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Machine Learning as a Service Market Key Players Change the View of the Global Face of Industry by 2028: Amazon Web Services, Inc., BigML, Inc.,…

Posted on April 26, 2021 by admin

Introduction: Global Machine Learning as a Service Market, 2020-28The research report on global Machine Learning as a Service market provides insightful data about market and all the important aspects related to it. The pattern in the Machine Learning as a Service industry gives an absolute overview of prime players by the weightlessness of their product definition, company summary, and business strategy at intervals in the market. a comprehensive analysis of the market performance throughout the years is offered in the research report. This analysis helps vendors and manufacturers to understand the change in the market dynamics over the years. In addition to that the research report also covers detailed analysis of all the crucial factors having an impact on the market growth. The detailed study of all the crucial aspects of the Machine Learning as a Service market is included in the market report such as market share, production, regions, key players, etc.

The study encompasses profiles of major companies operating in the Machine Learning as a Service MarketAmazon Web Services, Inc., BigML, Inc., Crunchbase Inc., Fair Isaac Corporation., Google LLC, H2O.ai., IBM, Microsoft Corporation, PREDICTRON LABS, and Yottamine Analytics, LLC.FPNV Positioning Matrix:The FPNV Positioning Matrix evaluates and categorizes the vendors in the Machine Learning as a Service 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.Competitive Strategic Window:The Competitive Strategic Window analyses the competitive landscape in terms of markets, applications, and geographies. The 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.Cumulative

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Global Machine Learning as a Service Market: Understanding ScopeThe comprehensive analysis of potential customer base, market values and future scope is included in the global Machine Learning as a Service market report. Along with that the research report on the global market holds all the vital information regarding the latest technologies and trends being adopted or followed by the vendors across the globe. The detailed study offers an important microscopic view of the industry to define manufacturers footprints by awareness of manufacturers worldwide sales and costs, and manufacturers production over the forecast era. Leading and influential players in the global Machine Learning as a Service market are narrowly analyzed on the basis of key factors in the competition analysis portion of the study. The study includes a detailed overview and reliable athlete sales estimates for the forecasted timeframe. The analysis also offers methodical references to the prevailing developments in business dynamics.

By the product type, the market is primarily split into: by Component (Services and Software),

By the end-users/application, this report covers the following segments: (Augmented & Virtual Reality, Fraud Detection & Risk Management, Marketing & Advertising, Predictive Analytics, and Security & Surveillance), by End

In addition, the study report also provides full documentation of past, present and future projections related to market size and volume. The study further presents the industrys leading and dominant business leaders with best practices and growth-friendly measures. The research also includes SWOT analysis for the global Machine Learning as a Service industry, PESTEL analysis and Potters Five Forces analysis. A competitive analysis of the Machine Learning as a Service industry and main product segments of the market is given in the study. The research report also offers the detailed analysis of performances of all the regions across the globe in market terms. The Machine Learning as a Service market report takes a detailed note on the major industrial events in past years. These events include several operational business decisions, innovations, mergers, collaborations, major investments, etc. The research report provides a 360 degree view of the global Machine Learning as a Service market.

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The key regions covered in the Machine Learning as a Service market report are:North America (U.S., Canada, Mexico)South America (Cuba, Brazil, Argentina, and many others.)Europe (Germany, U.K., France, Italy, Russia, Spain, etc.)Asia (China, India, Russia, and many other Asian nations.)Pacific region (Indonesia, Japan, and many other Pacific nations.)Middle East & Africa (Saudi Arabia, South Africa, and many others.)

The study objectives of this report are:

To analyze global Machine Learning as a Service status, future forecast, growth opportunity, key market and key players. To present the Machine Learning as a Service development in North America, Europe, China, Japan, Southeast Asia, India and Central & South America. To strategically profile the key players and comprehensively analyze their development plan and strategies. To define, describe and forecast the market by type, market and key regions.

In this study, the years considered to estimate the market size of Machine Learning as a Service are as follows:History Year: 2015-2019Base Year: 2019Estimated Year: 2020Forecast Year 2020 to 2026

For the data information by region, company, type and application, 2019 is considered as the base year. Whenever data information was unavailable for the base year, the prior year has been considered.

Table of Contents Chapter One: Report Overview 1.1 Study Scope1.2 Key Market Segments1.3 Players Covered: Ranking by Machine Learning as a Service Revenue1.4 Market Analysis by Type1.4.1 Global Machine Learning as a Service Market Size Growth Rate by Type: 2020 VS 20281.5 Market by Application1.5.1 Global Machine Learning as a Service Market Share by Application: 2020 VS 20281.6 Study Objectives1.7 Years Considered

Chapter Two: Global Growth Trends by Regions 2.1 Machine Learning as a Service Market Perspective (2015-2028)2.2 Machine Learning as a Service Growth Trends by Regions2.2.1 Machine Learning as a Service Market Size by Regions: 2015 VS 2020 VS 20282.2.2 Machine Learning as a Service Historic Market Share by Regions (2015-2020)2.2.3 Machine Learning as a Service Forecasted Market Size by Regions (2021-2028)2.3 Industry Trends and Growth Strategy2.3.1 Market Top Trends2.3.2 Market Drivers2.3.3 Market Challenges2.3.4 Porters Five Forces Analysis2.3.5 Machine Learning as a Service Market Growth Strategy2.3.6 Primary Interviews with Key Machine Learning as a Service Players (Opinion Leaders)

Chapter Three: Competition Landscape by Key Players 3.1 Global Top Machine Learning as a Service Players by Market Size3.1.1 Global Top Machine Learning as a Service Players by Revenue (2015-2020)3.1.2 Global Machine Learning as a Service Revenue Market Share by Players (2015-2020)3.1.3 Global Machine Learning as a Service Market Share by Company Type (Tier 1, Tier Chapter Two: and Tier 3)3.2 Global Machine Learning as a Service Market Concentration Ratio3.2.1 Global Machine Learning as a Service Market Concentration Ratio (CRChapter Five: and HHI)3.2.2 Global Top Chapter Ten: and Top 5 Companies by Machine Learning as a Service Revenue in 20203.3 Machine Learning as a Service Key Players Head office and Area Served3.4 Key Players Machine Learning as a Service Product Solution and Service3.5 Date of Enter into Machine Learning as a Service Market3.6 Mergers & Acquisitions, Expansion Plans

