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Category Archives: Machine Learning
19 Impact on Global Machine Learning Artificial intelligence Market to Grow at a Stayed CAGR from 2020 to 2026 – Cole of Duty
The 19 Impact on Global Machine Learning Artificial intelligence market research report added by Market Study Report, LLC, is a thorough analysis of the latest trends prevalent in this business. The report also dispenses valuable statistics about market size, participant share, and consumption data in terms of key regions, along with an insightful gist of the behemoths in the 19 Impact on Global Machine Learning Artificial intelligence market.
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Telecommuters: tired of the constant embarrassment of showing up to video conferences wearing nothing but your underwear? Save the humiliation and all those pesky trips down to HR with Safe Meeting, the new system that uses the power of artificial intelligence to turn off your camera if you forget that casual Friday isnt supposed to be that casual.
The following infomercial is brought to you by [Nick Bild], who says the whole thing is tongue-in-cheek but we sense a certain degree of necessity is the mother of invention here. Its true that the sudden throng of remote-work newbies certainly increases the chance of videoconference mishaps and the resulting mortification, so whatever the impetus, Safe Meeting seems like a great idea. It uses a Pi cam connected to a Jetson Nano to capture images of you during videoconferences, which are conducted over another camera. The stream is classified by a convolutional neural net (CNN) that determines whether it can see your underwear. If it can, it makes a REST API call to the conferencing app to turn off the camera. The video below shows it in action, and that it douses the camera quickly enough to spare your modesty.
We shudder to think about how [Nick] developed an underwear-specific training set, but we applaud him for doing so and coming up with a neat application for machine learning. Hes been doing some fun work in this space lately, from monitoring where surfaces have been touched to a 6502-based gesture recognition system.
Go here to see the original:
Machine Learning Takes The Embarrassment Out Of Videoconference Wardrobe Malfunctions - Hackaday
Machine Learning Chip Market Is Thriving Worldwide to reach $8,272 Million by 2022 | Advanced Micro Devices, Inc., Google Inc., Graphcore, Intel…
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.
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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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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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…
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 in Manufacturing Marketby Component, Technology (ML, Computer Vision, NLP), Application (Cybersecurity, Robot, Planning), Industry (Electronics, Energy, Automotive, Metals and Machine, Food and Beverages) Global Forecast to 2027
Automotive Artificial Intelligence (AI) Marketby Component (Hardware, Software), Technology (Machine Learning, Computer Vision), Process (Signal Recognition, Image Recognition) and Application (Semi-Autonomous Driving) - Global Forecast to 2027
Artificial Intelligence in Healthcare Marketby Product (Hardware, Software, Services), Technology (Machine Learning, Context-Aware Computing, NLP), Application (Drug Discovery, Precision Medicine), End User, And Geography - Global Forecast to 2025
Artificial Intelligence in Security Marketby Offering (Hardware, Software, Service), Security Type (Network Security, Application Security), Technology (Machine Learning, NLP, Context Awareness,), Solution, End-User, and Region - Global Forecast to 2027
Artificial Intelligence in Retail Marketby Product (Chatbot, Customer Relationship Management), Application (Programmatic Advertising), Technology (Machine Learning, Natural Language Processing), Retail (E-commerce and Direct Retail)- Forecast to 2025
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Special Operations Commands Gen. Richard Clarke with students at the Special Forces Qualification Course.
WASHINGTON: Special Operations Command is in a war for influence with adversaires from non-state groups to state-funded information operations, the commands top general said recently, and is rushing to fund artificial intelligence and machine learning programs to find an edge.
Were going to have to have artificial intelligence and machine learning tools, specifically for information ops that hit a very broad portfolio, SOCOM commander Gen. Richard Clarke said recently, because were going to have to understand how the adversary is thinking, how the population is thinking, and work in these spaces.
Special Operations have cultivated an image in popular culture over two decades of constant war in the Middle East as almost superhuman door kickers dropping from the sky to blast their way quickly through an objective, disappearing as quickly as they had arrived. That view has in part led policymakers and the public to look to these troops as a solution to almost any problem, placing an enormous burden on a force of about 70,000 troops.
Clarke said that kinetic mission wont change any time soon, but other missions the various tribes of SOCOM and SOF have always performed intelligence gathering, training and advising, and influence operations need to be reprioritized.
We need coders, he told the virtual Special Operations Forces Industry Conference last month. Weve been having discussions internally that the most important person on the mission is no longer the operator kicking down the door, but the cyber operator who the team has to actually get to the environment so he or she can work their cyber tools into the fight.
SOCOM has started using AI in developing information operations in places like Afghanistan, but the commands interest is hardly limited to that space.
Acquisition chief Jim Smith told the conference his team is looking at a wide range of applications for employing AI, including intel gathering and fusion, surveillance and reconnaissance, precision fires, and health and training efforts. All of these functions are time and manpower-intensive, requiring long hours and entire teams to collect, understand, analyze, and move data, sometimes forcing troops to react as opposed to seizing initiative.
