Deep learning, a technology based on artificial neural networks, has revolutionized artificial intelligence in the space of a few years. But what exactly is it?

Used by Siri, Cortana and Google Now to understand speech and recognize faces, deep learning is often confused with the concept of artificial intelligence (AI), so much so that the two terms are thought to be synonymous. However, this isn’t the case at all. Deep learning is a branch of machine learning, which in turn is a subset of AI. Here’s how it works.

Take a plunge into the depths of deep learning

Born with the development of computers, research in AI was quickly characterized by the emergence of different currents. One of them sought inspiration from the workings of the human brain in an attempt to create artificial neural networks. An initial neural machine was created by two Harvard University researchers as early as 1951. But development in the field only took off in recent decades, which were marked by major advances in the performance of computers. These also paved the way for the concept of deep learning, which depends on neural networks with many hidden layers.

Put simply, deep learning is a technology that teaches a machine to represent the world. It is a training technique that can enable a program to recognize the content of an image or to understand the spoken word. In the past, to accomplish such tasks engineers would explain to machines how to represent images. With deep learning, the machines take on this job themselves.

An extension of supervised learning

To understand how machines are capable of such a feat, you have to start with supervised learning. This is a standard technique in AI, which consists of feeding a machine with large amounts of data. For example, to train a program to recognize automobiles, it is fed tens of thousands of images of automobiles, which are labeled as such. Once this training has been completed — and it may take several hours or even days — the program will be able to recognize automobiles even in images it has never seen before.

Deep learning also uses supervised learning, but the internal architecture of the machine is different, because each of the thousands of units making up the neural network performs small, simple calculations.

A researcher at the French National Center for Scientific Research (CNRS) Yann Ollivier explains this process with an example: “How does a machine recognize a picture of a cat? The most salient characteristics are the eyes and ears. So how does it recognize a cat’s ear? It is distinguished by an angle of about 45 degrees. To recognize the presence of a line, a first layer of neurons will identify a difference in the pixels above and below it: this will generate a level one characteristic. The second layer will work on these features and combine them. If there are two lines that meet at 45°, it will start to recognize a cat’s ear triangle. And so on…” At every stage of this ongoing analysis, the neural network gains a deeper understanding of the content of an image.

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We live in a world surrounded by various software and applications and tend to shovel all the data related to our existence without any hesitation. Interestingly public and private sector organizations collect this information, store it and leverage it for personal and public benefits. The big data generated at such a massive scale help analyze people’s preferences and dictate their conduct based on it.

The big data technologies are primarily impacting sectors including banking and finance, media and entertainment, healthcare, education, agriculture and online retail in various regions.

Specifically, the year 2019 witnessed the insurgence of big data technologies and how it is paving way for various countries and regions to envelop established businesses. The up-surging trends predict the new age technologies like big data will soon become mainstream while further helping conceive novel business models.

Here is the list of 10 significant countries and regions across the globe that are leading big data adoption among others.

United States

In the US, the rise in data collection from unstructured sources is one of the major driving factors for its big data market. The increase in this market is owing to the high demand sectors such as public utilities. As various public sector units are using mobile, social networks, and weblogs for their businesses today, these applications and websites are responsible for generating a huge amount of data regularly. This data is utilized for business insights and forecasts. Therefore, big data technologies help the US market provide an edge to the business by collecting, converting, and analyzing the raw data from various sources into meaningful business information.

Canada

According to a market report, the Canadian Big Data and Analytics market reached US$ 1,866.6 million in 2017 and it is expected to grow with CAGR of 9.4 percent by 2022. For businesses in Canada, big data technologies are one of the top software investment priorities in order to derive meaningful insights as well as for automation purposes. Experts believe that big data and analytics implementation is forming a foundation for the future adoption of AI-related technologies.

