Hi dear, welcome to the fascinating way of Jira’s concept of learning. Today I will tell you about the creation of projects in Jira. This is the most common and important feature because if you are using Jira then it is very important for you to know how to create the project in Jira so that you can start your journey with Jira. You will see what is the agenda in this article? So, in this article, we will learn how to create a project, what the classic project is and how to work with that, and how to create boards so, we will start from the first one. You will see first how to create projects so, let’s go to the cloud instance. There will be the cloud instance that you have created. In this section, you will see there is the default page of a Jira. If you want to change your default page in Jira then you can do this I will explain the setting later in this course in the article

How to create a project?

We will, we are going to create the projects so, you will go into the project section and you will see, you will haven’t create any project yet so, it will show you currently have no projects so for creating the project you will click on the create project button and you will see, it gives you options to enter your project name and to choose the template right now it’s showing the scrum template but if you want to change your project template then you can simply click on the change template and you will get a load of options available. There, you can choose according to your requirements the Kanban, scrum, task tracking, and bug tracking templates and many more are available there but you will use the scrum template so. You will go with that scrum one and select that scrum one. You will mention the project name and project name is project a. Once you will click on the advanced option you will see the key of a project. The key of a project should be very descriptive and easy to use. What is the logic behind the project key? It takes the first letter of our project name and if you want to name and if you want to change the project key then you would recommend changing it at the time of the creation of the projects because it is very difficult to change the project T later once you will create the project and adding issues or your requirements in the project so I would recommend you to change your project T then please change at the time of the creations so you will go and click on create button it will land you in that page where you will see the backlog options in the backlog you can create your requirements in the form of a story in the form of the task and you can create the epics.

I will explain to you what are the effects stories and tasks you can see the other issues types are available story tasks bugs and there are the options of creating the epic so, what are these issues types? So, you will see, what are options are available in that project you will see when you will create the project in Jira then it creates automatically a bowl. You can say this is a PA board if you want to create your own board then you will click on the plus icon and you can create the board it could be a scrum or it could be a Kanban board it’s up to you but for now, you will cancel it and the second option is a backlog as I told you, you can collect your requirements in the backlog and the third one is the active sprint then you don’t have any experience available there.      

What is a classic project?

So, it will look like as there are the three columns are available to do in progress and then I will explain to you how you can change these columns and how can you add more columns in this active sprint board and there are report options are available. As you will see, there are a lot of reports are available burndown charts, burnup charts sprint reports velocity, and the reports which are related to the issue analysis forecast and management and the others. You will see, there are no available sprints for this board that’s why this is the reason it will not show any report there so once you will start your sprint then how can you start your sprint? You will be able to see the report there so, you will back to the other one. You will see relays this is the best option. With the help of these release options you can create the versions and how you will create diversion you can manage your releases there next option is issues and filters all issues that you will create in that project will be listed down there so, you can choose all issues and it will show you all the issues in a list. I will show you how but this time you don’t have any issues so, you cannot see it and you will see there the other options are available. You will see open issues your reported issues recently viewed issues or recently created resort or updated issues as well so, I shall show you once you will create issues in the project and the next project is pages. You can show any pages which are related to your project there and the next one is the components. You can create the components and you can categorize your issues as for the components that are the best feature in the Jira and you can add the other items may be.

If you will click on that then you can add the repose repository link pages Ning or any shortcut which are frequently used so you will go into the last one and which is a project setting in that option that contains a lot of features. So first, we will start from the details you will see that you can change the project name and the project key there but you will be able to see that option because you will be admin and have access to change that project key but you will haven’t admin access then you can’t change it you can mention any URL but you can’t change the project type. If you want to change the project type then you cannot do it there for this you will have to create a new project and move your all the issues on that particular project type and there you can select an image and you may be put your project low logo and any other related images. You can write their description and mention the project lead there and you can set the default assignee as well the next option is people. In this option, you can add the people which are going to work into your project. So simply, you can add the people by clicking that arrow options you can choose the role and type the name then it will show automatically has done. I will explain to you later how you will create the roles and how can you add more team members? So that you can add the people to your project. The third one is a summary where you will be able to see the complete summary of your project. 

