Edge computing, as known to many, has surpassed the expectation levels in terms of performance delivered and objectives achieved. Over the last couple of years, it has been a common scenario to observe companies making huge tech investments as a part of their digital transformation journey. On the same lines, cloud companies see new opportunities by fusing Edge computing and AI, or Edge AI. This form of AI has left everyone spellbound with the impact it has left behind. Virtual assistants like Amazon’s Alexa and Apple’s Siri that have dominated the world of late are a result of Edge AI Here is everything you need to know about Edge AI and beyond.
To know about Edge AI, it is important to know about AI and Edge computing individually for the sole reason that the amalgamation of the two would result in Edge AI. AI usually revolves around data transmission and computation of complex machine learning algorithms. On the other hand, Edge computing is responsible for moving AI and machine learning to the network’s edge, where the data generation and computation actually take place. With Edge AI, the final outcome is faster computing, better data security, and efficient control over the continuous operation. No wonder, why Edge AI stands the potential of enhancing the performance of AI-enabled applications. Well, there’s more to it. Edge AI also facilitates the autonomous application of deep learning models, machine learning, and advanced algorithms on the Internet of Things (IoT) devices itself – all this away from the cloud services.
Edge AI has a wide range of benefits to offer to various industries. Some of the benefits include – reducing the field-related issues, taking customer satisfaction to a different level altogether, a product taking an inspection of less than a minute, and extremely efficient predictive maintenance and asset management to name a few.
The combination of Machine learning with that of Edge AI has a magical impact that words cannot suffice. A lot of businesses especially the ones that rely on IoT devices have benefited a lot from this. Some of the advantages that Machine learning with the aid of Edge AI can bring forth are –
Low-Cost Digital Infrastructure
Edge AI holds the potential to eliminate the exorbitant expenses incurred on the AI or machine learning processes that are carried out on cloud-based data centers.
Privacy
Data is the most precious asset and consumers have always been concerned about how their data is being protected. With Edge AI, companies can deliver AI-enabled personalized features in their applications and make their users understand how their data is being collected and stored. This is an excellent way to enhance the brand loyalty of the customers.
Reduced Latency
With Edge AI, the requirement to send huge amounts of data across networks and devices is eliminated, thus improving the user experience.
Challenges of Edge AI
Poor data quality turns out to be a major obstacle when it comes to the research and development pertaining to Edge AI. In addition to this, industry experts also claim that the decentralized nature of Edge computing increases its security features, thus implying that the vulnerable security feature is yet another challenge of Edge AI. Lastly, the fact that machine learning requires great computational power is good enough a fact to conclude that limited machine learning power would be a hindrance for Edge AI.
Right from virtual assistants, facial recognition, enhancement of industrial IoT security, emergency medical care to autonomous vehicles, Edge AI has carved a niche for itself. This list keeps growing with every passing day. The day when Edge AI will emerge out to be a traditional day-to-day technology is not far behind.
Source: analyticsinsight.net
