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News Article | May 24, 2017
Site: www.businesswire.com

PLANTATION, Fla.--(BUSINESS WIRE)--Today, DHL introduced a new integral part of its Resilience360 supply chain risk management platform called DHL Supply Watch. The extension of DHL’s early warning system uses machine learning and natural language processing to detect disruptions in a company’s supply base before they cause financial losses or long lasting reputational damage. With Supply Watch, DHL Resilience360 is adding a broad range of new risk categories to the system’s existing portfolio to monitor supplier risks on a company level, including financial indicators, mergers & acquisition, environmental damages, supply shortages, quality issues and labor disputes, using publically available data found by monitoring of online and social media sources. “We provide our customers with a solution that detects and mitigates potential supplier failures before they happen, allowing them to focus on early risk mitigation and auditing activities of their most relevant suppliers and third parties,” says Tobias Larsson, Head of Resilience360, DHL Customer Solutions & Innovation. “The insights and transparency customers gain through Supply Watch are another example of how digitalization can benefit end-to-end supply chain operations, through building resilient supply chains and enabling businesses to be more competitive.” DHL Resilience360 Supply Watch monitors some 140 different risk categories including financial, environmental and social factors among risks resulting from crime, labor breaches, quality defects and supply chain perils such as shortages, capacity constraints and delays. Using advanced Machine Learning (ML) and Natural Language Processing (NLP) technologies, the adopted system analyzes data based on the monitoring of up to 30 million posts from more than 300,000 online and social media sources to detect potential supply chain disruptions. Instances such as the bankruptcy of one of the world’s top ten container shipping lines, which led to capacity shortages and supply chain disruptions worldwide, was for many businesses unexpected, despite indications that were made public before. DHL Supply Watch closes this gap and flags potential risks at an early stage. The recent global ‘WannaCry' ransomware attack is another example for a situation in which the system could help to identify which suppliers may have reportedly been affected, and therefore allowing companies working with them to take appropriate precautions in their supply chain. The development of the intelligent Supply Watch system was supported by leading linguistics experts and data scientists to establish a reliable analysis of both content and context of online discussions. Supply Watch is capable of understanding human language and evaluates how people talk about risk-relevant events and situations around the world. Among different risk types which may affect supply chains, many are particularly hard to detect, such as quality issues. Monitoring and analyzing discussions and articles in online and social media about such concerns, information about recalls, protest or delays, helps identify early indicators of supplier and partner distress. This feature distinguishes the system significantly from conventional search approaches as it operates nearly in real-time. The fast and effective solution enables users to address emerging issues quickly, preventing reputational and financial losses. DHL Supply Watch is available independently but can be fully integrated into the DHL risk assessment and incident monitoring solution Resilience360. DHL Resilience360 Supply Watch is being launched from the Gartner Supply Chain Executive Conference 2017 in Phoenix, Arizona, where participants have the opportunity to experience a live demonstration of the solution. Further information about DHL Resilience360 are available online at http://www.resilience360.dhl.com You can find the press release for download as well as further information on http://www.dpdhl.com/pressreleases DHL – The logistics company for the world DHL is the leading global brand in the logistics industry. Our DHL family of divisions offer an unrivalled portfolio of logistics services ranging from national and international parcel delivery, e-commerce shipping and fulfillment solutions, international express, road, air and ocean transport to industrial supply chain management. With about 350,000 employees in more than 220 countries and territories worldwide, DHL connects people and businesses securely and reliably, enabling global trade flows. With specialized solutions for growth markets and industries including technology, life sciences and healthcare, energy, automotive and retail, a proven commitment to corporate responsibility and an unrivalled presence in developing markets, DHL is decisively positioned as “The logistics company for the world”. DHL is part of Deutsche Post DHL Group. The Group generated revenues of more than 57 billion euros in 2016.


