Artificial Intelligence (AI) has rapidly evolved in recent years, leading to groundbreaking innovations and transforming various industries. One crucial factor driving this progress is the availability and quality of training data. As AI models continue to grow in size and complexity, the demand for training data is skyrocketing.
The Growing Importance of Training Data
At the heart of AI lies machine learning, where models learn to recognize patterns and make predictions based on the data they are fed. In order to improve their accuracy, these models require large amounts of high-quality training data. The more data that AI models have at their disposal, the better they can perform in various tasks, from language translation to image recognition.
As AI models continue to grow in size, the demand for training data has increased exponentially. This growth has led to a surge in interest in data collection, annotation, and management. Companies that can provide AI developers with access to vast, high-quality datasets will play a vital role in shaping the future of AI.
The State of AI Models Today
One notable example of this trend is the state-of-the-art GPT-3, released in 2020. According to ARK Invest’s “Big Ideas 2023” report, the cost to train GPT-3 was a staggering $4.6 million. GPT-3 consists of 175 billion parameters, which are essentially the weights and biases adjusted during the learning process to minimize error. The more parameters a model has, the more complex it is and the better it can potentially perform. However, with increased complexity comes a higher demand for quality training data.
GPT-3’s performance, and now GPT-4, has been impressive, demonstrating a remarkable ability to generate human-like text and solve a wide range of natural language processing tasks. This success has further fueled the development of even larger and more sophisticated AI models, which in turn will require even larger datasets for training.
The Future of AI and the Need for Training Data
Looking ahead, ARK Invest predicts that by 2030, it will be possible to train an AI model with 57 times more parameters and 720 times more tokens than GPT-3 at a much lower cost. The report estimates that the cost of training such an AI model would drop from $17 billion today to just $600,000 by 2030.
For perspective, the current size of Wikipedia’s content is approximately 4.2 billion words, or roughly 5.6 billion tokens. The report suggests that by 2030, training a model with an astounding 162 trillion words (or 216 trillion tokens) should be achievable. This increase in AI model size and complexity will undoubtedly lead to an even greater demand for high-quality training data.