If we need to introduce artificial intelligence (AI), it is partly a discipline and cognitive and intellectual capabilities embodied by computer systems or combinations of algorithms to perform certain tasks with a form of intelligence resembling that of humans. This is one definition among many, as one could say the complexity of AI is comparable to human nature.
This means that, like humans, there is no singularity in the definition or types of AI. However, it can be understood through certain steps, starting with "natural language processing," which is the way of conveying information. Secondly, it involves how to represent and store the knowledge understood and acquired, and then how to internalize and leverage it in response to a question.
Making tangible what is intangible is a task made possible by humans, raising two essential questions: how do they do it and why does it matter to them? AI, which is at the center of all current conversations and also the goal of all IT strategies, is currently experiencing its golden age.
With a very rapid pace of evolution in this field, innovation strategies that choose an open-source approach are favored because they allow for agility and transparency in AI-related usage. Open source, as most software publishers in this ecosystem have understood, is the way to materialize AI. Today, open-source communities are the starting point for innovation, particularly in the field of AI.
One can completely affirm that AI is redefining the use of traditional computing, as a technology that, for the first time in a long time, stimulates innovation, generates debates about its scope, and continually pushes back boundaries. We are facing a major conclusion, after many stages passed over the last twenty years: it is now possible to unleash the full power of AI.
This power, precisely, depends directly on the power that IT actors attribute to AI, depending on their market position. Gartner describes AI as having "the potential to provide immense added value to companies by enabling them to increase productivity, improve decision-making, and generate new growth and innovation opportunities," yet with a caveat: some companies restrict the use of AI to a single type of task, thus limiting its scope. There is no doubt that artificial intelligence is a major asset for evolving industries and governments, whether in data analysis, fraud detection and prevention, or major advances in healthcare.
Generative AI is the concept that has made the most noise, in the era of information access, as a technology capable of creating new content from Deep Learning models trained with large datasets. This type of AI model is used to generate new data, contrary to discriminative AI models, which enable classifying data according to their differences. They are then used to create text, images, and code, with examples like chatbots, image creation and editing, software code assistance, and scientific research.
Businesses need, from software publishers, solutions to deploy generative AI technologies daily (for instance, offering analysis or even intelligent remediation of platforms for system administrators, generating code in assistance mode for developers, etc.). Publishers must maintain end-to-end control over these technologies.
One of the major advantages here is mastering the entire lifecycle of a machine learning model, such as an LLM, by preparing the dataset, training the model with data scientists, scaling it with operators, and finalizing production within enterprise applications.
It is not only users who are impacted by artificial intelligence: it is also the case for companies, some of which choose a digital and cultural transformation strategy as their sole guiding line for future projects. The risk for those who refuse to get on board is to be left behind. It is essential to evolve simultaneously with AI to hope to unlock its full potential.