As dispute over research, diversity grows.

Google fired staff scientist Margaret Mitchell on Saturday, they both said, a move that fanned company divisions on academic freedom and diversity that were on display since its December dismissal of AI ethics researcher Timnit Gebru.

Google said in a statement Mitchell violated the company’s code of conduct and security policies by moving electronic files outside the company.

Mitchell, who announced her firing on Twitter, did not respond to a request for comment.

Google’s ethics in artificial intelligence work has been under scrutiny since the firing of Gebru, a scientist who gained prominence for exposing bias in facial analysis systems.

The dismissal prompted thousands of Google workers to protest.

She and Mitchell had called for greater diversity and inclusion among Google’s research staff and expressed concern that the company was starting to censor papers critical of its products.

Gebru said Google fired her after she questioned an order not to publish a study saying AI that mimics language could hurt marginalised populations.

Mitchell, a co-author of the paper, publicly criticised the company for firing Gebru and undermining the credibility of her work.

The pair for about two years had co-led the ethical AI team, started by Mitchell.

Google AI research director Zoubin Ghahramani and a company lawyer informed Mitchell’s team of her firing on Friday in a meeting called at short notice, according to a person familiar with the matter.

The person said little explanation was given for the dismissal. Google declined to comment.

The company said Mitchell’s firing followed disciplinary recommendations by investigators and a review committee.

It said her violations “included the exfiltration of confidential business-sensitive documents and private data of other employees”. The investigation began Jan. 19.

Google employee Alex Hanna said on Twitter the company was running a “smear campaign” against Mitchell and Gebru, with whom she worked closely. Google declined to comment on Hanna’s remarks.

Google has recruited top scientists with promises of research freedom, but the limits are tested as researchers increasingly write about the negative effects of technology and offer unflattering perspectives on their employer’s products.

Reuters reported exclusively in December that Google introduced a new “sensitive topics” review last year to ensure that papers on topics such as the oil industry and content recommendation systems would not get the company into legal or regulatory trouble. Mitchell publicly expressed concern that the policy could lead to censorship.

Google reiterated to researchers in a memo and meeting on Friday that it was working to improve pre-publication review of papers.

It also announced new policies on Friday to handle sensitive departures and evaluate executives based on team diversity and inclusion.

The post Google fires second AI ethics leader appeared first on Artificial Intelligence.

O&G giant Total and Google Cloud are launching a new artificial intelligence (AI) tool to help accelerate the deployment of residential solar panels.

Together they have developed Solar Mapper, a tool that can provide accurate and rapid estimates for the solar potential of homes using a brand-new AI algorithm.

The pair have claimed it can provide better results than similar tools due to the quality of the data extracted from satellite images, the sharpness of the estimation of the solar potential, the relevance of the technology to be installed and the global geographical cover of the tool.

Solar Mapper is set to be rolled out through Europe initially, before going global. It can provide more than 90% geographical cover of France – Total’s home market – encouraging more people to assess the solar potential of their homes.

“Solar Mapper will enable Total to faster deploy solar panels on the houses’ roofs, in order to provide its customers with more affordable and more accessible solar energy,” said Marie-Noëlle Séméria, Total’s chief technology officer.

“By combining Total’s expertise in solar energy with Google Cloud’s expertise in artificial intelligence and databases, we were able to develop an attractive and innovative offer together in just 6 months.”

The tool will initially just be for residential buildings, but Total has said it is aiming to develop a version for industrial and commercial buildings in the future.

Solar Mapper is a further move by the French O&G company to establish itself within the renewable energy sector, having announced its net zero emission by 2050 ambition in May.

This is not the first tool launched by Google to evaluate the solar potential of properties, having brought its Sunroof platform to British households together with E.On in 2018.  

The post Total and Google to launch AI tool Solar Mapper in Europe appeared first on Artificial Intelligence.

You may not realize this, but you interact with AI technology on a consistent, if not daily basis. And if you do recognize it, chances are good that you take it for granted. Whether it’s a Spotify playlist, an Alexa reply, or one of the myriad cool things Google Assistant does, it’s powered by AI and cloud technology.

