Google's New Technology to Train AI 13 Times Faster

Google continues its advancements in artificial intelligence. The tech giant’s new AI training technology offers 13 times faster speeds and 10 times greater efficiency.
Google's New Technology to Train AI 13 Times Faster

Among the most important details in the field of generative AI are training and the energy required for this training. Companies aim to make training processes as fast as possible while using less energy. Google’s AI unit, DeepMind, has developed a new AI training technology that achieves significant gains in both aspects.

AI Training to Become Cheaper

According to research published by Google DeepMind, the new AI training technology is called JEST (Joint Example Selection). JEST increases training speed by 13 times while achieving 10 times higher energy efficiency. DeepMind’s JEST method significantly differs from traditional AI model training techniques.

Traditional training techniques focus on individually prioritized data points, whereas JEST performs training based on entire data sets. With JEST, Google first creates a smaller AI model to rate data quality and rank sets by quality. The ranking is then compared to a larger, lower-quality set. This allows the small JEST model to identify the most ideal data, and the larger model is trained based on these findings.

Google's New Technology to Train AI 13 Times Faster

The biggest weakness of the model is its need for high-quality data. Without high-quality data, the model’s outputs cannot be expected to be optimal. However, once high-quality data is obtained, significant gains in model training can be achieved with JEST. As is known, generative AI consumes much more energy than other data centers both during training and operation.

For example, a single ChatGPT query consumes 10 times more energy than a Google search. Additionally, training GPT-4 reportedly cost OpenAI $100 million. With more advanced models and larger datasets, this figure is projected to reach the $1 billion mark soon.

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