Musk says that human data for AI training is running out

AI Data | Jan 10, 2025

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AI Hits a Data Wall. WHAT What Does This Mean for Fintech and Investors?

The data feedback loop in AI is predicted a few years ago to end in ‘model collapse’. Well, as reported by a Guardian articleElon Musk just boldly declared that artificial intelligence systems have exhausted all human-made (publicly available) data available for training — and this happened last year. Today, AI developers are faced with a drawback that Industry may be pushed towards alternative data sources such as synthetic (AI creates itself) and alternative methods. The implications of this shift could be huge, as fintechs rely on AI for a lot of the heavy lifting including everything from fraud detection to trading algorithms. So, what does this mean?

The Data Wall Problem

The limitation of available training source data can limit innovation and create bottlenecks in the applications sector, so the exhaustion of human data supply on the ground is a real problem. AI competition is fierce worldwide, and data-hungry AI models are consuming data faster than ever. Without access to new data, AI models can stop or lose their content to detect fraud, create personalized customer experiences, or even identify new market insights and opportunities. The idea of ​​’model breakdown’ is when the the quality of the data deteriorates to the point where more errors are introduced, compounding the problemsand deviation from the truth with too many’hallucinations‘. AI systems trained on AI outputs can get caught in a feedback loop where the models lose accuracy and diversity over time, resulting in bias and the spread of misinformation. There is also an issue of disproportionately relying on ‘minority data’ (less popular data or non-mainstream data) to train models, potentially distorting the truth.

Synthetic Data and Alternatives

More and more developers and AI experts are turning to synthetic data despite the risks described above. If there is a lack of training data, the AI ​​can create itself synthetic data to train itself by running simulated scenarios that are not often seen in real life. For example, a fintech company might use synthetic data to mimic unusual fraud patterns or test for unusual market conditions. Models that rely on too much synthetic data can generate inaccurate or trivial outputs that can have serious implications, especially in finance where reliability and accuracy are paramount.

See: Is Nuclear Fuel the Data-Driven Future?

Developers are looking for untapped, new sources of human-generated data. Data exchange markets are becoming more popular as a result. Amazon Web Services, for example, runs the AWS Data Exchangewhich allows companies to securely access and use third party datasets for AI training and analytics including sensitive information such as financial data or healthcare. Another option to check is Snowflake Data Marketplaces which is part of Snowflake’s Data Cloud. It is a platform that offers live, ready-to-query data across finance, consumer behavior, and public sector data.

Depending on the application and AI model, developers can turn to niche or specialized data sources such as physical records that are digitized, such as books, manuscripts or other archives. Cultural data sources from different regions or countries can be used as inputs for AI training data. Behavioral data may be collected from trading/investment activity or interactions with websites, apps, and games. Data can also be retrieved from real time sources such as IoT devices with sensors in smart homes, cars, and smart cities. Data can be collected in real time from Human-AI interactions as well, such as when customer support bots or conversational tools interact with humans. Project owners can also provide incentives and crowdsourced data online, depending on their requirements.

Strategic Shifts in AI Development

1. Multi-modal models. AI systems move beyond text and incorporate many different data sources such as VISUALS (charts, infographics, satellite imagery etc.), audio (voice patterns, phone calls, meetings), and transaction data (ie, payment trends). See: Google’s Vision for a Real-Time, Multimodal AI Assistant

2. Smaller, specialized models. Instead of being the leading Large Language AI model for all things, which requires a lot of training data, innovators are creating AI systems for specific tasks. These smaller solutions require less data while still delivering the desired results. it Small language models are also better for the environment because they use less energy.

See: RBC and Cohere Partner on ‘North for Banking’ AI Platform

3. Efficiency above the scale. A priority for AI researchers is to improve their AI models by making them more efficient by interpreting the data they have in a compelling way. more insights from less information.

4. Mixed training methods. This strategy combines synthetic data with real life in a balanced way to reduce risks and errors. See NVIDIA, Google, OpenAI for example.

5. Federated learning. A way to train AI models that on multiple devices/servers store and process data locally, instead of sending that data to a central server. This improves data privacy and security, and the raw data generated never leaves the local storage location.

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The lack of data will prompt new approaches and create new investment opportunities in the creation of synthetic data, AI privacy tools, and alternative data collection methods. At the same time, AI companies that rely heavily on synthetic data or outdated models may be less accurate with reduced performance, so caution is needed. Most companies should use data more intelligently, explore new data sources, and special strategies to compete.

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