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Monthly Archives: March 2021
Nanotechnology-Based Medical Devices Market Size, Share, Growth, Analysis and Forecast 2024 – The Courier
Posted: March 11, 2021 at 12:11 pm
Due to the rising geriatric population and increasing government policies, the global Nanotechnology- based medical devices market is growing at a substantial pace. Various products have added to the market size of nanotechnology-based medical devices, such as biochips, active implantable devices, and medical textile and wound dressings.
Owing to increased out-of-pocket healthcare spending, rising prevalence of age-related and lifestyle-related conditions such as coronary diseases and hearing diseases, and accessibility of insurance coverage and reimbursement for medical procedures, the industry has experienced strong demand for active implantable devices over the last few years.
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By products, the market is subdivided into active implantable devices, medical textiles, and wound dressings, biochips, implantable materials, and others.Active implantable devices segment anticipated the largest share in the market due to the increasing out-of-pocket healthcare expenditure.
By application, the market is subdivided into therapeutic applications, research applications, and diagnostic applications.
The key growth factors for the nanotechnology-based medical device market are growing government funding, the increasing prevalence of age-related and lifestyle-related disorders such as cardiovascular diseases and hearing diseases, large-scale advancement of nanotechnology, the presence of well-structured distribution networks, and the expanding geriatric population.
The growth of the nanotechnology-based medical device market is also encouraged by an ample mid-to-late-stage product pipeline, enhanced understanding of preventive measures in healthcare, accessibility of insurance coverage and payment for medical services, increased healthcare spending, advancements in technology, and immense growth opportunities in emerging markets.
Strict and time-consuming product approval processes, insufficient standards and absence of regulatory approval frameworks, and elevated cost of medical devices are the major challenges for the growth of nanotechnology-based medical devices market.
Recent News:
In October 2018, Stryker acquired HyperBranch Medical Technology, Inc, to improve its cranial closure portfolio and strengthens its position in neurotechnology business.
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Competitive Insights:
3M Company, AAP Implantate AG, Smith & Nephew PLC, Dentsply Sirona Inc., Perkinelmer, Inc., Stryker Corporation, Mitsui Chemicals, Inc., and Starkey Hearing Technologies are the key players offering nanotechnology-based medical devices.
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Global Image Recognition Market Comprehensive Study Explores Huge Growth in Future KSU | The Sentinel Newspaper – KSU | The Sentinel Newspaper
Posted: at 12:11 pm
This report focuses on the application by analyzing the consumption and its growth rate of each application and production, consumption, export and import in each region. It concentrates on manufacturing analysis, including key materials analysis, cost structure analysis and process analysis. It prospects the whole market, including global production and revenue forecast, regional forecast. It also foresees the market by type and application and concludes the research findings and refines all the highlights of the study. The report comprises evaluation of production process, methodologies, plant locations, raw material sources, serving segments, product specifications, product line-up, import-export, technologies, equipment, value chain, pricing structure, manufacturing cost, brands, patents, and participants global presence.
This report enfolds the imperative and insightful consuls that provides Shrewd acumen to market players to make informed business decisions and build the most remunerative strategies to achieve success in the market. The report defines and categorize the market and also scrutinize and predict the market size and share in terms of value and volume. Market forecast including marketing volumes, Value and Utilization is provided by regions, by types, and by applications.
Image recognition marketis set to witness a healthyCAGR of 22.25%in the forecast period. This rise in the market value can be attributed due to increasing demand of image recognition technologies and applications in gaming consoles and mobile computing devices.
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If you are involved in the Image Recognition industry or intend to be, then this study will provide you comprehensive outlook. Its vital you keep your market knowledge up to datesegmented byTechnology (Code Recognition, Digital Inage processing, Facial recognition, Object Recognition, Pattern recognition, Optical Character Recognition), Component (Hardware, Software, Services), Application (Scanning & Imaging, Security & Surveillance, Image Search, Augmented reality, Marketing & Advertising), Deployment Type (On-Premise, Cloud), Industry (BFSI, Media & Entertainment, Retail & Consumer Goods, IT & Telecom, Government, Healthcare, Transportation & Logistics, Education, Gaming), Geography
What are the major market growth drivers?
Growing adoption of image recognition applications, which is fueling the growth of the market
Increasing demand for security applications and products enabled with the image recognition function, is boosting the market growth
Surging uses of high bandwidth data services, had increased the market size of image recognition
Growing use of artificial intelligence by the companies globally, is driving the growth of the market
Rapid Business Growth Factors
In addition, the market is growing at a fast pace and the report shows us that there are a couple of key factors behind that. The most important factor thats helping the market grow faster than usual is the tough competition.
Key Insights that Study is going to provide:
Gap Analysis by Region. Country Level Break-up will help you dig out Trends and opportunity lying in specific area of your business interest.
