Credits : Gsmarena


Android 9 Pie is out today and it comes with Google’s first ever gesture-based navigation system. That, however, is opt-in at the moment, for devices that are getting updated to Pie. You can enable it if you want, otherwise you’re still able to use the traditional navigation with three software buttons – Back, Home, and Recents.

This situation will not remain the same going forward. For Google devices launching in the future, including the Pixel 3 and Pixel 3 XL which should be made official at an event on October 4, the traditional buttons will be gone.

Thus, the one and only option you’ll get is the new gesture-based navigation with the center pill and Back button on the left (which appears only when it’s necessary). The information has emerged through an interview by Android Central with EK Chung, Google’s UX manager for Android handheld and Pixel.

The company chose to retire the multitasking button because user diagnostics showed that very few people actually used it on a regular basis. Throughout user testing performed on the new gesture system with “normal” consumers, Google found that their most-loved feature was the ability to quickly jump between apps by sliding the pill to the right.

As is par for the course in the Android world, just because Google has its own gesture system now it doesn’t mean that the same exact one will be used by other OEMs. They are still free to ship handsets with the old three-button navigation, or even their own gestures like OnePlus and Motorola have already done. Fragmentation definitely isn’t going away anytime soon.

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Can machine learning be used to accelerate the development of traditional software development lifecycle? As artificial intelligence and other techniques get increasingly deployed as key components of modern software systems, the hybridisation of AI and ML and the resultant software is inevitable. According to a research paper from the University of Gothenburg, AI and ML technologies are increasingly being componentised and can be more easily used and reused, even by non-experts. Recent breakthroughs in software engineering have helped AI capabilities to be effectively reused via RESTful APIs as automated cloud solution2s.

The ML Impact

AI will play a key role in the design, creation and testing of software. According to a 2016 Forrester Research survey, AI can also help in code generation. The survey further revealed that if an AI software system is given a business requirement in natural language, it can write the code to implement it — or even come up with its own idea and write a program for it. For example, Microsoft’s Intellisense has been integrated with Visual Studio to enhance the developer experience. In fact, a 2017 State of Testing Survey revealed that testers will spend more time and their resources on testing mobile and hybrid applications, with the time spent on actual development shrinking.

Examples Of AI Integration In The Software Development Cycle

Google bugspot tool w3C: As the blog points out, 50 percent of the code changes every month. And as Google’s code base and teams increase in size, it becomes more unlikely that the submitter and reviewer will even be aware that they’re changing a hot spot. The Bug prediction tool uses ML algorithms and statistical analysis to find out whether a piece of code is flawed or not and whether it falls in the confidence range. Source-based metrics that could be used for prediction are how many lines of code, how many dependencies are required and whether those dependencies are cyclic1, the Google Engineering blog indicates.

Stack Overflow AutoComplete: Code Complete by Emil Schutte is a good case in point where the developer leveraged Stack Exchange data to crank out fully functional code based on the intentions inferred from existing code.

Deep Code: Then there is AI programming tool developed by a Zurich based startup DeepCode which is being positioned as a new AI code assistant. The tool learns from a corpus of 250,000 rules, from the public and private GitHub repositories, thereby telling programmers how to fix their code. In simpler terms, it does a thorough code review. It is a good tool to find bugs in code and helps developers deliver clean and reliable code.

Areas Where AI Will Play A Pivotal Role

Bug fixing: This is one of the biggest areas being revolutionised with AI technologies. Given the huge volume of data that needs to be tested and human error due to overlooked bugs, software testing tools such as bugspots show us that programs can leverage AI algorithms to auto-correct themselves with minimum intervention of a human programmer.

Code Optimization: Compilers fix old code without needing the original source, that too in a short period of time. Compilers are programs that process high-level programming language and convert it into machine language or instruction1s that can be performed by machines. For example, the Helium software developed by Adobe and MIT Computer Science and Artificial Intelligence Laboratory automated the task of fixing old code, without requiring the original source. It thereby made the next generation of code faster. This task which would take an engineer up to three months or more, was reduced to mere days. The Helium software was used to optimise the performance of Photoshop filters by up to 75 percent.

