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Definition of training
The training of an artificial intelligence model is the process by which he learns from large amounts of data by adjusting his internal parameters (weight and bias of artificial neurons). This phase is extremely greedy in calculation and requires massive computer infrastructure.
Why is training crucial?
This is the stage where AI is built And where she acquires her comprehension and generation capacities.
- The higher the data volume and the computing power, the more efficient the model.
- Models like GPT-4 require several months of training On supercomputers equipped with thousands of GPUs.
Technological issues
1️⃣ Massive energy consumption 🌍
- Full training can consume the energy equivalent of several hundred households over a year.
- Ex. GPT-3 would have required 1,287 MWhor the carbon footprint of a Paris-New York flight in Boeing 747.
2️⃣ Financial cost 💰
- Cause a model like GPT-4 costs several hundred million euros.
- Only giants as OPENAI, Google, Meta and Microsoft can finance such infrastructure.
3️⃣ Calculation time ⏳
- The training of an LLM takes Several weeks or even several months.
- Techniques like Distributed training and the Mixture of Experts (MOE) allow you to speed up the process.
The future of training
✅ Infrastructure optimization To reduce energy consumption.
✅ Use of smaller and specialized models To limit costs.
✅ Progression of federated learning architectures To cause AI without centralizing data.