More and more, companies are turning to cloud technology for AI applications, and that means the demand for developers and architects is steadily rising.

The Google Cloud Platform, one of the largest in the space, is a suite of computing services and tools that power Google’s Search, YouTube, and much more. According to Glassdoor, a GCP Cloud Architect can pull in a starting salary of $120,000- $160,000. Ready for a piece of that?

Google Cloud computing isn’t exactly something you master overnight. Hell, it could take you weeks just to form a basic understanding of it. It takes time to learn topics like deploying and implementing cloud solutions, software-defined networking, or virtual private clouds.

Fortunately, you can kick-start your education with some online training. Take the Google Cloud Certifications Practice Tests + Courses Bundle, for instance. This comprehensive online training features 43 hours of lectures and other tools to help prepare you for a career in the emerging field.

Sign up, and you’ll get lifetime access to the training so feel free to really dig in and learn things. Or, if you’re like many of us, drop in and out and spend the rest of the pandemic period fine-tuning yourself.

Considering how incredibly valuable the information in this 7-course bundle is, $29.99 is a small price to pay. It’s worth more than $630 if you were to purchase yourself, but we’d never let you pay that much.

Save even more!

In addition to the savings above, when you buy through AndroidGuys Deals, for every $25 spent, you get $1 credit added to your account. What’s more, should you refer the deal via social media or an email that results in a purchase, you’ll earn $10 credit in your account.

The post Unlock a new career in Google Cloud with this mastery bundle appeared first on Artificial Intelligence.

Microsoft CEO Satya Nadella announced the preview of Azure Orbital at Microsoft Ignite 2020 in New Orleans. According to Microsoft, Orbital is ‘Ground Station as a Service (GSaaS)’, which is aimed at helping its customers to communicate with, and analyse data from, their satellites or spacecrafts on a subscription basis.

The Redmond headquartered company, however, has competition in the skies. Almost five months earlier, International Business Machines Corp. (IBM) had announced a beta of its Cloud Satellite service. But it is Amazon Web Services Inc. (AWS), the cloud computing arm of Amazon.com, which has a head start in space.

Around two years ago, it launched the AWS Ground Station to allow its customers to control their satellite communications, process data, and scale operations without having to build or manage their own ground station infrastructure. On 30 June, AWS said it was establishing a new space unit called the Aerospace and Satellite Solutions.

These are but a few cases in point to demonstrate that leading cloud computing service providers have begun flexing their muscles in space too. But why is there a sudden race to outer space?

According to the International Telecommunication Union (ITU), non-geostationary satellite orbits (NGSOs) such as medium earth orbits (MEO) and low earth orbits (LEO) are being increasingly used worldwide. NGSOs, unlike fixed or geostationary satellite orbits, move across the sky during their orbit around the earth. With space launches becoming more affordable and accessible, a slew of private companies are starting to rely on this new array of satellites.

They are used for applications like weather forecasting, surface imaging, communications, and video broadcasts. However, the data from these satellites need to be processed and analysed in data centres on the ground, which explains the term ‘ground stations’.

While the cost of the satellite itself is falling, building and running ground stations can cost up to $1 million or more, according to a recent blog post by Jeff Barr, chief evangelist for AWS. Complex data processing also requires a lot of computing power, and the huge data storage requirements only add to the cost.

Leading cloud computing service providers are now starting to offer satellite operators the option to use these ground stations on a ‘pay-per-use’ or subscription basis, thus, helping the latter save on capital expenditure costs by employing an operating expenditure model.

These ground stations, thus, can help satellite operators download high-resolution imagery faster, more regularly, and analyse the data with artificial intelligence (AI) tools—all of which results in faster and enhanced monitoring of changing climate patterns, forests and agriculture, among other things.

While Microsoft and IBM are testing their services, AWS Ground Station already has customers such as NASA’s Jet Propulsion Laboratory and satellite operators Iridium Communications and Spire Global. It also has private sector customers such as Lockheed Martin, Maxar Technologies and Capella Space.

