#APP Store Monetisation Market Analysis

[lalsingh228-blog]

APP Store Monetisation Market to See Huge Growth by 2029

Global APP Store Monetisation Market Report from AMA Research highlights deep analysis on market characteristics, sizing, estimates and growth by segmentation, regional breakdowns & country along with competitive landscape, player’s market shares, and strategies that are key in the market. The exploration provides a 360° view and insights, highlighting major outcomes of the industry. These insights help the business decision-makers to formulate better business plans and make informed decisions to improved profitability. In addition, the study helps venture or private players in understanding the companies in more detail to make better informed decisions. Major Players in This Report Include, Apple  (United States), Tencent (China), Alibaba (China), 360 (China), Google  (United States), Xiaomi (China), Baidu (China), Wandoujia (China), HiMarket (Brazil), 91 Mobile Assistant (India). Free Sample Report + All Related Graphs & Charts @: https://www.advancemarketanalytics.com/sample-report/5990-global-app-store-monetisation-market The App Store Monetization is a technique of producing cash from a cellular app that does now not require customers to pay for it. Users are usually hesitant to pay for purposes due to the fact the good sized majority of them are reachable for free. Collecting consumer statistics and promoting it to 1/3 parties, more often than not marketers, is one of the most time-honored methods to monetise an app. In-app advertising and marketing are the most conventional strategy to monetise your cellular apps. For apps that favor to continue to be free in the app store, this method gives a beneficial supply of cash.

Market Drivers

Increasing use of Technologies such as Machine Learning and Artificial Intelligence in Mobile Apps

Market Trend

Developers are Focusing on Experience rather than Pure Revenue

Opportunities

Growing E-commerce Sector

Challenges

High-quality Native ads Effective, however Creating them is Tough

Enquire for customization in Report @: https://www.advancemarketanalytics.com/enquiry-before-buy/5990-global-app-store-monetisation-market In this research study, the prime factors that are impelling the growth of the Global APP Store Monetisation market report have been studied thoroughly in a bid to estimate the overall value and the size of this market by the end of the forecast period. The impact of the driving forces, limitations, challenges, and opportunities has been examined extensively. The key trends that manage the interest of the customers have also been interpreted accurately for the benefit of the readers. The APP Store Monetisation market study is being classified by Monetization (Sponsorships, Freemium, Sell In-App Features, One-time Paid Apps, Paid Apps with Additional Features for a Fee, Sell Merchandize through your App, CPI Network), Technologies (Java, E-Commerce, Microsoft, Mobile, Cloud, Open Source, Others) The report concludes with in-depth details on the business operations and financial structure of leading vendors in the Global APP Store Monetisation market report, Overview of Key trends in the past and present are in reports that are reported to be beneficial for companies looking for venture businesses in this market. Information about the various marketing channels and well-known distributors in this market was also provided here. This study serves as a rich guide for established players and new players in this market. Get Reasonable Discount on This Premium Report @ https://www.advancemarketanalytics.com/request-discount/5990-global-app-store-monetisation-market Extracts from Table of Contents APP Store Monetisation Market Research Report Chapter 1 APP Store Monetisation Market Overview Chapter 2 Global Economic Impact on Industry Chapter 3 Global Market Competition by Manufacturers Chapter 4 Global Revenue (Value, Volume*) by Region Chapter 5 Global Supplies (Production), Consumption, Export, Import by Regions Chapter 6 Global Revenue (Value, Volume*), Price* Trend by Type Chapter 7 Global Market Analysis by Application ………………….continued This report also analyzes the regulatory framework of the Global Markets APP Store Monetisation Market Report to inform stakeholders about the various norms, regulations, this can have an impact. It also collects in-depth information from the detailed primary and secondary research techniques analyzed using the most efficient analysis tools. Based on the statistics gained from this systematic study, market research provides estimates for market participants and readers. Contact US : Craig Francis (PR & Marketing Manager) AMA Research & Media LLP Unit No. 429, Parsonage Road Edison, NJ New Jersey USA – 08837 Phone: +1 201 565 3262, +44 161 818 8166 sales@advancemarketanalytics.com

[systango-technologies]

Emerging AI Business Opportunities: Future Trends and Innovations

In the ever-evolving landscape of technology, Artificial Intelligence (AI) continues to transform various industries and open up new avenues for business ventures. From BI tools for data visualisation to innovative AI-driven applications, these trends are reshaping how businesses operate and succeed. Let's delve into ten promising AI-powered business concepts poised to shake up industries in 2024, paving the way for innovative success stories.

Empowering Mental Health Support with AIThe mental health sector is rapidly expanding, creating opportunities for AI-driven platforms that offer tailored counselling and round-the-clock assistance. These platforms can potentially significantly impact by providing premium subscriptions and in-app purchases as revenue streams, leveraging advanced business analytics software for user insights.

Redefining Shopping with Augmented Reality (AR) AssistantsThe AR industry is forecasted to hit $198 billion by 2025. AR shopping assistants can transform the e-commerce landscape through virtual try-ons and room visualisations, monetising their services through premium features and seamless e-commerce integrations, enhanced by business intelligence software.

Personalised Recipes Curated by AIAI-driven recipe platforms can deliver personalised meal suggestions and nutritional insights, tapping into the growing market for online recipe apps. Revenue streams could include subscriptions and partnerships with grocery stores, supported by best business analytics software to refine offerings.

Personal Finance Coaching Enhanced by AIPlatforms powered by AI that offer tailored financial advice and investment suggestions can capitalise on the expanding personal finance software market. Revenue opportunities may arise from advanced analytical tools and premium subscription models, all optimised through business intelligence software.

