MacHinE lEarning

MacHinE lEarning BY BusinEss wirEnEws

ARTIFICIAL INTELLIGENCE MARKET BY TECHNOLOGY, HARDWARE, END-USER INDUSTRY, AND REGION – GLOBAL FORECAST TO 2026 – RESEARCHANDMARKETS.COM

“Artificial Intelligence (Chipsets) Market by Technology (Machine learning, Natural Language Processing, Context Aware Computing, Computer Vision), Hardware (Processor, Memory, Network), End-User Industry, and Region – Global Forecast to 2026”

The AI (chipsets) market is expected to be valued at USD 7.3 billion in 2020 and is likely to reach USD 57.8 billion by 2026, at a CAGR of 40.1% during the forecast period.

Major drivers for the market are increasingly large and complex datasets driving the need for AI, the adoption of AI for improving consumer services & reducing operational costs, the growing number of AI applications, the improving computing power, and growing adoption of deep learning and neural networks.

The major restraint for the market is the lack of a skilled workforce. Critical challenges facing the AI (chipsets) market include low return on investment, creating models & mechanisms for AI, and the availability of limited structured training data. Underlying opportunities in the AI (chipsets) market include increasing focus on developing human-aware AI systems and bringing AI to edge devices.

The machine learning technology is expected to account for the largest size of the AI (chipsets) market during the forecast period.

Machine learning’s ability to collect & handle big data & its applications in real-time speech translation, autonomous robots, and facial analysis are fuelling its growth. AI constitutes various technologies that play a vital role in developing its ecosystem. As AI enables machines to perform activities similar to those performed by human beings, enormous market opportunities have opened up.

The predictive maintenance and machinery inspection application in the manufacturing industry of the AI (chipsets) market is projected to hold the largest share during the forecast period.

The predictive maintenance and machinery inspection application held the largest share among the manufacturing applications of the AI (chipsets) market in 2019. Extensive use of computer vision cameras in machinery inspection, adoption of the Industrial Internet of Things (IIoT), and use of big data in the anufacturing industry are the factors driving the growth of the predictive maintenance and machinery inspection application. The increasing demand for

reducing the operational costs and machine downtime is also supplementing the growth of predictive maintenance and machinery inspection application in industries.

The cybersecurity industry held the largest size of the AI (chipsets) market in 2019.

AI is significantly used in antivirus and anti-malware solutions owing to the rise in cybersecurity attacks across the world. Increasing use of mobile devices for a wide range of applications, such as social networking, e-mails, remote monitoring, phone banking, and data storage, opens doors for hackers to attack, thereby making networks more vulnerable to risks. The rapid adoption of cloud-based services, along with the user-friendly approach of antivirus/anti-malware solutions, is contributing to the growth of this end -user industry of the AI (chipsets) market.

Impact of COVID-19 on the AI (chipsets) market

The market is likely to witness a slight plunge in terms of year-on-year growth in 2020. This is largely attributed to the affected supply chains and limited adoption of AI in various end-user industries in 2020 due to the lockdowns and shifting priorities of different industries. The ongoing COVID-19 pandemic has caused disruptions in economies. It is likely to cause supply chain mayhem and eventually force companies and entire industries to rethink and adapt to the global supply chain model. Many manufacturing companies have halted their production, which has collaterally damaged the supply chain and the industry.

This disruption has caused a delay in the adoption of AIbased software and hardware products. The industries have started to restructure their business model for 2020, and many SMEs and large manufacturing plants have halted/postponed any new technology upgrade in their factories to recover from the losses caused by the lockdown and economic slowdown. COVID-19 has impacted the educational industries rather positively, with ed-tech companies adopting AI technology to impart education during the lockdown. Ed-tech firms have deployed AI tools to enhance online learning and virtual classroom experience for students.

Market Dynamics Drivers

- Increasingly Large and Complex Dataset Driving the Need for Ai - Adoption of AI for Improving Consumer Services and Reducing Operational Cost - Growing Number of AI Applications Improving Computing Power - Growing Adoption of Deep Learning and Neural Networks

Restraints

- Lack of Skilled AI Workforce

Opportunities

- Increasing Focus on Developing Human-Aware AI Systems - Bringing AI to Edge Devices

Challenges

- Low Return on Investment - Creating Models and Mechanisms for AI Limited Structured Data

Company Profiles

- NVIDIA - Intel - Xilinx - Samsung - Micron - Qualcomm Technologies - IBM - Google - Microsoft - Amazon Web Services (AWS) - AMD - General Vision - Graphcore - Mediatek - Huawei Technologies - Fujitsu - Wave Computing - Mythic - Zero Asic - Koniku - Tenstorrent - Sambanova - Kalray - XMOS - Greenwaves Technologies

For more information about this report visit : https://www.researchandmarkets.com/r/ou68rd