Testing: AI-driven testing has been around for some time and there is a slew of open source tools that use AI for generating test cases and perform regression testing. For example, Appvance, pegged as an AI-driven software test automation tool uses AI for performance and load testing and to generate test cases based on user behaviour. Meanwhile, deploys machine learning to accelerate authoring, execution, and maintenance of automated tests. As one user points out, the tool becomes smarter when more tests are run. Then there is Functionize the machine learning based testing platform which uses ML for functional testing for web and mobile applications, thereby reducing the time to manage test infrastructures.

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In this Motion Control Software Market research report, the major factors driving the growth of this market were documented and the business partners & end operators were long-winded. The configuration of the business division, examples and complications manipulating the market internationally are similarly a piece of this broad analysis. Numerous gatherings and meetings were led by the noticeable pioneers of this industry to get steadfast and refreshed insights concerned to the market.

The assembling business is developing quickly and there is an expanded need to improve the assembling forms. The establishment of cutting edge mechanization frameworks, for example, movement control framework helps in lessening surrenders, expanding process representation, and enhancing process productivity. Process automation helps in settling the mind boggling undertaking of examining constant information and in lessening human intercession and the time taken for operational procedures.

The incorporation of remote innovation and remote gadgets have profited the movement control programming market as information can be recovered from every one of the gadgets on the up and up and can be put away in distributed storage. Expanded network amongst equipment and programming enhances the proficiency of the modern procedures and enhances the vital basic leadership capacity of the clients. Merchants are creating remote inserted chips that can be introduced in engines and are perfect with movement control programming. Cloud innovation helps in information sharing as the information can be recouped and gotten too safely. This will expand the requirement for cloud-based and remote innovation which will be one of the key patterns contributing towards the development of the movement control programming market.

The leading vendors in the market are –



National Instruments

Physik Instruments

Rockwell Automation

The other prominent vendors in the market are SIGMATEK, LINAK, 3S-Smart Software Solutions GmbH & CODESYS, Mitsubishi Electric, Galil, Trio Motion Technology, and Siemens.

The report segments the Motion Control Software market on the basis of key criteria and studies each of the segments along with their sub-segments in a detailed manner. Revealing the top segment, the segment with sluggish growth, and also the fastest growing segments, the report proves to be valuable for those wishing to invest in the global Motion Control Software_ market. Readers are able to make correct and smart decisions regarding investments in this market, thereby making profits and securing a strong foothold in the market in the future.

A detailed overview of key market drivers, trends, restraints and analyzes the way they affect the Motion Control Software_ market in a positive as well as the negative aspect. The regions which are covered in this report are North America, Europe, Asia Pacific, Middle East & Africa and Latin America. Considering the given forecast period and precisely studying each and every yearly data, a report is been drafted to ensure the data is as expected by client. A detailed study of the competitive landscape of the global Motion Control Software market have been given, presenting insights into the company profiles, product portfolio, financial status,  recent developments, mergers and acquisitions, and the SWOT analysis.

 Segmentation by product type and analysis of the motion control software market


Material handling

Semiconductor machinery

Packaging and labeling machinery

The motion control software is primarily used in robotics as the software eases the motion control process, provides assistance to robots by offering real-time instruction, and increases the accuracy of angular movements of robots. Additionally, the software also provides precise algorithms and inputs that increases the speed and efficiency of the robots.

Geographical segmentation and analysis of the motion control software market




The main points which are answered and covered in this Report are-

What will be the total market size in the coming years till 2023?

What will be the key factors which will be overall affecting the industry?

What are the various challenges addressed?

Which are the major companies included?