Elevating Virtual Events with AI EnhancementsWith virtual events projected to reach a $404 billion market size by 2027, platforms incorporating AI features like real-time translations and personalised content stand to thrive. Income sources may stem from sponsorships, ticket sales, and subscription services, maximising user engagement with BI tools for data visualisation.

Transforming Fashion Design Through AI OptimisationAI-infused design platforms offering virtual try-ons and trend analyses can tap into the flourishing fashion industry. Monetisation strategies could involve collaborations with brands, premium service offerings, and subscription models, all guided by business analytics software insights.

Predictive Maintenance in Renewable Energy Driven by AIPredictive maintenance systems leveraging AI in renewable energy sectors can boost equipment efficiency in a market expected to reach $2.15 trillion by 2025. Revenue avenues might include partnerships with energy companies and premium analytics services, enhanced by business intelligence software.

A Sustainable Lifestyle Promoted via AI-Integrated PlatformsAmidst the growth of sustainable product markets, AI-powered platforms suggesting eco-friendly products may witness substantial demand. Monetisation strategies could encompass affiliate sales and exclusive memberships, powered by data insights from the best business analytics software.

AI-Facilitated Language Learning Infused with Cultural InsightsLanguage learning enriched with cultural context via AI platforms may capture a portion of an extensive global language learning market estimated at $191 billion+ by 2028. Revenue sources might include subscriptions and collaborations with language institutions, optimised using business analytics software.

Optimising Influencer Marketing Through Artificial IntelligenceArtificial intelligence tools enhancing influencer campaigns based on audience data analysis can drive improved ROI within the burgeoning influencer marketing arena. Potential revenue models might feature tiered subscriptions and performance-linked fees, all refined using business intelligence software.

ConclusionAI presents lucrative business prospects in 2024 across diverse sectors such as mental health support and renewable energy maintenance. Enterprising individuals embracing these trends stand poised for growth and innovation. The future brims with immense possibilities, demonstrating that now is an opportune moment to act. Collaborating with experts like Systango could help realise your aspirations within this dynamic realm of artificial intelligence. Schedule a consultation today to explore how we can bolster your journey towards success in this thriving world driven by artificial intelligence.

Original Source - https://systango.medium.com/emerging-ai-business-opportunities-future-trends-and-innovations-77c07d5d07b2

[helloancycruzworld]

Global APP Store Monetisation Market 2019 by Size, Development Status, Trends and Forecast to 2023

Global APP Store Monetisation Market 2019 by Size, Development Status, Trends and Forecast to 2023

According to this study, over the next five years the APP Store Monetisation market will register a xx% CAGR in terms of revenue, the global market size will reach US$ xx million by 2023, from US$ xx million in 2017. In particular, this report presents the global revenue market share of key companies in APP Store Monetisation business, shared in Chapter 3.

This report presents a comprehensive…

[reporthive-blog]

Global APP Store Monetisation Market Size, Status and Forecast 2022

This report studies the global APP Store Monetisation market, analyzes and researches the APP Store Monetisation development status and forecast in United States, EU, Japan, China, India and Southeast Asia.

This report focuses on the top players in global market, like Apple Tencent Alibaba 360 Google Xiaomi Baidu Wandoujia HiMarket 91 Mobile Assistant Anzhi Market

Request a sample of this report @ https://www.reporthive.com/enquiry.php?id=1153177&req_type=smpl

Market segment by Regions/Countries, this report covers United States EU Japan China India Southeast Asia

Market segment by Type, the product can be split into Free with Ads Paywalls (Subscription or Download) Sponsorship Other

Market segment by Application, APP Store Monetisation can be split into Game Shopping Travel Working Other

If you have any special requirements, please let us know and we will offer you the report as you want.

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Table of Contents

1 Industry Overview of APP Store Monetisation 1.1 APP Store Monetisation Market Overview 1.1.1 APP Store Monetisation Product Scope 1.1.2 Market Status and Outlook 1.2 Global APP Store Monetisation Market Size and Analysis by Regions 1.2.1 United States 1.2.2 EU 1.2.3 Japan 1.2.4 China 1.2.5 India 1.2.6 Southeast Asia 1.3 APP Store Monetisation Market by Type 1.3.1 Free with Ads 1.3.2 Paywalls (Subscription or Download) 1.3.3 Sponsorship 1.3.4 Other 1.4 APP Store Monetisation Market by End Users/Application 1.4.1 Game 1.4.2 Shopping 1.4.3 Travel 1.4.4 Working 1.4.5 Other

2 Global APP Store Monetisation Competition Analysis by Players 2.1 APP Store Monetisation Market Size (Value) by Players (2016 and 2017) 2.2 Competitive Status and Trend 2.2.1 Market Concentration Rate 2.2.2 Product/Service Differences 2.2.3 New Entrants 2.2.4 The Technology Trends in Future

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[viditure]

The data model strategy: leverage the value of user-centric analytics

Digital interactions and the platforms that come with them have been constantly evolving over the last 20 years. The same applies to the tools used by digital professionals, which are becoming increasingly advanced. This has encouraged companies to create multiple specialised teams, which has inevitably created siloes. 

In AT Internet’s recent DAA webinar, our Data Expert Declan Owens highlighted the importance for companies to introduce a unified data model approach across their organisation. By ensuring that their analytics are at the heart of their digital product & marketing strategies, companies can ensure that it is fully aligned with their business needs.  

AT Internet’s new Analytics Suite provides the essential tools and ideas to implement a data model strategy into your business and leverage the true value of your digital analytics. 

Today’s analytics challenges 

In the beginning AT Internet simply had the internet user. They went online via a desktop and hey presto – straightforward analytics. Zooming up to 2020, the customer journey is now far more sophisticated, composed of multiple touchpoints, that can be physical, digital or both!   

