Contact form
Your request has been sent.
Please enter your name
Please enter your email
Please enter a correct email address
Please enter your company name
Please enter your message

3 questions to Bruno Thuillier - CTO of Alpha Mos

← All resources
3 questions to Bruno Thuillier - CTO of Alpha Mos

Over his 30-year career, Bruno Thuillier has helped launch and scale some of the world’s most innovative telecommunication products, mobile phones and sensors. Before co-founding BOYDSense, Bruno was the Chief Technology Officer at Sensitive Object. He also served as Vice President of R&D at Siemens Mobile Phones, and studied at the Conservatoire National des Arts et Metiers. Now he acts as CTO of Alpha MOS and BOYDSense.


Explain us concretely how Alpha Mos AI solutions work? What needs do your solutions solve?

Alpha MOS sensory measurement solutions based on Artificial Intelligence are designed to measure and quantify product characteristics from human sensory perceptions, such as sight, smell or taste. These solutions are commonly used in the food, cosmetics and pharmaceutical industries to help assess the quality and sensory properties of products.

The operation of these solutions can vary depending on the applications and needs of our customers but generally include four distinct phases:

  • Data Collection: Sensory sensors collect data about the physical characteristics of products. This data is then converted into digital signals.
  • Data processing: Artificial intelligence algorithms are used to process the digital signals and extract relevant information about the sensory characteristics of the product.
  • Data Analysis: The processed data is analyzed to determine trends, relationships and differences between product samples.
  • Interpretation of results: The results of the analysis are interpreted to provide information about the quality, flavor, odor, texture and other sensory properties of the products.
  • By using our solutions, our customers can objectively measure the sensory properties of their products and improve their quality. AI-based sensory measurement solutions also reduce the subjectivity associated with human sensory evaluations, which can improve the reliability of results.


Why can the sense of smell be seen as complicated to grasp from an AI perspective? What are some of the challenges you face?

Smell is complicated to understand from an AI perspective for several reasons.

First, odors are complex chemical stimuli that are perceived by the human olfactory system. There are thousands of different volatile organic compounds that can be mixed in the air to create an odor. Each volatile organic compound interacts with specific odorant receptors in the nose, creating a unique neural response in the brain. This complexity makes it very difficult to create an artificial olfactory sensor that can detect and quantify odors.

In addition, the sense of smell is very subjective and varies from person to person. Olfactory perceptions can also vary according to context, age, health and other individual factors. This makes it difficult to define a universal reference model for odor identification.

Finally, it is difficult to replicate the environmental conditions necessary to detect odors accurately. The concentration of volatile organic compounds, air humidity, temperature, and other factors can influence odor perception.

To address these challenges, our researchers are working to create artificial odor sensors that are capable of detecting complex mixtures of volatile organic compounds, objectively characterizing odors, and mimicking the environmental conditions in which odors are detected. Artificial intelligence models can also be trained to recognize odor patterns based on data from artificial olfactory sensors, as well as data from sensory evaluations by humans. This approach can help establish reference patterns for odor identification and improve the accuracy of olfactory detection.


According to you, what will be the next revolution in AI?

It is difficult to predict with certainty what the next AI revolution in sensory analysis will be, as advances in this field are happening rapidly. The next revolution in sensory intelligence, which refers to the ability of machines and systems to process and interpret information from sensory sensors, could include the following developments:

  • The integration of more advanced sensors: Modern sensors are becoming more sophisticated and can provide more detailed information about their environment. In the future, the integration of sensors such as gas, temperature and pressure sensors into sensory analysis systems could provide even richer and more accurate information.
  • The use of deep learning: The use of deep learning in sensory analysis could allow machines to learn from large amounts of sensory data and provide more relevant patterns and relationships.
  • Integrating augmented and virtual reality technologies: Augmented and virtual reality technologies allow users to visualize and interact with sensory data in an immersive way. Integrating these technologies into sensory analysis systems could allow users to visualize data in a more intuitive way and make more informed decisions.


Related articles
3 questions to Karim Beguir
3 questions to Karim Beguir
3 questions to Karim Beguir
3 questions to Joseph Ozanne, specialist in AI simulation of racing yachts
3 questions to Joseph Ozanne, specialist in AI simulation of racing yachts
3 questions to Joseph Ozanne, specialist in AI simulation of racing yachts
3 questions to Eric Sibony - Co-founder & Product Director of Shift Technology
3 questions to Eric Sibony - Co-founder & Product Director of Shift Technology
3 questions to Eric Sibony - Co-founder & Product Director of Shift Technology