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Today, as the research advances at a rapid pace, it is evident to observe breakthroughs in AI technology. Already, technology is driving automation for industrial operations. Machine learning, the subset technology of AI has also gained significance in development to enhance deep learning applications. Analysis of current AI applications can provide an insight into the future of the technology and predictions can be made for the journey of technology.
AI in Processor Chips
AI relies on processors that complement CPU. Unlike software, they require much higher computational power which currently the fastest and most advanced CPU lacks to deliver for AI training. Considering the same, leaders in chip making industry are planning to design chips specifically for AI operations such as natural language processing, computer vision, speech recognition. The market for AI-enabled chips already exists as the automobile and healthcare sector lure to espouse them for delivering enhanced customer services. In the proximate future, the demand for customized AI chips will increase from hyperscaled infrastructure to meet their requirements.
AI and IoT to Merge at Edge
Edge computing pervades nearly in all industries and requires treatment of its bottlenecks. AI holds the potential for the same. Converging IoT devices with ML will enable utilizing deep neural networks to handle video frames, speech synthesis, and unstructured data generated by IoT devices. Predictive maintenance, root cause analysis, and outlier detection are some much-spoken applications of AI integrated edge computing.
Reusability of Neural Network Framework
Currently, a trained and evaluated model for a specific framework is intricate to port it in another framework. As a result AI adoption is facing obstruction. Data scientist and developers are working over the same and leaders from various verticals have formed an Open Neural Network Exchange (ONNX) to make the same possible. Interoperability among neural networks is evident become essential in industries, and all key players will rely on it.
AI to Automate DevOps
Integrating AI in DevOps seems to be another futuristic IT application of the technology. Present huge datasets require the power of ML to got predictive from reactive. Applying AI and ML in Ops will bring intelligence to organizations enabling them to perform accurately precise root cause analysis.