The Global Machine Learning Chip Market Size Is Expected To Reach $8,272 Million In 2022 From $4,495 Million In 2015, Growing At A Cagr Of 9.4% From 2016 To 2022. The Global Machine Learning Chip Market report draws precise insights by examining the latest and prospective industry trends and helping readers recognize the products and services that are boosting revenue growth and profitability. The study performs a detailed analysis of all the significant factors, including drivers, constraints, threats, challenges, prospects, and industry-specific trends, impacting the market on a global and regional scale. Additionally, the report cites worldwide market scenario along with competitive landscape of leading participants.

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Leading Players in the Machine Learning Chip Market:

The Machine Learning Chip market analysis is intended to provide all participants and vendors with pertinent specifics about growth aspects, roadblocks, threats, and lucrative business opportunities that the market is anticipated to reveal in the coming years. This intelligence study also encompasses the revenue share, market size, market potential, and rate of consumption to draw insights pertaining to the rivalry to gain control of a large portion of the market share.

By Type

By Application

Competitive landscape

The Machine Learning Chip Industry is extremely competitive and consolidated because of the existence of several established companies that are adopting different marketing strategies to increase their market share. The vendors engaged in the sector are outlined based on their geographic reach, financial performance, strategic moves, and product portfolio. The vendors are gradually widening their strategic moves, along with customer interaction.

Machine Learning Chip Market Segmented by Region/Country: US, Europe, China, Japan, Middle East & Africa, India, Central & South America

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Points Covered in the Report:

Fundamentals of Table of Content:

1 Report Overview1.1 Study Scope1.2 Key Market Segments1.3 Players Covered1.4 Market Analysis by Type1.5 Market by Application1.6 Study Objectives1.7 Years Considered

2 Global Growth Trends2.1 Machine Learning Chip Market Size2.2 Machine Learning Chip Growth Trends by Regions2.3 Industry Trends

3 Market Share by Key Players3.1 Machine Learning Chip Market Size by Manufacturers3.2 Machine Learning Chip Key Players Head office and Area Served3.3 Key Players Machine Learning Chip Product/Solution/Service3.4 Date of Enter into Machine Learning Chip Market3.5 Mergers & Acquisitions, Expansion Plans

4 Breakdown Data by Product4.1 Global Machine Learning Chip Sales by Product4.2 Global Machine Learning Chip Revenue by Product4.3 Machine Learning Chip Price by Product

5 Breakdown Data by End User5.1 Overview5.2 Global Machine Learning Chip Breakdown Data by End User

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Machine Learning Chip Market Is Thriving Worldwide to reach $8,272 Million by 2022 | Advanced Micro Devices, Inc., Google Inc., Graphcore, Intel...

London, June 03, 2020 (GLOBE NEWSWIRE) -- Artificial intelligence has emerged as the most potent technologies over the past few years, that is transitioning the landscape of almost all industry verticals. Although enterprise applications based on AI and machine learning (ML) are still in the nascent stages of development, they are gradually beginning to drive innovation strategies of the business.

In the supply chain and logistics industry, artificial intelligence is gaining rapid traction among industry stakeholders. Players operating in the supply chain and logistics industry are increasingly realizing the potential of AI to solve the complexities of running a global logistics network. Adoption of artificial intelligence in the supply chain is routing a new era or industrial transformation, allowing the companies to track their operations, enhance supply chain management productivity, augment business strategies, and engage with customers in digital world.

Theartificial intelligence in supply chain market is expected to grow at a CAGR of 45.3% from 2019 to 2027 to reach $21.8 billion by 2027. The growth in this market is mainly driven by rising awareness of artificial intelligence and big data & analytics and widening implementation of computer vision in both autonomous & semi-autonomous applications. In addition, consistent technological advancements in the supply chain industry, rising demand for AI-based business automation solutions, and evolving supply chain complementing growing industrial automation are further offering opportunities for vendors providing AI solutions in the supply chain industry. However, high deployment and operating costs and lack of infrastructure hinder the growth of the artificial intelligence in supply chain market.

In this study, the globalAI in supply chain market is segmented on the basis of component, application, technology, end user, and geography.

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Based on component, AI in supply chain market is broadly segmented into hardware, software, and services. The software segment commanded the largest share of the overall AI in supply chain market in 2019. This can be attributed to the increasing demand for AI-based platforms and solutions, as they offer supply chain visibility through software, which include inventory control, warehouse management, order procurement, and reverse logistics & tracking.