In terms of analytics, this is compounded by the fact that each specific technology has traditionally had its own set of tools, from web analytics to app analytics, or even voice analytics, chatbot analytics, etc. The silo effect also applies to digital professionals who have also tended to focus and worked on each specific technology, with little or no interaction. 

The same goes for the vocabulary used in analytics – page_views can also be equivalent to  screen_loads, app_open, message_sent, action_sent, etc. depending on the technology. And translating all these events into concrete steps in the customer/user journey such as conversion can be challenging to say the least.  

The convoluted nature of modern analytics can also create roadblocks when trying to effectively monetise your business and optimise costs/service levels. Even meeting simple KPIs can be extremely time consuming, such as understanding how content consumption has evolved over time, the weekly average share of subscriptions between digital and physical, or purchasing patterns across all your brands. 

If you want to succeed in the highly competitive market, it is therefore essential to rely on a single, unified data model. 

Implementing user-centric analytics 

There are a multitude of ways that companies can organise their data. However, it’s critical to have a central analytics strategy regardless of the governance model applied within the company – and ideally a team that can build value from the core of business, then offer it to the rest of the organisation.  

Becoming user-centric is challenging due the range of platforms and devices, the need for specific teams and analytics tools, as well as the difficulty of centralising the structure of the data. 

Creating a unified ‘hybrid’ digital analytics architecture   

Today’s analytics market can be boiled down to three approaches – each with their advantages and drawbacks: 

Marketing analytics 

These are designed to meet specific digital marketing issues such traffic acquisition, monetisation, and include numerous metrics and specific analyses. They provide useful Out of the Box (OOTB) & marketing-specific reports but can quickly reach their limits when it comes to analysing very specific company concepts. They also aren’t flexible enough for more advanced users – you can’t go the extra mile.

Product analytics

 These have a high degree of flexibility, allowing them to measure interactions that are very specific to the development of a product or service – a strong selling point for Product Managers, Product Owners etc. Their drawback is that the flexibility can make them complex when it comes to having reliable and exhaustive data – as very few analyses are ready to use. This means they fall short in terms of data democratisation and spreading data across the organisation. 

In-house analytics

 Tools developed in-house can offer end-to-end customisation capabilities while offering configurable computing power. But beyond the exceptional skills required to successfully complete in-house projects, there are numerous functional trade-offs such as development costs, risks, technical debt, maintenance, etc. The Total Cost of Ownership can therefore skyrocket over time in the race to stay ahead of the competition. 

To ensure that both your analytics and your business are user-centric, it’s vital combine the best elements of these approaches.  

Firstly, you need to be able to effectively log events and their properties, i.e. understand exactly what actions the user has taken – page loads, conversion etc. Then it’s necessary to make sure your data is being ingested through a structured and consistent pipeline – from collection, to cleaning, storage and activation. Finally, it’s essential to have users at the centre – avoiding on overflow of basic events that need to be cleaned up. 

Aligning the terminology 

A major part of streamlining your analytics across all touchpoints is to align the naming of all the relevant actions. This could mean renaming all your events as page_load’s or simply interactions. This also applies to properties which often have different denominations that describe the same thing, e.g. visitor_id vs. user_id. The key is to move as quickly as possible to understanding if the user has converted or not and why. 

Storing the events  

By using separate tools for your different types of analytics, e.g. web and app, you’re multiplying the number of tables on which the data is stored. This has serious ramifications in terms of the effective use of time, accuracy of the results and effectively sharing the analysis with the relevant parties. By storing all your data in a single model, you link everything together and drastically simplify the analysis of the customer journey. 

Simplifying the data pipeline 

User-centricity means effective data collection, storage and activation. The data needs to be collected across all user touchpoints. It then needs to be stored cleanly and neatly. It can then be used to provide decision-ready reporting to users across your organisation, or by the data science team to build recommendation engines etc. 

Setting up a Unified Data Model 

By mapping out the KPIs, you can align your teams around the same analytics objectives. It’s then necessary to design your Data model by identifying the main events and associated properties that you’ll need to measure. Next is gathering interest from key stakeholders and demonstrating how they can leverage value from the data. 

Finally is making sure you have the optimal digital analytics tools to adapt to your strategy. This means choosing a solution that enables you to sustain the effectiveness of your data model over the long term. 

How AT Internet meets these challenges 

Combining analytics approaches 

AT Internet’s new Analytics Suite has a range of tools that respond to the needs of Marketing and Product analytics.  

This includes rapid ‘Marketing analytics-style’ data collection, with over 400 standard properties. As soon as the data is collected, the data model is ready to process and store it. It can then be used in a range of reporting and data mining user interfaces. 

It’s also possible to add up to 1000 custom properties and store them in the data model – all of which can be managed/viewed transparently. 

To go even further, such as importing CRM or customer platform data and enriching it, you can incorporate an OOTB approach, as with Product analytics. This means that you keep a unified data model and maintain the flexibility of using multiple tools.  

The Analytics Suite also allows you to benefit from rapid activation tools that provide rapid dashboarding & reporting, as well as data mining. Our data model can be directly accessed to feed tools further down the pipeline. In short, you never have to extract the data and re-work it, everything is enriched in the same platform – from OOTB reports, to a high level of customisation, an entirely user centric approach, and advanced flexibility. 

User Centric analytics 

AT Internet’s range of tools are perfectly suited to analyse complex non-linear user journeys across a range of platforms and devices.  

By taking a user centric approach on all levels – monitoring a series of events that cover all user interactions with a brand – we make all of the events user centric. All events collected in the platform are referenced under the same user_id, whether they’re from website visits, apps or audio/visual etc. This makes it fast and simple to find key information such as the number of visitors, visits or the time spent on different elements. 