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Machine Learning in Finance Market 2021 Industry Size, Share, Growth and Top Companies Analysis- Ignite Ltd, Yodlee, Trill AI, MindTitan, Accenture,…

Posted on April 26, 2021 by admin

DataIntelo has Published a brand-new market research study on the international Machine Learning in Finance Market. This industry report incorporates comprehensive market analysis about the chances that has emerged as a result of this COVID-19 pandemic. Whats more, it gives key insights about the creative approaches which are used by leading business players amidst the pandemic.

Major Players Covered in the Report:

Ignite LtdYodleeTrill A.I.MindTitanAccentureZestFinance

Get Free Sample Report: https://dataintelo.com/request-sample/?reportId=201715

The report covers the market drivers, restraints, threats, opportunities, and challenges which are anticipated to modify the dynamics of this market throughout the forecast period, 2021-2028. These afore-mentioned important parameters are expected to assist the reader make critical business decisions readily. The Machine Learning in Finance market research report offers information regarding the drivers, restraints, chances, pricing variables & tendencies, gains, revenue generation, and emerging trends of this market.

5 Crucial Insights That Are Covered in the Machine Learning in Finance Market Report

The global Machine Learning in Finance market is segmented on the basis of:

Products

Supervised LearningUnsupervised LearningSemi Supervised LearningReinforced Leaning

Application

BanksSecurities CompanyOthers

Regions

North America

Europe

Asia Pacific

Latin America

Middle East & Africa

Buy the complete report https://dataintelo.com/checkout/?reportId=201715

The Market Research report comprises revenue share, potential growth opportunities, and theorized growth rate of this market in above areas. DataIntelo has contemplated social-economic variables of the nations in the area to examine the regional market. Whats more, it has included the effect of the COVID-19 outbreak on the area and commerce regulations and government policies & policies which shapes the rise of the market in these areas.

Below is the TOC of the report:

Executive Summary

Assumptions and Acronyms Used

Research Methodology

Machine Learning in Finance Market Overview

Global Machine Learning in Finance Market Analysis and Forecast by Type

Global Machine Learning in Finance Market Analysis and Forecast by Application

Global Machine Learning in Finance Market Analysis and Forecast by Sales Channel

Global Machine Learning in Finance Market Analysis and Forecast by Region

North America Machine Learning in Finance Market Analysis and Forecast

Latin America Machine Learning in Finance Market Analysis and Forecast

Europe Machine Learning in Finance Market Analysis and Forecast

Asia Pacific Machine Learning in Finance Market Analysis and Forecast

Asia Pacific Machine Learning in Finance Market Size and Volume Forecast by Application

Middle East & Africa Machine Learning in Finance Market Analysis and Forecast

Competition Landscape

Why to Choose DataIntelo?

The companys research team has been constantly monitoring the Machine Learning in Finance market since few years, which has helped them to include actionable insights that can confer the esteemed reader with the leverage to grow their enterprise with a high CAGR and gain stellar ROI in the market.

Many regions are observing the second wave of the COVID-19 pandemic that has persuaded industry players to reanalyse their decisions and deploy strategies for the new normal. The research team has conducted interviews with the industry experts and top-executives amidst the pandemic to get in-depth insights of the market in a detailed manner. They have used Porters Five Analysis and implemented robust methodology to understand the complex nature of the global Machine Learning in Finance market.

The team provides quarterly updates of the market, that includes products latest developments, strategies implemented by top players, and latest trends of the market. Additionally, the research team can customize the report in accordance to the requirements.

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DataIntelo Has extensive knowledge in the development of tailored market research reports in many industry verticals. We ensure in-depth market evaluation including producing creative business plans for the new entrants and also the emerging players of this marketplace. Our firm offers market hazard evaluation, market opportunity analysis, and profound insights into the present and market situation.

To supply the utmost quality of the research report, we spend in analysts which hold stellar Expertise in the company domain and also have excellent analytical and Communication abilities. Our committed staff undergo quarterly training that Helps them to admit the most recent industry practices and also to serve the Customers together with the foremost customer experience.

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Machine Learning in Finance Market 2021 Industry Size, Share, Growth and Top Companies Analysis- Ignite Ltd, Yodlee, Trill AI, MindTitan, Accenture,...

Machine Learning Forecast Machine Learning Markets Trying to Break Out by Microsoft Corporation (Washington, US), IBM Corporation (New York, US), SAP…

Posted on April 26, 2021 by admin

The Machine Learning Market study is a perfect mix of qualitative and quantitative information and to get better understanding on how stats relates to growth, market sizing and share, the study is started with market overview and further detailed commentary is showcased on changing market dynamics that includes Influencing trends by regions, growth drivers, long term opportunities and short term challenges that industry players are facing. Furthermore, Market Factor Analysis gives insights on how various regulatory affairs, economic factors and policy action are factored in the past and future growth scenarios by various business segments and applications. The Competitive Landscape provides detailed company profiling of players and draws attention on development activities, SWOT, financial outlook and major business strategic action taken by players.

Industries and markets are ever-evolving; navigate these changes with ongoing research conducted by Adroit Market Research; Address the latest insights released on Global Machine Learning Market.

Relevant features of the study that are being offered with major highlights from the report:

1) Can Market be broken down by different set of application and types?

Additional segmentation / Market breakdown is possible subject to data availability, feasibility and depending upon timeline and toughness of survey. However a detailed requirement needs to be prepared before making any final confirmation.