News Article | May 25, 2017
Site: globenewswire.com

BEIJING, May 25, 2017 (GLOBE NEWSWIRE) -- Gridsum Holding Inc. (“Gridsum” or the “Company”) (NASDAQ:GSUM), a leading provider of cloud-based big-data analytics, machine learning and AI solutions in China, today announced that, as a part of its strategic evolution, it has consolidated all of its artificial intelligence (“AI”) activities strategically, technically and organizationally into a new division called the Gridsum Prophet. Gridsum is a first mover in China in big data intelligence. Since 2005, the Company has utilized a distributed big-data computing architecture, developed and implemented sophisticated natural language processing (“NLP”), and leveraged machine learning directed toward large enterprise clients. During that time, from serving large enterprise customers, the Company has accumulated deep domain knowledge and expertise as well as a massive amount of data that fuels its machine learning algorithms. Since this early inception, the Company has continued to stay at the forefront through focus and investment, hiring and training extraordinary engineers and architects and, importantly, playing an active and leading role in the AI academic and developer communities. The Company believes it is currently the technology market leader in the China enterprise-focused AI space with a global best-of-breed Enterprise-AI engine. As of March 31, 2017, Gridsum has filed 1,653 patent applications in China, of which 583 are big-data focused and 148 are explicitly Enterprise-AI focused. The Company believes this is the largest number of Enterprise-AI focused patents for a company in China today. Mr. Guosheng Qi, Chief Executive Officer of Gridsum, stated, “We are excited to launch our new AI engine, Gridsum Prophet. AI is playing an increasingly key role in developing and optimizing our intelligent products and solutions across the board and providing quantifiable value-add to our enterprise and other customers. Through Gridsum Prophet, we are taking strategic moves to further accelerate our AI development in a more concentrated way. New product features driven by Gridsum Prophet have already been key in a double-digit number of both client wins and subscription renewals with substantial ARPU uplift.” In terms of scope, Gridsum Prophet encompasses all of the Company’s AI capabilities: machine learning, natural language processing, image recognition, predictive industry modeling, and knowledge graph. Gridsum Prophet powers the Company’s intelligent products and solutions across the matrix of clients and industry verticals. It also allows the Company to see cross-industry, product and demographic correlations and relationships to add further value for its clients. Gridsum Prophet is an explicit and important development area for the Company and is increasingly key in creating new differentiated product features for the Company’s existing products (e.g. marketing automation) and is central in defining and shaping new products, such as the Company’s social listening and brand management suite as well as its legal services and Intelligent CRM product suites. Gridsum Prophet already plays a key role in driving quantifiable value for the Company’s customers. It also defines a key competitive advantage for Gridsum versus other local and global players. This is the case in the marketing technology and broader Business Intelligence ecosystems where Gridsum Prophet is instrumental for the Company in driving new customer acquisition as well as ARPU growth with existing customers. Gridsum Prophet is also key to the Company’s legal services product suite which, in some areas, Gridsum believes is consistent with passing a “soft” Turing Test - delivering results in a few seconds consistent with multiple man-hours of work by a team of junior lawyers and paralegals. Gridsum Prophet is also central to the development and success, so far in current Beta trials, for the Company’s Intelligent CRM solution. Broadly speaking, consumer AI often leverages the ubiquitous consumer Internet ecosystems across e-commerce, social, mobile, entertainment and others and focuses to quickly solve or facilitate relatively simple (from a mathematical standpoint) but often time-consuming, “painful” or distracting consumer challenges. Consumer AI is incredibly broad in scope, having the potential to revolutionize and facilitate the way people live and, within the next 10 years, will likely touch the lives of most of the people on the planet. Enterprise AI is different in focus, structure, development, management, application and goals. It requires a very different and focused “organizational DNA” which is particularly rare in China (and elsewhere in Asia). It is focused on creating immediate and quantifiable value for companies with an immediate KPI impact and evolving and increasing that value-add over time. Enterprise AI hence requires deep domain expertise and knowledge of the target industry and its ecosystem. This allows Gridsum to understand the challenges and opportunities where AI technologies can be effectively applied to delivering immediate and quantifiable value to an enterprise customer whether it is improving efficiency, reducing cost, or allocating marketing budget for optimized ROI. These are often industry-specific drivers and dynamics, typically requiring more focus and depth in a narrower area than consumer AI. To accomplish this, enterprise AI tends to heavily leverage supervised learning techniques to infuse human expertise into the resulting intelligent solutions. To drive high-value machine learning, Gridsum utilizes its growing team of industry experts and data scientists who also help tailor the Company’s products, solutions, features and UI for specific industries and verticals. This is a key differentiator of enterprise AI (and Gridsum) when compared to the demands and development process for typical consumer AI. Gridsum uses an iterative learning process whereby high quality raw data is sourced. That data is organized across multiple dimensions and machine learning then produces intermediate results from analyzing the complex relationships within the data. The Company’s industry experts review the results suggested by the system and, based on deep industry expertise, tag these results as relevant, less relevant or irrelevant which is then fed back into the system’s machine learning cycle to further optimize the results, thereby forming a positive optimizing feedback loop. In addition to the Company’s strong emphasis on supervised