Related videos:

The post How to create a new project in Jira? appeared first on Artificial Intelligence.

Fundstrat Digital Asset Consulting has produced another report updating its clients on BSV ecosystem progress. The report, released exclusively to investors last week, refers to BSV as “a Web 3.0 platform,” describes the concept of “Blockchain as a Service (BaaS)”, big data application potential, and gives some examples of the companies building on the network in different ways.

Similar to previous reports, Fundstrat (headed by well-known Wall Street strategist Thomas Lee) describes BSV’s “unique technological approach” as its most compelling aspect. Its large-scale data processing capabilities, combined with the security and verifiability of blockchain records, mean that developers are building real applications with real use cases. Industries seriously looking at the BSV network include supply chains, healthcare, payments, real estate, e-government services and more.

The emergence of BaaS infrastructure solutions will make enterprise adoption easier, the report says. Some of the projects it profiles are aimed at building solutions for others to come onboard without first spending years studying Bitcoin and blockchain history and the technical challenges of integrating their systems.

Fundstrat has published previous reports on BSV to its list of investor clients, in November 2019 and February and July 2020. Those reports also focused on BSV’s big data focus and “unique vision” compared to other blockchain networks, and highlighted the growing number of businesses building its infrastructure.

It says:

“The BSV and BTC networks are going after very different use cases. BSV is positioning itself as a Web 3.0 platform upon which Dapps and enterprise data applications are built that can store data directly on the main block chain layer (efficiently, for low fees) due to its unbounded block size cap. This approach has tradeoffs but is unique relative to other blockchains as it allows for certain types of big data applications that may not be well suited for other networks.”

The report includes a PowerPoint presentation that gives a general overview of the BSV network and its architecture. It details the functions of services like mAPI and MinerID for obtaining transaction fee information, and the relationships between miners (transaction processors), pools and ordinary users. It describes the structure of the BSV Metanet and provides statistics on BSV unit price, volumes, transaction counts, exchanges and average per-transaction fees (notably, these are US$0.0013 on BSV, $8.22 on Ethereum and $10.16 on BTC).

In targeting the “big data” enterprise market, BSV should serve the “Three Vs of Big Data” which are volume, velocity and variety. The report lists the benefits of users’ ability to own their own data and keep it both private and secure, noting the massive costs to organizations in the event of a data breach.

Companies named in the report include UNISOT, Veridat, nChain, TAAL, Fabriik, Planaria, MetaStreme, Mattercloud, FYX, BitBoss, Haste, Transmira, Twetch and Relica, EHR Data, and wallets HandCash, RelayX, DotWallet and Money Button. These companies cover a wide variety of big data use scenarios, showing BSV’s versatility and potential business models.

Fundstrat does warn that becoming involved with BSV carries risk due to the criticism it often receives from other sectors of the blockchain industry, saying “We recognize that there is a subset of the crypto community that does not have a favorable view of certain BSV supporters and the BSV coin or anyone who associates with it as an extension.”

We should note that much of that criticism comes from coordinated attacks on BSV from those who stand to lose the most commercially should BSV’s superior technology continue to prove itself over existing platforms. ETH routinely fails to scale to perform contract functions at both technical and economic levels, while BTC’s deliberately crippled capacity make its transaction fees prohibitively high to use as a payments network.

However, the report does mention that the blockchain industry is maturing and “maximalism fading.” While BSV would need to prove it can overcome this polarization and move past its “early reputational criticisms” in order to succeed, BSV’s focus on enterprise tier data applications and the BaaS infrastructure could convince more outsiders to take the plunge. Building a healthy user community was critical to achieving success, it concluded.

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Concerning staff activity, CPSC recently hired a Chief Technologist with a background in AI and ML to address the use of AI in consumer products. The CPSC also recently established an “AI/ML Working Group” and held a virtual forum on AI and ML in March 2021.

