/expertise

Artificial Intelligence Is Not Magic

It is built brick by brick through the integration of technologies from the fields of mathematics, computer science, statistics and data science.

Expertises, AI Is Not Magic (rabbit)

/expertise

Artificial Intelligence Is Not Magic

It is built brick by brick through the integration of technologies from the fields of mathematics, computer science, statistics and data science.

Data Mining (icon)

Data Mining

Data mining is a set of processing and analysis techniques used to identify connections and correlations in large volumes of data. It is used to convert raw data into meaningful and useful information. Data Mining uses techniques (usually algorithms) from the fields of Machine Learning and Statistics which include:

  • Data preprocessing gathers, normalizes and cleans data in order to make it accessible and usable by algorithms.
  • Outlier detection aims to identify entries that significantly deviate from the rest of a dataset (input error, exceptional atypical value, etc.) in order to delete, keep or correct them.
  • Data visualization is the representation of information and data through visual elements (e.g., graphs, charts, etc.). It can be an end in itself or help Data Scientists see and understand features, trends and patterns in data.
  • Pattern mining is a set of techniques that help to find and identify relationships (causality, sequential, etc.) between variables in a dataset.
  • Clustering divides data into groups with similar properties. This technique is useful to find similarities between unlabeled data or to check the quality of a data labeling process.
  • Predictive analysis aims to predict future values and trends based on known preprocessed and labeled data. To do this, it looks for patterns that can lead to predictions and plausible models.

Data Mining can be a stand-alone solution (for instance with data visualization in the context of Business Intelligence projects) or a prelude and the raw material for the development of a more complex Machine Learning model.

Expertise - Artificial Intelligence (icon)

Artificial Intelligence

Artificial intelligence, or AI, is an umbrella term for a set of domains and techniques that enable machines to perceive their environment, analyze what they perceive, and act to solve a problem or achieve a specific goal.

IA brain illustration

Current applications of Artificial Intelligence are often based on Machine Learning algorithms (sets of unambiguous instructions that can be executed by a computer). Over the last decade, the rapid progress in the field of Machine Learning has been a driving force in the increasing use of AI in industry and enterprise.

Expertise - Machine Learning (icon)

Machine Learning

Machine Learning is a branch of Artificial Intelligence. It relies on methods, or algorithms, to automatically create models from data. Unlike explicit rule-based systems (which always perform a task in the same way), Machine Learning models learn from experience, adapt and improve their performance when they are exposed to more data.

Machine Learning algorithms are “trained” to recognize features or patterns in data and derive “lessons” from them. They then apply these “lessons” to new data to make predictions and/or decisions.

There are two main types of Machine Learning:

  • Supervised learning exposes algorithms to labeled training datasets in which inputs and outputs are predefined by data scientists. This method is used to perform classification tasks (assigning a class to a data item) or regression tasks (assigning a mathematical value, number or percentage, to a data item).
  • Unsupervised learning exposes algorithms to unlabeled datasets in which they seek to identify recurring features or connections. This method is used for clustering tasks (grouping data into the most homogeneous sets possible) or collaborative filtering (e.g. personalized recommendation system).

Reinforcement learning, a behavioral approach to learning, complements the two main types. It involves setting up a system of “rewards” and “punishments” in a closed environment. This system allows the algorithm to develop, by trial and error, the optimal sequence of actions or decisions to solve a problem.

Machine Learning models have various structures and learning processes. “Classic” ML methods are often integrated with, or used alongside, more recent approaches based on Artificial Neural Networks (ANN).

ARTIFICIAL NEURAL NETWORKS

Artificial Neural Networks or ANNs are computer systems inspired by the human brain’s functioning. These systems are composed of interconnected units (like neurons) organized in multiple layers. First, an input layer where data from external sources enters the system. Then, one or more hidden layers that process the inputs and apply weights, biases and thresholds to them. Finally, an output layer where one or more conclusions (in which the network has varying degrees of confidence) emerge.

Deep Learning (icon)

Deep Learning

Deep Learning is a subfield of Machine Learning. Deep Learning models are based on Deep Neural Networks which are ANNs with several hidden layers. In these networks, each layer refines the findings of the previous layer. The term “deep” refers to the depth of the Neural Network which is defined by the number of hidden layers it contains.

Deep Learning - Neural Network (illustration)

In most Deep Neural Networks, computations flow in one direction only, from input layer to output layer, in a process called feed-forward. However, it is also possible to train a model to identify errors in each neuron and to correct them (by assigning weights that are sent back to previous layers). This process known as back-propagation allows to refine and optimize a model.

Deep Learning is an important advance in the field of Machine Learning. Its development has made possible most of the recent achievements in AI.

Deep Learning/Machine Learning comparison table
Natural Language Processing (icon)

Natural Language Processing

At the crossroads of linguistics and computer science, NLP is a branch of artificial intelligence that helps computers decipher and understand human language (written or spoken). NLP uses algorithmic models to identify and extract elements and rules from unstructured natural language. It then uses this information to turn natural language into data that can be processed by a computer system.

Computer Vision (icon)

Computer Vision

Computer Vision is another branch of artificial intelligence. This technology reproduces some complex aspects of human vision and allows computers to recognize and process objects, people or movements in images and videos. Computer vision has greatly benefited from the advances in the field of Deep Learning. It is now able to match or surpass human performance in certain tasks of object detection and identification.

MLOps icone

MLOps

Although ML code is the essence of a Machine Learning solution and drives all the decisions, it is only a small part of the overall system that needs to be developed for a solution to be efficient and reliable.

MLOps (or Machine Learning Operations) is an emerging engineering discipline that aims to unify the development and deployment of Machine Learning models. Inspired by DevOps, MLOps involves creating an automated production environment (ML pipeline), from data collection and preparation to model deployment and monitoring. Its goal is to sustain the performance of an ML model over time and thus ensure the success of an automation solution.

MLOps illustration

Research & development

Early on, Inceptive made the choice to invest in internal R&D to guarantee its technological independence. This ongoing commitment combined with a thorough technology watch and skills in applied mathematics and software development, has allowed us to develop several proprietary tools that include our Robo Fabrica and Igloo platforms.

Igloo is a global and modular Data Mining and Machine Learning platform. It is built around three modules. The first one, Inceptive Machine Learning Engine (MLE), is the algorithmic heart of the platform. The second, Inceptive Studio, is the interface through which Inceptive MLE is controlled and configured. Finally, Inceptive Server delivers the models developed with Inceptive Studio to the end user.

Igloo, Proprietary Machine Learning platform

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