** An additional country of your interest can be included at no added cost feasibility test would be conducted by Analyst team of ADROIT MARKET RESEARCH based on the requirement shared and accordingly deliverable time will also be disclosed.

2) How Study Have Considered the Impact of Economic Slowdown of 2020?

Analyst at Adroit Market Research have conducted special survey and have connected with opinion leaders and Industry experts from various region to minutely understand impact on growth as well as local reforms to fight the situation. A special chapter in the study presents Impact and Market factor Analysis on Global Machine Learning Market along with tables and graphs related to various country and segments showcasing impact on growth trends.

3) Which companies are profiled in current version of the report? Can list of players be customize based on regional geographies we are targeting

Considering heat map analysis and based on market buzz or voice the profiled list of companies in the report are Microsoft Corporation (Washington, US), IBM Corporation (New York, US), SAP SE (Walldorf, Germany), SAS Institute Inc. (North Carolina, US), Google, Inc. (California, US), Amazon Web Services Inc. (Washington, US), Baidu, Inc. (Beijing, China), BigML, Inc. (Oregon, US), Fair Isaac Corporation (FICO) (California, US), Hewlett Packard Enterprise Development LP (HPE) (California, US), Intel Corporation (California, US), KNIME.com AG (Zurich, Switzerland), RapidMiner, Inc. (Massachusetts, US), Angoss Software Corporation (Toronto, Canada), H2O.ai (California, US), Alpine Data (California, US), Domino Data Lab, Inc. (California, US), Dataiku (Paris, France), Luminoso Technologies, Inc. (Massachusetts, US), TrademarkVision (Pennsylvania, US), Fractal Analytics Inc. (New Jersey, US), TIBCO Software Inc. (California, US), Teradata (Ohio, US), Dell Inc. (Texas, US), and Oracle Corporation (California, US). Yes, further list of players can also be customized as per your requirement keeping in mind your areas of interest and adding local emerging players and leaders from targeted geography.

** List of companies covered may vary in the final report subject to Name Change / Merger & Acquisition Activity etc. based on the difficulty of survey since data availability needs to be confirmed by research team especially in case of privately held company. Up to 2 players can be added at no additional cost.

To comprehend Global Machine Learning market dynamics in the global market, the worldwide Machine Learning market is analyzed across major geographical regions. Adroit Market Research Market Intelligence also provides customized specific regional and country-level reports, see below break-ups.

North America: United States, Canada, and Mexico.

South & Central America: Argentina, Chile, LATAM, and Brazil.

Middle East & Africa: Saudi Arabia, UAE, Israel, Turkey, Egypt and South Africa.

Europe: UK, France, Italy, Germany, Spain, BeNeLux, and Russia.

Asia-Pacific: India, China, Japan, South Korea, Indonesia, Thailand, Singapore, and Australia.

2-Page company profiles for 10+ leading players is included with 3 years financial history to illustrate the recent performance of the market. Latest and updated discussion for 2019 major macro and micro elements influencing market and impacting the sector are also provided with a thought-provoking qualitative remarks on future opportunities and likely threats. The study is a mix of both statistically relevant quantitative data from the industry, coupled with insigAdroit Market Researchul qualitative comment and analysis from Industry experts and consultants.

Global Machine Learning Product Types In-Depth: by ServiceProfessional ServicesManaged ServicesMachine learning market by Deployment Model:CloudOn-premises

Global Machine Learning Major Applications/End users:by Organization Size:SMEsLarge Enterprises

Market Sizing by Geographical Break-down: North America (Covered in Chapter 9), United States, Canada, Mexico, Europe (Covered in Chapter 10), Germany, UK, France, Italy, Spain, Russia, Others, Asia-Pacific (Covered in Chapter 11), China, Japan, South Korea, Australia, India, South America (Covered in Chapter 12), Brazil, Argentina, Columbia, Middle East and Africa (Covered in Chapter 13), UAE, Egypt & South Africa

To ascertain a deeper view of Market Size, competitive landscape is provided i.e. Comparative Market Share Revenue Analysis (Million USD) by Players (2018-2019) & Segment Market Share (%) by Players (2018-2019) and further a qualitative analysis of all players is made to understand market concentration rate.

Competitive Landscape & Analysis:

Major players of Machine Learning Market are focusing highly on innovation in new technologies to improve production efficiency and re-arrange product lifecycle. Long-term growth opportunities for this sector are captured by ensuring ongoing process improvements of related players following NAICS standard by understanding their financial flexibility to invest in the optimal strategies. Company profile section of players such as Microsoft Corporation (Washington, US), IBM Corporation (New York, US), SAP SE (Walldorf, Germany), SAS Institute Inc. (North Carolina, US), Google, Inc. (California, US), Amazon Web Services Inc. (Washington, US), Baidu, Inc. (Beijing, China), BigML, Inc. (Oregon, US), Fair Isaac Corporation (FICO) (California, US), Hewlett Packard Enterprise Development LP (HPE) (California, US), Intel Corporation (California, US), KNIME.com AG (Zurich, Switzerland), RapidMiner, Inc. (Massachusetts, US), Angoss Software Corporation (Toronto, Canada), H2O.ai (California, US), Alpine Data (California, US), Domino Data Lab, Inc. (California, US), Dataiku (Paris, France), Luminoso Technologies, Inc. (Massachusetts, US), TrademarkVision (Pennsylvania, US), Fractal Analytics Inc. (New Jersey, US), TIBCO Software Inc. (California, US), Teradata (Ohio, US), Dell Inc. (Texas, US), and Oracle Corporation (California, US)includes vital information like legal name, website, headquarter, its market position, distribution and marketing channels, historical background and top 4 closest competitors by Market capitalization / turnover along with sales contact information. Each company / manufacturers revenue figures, growth rate, net profit and gross profit margin is provided in easy to understand tabular format for past 3 years and a separate section on market entropy covering recent development activities like mergers &acquisition, new product/service launch, funding activity etc.

