Top AI Trends To Watch Out For In 2023: What No One Is Talking About
Artificial Intelligence (AI) is revolutionizing various industries and has become a powerful tool for businesses and organizations to improve their efficiency, accuracy, and profitability. As the technology continues to evolve, new trends are emerging in the field of AI that are set to change the way we work and live.
In this blog, we will discuss the top 2 AI trends that are currently shaping the future of technology and their real-world applications.
Edge AI is a trend that involves processing data on devices that are closer to the source, such as sensors or smart devices, rather than sending data to the cloud for processing. Edge AI algorithms allow devices to analyze and respond to data in real-time, without the need for an internet connection. This trend is gaining popularity as it reduces latency, improves data security, and saves bandwidth.
Real-world Examples of Edge AI:
- Autonomous vehicles: Edge AI is being used in self-driving cars to analyze sensor data in real-time, allowing the car to respond to changing road conditions and avoid collisions.
- Smart homes: Edge AI is being used in smart home devices, such as thermostats and security systems, to analyze data from sensors and make real-time decisions about temperature and security.
- Industrial IoT: Edge AI is being used in industrial IoT applications, such as predictive maintenance and quality control, to analyze data from sensors and predict when machines will need maintenance or when defects will occur.
- The global edge AI software market is expected to grow from $470 million in 2020 to $1.5 billion by 2025, at a Compound Annual Growth Rate (CAGR) of 26.5%. (Source: MarketsandMarkets)
- The edge computing market is expected to reach $250.6 billion by 2024, growing at a CAGR of 12.4%. (Source: MarketsandMarkets)
Federated learning is a trend that involves training machine learning models on data that is distributed across multiple devices or servers, without the need to share the data itself. This allows for privacy-preserving machine learning, as sensitive data does not need to be shared outside of the device or server where it is stored. Federated learning also allows for faster and more efficient machine learning, as the models are trained on local data rather than centralized data.
Real-world Examples of Federated Learning:
- Health monitoring: Federated learning is being used in healthcare applications, such as monitoring heart disease, to train machine learning models on data from wearable devices without sharing sensitive data outside of the device.
- Financial services: Federated learning is being used in financial services applications, such as credit scoring, to train machine learning models on data from multiple banks without sharing sensitive data outside of the banks.
- Traffic management: Federated learning is being used in traffic management applications, such as predicting traffic congestion, to train machine learning models on data from multiple sources, such as GPS devices and traffic cameras, without sharing sensitive data outside of the sources.
- The global federated learning market is expected to grow from $117 million in 2020 to $201 million by 2025, at a CAGR of 11.4%. (Source: MarketsandMarkets)
- By 2025, it is estimated that 50% of enterprise data will be processed at the edge, with federated learning being a key driver of this trend. (Source: IDC)
The above-mentioned AI trends, Edge AI and Federated Learning, are transforming various industries and have the potential to revolutionize the way we interact with machines. These trends are expected to continue to grow in popularity and to have a significant impact on businesses and organizations in the coming years.
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