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SirWin
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SirWin

Why streaming data is the key to smarter AI

Sponsored
Why streaming data is the key to smarter AI

Disclosure: This article does not represent investment advice. The content and materials featured on this page are for educational purposes only.

Streaming data revitalizes AI by providing real-time insights, enabling technologies to become more adaptive and responsive, transforming everything from fraud detection to predictive maintenance.

Artificial intelligence (AI) is rapidly transforming our world, from facial recognition software to self-driving cars. But for AI to reach its full potential, it needs a constant flow of information – a firehose of data that traditional methods often struggle to handle.

This is where streaming data comes into play: the real-time lifeblood fueling the evolution of smarter AI. Traditionally, AI relied on massive, static datasets. However, this approach had limitations. Imagine training an AI on historical weather data to predict future patterns. While valuable, such data wouldn’t account for sudden changes like a developing storm. 

Here’s where streaming data comes in. Think of it as a live data broadcasting protocol, continuously feeding real-time information to, from, and between AI models and AI agents. This allows AI to adapt and react to constantly evolving situations, making it significantly more powerful and versatile.

The power of streaming data pipelines

So, how does streaming data empower artificial intelligence? The magic lies in streaming data pipelines – software infrastructure that takes in, processes, and analyzes real-time data streams. These pipelines act as the bridge between the real world and the AI system. They continuously filter, clean, and transform data, ensuring the AI receives the most relevant and accurate information possible.

This real-time processing unlocks many benefits for AI. Consider fraud detection systems in the financial sector, for example. Every year, scammers get craftier and more cunning as a result of technological breakthroughs. From 2021 to 2022, the median loss for victims of scams doubled. And, according to a report published by the FTC in 2023, consumers in the US lost an estimated $300 billion to fraudulent texts alone in 2022.

Now, banks are leveraging AI, hoping to cut down on impersonation schemes and mitigate the impacts of fraud and various scams. In the past, AI might have relied on analyzing past transactions to identify fraudulent activity. With streaming data pipelines, however, AI can analyze real-time transactions, allowing for immediate detection and prevention of fraudulent activity.

Deep learning and machine learning thrive on streaming data

Streaming data is particularly beneficial for two key areas of AI: deep learning and machine learning. Deep learning algorithms, inspired by the human brain, require vast amounts of data to learn and improve. Streaming data provides a continuous stream of new information, allowing deep learning models to constantly refine their decision-making capabilities.

Machine learning also benefits greatly. Machine learning algorithms learn from data to make predictions. Streaming data ensures these algorithms are continuously exposed to new information, enabling them to adapt their predictions and become more accurate over time.

Unlocking the power of AI with streaming data

The applications of streaming data in AI are vast and ever-growing. Here are just a few examples of the many use cases for AI across various industries:

  • Personalized experiences: Streaming data on user behavior allows artificial intelligence to personalize recommendations in real time, whether it’s suggesting products on an e-commerce platform or curating content on a streaming service. This can significantly enhance user engagement and satisfaction.
  • Predictive maintenance: In industrial settings, streaming sensor data can be used by AI to predict equipment failures before they occur, preventing costly downtime and ensuring smooth operations. Imagine an AI system in a wind farm analyzing real-time sensor data from wind turbines to predict potential malfunctions, allowing for preventative maintenance and avoiding energy production loss.
  • Traffic management: Streaming data from traffic cameras and sensors allows AI to optimize traffic flow in real time, reducing congestion and improving commute times. This can significantly impact urban planning and infrastructure development.
  • Cybersecurity: By analyzing network traffic data in real time, AI can identify and respond to cyber threats much faster, protecting systems from attacks. With the ever-increasing threat landscape in the blockchain world, robust AI-powered security systems utilizing streaming data are crucial for ensuring the safety and security of decentralized networks.

The future of streaming data and AI

As artificial intelligence technology continues to evolve, streaming data will play an increasingly crucial role. The ability to process and analyze real-time data streams will be essential for developing even more sophisticated AI applications. But did you know that there’s a looming data shortage? According to research published by Epoch, it’s estimated that AI firms may run out of data as early as 2026.

Thankfully, firms like Streamr are helping to ensure the data keeps flowing by connecting AI systems with open and pay-to-access real-time data streams. To prepare for the near future when generative AI content overtakes human-generated content, live media streaming must scale P2P distribution. This decentralized solution can help prevent overload on centralized platforms struggling even to handle video streaming bandwidth requirements.

The possibilities are endless. Streaming data is the fuel that will power the next generation of intelligent systems, shaping a future where artificial intelligence seamlessly integrates with our lives, solving problems and creating new opportunities we can only begin to imagine.

Disclosure: This content is provided by a third party. crypto.news does not endorse any product mentioned on this page. Users must do their own research before taking any actions related to the company.