Table of Content:

Global Motion Control Software Market Research Report 2018-2023

Chapter 1: Motion Control Software Market Overview

Chapter 2: Global Economic Impact

Chapter 3: Competition by Manufacturer

Chapter 4: Production, Revenue (Value) by Region (2018-2023)

Chapter 5: Supply (Production), Consumption, Export, Import by Regions (2018-2023)

Chapter 6: Production, Revenue (Value), Price Trend by Type

Chapter 7: Analysis by Application

Chapter 8: Manufacturing Cost Analysis

Chapter 9: Industrial Chain, Sourcing Strategy and Downstream Buyers

Chapter 10: Marketing Strategy Analysis, Distributors/Traders

Chapter 11: Market Effect Factors Analysis

Chapter 12: Market Forecast (2018-2023)

Chapter 13: Appendix

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Google LLC today released Jib, a new open-source tool that aims to make software containers and the Java programming language work more seamlessly together.

The two technologies are both mainstays of application development in the enterprise. Java has been used to write business software for decades and remains ubiquitous to this day. Software containers are a popular means of building portable applications that work across different kinds of infrastructure.

Google has created Jib to take the hassle out of packaging Java code into containers. This task is a tedious, multistage process when performed the traditional way. A developer has to install and run the Docker container engine, write a set of instructions known as a Dockerfile to define how an application should be built and then push the finished container image to a repository for safekeeping.

Jib consolidates the process into a single step. According to Google, the tool doesn’t require users to install Docker and can figure out how to build an application without needing any specially crafted instructions. Instead of a Dockerfile, the software analyzes project data from the user’s development environment.

According to Google, Jib also uses the collected information to organize software components into “layers.” When a developer updates their application, the tool only rebuilds the relevant layer instead of the entire code base to reduce build times.

“Jib takes advantage of image layering and registry caching to achieve fast, incremental builds,” explained Google engineers Appu Goundan and Qingyang Chen. “It reads your build config, organizes your application into distinct layers (dependencies, resources, classes) and only rebuilds and pushes the layers that have changed. When iterating quickly on a project, Jib can save valuable time on each build by only pushing your changed layers to the registry instead of your whole application.”

By making it easier for Java developers to use containers, Jib could help further expand the adoption of the technology in the enterprise. The tool is the latest addition to the already extensive portfolio of open-source projects that Google has built up in recent years. The company’s most well-known contribution to the container ecosystem is Kubernetes, the go-to software for managing large Docker clusters.

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Credits : Infoq

 Key Takeaways

  • In enterprise test scenarios, software needs to be tested in the same way as it will run in production, in order to ensure that it will work as expected.
  • A common challenge is that microservice applications directly or indirectly depend on other services that need to be orchestrated within the test scenario.
  • This article shows how container orchestration provides an abstraction over service instances and facilitates in replacing them with mock instances.
  • Additionally, service meshes enable us to re-route traffic and inject faulty responses or delays to verify our services’ resiliency.
  • The article contains sample code from an accompanying example Java-based coffee shop application deployed to and tested on Kubernetes and Istio.

In enterprise test scenarios, software needs to be tested in the same way as it will run in production, in order to ensure that it will work as expected. A common challenge is that microservice applications directly or indirectly depend on other services that need to be orchestrated within the test scenario.

This article shows how container orchestration provides an abstraction over service instances and facilitates in replacing them with mock instances. On top of that, service meshes enable us to re-route traffic and inject faulty responses or delays to verify our services’ resiliency.

We will use a coffee shop example application that is deployed to a container orchestration and service mesh cluster. We have chosen Kubernetes and Istio as example environment technology.

Test Scenario

Let’s assume that we want to test the application’s behavior without considering other, external services. The application runs in the same way and is configured in the same way as in production, so that later on we can be sure that it will behave in exactly the same way. Our test cases will connect to the application by using its well-defined communication interfaces.

External services, however, should not be part of the test scenario. In general, test cases should focus on a single object-under-test and mask out all the rest. Therefore, we substitute the external services with mock servers.

