{"id":90829,"date":"2024-10-15T09:01:31","date_gmt":"2024-10-15T07:01:31","guid":{"rendered":"https:\/\/intercoaching.fr\/?p=90829"},"modified":"2024-10-15T09:01:33","modified_gmt":"2024-10-15T07:01:33","slug":"dont-confuse-artificial-intelligence-take-the-test-for-yourself","status":"publish","type":"post","link":"https:\/\/intercoaching.fr\/en\/dont-confuse-artificial-intelligence-take-the-test-for-yourself\/","title":{"rendered":"Don’t confuse artificial intelligence: take the test for yourself!"},"content":{"rendered":"

The rise of artificial intelligence (AI) has considerably changed our relationship with technology, making interfaces previously reserved for specialists accessible to the general public. Faced with the diversity of solutions offered by giants such as Google with its model Gemini<\/strong> or Meta with Llama<\/strong>, choose between different AI models<\/strong> may seem complex. However, it is essential to understand that these systems are not all equal. To discern which one best meets your expectations, a comparative approach is necessary: \u200b\u200binteract with these technologies on subjects that you understand. An initiative like that of the Compar:IA platform, supported by the interministerial digital department, offers an excellent springboard for exploring these technologies. With this test, not only do you evaluate the relevance of the answers obtained, but you also acquire a critical awareness of the underlying energy and cultural issues. The differences of algorithmic production<\/strong> and the impact of the training language on the results are indicative of the need to diversify documentary sources for a more inclusive model.<\/p>\n\n\n\n

Artificial intelligence (AI) is omnipresent in our daily lives, but the plethora of models and platforms often makes it difficult to choose the solution best suited to our needs. This article will guide you to compare these different AI models, understand their performance and take into account criteria such as energy cost and the cultural origin of the training data.<\/p>\n\n\n

Understanding artificial intelligence models<\/h2>\n\n\n

Immersing yourself in the world of AI is not easy, especially with the complexity of the underlying architectures. Big names such as Google with Gemini, Meta with Llama, or even the Microsoft and Mistral platforms, have developed high-performance solutions with varied characteristics. Using these models yourself is the most effective approach to assess their suitability.<\/p>\n\n\n

Evaluation through experimentation<\/h3>\n\n\n

The best way to judge a artificial intelligence<\/strong> is to question it directly. By asking questions on a subject that you know, you can compare the answers and thus judge their accuracy and relevance. This helps to highlight the strengths and weaknesses of each model.<\/p>\n\n\n

An educational initiative: Compar:IA<\/h2>\n\n\n

To democratize access to these technologies, the state start-up Compar:IA, supported by the interministerial digital department, offers a platform where the user can compare two AIs anonymously. This allows for a neutral and fun evaluation of the different solutions available.<\/p>\n\n\n

Interact with AI responsibly<\/h3>\n\n\n

All interactions with AI require compliance with certain principles: avoiding personal information, not using machines for harmful purposes and being aware of possible errors in the answers provided. These precautions ensure safe and ethical use of AI technologies.<\/p>\n\n\n

Measuring ecological impact<\/h2>\n\n\n

In addition to technical performance, it is crucial to take into account the energy balance<\/strong> AIs. Tests show that consumption can vary considerably, with energy expenditure ranging from 4 to 65 kW\/h and CO\u00b2 emissions ranging from 2 to 40 grams per consultation.<\/p>\n\n\n

Putting the impact into perspective<\/h3>\n\n\n

Compar:IA provides practical conversions to better understand these numbers: the energy spent can be compared to lighting an LED bulb or watching online videos. These indicators encourage more conscious and responsible use of AI.<\/p>\n\n\n

Cultural influence on AI responses<\/h2>\n\n\n

The origin of an AI model\u2019s training data directly influences its responses. Thus, biased results may appear, favoring the dominant cultural contexts of Anglo-Saxon countries. To overcome this, training with French-speaking libraries is crucial, and this is where the ComparIA platform plays an important role under the aegis of the French Ministry of Culture.<\/p>\n\n\n

Develop critical thinking<\/h3>\n\n\n

Users should be aware of the plurality of responses generated by the algorithms<\/strong> and adopt a critical attitude towards the results. This analysis prevents us from being influenced by potentially inaccurate or truncated information.<\/p>\n\n\n\n

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