In this study, the years considered to estimate the market size of Global Machine Learning are as follows:

History Year: 2014-2019, Base Year: 2019, Forecast Year 2020 to 2025

Key Stakeholders / Target Audience Covered:

In order to better analyze value chain/ supply chain of the Industry, a lot of attention given to backward & forward Integration

Machine Learning Manufacturers

Machine Learning Distributors/Traders/Wholesalers

Machine Learning Sub-component Manufacturers

Industry Association

Downstream Vendors

Actual Numbers & In-Depth Analysis of Machine Learning Market Size Estimation, Business opportunities, Available in Full Report.

Thanks for reading this article, you can also get individual chapter wise section or region wise report version like North America, LATAM, West Europe, MENA Countries, Southeast Asia or Asia Pacific.

About Us

Adroit Market Research is an India-based business analytics and consulting company incorporated in 2018. Our target audience is a wide range of corporations, manufacturing companies, product/technology development institutions and industry associations that require understanding of a markets size, key trends, participants and future outlook of an industry. We intend to become our clients knowledge partner and provide them with valuable market insights to help create opportunities that increase their revenues. We follow a code Explore, Learn and Transform. At our core, we are curious people who love to identify and understand industry patterns, create an insightful study around our findings and churn out money-making roadmaps.

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Machine Learning Forecast Machine Learning Markets Trying to Break Out by Microsoft Corporation (Washington, US), IBM Corporation (New York, US), SAP...

Global Machine Learning in Education Market Analysis, Share, Regional Outlook, Competitive Strategies and Forecast by 2025 NeighborWebSJ -…

Posted on April 26, 2021 by admin

A fresh report on Worldwide Machine Learning in Education Market 2021 quotes a critical analysis for its business on a regional and international level. It clarifies how companies procurement expenditure, Machine Learning in Education business plans, media speculate, marketing/sales, practices, and Machine Learning in Education company plan are set to alter in 2021. The report permits you to examine different Machine Learning in Education market predictions together with challenges, provider selection standards, the present Machine Learning in Education market size and investment opportunities, and advertising budgets of senior-level officials. The report also determines the anticipated Machine Learning in Education expansion of buyers and suppliers combined with funds spending and e-procurement. The global Machine Learning in Education marketplace report not only assesses perspectives and strategies of Machine Learning in Education company decision-makers and rivals but investigates their activities circling company priorities. Additionally, the Machine Learning in Education report offers accessibility to data categorized by business type and dimensions, area.

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In contrast to the present market situation, the net Machine Learning in Education business report shows various facts connected with driving variables, trends, opportunities, limitations, together with major Machine Learning in Education challenges encountered by business players. The net Machine Learning in Education marketplace account has considered each the major along with minor facets concerning the maturation of the Machine Learning in Education marketplace.

IBMMicrosoftGoogleAmazonCognizanPearsonBridge-UDreamBox LearningFishtreeJellynoteQuantum Adaptive Learning

Listing offers a particular evaluation of the Machine Learning in Education marketplace by assessing the changing competitive aspects of the global marketplace. It delivers a particular evaluation of the net Machine Learning in Education market so you believe you ought to always get the greener grass in the side. This analysis further includes the effect of the coronavirus on leading companies within the Machine Learning in Education marketplace and gives a whole evaluation of COVID-19 effect investigation from the market by type, program, and areas for example (Americas, APAC, and also EMEA).

Fundamental Machine Learning in Education information because of the institutions, for example, market volume, percentage share, carrier information, product pictures are also exhibited. The Level of the Worldwide Machine Learning in Education Market list is Based on the following:

To study and forecast the Machine Learning in Education marketplace step and provides for esteem and volume. Evaluation of Machine Learning in Education compounds sources and numbers of how downstream buyers are given. To dissect present and future risks and replacement hazard with this Machine Learning in Education report provides better esteem to your client demands and their changing inclinations together with monetary/political ecological change. Inclining Machine Learning in Education marketplace numbers, respect, utilization, costs, as well as the cost is offered by places, by forms, by producers, and from applications till the forecast season 2027.

Form Analysis of the Machine Learning in Education market:

Cloud-BasedOn-Premise

Application Assessment of the Machine Learning in Education market:

Intelligent Tutoring SystemsVirtual FacilitatorsContent Delivery SystemsInteractive WebsitesOthers

The Machine Learning in Education study report profound evaluation, providing an extensive analysis of global marketplace prognosis, review, utilization, and measurement of the entire sector by diverse geological places. The Machine Learning in Education report has been produce through key heights of study with respect to the business. The run down of important Machine Learning in Education organizations/contenders is additionally comprised from the accounts along with the appendix together with decisions.

The clear insights of this Machine Learning in Education marketplace in addition to the opportunities, dangers, and market expansion are covered in this Machine Learning in Education analysis report. It exfoliates present Machine Learning in Education marketplace branches to predict expanding ones and provides detailed business segmentation Machine Learning in Education according to product types, software Machine Learning in Education, and important geographies. A comprehensive study of this Machine Learning in Education market share and its own participation can be cited in the report.

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The primary institutions in the Worldwide Machine Learning in Education market are poised to give you an general entire overview of the growth processes, financial status and administrations, and in addition Machine Learning in Education recent co ordinated campaigns in addition to progress.

The Machine Learning in Education business report basically covers the subjects of focus identified with the Machine Learning in Education company exactly like the business definition, an assortment of use, ask and supply requirement. A thorough analysis of the Machine Learning in Education report will aid all the market players using analyzing the current patterns and essential small business procedures. This aggressive and top to base evaluation of the Machine Learning in Education industry marketplace will forecast the business growth in light of their progress openings, development parts and viability of speculation. Organizing Machine Learning in Education business approaches by sectioning the fragments and present business portions will be the facilitate and surely will similarly be useful to perusers. Finally, the report Worldwide Machine Learning in Education market reflects growth process, data distribution, benchmark division, begin looking into discoveries as well as the choices.