learning, it is important to note that Gridsum utilizes deep learning, supervised or unsupervised where appropriate, in areas such as abnormal pattern discovery, image recognition and customer value prediction. To further explain how Gridsum Prophet works in practice, below are three specific examples of the engine in action. The Company estimates that over 35% of Internet traffic in China can be defined as “suspicious” (including non-human/bot and “malvertising”). Powered by the vast amount of online behavior data that the Company has been collecting and analyzing for over a decade across a broad range of industries and diverse sets of computing devices and software systems, Gridsum’s supervised and deep learning algorithms continuously improve the coverage and accuracy of click fraud detection and prevention within its Web Dissector and other products. By consolidating all of its abnormal pattern recognition technology into the Prophet engine, the system is now leveraging cross-industry and cross-product machine learning resulting in improved performance. This helps the Company’s clients to substantially and quantifiably improve their ROI from their digital market dollars. By mining historical media buying and performance data and leveraging Gridsum’s deep knowledge and expertise in the China marketing space, the Company’s data science team has developed an automated industry modeling and marketing budget allocation model. This means that a customer can input a desired range of KPI results and the system will then define and allocate a marketing budget to achieve this. This also allows a customer to have an accurate prediction of likely KPI outcomes from their marketing budget and strategy. This heavily leverages Gridsum’s supervised machine learning algorithms that the Company has already successfully used to help clients in the financial industry to allocate their annual digital marketing budget across media channels to achieve optimal return on investment. This moves Gridsum’s products up the value chain to a marketing strategy creation level, and has already resulted in significantly increased ARPU for a number of clients This is increasingly important in China today where the largest brands are finding it increasingly difficult to differentiate in an increasingly “noisy” and crowded landscape defined by a proliferation of lower-end brands. The Gridsum Prophet engine here also importantly helps clients to explore new media such as newsfeed, short video and social video in an ROI-focused, controlled, data-driven and results-oriented manner. These features have recently been a key differentiator in a number of the Company’s new customer additions. In another demonstration of the power of data and deep learning, Gridsum has built a customer value prediction system by analyzing the customers’ online (spanning mobile app, browser and PC) and offline behavior data (contained within a customer’s CRM system and other areas). Using predictive modeling and machine learning, the system has already been helping clients in the automotive industry to effectively improve their sales and call center performance by reducing the average number of calls needed for customer acquisition by as much as 90%. Going forward, over the medium and longer term, Gridsum Prophet opens the opportunity for significant future product development. For example: Gridsum Holding Inc. is a leading provider of cloud-based big-data analytics, machine learning and AI solutions for multinational and domestic enterprises and government agencies in China. Gridsum’s core technology, the Gridsum Big Data Platform, is built on a distributed computing framework and performs real-time multi-dimensional correlation analysis of both structured and unstructured data. This enables Gridsum’s customers to identify complex relationships within their data and gain new insights that help them make better business decisions. The Company is named “Gridsum” to symbolize the combination of distributed computing (Grid) and analytics (sum). As a digital intelligence pioneer, the Company’s mission is to help enterprises and government organizations in China use data in new and powerful ways to make better informed decisions and be more productive. For more information, please visit http://www.gridsum.com/. This announcement contains forward-looking statements. These forward-looking statements are made under the "safe harbor" provisions of the U.S. Private Securities Litigation Reform Act of 1995. These statements can be identified by terminology such as “may,” "will," "expects," "anticipates," “aims,” "future," "intends," "plans," "believes," "estimates," “likely to” and similar statements. Among other things, quotations from management in this announcement, Gridsum’s financial outlook as well as Gridsum's strategic and operational plans contain forward-looking statements. Gridsum may also make written or oral forward-looking statements in its reports filed with, or furnished to, the U.S. Securities and Exchange Commission, in its annual reports to shareholders, in press releases and other written materials and in oral statements made by its officers, directors or employees to third parties. Statements that are not historical facts, including statements about Gridsum's beliefs and expectations, are forward-looking statements. Forward-looking statements involve inherent risks and uncertainties. A number of factors could cause actual results to differ materially from those contained in any forward-looking statement, including but not limited to the following: unexpected difficulties in Gridsum's pursuit of its goals and strategies; the unexpected developments, including slow growth, in the digital intelligence market; reduced demand for, and market acceptance of, Gridsum's solutions; difficulties keeping and strengthening relationships with customers; potentially costly research and development activities; competitions in the digital intelligence market; PRC governmental policies relating to media, software, big data, the internet, internet content providers and online advertising; and general economic and business conditions in the regions where Gridsum provides solutions and services. Further information regarding these and other risks is included in Gridsum’s reports filed with, or furnished to, the Securities and Exchange Commission. All information provided in this press release and in the attachments is as of the date of this press release, and Gridsum undertakes no duty to update such information except as required under applicable law.


