Introduction
There will be no training data available for the coming years to make AI models.
AI companies are rapidly coming up with new AI models to beat their competitors in the race. The amount of data used to train them is quite huge for instance is mind-blowing. So the day is near when they would face a deficiency in training data.
In a research conducted by Epoch, projections have been generated to try to estimate when AI researchers might expect to run out of data. They estimate that computer scientists could exhaust the stock of high-quality linguistic data by 2026.
This would severely affect the production of AI models, particularly large language models.
You might think that there is plenty of data available on the web that could be used to train models. But that’s not how things work.
To train powerful and high quality algorithms, companies require a lot of data. Take the example of ChatGPT for instance, it was trained on 570 gigabytes of text data or about 300 billion words !!
Similarly, the stable diffusion algorithm was trained on a dataset that comprised of 5.8 billion image-text pairs. If an algorithm is trained on an insufficient amount of data, it will produce inaccurate or low-quality outputs.
Hence, the quality of training data matters a lot. Low-quality data are easy to source but they cannot be used to train AI models.
Should we be worried?
Is there any solution to tackle this problem?
While the above points might alarm AI companies the situation may not be as bad as it seems. We still don’t how AI models will develop in the future.
An option to solve this issue is to use AI to create synthetic data and later train systems with the help of it. In other words, developers can simply generate the data they need, curated to suit their particular AI model. And that’s what Nvidia has done already.
NVIDIA announced Nemotron-4 340B, a family of open models that developers can use to generate synthetic data for training large language models (LLMs). It gives developers a free, scalable way to generate synthetic data that can help build powerful LLMs.
The Nemotron-4 340B family includes base, instruct, and reward models that form a pipeline. The models are optimized to work with NVIDIA NeMo, an open-source framework for end-to-end model training, including data curation, customization, and evaluation.
The Nemotron-4 340B Instruct model creates diverse synthetic data that mimics the characteristics of real-world data, helping improve data quality to increase performance.
Later to boost the quality of the AI-generated data, use the Nemotron-4 340B Reward model to filter for high-quality responses.
The Instruct model underwent extensive safety evaluation across a wide range of risk indicators. Users should still perform a careful evaluation of the model’s outputs to ensure the synthetically generated data is suitable, safe, and accurate for their use case.
Artificial intelligence turns out a savior for us here. Let us see what the future holds for us ahead!
Stay tuned for more such AI-related articles and updates.