It highlights the principal players in Machine Learning in Education advertising in addition to their different approaches and approaches utilized. Market dynamics which keep shifting over time plus an comprehensive look at marketplace resources Machine Learning in Education are also mentioned.

It plays a broader study of previous and present marketplace trends Machine Learning in Education to forecast future market growth concerning value and volume. Additionally, it computes basic business parameters Machine Learning in Education for example industrial advancement and expansion and Machine Learning in Education offers basic market amounts in the kind of tables, pie graphs, charts and flowcharts.

Major business applications Machine Learning in Education will also be decided on the basis of achievement and performance. The sanctuary of Unstable Industries to boost their ledge from the Machine Learning in Education marketplace can be discussed.

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What Is the Real Threat of Artificial Intelligence to the Architecture Profession? – Archinect

Posted on April 26, 2021 by admin

Artificialintelligence could potentially help us streamline the morerudimentary and tedious aspects of design to free up more time forcreative problem solving and response to human needs. It would beunreasonable to say that any AI that we could conceive in our lifetimewould be able to intimately familiarize itself with the breadth ofhuman experience to allow it to make accurate determinationsabout the things we need. Oftentimes the things we need are rooted inaesthetics that are meant to facilitate our emotional well-being orthey are otherwise rooted in cultural lineages and traditions thatare difficult as yet to quantify.

Itis obvious that with more precise data, and with the use of moreintelligent software, architects and designers will be able toenhance their work in many predictable ways such as optimizingbuilding maintenance and security, streamlining BIM workflow, andincreasing the sustainability of a design through more accurate environmental analysis.Having said this, I feel that the notion of having better toolsallowing you to do your job better and faster is not necessarily worthdiscussing. What is of interest is whether or not improving thesetools will eventually make their very users obsolete by becoming theusers themselves. From this, it's important to consider what makeshumans particularly useful (as users of these tools).

Whatmakes a thing intelligent is not only access to data but also theability to identify connections between disparate pieces ofinformation and use those connectionsto solve problems to develop intuition. The way we identifyconnections between bits of data as humans is by working withincertain logical frameworks that allow us to create a relationshipbetween things that would otherwise feel random and/or arbitrary. Forexample, one such framework is cause and effect. You know not to lookdirectly at the sun because it hurts your eyes when you do that. Aconnection was drawn between the action and the subsequent pain thatinformed the newly-developed behavior that followed.

Architectsspend several years developing skills that give them a heighteneddegree of spatial awareness. Beyond that, many are also skilled atbridging gaps between their lived experience, their ability toidentify sociocultural cues and traditions (humans are particularlyskilled at this because we are the only species that placessentimental value onto objects, as far as our current understandinggoes) and their technical skills in order to come up with cleversolutions to a specific set of problems. For better or for worse, adesigners individualism seeps through when they make decisionsthat influence the emotional impact of space using their livedexperiences their memories as a basis.

Forthe same reason that it is difficult to imagine fully relating toanother human being because your lived experiences are fundamentallydifferent, it is difficult to imagine how digital intelligences wouldbegin to develop a sense of ego and then use that to make aestheticchoices about a space. If that were to happen, the point wouldessentially be moot because we would come to a point where it wouldbe redundant to distinguish between supposedly "real" humanityand a synthetic one. It wouldnt bea matter of asking what the future of architecture and design lookslike under the influence of AI because AI would not exist humanintelligence and artificial intelligence would essentially be thesame, and so any useful distinction (those that look beyond thepedantic notion of humans being made from organic matter as opposedto AI, which is created with synthetic material) will probablycease to exist. Would AI eventually reach a point where designersbecome obsolete? Probably not until we reach a level ofsophistication with AI that is indistinguishable from our owncomplexity, but in that case, it wouldnt be a matter of AI vs.designers, it would simply mean that there are more designers.

Anexample of how AI is already integrated in architecture can be seenat The Bartlett, wherein their space syntax software "depthmapX" can generate accurate spatial analyses that remove the need toactually visit the site.Granted, there is as yet no way for such a software to tell you, forinstance, how a certain place "feels" or how culturallysignificant certain elements at a site are, but any physical orspatial data that can be quantified is still perfectly fair game.This not actually limited to just environmental analysis. In much thesame way that analytics companies gather our social and behavioraldata to essentially generate profiles on us to create more successfulmarketing campaigns, in an architectural setting, this data can beused to democratize development. With this data, software may be able to prioritize certain projects, calculate population growth andcategorize streets or neighborhoods by usage and density (and thenfurther categorize those things into time of day).

Still moreinteresting integrations of AI in architecture can be seen in aninstallation called Ada as part of Microsoft's Artist in Residenceprogram. Ada is a pavilion that incorporates AI to generate aperformative environment based on analyses of its users. Itcollects data from facial expressions and vocal tones and translatesthat data into certain colors and materials based on specificsentiments that it perceives from this data.What it becomes is this vehicle for a uniquely responsivearchitecture that allows designers to expand their conceptualizationprocess to encompass not only what a certain building or space mustbe but also what it couldbe. The question that arises here is how this data is beingperceived and translated by the AI and who programs it to perceivethings in this way and these things are determined by a variety ofcultural and social biases. Perhaps the challenge will come fromattempting to get the AI to understand certain illogical humanbehaviors that are rooted in cultural stigma such as Americans' preference for private vehicles over robust public transportationnetworks and infrastructure. Logic isn't standardized becauseculture and experience inform a person's idea of what is logical.

Thethreat is actually posed not by artificial intelligence itself but byusers who deem AI to be a cheaper, more efficient means to an end. Aswe are encouraged to indulge in our consumerist tendencies, we becomeless concerned with creating spaces that we can emotionally connectto and see ourselves in and more about acquiring material things. Inthis case, it is about acquiring four walls and a roof as quickly and efficientlyas possible. While it is indeed possible for architectural firms toadapt to this and begin implementing AI technologies to help themfill in gaps in their output (such as Ada or depthmapX), largercompanies that have an edge in gathering data (especially if thatdata is deemed proprietary) will have negative influences on thecompetitive environment of the field.

