If you asked a Gen AI model to write lyrics to a song like the Beatles would have and if it did an impressive job, there’s a reason for it. Or, if you asked a model to write prose in the style of your favorite author and it precisely replicated the style, there’s a reason for it.Even simply, you’re in a different country and when you want to translate the name of an interesting snack you find on a supermarket aisle, your smartphone detects labels and translates the text seamlessly.AI stands at the fulcrum of all such possibilities and this is primarily because AI models would have been trained on vast volumes of such data – in our case, hundreds of The Beatles’ songs and probably books from your favorite writer.With the rise of Generative AI, everyone is a musician, writer, artist, or all of it. Gen AI models spawn bespoke pieces of art in seconds depending on user prompts. They can create Van Gogh-isque art pieces and even have Al Pacino read out Terms of Services without him being there.Fascination aside, the important aspect here is ethics. Is it fair that such creative works have been used to train AI models, which are gradually trying to replace artists? Was consent acquired from owners of such intellectual properties? Were they compensated fairly?Welcome to 2024: The Year of Data WarsOver the last few years, data has further become a magnet to attract the attention of firms to train their Gen AI models. Like an infant, AI models are naïve. They have to be taught and then trained. That’s why companies need billions, if not millions, of data to artificially train models to mimic humans.For instance, GPT-3 was trained on billions (hundreds of them) of tokens, which loosely translates to words. However, sources reveal that trillions of such tokens were used to train the more recent models.With such humongous volumes of training datasets required, where do big tech firms go?Acute Shortage Of Training DataAmbition and volume go hand in hand. As enterprises scale up their models and optimize them, they require even more training data. This could stem from demands to unveil succeeding models of GPT or simply deliver improved and precise results.Regardless of the case, requiring abundant training data is inevitable.This is where enterprises face their first roadblock. To put it simply, the internet is becoming too small for AI models to train on. Meaning, that companies are running out of existing datasets to feed and train their models.This depleting resource is spooking stakeholders and tech enthusiasts as it could potentially limit the development and evolution of AI models, which are mostly closely connected with how brands position their products and how some plaguing concerns in the world are perceived to be tackled with AI-driven solutions.At the same time, there is also hope in the form of synthetic data or digital inbreeding as we call it. In layperson’s terms, synthetic data is the training data generated by AI, which is again used to train models.While it sounds promising, tech experts believe the synthesis of such training data would lead to what is called Habsburg AI. This is a major concern to enterprises as such inbred datasets could possess factual errors, bias, or just be gibberish, negatively influencing outcomes from AI models.Consider this as a game of Chinese Whisper but the only twist is that the first word that gets passed on might be meaningless as well.The Race To Sourcing AI Training Data
Licensing is an ideal way to source training data. Though potent, libraries and repositories are finite sources. Meaning, they can’t suffice the volume requirements of large-scale models. An interesting statistic shares that we might run out of high-quality data to train models by the year 2026, weighing the availability of data on par with other physical resources in the real world.One of the largest photo repositories – Shutterstock has 300 million images. While this is enough to get started with training, testing, validating, and optimizing would need abundant data again.However, there are other sources available. The only catch here is they are color-coded in grey. We are talking about the publicly available data from the internet. Here are some intriguing facts:Over 7.5 million blog posts are taken live every single dayThere are over 5.4 billion people on social media platforms like Instagram, X, Snapchat, TikTok, and more.Over 1.8 billion websites exist on the internet.Over 3.7 million videos are uploaded on YouTube alone every single day.Besides, people are publicly sharing texts, videos, photos, and even subject-matter expertise through audio-only podcasts.These are explicitly available pieces of content.So, using them to train AI models must be fair, right?This is the grey area we mentioned earlier. There is no hard-and-fast opinion to this question as tech companies with access to such abundant volumes of data are coming up with new tools and policy amendments to accommodate this need.Some tools turn audio from YouTube videos into text and then use them as tokens for training purposes. Enterprises are revisiting privacy policies and even going to the extent of using public data to train models with a pre-determined intention to face lawsuits.Counter MechanismsAt the same time, companies are also developing what is called synthetic data, where AI models generate texts that can be again used to train the models like a loop.On the other hand, to counter data scrapping and prevent enterprises from exploiting legal loopholes, websites are implementing plugins and codes to mitigate data-scaping bots.What Is The Ultimate Solution?The implication of AI in solving real-world concerns has always been backed by noble intentions. Then why does sourcing datasets to train such models have to rely on grey models?As conversations and debates on responsible, ethical, and accountable AI gain prominence and strength, it’s on companies of all scales to switch to alternate sources that have white-hat techniques to deliver training data.This is where Shaip excels at. Understanding the prevailing concerns surrounding data sourcing, Shaip has always advocated for ethical techniques and has consistently practiced refined and optimized methods to collect and compile data from diverse sources.White Hat Datasets Sourcing Methodologies
Our proprietary data collection tool has humans at the center of data identification and delivery cycles. We understand the sensitivity of use cases our clients work on and the impact our datasets would have on the outcomes of their models. For instance, healthcare datasets have their sensitiveness when compared to datasets for computer vision for autonomous cars.This is exactly why our modus operandi involves meticulous quality checks and techniques to identify and compile relevant datasets. This has allowed us to empower companies with exclusive Gen AI training datasets across multiple formats such as images, videos, audio, text, and more niche requirements.Our PhilosophyWe operate on core philosophies such as consent, privacy, and fairness in collecting datasets. Our approach also ensures diversity in data so there is no introduction of unconscious bias.As the AI realm gears up for the dawn of a new era marked by fair practices, we at Shaip intend to be the flagbearers and forerunners of such ideologies. If unquestionably fair and quality datasets are what you’re looking for to train your AI models, get in touch with us today.