The alternative that constitutes the choice of open source is embodied in four main arguments linked to AI.
The generative AI, popularized since 2022 by chatbots or even well -known tools like Chatgpt, crystallizes a large part of the discussion on artificial intelligence (AI) around it. The generative part of AI thus supports the vast majority of the concerns of IT decision -makers, who cover both investments in the cloud and the evolution of the course of NVDIA action, including the purchase of dedicated equipment. There is also an ignorance of the challenges of these types of projects on the part of IT actors who nevertheless commit in it. This development of the generative AI imposes on publishers, already specialists in traditional predictive AI projects, to extend their knowledge, in particular with major language models (LLM). The market players voluntarily maintain these in an opaque owner logic, such as “black boxes” in which users of cloud and SaaS solutions and devoid of cost control capacities would be captive.
For independent technology and costs transparency
Why choose open source? First of all, it is a question of abolishing the dependence which weighs on the actors of the owner and give them more autonomy, but it is also of economic issues. Indeed, the error for users would be to choose “closed source” solutions, that is to say closed, which do not allow any evolution on one side or the other, while the AI market has not yet reached its stage of maturity. Thanks to open source, on the contrary, users are masters of their services, and can deploy them on their private, public or their own sites, while avoiding additional costs. Existing Open Source models today provide performance and reliability levels comparable to owners’ equivalents; They are also more flexible on their size, and therefore their consumption, than the cloud alternatives.
Improve the cleanliness of data models
The transparency established on the data of AI models thanks to the open source makes it possible to respond to the intellectual property issues, which find their limit in the “black box” type model. Indeed, existing generative AI tools are often based on all accessible data on the Internet, which they have used to train their models. No user who uses AI to generate content is immune to a legal attack launched by the owners of the data used, despite the clauses that govern the use of AI tools.
The identified presence of bias in data sources and training methods is a second point of concern, which can impact the result. In order to guarantee the cleanliness and reliability of the data, but also to defend the open source community in the event of a problem, it is advisable to rely on open source data games; If all users can easily locate and access source equipment, it will be easier to solve problems on intellectual property and these will also be less frequent.
AI simplifies customization of uses
The generative AI is used by each company in a different way, to serve its specific objectives linked to its activity. One of the main prerequisites is the company’s ability to integrate the data it has in a generic model it has, to achieve results aligned with its objectives and priorities. If the objective is to guarantee a better customer experience using AI, the company must use data in number and specific to its customers within its model. If the latter is in open source mode, it brings both ease, efficiency and savings compared to the SaaS model, which is why the cloud is often abandoned by companies.
On another level, an injunction to minimize the level of “intelligence” of the models is making its way, in order to reduce their footprint, their GPU consumption and their size. Indeed, the issue around the consumption of energy, resources and infrastructure linked to the use of AI becomes more and more important.
In this perspective, it is necessary to limit the use of technology to specific tasks, therefore to have a “specialized” model and not a versatile and very powerful model while being under-exploited. In order to apply this approach, it is first necessary to assess the parameters of each model and their degree of relevance: a mission that can be fulfilled an open source, in a very transparent way. Depending on the results of this evaluation, the relevance of a small model of language (SML) can emerge, or that of open source projects which lead to the LLM on a specific area of knowledge, simplifying deployment while being more profitable. This approach contributes to democratizing AI and its use, opening up new perspectives for all users, unimportant their level of technicality.
Initiate the framework of a traceable AI
Freeing yourself from emerging market players and creating your own model is a ambition that many users have shared at the time of the generative AI explosion. Two years later, it was an extremely expensive strategic choice at present, both from a human and material point of view. It is imperative indeed to rely on a basic model (also called “Foundation Model”), like GPT, Llama, Mistral or Granite.
The choice that amounts to companies now, to take advantage of the stability and solidity of the open source ecosystem, is to opt for an open source model rather than a model in large SaaS mode; The open source is accompanied by the advantages of members of the community behind.
With this approach, the use cases of AI models should standardize and be less and less diverse, thanks to the identification of those that work well and the general use of underlying tools on the different models. Thanks to this, it will be possible to draw up a map identifying the LLM models according to several parameters: faults, forces, the level of security.
Developers and users, who can quickly find themselves lost in the face of AI evolution pace, can benefit from this type of device and simplify their decision -making. The open source adapts particularly to the frantic pace of AI development, and offers a more suitable alternative route than the opacity of the “black box” model practiced by the major players in the field. The system will only improve and avoiding drifts, as more users will look into the subject of AI and ask questions related to uses and ethics.