Lucrative market

The worldwide cloud infrastructure services market continued to surge in the April-June quarter of this calendar year to touch $34.6 billion, according to research firm Canalys. The growth was attributed to the consumption of cloud-based services for online collaboration and remote working tools, e-commerce, remote learning, and content streaming which hit new records during the lockdown.

During this period, AWS was the leading cloud service provider, accounting for 31% share of the total spend. Microsoft Azure came second, followed by Google Cloud and Alibaba Cloud.

The revenue of the cloud unit of Amazon totalled $10.81 billion in the April-June quarter of this calendar year, accounting for 12% of its parent’s revenue.

Microsoft, on the other hand, said its “commercial cloud surpassed $50 billion in annual revenue for the first time” for the quarter ended June 30 (which is also its financial year ending). But it does not spell out what this ‘commercial cloud’ consists of.

Nevertheless, the space forays will only add to the revenue of all these companies.

Battle lines in India

Space deals will add spice in India too. India’s cloud computing market was estimated at $2.5 billion in 2018, dominated by infrastructure as a service (IaaS) and software as a service (SaaS), according to industry body Nasscom. It is forecast to touch over $7 Billion in 2022.

AWS, Microsoft and Google are leaders on the local turf too. Last August, for instance, Microsoft signed a deal with Reliance Jio Infocomm Limited (Jio)—a subsidiary of Mukesh Ambani-owned Reliance Industries Ltd (RIL). The agreement included deploying the Microsoft Azure cloud platform in Jio’s data centers in locations across India.

This January, Google Cloud signed a deal with Bharti Airtel to cater to small and medium enterprises (SMEs) in India. However, Google said this July that it was pumping in $4.5 billion into Airtel’s rival Jio Platforms in exchange for a 7.7% stake. Not surprisingly, a month later, Bharti Airtel announced a multi-year agreement with AWS to deliver cloud solutions to big companies and SMEs in India.

According to Alok Shende, Managing Director of Ascentius Insights, the “fusion of cloud computing with networking, linked by a satellite, is expected to shave off milliseconds in transferring data from source to destination. This is the holy grail in many applications, more specifically in finance and in mission-critical applications. There are many India-centric applications (like defence and in the stock markets) where this could play a powerful role.”

He believes that “for Microsoft, particularly, this move opens a new avenue to entrench itself in the enterprise market where it has traditionally been a strong player on the application side but has lost the leadership position in terms of market share for cloud.”

Jayanth Kolla, founder and partner of Convergence Catalyst points out that India has always been a strong player in the space sector with the Indian Space Research Organization (Isro) developing and launching satellites at a fraction of global costs. He believes that the Indian government’s decision to open up India’s space sector to private players is an encouraging sign.

“It has already resulted in Indian space tech startups such as Pixxel, Bellatrix Aerospace, Vesta Space and Agnikul raising over $20 million funding from venture capitalists (VCs) in the last six months. TV media, agriculture, telemedicine and logistics are a few sectors that can benefit from strong satellite communication and space technology development. The ground station services launch by Microsoft and AWS will only expedite this ecosystem development significantly in India,” says Kolla.

Sanchit Vir Gogia, chief analyst and founder of Greyhound Research, concurs that the timing of this space move is right since many organizations are now beginning to try new use-cases by tapping into geospatial data (data related to a specific location on earth) that is omnipresent, given the proliferation of devices and edge computing devices.

“This space is increasingly getting busy with the likes of AWS and IBM investing money and resources to cater to this opportunity,” notes Gogia. He cautions, however: “We believe the trick in making such an offering successful is to ensure that it is cheap to start with, since most of these projects are nothing more than trials and, hence, have an extremely high failure rate.”

The distributed cloud

Space is just an additional frontier for the leading cloud services providers. It all began when companies, which traditionally used servers for their computing needs, realised that they could lower costs by accessing IT resources over the internet, and paying only for the services they needed, reducing capex—a trend we now know as cloud computing.