Based on technology, AI in supply chain market is broadly segmented into machine learning, computer vision, natural language processing, and context-aware computing. In 2019, the machine learning segment commanded the largest share of the overall AI in supply chain market. This growth can be attributed to the growing demand for AI-based intelligent solutions; increasing government initiatives; and the ability of AI solutions to efficiently handle and analyze big data and quickly scan, parse, and react to anomalies

Based on application, AI in supply chain market is broadly segmented into supply chain planning, warehouse management, fleet management, virtual assistant, risk management, inventory management, and planning & logistics. In 2019, the supply chain planning segment commanded the largest share of the overall AI in supply chain market. The growth of this segment can be attributed to the increasing demand for enhancing factory scheduling & production planning and the evolving agility and optimization of supply chain decision-making. In addition, digitizing existing processes and workflows to reinvent the supply chain planning model is also contributing to the growth of this segment.

Based on end user, artificial intelligence in supply chain market is broadly segmented into manufacturing, food & beverage, healthcare, automotive, aerospace, retail, and consumer packaged goods sectors. The retail sector commanded the largest share of the overall AI in supply chain market in 2019. This can be attributed to the increase in demand for consumer retail products.

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Based on geography, the global artificial intelligence in supply chain market is categorized into five major geographies, namely, North America, Europe, Asia Pacific, Latin America, and Middle East & Africa. In 2019, North America commanded for the largest share of the global artificial intelligence in supply chain market, followed by Europe, Asia-Pacific, Latin America, and the Middle East & Africa. The large share of the North American region is attributed to the presence of developed economies focusing on enhancing the existing solutions in the supply chain space, and the existence of major players in this market along with a high willingness to adopt advanced technologies.

On the other hand, the Asia-Pacific region is projected to grow at the fastest CAGR during the forecast period. The high growth rate is attributed to rapidly developing economies in the region; presence of young and tech-savvy population in this region; and growing proliferation of internet of things (IoT); rising disposable income; increasing acceptance of modern technologies across several industries including automotive, manufacturing, and retail; and broadening implementation of computer vision technology in numerous applications. Furthermore, the growing adoption of AI-based solutions and services among supply chain operations, increasing digitalization in the region, and improving connectivity infrastructure are also playing a significant role in the growth of this market in the region.

The globalAI in supply chain market is fragmented in nature and is characterized by the presence of several companies competing for the market share. Some of the leading companies in the artificial intelligence in supply chain market are from the core technology background. These include IBM Corporation (U.S.), Microsoft Corporation (U.S.), Google LLC (U.S.), and Amazon.com, Inc. (U.S.). These companies are leading the market owing to their strong brand recognition, diverse product portfolio, strong distribution & sales network, and strong organic & inorganic growth strategies. The other key players in the global artificial intelligence in supply chain market are Intel Corporation (U.S.), Nvidia Corporation (U.S.), Oracle Corporation (U.S.), Samsung (South Korea), LLamasoft, Inc. (U.S.), SAP SE (Germany), General Electric (U.S.), Deutsche Post DHL Group (Germany), Xilinx, Inc. (U.S.), Micron Technology, Inc. (U.S.), FedEx Corporation (U.S.), ClearMetal, Inc. (U.S.), Dassault Systmes (France), and JDA Software Group, Inc. (U.S.), among others.

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Artificial Intelligence (AI) in Supply Chain Market is projected to reach $21.8 billion by 2027, Growing at a CAGR of 45.3% from 2019- Meticulous...

DeepMind has recently releasedAcme, a library with an objective to simplify the development ofreinforcement learningalgorithms and agent building blocks. This application can be run at various scales of execution and it is achieved by enabling AI-driven agents to enable simple agent implementations. Acme can be used to create agents with greater parallelization than in previous approaches as per reports. This tool can be used by researchers to reproduce published RL algorithms or rapidly prototype ideas. Acme aims to make the results of various reinforcement learning (RL) algorithms developed in academia and industrial labs easier to reproduce and extend.

Acme strives to bring simple, efficient, and readable agents, that serve both as reference implementations of popular algorithms and as strong baselines, while still providing enough flexibility to do novel research. The design of Acme also attempts to provide multiple points of entry to the RL problem at differing levels of complexity.

Github: https://github.com/deepmind/acme

Paper: https://arxiv.org/pdf/2006.00979.pdf

Installation

To installacmecore:

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Asif Razzaq is an AI Tech Blogger and Digital Health Business Strategist with robust medical device and biotech industry experience and an enviable portfolio in development of Health Apps, AI, and Data Science. An astute entrepreneur, Asif has distinguished himself as a startup management professional by successfully growing startups from launch phase into profitable businesses. This has earned him awards including, the SGPGI NCBL Young Biotechnology Entrepreneurs Award.