Container Orchestration

Reconfiguring the application to use the mock servers instead of the actual backends contradicts the idea of running the microservice in the same way as in production, since this would chance configuration. However, if our application is deployed to a container orchestration cluster, such as Kubernetes, we can use the abstracted service names as configured destinations and let the cluster resolve the backend service instances.

The following example shows a gateway class that is part of the coffee shop application and connects against the coffee-processor host on port 8080.

public class OrderProcessor {

    // definitions omitted ...

    private void initClient() {
        final Client client = ClientBuilder.newClient();
        target ="http://coffee-processor:8080/processes");

    public void processOrder(Order order) {
        OrderStatus status = retrieveOrderStatus(order);

    // ...

    private JsonObject sendRequest(final JsonObject requestBody) {
        Response response = target.request()

        // ...

        return response.readEntity(JsonObject.class);

    // definitions omitted ...

This host name is resolved via the Kubernetes cluster DNS and this will direct traffic to one of the running processor instances. The instance that backs the coffee-processor service, however, will be a mock server, WireMock in our example. This substitution is transparent to our application.

The system test scenario not only connects against the application to invoke the desired business use case, but will also communicate with the mock server, on a separate admin interface, to control its response behavior and to verify whether the application invoked the mock in the correct way. It is the same idea as for class-level unit tests, usually realized by JUnit and Mockito.



External Services

This setup allows us to mock and control services that run inside of our container orchestration cluster. But what if the external service is outside of the cluster?

In general, we can create a Kubernetes service without selectors that points to an external IP, and rewrite our application to always use that service name which is resolved by the cluster. By doing so, we define a single point of responsibility, where the service will route to.

The following code snippet shows an external Kubernetes service and endpoints definition which routes coffee-shop-db to an external IP address

kind: Service
apiVersion: v1
  name: coffee-shop-db
  - protocol: TCP
    port: 5432

kind: Endpoints
apiVersion: v1
  name: coffee-shop-db
  - addresses:
      - ip:
      - port: 5432

Within different environments, the service might route to different database instances.

Service Meshes

Service meshes enable us to transparently support technical cross-cutting communication concerns to microservices. As of today, Istio is one of the most-used service mesh technology. It adds sidecar proxy containers, co-located with our application containers, which implement these additional concerns. The proxy containers also allow to purposely manipulate or slow down connection for resiliency testing purposes.

In a end-to-end test, we can introduce faulty or slow responses to verify whether our applications handles these faulty situations properly.

The following code snippet shows an Istio virtual service definition that annotates the route to coffee-processor with a 3 second delay for 50% and failures for 10% of the responses.

kind: VirtualService
  name: coffee-processor
  - coffee-processor
  - route:
    - destination:
        host: coffee-processor
        subset: v1
        fixedDelay: 3s
        percent: 50
        httpStatus: 500
        percent: 10

kind: DestinationRule
  name: coffee-processor
  host: coffee-processor
  - name: v1
      version: v1

Now, we can run additional tests and verify how our application reacts to these increased response times and failure situations.

Besides the possibility to inject faulty responses, service mesh technology also allows to add resiliency from the environment. Proxy containers can handle timeouts, implement circuit breakers and bulkheads without requiring the application to handle these concerns.


Container orchestration and service meshes improve the testability of microservice applications by extracting concerns from the application into the operational environment. Service abstractions implement discovery and allow us to transparently substitute services or to re-route. Service meshes not only allow more complex routing but also allow us to inject failures or slow responses in order to put our applications under pressure and verify their corresponding behavior.

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As of PHP 7.1, the php-mcrypt was deprecated. And as of PHP 7.2 it was completely removed. This is a problem, since a number of server software titles still depend upon this encryption tool. And because software like Nextcloud, ownCloud, and many more have yet to shift that dependency, you might find yourself unable to install without mcrypt on the system. What do you do? No matter how many times you run either apt-get install php-mcrypt or yum install php-mcrypt, it won’t work.