Japan

In Japan, the big data market is influenced by several drivers, restraints, and opportunities. The key factors of this market are – a sudden rise in the application of social media in order to study the customer behavior pattern by the businesses to make their strategies more effective. The growth in the amount of transitional information also acts as a driver for accelerating the big data market in Japan. Additionally, the proliferation of real-time information from varied sources – web, log files, handheld devices (mobile devices), sensors and others are expected to create ample opportunities for big data.

China

A market report suggests that the big data sector in China will continue to depict steady expansion by 2023. Policy support and technology integration are major factors driving this growth. The report revealed that revenue of the domestic big data sector is predicted to reach US$ 9.6 billion in 2019 and is expected to grow at CAGR of 23.5 percent during 2019-2023. It also said that the size of China’s big data market is likely to hit US$ 22.49 billion by 2023. Observing the up surging graph of big data, the report predicts that AI platforms will become the third-largest sub-market of the overall big data industry by 2023 while replacing commercial services.

UK

Big data technologies in the UK are expected to grow at an astonishing rate in the coming years. Although the focus of this upsurge is on the UK it is being assumed that it will impact the other European regions positively. Research indicates that telecoms firms are already strongly adopting Big Data solutions, but other industries are catching up and expressing a strong interest in its adoption as well. Also, retail banking is likely to overtake telecoms soon for Big Data analytics affecting the industry to its core.

South Korea

The government of South Korea has decided to expand the domestic data market to 10 trillion won by 2022, which has been recently recorded around 6.3 trillion won. The Fourth Industrial Revolution Committee Chairman Chang Byeong-kyu said, “The government’s data policy that does not comply with the changes of the times is acting as a stumbling block to the spread of the Fourth Industrial Revolution. A paradigm shift in data use is required to strengthen the competitiveness of the nascent big data industry.” Reportedly the Korean ministries will nurture big data specialized centers for the abundant generation and collection of raw data by industries.

Russia

In Russia, the main consumers of Big Data technologies are banks, telecom operators and large retailers. The main problems of development of the direction of Big Data are the shortage of qualified personnel, lack of sufficient experience of the Russian implementations and also the high cost of solutions. However, the IRI notes that in the next five years the big data market in Russia will grow by 10 times, to 300 billion rubles by 2024. According to experts, in the upcoming future, there will be more implementations using the technology of Big Data in the public sector. The extensive data arrays which are saved up by federal state agencies serve as a great resource that can be used for the development of digital society. It can also help increase in process performance of public administration.

India

A report suggests that there has been a proliferation in the Big Data and AI ecosystem with several large and small players entering in the last few years. This is expected to lead India in becoming one of the largest Big Data Analytics Market in the world with better use cases and significant opportunities for data scientists in the future. Reportedly, the competitive landscape of the Big Data Technology & Services market in India remains largely fragmented owing to its well-established IT services industry that has been able to leap into the data revolution rapidly. Besides, a number of emerging players have specialized in catering to the Big Data needs of the world across various End-User verticals.

Middle East Region

With a huge amount of data generation, the growth of big data in the Middle East market is because of the adoption of IoT and cloud, upcoming mobile BI and domain-specific solutions, and growth in competition which demands simplified and enhanced data visualization tools. The big data technologies are being used in multiple sectors, such as automobile, construction, healthcare, and energy & power, etc. There, big data is further processed and analyzed to gain worthy business insights. The current scenario of big data in the Middle East region implies that several companies are deploying IoT to provide cutting-edge solutions and insights, which, in turn, will boost the growth of the big data, business intelligence, and analytics market.

South Africa

Leveraging disruptive technologies such as IoT, AI, machine learning, social media, and cloud to enhance citizen engagement, drive operational efficiencies, and improve flexibility inevitably creates massive amounts of data in the South African region. Extracting actionable intelligence from this data requires advanced big data technologies to support various line-of-business functions, which in turn requires appropriate skills and funding. According to research, the wide implementation of advanced, easy-to-use big data solutions is becoming a priority for many South African organizations. In fact, C-suite executives from the government and the public sector find this technology especially valuable and look up to develop their approach to extracting relevant insights and making more efficient business decisions.