Informed by the discussions held with various stakeholders at this forum, the CPSC staff has proposed a framework in the report for evaluating the potential safety impact of AI and ML in consumer products. The framework’s first step involves screening products for AI and ML “components.” The CPSC and stakeholders have identified the following components to be essential to producing an AI capability: data sources, algorithms, computations, and connections. Likewise, the CPSC and stakeholders have found the following components to define ML capabilities: assessing and monitoring outputs, analyzing and modeling changes, and adjusting and adapting behavior over time. The framework’s second step involves assessing the functions and features of consumer products’ AI and ML capabilities. The third step involves understanding how products’ AI and ML capabilities may impact consumers, which can be accomplished by studying the nature of the technology, how it is implemented in the product, and how the consumer might use the product. The final step involves ascertaining if, and to what extent, AI and ML capabilities may transform the product and/or its use over time.

The framework demonstrates the CPSC’s focus on the impact AI may have on the long-term safety of products that may operate differently as they evolve over time.  Identifying a defect that could present a safety risk can be challenging in an AI product because the product in the hands of consumers is not the same product that came off the manufacturing line.  If those changes over time could result in emerging safety issues, those risks would need to be reported to the CPSC.  The timing of those reports could be tricky if a defect emerges due to product transformations in only certain applications or use cases, but may not emerge in some or all products, or in all products at the same time given different usage patterns.  The framework appears to be designed to explore these issues.  It is also broad enough to allow the CPSC to do more than spot emerging defects and hazards.  It should also allow them to identify safety advantages in AI and ML technologies that may enable products to identify and mitigate potential hazards before they manifest thereby eliminating risks and increasing the overall safety of products.

The report also outlines a set of recommendations for future steps the CPSC can take to address AI and ML-related product safety concerns. First, the CPSC should continue building upon the discussions it held with stakeholders at the March 2021 forum. Second, and arguably most significantly, the CPSC should focus on voluntary standards development with respect to AI and ML, as it has done with other technologies. And it should leverage private-public partnerships to do so.

The CPSC also plans to address product testing. Product testing is important because, in the past, the CPSC has faced the challenge of replicating product safety incidents due to the constant changes in AI and ML technologies. The CPSC is also exploring different collaborative efforts with various stakeholders, including interagency agreements. Additionally, the CPSC has expressed a desire to work with investigators, data scientists, and AI and ML subject matter experts, to develop tools for them so that they too can work with the CPSC’s partner stakeholders, and to hire new staff with AI, ML, or related technical backgrounds. Given these plans and efforts, the CPSC is likely to prioritize AI and ML in its fiscal year 2022 Operating Plan.

The post CPSC Publishes Report on Artificial Intelligence and Machine Learning appeared first on Artificial Intelligence.

The global Storage in Big Data Market was accounted for US$ 17,391.4 Mn in terms of value in 2019 and is expected to grow at CAGR of 20.4% for the period 2020-2027.

Big data storage refers to a compute and storage architecture that gathers and operates vast data sets and allows real-time data analytics. Many companies employ big data analytics to collect greater intelligence from metadata. Big data storage allows the storage and sorting of big data in such a way that it can be easily used, accessed, and processed by applications and services working on big data. Moreover, big data can be flexibly scaled as required. Many end-use industries employ big data storage including BFIS, media and entertainment, IT and telecommunications, healthcare and medical, transportation, logistics, retail, etc.

In a software-based storage solution, the storage controller software is disassociated from hardware and takes advantage of industry-standard hardware platforms, in order to deliver a complete range of storage services. This allows different solutions for data storage, data access interfaces, services, and can be delivered in various forms including on cloud. According to Intel Corporation’s study 2016, enterprises are shifting towards software-based storage as performance, capital expenses and scaling are the top three factors considered by data center managers. However, there are several approaches that can be used while deploying software-based storage such as Do-It-Yourself solutions, turnkey solutions and converged and hyper-converged solutions. Hence, these factors are expected to support growth of the global storage in the big data market in the near future.