News Article | May 25, 2017
Site: globenewswire.com

BEIJING, May 25, 2017 (GLOBE NEWSWIRE) -- Gridsum Holding Inc. (“Gridsum” or the “Company”) (NASDAQ:GSUM), a leading provider of cloud-based big-data analytics, machine learning and AI solutions in China, today announced that, as a part of its strategic evolution, it has consolidated all of its artificial intelligence (“AI”) activities strategically, technically and organizationally into a new division called the Gridsum Prophet. Gridsum is a first mover in China in big data intelligence. Since 2005, the Company has utilized a distributed big-data computing architecture, developed and implemented sophisticated natural language processing (“NLP”), and leveraged machine learning directed toward large enterprise clients. During that time, from serving large enterprise customers, the Company has accumulated deep domain knowledge and expertise as well as a massive amount of data that fuels its machine learning algorithms. Since this early inception, the Company has continued to stay at the forefront through focus and investment, hiring and training extraordinary engineers and architects and, importantly, playing an active and leading role in the AI academic and developer communities. The Company believes it is currently the technology market leader in the China enterprise-focused AI space with a global best-of-breed Enterprise-AI engine. As of March 31, 2017, Gridsum has filed 1,653 patent applications in China, of which 583 are big-data focused and 148 are explicitly Enterprise-AI focused. The Company believes this is the largest number of Enterprise-AI focused patents for a company in China today. Mr. Guosheng Qi, Chief Executive Officer of Gridsum, stated, “We are excited to launch our new AI engine, Gridsum Prophet. AI is playing an increasingly key role in developing and optimizing our intelligent products and solutions across the board and providing quantifiable value-add to our enterprise and other customers. Through Gridsum Prophet, we are taking strategic moves to further accelerate our AI development in a more concentrated way. New product features driven by Gridsum Prophet have already been key in a double-digit number of both client wins and subscription renewals with substantial ARPU uplift.” In terms of scope, Gridsum Prophet encompasses all of the Company’s AI capabilities: machine learning, natural language processing, image recognition, predictive industry modeling, and knowledge graph. Gridsum Prophet powers the Company’s intelligent products and solutions across the matrix of clients and industry verticals. It also allows the Company to see cross-industry, product and demographic correlations and relationships to add further value for its clients. Gridsum Prophet is an explicit and important development area for the Company and is increasingly key in creating new differentiated product features for the Company’s existing products (e.g. marketing automation) and is central in defining and shaping new products, such as the Company’s social listening and brand management suite as well as its legal services and Intelligent CRM product suites. Gridsum Prophet already plays a key role in driving quantifiable value for the Company’s customers. It also defines a key competitive advantage for Gridsum versus other local and global players. This is the case in the marketing technology and broader Business Intelligence ecosystems where Gridsum Prophet is instrumental for the Company in driving new customer acquisition as well as ARPU growth with existing customers. Gridsum Prophet is also key to the Company’s legal services product suite which, in some areas, Gridsum believes is consistent with passing a “soft” Turing Test - delivering results in a few seconds consistent with multiple man-hours of work by a team of junior lawyers and paralegals. Gridsum Prophet is also central to the development and success, so far in current Beta trials, for the Company’s Intelligent CRM solution. Broadly speaking, consumer AI often leverages the ubiquitous consumer Internet ecosystems across e-commerce, social, mobile, entertainment and others and focuses to quickly solve or facilitate relatively simple (from a mathematical standpoint) but often time-consuming, “painful” or distracting consumer challenges. Consumer AI is incredibly broad in scope, having the potential to revolutionize and facilitate the way people live and, within the next 10 years, will likely touch the lives of most of the people on the planet. Enterprise AI is different in focus, structure, development, management, application and goals. It requires a very different and focused “organizational DNA” which is particularly rare in China (and elsewhere in Asia). It is focused on creating immediate and quantifiable value for companies with an immediate KPI impact and evolving and increasing that value-add over time. Enterprise AI hence requires deep domain expertise and knowledge of the target industry and its ecosystem. This allows Gridsum to understand the challenges and opportunities where AI technologies can be effectively applied to delivering immediate and quantifiable value to an enterprise customer whether it is improving efficiency, reducing cost, or allocating marketing budget for optimized ROI. These are often industry-specific drivers and dynamics, typically