Fortunately, there’s a solution. Said solution falls onto the shoulders of the pecl command. PECL is the PHP Extension Community Library, which serves as a repository for PHP extensions. Through this repository, you can install mcrypt.

What is mcrypt?

The mcrypt extension is a replacement for the UNIX cryptcommand. These commands serve as a means to encrypt files on UNIX and Linux systems. The php-mcrypt extension serves as an interface between PHP and mcrypt.

Getting mcrypt installed

I’m going to walk you through the process of getting mcrypt installed on Ubuntu Server 16.04. It’s not challenging once you have the necessary dependencies added to your system. With mcrypt installed, you can continue with the installation of the software that depends upon this extension.

With that said, how do we install mcrypt? First, open up a terminal window and install the necessary dependencies with the commands:

sudo apt-get -y install gcc make autoconf libc-dev pkg-config
sudo apt-get -y install php7.2-dev
sudo apt-get -y install libmcrypt-dev

Once the dependencies have been installed, you can install mcrypt with the command:

sudo pecl install mcrypt-1.0.1

And there you go. Mcrypt is now installed. Go back to the process of installing whatever server software that depends upon this extension and you should be good to go.

Not gone, just moved

Don’t worry: mcrypt is not gone. It’s just been moved out of PHP and into PECL. But for those who have been installing via php-mcrypt for years, this makes for a pretty big shift. Now, instead of being able to install mcrypt with a single command, you have four to deal with. Even so, at least you still have mcrypt available. Eventually, however, I believe the mcrypt dependency will be migrated to another tool (such as OpenSSL).

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The largest geographic markets by consumption in the information technology market are the Americas, Asia and Europe. The Americas was the largest region in the information technology market in 2016, accounting for 39.0% market share.

The Information Technology Global Market Briefing provides strategists, marketers and senior management with the critical information they need to assess the information technology market.

Reasons to Purchase:

  • Outperform competitors using accurate up to date demand-side dynamics information.
    Identify growth segments for investment.
  • Facilitate decision making on the basis of historic and forecast data and the drivers and restraints on the market.
  • Develop strategies based on likely future developments.
  • Suitable for supporting your internal and external presentations with reliable high quality data and analysis.
  • Gain a global perspective on the development of the market.
  • The market characteristics section of the report defines and explains the market.

    The market size section gives the information technology market revenues, covering both the historic growth of the market and forecasting the future.

    Comparison With Other Markets section outlines the information technology market share among the other manufacturing markets.

    Historic and Forecast Growth Comparison With Other Markets section compares the information technology markets historic and forecast growth rate with other manufacturing markets.

    Drivers and restraints looks at the external factors supporting and controlling the growth of the market.

  • Market segmentations break down the key sub sectors which make up the market. The regional breakdowns section gives the size of the market geographically.

    Competitive landscape gives a description of the competitive nature of the market, market shares, and a description of the leading companies. Key financial deals which have shaped the market in the last three years are identified.

    The trends and strategies section highlights the likely future developments in the information technology market and suggests approaches.

  • Scope of the Report:

    Markets covered: Telecommunications, IT Services, Software Publishers, Computer Hardware Companies mentioned: Apple, AT&T, Verizon, Amazon, Hewlett-Packard, Microsoft, IBM, Google, Comcast, Intel

    Geographic scope: Americas, Europe, Asia, Middle East and Africa, Oceania.

    Time series: Five years historic and forecast.

    Data: Market value in $ billions.

    Data Segmentation: Regional breakdowns, market share of competitors, key sub segments.

    Sourcing and Referencing: Data and analysis throughout the report is sourced using end notes.

  • The information technology (IT) industry deals with the application of computers, computer peripherals and telecommunications equipment to store, retrieve, transmit and maneuver data. It involves computer networking, broadcasting, systems design services and information distribution technologies like television and telephones.