Which are Compay Profile Plays Major Role in Storage in Big Data (AML) Market?
Key players operating in the global storage in big data market are MemSQL Inc., Google Inc., Hitachi Data Systems Corporation, Microsoft Corporation, Hewlett Packard Enterprise, Amazon Web Services, Inc., Teradata Corporation, VMware, Inc., SAP SE, IBM Corporation, Oracle Corporation, Dell EMC, and SAS Institute Inc.

What are the Key Segments in the Market By Types?
Global Storage in Big Data Market, By Segment:
• Hardware
◦ DAS – internal (OEM)
◦ DAS – external (OEM)
◦ DAS – other (ODM Direct)
◦ ESCON/FICON
◦ NAS
◦ SAN
◦ Tape Systems and Media
• Software
• Services

What are the Key Segments in the Market By End-use Sector ?
Global Storage in Big Data Market, By Industry:
• BFSI
• IT and Telecommunications
• Transportation, Logistics & Retail
• Healthcare and Medical
• Media and Entertainment
• Others

The post Storage in Big Data Market Analysis 2021-2027 Research Report appeared first on Artificial Intelligence.

Artificial intelligence and machine learning have moved from the backrooms of computer science into the mainstream. Their impact is being felt in everything – from how we shop through to finance markets and medical research, as well as the agriculture and manufacture industries.

That’s according to AI firm Appier, who has released its AI Predictions and Trends to Watch in 2021. 

According to the company, larger models have been trained in separated modality. For instance, GPT-3 is the first 100-billion-parameter model for natural language processing (NLP). Recently, a-trillion-parameter model (T5-XXL) has also been trained. They can be used to write articles, analyse text, perform translations and even create poetry.

“In parallel, we’ve seen models used for image recognition and generation greatly improved as they have also been trained with more data sets,” Appier says.

“What we are seeing emerge is the power that can come from combining two or more AI models without changing these large models. 

“In this way, combining these large models becomes affordable. That will allow us to use AI to interpret text and generate a completely new image.”

 The following are the current observations and predictions of AI applications in five major fields:

The E-Commerce Boom Is AI-Driven

Over the last year, online commerce has grown significantly and is expected to continue to increase. COVID-19 restrictions have resulted in people spending much more time online — not just shopping but in online meetings, playing games, accessing social media and using apps. 

The growing digital journeys undertaken by people have generated more data that can be used to understand human behaviour. However, more data also brings a greater complexity. 

Today, there’s no single, most effective channel for reaching customers. Reaching the right customer on the right channel at the right time is complicated for humans, but that complexity can be overcome through the use of AI.

AI gives marketers a way to influence customer’s behaviour at a pace and scale previously thought impossible. AI not only finds the right customers, but also accesses the often-forgotten long tail of customers. It can also effectively generate creatives and develop customised content for different customers, and test the performance for different creatives to increase user engagement.

Data-Driven Finance Relies on AI

The main application of AI in finance has been in high-frequency trading where transactions are conducted between machines faster than people can communicate. This will continue in both traditional finance and in the world of cryptocurrencies, where we see different AIs engage in ‘warfare’.

Investors have been using AI to make long-term predictions — which has required systems that can understand investors’ long-term targets. These were typically centred around measures such as revenues, incomes and profits.

While high-frequency trading strategies are important, there is another factor to show that cryptocurrencies are far more challenging to predict. Much of what we see in cryptocurrency markets is driven by ‘human madness’. While AI models struggle with this today, we can expect the AI models of the future to evolve and do a better job of predicting this behaviour through closely monitoring trends in media and social networks.

AI in Healthcare and Biomedical Research

The prototype of messenger RNA (mRNA) COVID-19 vaccines was developed in days thanks to the digitisation tools of genetic code sequencing and the transcription tools of making mRNA from genetic code sequence.

With the help of AI to predict new mutations in the Sars-Cov-2 virus, the process of developing mRNA vaccines will be even faster. AI can also be used as a diagnostic tool to read x-rays, based on the sound of someone coughing and indicate whether the patient is likely to be suffering from COVID-19 or some other illness.