Inorder to prevent the consolidation of an immense amount ofdecision-making power in the hands of a small group of alreadyresource-rich entities, architects and designers should aim toliberalize pertinent data so that anybody can have access to them.Data sets should be available for public use and perhaps managed byan international body. We see this occurring more and more frequentlyin the design world through the emergence of open-source programs,plans, and data such as Wheelmap, which is an urbanism platformdesigned to help people identify and share accessible spaces aroundthe world. Decentralizing design in this way may prove beneficial to society asa whole by giving more people greater access to quality design that ismost often reserved for people with the capital to access the finestpieces.

Sebastian Errazuriz has a rather bleak albeit realistic perspectiveregarding the impact of AI on the architecture industry. Approachingit purely from a brass tacks perspective, architects are largelyexpendable mainly because they take a lot of time and resources toget equipped with the skills needed to become architects. Beyondthat, the level of coordination between all these different entitiesmakes it so that it's normalfor projects to take 2, 3, or even 10 years to finish. How could anyof that possibly compete with a program that is unbiased andunburdened by ego, that can learn anything in a matter of seconds,and that can communicate and coordinate with other equally egolessprograms with complete fluidity (more fluidly than we can evencommunicate with our own selves). His suggestion is that architectsshould take their advanced spatial awareness and apply it in a techlandscape wherein they would apply their skills more abstractly todesign other kinds of systems.

Aswith almost every other profession, architects are on the precipiceof a reckoning with their roles in society moving forward. This ismainly due to the fact that we recognize that AI isnt just a tool it has the potential to eventually surpass our ability to doanything. What is particularly new about this is that it will causeus to fundamentally re-evaluate our relationship with our labor, andwhat our role in society will be if our ability (or even our need)to work is taken away. While it may not necessarily be a matter ofthe utmost urgency, it would be prudent for architects to reflect onhow they can synthesize the more intangible aspects of their skillsets in order to be more equipped to navigate these rapidly shiftingenvironments.

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What Is the Real Threat of Artificial Intelligence to the Architecture Profession? - Archinect

Researchers Have Uncovered Yet Another Secret of the Dead Sea Scrolls, This Time Using Artificial Intelligence – artnet News

Posted on April 26, 2021 by admin

It turns out there are still more mysteries to uncover about theDead Sea Scrolls.

The latest discovery, made with the help of artificial intelligence, is that the artifacts were likely transcribed by two different writers, despite the fact that all the handwriting looks similar.

We will never know their names. But after 70 years of study, this feels as if we can finally shake hands with them through their handwriting, Mladen Popovi, a bible studies professor and a member of the three-person team from the University of Groningen in the Netherlandsbehind the study, said a statement. This opens a new window on the ancient world that can reveal much more intricate connections between the scribes that produced the scrolls.

Written on 17 sheets of parchment, the manuscript is 24 feet long and is the oldest complete copy of a book of the bible by about 1,000 years. Using A.I. pattern-recognition technology, experts singled out theHebrew letter aleph, which appears in the scroll over 5,000 times, to identify the hand of two main writers, reportsCourthouse News.

Kohonen maps (blue colormaps) of the character aleph and bet from the Dead Sea Scrolls Great Isaiah Scroll used to analyze the handwriting. Image courtesy of Maruf A. Dhali, University of Groningen.

The initial discovery of the first Dead Sea Scroll by a Bedouin shepherd in theQumran caves in 1947 proved one of the 20th centurys most significant archaeological finds.The scrolls, the earliest biblical manuscripts, are written primarily in Hebrew, with sections in Aramaic and Greek.

The new study is the part of European Research Council-funded 1.5 million ($1.8 million) The Hands that Wrote the Bible project. The first findings, published yesterday in the journalPLOS ONE, and presented earlier this month at the universitys Digital Palaeography and Hebrew/Aramaic Scribal Culture conference,offer fresh clues as to the origins of the scrolls, which are believed to be the work of a Jewish sect known as the Essenes.

Greyscale image of column 15 of the Dead Sea Scrolls Great Isaiah Scroll, the corresponding binarized image using BiNet, and the cleaned-corrected image. From the red boxes of the last two images, one can see how the rotation and the geometric transformation is corrected to yield a better image for further processing. Image courtesy of University of Groningen.

Examining each letter both as a whole and in microscopic detail, A.I. was able to identify minute differences in the way characters were formed.

The first step was using digital imaging to capture each aleph. Then, the researchers trained the algorithm to separate the inked letters from the papyrus or leather on which they were written. This process, called binarization, was achieved through a state-of-the-art artificial neural network and deep learning.

The A.I. then considered each alefs shape and curvature to deduce information about the original scribes biomechanical traits, like the way they held their pen.The ancient ink traces relate directly to a persons muscle movement and are person specific, the studys co-author Lambert Schomaker, aprofessor of computer science and A.I., said in a statement.

Comparing all of the alefs, the A.I.s findings confirmed experts long-held suspicion that the writer of the Great Isaiah Scroll likely switched about halfway through. With the intelligent assistance of the computer, we can demonstrate that the separation is statistically significant,Popovi said.

The AI analysis identified normalized average character shapes in the Dead Sea Scrolls Great Isaiah Scroll. Image courtesy of Maruf A. Dhali, University of Groningen.

The similarity in the handwriting suggests that the two scribes received the same training, possibly at some kind of ancient scribal school. (It is also a possibility that the differences could be attributed to a single writer getting fatigued, changing writing instruments, or getting injured, but the two-scribe explanation is the most straightforward.)

There are plans to conduct further A.I. analysis on other Dead Sea Scroll text using the same methodology.

Analysis of handwriting in the Great Isaiah Scroll, the longest of the Dead Sea Scrolls. Image courtesy of Mladen Popovic, University of Groningen.