Market Share & Sales Revenue by Key Players & Emerging Regional Players.[Some of the players covered in the study are Qualcomm Technologies, Inc., NEC Corporation, Google., LTUTech, Catchoom, Honeywell International Inc, Hitachi, Ltd., Wikitude and others]
A separate chapter on Market Entropy to gain insights on Leaders aggressiveness towards market [Merger & Acquisition / Recent Investment and Key Developments]
Patent Analysis** No of patents / Trademark filed in recent years.
Competitive Landscape:Company profile for listed players with SWOT Analysis, Business Overview, Product/Services Specification, Business Headquarter, Downstream Buyers and Upstream Suppliers.
May vary depending upon availability and feasibility of data with respect to Industry targeted
Business Strategies
Key strategies in the Global Image Recognition Market that includes product developments, partnerships, mergers and acquisitions, etc discussed in this report. The potential of this enterprise section has been rigorously investigated in conjunction with main market challenges.
Key Market Competitors:Image Recognition Market
Few of the major competitors currently working in the global image recognition market are Qualcomm Technologies, Inc., NEC Corporation, Google., LTUTech, Catchoom, Honeywell International Inc, Hitachi, Ltd., Wikitude GmbH, Slyce, Attrasoft, Inc., JASTEC Co. Ltd., Apple Inc., Facebook, Twitter, IDEMIA, Gemalto NV, Ayonix Face Technologies, Cognitec Systems GmbH, Aware, Inc., Daon, Neurotechnology, Herta Security, KeyLemon Ind. among others.
Market Dynamics:
Set of qualitative information that includes PESTEL Analysis, PORTER Five Forces Model, Value Chain Analysis and Macro Economic factors, Regulatory Framework along with Industry Background and Overview
Key Developments in the Market:
In July 2019, First Insight Inc., announced New insight customer experience platform which is fast and combines enhanced customer insights with the product level analytics which increases the efficiency and productivity of the user and can enhance the overall customer experience.
In January 2017, Qualcomm Technologies Inc., and ODG announced the first augmented reality smart glasses with new snapdragon 835 processor enabled which uniquely delivers power efficiency and performance required for the compact AR/VR smart glasses.
In October 2016, NEC Corporation announced the global launch of their NeoFace image data mining that utilizes artificial intelligence to accurately search the video footages for the required individual. The software can be utilized for various applications like criminal investigation, searching lost child and proving improved customer service.
Some extract from Table of Contents
Overview of Global Image Recognition Market
Image Recognition Size (Sales Volume) Comparison by Type
Image Recognition Size (Consumption) and Market Share Comparison by Application
Image Recognition Size (Value) Comparison by Region
Image Recognition Sales, Revenue and Growth Rate
Image Recognition Competitive Situation and Trends
Strategic proposal for estimating availability of core business segments
Players/Suppliers, Sales Area
Analyze competitors, including all important parameters of Image Recognition
Global Image Recognition Manufacturing Cost Analysis
The most recent innovative headway and supply chain pattern mapping
Thanks for reading this article; you can also get individual chapter wise section or region wise report version like North America, Europe, MEA or Asia Pacific.
Table Of Contents Is Available[emailprotected]https://www.databridgemarketresearch.com/toc/?dbmr=global-image-recognition-market
To comprehend Global Image Recognition market dynamics in the world mainly, the worldwide Image Recognition market is analyzed across major global regions. DBMR also provides customized specific regional and country-level reports for the following areas.
North America: United States, Canada, and Mexico. South & Central America: Argentina, Chile, and Brazil. Middle East & Africa: Saudi Arabia, UAE, Turkey, Egypt and South Africa. Europe: UK, France, Italy, Germany, Spain, NORDIC {Sweden, Norway, Finland, Denmark etc}, BENELUX {Belgium, The Netherlands, Luxembourg}, and Russia. Asia-Pacific: India, China, Japan, South Korea, Indonesia, Singapore, and Australia.
Why Is Data TriangulationImportantIn Qualitative Research?
This involves data mining, analysis of the impact of data variables on the market, and primary (industry expert) validation. Apart from this, other data models include Vendor Positioning Grid, Market Time Line Analysis, Market Overview and Guide, Company Positioning Grid, Company Market Share Analysis, Standards of Measurement, Top to Bottom Analysis and Vendor Share Analysis. Triangulation is one method used while reviewing, synthesizing and interpreting field data. Data triangulation has been advocated as a methodological technique not only to enhance the validity of the research findings but also to achieve completeness and confirmation of data using multiple methods
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Face and Voice Biometrics Market 2021 | Covid19 Impact Analysis | Business Outlook, Growth, Revenue, Trends and Forecasts 2026 | 3M Cogent (USA), NEC…
Posted: at 12:11 pm
Face and Voice Biometrics Market describes an in-depth evaluation and professional study on the present and future state of the Face and Voice Biometrics market across the globe, including valuable facts and figures. Face and Voice Biometrics Market provides information regarding the emerging opportunities in the market & the market drivers, trends & upcoming technologies that will boost these growth trends. The report provides a comprehensive overview including Definitions, Scope, Application, Production and CAGR (%) Comparison, Segmentation by Type, Share, Revenue Status and Outlook, Capacity, Consumption, Market Drivers, Production Status and Outlook and Opportunities, Export, Import, Emerging Markets/Countries Growth Rate. The report presents a 360-degree overview of the competitive landscape of the industries. The Face and Voice Biometrics market report assesses the key regions (countries) promising a huge market share for the forecast period.
