Support in artificial intelligence

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Our Services

We offer a variety of services to support companies that want to develop AI-based solutions. Whether your project is embryonic or well underway, our services will meet your needs.

Exploratory evaluation

Quickly learn about the potential applications of AI in your business.

Personalized coaching

Benefit from experts advice to steer your AI project in the right direction when you need it.

Solution development

Let us take care of the core of your AI project to bring it to a successful conclusion, from the proof of concept to production.

Tailor-made training

Sessions led by our AI experts, adapted to your business field and the level of knowledge of your staff.

Our specializations

Our diversified and complementary specializations allow us to have a multidisciplinary approach to solve problems with ingenuity. Our main areas of expertise include, among others:

Artificial intelligence, abbreviated as AI, is a vast field dedicated to the study and application of systems demonstrating capabilities typically associated with human intelligence. This definition encompasses a range of subfields that generally aim to automate a task. Thus, a more pragmatic definition of AI is that it addresses problems of automatic prediction and decision-making.

The recent interest of the public in AI comes from one of its subfields: machine learning (which includes deep learning). Recent advances in this field have made it possible to solve much more complex tasks, long considered feasible only by humans, such as driving, chess, X-ray diagnostics, etc.

Natural Language Processing (NLP) addresses any problem that involves a linguistic aspect. The main goal of NLP is to produce a language model, i.e. a numerical model capable of capturing the semantic and grammatical elements of a language, which can subsequently be used to achieve a related goal. Examples include identifying the topic or themes of a document, extracting specific information, summarizing a text, answering questions, and even maintaining a conversation. In particular, deep learning has recently led to significant improvements in machine translation models and automatic text generators, which has in turn led to a significant uptake of NLP by the industry.

Computer vision is the field of artificial intelligence focusing on image analysis and processing. It allows the computer to recognize what an image contains. In recent years, computer vision has grown considerably with the emergence of deep learning.

Computer vision has many fields of application. The typical case is object detection such as pedestrian detection, necessary in self-driving cars. Another case is image segmentation, in which one tries to annotate the different parts of an image. For example, one may want to annotate the parts of an image referring to animals on pictures taken by a camera in the forest.

Machine learning is based on solid theoretical foundations, involving advanced notions in mathematics, statistics and algorithmic. Theory not only explains why a learning algorithm works, but also provides performance, efficiency, anonymity and fairness guarantees. In other words, it allows to upper bound the number of errors that a model will make, to compare the running time of different algorithms, to ensure that confidential data remains confidential, and that minority groups are not put at a disadvantage. Being able to theoretically model a need can therefore be essential in certain critical or regulated fields such as health and insurance, an expertise that we master.

Reinforcement learning is a learning paradigm that aims to solve sequential decision problems through an approach that maximizes rewards and minimizes penalties. This formulation, directly inspired by animal learning theory, is both powerful and versatile, with applications in self-driving cars, advertising targeting, robotics and games. In fact, it is reinforcement learning that lies behind the resounding success of the AlphaZero program, world champion in the game of chess, Go and shogi.

Bioinformatics is a broad field involving the use of computer tools in relation to biological data. In particular, artificial intelligence offers an analysis and processing capacity that can revolutionize certain tasks in biology. Examples of applications are prediction of interaction between molecules, study of viral mutations, phenotype classification based on DNA sequencing, and knowledge extraction from scientific publications.

Artificial intelligence, abbreviated as AI, is a vast field dedicated to the study and application of systems demonstrating capabilities typically associated with human intelligence. This definition encompasses a range of subfields that generally aim to automate a task. Thus, a more pragmatic definition of AI is that it addresses problems of automatic prediction and decision-making.

The recent interest of the public in AI comes from one of its subfields: machine learning (which includes deep learning). Recent advances in this field have made it possible to solve much more complex tasks, long considered feasible only by humans, such as driving, chess, X-ray diagnostics, etc.

Natural Language Processing (NLP) addresses any problem that involves a linguistic aspect. The main goal of NLP is to produce a language model, i.e. a numerical model capable of capturing the semantic and grammatical elements of a language, which can subsequently be used to achieve a related goal. Examples include identifying the topic or themes of a document, extracting specific information, summarizing a text, answering questions, and even maintaining a conversation. In particular, deep learning has recently led to significant improvements in machine translation models and automatic text generators, which has in turn led to a significant uptake of NLP by the industry.

Computer vision is the field of artificial intelligence focusing on image analysis and processing. It allows the computer to recognize what an image contains. In recent years, computer vision has grown considerably with the emergence of deep learning.

Computer vision has many fields of application. The typical case is object detection such as pedestrian detection, necessary in self-driving cars. Another case is image segmentation, in which one tries to annotate the different parts of an image. For example, one may want to annotate the parts of an image referring to animals on pictures taken by a camera in the forest.

Machine learning is based on solid theoretical foundations, involving advanced notions in mathematics, statistics and algorithmic. Theory not only explains why a learning algorithm works, but also provides performance, efficiency, anonymity and fairness guarantees. In other words, it allows to upper bound the number of errors that a model will make, to compare the running time of different algorithms, to ensure that confidential data remains confidential, and that minority groups are not put at a disadvantage. Being able to theoretically model a need can therefore be essential in certain critical or regulated fields such as health and insurance, an expertise that we master.

Reinforcement learning is a learning paradigm that aims to solve sequential decision problems through an approach that maximizes rewards and minimizes penalties. This formulation, directly inspired by animal learning theory, is both powerful and versatile, with applications in self-driving cars, advertising targeting, robotics and games. In fact, it is reinforcement learning that lies behind the resounding success of the AlphaZero program, world champion in the game of chess, Go and shogi.

Bioinformatics is a broad field involving the use of computer tools in relation to biological data. In particular, artificial intelligence offers an analysis and processing capacity that can revolutionize certain tasks in biology. Examples of applications are prediction of interaction between molecules, study of viral mutations, phenotype classification based on DNA sequencing, and knowledge extraction from scientific publications.

Our Values

Baseline aims to democratize data science and artificial intelligence so that as many people as possible can benefit from it. As such, our actions rely on three main values.

Accessibility

We want companies of all sizes to benefit from advances in artificial intelligence. This is why our support is adapted to each client's situation and aims to keep them in control of their projects.

Fairness

A successful project treats everyone concerned equitably. The importance we grant to fairness is reflected in our internal management, but also in our sensitivity to human issues surrounding our projects.

Innovation

Innovation is essential to the success of businesses in the 21st century. Our services help them develop original strategies adapted to the challenges they face by leveraging their data.

About us

Baseline Logo

Baseline is an initiative resulting from our desire to share with companies the expertise we acquired during our graduate studies. It is the outcome of a collective will to share resources, to give ourselves greater visibility and to pool common expenses and risks, while respecting our individual needs. Thus, it is in line with the saying "strength in numbers".

It is in this context that we have chosen to make Baseline a work cooperative. This business model places its worker members on an equal footing, where everyone can actively contribute to the management and development of the organization. The cooperative model allows us to put forward our values of democracy, equity and commitment, and promotes discussion and collaboration among its members.

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Our Members

We are graduate students united by our passion for data science. Our team is made up of members from a variety of academic backgrounds such as computer science, mathematics, physics, actuarial sciences, and finance.
David Beauchemin

David Beauchemin

Mathieu Godbout

Mathieu Godbout

Jean-Samuel Leboeuf

Jean-Samuel Leboeuf

Gaël Letarte

Gaël Letarte

Frédérik Paradis

Frédérik Paradis

Dominique Pothier

Dominique Pothier

Simon Provencher

Simon Provencher

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