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The Global AI in Fintech Market report provides in-depth analysis and insights into developments impacting businesses and enterprises on global and regional level. The report covers the global AI in Fintech Market performance in terms of revenue contribution from various segments and includes a detailed analysis of key trends, drivers, restraints, and opportunities influencing revenue growth of the global AI in Fintech market. This report studies the global AI in Fintech Market size, industry status and forecast, competition landscape and growth opportunity. This research report categorizes the global AI in Fintech Market by companies, region, type and end-use industry. The report titled “Global AI in Fintech Market Size, Status and Forecast 2019-2023” offers a primary impression of the AI in Fintech industry covering different product Scope, Characterizations, Classifications, Objectives, and Participants in the industry chain structure. The AI in Fintech market report explains the key growth factors and limitations that remarkably influence the market, and also provides the information about the previous and current status of the AI in Fintech market at global level.

The recent report on “AI in Fintech market” offered by a comprehensive investigation into the geographical landscape, industry size along with the revenue estimation of the business. Additionally, the report also highlights the challenges impeding market growth and expansion strategies employed by leading companies in the “AI in Fintech market.” The AI in Fintech Market study is structured in a chapter-wise manner. The report examines the market based on explicit key segments under various categories. The segments are checked for their growth in nature in the upcoming years. The AI in Fintech market report contains a pelagic as well as organized data. Next, in this report, you will find the competitive scenario of the major market players focusing on their sales revenue, customer demands, company profile, import/export scenario, business strategies that will help the emerging market segments in making major business decisions. The market contains the ability to become one of the most lucrative industries as factors related to this market such as raw material affluence, financial stability, technological development, trading policies, and increasing demand are boosting the market growth. The report includes the description about the factors that considerably enhance and downgrade the growth of the market profound explanation of the market’s past data; alongside the current investigated data, and forecast the development trend of the AI in Fintech market.

Competitors and Regional Analysis of AI in Fintech Market:

The report analyzes competition and the latest developments in the future AI in Fintech market. On the basis of major manufacturers, the global AI in Fintech market is segmented based on the key manufacturers, growth rate, revenue, research and modification taking place. In addition, it gives rise to opportunities for companies in the market. Some of the outstanding manufacturers in the AI in Fintech market enclosed in the report are Microsoft, Google, Ibm, Intel, Inbenta Technologies, Nuance Communications, Complyadvantage.Com, Salesforce.Com, Amazon Web Services, Samsung, Ipsoft, Next It Corp.

The AI in Fintech Market report mainly includes the major company profiles with their annual sales & revenue, business strategies, company major products, profits, industry growth parameters, industry contribution on global and regional level. This report covers the global AI in Fintech Market performance in terms of value and volume contribution. This section also includes major company analysis of key trends, drivers, restraints, challenges, and opportunities, which are influencing the global AI in Fintech Market. Impact analysis of key growth drivers and restraints, based on the weighted average model, is included in this report to better equip clients with crystal clear decision-making insights. The AI in Fintech Market report provides company market size, share analysis in order to give a broader overview of the key players in the market. The market report formation requires detailed research and analysis to realize the market growth; and different scientific strategies, including SWOT analysis to get the information suitable to evaluate the upcoming monetary variations associated to the current situation and growth pattern of the market. The geographical regions also play an important role in enhancing the growth and development of the global AI in Fintech market. The report has all the vital information regarding supply and demand, market development enhancers, market share, sales distributors, and more advocated in a very formal pattern.

This report studies the market across regions such as North America, Europe, Latin America, Asia Pacific, Middle East, and Africa. The regional market will get an advantage from the well-established AI in Fintech framework and the high level of digitizing in the region’s sector.