Top Key players of Face and Voice Biometrics Market Covered In The Report:3M Cogent (USA)NEC Corporation of America (USA)AcSys Biometrics Corp. (Canada)AGNITiO S.L. (Spain)Cognitec Systems GmbH (Germany)Nuance Communications (USA)Eurotech S.P.A (Italy)Ivrnet Inc. (Canada)Kimaldi ElectronicsS.L. (Spain)National Security Resources (USA)Neurotechnology (Lithuania)PSP Security Co. Ltd (Hong Kong)SAFRAN Group (France)Sensible Vision (USA)Sensory (USA)Suprema (Korea)VoiceTrust eServices (Canada)VoiceVault (USA)
Key Market Segmentation of Face and Voice Biometrics:
By Type, Face and Voice Biometrics market has been segmented into:
Face BiometricsVoice Biometrics
By Application, Face and Voice Biometrics market has been segmented into:
Application AApplication BApplication C
The Face and Voice Biometrics report gives detail complete examination to territorial sections that covered The USA, Europe, Japan, China, India, Southeast Asia, South America, South Africa, and Rest of World in Global Outlook Report with Face and Voice Biometrics Market definitions, characterizations, delivering reports, cost structures, advancement strategies, and plans. The results and information are top notches in the Face and Voice Biometrics report utilizing outlines, diagrams, pie graphs, and other pictorial portrayals concerning its Current Trends, Dynamics, and Face and Voice Biometrics Business Scope, Key Statistics and CAGR Analysis of top key players.
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Key Highlights from Face and Voice Biometrics Market Study:
Income and Sales Estimation Historical Revenue and deals volume is displayed and supports information is triangulated with best down and base up ways to deal with figure finish market measure and to estimate conjecture numbers for key areas shrouded in the Face and Voice Biometrics report alongside arranged and very much perceived Types and end-utilize industry. Moreover, macroeconomic factor and administrative procedures are discovered explanation in Face and Voice Biometrics industry advancement and perceptive examination.
Assembling Analysis The Face and Voice Biometrics report is presently broke down concerning different types and applications. The Face and Voice Biometrics market gives a section featuring the assembling procedure examination approved by means of essential data gathered through Industry specialists and Key authorities of profiled organizations.
Competition Analysis Face and Voice Biometrics Leading players have been considered relying upon their organization profile, item portfolio, limit, item/benefit value, deals, and cost/benefit.
Demand and Supply and Effectiveness Face and Voice Biometrics report moreover gives support, Production, Consumption and (Export and Import).
Which prime data figures are included in the report? -Market size (Last few years, current and expected)-Market share analysis as per different companies)-Market forecast)-Demand)-Price Analysis)-Market Contributions (Size, Share as per regional boundaries)
Who all can be benefitted out of this report? -Market Investigators-Teams, departments, and companies-Competitive organizations-Individual professionals-Vendors, Buyers, Suppliers-Others
What are the crucial aspects incorporated in the report? -Industry Value Chain-Consumption Data-Market Size Expansion-Key Economic Indicators
Strategic Points Covered in TOC:
Chapter 1: Introduction, market driving force product scope, market risk, market overview, and market opportunities of the global Face and Voice Biometrics market.
Chapter 2: Evaluating the leading manufacturers of the global Face and Voice Biometrics market which consists of its revenue, sales, and price of the products.
Chapter 3: Displaying the competitive nature among key manufacturers, with market share, revenue, and sales.
Chapter 4: Presenting global Face and Voice Biometrics market by regions, market share and with revenue and sales for the projected period.
Chapter 5, 6, 7, 8 and 9: To evaluate the market by segments, by countries and by manufacturers with revenue share and sales by key countries in these various regions.
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Automated Fingerprint Identification System Market Research Report by Component, by Search Type, by Application – Global Forecast to 2025 – Cumulative…
Posted: at 12:11 pm
Automated Fingerprint Identification System Market Research Report by Component (Hardware and Software), by Search Type (Latent Print to Latent Print Search and Tenprint to Tenprint Search), by Application - Global Forecast to 2025 - Cumulative Impact of COVID-19
New York, March 09, 2021 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Automated Fingerprint Identification System Market Research Report by Component, by Search Type, by Application - Global Forecast to 2025 - Cumulative Impact of COVID-19" - https://www.reportlinker.com/p05913310/?utm_source=GNW
Market Statistics:The report provides market sizing and forecast across five major currencies - USD, EUR GBP, JPY, and AUD. This helps organization leaders make better decisions when currency exchange data is readily available.
