If you asked a Gen AI model to write lyrics like the Beatles and it did an impressive job, there’s a reason why. Or if you asked a model to write prose in the style of your favorite author and it copied that style exactly, there’s a reason why.
Quite simply, when you’re in another country and want to translate the name of an interesting snack you found in the supermarket aisle, your smartphone will detect the label and seamlessly translate the text.
AI is at the center of all these possibilities, and that’s largely because AI models are trained on massive amounts of data — in our case, hundreds of Beatles songs and maybe books from your favorite author.
The rise of Generative AI has made it possible for everyone to become a musician, writer, artist, or all of the above. Gen AI models can generate custom artwork in seconds based on user prompts. They can create Van Gogh-style artwork, and even make Al Pacino read terms of service without being there.
Interesting aside, the important aspect here is ethics. Is it fair that such creations are used to train AI models that are increasingly trying to replace artists? Did they get consent from the owners of such intellectual property? Were they fairly compensated?
Welcome to 2024: The Year of Data Wars
Over the past few years, data has become a magnet for companies to train Gen AI models. Like infants, AI models are naive. They need to be taught and trained. That’s why companies need billions, if not millions, of data to artificially train models to mimic humans.
For example, GPT-3 was trained on billions (hundreds) of tokens, which roughly translate into words, but according to sources, trillions of these tokens have been used to train the latest models.
With such a huge need for training data sets, where should big tech companies go?
Severe lack of training data
Ambition and volume are closely related. As companies scale and optimize their models, they need more training data. This can come from the desire to release a follow-up model of GPT or simply to provide improved and more accurate results.
In any case, it is inevitable that abundant training data is required.
Here, companies face their first hurdle. Simply put, the Internet is becoming too small for AI models to train on. In other words, companies are running out of existing data sets to feed and train their models.
This depleting resource is making stakeholders and tech enthusiasts nervous, as it could limit the development and advancement of AI models, which are largely tied to how brands position their products and how they perceive some of the world’s most pressing problems to be solved with AI-based solutions.
At the same time, there is hope in the form of synthetic data, or what we call digital inbreeding. In layman’s terms, synthetic data is training data generated by AI that is then used to train the model.
While it sounds promising, tech experts believe that the synthesis of this training data will lead to what is known as Habsburg AI. This is a huge concern for businesses, as these inbred data sets can in fact contain errors, biases, or just plain gibberish, negatively affecting the results of AI models.
Think of this as a Chinese whisper game, but the only difference is that the first word spoken may not make sense.
AI training data sourcing competition
Shutterstock, one of the largest photo repositories, has 300 million images. This is enough to start training, testing, validation, and optimization, but again, you need a lot of data.
But there are other sources. The only problem here is that they are colored in gray. We are talking about publicly available data on the Internet. Here are some interesting facts:
- Over 7.5 million blog posts are published in real time every day.
- There are over 5.4 billion people on social media platforms like Instagram, X, Snapchat, and TikTok.
- There are over 1.8 billion websites on the Internet.
- Over 3.7 million videos are uploaded to YouTube alone every day.
Plus, people are sharing text, video, photos, and even expertise publicly through audio-only podcasts.
This is clearly usable content.
So shouldn’t it be fair to use this to train AI models?
This is the gray area we mentioned earlier. There is no firm opinion on this question, as technology companies with access to such vast amounts of data are coming up with new tools and policy changes to meet these needs.
Some tools transcribe audio from YouTube videos into text and then use it as tokens for training purposes. Companies are going so far as to use public data to train models with pre-determined intent that they are rethinking their privacy policies and even facing lawsuits.
response mechanism
At the same time, companies are also developing so-called synthetic data, where AI models generate text and use it in a loop to train the model.
Meanwhile, to combat data scraping and prevent businesses from exploiting legal loopholes, websites are implementing plugins and code to mitigate data scraping bots.
What is the ultimate solution?
The significance of AI in solving real-world problems has always been supported by noble intentions. So why should we rely on gray models to source data sets to train such models?
As the conversation and debate around responsible, ethical, and explainable AI gains importance and momentum, it is time for businesses of all sizes to turn to alternative sources of white hat technology to provide training data.
This is the place Saif Outstanding Abilities. Understanding the prevailing concerns surrounding data sourcing, Shaip has always advocated ethical techniques and has consistently implemented sophisticated and optimized methods for collecting and compiling data from a variety of sources.
White Hat Dataset Sourcing Methodology
That’s why our approach involves meticulous quality checks and the ability to identify and compile relevant datasets. This has enabled us to provide companies with exclusive Gen AI training datasets across multiple formats such as images, videos, audio, text, and more niche requirements.
Our Philosophy
We operate under core philosophies such as consent, privacy, and fairness when collecting datasets. Our approach also ensures that the data is diverse, so unconscious bias is not introduced.
As the AI field prepares for the dawn of a new era characterized by fair practices, we at Shaip aim to be the standard-bearers and pioneers of such ideals. If you need a fair and high-quality dataset to train your AI models, contact us today.