MacHinE lEarning BY BusinEss wirEnEws

GLOBAL MACHINE LEARNING MARKET 2020-2024 | INCREASING ADOPTION OF CLOUD-BASED OFFERINGS TO BOOST THE MARKET GROWTH | TECHNAVIO

Machine LearTechnavio has published e latest market research report titled Global Machine Learning Market 2020-2024

The machine learning market is expected to grow by USD 11.16 billion during 2020-2024, according to the latest market research report by The rising adoption of cloud computing services globally is increasing the adoption of cloud-based applications aimed at multiple end-user industries. The inherent benefits of cloud computing, such as minimal cost for computing, network and storage infrastructure, scalability, reliability, and high resource availability, encourage enterprises to adopt cloud-based solutions in their business models. Machine learning adopted via the cloud enables enterprises to experiment with machine learning technologies and capabilities at a fraction of the cost of setting up an in-house machine learning team and infrastructure. Machine learning also helps enterprises to scale up the production workload of their projects with the increase in data. The above-mentioned advantages of cloud-based offerings are expected to drive the growth of the global machine learning market. As per Technavio, the increasing technology investments in retail industry will have a positive impact on the market and contribute to its growth significantly over the forecast period. This research report also analyzes other significant trends and market drivers that will

influence market growth over 2020-2024.

Machine Learning Market: Increasing Technology Investments in Retail Industry

The digital revolution in the retail sector with the growing number of e-commerce websites is enabling retail companies to shift their focus on identifying customer buying patterns to drive growth. Retailers need to manage and track the inventory movement of a large number of items across various categories, as well as track consumers’ purchase options and purchase behaviors. The need for retail companies to monitor and manage customer requirements, enhance their decision-making, save cost, and automate processes is driving the adoption of machine learning. Retail companies are using machine learning for purposes such as inventory optimization, fraud detection, demand forecasting by price optimization, marketing, and product placement with behavioral tracking, customer servicing, and customer churn prediction. Thus, the growing adoption of advanced technologies in retail industry will be one of key trends in the market during the forecast period.

“Factors such as the growing number of acquisitions and partnerships, and the application of machine learning to IoT data will have a significant impact on the growth of the machine learning market value during the forecast period,” says a senior analyst at Technavio.

For more information about this report visit :

http://www.businesswire.com/news/home/20200327005177/en

Manufacturing updatE aMar Hanspal

Five Predictions For The Manufacturing Industry In 2021

There’s no shortage of articles that will opine about how tumultuous 2020 was for the economy, jobs, and industry as the world grappled with the impact of COVID-19 on business and daily life. However, there’s reason to be optimistic, especially in manufacturing.

Despite 60% of manufacturers feeling the impact of COVID on operations, a recent survey of senior leaders of manufacturing and distribution companies noted significant or modest growth in company revenue during the pandemic. Demand for products is surging, requiring new and innovative production methods, and many manufacturers have stepped up to the plate. As we close out the year, we’ll better understand just how much manufacturing changed in 2020. But economic uncertainty aside, the unprecedented supply chain disruptions of the year are a blessing in disguise for manufacturers, as they encouraged the often stagnant industry to move faster and become more resilient than ever before. If there were a year to push the industry forward towards progress, this was it.

Smart industry robot arms for digital factory production technology showing automation manufacturing process of the Industry 4.0 or 4th industrial revolution and IOT software to control operation .

In 2021, manufacturing will see a fundamental shift in how its leaders view progressive change - from unrealized vision to practical reality. As a result, an industry that’s nimbler and more flexible will emerge. Here are five ways I believe the industry will evolve (for the better) next year - some long in the making, others resulting from the 2020 effect:

We’ll see a shift to localized production

In 2021, the industrial manufacturing sector will take a page from the consumer-driven “farm to table” trend that has taken hold in the agriculture industry over the last decade, with a shift to localized production. This will primarily be driven by the threat of ongoing trade war/ tariffs threatening global supply chains, encouraging manufacturers to move production activity closer to the customer. In the future, manufacturers will want to build where they sell for several reasons, including faster time to market, lower working capital, government policies, and increased resiliency. This won’t be an easy or overnight shift. Naturally, the larger the manufacturer, the longer and more expensive any reshoring process will be, but the challenges of 2020 have created more urgency in adopting this type of production.

Digital transformation of the factory floor will accelerate

The pandemic reminded manufacturers about the fragility of relying on labor, access to physical space, and centralized factories half-way around the world to produce goods. Fortunately, advanced technology – sensors, machine learning, computer vision, robotics, cloud computing, edge computing, and 5G network infrastructure – has proven to increase supply chain resiliency for manufacturers who adopt it. While manufacturing lines present a unique set of challenges, tech companies will continue to focus on bringing the value of these advancements to verticalized settings as the industry realizes they must diversify their factory operations and embrace Industry 4.0 technology to become more resilient.

Reference: https://www.forbes.com/sites/amarhanspal/2020/12/07/five-predictions-for-the-manufacturing-industry-in-2021/?sh=768dd2a28ca4