1. The Global Automated Fingerprint Identification System Market is expected to grow from USD 5,674.47 Million in 2019 to USD 14,240.61 Million by the end of 2025.2. The Global Automated Fingerprint Identification System Market is expected to grow from EUR 5,066.64 Million in 2019 to EUR 12,486.43 Million by the end of 2025.3. The Global Automated Fingerprint Identification System Market is expected to grow from GBP 4,446.81 Million in 2019 to GBP 11,100.47 Million by the end of 2025.4. The Global Automated Fingerprint Identification System Market is expected to grow from JPY 618,563.44 Million in 2019 to JPY 1,519,833.94 Million by the end of 2025.5. The Global Automated Fingerprint Identification System Market is expected to grow from AUD 8,163.90 Million in 2019 to AUD 20,679.25 Million by the end of 2025.
Market Segmentation & Coverage:This research report categorizes the Automated Fingerprint Identification System to forecast the revenues and analyze the trends in each of the following sub-markets:
"The Software is projected to witness the highest growth during the forecast period"
Based on Component, the Automated Fingerprint Identification System Market studied across Hardware and Software. The Hardware further studied across Displays, Fingerprint Input Modules, Microprocessors/Microcontrollers, and Sensors. The Software further studied across Database Fingerprints and Matchers. The Hardware commanded the largest size in the Automated Fingerprint Identification System Market in 2019. On the other hand, the Software is expected to grow at the fastest CAGR during the forecast period.
"The Tenprint to Tenprint Search is projected to witness the highest growth during the forecast period"
Based on Search Type, the Automated Fingerprint Identification System Market studied across Latent Print to Latent Print Search and Tenprint to Tenprint Search. The Tenprint to Tenprint Search commanded the largest size in the Automated Fingerprint Identification System Market in 2019, and it is expected to grow at the fastest CAGR during the forecast period.
"The Government is projected to witness the highest growth during the forecast period"
Based on Application, the Automated Fingerprint Identification System Market studied across Banking & Finance, Government, Healthcare, Hospitality, Institutional, and Transportation. The Institutional commanded the largest size in the Automated Fingerprint Identification System Market in 2019. On the other hand, the Government is expected to grow at the fastest CAGR during the forecast period.
"The Asia-Pacific is projected to witness the highest growth during the forecast period"
Based on Geography, the Automated Fingerprint Identification System Market studied across Americas, Asia-Pacific, and Europe, Middle East & Africa. The Americas region surveyed across Argentina, Brazil, Canada, Mexico, and United States. The Asia-Pacific region surveyed across Australia, China, India, Indonesia, Japan, Malaysia, Philippines, South Korea, and Thailand. The Europe, Middle East & Africa region surveyed across France, Germany, Italy, Netherlands, Qatar, Russia, Saudi Arabia, South Africa, Spain, United Arab Emirates, and United Kingdom. The Americas commanded the largest size in the Automated Fingerprint Identification System Market in 2019. On the other hand, the Asia-Pacific is expected to grow at the fastest CAGR during the forecast period.
Company Usability Profiles:The report deeply explores the recent significant developments by the leading vendors and innovation profiles in the Global Automated Fingerprint Identification System Market including Dermalog Identification Systems GmbH, Fujitsu Limited, HID Global Corporation by Assa Abloy AB, Id3 Technologies, IDEMIA, Innovatrics, M2SYS Technology, Maxar Technologies Inc., NEC Corporation, Neurotechnology, Papillon Systems, Sonda Technologies Ltd., Suprema, Inc., and Thales S.A..
Cumulative Impact of COVID-19:COVID-19 is an incomparable global public health emergency that has affected almost every industry, so for and, the long-term effects projected to impact the industry growth during the forecast period. Our ongoing research amplifies our research framework to ensure the inclusion of underlaying COVID-19 issues and potential paths forward. The report is delivering insights on COVID-19 considering the changes in consumer behavior and demand, purchasing patterns, re-routing of the supply chain, dynamics of current market forces, and the significant interventions of governments. The updated study provides insights, analysis, estimations, and forecast, considering the COVID-19 impact on the market.
360iResearch FPNV Positioning Matrix:The 360iResearch FPNV Positioning Matrix evaluates and categorizes the vendors in the Automated Fingerprint Identification System Market on the basis of Business Strategy (Business Growth, Industry Coverage, Financial Viability, and Channel Support) and Product Satisfaction (Value for Money, Ease of Use, Product Features, and Customer Support) that aids businesses in better decision making and understanding the competitive landscape.
360iResearch Competitive Strategic Window:The 360iResearch Competitive Strategic Window analyses the competitive landscape in terms of markets, applications, and geographies. The 360iResearch Competitive Strategic Window helps the vendor define an alignment or fit between their capabilities and opportunities for future growth prospects. During a forecast period, it defines the optimal or favorable fit for the vendors to adopt successive merger and acquisition strategies, geography expansion, research & development, and new product introduction strategies to execute further business expansion and growth.