requiring more focus and depth in a narrower area than consumer AI. To accomplish this, enterprise AI tends to heavily leverage supervised learning techniques to infuse human expertise into the resulting intelligent solutions. To drive high-value machine learning, Gridsum utilizes its growing team of industry experts and data scientists who also help tailor the Company’s products, solutions, features and UI for specific industries and verticals. This is a key differentiator of enterprise AI (and Gridsum) when compared to the demands and development process for typical consumer AI. Gridsum uses an iterative learning process whereby high quality raw data is sourced. That data is organized across multiple dimensions and machine learning then produces intermediate results from analyzing the complex relationships within the data. The Company’s industry experts review the results suggested by the system and, based on deep industry expertise, tag these results as relevant, less relevant or irrelevant which is then fed back into the system’s machine learning cycle to further optimize the results, thereby forming a positive optimizing feedback loop. In addition to the Company’s strong emphasis on supervised learning, it is important to note that Gridsum utilizes deep learning, supervised or unsupervised where appropriate, in areas such as abnormal pattern discovery, image recognition and customer value prediction. To further explain how Gridsum Prophet works in practice, below are three specific examples of the engine in action. The Company estimates that over 35% of Internet traffic in China can be defined as “suspicious” (including non-human/bot and “malvertising”). Powered by the vast amount of online behavior data that the Company has been collecting and analyzing for over a decade across a broad range of industries and diverse sets of computing devices and software systems, Gridsum’s supervised and deep learning algorithms continuously improve the coverage and accuracy of click fraud detection and prevention within its Web Dissector and other products. By consolidating all of its abnormal pattern recognition technology into the Prophet engine, the system is now leveraging cross-industry and cross-product machine learning resulting in improved performance. This helps the Company’s clients to substantially and quantifiably improve their ROI from their digital market dollars. By mining historical media buying and performance data and leveraging Gridsum’s deep knowledge and expertise in the China marketing space, the Company’s data science team has developed an automated industry modeling and marketing budget allocation model. This means that a customer can input a desired range of KPI results and the system will then define and allocate a marketing budget to achieve this. This also allows a customer to have an accurate prediction of likely KPI outcomes from their marketing budget and strategy. This heavily leverages Gridsum’s supervised machine learning algorithms that the Company has already successfully used to help clients in the financial industry to allocate their annual digital marketing budget across media channels to achieve optimal return on investment. This moves Gridsum’s products up the value chain to a marketing strategy creation level, and has already resulted in significantly increased ARPU for a number of clients This is increasingly important in China today where the largest brands are finding it increasingly difficult to differentiate in an increasingly “noisy” and crowded landscape defined by a proliferation of lower-end brands. The Gridsum Prophet engine here also importantly helps clients to explore new media such as newsfeed, short video and social video in an ROI-focused, controlled, data-driven and results-oriented manner. These features have recently been a key differentiator in a number of the Company’s new customer additions. In another demonstration of the power of data and deep learning, Gridsum has built a customer value prediction system by analyzing the customers’ online (spanning mobile app, browser and PC) and offline behavior data (contained within a customer’s CRM system and other areas). Using predictive modeling and machine learning, the system has already been helping clients in the automotive industry to effectively improve their sales and call center performance by reducing the average number of calls needed for customer acquisition by as much as 90%. Going forward, over the medium and longer term, Gridsum Prophet opens the opportunity for significant future product development. For example: Gridsum Holding Inc. is a leading provider of cloud-based big-data analytics, machine learning and AI solutions for multinational and domestic enterprises and government agencies in China. Gridsum’s core technology, the Gridsum Big Data Platform, is built on a distributed computing framework and performs real-time multi-dimensional correlation analysis of both structured and unstructured data. This enables Gridsum’s customers to identify complex relationships within their data and gain new insights that help them make better business decisions. The Company is named “Gridsum” to symbolize the combination of distributed computing (Grid) and analytics (sum). As a digital intelligence pioneer, the Company’s mission is to help enterprises and government