In the biomedical domain, sequences of codes, such as DNA or amino acid, are commonly used. Since sequences of codes can be treated as a type of language with hidden structure, the architecture used in NLP models can be potentially used to understand and generate sequences of codes in the biomedical domain as well. 

One example in early 2021 is that biomedical researchers used language model architecture to predict virus mutations and to understand protein folding — a key challenge in the creation of some of the vaccines now available. This finding is actually adapting the architecture of one model to solve problems in the biomedical domain.

Machine learning and AI don’t replace clinicians and researchers; they allow these professionals to work faster and rapidly test hypotheses. 

Instead of waiting for cell cultures to grow in the physical world, they can use these models to understand what will happen much faster in the digital simulation. 

As more and more people wear devices that can monitor heart rate, body temperature, blood pressure and other critical factors, the data can be used to give doctors greater insight into a patient’s condition. It also aids accuracy when making diagnoses as doctors and other clinicians are no longer reliant on patient recollections.

The Future of Education

Curricula and textbooks have typically been developed to serve large populations of ‘average’ students. These materials include content designed for a wide gamut of different abilities. 

However, experts, such as Sir Ken Robinson, point out that the ‘conveyor belt’ model of education doesn’t take into account the individual abilities and needs of students. Therefore, we have seen AI being used to revolutionise the way curricula is created and delivered. 

It can be used to provide more personalised curricula or personal problem sets for students. Instead of every student working through the same set of problems or questions, they receive a set that are customised to their specific level.

For example, a student may be very strong with fractions in mathematics, but have a problem with trigonometry. Instead of putting the student through the standard curriculum, he or she would spend less time on fractions and more time on trigonometry. As a student proceeds through a course, AI will monitor his progress and self-modify to meet the specific needs of that student.

With so much content now available online, cheating and plagiarism has become a huge issue. While detecting plagiarism is quite easy — there is already AI that can detect direct copying and similar text where just a few words or the tense are altered — there are other challenges. For example, a student may take content from one language and translate it to another. This is harder to detect, but AI is being developed to solve this problem. Similarly, image interpretation AI is being developed to find instances where arts students copy or imitate a design.

Smart Farming and Factories

Factories and farms are using data in innovative ways too. However, they differ from many other AI applications as they don’t focus on end-users. Instead, they focus on products, produce and machines. This requires an investment in sensors, robots and automation, and the optimisation of operations.

The biggest development we are seeing in this area is in the generalisation of findings between different areas. For example, if AI is being used to increase yields in an apple crop, can those AI models be reapplied for the growing of other fruits such as bananas or peaches? Similarly, if a factory is manufacturing LCD panels and has found ways to increase their yield rates, can those tools and lessons be applied to other manufacturing processes and factories?

Perhaps the biggest prediction to make about AI in 2021 and beyond can be summarised in one word: leverage, Appier says.

“Using existing AI model architecture, combining well developed models and finding ways to generalise existing models to other applications will continuously increase the impact of AI along with accelerated digital transformation across many domains.”

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MediaNama held an online discussion on the impact of data policies on Artificial Intelligence (AI) on January 28, 2021. We have summarised the key issues and concerns raised during the discussion in a report

Given the rapid commercial and government deployments of AI technologies, we felt it was imperative to discuss the impact of AI development on rights and privacy. This is crucial particularly in context of upcoming regulations in India on usage of data i.e. the proposed Personal Data Protection Act and a framework for Non Personal Data. 

We spoke to experts in the lending industry about how they collect data and use AI to generate insights about borrowers, and the broader utility of using data and AI for real-world decision making. We discussed the privacy implications of using AI in the context of proposed government regulations, and further explored the relationship between data and the development of AI, the impact of AI on data governance, fairness, accountability, transparency of algorithms, and how anonymisation, inferred data, and consent would interact with usage of data for AI.