Many companies today use private clouds (on-premise), public clouds (on a network, typically the internet) and hybrid clouds (combining public and private). User companies, though, became wise and began adopting a ‘multi-cloud’ vendor approach to avoid being locked in by any single technology or cloud vendor.

With billions of devices getting connected to each other as part of the Internet of Things (IoT) trend, computing is now also getting done at the so-called “edge”, which simply means near the source of the data.

General Electric Co. (GE), for instance, believes cloud computing is best suited to situations that demand actions such as significant computing power, management of huge data volumes from across plants, asset health monitoring and machine learning. Edge computing, on the other hand, makes sense in places like mines or offshore oil platforms that have bandwidth constraints, which make it impractical or very expensive to transmit data from machines to the cloud.

During his speech at the Ignite event, for instance, Nadella pointed out that Microsoft was “extending Azure from under the sea to outer space”. He was referring to Project Natick that aims to serve customers in areas near large bodies of water. Natick uses AI to monitor signs of failure in its servers and other equipment.

Going forward, Microsoft says it will explore powering a Natick data center by “a co-located ocean-based green power system, such as offshore wind or tide, with no grid connection”.

Similarly, other than deploying internet balloons in space to provide broadband services, Google also provides services to companies like Planet Labs Inc. The US-based aerospace and data analytics company uses Google Cloud platform to process all of its satellite images and Google Cloud storage to host its image archive.

These moves have given rise to a trend called ‘Distributed Cloud’, which research firm Gartner describes as “distribution of public cloud services to different physical locations”.

By 2023, posits a 22 January note by Gartner, “the leading cloud service providers will have a distributed ATM-like presence to serve a subset of their services for low-latency application requirements… ‘Micro data centers’ will be located in areas where a high population of users congregates, while pop-up cloud service points will support temporary requirements like sporting events and concerts.”

Greyhound Research believes offerings such as ground stations will be highly valuable in the next wave of investments in more distributed computing environments. “More than 7 in 10 of our end-user inquiries with global majors have confirmed that organizations, in the next 3-5 years, will use a large variety of computing environments and make them more contextual to the use-case,” says Gogia. “This change is likely to be paced multiple times, given the investments in edge networks and 5G that allow remote sites in utilities, oil and gas, manufacturing, and many other scenarios,” he adds.

The distributed cloud market is forecast to reach $3.9 billion by 2025, growing at a CAGR of 24.1% during the forecast period from 2020-2025, according to market research firm, IndustryARC. Security, though, remains a concern if proper protocols and policies are not adhered to in a distributed cloud.

For now, though, ground stations that cater to satellite companies will remain one big component of the distribution cloud. A race is clearly on and all the main players are looking up at the sky.

The post Cloud computing is betting on outer space appeared first on Artificial Intelligence.

Google Cloud and Anaplan, Inc. announced a strategic partnership to offer Anaplan’s platform for enterprise planning and business performance on Google Cloud. As Anaplan’s first public cloud offering, the partnership brings customers added intelligence, choice, and scalability to solve complex business problems and link business strategy to operational drivers and financial outcomes. An Anaplan customer since 2016, Google has deployed the Anaplan platform on Google Cloud with multiple use cases across sales, supply chain and finance.

Transforming enterprise planning in the cloud

With Anaplan on Google Cloud, global enterprises can leverage the leading performance of Anaplan’s in-memory, Hyperblock® calculation engine running on Google Cloud’s global, high-performance, scalable and secure infrastructure. In addition, the partnership will enable Anaplan to expand its global reach so customers can benefit from using Anaplan at closer proximity to their operations with greater choice and control to comply with local and industry-specific data residency and compliance requirements.

To help businesses improve decision-making and planning, Google Cloud and Anaplan, together with services partners, will offer an extended platform for data storage and analytics, leveraging BigQuery and Google Cloud’s AI/ML capabilities. These integrations will enable organizations to blend first-party Anaplan data with third-party data sources for dynamic, real-time modeling of large amounts of critical data to deliver new business insights.