The report provides insights on the following pointers:1. Market Penetration: Provides comprehensive information on the market offered by the key players2. Market Development: Provides in-depth information about lucrative emerging markets and analyzes the markets3. Market Diversification: Provides detailed information about new product launches, untapped geographies, recent developments, and investments4. Competitive Assessment & Intelligence: Provides an exhaustive assessment of market shares, strategies, products, and manufacturing capabilities of the leading players5. Product Development & Innovation: Provides intelligent insights on future technologies, R&D activities, and new product developments
The report answers questions such as:1. What is the market size and forecast of the Global Automated Fingerprint Identification System Market?2. What are the inhibiting factors and impact of COVID-19 shaping the Global Automated Fingerprint Identification System Market during the forecast period?3. Which are the products/segments/applications/areas to invest in over the forecast period in the Global Automated Fingerprint Identification System Market?4. What is the competitive strategic window for opportunities in the Global Automated Fingerprint Identification System Market?5. What are the technology trends and regulatory frameworks in the Global Automated Fingerprint Identification System Market?6. What are the modes and strategic moves considered suitable for entering the Global Automated Fingerprint Identification System Market?Read the full report: https://www.reportlinker.com/p05913310/?utm_source=GNW
About ReportlinkerReportLinker is an award-winning market research solution. Reportlinker finds and organizes the latest industry data so you get all the market research you need - instantly, in one place.
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What Is Artificial Intelligence? Whether You’re a Student, Professional, or Scientist, Here’s What It Means. – Entrepreneur
Posted: at 12:10 pm
March10, 20215 min read
Opinions expressed by Entrepreneur contributors are their own.
Artificial Intelligence (AI) is revolutionizing the way we live and work, so today I invite you to learn more about this topic by approaching it from three different and increasingly complex segments.
Broadly speaking, we can refer to AI as the simulation of human intelligence by machines. In other words, a discipline that tries to create systems capable of learning and reasoning like people .
Importantly, Artificial Intelligence is the most debated technology of the 21st century. Today, it is widely used to solve complex problems and facilitate human tasks.
Do you still have doubts about what artificial intelligence is? Join me in the following segments.
Image: Depositphotos.com
Imagine a machine that organizes your closet the way you like itorserves refreshments to your friends during a party. That is, a robot that performs the activities that you do daily. Well, this is precisely what Artificial Intelligence makes possible. Through algorithms and mathematical functions, AI provides machines with intelligence similar to that of a human to perform everyday tasks such as playing soccer with you or even giving you some dance lessons.
Through Artificial Intelligence, machines can learn, reason and solve problems three capabilities that make the robot artificially intelligent.
Image: Depositphotos.com
Artificial intelligence works by combining large amounts of data with fast, iterative processing, as well as intelligent algorithms that allow software to automatically learn and identify patterns. The goal of AI is to create systems that can function intelligently and independently of human beings AND perform tasks in the professional field such asthe construction of buildings or in factories automated by intelligent robots.
Artificial intelligence has subfields, among which the following stand out:
Natural language processing: The ability of computers to analyze, understand, and generate human language, including speech. Some products in this area are Amazon Alexa andGoogle Voice.
Image: Depositphotos.com
To develop this segment, I am going to focus on the fact that Artificial Intelligence allows machines tosolve problems as people do The interesting thing here is to compare them with humans and understand how they interact with them. To do this, the following points must be taken into account:
It is worth mentioning that there are currently 4 types of Artificial Intelligence, according to Professor Arend Hintze:
Purely reactive: Here, machinesdo not have the ability to form memories, therefore they cannot use their experience in decision-making. These are machines that make predictions based on parameters. An example of this type is Deep Blue, the IBM-made supercomputer that defeated chess grandmaster and world champion Garri Kasparov in 1997.
Limited memory: Machines of this type do store memories and use that "past" to make decisions.That is, they use the knowledge of their previous experiences to elaborate a response. However, this information is kept for a certain time, so their vision of the past is very "limited."To put it in another way, they store only a limited number of recent experiences that they use to perform their calculations and act in the environment they do not use that experience to learn it forever, as people would. An example of this type is autonomous cars.
Theory of mind: One of the objectives of Artificial Intelligence is to ensure that machines can emulate the learning process that people have. The machines of this classification aremore advanced, and they are capable of processing and expressing emotions.Theoretical knowledge of these emotions allows them to know how people or elements in their environment think and feel,to predict what we expect them to do, and to later adjust their behavior according to those predictions.
Self-awareness: This isthe highest step in the hierarchy proposed by Professor Hintze. Here,machines are aware of themselves, recognize their internal states and become aware of the place and position they occupy within an environment. In this case, theynot only predict behaviors but also emotions, because they understand what theyentail in all theircomplexity.
Amazing isn't it?