Those tasks are becoming more critical as defense budgets tighten and adversaries catch up and even surpass US capabilities across a wide range of technologies and capabilities.
So how do we use artificial intelligence and machine learning to get those sensors to interoperate autonomously and provide feedback to a single operator to enable that force to maneuver on the objective? Smith asked, noting that this is one of the biggest issues his office is coping with/.
Think of those small UAVs or your small ground vehicles and give them enough artificial intelligence and machine learning to be able to be autonomous, so that they can clear a building or they can clear a tunnel, which then allows the maneuver force to focus on other tasks.
These technologies could also help operators in the field launch countermeasures to intercept and disrupt enemy communications, which right now can be a slow process.
Today the way we do that is we have a library of threat radar signatures Smith said, and if you see one of those threat radars in our library we counter it. So SOCOM is looking for ways to use machine learning to identify anomalies in this space so it wasnt just the threat radars we had loaded into the library, that were already known, but maybe its a new radar that we havent seen before or a radar that we didnt realize was operating in that theater that we could identify.
Smith said his approach is to bake in AI and machine learning requirements with every program that SOCOM develops from here on out.
What were starting to see is our industry partners coming in on proposals and theyre baking in artificial intelligence and machine learning, he said. Thats exactly where we want to be.
InterDigital, Blacknut, and Nvidia unveil worlds first Cloud gaming solution with AI-enabled user interface – TelecomTV
WILMINGTON, Del., June 03, 2020 (GLOBE NEWSWIRE) -- InterDigital, Inc. (NASDAQ:IDCC), a mobile and video technology research and development company, today introduced the worlds first cloud gaming solution with an AI and machine learning-enabled user interface, presented in collaborative partnership with cloud gaming trailblazer Blacknut and in cooperation with GPU pioneer Nvidia. The tripartite collaboration represents the first time that an AI and machine learning-driven user interface is utilized, wearable-free, with a live cloud gaming solution. The technology demonstrates the incredible potential of integrating localized and far-Edge enabled AI capabilities into home gaming experiences.
The AI and machine learning-enabled user interface is connected to a cloud gaming solution that operates without joysticks or wearable accessories. The demonstration leverages unique technologies, including real-time video analysis on home and local edge devices, dynamic adaptation to available compute resources, and shared AI models managed through an in-home AI hub, to implement a cutting-edge gaming experience.
In the demonstration, users play a first-person view snowboarding game streamed by Blacknut and displayed on a commercial television. Users do not require a joystick or handheld controller to play the game; instead, their movements and interactions are tracked by AI processing of the live video capture of the users movements. The users presence is detected using an AI model and his or her body movements are matched with the snowboarder in the game, in real time, using InterDigitals low latency Edge AI running on a local AI accelerator. The groundbreaking demo addresses the challenges of ensuring the lowest possible end-to-end latency from gesture capture to game action, while accelerating inference of concurrent AI models serving multiple applications to deliver an interactive and more seamless gaming experience. This demonstration enables AI and machine learning tasks to be completed locally, revolutionizing our current implementation of cloud gaming solutions.
We are so proud of the work of this demonstration, as it displays the real potential of AI and edge computing, highlights the power of industry collaboration, and helps blaze a trail for new cloud gaming capabilities. Of course, such a success would not have been possible without the utmost implication of all the teams from Interdigital, Blacknut, and Nvidia, and I would like to take the opportunity to credit and thank their outstanding work, said Laurent Depersin, Director of the Home Experience Lab at InterDigital.
The far-Edge AI and machine learning technologies put forth by InterDigital bring a plethora of new capabilities to the cloud gaming experience. Far-Edge AI enables low-latency analysis to deliver an interactive and entertaining experience, reduces cloud computing costs by leveraging available computing resources, and saves significant bandwidth by prioritizing up-linking. In addition, far-Edge AI in edge cloud architecture offers an important solution for privacy concerns by localizing computing and supports a variety of new and emerging vertical applications beyond gaming, including smart home and security, remote healthcare, and robotics.
Cloud gaming with far-Edge AI leverages artificial intelligence and localized Edge computing to showcase the ways an interactive television or gaming experience can be enhanced by the localized AI analysis of a cameras video stream. Ongoing research in the real-time processing of user generated data will drive new innovations and vertical applications in the home, from cloud gaming to remote medical care, and those innovations will be enhanced by the ability to execute artificial intelligence models under low latency conditions.
Blacknuts mission is to bring to our customers unlimited hours of gaming fun in the simplest manner, said Pascal Manchon, CTO at Blacknut. Our unique cloud gaming solution allows to free games from dedicated consoles or hardware. Using AI and machine learning to transform the human body itself in a full-fledge game controller was challenging but Blacknuts close collaboration with Interdigital and NVidia led to outstanding performances. And yes, it is addictive and fun to play this way!
Cloud gaming is an exciting industry use case that leverages innovations in network architecture, video streaming and content delivery to shape the future of interactive gaming and entertainment. This worlds first cloud gaming solution, and the broader exploration of AI-enabled cloud solutions, would not be possible without a commitment to collaboration with industry leaders and partners.