This article was first published on June 8, 2023. 

Over 150 years ago, statistician and founder of modern nursing, Florence Nightingale, created data visualisations that aimed to influence societal behavior. By applying data to address issues like poor sanitation and overcrowding, Nightingale revolutionized the way we care for people.

Despite being known as the Lady with the Lamp for her night rounds as a nurse, Nightingale was a visionary who understood the power of data long before data analysis became a recognized term in medical research. Her vision focused on making data clear and understandable.

With the rise of automation and AI in our daily lives, conversations about data have intensified. As AI evolves, it is crucial to evaluate our data sources, reliability, and future data requirements.

The debate over whether we are headed towards an AI takeover is gaining traction among technology leaders and researchers. From Elon Musk to Steve Wozniak to healthcare experts, many are advocating for AI regulation to prevent unchecked advancements.

AI models rely on quality data inputs

As AI transforms various industries, including healthcare and finance, the demand for data to train AI models has increased significantly. But what happens when we face data shortages?

Poor data inputs pose risks to AI models. Can secure blockchain data offer a solution to mitigate the impact of data scarcity? The answer lies in the collaboration among stakeholders to maximize the data available for AI training.

According to Hugo Philion, CEO and Co-Founder of Flare Networks, “The Flare Time Series Oracle can bring time-varying data, such as stock market indices and weather, onto the blockchain in a decentralized manner.”

Pooling diverse data sources to create comprehensive datasets can enhance the effectiveness of AI models.

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Managing vast data sources and selecting the right inputs to extract relevant information are crucial tasks in utilizing data for informed decision-making.

Using data for informed decision making

Currently, there are limited incentives for local governments to enhance efficiency. Transparency in planning, regulations, and infrastructure development is essential for building trust and stability in local economies. On-chain data ensures secure record-keeping like never before.

Accurate data provision is a top priority for TangleHUB, a decentralized storage solution working with IOTA. However, unpredictability in data inputs poses challenges for developing future products and solutions.

Optimized data management can lead to reduced waste and effective allocation of local budgets, benefiting the economy and creating more employment opportunities.

Unlocking the power of decentralized data

In the blockchain realm, questions arise about data sharing for efficient AI management. Ensuring fair data sharing and privacy protection are key considerations for utilizing private data in AI applications.

Empowering better care, health, and lifestyle management through self-serving analytics is becoming more prevalent, especially in fitness, health, and work sectors. Individuals are increasingly aware of their data rights and usage.

The marriage of blockchain and AI has the potential to transform public services significantly. By leveraging blockchain technology to track and control data, users and AI systems can benefit from secure and reliable data sources.

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This article was first published on September 25, 2023

The post Running on empty: What happens when AI models run out of data? appeared first on e27.