organizations in China use data in new and powerful ways to make better informed decisions and be more productive. For more information, please visit http://www.gridsum.com/. This announcement contains forward-looking statements. These forward-looking statements are made under the "safe harbor" provisions of the U.S. Private Securities Litigation Reform Act of 1995. These statements can be identified by terminology such as “may,” "will," "expects," "anticipates," “aims,” "future," "intends," "plans," "believes," "estimates," “likely to” and similar statements. Among other things, quotations from management in this announcement, Gridsum’s financial outlook as well as Gridsum's strategic and operational plans contain forward-looking statements. Gridsum may also make written or oral forward-looking statements in its reports filed with, or furnished to, the U.S. Securities and Exchange Commission, in its annual reports to shareholders, in press releases and other written materials and in oral statements made by its officers, directors or employees to third parties. Statements that are not historical facts, including statements about Gridsum's beliefs and expectations, are forward-looking statements. Forward-looking statements involve inherent risks and uncertainties. A number of factors could cause actual results to differ materially from those contained in any forward-looking statement, including but not limited to the following: unexpected difficulties in Gridsum's pursuit of its goals and strategies; the unexpected developments, including slow growth, in the digital intelligence market; reduced demand for, and market acceptance of, Gridsum's solutions; difficulties keeping and strengthening relationships with customers; potentially costly research and development activities; competitions in the digital intelligence market; PRC governmental policies relating to media, software, big data, the internet, internet content providers and online advertising; and general economic and business conditions in the regions where Gridsum provides solutions and services. Further information regarding these and other risks is included in Gridsum’s reports filed with, or furnished to, the Securities and Exchange Commission. All information provided in this press release and in the attachments is as of the date of this press release, and Gridsum undertakes no duty to update such information except as required under applicable law.


BOULDER, Colo.--(BUSINESS WIRE)--The automotive industry has seen the promise of artificial intelligence (AI) technology, and is among the industries at the forefront of using AI to augment human actions and to mimic the actions of humans, while also harnessing the advanced reaction times and pinpoint precision of machine-based systems. According to a new report from Tractica, both semi-autonomous and the fully autonomous vehicles of the future will rely heavily on AI systems. “Artificial intelligence is not restricted to autonomous driving, however,” says principal analyst Keith Kirkpatrick. “Suppliers and automakers realize that AI can also be used to make life in the car more convenient and personalized, for both the driver and the passengers, in addition to enabling greater automation for a range of areas including vehicle maintenance, customer service, fleet management, and navigation, among others.” Tractica forecasts that the top use cases for automotive AI applications, in order of revenue potential, are as follows: 1. Machine/vehicular object detection/identification/avoidance 2. Personalized services in cars 3. Building generative models of the real world 4. Predictive maintenance 5. Localization and mapping 6. Sensor data fusion in machinery 7. Predicting demand for on-demand taxis 8. Simulating worlds for AI training 9. Automated on-road customer service 10. Truck platooning 11. Vehicle network and data security 12. Surge pricing for on-demand taxis 13. Virtual testing and simulation for racing cars 14. Driver face analytics and emotion recognition 15. Gesture recognition Tractica forecasts that, together, these use cases will drive AI hardware, software, and services revenue in the automotive industry from $404 million in 2016 to $14.0 billion by 2025, representing a compound annual growth rate of 48.3%. Tractica’s report, “Artificial Intelligence for Automotive Applications”, provides detailed market forecasts for AI hardware, software, and services in the automotive market during the period from 2016 through 2025. The technologies covered include machine learning, deep learning, NLP, computer vision, machine reasoning, and strong AI. The forecast covers 15 key use cases for automotive AI, segmented by world region. Profiles are also included for 30 key participants in the emerging automotive AI market ecosystem. An Executive Summary of the report is available for free download on the firm’s website. Tractica is a market intelligence firm that focuses on human interaction with technology. Tractica’s global market research and consulting services combine qualitative and quantitative research methodologies to provide a comprehensive view of the emerging market opportunities surrounding Artificial Intelligence, Robotics, User Interface Technologies, Wearable Devices, and Digital Health. For more information, visit www.tractica.com or call +1.303.248.3000.