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DeepMind releases Acme: A library of reinforcement learning components and agents - MarkTechPost

Image: The Respiratory Epithelium (Photo courtesy of Wikimedia Commons)

In the study, the scientists identified ciliated cells as the major target of SARS-CoV-2 infection. The bronchial epithelium acts as a protective barrier against allergens and pathogens. Cilia removes mucus and other particles from the respiratory tract. Their findings offer insight into how the virus causes disease. The scientists infected HBECs in an air-liquid interface with SARS-CoV-2. Over a period of three days, they used single-cell RNA sequencing to identify signatures of infection dynamics such as the number of infected cells across cell types, and whether SARS-CoV-2 activated an immune response in infected cells.

The scientists utilized advanced algorithms to develop working hypotheses and used electron microscopy to learn about the structural basis of the virus and target cells. These observations provide insights about host-virus interaction to measure SARS-CoV-2 cell tropism, or the ability of the virus to infect different cell types, as identified by the algorithms. After three days, thousands of cultured cells became infected. The scientists analyzed data from the infected cells along with neighboring bystander cells. They observed ciliated cells were 83% of the infected cells. These cells were the first and primary source of infection throughout the study. The virus also targeted other epithelial cell types including basal and club cells. The goblet, neuroendocrine, tuft cells, and ionocytes were less likely to become infected.

The gene signatures revealed an innate immune response associated with a protein called Interleukin 6 (IL-6). The analysis also showed a shift in the polyadenylated viral transcripts. Lastly, the (uninfected) bystander cells also showed an immune response, likely due to signals from the infected cells. Pulling from tens of thousands of genes, the algorithms locate the genetic differences between infected and non-infected cells. In the next phase of this study, the scientists will examine the severity of SARS-CoV-2 compared to other types of coronaviruses, and conduct tests in animal models.

Machine learning allows us to generate hypotheses. Its a different way of doing science. We go in with as few hypotheses as possible. Measure everything we can measure, and the algorithms present the hypothesis to us, said senior author David van Dijk, PhD, an assistant professor of medicine in the Section of Cardiovascular Medicine and Computer Science.

Related Links:Yale School of Medicine

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Yale Researchers Use Single-Cell Analysis and Machine Learning to Identify Major COVID-19 Target - HospiMedica

Ananya Gupta

When Ananya Gupta told us about the tool that she developed, GRIT Parity (Gender Representation in Textbooks), to find out if textbooks are gender-neutral or not, we were surprised. We had to try it to believe it. So, we checked out her website gritparity.com and tried out thetool by uploading a Social Sciences textbook..The results stated that the textbook was not gender-neutral, in fact, around 96 per cent of the textbook used images and names of men and a small percentage of women. We were truly amazed by the tool but what amazed us even more that something so sophisticated was developed by a eleventh grader. And she developed the tool using her skills in Machine Learning and Artificial Intelligence.Ananya, who developed an interest in Machine Learning and Artificial Intelligence when she was in class VIII, says, "I loved these subjects to the extent that I started learning them on my own. I referred to several YouTube videos, signed up for free courses on Coursera, Udemy and many other websites. When I was in class VIIII, I developed a mobile application with the aim to solve the wet waste problem in Bengaluru. I also exhibited this app at Technovation and won a prize for it. It encouraged me to know more about these subjects. In the last two years, my desk has been full of books related to ML and AI."

But what led Ananya to develop GRIT Parity in the first place? A few months ago, she co-founded the GirlUP chapter at the International Business School. GirlUP is a global UN initiative to promote gender equality. Ananya used to visit government and low-income schools across the city to promote STEM education among young students. She explains, "My visits made me realise that there were deep thoughts of gender bias instilled in the minds of these children. I conducted a survey in these schools to find out why it existed and what they feel about themselves. I understood that the main reasons for these thoughts were the society at large, which is prejudiced against women, and textbooks as well."She continues, "I felt that our textbooks need to be revised because they show men doing various jobs whereas women were only restricted to activities like cooking, washing clothes and jobs like nurse, garbage collector etc. This text and teaching can affect young girls' lives and the career choices they make in the future. So, I decided to develop a tool that could help find out the gender neutrality percentage in any textbook. And it has been my dream to use ML and AI to make a social impact."

It is quite easy to use the GRIT Parity tool. Ananya, who took help from the mentors in the industry, says, "These days, textbooks are readily available in PDF format either on private or government websites. All you have to do is upload the PDF ingritparity.comand the tool analyses the textbook in three different ways. It shows the number of stories that have a majority of male characters, it shows careers that both genders are involved in and compares the number of images used in it." According to Ananya, a lot of girls who are talented are not able to achieve what they want due to a lack of confidence or because of the kind of prejudice they come across in society. She does not want to stop here as she believes that now it is time to make some changes to our textbook. "The first step towards changing this prejudice among students is to represent women in a better light and on par with men in textbooks. I am planning to work closely with the Government of Karnataka as well as the Education Minister to achieve this. I have a long way to go but there is no stopping me," she concludes.

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Why you must check out this online tool by a class 11 student that detects gender neutrality in textbooks - EdexLive