Tech Companies Start Utilizing Synthetic Data To Feed Their Advanced AI Models

Information is available freely on the internet, but there are boundaries that one shall not cross.

And that is bulk retrieval.

AI works by consuming large amount of information, in order to extract the patterns and learn from them. The thing is, tech companies behind the AIs are scouting every corners of the web, in the pursuit to feed its ever-hungry products.

In the process, it's even said that AI chatbots are running "out of text in the universe" to learn from.

This is apparently true, because according to the CEO of AI firm Cohere, Aiden Gomez, synthetic data is already being used to train AI models.

As explained by Aidan Gomez:

Then, there is the cost of human-generated data, that according to Gomez, is "extremely expensive".

This happens because the internet, while too vast for anyone or anything to read and consume, it is still limited in size. Furthermore, various platforms like Reddit and Twitter, are also starting to charge huge money for other companies to scrape their data through APIs.

To also tackle issues that can include copyright infringement and privacy violations, AI companies like OpenAI, Microsoft and Cohere, are starting to rely on synthetic data to train their AI products.

Synthetic data, just like what the name suggests is 'synthetic data' - it's a computer-generated information, and isn't created by human.

So instead of forcing themselves to scrape data from digital books, news articles, blogs, social media and more, synthetic data is used to replace some of the missing information.

Citing the limited availability of "organic" human-generated data in the World Wide Web, the researchers then give feedback and fill in the gaps through reinforcement learning by human feedback (RLHF).

In other words, AI companies are doing everything they can to satisfy the hunger of their AI products, to even utilize AI-generated information, or create a sort of infinite loop, where training is achieved on data that's already been artificially created.

Gomez revealed that the use of synthetic data is already huge but that it’s not broadcast widely.

In the past, synthetic data is already being used to train AI models, and that the method has already been the focus of several research papers.

But what Gomez is highlighting here, is that tech companies are running out of human-made eligible data, to improve their cutting-edge AI technologies.

It's only this time, that companies are clearly worrying about data availability and provenance.

While synthetic data is may be artificial, it actually reflects real-world data, both mathematically or statistically.

Research demonstrates that synthetic data can be as good or even better for training an AI model than data based on actual objects, events or people.

However, there are disadvantages of using synthetic data.

As realized by researchers, the use of synthetic data, AI-generated information that already contains biases, will continue teaching biases. This is because the same biases will be included, digested, and amplified in subsequent training iterations, increasing its the biases' relevancy.

Then, there are issues about AI "hallucination".

And on a more-recent finding, it's realized that AI systems can get "MAD" if trained with AI-generated content.

All of these should force human intervention to ensure that only high-quality datasets are included in the training.

So here, while creating and feeding synthetic data to train AI models could lead to breakthroughs, companies also have to be careful not to use poor synthetic data which could lead to degradation over time.