Market Segmentation

Global AI in Fintech Market by Type:

Cloud, On-Premises
Virtual Assistants (Chatbots), Business Analytics And Reporting, Customer Behavioral Analytics,

Important AI in Fintech Market Data Available In This Report:

1) Emerging opportunities, competitive landscape, revenue share of main manufacturers.
2) This report discusses the AI in Fintech Market summary; market scope gives a brief outline of the AI in Fintech Market.
3) Strategic recommendations, forecast growth areas of the AI in Fintech Market.
4) Key performing regions (APAC, EMEA, Americas, Other) along with their major countries are detailed in this report.
5) Challenges for the new entrants, trends market drivers.
6) Company profiles, product analysis, Marketing strategies, emerging market segments and comprehensive analysis of AI in Fintech Market.
7) AI in Fintech Market share year-over-year growth of key players in promising regions.

Key Research:

The main sources are industry experts from the global AI in Fintech industry, including management organizations, processing organizations, and analytical services providers that address the value chain of industry organizations. We interviewed all major sources to collect and certify qualitative and quantitative information and to determine future prospects. Through interviews in the industry experts industry, such as CEO, vice president, marketing director, technology and innovation director, founder and key executives of key core companies.

The post Global AI in Fintech Market Regional Analysis 2019 – 2023 : Microsoft, Google, Inbenta Technologies, Nuance Communications appeared first on Artificial Intelligence.

The Apache Software Foundation (ASF) recently announced that SINGA, a framework for distributed deep-learning, has graduated to top-level project (TLP) status, signifying the project’s maturity and stability. SINGA has already been adopted by companies in several sectors, including banking and healthcare.

Originally developed at the National University of Singapore, SINGA joined ASF’s incubator in March 2015. SINGA provides a framework for distributing the work of training deep-learning models across a cluster of machines, in order to reduce the time needed to train the model. In addition to its use as a platform for academic research, SINGA has been used in commercial applications by Citigroup and CBRE, as well as in several health-care applications, including an app to aid patients with pre-diabetes.

The success of deep-learning models has been driven by the use of very large datasets, such as ImageNet with hundreds of thousands of images, and complex models with millions of parameters. Google’s BERT natural-language model contains 300 million parameters and is trained on nearly 3 billion words. However, this training often requires hours, if not days, to complete. To speed up this process, researchers have turned to parallel computing, which distributes the work across a cluster of machines. According to Professor Beng Chin Ooi, leader of the research group that developed SINGA:

It is essential to scale deep learning via distributed computing as…deep learning models are typically large and trained over big datasets, which may take hundreds of days using a single GPU.

There are two broad parallelism strategies for distributed deep-learning: data parallelism, where multiple machines work on different subsets of the input data, and model parallelism, where multiple machines train different sections of the neural-network model. SINGA supports both of these strategies, as well as a combination of the two. These strategies do introduce some communication and synchronization overhead, required to coordinate the work among the machines in the cluster. SINGA implements several optimizations to minimize this overhead.

Acceptance as a top-level project means that SINGA has passed several milestones related to software quality and community, which in theory makes the software more attractive as a solution. However, one possible barrier to adoption is that instead of building upon an existing API for modeling neural networks, such as Keras, SINGA’s designers chose to implement their own. By contrast, the Horovod framework open-sourced by Uber allows developers to port existing models written for the two most popular deep-learning frameworks, TensorFlow and PyTorch. PyTorch in particular is the framework used in a majority of recent research papers.

ASF has several other top-level distributed-data processing projects that support machine-learning, including Spark and Ignite. Unlike these, SINGA is designed specifically for deep-learning’s large models. ASF is also home to MXNet, a deep-learning framework similar to TensorFlow and PyTorch, which is still in incubator status. AWS touted MXNet as its framework of choice in late 2016, but MXNet still hasn’t achieved widespread popularity, hovering at just under 2% in KDNugget’s polls.