We also enrich the user-centric data to give you far more perspective and more capacity to personalise the experience. This means it’s simple to add a range of elements to your analysis such as different age groups, behaviours, levels of maturity, as well as to subsequent data science projects.  

While Analytics Suite’s flexible data pipeline and centralised data model are built from the ground up, simple to put in place, and designed to meet all your relevant needs. 

How a data model strategy will boost your business value 

With the new Analytics Suite, AT Internet makes it simple to meet your main KPIs. To discover how your content consumption has evolved over the last year, we allow you to track the entirety of the cross-device user journey with a single tool, use the same vocabulary throughout the analysis, and carry out the same calculations. 

With our Axon module, you can carry out advanced anomaly prediction powered by machine learning. This tool allows you monitor any drop in traffic based on your normal rates of consumption and lets you set up automatic alerts to notify the relevant Sales teams. They can then put in place the relevant retention steps to reduce user churn. 

We also cover the issue of data governance – which is not only about quality but having adequate data privacy protection. AT Internet is committed to protecting user privacy in full compliance with global regulations. The Analytics Suite categorises the purpose for the collected data, as well as the legal basis for the collection. This involves clearly listing user consent/opt-in in relation to the specific country regulation involved – which in turn minimises the legal risk for companies and ensures that collection is ethical and in line with the respect for user rights. 

Finally is the key importance of data democratisation – i.e. ensuring that decision-ready data is accessible to everyone in the organisation. AT Internet’s dashboarding and automatic reporting tools are integrated as standard elements in the data model and are fully turnkey from the outset.  

Keen to find out more? Listen again to Declan’s DAA webinar and request a free demo today. 

Article The data model strategy: leverage the value of user-centric analytics first appeared on Digital Analytics Blog.

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[reportsandmarkets]

APP Store Monetisation Market Size, Status and Forecast 2022

APP Store Monetization  Global APP Store Monetisation market, analyzes and researches the APP Store Monetisation development status and forecast in United States, EU, Japan, China, India and Southeast Asia. This report focuses on the top players in global market, like Apple, Tencent, Alibaba, 360, Google, Xiaomi, Baidu, Wandoujia, HiMarket, 91 Mobile Assistant, Anzhi Market. Get Free Sample…

[jacobwill176]

– The “Global APP Store Monetisation Market ” report focuses on the market status, future forecast, growth opportunities, market trends and leading players.

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APP Store Monetisation Market Analysis 2022: Impressively growing Latest Technology Trends with Top Manufactures and Business Opportunities

The Market Research Study titled Global (North America, Europe, Asia-Pacific, South America, Middle East and Africa) APP Store Monetisation Market 2012, Forecast to 2022 has been added to ‘Worldwide Market Reports’ offering. The report makes worthwhile and crucial projections regarding market for the upcoming years. It highlights market size, industry dynamics, and updates of the market with respect to opportunities, challenges, threats, constraints, current trends, and cost.

Above information will give you reliable principals, geological areas, participants, product type, and applications. Past and present data was used as a base of this research. The reader of this report can utilize the metrics such as revenue, cost, gross, gross margin, and the year-on-year growth rate that will help in future progress of the market for a period from 2012 till 2022.

This report delivers the competitive outlook of APP Store Monetisation markets key players and leading companies. The cost-effective data has been explained in the form of tables, figures, charts, and graphs. Experts have conducted primary and secondary research to obtain important statistics of the industry with the help of SWOT analysis.

Request for free Sample Reports at https://www.worldwidemarketreports.com/sample/47871

Global Top key Vendors:

Apple Tencent Alibaba 360 Google Xiaomi Baidu Wandoujia HiMarket 91 Mobile Assistant Anzhi Market

Market segment by Type, the product can be split into Free with Ads Paywalls (Subscription or Download) Sponsorship Other Market segment by Application, APP Store Monetisation can be split into Game Shopping Travel Working Other Description:

Global size of market 2013 to 2017, and development forecast 2012-2022

Market status and development trend by types and applications

Industry segment by application

Prominent manufacturers/suppliers of APP Store Monetisation along with company profiles, product introduction, and position

Key success factors and market share overview

Market competition by manufacturers

Market growth drivers, restraints, challenges, risks, limitation

Huge-growth segments of the market and their future scope

Manufacturing cost analysis and industrial chain analysis

We have also added regional and country-level market analysis for the following areas:

United States EU Japan China India Southeast Asia

Get Report Discount at https://www.worldwidemarketreports.com/discount/47871

Questions Covered in Worldwide APP Store Monetisation Industry Research Report:

What is the current size of the market both global and regional?

Which global market tendencies, barriers and challenges the key competitors of market have faced?

What are the long-lasting and defects of the industry?

Over the next few years which application segments will perform well?

How the market is expected to develop in the forecast period from 2012-2022?

Who are the key players in the market and what are their contributions in the overall revenue growth?

How market share changes their values by different manufacturing brands?

What are major end result and effect of the five strengths study of industry?

The report presents research conclusions, findings which can offer a summarized view of the APP Store Monetisation. You will be able to understand SWOT examination and venture return investigation, and other aspects such as the principle locale, economic situations with benefit, generation, request, limit, supply, and market development rate and figure.

Tel: +1 415 871 0703

Email: sales@worldwidemarketreports.com

[rohitamecoresearchblog-blog]

Global APP Store Monetisation Market 2018 by Industry Research, Growth, Segmentation, Key Players Analysis and Forecast 2025

Global APP Store Monetisation Market 2018 by Industry Research, Growth, Segmentation, Key Players Analysis and Forecast 2025

The Exhaustive Study for “Global APP Store Monetisation Market” is added on Ameco Research.  The report covers the market aspect and its growth forecasts across the coming years. It also includes a review of the key merchants moving in this market.