“We are committed to helping businesses modernize and accelerate their most critical operations with Google Cloud,” said Thomas Kurian, CEO at Google Cloud. “Through our partnership with Anaplan, we are bringing highly performant infrastructure and intelligent innovation to enterprise planning, ultimately helping organizations make better business decisions.”

“The way our customers fundamentally execute business has changed,” said Frank Calderoni, Chairman and CEO, Anaplan. “Anaplan is committed to helping business leaders take the right actions right now, while systematically building for future readiness. That’s why we chose to extend Anaplan to run on Google Cloud. We are proud to have Google as a long-time customer and now Google Cloud as a partner, and we are excited to go to market together to accelerate our customers’ digital transformation journey to the cloud and deliver innovative solutions that turn change into advantage.”

Innovating with consulting and systems integrator partners

Global consulting systems integrator partners, including Deloitte, Slalom, Wipro, and others, manage many of the world’s largest, enterprise-scale deployments of Anaplan. This consortium of partners will support customers on their migrations to Anaplan running on Google Cloud’s and will develop robust solutions leveraging Anaplan, BigQuery, and Google Cloud’s AI and ML capabilities.

“We have seen first-hand the tangible client value that Anaplan and Google Cloud delivers by marrying real-time analytics to enterprise planning and operations execution,” said Tom Galizia, Principal, Deloitte Consulting LLP, and Deloitte Chief Commercial Officer for Alphabet. “With Anaplan on Google Cloud, we see tremendous opportunity to bring to market customized industry and functional solutions that deliver the intelligence and agility customers need for nimbler decision making.”

Google deploys Anaplan for end-to-end Connected Planning at enterprise scale

Google uses the Anaplan platform to orchestrate business performance at scale across multiple business units, line of business teams and geographies. A customer since 2016, Google has deployed Anaplan in multiple complex business scenarios, with use cases across financial planning and analysis, supply planning and sales and operations planning, call center forecasting, and sales finance and operations planning.

Under this strategic partnership, Anaplan has migrated the entirety of Google’s Anaplan use cases to run on Google Cloud. Anaplan’s work with Google has also demonstrated many of the benefits that enterprises can expect from Anaplan on Google Cloud’s, including expanded global access, faster time to value, and additional real-time insights into data from Anaplan and third-party sources.

The post Google Cloud And Anaplan Innovate To Transform Enterprise Planning appeared first on Artificial Intelligence.

DeepMind is one of the companies that are leading the AI charge and coming up with innovative uses of AI. This London-based AI lab has been under the umbrella of Alphabet since the latter acquired it in January 2014. While Google’s AI ventures have been keeping it running, DeepMind is most helpful when it comes to Google Maps. For years, it has been a challenge to design a machine-learning algorithm to train AI models and softwares to help in navigation, especially in unstructured surroundings. Therefore understanding how AI can learn about cruising through an environment and guide us in the future is always an area of interest for researchers.

The reason why it is an arduous task is primarily that long-range navigation is a complex cognitive task that relies on developing an internal representation of space, grounded by familiar landmarks and robust visual processing, that can simultaneously support continuous self-localization (“I am here”) and a representation of the goal (“I am going there”). This is where DeepMind’s deep reinforcement learning helps to solve the hitch. Besides, it is essential to address this as people rely on the accuracy of Google Maps to assist them. Every day, this app provides useful directions, real-time traffic information, and information on businesses to millions of people, along with accurate traffic predictions and estimated times of arrival (ETAs). As a result, it is crucial to mirror the ever-changing landscape of urban lands.

Recently, researchers at DeepMind teamed up with Google Maps to improve the accuracy of real-time ETAs by up to 50% in places like Berlin, Jakarta, São Paulo, Sydney, Tokyo, and Washington D.C. by using advanced machine learning techniques. At present, the Google Maps traffic prediction system consists of a route analyzer for processing traffic information to construct Supersegments (multiple adjacent segments of road that share significant traffic volume). It also has a Graph Neural Network model, which is optimized with various objectives and predicts the travel time for each Supersegment.