    The largest geographic markets by consumption in the information technology market are the Americas, Asia and Europe. The Americas was the largest region in the information technology market in 2016, accounting for 39.0% market share. Asia and Europe were the second and third largest region accounting nearly 27% and 25% market share respectively.

    Over the past five years there has been an increasing prevalence of low cost open source alternatives. Open source has become a preferred platform for developing new technology. In the past, software publishers would open source software that was not making money, but now companies are open sourcing software to increase its presence and share in the market. According to Allison Randal, President, Open Source Initiative, 78% of companies use open source solutions and 64% participate in open source projects indicating an increase in open source software platforms to build applications in 2015.

    About Us:

    Industrydataanalytics provides syndicated Market research reports to industries, organizations or even individuals with an aim of helping them in their decision making process.

    Industrydataanalytics has a targeted view to provide business insights and consulting to assist its clients to make strategic business decisions and achieve sustainable growth in their respective market domain.

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Credits : Eurekalert


An open-source movement simulator that has already helped solve problems in medicine, paleontology, and animal locomotion has been expanded and improved, according to a new publication in the open-access journal PLOS Computational Biology. The software, called OpenSim, has been developed by a team at Stanford University, led by first authors Ajay Seth, Jennifer Hicks, and Thomas Uchida, with contributions from users around the world. The new paper reviews the software’s wide range of applications and describes the improvements that can increase its utility even further.

The major challenges in creating movements “in silico” include formulating the underlying mathematical equations and ensuring the solution is accurate when calculating variables that are difficult to measure experimentally, such as the metabolic consumption of individual muscles and the stretch and recoil of tendons during movement. Physics-based models enable prediction of novel movements, both adaptive and maladaptive, such as excess hip rotation in response to leg muscle weakness. OpenSim combines methods from biology, neuroscience, mechanics, and robotics to address these challenges and create fast and accurate simulations of movement.

OpenSim has already been put to use determining whether Australopithecus afarensis had sufficient grip strength to make certain tools, based on fossilized bone discoveries; developing strategies to prevent ankle injuries during athletic performance; and optimizing a wearable robotic device for long jumps. Additional applications include predicting the locomotion patterns of extinct species and planning tendon-lengthening surgery for children with cerebral palsy.

Recent improvements include addition of more accurate models of muscle dynamics, joint kinematics, and assistive devices, which will aid in rehabilitation studies; the ability to create custom studies by combining existing tools in new ways; tools for importing motion-capture data in order to test simulations against experiments; and modern visualization tools for creating insightful animations of movement.

“The software is like a Swiss Army knife for the movement scientist,” said the lead authors. “It allows researchers with no special expertise in biomechanics to perform powerful and accurate simulations to test hypotheses, visualize solutions to problems, and communicate ideas. Because it incorporates decades of research about how humans and other animals move, and is constantly being augmented and enhanced by the community of users from so many different fields, OpenSim can accelerate discoveries in any field in which biological movement plays a role.”

Citation: Seth A, Hicks JL, Uchida TK, Habib A, Dembia CL, Dunne JJ, et al. (2018) OpenSim: Simulating musculoskeletal dynamics and neuromuscular control to study human and animal movement. PLoS Comput Biol 14(7): e1006223.

Funding: This work was supported by a) the National Institutes of Health through grants U54 GM072970, R24 HD065690, P2C HD065690, U54 EB020405, R01 HD033929, R01 NS055380, R01 HD046814, and R01 HD046774; b) Defense Advanced Research Projects Agency (DARPA) contracts, including W911QX-12-C-0018 and HR0011-12-C-0111, via subcontract 12-006 from Open Source Robotics Foundation, and c) European Commission grant FP7-ICT-248189. JLH and CLD received support from the National Science Foundation Graduate Fellowship Program; JLH, CLD, CFO, EMA, and JRY received support from the Stanford (University) Bio-X Graduate Fellowship; and CFO received support from the Siebel Scholars Program . The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing Interests: The authors have declared that no competing interests exist.

