Scaling AI innovation: The crucial roadblock of high-quality data instead of the model | Viewpoint
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In the rapidly evolving world of Artificial Intelligence (AI), a shift in focus is underway. For the past five years, capital and hype have primarily been directed towards model creation. However, as we move forward, the importance of high-quality, clean, and relevant data sets is becoming increasingly apparent.
Max Li, the founder and CEO of OORT, a data cloud for decentralized AI, underscores this shift. He asserts that the most critical assets for further AI development are not just vast amounts of data, but data sovereignty, targeted data selection, and clear governance.
This emphasis on data is not without reason. The biggest constraint in the future AI competition won't be compute but sourcing real, useful, and legal data. As model sizes increase, attention is shifting towards data acquisition.
Enterprises can leverage this trend by training models better aligned with their target audiences through data contributors. These contributors can not only provide unique, high-quality datasets but also become stakeholders in the AI industry.
Max Li, with his background in work on 4G LTE and 5G systems with Qualcomm Research and academic contributions to information theory, machine learning, and blockchain technology, is well-positioned to lead this data-centric revolution. He is also an accomplished author, having penned the book titled "Reinforcement Learning for Cyber-physical Systems."
This shift towards data is not just a change in focus, but a redefinition of the AI value chain. It now consists of not just model creation, but also data acquisition. The next frontier in AI lies with data stewards, aggregators, contributors, and platforms that bring them together.
In the new AI era, the control of data will determine the power. Embracing this reality and harnessing the potential of data contributors could be the key to unlocking the full potential of AI.
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