Apache SINGA version 2.0 was released in April, 2019. The source code is available on GitHub, and a list of open issues can be tracked in SINGA’s Jira project. According to ASF, upcoming features include “SINGA-lite for deep learning on edge devices with 5G, and SINGA-easy for making AI usable by domain experts (without deep AI background).

The post Deep-Learning Framework SINGA Graduates to Top-Level Apache Project appeared first on Artificial Intelligence.

Global Business Intelligence Software Market is forecast to bring about afairly desirable remuneration portfolio by the end of the forecast period.Certainly, the report not only includes a modest growth rate over the forecast time frame but also contains a reliable overview of this business. The study involves overall growth opportunities and valuation currently this market is holding. Additionally, the report involves classified segmentation of Business Intelligence Software market.

Global Business Intelligence Software Market: Key players

Looker
Microsoft
Tableau
Domo
Qlik
Zoho
SAP
Oracle
Cognos
SAS
Information Builders
Yellowfin
TIBCO
MicroStrategy
Targit
InetSoft

Market Segment by Type covers:

Mobile
Cloud

Market Segment by Applications can be divided into:

SMEs
Large Organization
Other

Regional analysis covers:
• North America (USA, Canada, and Mexico)
• Europe (Russia, France, Germany, UK, and Italy)
• Asia-Pacific (China Korea, India, Japan, and Southeast Asia)
• South America (Brazil, Columbia, Argentina, etc.)
• The Middle East and Africa (Nigeria, UAE, Saudi Arabia, Egypt, and South Africa)

Key Highlights of the Business Intelligence Software Market report:
• The key details related to Business Intelligence Software industry like the product definition, cost, variety of applications, demand and supply statistics are covered in this report
• Competitive study of the major players will help all the market players in analyzing the latest trends and business strategies
• Holistic study of market segments and sub-segments will help the readers in planning the business strategies
• Figure Global Production Market Share of Business Intelligence Software market by Types and by Applications in 2019

The report has provided quantitative and qualitative analysis along with absolute opportunity assessment in the report. Also, the report offers Porter’s Five Forces analysis and PESTLE analysis for more detailed contrast studies. Each section of the report has something valuable that helps companies for improving their sales and marketing strategy, gross margin, and profit margins. Using the report as a tool for gaining insightful Business Intelligence Software market analysis, players can identify the much-required changes in their operation and improve their approach to doing business.

The report provides comprehensive information to identify market segments that help to improve the quality of business decision-making based on demand, sales, and production based on application-level analysis and regional level. Further, the report has been analyzed graphically to make this report more effective and understandable. The experts have constructed the detailed study market 2019 in a structured format for better analysis.

Chapters involved in Business Intelligence Software market report:
Chapter 1: Market Overview, Drivers, Restraints and Opportunities, Segmentation overview
Chapter 2: Market Competition by Manufacturers
Chapter 3: Production by Regions
Chapter 4: Consumption by Regions
Chapter 5: Production, By Types, Revenue and Market share by Types
Chapter 6: Consumption, By Applications, Market share (%) and Growth Rate by Applications
Chapter 7: Complete profiling and analysis of Manufacturers
Chapter 8: Manufacturing cost analysis, Raw materials analysis, Region-wise manufacturing expenses
Chapter 9: Industrial Chain, Sourcing Strategy and Downstream Buyers
Chapter 10: Marketing Strategy Analysis, Distributors/Traders
Chapter 11: Market Effect Factors Analysis
Chapter 12: Market Forecast
Chapter 13: Business Intelligence Software Research Findings and Conclusion, Appendix, methodology and data source

The post Global Business Intelligence Software Market 2019 – Looker, Microsoft, Tableau, Domo, Qlik appeared first on Artificial Intelligence.

Thanks to the technological advancements in the field of data analytics, the global market for neutral network software is witnessing an exponential rise in its size and revenue. Since neutral network software is highly effective in reducing the cost and operational time in a number of enterprises, its usage in business application, such as such as fraud detection and risk assessment, is increased by leaps and bounds.