In 2017, the Global APP Store Monetisation Marketsize was xx million US$ and it is expected to reach xx million US$ by the end of 2025, with a CAGR of…

[teiraymondmccoy78]

Integrating AI Into Blockchain Can Help In More Ways Than You Think

Integrating AI Into Blockchain Can Help In More Ways Than You Think

Artificial intelligence is one of the major driving forces of innovations loved by entrepreneurs and users alike. It has disrupted numerous industries including customer service, agriculture, manufacturing, healthcare, tech support and many more.

Blockchain technology is another force to be reckoned with. Although it is presently popular in the BFSI sector, there are endless uses for Blockchain technology, such as, education, transportation, voting, law and enforcement, among others.

Advertisement

Blockchain, though powerful on its own, becomes enhanced to a whole new level when coupled with AI. New features and capabilities become unlocked, enhanced, and more secure through their convergence.

How AI Can Add On To Blockchain

The confluence of AI in blockchain creates perhaps what is the world’s most reliable technology enabled decision making systems that are virtually tamper-proof and provides solid insights and decisions. It holds several benefits like:

Improved business data models

Globalised verification systems

Innovative audits and compliance systems

Smarter finance

Transparent governance

Intelligent retail

Intelligent predictive analysis

Digital Intellectual Property Rights

Technical Enhancements That AI Can Enable

Security: With the implementation of AI, Blockchain technology becomes more secure by creating secure future application deployments. AI algorithms which are increasingly making decisions about whether financial transactions are fraudulent and should be blocked or investigated is a good example of it.

Efficiency: AI can help optimise calculations to reduce miner load which results in less network latency for faster transactions. AI enables to reduce the carbon footprint of blockchain technology. The cost which is applied upon miners would also be reduced along with the energy spent if AI machines replace the work done by miners. As the data on blockchains grows by the minute, AI’s data pruning algorithms can be also be applied on the blockchain data which automatically prunes the data which is not required for future use. AI can introduce even new decentralised learning systems such as federated learning or new data sharing techniques that make the system much more efficient.

Trust: The iron cast records of blockchain is considered one its USP. Applied in conjunction with AI means that users have clear records to follow the system’s thinking process. This, in turn, helps the bots trust each other, increasing machine-to-machine interaction and allowing them to share data and coordinate decisions at large.

  Better Management: When it comes to cracking codes, human experts get better over time with practice. A machine learning-powered mining algorithm will eliminate the need for human expertise as it could almost instantaneously sharpen its skills if it is fed the right training data. So, AI also helps in managing blockchain systems better.

Privacy And New Markets: Making private data secure invariably lead to it being sold, resulting in data markets/model markets. The markets get easy, secure data sharing that helps smaller players gain Blockchain’s privacy can be more increased by executing “Homomorphic encryption” algorithms. Homomorphic algorithms are the ones using which operations can be performed on encrypted data directly.

Storage: Blockchains are ideal for storing the highly sensitive, personal data which, when smartly processed with AI, can add value and convenience. Smart healthcare systems that make accurate diagnoses based on medical scans and records is a good example of that.

Real-Life Applications:

Triggmine, a product converges AI in blockchain within a single marketing automation system. It has led to the company being one of the world’s first decentralised platform for marketing automation.

Mithril is a social media app which makes content creation and engagement on social media worthwhile for users, in the form of crypto. Their social media platform enables what Mithril has termed “social mining” in which users are rewarded for the impact and influence of their content. They also make extensive use of AI in their overall working.

Bubblo is an AI-based real-time discovery app that uses blockchain technology and virtual reality to build a marketplace allowing users to monetise their personal information for rewards. It provides users personalised recommendation of bars, restaurants and other venues.

Back in India, Signzy couples artificial intelligence with the blockchain to make secure, compliant and user-friendly transactions in banks. It is harnessing the power of AI and blockchain to enable their clients to adapt digitisation and offer fully digital experiences to their users. It offers products such as RealKYC which is a bank-grade digital KYC and Digital contracts which is a secured digital contract enabled by Aadhaar and Biometrics

EVS is an employee verification platform created by SpringRole which verifies employees’ credentials and skills. It uses blockchain as a platform where the feedbacks registered are fed into an AI system along with using a smart contract method which digitally facilitates, verify, or enforce the negotiation or performance of any contract.

Outlook

As the industries are seeing newer developments, there will be more blockchain and non-blockchain based products looking to leverage the advantages that AI can offer.

The long-term potential for both blockchain and AI is staggering, so combining the two certainly makes such projects worth beneficial. It enhances each other’s capabilities as seen in few instances above while offering opportunities for better oversight and accountability. These developments simply point towards why AI should not be brought into blockchain.

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[vanessawestwcrtr5]

Integrating AI Into Blockchain Can Help In More Ways Than You Think

Integrating AI Into Blockchain Can Help In More Ways Than You Think

Artificial intelligence is one of the major driving forces of innovations loved by entrepreneurs and users alike. It has disrupted numerous industries including customer service, agriculture, manufacturing, healthcare, tech support and many more.

Blockchain technology is another force to be reckoned with. Although it is presently popular in the BFSI sector, there are endless uses for Blockchain technology, such as, education, transportation, voting, law and enforcement, among others.

Advertisement

Blockchain, though powerful on its own, becomes enhanced to a whole new level when coupled with AI. New features and capabilities become unlocked, enhanced, and more secure through their convergence.

How AI Can Add On To Blockchain

The confluence of AI in blockchain creates perhaps what is the world’s most reliable technology enabled decision making systems that are virtually tamper-proof and provides solid insights and decisions. It holds several benefits like:

Improved business data models

Globalised verification systems

Innovative audits and compliance systems

Smarter finance

Transparent governance

Intelligent retail

Intelligent predictive analysis

Digital Intellectual Property Rights

Technical Enhancements That AI Can Enable

Security: With the implementation of AI, Blockchain technology becomes more secure by creating secure future application deployments. AI algorithms which are increasingly making decisions about whether financial transactions are fraudulent and should be blocked or investigated is a good example of it.