The new findings come one month after Israel announced the discovery of the first new set of fragments from the ancient manuscripts in 60 years,unearthed from theso-called Cave of Horror,home to the bodies of Jewish families who died under siege during the Bar Kokhba revolt in the first century.

These Dead Sea Scrolls are like a time machine,Popovi told the New Scientist.They allow us to travel way back in time, even to the time that the Hebrew Bible was still being written.

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Researchers Have Uncovered Yet Another Secret of the Dead Sea Scrolls, This Time Using Artificial Intelligence - artnet News

The global race to regulate AI – Axios

Posted on April 26, 2021 by admin

Regulators in Europe and Washington are racing to figure out how to govern business' use of artificial intelligence while companiespush to deploy the technology.

Driving the news: On Wednesday, the EU revealed a detailed proposal on how AI should be regulated, banning some uses outright and defining which uses of AI are deemed "high-risk."

In the U.S., the federal government has yet to pass legislation specifically addressing AI, though some local governments have enacted their own rules, especially around facial recognition.

But Monday, the Federal Trade Commission laid out a tough restatement of its role enforcing laws related to AI, addressing the sale and use of algorithms that:

Acting FTC chairwoman Rebecca Slaughter told Axios: I am pleased that the European Commission shares the FTCs concerns about the risks posed by artificial intelligence... I look forward to reviewing the ECs proposal as we learn from each other in pursuit of transparency, fairness, and accountability in algorithmic decision making.

Why it matters: Artificial intelligence is no longer in its infancy and already has wide uses. Global governments are trying to wrap their arms around it, often taking different approaches.

What they're saying: The EU's move "is another wake-up call for the U.S. that it needs to retain its leadership position in the development in these sorts of legal frameworks," said Christian Troncoso, senior director of policy at BSA | The Software Alliance.

Be smart: Regulators move slower than technology. Just this week, the ACLU and dozens of other advocacy groups called on the Department of Homeland Security to stop using Clearview AI's facial recognition software.

The bottom line: Regulators want to get the details right, but they also believe they have a rare chance with AI to put legal and ethical guardrails around a new technology before it's already deployed everywhere. That window will close fast.

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The global race to regulate AI - Axios

China and Artificial Intelligence The Diplomat – The Diplomat

Posted on April 26, 2021 by admin

The U.S. National Security Commission on Artificial Intelligence released its final report recently, listing China as a strategic competitor to the United States in this field. The report describes China as a U.S. peer in many areas and an AI leader in some areas. This new technology allows machines to exhibit characteristics associated with human learning and problem-solving, and can be applied to areas such as facial and speech recognition, natural language processing, and automated reasoning.

While China has made technological strides in the AI field, the authors of the report view these developments as a threat. As recorded in the report, potential threatening applications can be made in a number of areas.

First, AI boosts the threat imposed by potential cyberattacks coming from China. Cyberattacks can be made more rapidly, with better precision, and in greater secrecy with the use of AI. Already, cyberattacks have been used to steal trade and government secrets. Intellectual property protection was a central issue in the China-U.S. trade war and may become more vulnerable as China accelerates its AI capabilities. Cyberattacks have also been used to disseminate disinformation, which was prevalent during the 2016 U.S. election, and spread self-replicating AI-generated malware. Use of AI-fused data for blackmail, deepfakes, or swarms are possible in the future.

Second, China plans to use AI to offset U.S. military superiority by implementing a type of intelligentized war that relies more on creation of alternative logistics, procurement, and training, as well as warfare algorithms. Battle networks will connect systems, and armed drones with autonomous functions will be employed. Soldiers will be trained in live and virtual environments that integrate AI. AI will speed up the process with which valuable targets can be identified and hit due to enhancements in collection and transmission of intelligence.

Third, Chinas use of AI in national intelligence will help government officials pinpoint trends and threats as well as use deception and expose sources and methods. AI renders social media information, satellite imagery, communications signals, and other sources of data more understandable and potentially actionable. Intelligence sources may be coupled with domestic and international surveillance. The authors assert that Chinas domestic use of AI is a chilling precedent for anyone around the world who cherishes individual liberty due to its use in domestic surveillance and repression.

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To combat these possibilities, the report emphasizes that the United States should ensure that it also builds its own AI capabilities and not fall behind. The authors recommend that the U.S. invest $40 billion in expanding and democratizing federal AI research and development. They also recommend that the U.S. create a Joint Interagency Task Force and Operations Center and provide additional funds to the Defense Advanced Research Projects Agency (DARPA) to counter social media disinformation. As a response to hacking and other attacks, the U.S. should develop AI-enabled defenses against cyberattacks and set up red teams for adversarial testing. The report also recommends that, in order to maintain military defense capabilities against AI-based attacks, the Department of Defense should invest in next-generation technologies and set up a joint warfighting network architecture this year.

Certainly, the world of AI will lead to the danger of automating and accelerating decisions that can harm other nations. One of the biggest issues is that AI-based applications may automatically authorize use of nuclear weapons. This emphasizes the need to ensure that AI cannot make security-critical decisions without some human intervention. As long as humans are somehow involved in the final decision, possessing the capability to engage in cyberattacks, intelligentized war, or national intelligence gathering does not in itself place other nations in direct jeopardy. Equally importantly, ensuring that there are open channels to negotiate disputes and keep the peace is the most critical aspect of reducing conflict.

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It is important for world powers to maintain AI capabilities in the event of a conflict, but these offensive AI-based technologies should not be the first response for the U.S. or any other nation. This can only serve to escalate conflict and increase the likelihood that two or more nations will engage in a broader war. Reliance on AI-based conflict should be viewed as a last resort, and diplomatic and economic relations should be used as the primary method of maintaining peace.

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China and Artificial Intelligence The Diplomat - The Diplomat

3 Sectors Revolutionized By the Power of Artificial Intelligence – TechBullion

Posted on April 26, 2021 by admin

There are two main technologies that are responsible for helping to create intelligent systems in our industries. One youve probably heard of is artificial intelligence (AI) and the other is machine learning (ML). These two developments are very similar, so what is the difference between AI vs ML?

