AI News, Global revenue of AI chip market 2018 and 2025 artificial intelligence

Artificial Intelligence Chipset Market Anticipated to Witness a CAGR of 33.6% From 2019 to 2025 | Grand View Research, Inc.

The globalartificial intelligence chipset marketsize is expected to reach USD 59.2 billion by 2025, according to a new report by Grand View Research, Inc.

An artificial intelligence chipset is built on the concept of adding a dedicated component in an electronic device, to execute machine learning tasks.

In addition, an increased amount of data has led to the need for high-speed processors and faster computing, which is addressed by incorporating artificial intelligence into the set of electronic components.

In addition, the adoption of smart homes and smart cities is directly influencing the AI chipset market’s growth by providing numerous opportunities.

(Request a Free Sample) Click the link Grand View Research has segmented the global artificial intelligence chipset market based on chipset type, workload domain, computing technology, vertical, and region: AI Chipset Type Outlook (Revenue, USD Million, 2014 –

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Supported by an interactive market intelligence platform, the team at Grand View Research guides Fortune 600 companies and prominent academic institutes in comprehending the global and regional business environment and carefully identifying future opportunities.

Artificial intelligence will be transformative across industry domains and verticals - IHS Technology

The powerful new technology of artificial intelligence (AI) is here to stay, disruptive in impact and pervasive in presence, its significant computational capabilities and vast potential for deep learning applicable to a sea of use cases in daily life and commerce.

IHS Markit estimates that global revenue for AI systems covering both hardware and software components will reach nearly $1 trillion in 2025, with a compound annual growth rate (CAGR) of 28% from $142 billion in 2017, as shown in the graph below.

China Artificial intelligence, as defined by IHS Markit, refers to the body of science that studies how machines are enabled to perform independent problem-solving, derive inferences, achieve learning, demonstrate the acquisition of knowledge, and make logical decisions based on applicable inputs continually being processed by the machines and by their own accumulated store of knowledge, all executed in a split-second duration closely resembling the complex calculations made by the human brain in real-life situations and events.

Under ML are several technologies and techniques, including neural networks and deep learning, which are computational models that try to emulate the structure and workings of the human brain, including process phases like training and inference.

Yet China has numerous advantages in many other areas where AI initiatives are involved, including exceptional government support and committed funding, which could propel the country past the US to become the world’s leading repository of AI expertise in the not-so-distant future.

The implementation of AI and associated machine-learning technologies in unmanned devices and robotics poses concerns on whether—and how—society will be able to maintain control of a potent and formidable technology without losing sight of the human factor and element.

The ethics question brings up the need, much discussed in circles today, for a framework of ethical principles that will govern the deployment of AI and autonomous machines in representative situations and operating scenarios, and how data gathered for AI-training purposes can be regulated and made secure.

The ASIC market for AI is, in fact, flourishing, driven by several startups that aim to offer completely new architectures in processing capability and memory interface to challenge traditional chip architectures found today in central processing units (CPUs), graphics processing units (GPUs), field-programmable gate arrays (FPGAs), and digital signal processors (DSPs).

To this end, the chip industry is looking at various innovative approaches, including new processor architecture where memory is closer to the computational core to reduce the burden of data movement, enabling high-processing parallelism and dedicated memory cells per processing core.

And within datacenters, new high-performance servers have been developed with AI and ML algorithms in mind given the importance of essential performance parameters like real-time delivery and low latency in new and critical applications.

HMI HMI, or the human-machine interface, is a basic but extremely powerful application that can be deployed in multiple applications and across several industries and domains, from consumer and mobile electronics to automotive, passing through smart homes and smart appliances or the industrial space.

machines and robotics Machine learning technologies, together with advanced electro-mechanical and sensory science, are enabling the rapid development and deployment of different autonomous entities able to accomplish a multiplicity of tasks with varying levels of complexity, traditionally the domain of humans in the past.

However, it is the consumer market that will retain the lead in the shipment of autonomous machines, with total worldwide shipments expected to reach 13.3 million units by 2025, up from 4.5 million units in 2018.

In automotive, the entire automotive supply chain—from semiconductor suppliers to original equipment manufacturers (OEMs)—is seriously investing in AI, where advanced technologies are paving the way toward realization of the self-driving, autonomous vehicle.

With generative design, users will be able to interactively specify the functional needs and goals of their design, including preferred materials, engineering constraints, and manufacturing processes, with a manufacture-ready design resulting from the process.

By providing the tools to assimilate large volumes of disparate, structured, and unstructured data produced by healthcare systems, it will be increasingly possible to analyze such data in their entirety to increase the speed and accuracy of diagnosis, evaluate therapy, and expand the ability of researchers to explore disease pathologies.

In video surveillance, AI will be poised to revolutionize an industry affected by various factors, including the aggressive competition in pricing among video surveillance products and players in the market, the outsize presence and prevalence of China, the rise of safe cities, and the continuing move from standard-definition to high-definition video.

While the Cloud and its large, virtualized processing power is being utilized to run video analytics, some analytics—such as crowd monitoring, counting, and object detection—will be run at the camera to save bandwidth and relieve computing capacity at the backend.

key differentiator in video surveillance will be software in the form of video management software (VMS), video content analysis (VCA), physical security information management (PSIM), and software used by central monitoring stations (CMS).

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