Efficiency: AI can help optimise calculations to reduce miner load which results in less network latency for faster transactions. AI enables to reduce the carbon footprint of blockchain technology. The cost which is applied upon miners would also be reduced along with the energy spent if AI machines replace the work done by miners. As the data on blockchains grows by the minute, AI’s data pruning algorithms can be also be applied on the blockchain data which automatically prunes the data which is not required for future use. AI can introduce even new decentralised learning systems such as federated learning or new data sharing techniques that make the system much more efficient.

Trust: The iron cast records of blockchain is considered one its USP. Applied in conjunction with AI means that users have clear records to follow the system’s thinking process. This, in turn, helps the bots trust each other, increasing machine-to-machine interaction and allowing them to share data and coordinate decisions at large.

  Better Management: When it comes to cracking codes, human experts get better over time with practice. A machine learning-powered mining algorithm will eliminate the need for human expertise as it could almost instantaneously sharpen its skills if it is fed the right training data. So, AI also helps in managing blockchain systems better.

Privacy And New Markets: Making private data secure invariably lead to it being sold, resulting in data markets/model markets. The markets get easy, secure data sharing that helps smaller players gain Blockchain’s privacy can be more increased by executing “Homomorphic encryption” algorithms. Homomorphic algorithms are the ones using which operations can be performed on encrypted data directly.

Storage: Blockchains are ideal for storing the highly sensitive, personal data which, when smartly processed with AI, can add value and convenience. Smart healthcare systems that make accurate diagnoses based on medical scans and records is a good example of that.

Real-Life Applications:

Triggmine, a product converges AI in blockchain within a single marketing automation system. It has led to the company being one of the world’s first decentralised platform for marketing automation.

Mithril is a social media app which makes content creation and engagement on social media worthwhile for users, in the form of crypto. Their social media platform enables what Mithril has termed “social mining” in which users are rewarded for the impact and influence of their content. They also make extensive use of AI in their overall working.

Bubblo is an AI-based real-time discovery app that uses blockchain technology and virtual reality to build a marketplace allowing users to monetise their personal information for rewards. It provides users personalised recommendation of bars, restaurants and other venues.

Back in India, Signzy couples artificial intelligence with the blockchain to make secure, compliant and user-friendly transactions in banks. It is harnessing the power of AI and blockchain to enable their clients to adapt digitisation and offer fully digital experiences to their users. It offers products such as RealKYC which is a bank-grade digital KYC and Digital contracts which is a secured digital contract enabled by Aadhaar and Biometrics

EVS is an employee verification platform created by SpringRole which verifies employees’ credentials and skills. It uses blockchain as a platform where the feedbacks registered are fed into an AI system along with using a smart contract method which digitally facilitates, verify, or enforce the negotiation or performance of any contract.

Outlook

As the industries are seeing newer developments, there will be more blockchain and non-blockchain based products looking to leverage the advantages that AI can offer.

The long-term potential for both blockchain and AI is staggering, so combining the two certainly makes such projects worth beneficial. It enhances each other’s capabilities as seen in few instances above while offering opportunities for better oversight and accountability. These developments simply point towards why AI should not be brought into blockchain.

Related

Provide your comments below

comments

Source link http://bit.ly/2V2N1PN

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Integrating AI Into Blockchain Can Help In More Ways Than You Think

Integrating AI Into Blockchain Can Help In More Ways Than You Think

Artificial intelligence is one of the major driving forces of innovations loved by entrepreneurs and users alike. It has disrupted numerous industries including customer service, agriculture, manufacturing, healthcare, tech support and many more.

Blockchain technology is another force to be reckoned with. Although it is presently popular in the BFSI sector, there are endless uses for Blockchain technology, such as, education, transportation, voting, law and enforcement, among others.

Advertisement

Blockchain, though powerful on its own, becomes enhanced to a whole new level when coupled with AI. New features and capabilities become unlocked, enhanced, and more secure through their convergence.

How AI Can Add On To Blockchain

The confluence of AI in blockchain creates perhaps what is the world’s most reliable technology enabled decision making systems that are virtually tamper-proof and provides solid insights and decisions. It holds several benefits like:

Improved business data models

Globalised verification systems

Innovative audits and compliance systems

Smarter finance

Transparent governance

Intelligent retail

Intelligent predictive analysis

Digital Intellectual Property Rights

Technical Enhancements That AI Can Enable

Security: With the implementation of AI, Blockchain technology becomes more secure by creating secure future application deployments. AI algorithms which are increasingly making decisions about whether financial transactions are fraudulent and should be blocked or investigated is a good example of it.

Efficiency: AI can help optimise calculations to reduce miner load which results in less network latency for faster transactions. AI enables to reduce the carbon footprint of blockchain technology. The cost which is applied upon miners would also be reduced along with the energy spent if AI machines replace the work done by miners. As the data on blockchains grows by the minute, AI’s data pruning algorithms can be also be applied on the blockchain data which automatically prunes the data which is not required for future use. AI can introduce even new decentralised learning systems such as federated learning or new data sharing techniques that make the system much more efficient.

Trust: The iron cast records of blockchain is considered one its USP. Applied in conjunction with AI means that users have clear records to follow the system’s thinking process. This, in turn, helps the bots trust each other, increasing machine-to-machine interaction and allowing them to share data and coordinate decisions at large.

  Better Management: When it comes to cracking codes, human experts get better over time with practice. A machine learning-powered mining algorithm will eliminate the need for human expertise as it could almost instantaneously sharpen its skills if it is fed the right training data. So, AI also helps in managing blockchain systems better.