As we can see in the three explanatory segments above, Artificial Intelligence is designed to work with humans and facilitate their daily tasks. We currently find it applied inbanks, online customer service, cybersecurity, virtual assistants, smartphones, cars, social networks, video games, and in many other aspects of daily life.
This panorama allows us to see that, in the near future, we will no longer think about how to use emerging technologies, but we will have to learn to relate to them and live with machines in a natural way.
If you want to learn more about Artificial Intelligence, I invite you to be part of Ai Lab School, the first practical education program in AI programming in Mexico. The course lasts 9 months, where you learn, based on projects, to develop solutions using advanced algorithms with AI. Once the course is finished, you access the next stage: job placement. Here, the Ai Lab School team guides and helps you apply for jobsin Silicon Valley and in technology hubs in the United States, in collaboration with the Mexican talent connection programTalentum Space.
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Artificial Intelligence in Genomics Market worth $1,671 million by 2025 – Exclusive Report by MarketsandMarkets – PRNewswire
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CHICAGO, March 11, 2021 /PRNewswire/ -- According to the new market research report "Artificial Intelligence In Genomics Market by Offering (Software, Services),Technology (Machine Learning, Computer Vision), Functionality (Genome Sequencing, Gene Editing), Application (Diagnostics), End User (Pharma, Research) - Global Forecasts to 2025", published by MarketsandMarkets, the global AI in Genomics market is projected to reach USD 1,671 million by 2025 from USD 202 million in 2020, at a CAGR of 52.7% between 2020 and 2025
Browse in-depth TOC on "Artificial Intelligence in Genomics Market"141 Tables24 Figures 154 Pages
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The need to control drug development and discovery costs and time, increasing public and private investments in AI in genomics, and the adoption of AI solutions in precision medicine are driving the growth of this market. However, the lack of a skilled AI workforce and ambiguous regulatory guidelines for medical software are expected to restrain the market growth during the forecast period.
Machine learning to dominate the AI in Genomics market in 2019
Based on technology, the Artificial Intelligence in GenomicsMarket is segmented into machine learning and other technologies. The machine learning segment dominated this market in 2019, as pharmaceutical companies, CROs, and biotechnology companies have widely adopted machine learning for drug genomics applications. This is because machine learning can extract insights from data sets, accelerating genomic research.
Diagnostics segment accounted for the largest share of the AI in Genomics market, by end user, in 2019
Based on application, the Artificial Intelligence in GenomicsMarket is segmented into diagnostics, drug discovery & development, precision medicine, agriculture & animal research, and other applications. Diagnostics was the largest application segment in genomics market in 2019. The large share of this segment can be attributed to the increasing research on diseases and the decreasing cost of sequencing.
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North America is the largest regional market for AI in Genomics in 2019
In 2019, North America accounted for the largest share of the AI in Genomics market, followed by Europe. The large share of North America can be attributed to the increasing research funding and government initiatives for promoting precision medicine in the US.
Prominent players in the Artificial Intelligence in GenomicsMarket are IBM (US), Microsoft (US), NVIDIA Corporation (US), Deep Genomics (Canada), BenevolentAI (UK), Fabric Genomics Inc. (US), Verge Genomics (US), Freenome Holdings, Inc. (US), MolecularMatch Inc. (US), Cambridge Cancer Genomics (UK), SOPHiA GENETICS (US), Data4Cure Inc. (US), PrecisionLife Ltd (UK),Genoox Ltd. (US), Lifebit (UK), Diploid (Belgium), FDNA Inc. (US), DNAnexus Inc. (US), Empiric Logic (Ireland), Engine Biosciences Pte. Ltd. (US)
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Artificial Intelligence (AI) in Drug Discovery Market by Component (Software, Service), Technology (ML, DL), Application (Neurodegenerative Diseases, Immuno-Oncology, CVD), End User (Pharmaceutical & Biotechnology, CRO), Region - Global forecast to 2024https://www.marketsandmarkets.com/Market-Reports/ai-in-drug-discovery-market-151193446.html
Genomics Market by Product & Service (System & Software, Consumables, Services), Technology (Sequencing, PCR), Application (Drug Discovery & Development, Diagnostic, Agriculture), End User (Hospital & Clinics, Research Centers) Global Forecast to 2025https://www.marketsandmarkets.com/Market-Reports/genomics-market-613.html
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Israels retrain.ai closes $13M to use AI to understand early signals in the changing jobs market – TechCrunch
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Israels retrain.ai, which uses AI and machine learning to read job boards at scale and gain insight into where the job market is going, has closed a $9 million Series A led by Square Peg. Since retrain.ais $4 million seed round last year was unannounced (led by Hetz Ventures, with TechAviv and .406 Ventures participating), that means its raised a total of $13 million. Its competitors include Pymetrics, which has raised $56.6 million, and Eightfold.ai, which has raised $176.8 million.