The data collected to train the machine learning model of DeepMind was extracted from authoritative data input from local governments and real-time feedback from users. The authoritative data lets Google Maps learn about speed limits, tolls, or road restrictions due to things like construction, excavation works, or COVID-19 shutdown. Meanwhile, feedback from users lets Google know that paved roads are better for driving than unpaved ones. It also helps Google to make a neural network model opt a long stretch of highway as efficient routes than a smaller shortcut road with multiple stops.

After collecting the data, in the Graph Neural Network, the model considers the local road network as a graph, with each route segment resembling as a node and edges that exist between segments that are consecutive on the same road or connected through an intersection.  When a message-passing algorithm gets executed, neural networks learned those messages and studied their effect on node states and edge. Now, in the real world, these Supersegments are road subgraphs, which were sampled at random in proportion to traffic density. When a single model was successfully trained via these subgraphs, the algorithm was then deployed at scale.

Through Graph Neural Network, researchers were able to carry spatiotemporal reasoning by incorporating relational learning biases to model the connectivity structure of real-world road networks. Google Maps product manager Johann Lau says, “We saw up to a 50 percent decrease in worldwide traffic when lockdowns started in early 2020. To account for this sudden change, we’ve recently updated our models to become more agile — automatically prioritizing historical traffic patterns from the last two to four weeks, and deprioritizing patterns from any time before that.

The post HOW DEEPMIND ALGORITHMS HELPED IMPROVE THE ACCURACY OF GOOGLE MAPS? appeared first on Artificial Intelligence.

The awardees are working to advance foundational and applied research to advance the state-of-the-art in artificial intelligence across a wide range of research areas including Algorithms and Theory, Computer Vision, Natural Language Understanding and Privacy & Security.

Google on Monday announced support for the five faculty members who are the recipient of this year’s Google India AI/ML Research Awards, a program focused on supporting exceptional AI research in India.

The awardees are working to advance foundational and applied research to advance the state-of-the-art in artificial intelligence across a wide range of research areas including Algorithms and Theory, Computer Vision, Natural Language Understanding and Privacy & Security.

The search giant said it received over a hundred proposals across various fields of AI. All of them went through an extensive review process involving global expert reviewers who assessed them on merit, innovation, connection to Google’s research efforts and alignment with the company’s overall research philosophy and AI Principles.

“We remain committed to investing in the development of the research ecosystem in India through various research grant-based and education programs, and continue to pursue cutting-edge research at Google Research India: an AI lab in Bangalore,” Google wrote in a blog post.

Here’s the list of Google India AI/ML Research Awardees 2020:

1. Arpita Patra: She is an Associate Professor at Dept. of Computer Science & Automation (CSA), EECS Division, Indian Institute of Science, Bangalore and is working on molding the use of Secure Multiparty Computation (MPC) techniques to ML that preserves the privacy of the user.
2. Anirban Dasgupta: He is an Associate Professor at the Indian Institute of Technology, Gandhinagar and is working on developing randomized algorithms for numerical tensor algebra.
3. Pawan Goyal: An Associate Professor at the Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur, Goyal is developing ways to build a conceptual understanding of natural language in AI Dialogue Systems.
4. Soma Biswas: She is an Associate Professor at the Indian Institute of Science, Bangalore and is working on making AI systems more robust. This work has widespread applications in image classification, detection, segmentation, etc.
5. Vasudev Verma: He is a Professor at the International Institute of Information Technology (IIIT), Hyderabad and is advancing his work on ‘Project ANGEL’ that seeks to utilize machine learning techniques for enhancing the well-being of teenagers, especially girls.

The post Google India AI/ML Research Awardees 2020 announced appeared first on Artificial Intelligence.

Google could soon offer an ethics consultation service to companies building Artificial Technology solutions. According to a report in Wired, the search giant may soon offer the service to companies developing AI projects. The report suggests that Google might launch the service before the end of 2020 and initially, it would offer only advice on tasks such as spotting racial bias or creating guidelines to govern the AI projects. The American multinational may in the future also offer other services such as audits of AI systems, etc. 

