ARTIFICIAL INTELLIGENCE


WHAT IS IT?

AI expert company

Neovision gives you its definition

“At present, AI (Artificial Intelligence) does not totally replace human beings (or only in rare cases). It helps increase them, to assist them in their daily lives (no, this is nothing new, a person who wears glasses is also “increased”). AIs are specific and they are designed to solve specific problems. We are still very far from being able to create a generalist AI (i.e. that would be able to perform tasks without any link and multiply applications). However, these AIs already find multiple applications that improve the performance of the companies that integrate them, whether in their production cycle, quality control, marketing, sales, or HR departments among others.”

 

— Excerpt from the article : Our definition of AI, Neovision’s expert viewpoint

An AI is a computer programm capable of processing a large volume of data very quickly.. It is thus more efficient than human beings on specific applications and allows for many automations and optimizations.
Schéma de l'Intelligence Artificielle et ses domaines

THE GENESIS

OF ARTIFICIAL INTELLIGENCE

At Neovision we create custom-built AI’s
with the aim of responding to very specific problems..
But how is an artificial intelligence created?What are the main steps ?
DATABASE

ACQUISITION & STRUCTURE

Data is the raw material needed to create AI. It must be representative of the real data that the algorithm will have to analyze. Without data, there can’t be any AI. The data must then be cleaned and annotated before being exploited by an algorithm.

Illustration Base de données
TRAINING

TO CREATE A MODEL

Once the database has been prepared, it is necessary to find the appropriate algorithm. Whether it is Open Source or designed by us, an algorithm will be adapted to certain types of data and use cases. Once selected, the algorithm will be trained on the prepared data.

ITERATIONS

TO OPTIMIZE PERFORMANCE

Following the training of the algorithm, we obtain a model, a mathematical function capable of applying a treatment on new data. This model is then tested and validated on the dataset. If the results are unsatisfactory, we will work again on the datasets and the models will need to be re-trained.

Intégration illustration
PRODUCTION LAUNCH

OF THE SOFTWARE SOLUTION

When the model is satisfactory, it is time to put it into production. First of all, a beta version of the solution must be quickly set up, so that the technology can be tested in a real environment. Subsequently, software engineering work is required to interface the software brick with the customer’s existing IT system.

CO-BUILT SOLUTIONS

The creation of an AI requires very high skills and knowledge
in mathematics and computer science. However, these skills are not enough
and partner involvement is essential, especially in data acquisition
iterations and integration.
For each step, you will find out what the customer needs to do
and what Neovision needs to do.
CUSTOMER :

 

The customer must be able to provide Neovision with a dataset that is representative of what they wish to observe and process. If this is not the case, Neovision will be able to assist the customer in the acquisition of new data.

 

 

 

NEOVISION :

 

Study of the quality and quantity of the data provided by the customer. Indeed, the volume of data and its representativeness are two essential aspects to the success of the project. In the event of a lack of data, Neovision can advise and support its customer in the acquisition of data.
CUSTOMER :

 

The customer must provide an exhaustive list of its technical constraints, expected performance and define the concrete use of the technology. The choice of the most relevant algorithm will be made based on this information.

 

 

 

NEOVISION :

 

In order to design a solution/strong>, our experts identify the most relevant algorithm. To do this, they survey the latest research papers and the best Open Source libraries. However, if no algorithm exists in the state of the art, our scientists will be able to create one from scratch
CUSTOMER :

 

The solution, designed and then prototyped, now needs to be tested. The role of the customer is to take part in this testing phase to get as much feedback as possible and to provide essential information. The goal is to identify areas for improvement and prioritize them to achieve the objectives set.

 

 

 

NEOVISION :

 

Neovision tests the prototyped technology and, by cross-referencing its observations with those reported by the customer, improves the technology (model parameterization, new learning, etc.).Iterations are important to be able to optimize AI and reach an optimal level of performance (variations between the controlled environment in R&D and the real environment where it will be in production).
CLIENT :

 

The customer needs to give Neovision comprehensive information about their IT environment. The more Neovision is aware of the solutions already in place, the better the integration and production of the solution will be.. One or more privileged contacts must be identified to ensure good communication, which is essential for good integration.

 

 

 

NEOVISION :
Neovision uses its software engineering skills. The major challenge of this phase is to interface the technology created. This technology must be able to be easily integrated into the customer’s IT system. Finally, Neovision makes its experts available to the customer to help them understand and use the technology.

AI APPLICATIONS

When we talk about artificial intelligence today,
we mainly refer to machine learning
and deep learning methods.

 

But to find real applications, these methods
must be applied to different fields such as computer vision,
natural language processing or predictive analysis.

 

Find below some concrete examples of AI applications.

COMPUTER VISION

Computer vision, consists of processing and analyzing visual data, i.e. images. This data is dense and structured and is relatively easy to exploit and most of the scientific advances in AI are first made on images.
SOME EXAMPLES :
  • Image, object, facial recognition
  • Detection of anomalies and defects
  • Text recognition
  • People detection

NATURAL LANGUAGE PROCESSING

Le Natural Language Processing, or NLP, allows to detect some words or expressions, feelings and emotions conveyed by the language. For example, thanks to NLP, it is possible to know what is the meaning of the word “crane” in a sentence (Is it the bird or the machine?)
SOME EXAMPLES :
  • User behavior and customer knowledge
  • Anonymization of data and documents
  • Automated semantic analysis
  • Chatbot and personal assistant
  • Automated translation
  • Speech recognition

PREDICTIVE ANALYSIS

Have you ever wanted to foresee the future? For a human being, this seems almost impossible because the variables can be so numerous and their correlations so subtle. By basing on a history of data and coupling it with correlated external data, it is possible to predict some events with great accuracy.
SOME EXAMPLES :
  • Predictive maintenance
  • User behavior and customer knowledge
  • Home automation / Smart building
  • Churn prevention
  • Prediction of sales and/or production
  • Logistics and stock optimization
  • Risk management / scoring for investments

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