The discussion was hosted with support from Facebook, Microsoft, and Flipkart. The Centre for Internet and Society was a community partner for the sessions. We livestreamed the entire discussion, catch it here:

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The report comes out as an intelligent and thorough assessment tool as well as a great resource that will help you to secure a position of strength in the global Machine Learning Artificial intelligence Market. It includes Porter’s Five Forces and PESTLE analysis to equip your business with critical information and comparative data about the Global Machine Learning Artificial intelligence Market. We have provided deep analysis of the vendor landscape to give you a complete picture of current and future competitive scenarios of the global Machine Learning Artificial intelligence market. Our analysts use the latest primary and secondary research techniques and tools to prepare comprehensive and accurate market research reports.

The latest Machine Learning Artificial intelligence market research report offers a comprehensive analysis of the changing trends, supply-demand scenario, high growth opportunities, and future prospects of this business sphere.

According to seasoned forecasters and analysts, the industry is slated to garner substantial returns, registering a y-o-y growth rate of XX% over the forecast period 20XX-20XX.

Further, the research document attempts to track the impact of Covid-19 pandemic on this vertical for a stronger realization of the possible growth avenues. Besides, it hosts a granular assessment of the various industry segments, followed by a competitive data analysis of prominent and emerging players in this domain.

Key pointers from the Machine Learning Artificial intelligence market report:

• Implications of Covid-19 pandemic on the growth matrix.
• Opportunity windows.
• Key industry trends.
• Statistical coverage of market size in terms of the sales volume and overall revenue.
• Growth rate forecasts for the market and sub-markets.
• Strengths and weaknesses of direct and indirect sales channels.
• A citation of the top traders, dealers, and distributors.

Machine Learning Artificial intelligence market segments covered in the report:

Geographical fragmentation: North America, Europe, Asia-Pacific, South America, Middle East & Africa

• Country-wise assessment of each regional market.
• Total sales, returns, and market share held by each geography.
• Revenue and growth rate predictions of each region over the forecast timeframe.

Product gamut:

• Deep Learning
• Natural Language Processing
• Machine Vision and Others

• Sales and revenue garnered by each product type.
• Market share captured by each product segment.
• Pricing patterns of each product category.

Application spectrum:

• Automotive & Transportation
• Agriculture
• Manufacturing and Others

• Product pricing in accordance with their application scope.
• Sales volume and net revenue amassed by each application segment over the analysis period.

Competitive outlook:

• AIBrain
• Amazon
• Anki
• CloudMinds
• Deepmind
• Google
• Facebook
• IBM
• Iris AI
• Apple
• Luminoso and Qualcomm

• Basic company details, inclusive of manufacturing units across the serviced regions.
• Product & service portfolio of the leading players.
• Records of the pricing model, sales, revenue, gross margins, and market share of the listed companies.
• SWOT analysis of each company.
• A rundown of the market concentration ratio, commercialization rate, market strategies and other business centric facets.

Table of Contents

• Report Overview: It includes six chapters, viz. research scope, major manufacturers covered, market segments by type, Machine Learning Artificial intelligence market segments by application, study objectives, and years considered.
• Global Growth Trends: There are three chapters included in this section, i.e. industry trends, the growth rate of key producers, and production analysis.
• Machine Learning Artificial intelligence Market Share by Manufacturer: Here, production, revenue, and price analysis by the manufacturer are included along with other chapters such as expansion plans and merger and acquisition, products offered by key manufacturers, and areas served and headquarters distribution.
• Market Size by Type: It includes analysis of price, production value market share, and production market share by type.
• Market Size by Application: This section includes Machine Learning Artificial intelligence market consumption analysis by application.

The post Research Report and Overview on Machine Learning Artificial intelligence Market, 2020-2025 appeared first on Artificial Intelligence.

(Version 2021) Lifesciences Data Mining And Visualization Market report published by Stratagem Market Insights is an in-depth analysis of the market covering its size, share, value, growth and current trends for the period of 2021-2028 based on the historical data. This research report delivers recent developments of major players with their respective market share. In addition, it also delivers detailed analysis of regional and country market.

Companies Mentioned of the Global Lifesciences Data Mining And Visualization Market:

Tableau Software, SAP SE, IBM, SAS Institute, Microsoft, Oracle, TIBCO Software, Information Builders, Dundas Data Visualization, Pentaho, InetSoft Technology.