As well as the funding, the company has secured a first deal with the Israeli Department of Labor to look at the changing nature of the Israeli job market in light of the pandemic.
With technology eating into the traditional labor market, retrain.ai says its platform can look at which jobs are being advertised, which jobs are going down in popularity and see early warning signals as to where new jobs are going to appear. This can help form policy for large organizations and governments.
Retrain.ais CEO is Dr. Shay David, who is best known for co-founding the video enterprise leader Kaltura, which first appeared at TechCrunchs first-ever conference in 2007. Isabelle Bichler-Eliasaf is the companys COO and Avi Simon is retrain.ais CTO.
Dr. Shay David said: What was once the regular tide of change in the workforce has evolved into a tsunami, especially pronounced by COVID-19 and its huge impact on the labor market this has been a wake-up call. Unemployment and underemployment is going to affect a billion people globally in the next few decades. Our vision is to help 10 million workers get the right jobs by 2025 and help organizations navigate efficiently through the wave of change.
Retrain.ai is the first investment by Square Pegs new $450 million fund. The VC previously invested in Canva, Stripe, Fiverr and Airwallex.
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Zumper is the first to bring AI to apartment rental leads – PRNewswire
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Leasing consultants report current industry methods for managing leads are not only highly inefficient and frustrating, they're also one of the industry's most significant challenges. By proactively identifying prospective renters who are more likely to lease immediately, both renters and leasing consultants benefit from a far more efficient, personalized experience.
PowerLeads AI features three main components. First, Power Prospects identifies and flags renters who are statistically proven to be up to 2X more likely to lease immediately. Additionally, our system will now surface and provide leasing agents over 50 unique renter characteristics more than 5X more information than the competition that will help leasing agents best meet renter needs.Lastly, PowerLeads AI provides access to up-to-the-minute information allowing leasing teams to see a prospect's interests in real time enabling them to better understand prospective renters' needs.
"Our studies showed that the vast majority of leasing teams want a way to understand which prospects are more likely to lease, so we knew that it was vital to solve this challenge with an industry-first solution," said Zumper's Chief Growth Officer, Tanguy Le Louarn. "In fact, 78% believe having more data on prospects would help them convert more leads. By using cutting edge AI and machine learning to provide predictive insights and data, we'll not only provide higher quality leads, we'll also enable a faster leasing process."
PowerLeads AI will go live on March 11, 2021. For more information about how PowerLeads AI can support advertising needs, please email [emailprotected]
About Zumper: Zumper is the fastest growing and third largest rental platform in North America, serving one in three U.S. adults.Zumper aims to make renting an apartment as easy as booking a hotel. With over 70 million users, Zumper's free online and mobile rental search marketplace has become the largest of any startup in the industry. Headquartered in San Francisco, Zumper has 200 employees across the U.S. and acquired PadMapper in 2016. The company has raised over $140 million in funding from investors including e.ventures, Greycroft, Dawn Capital, Kleiner Perkins, Goodwater Capital, Axel Springer, Stereo Capital, the Blackstone Group, Breyer Capital, Foxhaven Asset Management, Andreessen Horowitz, Greylock, NEA, CrunchFund, xfund, Divco West, MMC Technology Ventures, Scott Cook, and the DeWilde Family Trust. Learn more atZumper.comor email [emailprotected].
Interested in joining the Zumper team? Check out open positionshere.
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Your Computer Is On Fire draws on tech history to critique AI and the cloud – VentureBeat
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In a story last year about nine books I read about AI in 2020, I called Your Computer Is On Fire a book worth watching out for this year. Its released this week, and I was not disappointed. The premise of the book is that techno-utopianism should die because its too dangerous to be allowed to continue. This argument came up recently in the context of Amazon workers in factories with robotics getting hurt more often than workers in factories without robots. But once people throw away unrealistic visions of outcomes, the history of technology looks very different.
The book attempts to interrogate how the legacy of social constructs and media narratives have shaped computing. It invites people to think critically about notions of purity surrounding data, the concealment of the carbon footprint the cloud represents, the whiteness of robots, and the wires and resources involved with making the world wireless. Your computer is on fire in part, authors argue, because of automation that perpetuates racism and sexism, and the growth of resource-intensive datacenters and the cloud at a time when climate change is an existential threat for the planet.
The title of this book is meant to prepare you for a series of 16 provocative essays that consider the history of technology, media, and policy, from Siri disciplines and the cloud as a factory to how the internet will be decolonialized and tech for the Global South. Each essay takes readers on a journey through a topic to consider the ethical and societal implications of technology over the long term, an approach former Ethical AI team lead Margaret Mitchell suggested for Google.
Contributors to the collection of essays include Safiya Noble, author of Algorithms of Oppression, who wrote an essay about race and gender stereotypes that permeate robotics and the role of robotics in policing, prisons, and warfare.