Machine learning is a part of the broader term of AI, but it refers to the specific learning a machine does based on the data it is fed. ML can make decisions or predictions based on a historical set of data, and use its best decision-making ability to help provide an outcome.

Conversely, artificial intelligence refers to technologies that can mimic human behaviour and thinking. The goal of this development is to simulate human intelligence in systems so that they can function in a more complex and intelligent manner. Here are three sectors that are being revolutionized by AI technology, along with what it could mean for the future.

Insurance

The insurance industry is always changing alongside updates in technology. There are many factors and evolving regulations that give this industry the ability to harness the power of artificial intelligence to its benefit. Enhancing customer satisfaction Is one of the most important ways AI is revolutionizing insurance companies. It helps customers and companies have a faster claim processing due to the large information of data they process. AI automation can also help with loss prevention and protecting against fraudulent claims. Through deep learning, chatbots or data processing and management, there are many AI tools to help companies make the process smoother for them and their customers.

Entertainment

The scope of possibilities is endless when it comes to merging the entertainment industry with the powers of AI. One of the first real examples of AI in mainstream media was the film title with the same name, AI: Artificial Intelligence. The actual technology itself has made many incredible advancements since then, including the worlds first AI-Generated music and video content, which created new music based on previous learnings of popular sounds. User experience is a big factor in how AI is transforming the entertainment industry. Netflix, for example, learns personal preferences and offers suggestions, making a personalized entertainment experience for users.

Education

Artificial intelligence has been a big part of the education sector since its inception. However, its not only for technical learning; it also serves to make everyday learning much easier for students, teachers and even parents. There are AI-based games and software that help kids to learn different subjects in new ways, while being more engaging and impactful than older traditional methods. Personalization is one of the ways how intelligent tutoring systems are changing education. For example, some systems can learn how a student is performing based on past tests and create a personalized study pack for them. This is only the beginning of what AI will be able to do to further enhance our education process.

Artificial intelligence is one of the emerging technologies that can truly help revolutionize these sectors and many others. There is so much that we can teach machines and there is a lot they can learn from us and our data. Its truly amazing to see how this information and technology can revolutionize our world.

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3 Sectors Revolutionized By the Power of Artificial Intelligence - TechBullion

Global investment in telehealth, artificial intelligence hits a new high in Q1 2021 – FierceHealthcare

Posted on April 26, 2021 by admin

Telehealth investment hit an all-time high of $4.2 billion in just 139 deals in the first quarter, almost doubling the $2.2 billion raised in the same quarter a year ago, according to CB Insights.

That's the highest global funding for telehealth during one quarter on record, according to the company's first-quarter 2021 report. Funding also was up 18% from the $3.6 billion raised in the fourth quarter of 2020.

Industry executive discussions around telehealth and its role in care delivery remain active, based on mentions of telehealth during company earnings' calls, CB Insights reported. During the second quarter of 2020, there were close to 1,200 mentions of telehealth on earnings calls. And, while thatdropped to 517 mentions in the first quarter of 2021, it's still much higher than pre-COVID levels.

Meanwhile, as COVID-19 vaccines roll out, industry executive discussions on the topic are starting to taper off. The focus is shifting toward the pandemics long-term effects, such as virtual care, according to the report from CB Insights, a market intelligence firm.

The money poured into the telehealth sector helped to boost six companies to "unicorn" status: when a company's valuation hits$1 billion. Hinge Health, Dispatch Health, K Health, Innovaceer, Modern Health and Evidation all raised massive funding rounds in the first quarter that helped propel them to unicorn status.

RELATED:Digital health's red-hot quarter: $6.7B raised in 147 deals

The first quarter also saw companies that provide hybrid in-person and virtual care services bank late-stage "mega-rounds." Tech-enabled primary care provider Forward scored $225 million in a series D round, and Crossover Health clinched $168 million in a series D round.

The space also has attracted large acquirers, includingBoston Scientific's move tobuyremote cardiac monitoring company Preventice Solutions for $1.2 billion and Cigna subsidiary Evernorth's grab for MDLive.

Dollars for artificial intelligence startups also skyrocketed during the first quarter.

Globally, healthcare AI companies brought in a record-breaking $2.5 billion in the first quarter of 2021 in 111 deals. That's up 140% compared to $1 billion raised in the first quarter of 2020.

Healthcare AI also continues to gain attention from industry executives, as mentions of AI and machine learning in healthcare topped 2,200 during company earnings calls last quarter, according to CB Insights.

The AI sector's record-setting quarter was largely propelled by mega-rounds totaling about $1.5 billion. These rounds spanned applications from drug discovery to patient payments.

Insitro, which developeda machine learning platform to accelerate drug R&D and predict the success of drug targets in clinical trials, scored $400 million backed by Google Ventures and other investors.

RELATED:2020 breaks record in digital health investment with $9.4B in funding

Cedar, a digital health company using a machine-learning-powered payment platform to help healthcare providers engage patients with personalized messages, snagged $200 million led by Tiger Global Management.

Strive Health uses healthcare data such as clinical information, dialysis machines, claims data, demographics and more to monitor and predict kidney health. The company raised $140 million in new funding during the first quarter.

Back-office automation also attracted major funding with Infinitus Systems, which uses conversational AI to automate phone calls for providers as they collect data or check status updates, pulled in $21 million. Revenue cycle management startup Akasauses AI-powered RPA to automate and spot efficiencies in revenue cycle management. That company brought in $60 million backed byAndreessen Horowitz.

CB Insightsreported that global healthcare funding hit a new quarterly record in the first quarterwith a total of $31.6 billionin equity funding. Deal count grew by 9% to more than 1,500deals, the second-highest in the last 12 quarters.

Global digital health funding jumped by 9% quarter over quarter in the first quarter of 2021, to reachan all-time high of $9 billion.

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