This report examines all the key factors influencing growth of global Lifesciences Data Mining And Visualization market, including demand-supply scenario, pricing structure, profit margins, production and value chain analysis. Regional assessment of global Lifesciences Data Mining And Visualization market unlocks a plethora of untapped opportunities in regional and domestic market places. Detailed company profiling enables users to evaluate company shares analysis, emerging product lines, scope of Lifesciences Data Mining And Visualization in new markets, pricing strategies, innovation possibilities and much more.

Key Topics Covered:

Executive Summary

Lifesciences Data Mining And Visualization Market Landscape

• Market ecosystem
• Market characteristics
• Value chain analysis

Lifesciences Data Mining And Visualization Market Sizing

• Market definition
• Market segment analysis
• Market size 2021
• Market outlook: Forecast for 2021 – 2028

Five Forces Analysis

• Five forces summary
• Bargaining power of buyers
• Bargaining power of suppliers
• Threat of new entrants
• Threat of substitutes
• Threat of rivalry
• Market condition

Lifesciences Data Mining And Visualization Market Segmentation by Product

• Market segments
• Comparison by Product
• Lifesciences Data Mining And Visualization – Market size and forecast 2021 – 2028
• Market opportunity by Product

This report includes assessment of various drivers, government policies, technological innovations, upcoming technologies, opportunities, market risks, restrains, market barriers, challenges, trends, competitive landscape, and segments which gives an exact picture of the growth of the global Lifesciences Data Mining And Visualization market.

Key questions answered in the report:

• What is the growth potential of the Lifesciences Data Mining And Visualization market?
• Which product segment will grab a lion’s share?
• Which regional market will emerge as a frontrunner in the coming years?
• Which application segment will grow at a robust rate CAGR?
• What are the growth opportunities that may emerge in the Lifesciences Data Mining And Visualization industry in the years to come?
• What are the key challenges that the global Lifesciences Data Mining And Visualization market may face in the future?
• Which are the leading companies in the global Lifesciences Data Mining And Visualization market?
• Which are the key trends positively impacting the market growth?

Why choose Stratagem Market Insights?

Stratagem Market Insights is a management consulting organization providing market intelligence and consulting services worldwide. The firm has been providing quantified B2B research and currently offers services to over 350+ customers worldwide.

The post Trending Report: Lifesciences Data Mining And Visualization Market Wrap: Now Even More Attractive| Keyplayers- Tableau Software, SAP SE, IBM, SAS Institute appeared first on Artificial Intelligence.

Indian business landscape is likely to be influenced by trends like artificial intelligence, automation, and digital currencies in the second half of this year, says a report.

Investment Outlook report for 2017, which identified key trends that are likely to influence asset allocation decisions, noted that companies with disruptive leadership will thrive.

“The advent of artificial intelligence, sharing economies, automation, and digital currencies are likely to disrupt the face of the Indian business landscape as we know it today,” said Prateek Pant, head of products and solutions, Sanctum Wealth Management, in a research note.

The report said that key investments will be in innovation in the fields of energy, transportation and manufacturing, among others. The report noted that currently solar energy represents roughly 1 percent of energy production but by 2027, 57 percent of India’s total electricity capacity will come from non-fossil fuel sources.

Similar kind of disruption is also visible in the transportation sector. Electric cars accounted for 0.8 percent of new car sales in 2016, and their sales figure are witnessing a steady rise. “A shift to electricity and alternative sources of power looks set to positively impact the potential growth rate of the Indian economy,” the report said.

It further noted that artificial intelligence (AI) and automation will emerge as the next leg for smart manufacturing.

“As we embark on the second half of the calendar year, with markets in a strong, euphoric uptrend, we seek to identify the key trends that will, in the months to come, provide crucial inputs into asset allocation as well as bottom-up portfolio construction decisions,” said Shiv Gupta, founder and CEO at Sanctum Wealth Management.

The post Artificial Intelligence, digital currency, automation to influence business trends in India: Report appeared first on Artificial Intelligence.