We have to ask what is lost, who is harmed, and what should be forgotten with the embrace of artificial intelligence and robotics in decision-making. We have a significant opportunity to transform the consciousness embedded in artificial intelligence and robotics, since it is in fact a product of our own collective creation, Noble wrote in the book.
Another essay, by Nathan Ensmenger, argues that the cloud is a factory, and it examines the extent to which datacenters demand a lot of energy, water, and the mining of rare mineral resources like cobalt, which has led to accusations that Big Tech companies aided in the death or serious injury of children. That essay also walks through a comparison between Amazon online today and Sears mail-order catalogs a century ago, and compares Amazon transportation and distribution strategy to Standard Oil.
Understanding, for example, that in the past women made up much of computation work treated as menial and feminine for most of its early history helps illuminate ongoing problems of racism and sexism in tech environments that women especially Black women describe as toxic.
I also found something terribly human in an essay arguing that a network is not a network, which looks at the history of large networks built in Chile, Russia, and the United States. Benjamin Peters says that history shows that just because a network works does not mean it works as its designers intended.
[N]etwork projects are twice political for how they, first, surprise and betray their designers, and, second, require actual institution building and collaborative realities far richer than any design, Peters wrote.
Editors of the book include Mar Hicks, a tech historian at the Illinois Institute of Technology in Chicago and an associate editor of the IEEE Annals of History of Computing. They are joined by science and technology historian and University of California, Irvine professor Kavita Philip; Peters, a media historian and University of Tulsa professor; and Stanford University history professor Thomas Mullaney.
The editors take pains to state that the books conclusions arent meant to be an overly dark view of the future or to give people the impression things are hopeless. There is hope, they argue, but recent trends should act as an alarm.
What I also took away from this book is the continuing value of critical analysis. In a recent paper, researchers recommended reporters persist in sharp questioning, declaring, Technology journalism is a keystone of equitable automation and needs to be fostered for AI.
In the final pages of the book, Your Computer Is On Fire also addresses the role of media and the writers of narratives in tech and AI trends.
Tech will deliver on neither its promises nor its curses, and tech observers should avoid both utopian dreamers and dystopian catastrophists. The world truly is on fire, but that is no reason it will either be cleansed or ravaged in the precise day and hour that self-proclaimed prophets of profit and doom predict. The flow of history will continue to surprise, Peters writes.
Even if youre like me and follow trends in artificial intelligence through news, books, and research papers, you may still learn parts about the history of technology in this book that you didnt know, because this book extends across an arch of history. And as editors lay out in the afterword, they hope the messages contained within will be viewed as obvious decades from now.
This lens viewing computing and artificial intelligence across the span of decades and consideration of social and historical context was previously espoused by Ruha Benjamin, who last year argued in the context of deep learning that computational depth without historic or sociological depth is superficial learning. But the collection of impactful tech issues interrogated over the span of decades in this book makes it recommended reading for anyone interested in the impact of tech policy in businesses and governments, as well as people deploying AI or interested in the way people shape technology.
This book presents compelling arguments for essential topics at the center of business and society. By using computational history as a foundation, its able to, as Noble put it, underscore how much is at stake when we fail to think more humanistically about computing.
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Recap: Bias Issues and AI | Morgan Lewis – Tech & Sourcing – JDSupra – JD Supra
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Morgan Lewis partners Mike Pierides and Andrew J. Gray IV and associate Oliver Bell recently presented a webinar, Bias Issues and AI, as part of the Artificial Intelligence Boot Camp series.
Bias in AI can result from assumptions in the machine learning process, or as a result of data that is imbalanced or incomplete and does not reflect a true representation of the relevant population. Examples of such skewed data include datasets that are mislabeled or misrepresentative of reality, systematic errors in the collection of data, or valuable data that is completely excluded, which in turn creates biased outputs.
The implications of bias in AI are widespread, and can affect recruiting processes, credit referencing, and insurance decisions, to name a few. For example, if an employer uses an AI tool for recruiting that uses historical data from the companys past and current employees, most of whom are male, then the AI system may incorrectly learn that the ratio of preferable candidates should match this historical data, therefore resulting in a biased outcome. As the role of AI increases in decision making across industries, the risks of bias also increase due to the large scale of data that can be processed by machines.
The risks associated with bias in AI can result in statutory, contractual, and common law liability. Laws that prohibit discrimination, like the Fair Housing Act in the United States and the Equality Act in the United Kingdom, provide examples of how biased AI could lead to liability for organizations.
There are steps that can be taken, if not to completely remove bias in AI, then at least to mitigate it. Examples include choosing a suitable AI provider, performing regular audits of algorithms, and employing a diverse programming or control team with antibias training and a culture of transparency.
As we look to the future, the detrimental impact and effects of bias in AI will likely increase as we see a continued focus on ensuring diversity and inclusion, which is likely to lead to new or updated legislation which AI bias may fall foul of. There is also the possibility of AI-specific legislation that places obligations on companies regarding their use of AI.
For more details on bias in AI, view the presentation slides or watch the recorded event.
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