Privacy And New Markets: Making private data secure invariably lead to it being sold, resulting in data markets/model markets. The markets get easy, secure data sharing that helps smaller players gain Blockchain’s privacy can be more increased by executing “Homomorphic encryption” algorithms. Homomorphic algorithms are the ones using which operations can be performed on encrypted data directly.

Storage: Blockchains are ideal for storing the highly sensitive, personal data which, when smartly processed with AI, can add value and convenience. Smart healthcare systems that make accurate diagnoses based on medical scans and records is a good example of that.

Real-Life Applications:

Triggmine, a product converges AI in blockchain within a single marketing automation system. It has led to the company being one of the world’s first decentralised platform for marketing automation.

Mithril is a social media app which makes content creation and engagement on social media worthwhile for users, in the form of crypto. Their social media platform enables what Mithril has termed “social mining” in which users are rewarded for the impact and influence of their content. They also make extensive use of AI in their overall working.

Bubblo is an AI-based real-time discovery app that uses blockchain technology and virtual reality to build a marketplace allowing users to monetise their personal information for rewards. It provides users personalised recommendation of bars, restaurants and other venues.

Back in India, Signzy couples artificial intelligence with the blockchain to make secure, compliant and user-friendly transactions in banks. It is harnessing the power of AI and blockchain to enable their clients to adapt digitisation and offer fully digital experiences to their users. It offers products such as RealKYC which is a bank-grade digital KYC and Digital contracts which is a secured digital contract enabled by Aadhaar and Biometrics

EVS is an employee verification platform created by SpringRole which verifies employees’ credentials and skills. It uses blockchain as a platform where the feedbacks registered are fed into an AI system along with using a smart contract method which digitally facilitates, verify, or enforce the negotiation or performance of any contract.

Outlook

As the industries are seeing newer developments, there will be more blockchain and non-blockchain based products looking to leverage the advantages that AI can offer.

The long-term potential for both blockchain and AI is staggering, so combining the two certainly makes such projects worth beneficial. It enhances each other’s capabilities as seen in few instances above while offering opportunities for better oversight and accountability. These developments simply point towards why AI should not be brought into blockchain.

Related

Provide your comments below

comments

Source link http://bit.ly/2V2N1PN

[adrianjenkins952wblr]

Integrating AI Into Blockchain Can Help In More Ways Than You Think

Integrating AI Into Blockchain Can Help In More Ways Than You Think

Artificial intelligence is one of the major driving forces of innovations loved by entrepreneurs and users alike. It has disrupted numerous industries including customer service, agriculture, manufacturing, healthcare, tech support and many more.

Blockchain technology is another force to be reckoned with. Although it is presently popular in the BFSI sector, there are endless uses for Blockchain technology, such as, education, transportation, voting, law and enforcement, among others.

Advertisement

Blockchain, though powerful on its own, becomes enhanced to a whole new level when coupled with AI. New features and capabilities become unlocked, enhanced, and more secure through their convergence.

How AI Can Add On To Blockchain

The confluence of AI in blockchain creates perhaps what is the world’s most reliable technology enabled decision making systems that are virtually tamper-proof and provides solid insights and decisions. It holds several benefits like:

Improved business data models

Globalised verification systems

Innovative audits and compliance systems

Smarter finance

Transparent governance

Intelligent retail

Intelligent predictive analysis

Digital Intellectual Property Rights

Technical Enhancements That AI Can Enable

Security: With the implementation of AI, Blockchain technology becomes more secure by creating secure future application deployments. AI algorithms which are increasingly making decisions about whether financial transactions are fraudulent and should be blocked or investigated is a good example of it.

Efficiency: AI can help optimise calculations to reduce miner load which results in less network latency for faster transactions. AI enables to reduce the carbon footprint of blockchain technology. The cost which is applied upon miners would also be reduced along with the energy spent if AI machines replace the work done by miners. As the data on blockchains grows by the minute, AI’s data pruning algorithms can be also be applied on the blockchain data which automatically prunes the data which is not required for future use. AI can introduce even new decentralised learning systems such as federated learning or new data sharing techniques that make the system much more efficient.

Trust: The iron cast records of blockchain is considered one its USP. Applied in conjunction with AI means that users have clear records to follow the system’s thinking process. This, in turn, helps the bots trust each other, increasing machine-to-machine interaction and allowing them to share data and coordinate decisions at large.

  Better Management: When it comes to cracking codes, human experts get better over time with practice. A machine learning-powered mining algorithm will eliminate the need for human expertise as it could almost instantaneously sharpen its skills if it is fed the right training data. So, AI also helps in managing blockchain systems better.

Privacy And New Markets: Making private data secure invariably lead to it being sold, resulting in data markets/model markets. The markets get easy, secure data sharing that helps smaller players gain Blockchain’s privacy can be more increased by executing “Homomorphic encryption” algorithms. Homomorphic algorithms are the ones using which operations can be performed on encrypted data directly.

Storage: Blockchains are ideal for storing the highly sensitive, personal data which, when smartly processed with AI, can add value and convenience. Smart healthcare systems that make accurate diagnoses based on medical scans and records is a good example of that.

Real-Life Applications:

Triggmine, a product converges AI in blockchain within a single marketing automation system. It has led to the company being one of the world’s first decentralised platform for marketing automation.

Mithril is a social media app which makes content creation and engagement on social media worthwhile for users, in the form of crypto. Their social media platform enables what Mithril has termed “social mining” in which users are rewarded for the impact and influence of their content. They also make extensive use of AI in their overall working.

Bubblo is an AI-based real-time discovery app that uses blockchain technology and virtual reality to build a marketplace allowing users to monetise their personal information for rewards. It provides users personalised recommendation of bars, restaurants and other venues.

