyond lab demonstrations to real-world applications, BCIs are becoming a strategic topic for tech companies. Indeed, BCIs are poised to become the next must-have consumer gadget.

In my latest project, I had the opportunity to work on a project based on electrical…


When to use Behavioral Cloning, Use cases, Apprenticeship Learning via Reinforcement Learning and Imitation Learning

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The ability to learn through experience is a fundamental aspect of human intelligence. However, learning to perform a task relying only on one’s own experience can be arduous and time-consuming.

In reality, our ability to learn can also work by observing and imitating the behavior of those around us. Enabling this same ability in autonomous agents has become a key topic in the Machine Learning community. Many real-life applications could highly benefit from this ability, such as industrial robots. Indeed, programming robots to perform complex tasks is difficult with classical methods.

During one of my latest projects related to multi…


Active algorithms, hybrid BCI, Scalability, Bandwith, and Future of non-invasive BCI headsets

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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. Now, measuring brain activity is no longer limited to medical diagnostics but includes more applications aiming to change users’ lifestyles.

As part of a team in charge of innovation, I was involved in the development of several BCI prototypes. This article will present some of the current technical and commercial challenges of wireless BCI systems…


How to improve the scalability of Swarm robotics applications, Collective AI algorithms and hardware limits

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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 found in the context of a Machine-to-Machine economy (M2M).

Recently, I had the chance to collaborate with a team involved with swarm robotics. The goal was to leverage a group of small robots to identify water leaks in a large-scale facility.

In this article, I will explain the main issues…


New Business applications combined with Brain-computer interface and Generative Adversarial networks

Image by Ulrich Wechselberger from Pixabay

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 data corruption. In my latest project, we explored the idea of leveraging data augmentation methods such as generative adversarial networks to create synthetic EEG signals.

Indeed, data augmentation (DA) is a potential solution to address these issues. …


Differences with Reinforcement Learning and non-exhaustive list of use cases

Photo by Alex Perri on Unsplash

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 understanding of Generative Adversarial Imitation Learning (GAIL). As part of a team, my goal was to use GAIL to help a robot predict and understand human behaviors for safety purposes.

In this article, I will explain Generative Adversarial Imitation Learning, introduce its advantages and explain the limits of this approach.

The importance of learning human decision-making strategies


Introduction to Autonomous Economic Agent, Machine Learning and New Business Models in the Machine to Machine economy (M2M)

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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 pilot project. This new strategy included the creation of new business models, selection of a new Machine Learning approach, and identification of possible technological partners.

In this article, you will learn about key future concepts such as autonomous economic agents, discover M2M use cases, and how companies will have to…


How Machine Learning combined with Brain-computer interface can create new revenue streams

Photo by Morgan Housel on Unsplash

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 deep learning solutions to help improve BCIs accuracy, and drop in the cost of EEG (Electroencephalography) headsets, a growing number of startups and large tech firms are looking for new ways to generate revenue from brain data.

Indeed, the development of brain-computer interfaces will fuel a lucrative data market and…


Introduction to Homomorphic Encryption, use cases and impact on Machine Learning projects

Photo by Jon Moore on Unsplash

One of the main “issues” about AI projects is data privacy. Indeed, you might identify the best use case for your company and then realize that your business project depends on data you are not allowed to use since you can not comply with existing data privacy regulations (for good reasons). This situation hinders our ability to leverage AI in real-life business applications.

Indeed, most Machine Learning systems are fed by data that are very sensitive and personal (customer data, health records, CCTV footage, etc.).


How AI DAO could help create fully autonomous stores and new business models

Photo by Simon Bak on Unsplash

The need to automate retail stores has become a priority due to the recent sanitary crisis. Indeed, it has become essential for retailers to adapt their business models if they want to survive. In this context, I worked on a project related to AI decentralized autonomous organizations (AI DAO) and retail stores.

Can a store become independent? Can you invest in a store that does not require human workers? Can we make a store more intelligent by using distributed artificial intelligence?

In this article, I will explain why existing retailers are at risk and must urgently invest in new business…

Alexandre Gonfalonieri

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