The neural network software market is majorly driven by the remarkable rise in the demand for data archiving tools, used for organizing a massive amount of unorganized data created by various end users. Additionally, the high adoption rate of digital technologies and the increasing demand for predicting solutions are likely to boost this market in the near future. However, the slow digitization rate across emerging markets, dearth of technical expertise, and various other operational challenges may hinder the neural network software market’ growth over the forthcoming years.

Analytical software, data mining and archiving software, and optimization software are the key products available in this market. Currently, the demand for analytical software is higher than other neutral network software. However, the data mining and archiving software is expected to witness a high-paced demand growth over the next few years, thanks to the rising need for the classification and clustering of unorganized data. The significant areas where neural network software find application is financial operations, trading, business analytics, and product maintenance.

Global Neural Network Software Market: Overview

Large-scale digitization and seamless connectivity of a vast variety of electronic end-points and sensors are two important aspects common to all enterprises that call themselves technologically advanced and digitally competent. To be able to make use of the vast volumes of data generated from interactions between the connected entities and apply it for the benefit of the business, effective analytical, predictive tools are required. Artificial neural networks, the computational devices, which could be either an algorithm or an actual hardware, are modeled after the operations and structure of neural network of living beings.

Owing to their ability to learn from the inputs provided, much as their biological counterparts, artificial neural networks are considered to be the future of data analytics. A neural network software simulates an artificial neural network algorithm for use in a computer system and is used to apply the concepts of artificial neural networks to input data.

This report on the global neural network software market presents a detailed overview of the present growth dynamics of the market and its key segments. The report includes several forward-looking quantitative and qualitative projections about aspects such as market valuation, overall sales, demand and supply statistics in key regional markets, and overall future growth prospects. The neural network software market report also presents a detailed overview of the factors expected to have a notable impact on the overall development of the market in the next few years, including growth drivers, challenges, regulatory aspects across key regional markets, opportunities, and level of competition.

Global Neural Network Software Market: Geographical Dynamics

For the study, the global market for neural network software has been segmented in terms of geography into regions such as North America, Europe, Asia Pacific, and Middle East and Africa. Of these, North America is presently the leading market in terms of revenue contribution to the global market as well as technological advancements in the field of neural network. The region leads owing to the presence of a large number of technology companies excelling in the field of neural networks, large number of enterprises with highly digitized and technologically advanced ecosystems who could be potential buyers of neural network software.

In the next few years, however, regions such as Asia Pacific and Middle East and Africa are expected to emerge as the ones with the most promising growth prospects. Rising investment in smart cities, focus on digitization of processes and operations across industrial, commercial, and public sectors, and an increasing number of enterprises adopting technological implementation would foster the growth prospects of the neural network software market in these regions.

Global Neural Network Software Market: Competitive Landscape

Some of the world’s leading tech giants such as Google Inc., Microsoft Corporation, IBM, Intel Corporation, Qualcomm Technologies Inc., and Oracle are investing vast capital and human resources towards the development of neural networks that most closely resemble and work like the highly complex biological neural network. The market is also witnessing the entry of a large number of small- and medium-sized companies, which are helping the market gain strength through innovative neural network software solutions and systems for a vast range of applications.

Other than the technology companies mentioned above, some more of the neural network software market’s most notable vendors are GMDH, Llc, Neural Technologies Limited, Afiniti, SAP SE, Ward Systems Group, Inc., Alyuda Research, Llc., Slagkryssaren Ab, Starmind International Ag, Neuralware, Slagkryssaren AB, Swiftkey, and Starmind International AG.

About TMR Research:

TMR Research is a premier provider of customized market research and consulting services to business entities keen on succeeding in today’s supercharged economic climate. Armed with an experienced, dedicated, and dynamic team of analysts, we are redefining the way our clients’ conduct business by providing them with authoritative and trusted research studies in tune with the latest methodologies and market trends.

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