Global AI market to surge to US $1,811 billion by 2030

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Both innovation and demand for artificial intelligence, or AI, are already having a disruptive impact on the direction of the surveillance industry with a growing trend for smart cameras, innovative software analytics and an ever widening scope of vertical applications. 

As a huge growth sector in its own right, the global artificial intelligence market size is expected to reach USD 1,811.8 billion by 2030, according to a new report by Grand View Research, Inc. The figures anticipate this market to expand at a CAGR of 38.1% from 2022 to 2030. 

Taking these figures into account is almost inevitable that the security industry will benefit from this surge in demand for AI, as a huge portion of the data required for AI to work its magic, must first be collected via some form of detection device, whether it be a network surveillance camera, a PIR sensor, or voice recognition software, for example.

Artificial Intelligence (AI) denotes the concept and development of computing systems capable of performing tasks customarily requiring human assistance, such as decision-making, speech recognition, visual perception, and language translation. AI uses algorithms to understand human speech, visually recognise objects, and process information. These algorithms are used for data processing, calculation, and automated reasoning.

Artificial intelligence researchers continuously improve algorithms for various aspects, as conventional algorithms have drawbacks regarding accuracy and efficiency. These advancements have led manufacturers and technology developers to focus on developing standard algorithms. Recently, several developments have been carried out for enhancing artificial intelligence algorithms. For instance say the researchers, in May 2020, International Business Machines Corporation announced a wide range of new AI-powered services and capabilities, namely IBM Watson AIOps, for enterprise automation. These services are designed to help automate the IT infrastructures and make them more resilient and cost reduction.

Various companies are implementing AI-based solutions such as RPA (Robotic Process Automation) to enhance the process workflows to handle and automate repetitive tasks. AI-based solutions are also being coupled with the IoT (Internet of Things) to provide robust results for various business processes. For Instance, Microsoft announced to invest USD 1 billion in Open AI, a San Francisco-based company. The two businesses teamed up to create AI supercomputing technology on Microsoft's Azure cloud.

The Covid – 19 pandemic has emerged as an opportunity for AI-enabled computer systems to fight against the epidemic as several tech companies are working on preventing, mitigating, and containing the virus. For instance, Leeway Hertz, a U.S.-based custom software development company, offers technology solutions using AI tools and techniques, including the Face Mask Detection System to identify individuals without a mask and the Human Presence System to monitor patients remotely. Besides, Voxel51 Inc., a U.S.-based artificial intelligence start-up, has developed Voxel51 PDI (Physical Distancing Index) to measure the impact of the global pandemic on social behaviour across the world.

AI-powered computer platforms or solutions are being used to fight against Covid – 19 in numerous applications, such as early alerts, tracking and prediction, data dashboards, diagnosis and prognosis, treatments and cures, and maintaining social control. Data dashboards that can visualise the pandemic have emerged with the need for coronavirus tracking and prediction. For instance, Microsoft Corporation's Bing's AI tracker gives a global overview of the pandemic's current statistics.

Artificial Intelligence is becoming vital to big data as the technology allows the extraction of high-level and complex abstractions through a hierarchical learning process. The need for mining and extraction of significant patterns from high-volume information is driving the growth of artificial intelligence in big data analytics. Furthermore, the technology helps overcome challenges associated with big data analytics, such as the trustworthiness of data analysis, format variation of raw information, highly distributed input sources, and imbalanced input data. Another challenge includes a lack of efficient storage and poor information retrieval as it is collected in large quantities and made available across numerous domains. These challenges are overcome by using semantic indexing to facilitate comprehension and knowledge discovery.


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