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

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The Google Cloud Next 18 conference got underway on Tuesday and Dr. Fei Fei Li, Google’s chief AI scientist demonstrated a new AI system called Google Contact Center AI. This is designed to be the next generation of automated customer-service voices.

Li took the audience through a demonstration, showing off a system that deftly understood natural language and quickly responded to questions with pertinent answers. The software and human on the phone had what sounded like a normal conversation between a customer and a customer-service rep. It appears the days of keying into our handsets 1 for yes, and 2 for no may soon be a thing of the past.

The presentation echoed a similar demo at I/O this year. That’s when Google CEO Sundar Pichai showed off Duplex, the restaurant-booking digital assistant that sounded so human that people speaking to it on the phone were unaware they were talking to an automated system.

That freaked out some journalists and technology ethicists. The company was criticized for using a technology that could fool humans or snatch away jobs. This time, Li seemed intent on making sure that everyone understood that Google’s AI is not intended to put humans out of work.

“Contact Center AI is an example for our passion for bringing AI to every industry all the while elevating the role of human talent,” Li told the audience. “We’re creating technology that’s not just powerful but that’s also trustworthy.”

Google’s management has said that AI is core to the company’s future and mission and leaders are trying to exploit it in retail, autonomous cars, search and advertising. Wall Street analysts have begun to predict big future earnings as a result of AI.

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To grow at the Highest CAGR Increasing Demand of Global Robot Software Market Analysis & Forecasts Report By 2023 Along with Top Key Players like IBM, ABB, Nvidia, Cloud minds, Liquid Robotics, Brain Corp

This press release was orginally distributed by SBWire

Pune, India — (SBWIRE) — 07/23/2018 — The Research Report “Global Robot Software Market” on the basis of software types, robot types, deployment models, organization sizes, verticals, and regions. The report analyzes the major factors influencing the market growth, such as drivers, restraints, opportunities, and challenges. It aims to strategically analyze the micro markets with respect to individual growth trends, prospects, and their contribution to the market.

“The Global Robot Software Market is expected to grow from USD 1,142.2 Million in 2017 to USD 7,527.1 Million by 2022, at a Compound Annual Growth Rate (CAGR) of 45.8%.”

The report provides detailed insights into the Global Robot Software Market based on software types, robot types, deployment models, organization sizes, verticals, and regions. Among the software types, recognition software is expected to have the largest market size during the forecast period. This software is integrated behind the robots that offer them the cognitive ability to recognize the object and respond to them appropriately.

Some prominent players in the Global Robot Software Market are IBM, ABB, Nvidia, Cloud minds, Liquid Robotics, Brain Corp, Aibrain, Furhat Robotics, Neurala, Energid Technologies, H2o AI, Oxbotica.

In the end, the world Robot Software industry report delivers high-level information both in terms of quality and quantity. It also gives a summary of the Robot Software vendor, dealer, contributors to the Robot Software market together with research findings, data source, and appendix.

The report gives in depth industry analysis on Robot Software Market. It helps in visualizing the composition of the market across each indication, in terms of type and applications, highlighting the key commercial assets and players. Report Pinpoint growth sectors and identify factors driving change.

Table of Content:
Chapter 1 Robot Software Market Overview
Chapter 2 Global Economic Impact on Robot Software Market Industry
Chapter 3 Global Robot Software Market Competition by Manufacturers
Chapter 4 Global Robot Software Production, Revenue (Value) by Region (2017-2022)
Chapter 5 Global Robot Software Supply (Production), Consumption, Export, Import by Regions (2017-2022)
Chapter 6 Global Robot Software Production, Revenue (Value), Price Trend by Type
Chapter 7 Global Robot Software Market Analysis by Application
Chapter 8 Robot Software Manufacturing Cost Analysis
Chapter 9 Industrial Chain, Sourcing Strategy and Downstream Buyers
Chapter 10 Marketing Strategy Analysis, Distributors/Traders

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