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Many organisations see Artificial Intelligence as the solution to a lot of uncertainty like economic uncertainty, labour shortages, supply chain challenges, etc, bringing improved efficiency, differentiation, automation, and cost savings to airports, stores, and hospitals, among other places, which is why Edge AI trends have been accelerated.
Edge AI is AI that operates locally rather than in the cloud. Because of lightweight models and lower-cost high-performance GPUs, its implementation will become more accessible and less expensive in 2023. Edge AI enables the powering of scalable, mission-critical, and private AI applications. Because Edge AI is a new technology, many Edge AI applications are expected in the near future such as AI healthcare, Smart AI vision, Smart energy, and intelligent transportation system. According to Markets and Markets Research, the global Edge AI software market will grow from $590 million in 2020 to $1.83 trillion by 2026. Let's take a look at the top 10 Edge AI Trends in 2023:
With businesses looking for new ways to cut costs and gain a competitive advantage, AI projects are likely to become more common. Edge AI use cases, in particular, can help increase efficiency and reduce costs, making them an appealing location to direct new investments.
Improved tools for labelling data and automatic tweaking of neural net structures are two promising aspects of automated machine learning. When the process of selecting and refining a neural network model is automated, the cost and time to market for new solutions for AI will be reduced. Gartner predicts that the future of operationalizing these models will revolve around refining the PlatformOps, MLOps, and DataOps processes. Gartner refers to these advanced features collectively as XOps.
AI functional safety is related to the trend of human-machine collaboration. More companies are looking to use AI to add proactive and flexible safety measures to industrial environments, as seen in autonomous vehicles. The functional safety has been used in industrial settings in a binary fashion, with the primary role of the safety function being to immediately stop the equipment from causing any harm or damage when an event is triggered.
The increasing use of AI in security operations is the next logical step in the evolution of automated defences against cyber threats. The use of artificial intelligence (AI) in cybersecurity extends beyond the capabilities of its forerunner, automation, and includes tasks like the routine storage and safeguarding of sensitive data.
AI was once considered experimental, but according to IBM research, 35% of companies today report using AI in their business, with an additional 42% exploring AI. Edge AI use cases can help improve efficiency and lower costs, making them an appealing place to direct new investments. Supermarkets and big box stores, for example, are investing heavily in AI at self-checkout machines to reduce loss due to theft and human error.
The more recent type of process mining digs deeper to discover the outcomes of people's interactions with various objects that trigger business process events. There are numerous techniques and AI models available, ranging from clicking the mouse for a specific goal to accessing files, papers, web pages, and so on, all of which necessitate different information transformations in various ways.
Edge AI along with new data processing and automation capabilities, supports a diverse ecosystem of evolving networks in ways that cloud-based solutions cannot. Furthermore, self-driving cars, virtual reality, and any other use case that requires real-time alerts require Edge AI and 5G for the fast processing it promises. As a result, 5G is promoting the Edge.
Due to the limited data storage and computational power of these resource-constrained devices, performing deep learning in low-power IoT devices has always been difficult. Edge AI models are now cost-effective enough to operate at the edge, allowing devices to complete their own data processing and generate insights without relying on cloud-based AI.
The term "digital twin" refers to physically accurate virtual representations of real-world assets, processes, or environments that are perfectly synchronized. The explosion of IoT sensors and data that is driving both of these trends is what connects digital twins to the physical world and edge computing.
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