Google’s own ethics-based controversies

Google’s plan to offer ethics advice to companies building AI solutions is being mocked even before it could be officially launched and there is a reason for that. Google doesn’t have a good track record when it comes to the company’s own ethics policy as it has been mired in several controversies in the past few years.

In 2015, the company had to apologise for a technical glitch in its Photos app that identified Black people as gorillas. In 2018, the company face backlash from its own employees after agreeing to develop surveillance software for the US defence department. 

The California-based company in 2018 reportedly tested a secret search engine it had developed for China, an authoritarian country where content is censored and monitored by the State. Company’s CEO Sundar Pichai had even testified for the same in the US Congress, where he admitted that Google was working on a search engine for China.

Following these controversies, the company issued a set of ethical principal for the use of its AI. Big companies pay Google for cloud computing services and as per reports some of them are already taking ethical help for the firm, which may have possibly prompted the Alphabet Inc-owned to dive into it. 

The post Google Could Soon Provide Ethics Service To Companies Building AI Solutions appeared first on Artificial Intelligence.

A new Research Report published by GMA under the title Global Cognitive/Artificial Intelligence Systems Market (COVID 19 Version) can grow into the world’s most important market which has played an important role in making progressive impacts on the global economy. The Global Cognitive/Artificial Intelligence Systems Market Report presents a dynamic vision for concluding and researching market size, market hope and competitive environment. The study is derived from primary and secondary Research and consists of qualitative & Quality analysis. The main company in this Research is IBM, Microsoft, Google

Primary Research:

We interviewed various key sources of supply and demand in the course of the Primary Research to obtain qualitative and quantitative information related to this report. Main sources of supply include key industry members, subject matter experts from key companies, and consultants from many major firms and organizations working on the Global Cognitive/Artificial Intelligence Systems Market .

Secondary Research:

Secondary Research was performed to obtain crucial information about the business supply chain, the company currency system, global corporate pools, and sector segmentation, with the lowest point, regional area, and technology-oriented perspectives. Secondary data were collected and analyzed to reach the total size of the market which the first survey confirmed.

Some Key Research Questions & answers:

What Is impact of COVID 19 on Global Cognitive/Artificial Intelligence Systems Market ?
Before COVID 19 Global Cognitive/Artificial Intelligence Systems Market Size Was XXX Million $ & After COVID 19 Excepted to Grow at a X% & XXX Million $.
Who are the Top Key Players in the Global Cognitive/Artificial Intelligence Systems Market and what are their priorities, strategies & developments?
Lists of Competitors in Research is: IBM, Microsoft, Google
What are the Types & Applications of the Global Cognitive/Artificial Intelligence Systems Market ?
Application’s cover in these Reports Is: Manufacturing, Healthcare, Consumer And Retail, Automotive, BFSI, Aerospace And Defence and Others, ,
Types Cover in this Research: Robotics, Consumer Electronics, Drones, Autonomous Cars and Others, ,

All percent shares, breaks, and classifications were determined using the secondary sources and confirmed through the primary sources. All parameters that may affect the market covered in this study have been extensively reviewed, researched through basic investigations, and analyzed to obtain final quantitative and qualitative data. This has been the study of key quantitative and qualitative insights through interviews with industry experts, including CEOs, vice presidents, directors and marketing executives, as well as annual and financial reports from top market participants

Table of Content:

1 Report Summary

1.1 Research Scope
1.2 Key Market Segments
1.3 Target Player
1.4 Market Analysis by Type Robotics, Consumer Electronics, Drones, Autonomous Cars and Others, ,
1.5 Market by Application Manufacturing, Healthcare, Consumer And Retail, Automotive, BFSI, Aerospace And Defence and Others, ,
1.6 Learning Objectives
1.7 years considered