Back in India, Signzy couples artificial intelligence with the blockchain to make secure, compliant and user-friendly transactions in banks. It is harnessing the power of AI and blockchain to enable their clients to adapt digitisation and offer fully digital experiences to their users. It offers products such as RealKYC which is a bank-grade digital KYC and Digital contracts which is a secured digital contract enabled by Aadhaar and Biometrics

EVS is an employee verification platform created by SpringRole which verifies employees’ credentials and skills. It uses blockchain as a platform where the feedbacks registered are fed into an AI system along with using a smart contract method which digitally facilitates, verify, or enforce the negotiation or performance of any contract.

Outlook

As the industries are seeing newer developments, there will be more blockchain and non-blockchain based products looking to leverage the advantages that AI can offer.

The long-term potential for both blockchain and AI is staggering, so combining the two certainly makes such projects worth beneficial. It enhances each other’s capabilities as seen in few instances above while offering opportunities for better oversight and accountability. These developments simply point towards why AI should not be brought into blockchain.

Related

Provide your comments below

comments

Source link http://bit.ly/2V2N1PN

[courtneyvbrooks87]

Integrating AI Into Blockchain Can Help In More Ways Than You Think

Integrating AI Into Blockchain Can Help In More Ways Than You Think

Artificial intelligence is one of the major driving forces of innovations loved by entrepreneurs and users alike. It has disrupted numerous industries including customer service, agriculture, manufacturing, healthcare, tech support and many more.

Blockchain technology is another force to be reckoned with. Although it is presently popular in the BFSI sector, there are endless uses for Blockchain technology, such as, education, transportation, voting, law and enforcement, among others.

Advertisement

Blockchain, though powerful on its own, becomes enhanced to a whole new level when coupled with AI. New features and capabilities become unlocked, enhanced, and more secure through their convergence.

How AI Can Add On To Blockchain

The confluence of AI in blockchain creates perhaps what is the world’s most reliable technology enabled decision making systems that are virtually tamper-proof and provides solid insights and decisions. It holds several benefits like:

Improved business data models

Globalised verification systems

Innovative audits and compliance systems

Smarter finance

Transparent governance

Intelligent retail

Intelligent predictive analysis

Digital Intellectual Property Rights

Technical Enhancements That AI Can Enable

Security: With the implementation of AI, Blockchain technology becomes more secure by creating secure future application deployments. AI algorithms which are increasingly making decisions about whether financial transactions are fraudulent and should be blocked or investigated is a good example of it.

Efficiency: AI can help optimise calculations to reduce miner load which results in less network latency for faster transactions. AI enables to reduce the carbon footprint of blockchain technology. The cost which is applied upon miners would also be reduced along with the energy spent if AI machines replace the work done by miners. As the data on blockchains grows by the minute, AI’s data pruning algorithms can be also be applied on the blockchain data which automatically prunes the data which is not required for future use. AI can introduce even new decentralised learning systems such as federated learning or new data sharing techniques that make the system much more efficient.

Trust: The iron cast records of blockchain is considered one its USP. Applied in conjunction with AI means that users have clear records to follow the system’s thinking process. This, in turn, helps the bots trust each other, increasing machine-to-machine interaction and allowing them to share data and coordinate decisions at large.

  Better Management: When it comes to cracking codes, human experts get better over time with practice. A machine learning-powered mining algorithm will eliminate the need for human expertise as it could almost instantaneously sharpen its skills if it is fed the right training data. So, AI also helps in managing blockchain systems better.

Privacy And New Markets: Making private data secure invariably lead to it being sold, resulting in data markets/model markets. The markets get easy, secure data sharing that helps smaller players gain Blockchain’s privacy can be more increased by executing “Homomorphic encryption” algorithms. Homomorphic algorithms are the ones using which operations can be performed on encrypted data directly.

Storage: Blockchains are ideal for storing the highly sensitive, personal data which, when smartly processed with AI, can add value and convenience. Smart healthcare systems that make accurate diagnoses based on medical scans and records is a good example of that.

Real-Life Applications:

Triggmine, a product converges AI in blockchain within a single marketing automation system. It has led to the company being one of the world’s first decentralised platform for marketing automation.

Mithril is a social media app which makes content creation and engagement on social media worthwhile for users, in the form of crypto. Their social media platform enables what Mithril has termed “social mining” in which users are rewarded for the impact and influence of their content. They also make extensive use of AI in their overall working.

Bubblo is an AI-based real-time discovery app that uses blockchain technology and virtual reality to build a marketplace allowing users to monetise their personal information for rewards. It provides users personalised recommendation of bars, restaurants and other venues.

Back in India, Signzy couples artificial intelligence with the blockchain to make secure, compliant and user-friendly transactions in banks. It is harnessing the power of AI and blockchain to enable their clients to adapt digitisation and offer fully digital experiences to their users. It offers products such as RealKYC which is a bank-grade digital KYC and Digital contracts which is a secured digital contract enabled by Aadhaar and Biometrics

EVS is an employee verification platform created by SpringRole which verifies employees’ credentials and skills. It uses blockchain as a platform where the feedbacks registered are fed into an AI system along with using a smart contract method which digitally facilitates, verify, or enforce the negotiation or performance of any contract.

Outlook

As the industries are seeing newer developments, there will be more blockchain and non-blockchain based products looking to leverage the advantages that AI can offer.

The long-term potential for both blockchain and AI is staggering, so combining the two certainly makes such projects worth beneficial. It enhances each other’s capabilities as seen in few instances above while offering opportunities for better oversight and accountability. These developments simply point towards why AI should not be brought into blockchain.

Related

Provide your comments below

comments

Source link http://bit.ly/2V2N1PN