2 Global Growth Trends

2.1 Global Global Cognitive/Artificial Intelligence Systems Market Size
2.2 Trends of Global Cognitive/Artificial Intelligence Systems Market Growth by Region
2.3 Corporate trende
3 Global Cognitive/Artificial Intelligence Systems Market shares by key players
3.1 Global Cognitive/Artificial Intelligence Systems Market Size by Manufacturer
3.2 Global Cognitive/Artificial Intelligence Systems Market Key players Provide headquarters and local
3.3 Major Players Products / Solutions / Services
3.4 Enter the Barriers in the Global Cognitive/Artificial Intelligence Systems Market
3.5 Mergers, acquisitions and expansion plans

Continue……………………………………..

GMA global research and market intelligence consulting organization is uniquely positioned to not only identify growth opportunities but to also empower and inspire you to create visionary growth strategies for futures, enabled by our extraordinary depth and breadth of thought leadership, research, tools, events and experience that assist you for making goals into a reality. Our understanding of the interplay between industry convergence, Mega Trends, technologies and market trends provides our clients with new business models and expansion opportunities. We are focused on identifying the “Accurate Forecast” in every industry we cover so our clients can reap the benefits of being early market entrants and can accomplish their “Goals & Objectives”.

The post Cognitive/Artificial Intelligence Systems Market Is Booming Worldwide | IBM, Microsoft, Google appeared first on Artificial Intelligence.

The Machine Learning Operationalization Software market growth prospects have been showing great promise all over the world with immense growth potential in terms of revenue generation and this growth of the Machine Learning Operationalization Software market is expected to be huge by 2026.The growth of the market is driven by key factors such as manufacturing activity in accordance with the current market situation and demand that seems to be seeing a major upward trend in some time, risks of the market, acquisitions, new trends, assessment of the new technologies and their implementation.

Top Companies covering This Report :- SAS, Microsoft, ParallelM, Algorithmia, H20.ai, TIBCO Software, SAP, IBM, Domino, Seldon, Datmo, Actico, RapidMiner .

This report has been detailed and is structured in a manner that covers all of the aspects required to gain a complete understanding of the pre-market conditions, current conditions as well as a well-measured forecast in order for the client to establish a strong position in the Machine Learning Operationalization Software market.

The report has been segmented and discussed in detail with the consideration of the COVID-19 situation, and is as per the examined essential aspects such as sales, revenue, market size, and other aspects that are crucial to post good growth numbers in the market and emerge as a leader in the market.

Description:

In this report, we are providing our readers with the most updated data on the Machine Learning Operationalization Software market and as the international markets have been changing very rapidly over the past few years the markets have gotten tougher to get a grasp of and hence our analysts have prepared a detailed report while taking in consideration the history of the market and a very detailed forecast along with the market issues and their solution.

The given report has focused on the key aspects of the markets to ensure maximum benefit and growth potential for our readers and our extensive analysis of the market will help them achieve this much more efficiently. The report has been prepared by using primary as well as secondary analysis in accordance with porter’s five force analysis which has been a game-changer for many in the Machine Learning Operationalization Software market. The research sources and tools that we use are highly reliable and trustworthy. The report offers effective guidelines and recommendations for players to secure a position of strength in the Machine Learning Operationalization Software market. The newly arrived players in the market can up their growth potential by a great amount and also the current dominators of the market can keep up their dominance for a longer time by the use of our report.

Machine Learning Operationalization Software Market Type Coverage: –

Cloud Based
On Premises

Machine Learning Operationalization Software Market Application Coverage: –

BFSI
Energy and Natural Resources
Consumer Industries
Mechanical Industries
Service Industries
Publice Sectors
Other

Market Segment by Regions, regional analysis covers

North America (United States, Canada, Mexico)

Asia-Pacific (China, Japan, Korea, India, Southeast Asia)

South America (Brazil, Argentina, Colombia, etc.)

Europe, Middle East and Africa (Germany, France, UK, Russia and Italy, Saudi Arabia, UAE, Egypt, Nigeria, South Africa)

Competition analysis

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