http://www.marketsandmarkets.com/Market-Reports/cognitive-computing-vendor-comparison-47084149.html Early buyers will receive 10% customization on this report. This report is instrumental in helping the stakeholders, such as cognitive computing vendors, system integrators, value-added resellers, and other channel partners, in making profitable business choices for the deployment of cognitive computing solutions. Most of the vendors attempt to provide a complete cognitive computing suite or platform, which caters to various industry verticals, such as healthcare, IT and telecom, manufacturing, energy, life sciences, and government and defense. They compete to provide cognitive computing solutions that offer faster, simplified, and more flexible processes to suit the needs of their clients. Vendors mostly focus on those organizations that seek holistic cognitive computing solutions to get valuable insights from very large and complex unstructured data sets, for making critical business decisions based on the insights received. The following vendors are included in the report: The MnM DIVE methodology involves extensive research to identify the key vendors offering cognitive computing solutions. A comprehensive list of the cognitive computing vendors was prepared through secondary research by referring to annual reports, press releases, and investor presentations of companies; white papers; directories; and databases. Based on the breadth of product offering, organization size, and other selection criteria, the list was narrowed down to select the top 25 vendors. During the production cycle of the report, in-depth interviews were conducted with various primary respondents, which include key opinion leaders, subject matter experts, directors, and C-level executives of selected cognitive computing vendors to obtain and verify critical qualitative and quantitative information. This primary data was collected mainly through questionnaires, emails, and telephonic interviews. After the completion of the data gathering and verification processes, the scores and weightage for the shortlisted vendors against each parameter were finalized. Based on the extensive secondary and primary research, each criterion for the selected vendors was scored on a scale ranging from 0 to 10. After the ratings were finalized, each vendor was placed in the MnM DIVE matrix based on their score for product offerings and business strategies in the cognitive computing market. Browse Related Reports Affective Computing Market by Technology (Touch-Based and Touchless), Software (Speech Recognition, Gesture Recognition, Facial Feature Extraction, Analytics Software, & Enterprise Software), Hardware, Vertical, and Region - Forecast to 2021 http://www.marketsandmarkets.com/Market-Reports/affective-computing-market-130730395.html Emotion Detection and Recognition Market by Technology (Bio-Sensor, NLP, Machine Learning), Software Tool (Facial Expression, Voice Recognition), Service, Application Area, End User, and Region - Global Forecast to 2021 http://www.marketsandmarkets.com/Market-Reports/emotion-detection-recognition-market-23376176.html Know More About our Knowledge Store @ http://www.marketsandmarkets.com/Knowledgestore.asp MarketsandMarkets™ provides quantified B2B research on 30,000 high growth niche opportunities/threats which will impact 70% to 80% of worldwide companies' revenues. Currently servicing 5000 customers worldwide including 80% of global Fortune 1000 companies as clients. Almost 75,000 top officers across eight industries worldwide approach MarketsandMarkets™ for their painpoints around revenues decisions. Our 850 fulltime analyst and SMEs at MarketsandMarkets™ are tracking global high growth markets following the "Growth Engagement Model - GEM". The GEM aims at proactive collaboration with the clients to identify new opportunities, identify most important customers, write "Attack, avoid and defend" strategies, identify sources of incremental revenues for both the company and its competitors. MarketsandMarkets™ now coming up with 1,500 MicroQuadrants (Positioning top players across leaders, emerging companies, innovators, strategic players) annually in high growth emerging segments. MarketsandMarkets™ is determined to benefit more than 10,000 companies this year for their revenue planning and help them take their innovations/disruptions early to the market by providing them research ahead of the curve. MarketsandMarkets' flagship competitive intelligence and market research platform, "RT" connects over 200,000 markets and entire value chains for deeper understanding of the unmet insights along with market sizing and forecasts of niche markets. Visit Our Blog @ http://www.marketsandmarketsblog.com/market-reports/telecom-it Connect with us on LinkedIn @ http://www.linkedin.com/company/marketsandmarkets


DUBLIN--(BUSINESS WIRE)--Research and Markets has announced the addition of the "Artificial Intelligence in Healthcare Market by Offering (Hardware, Software and Services), Technology (Deep Learning, Querying Method, NLP, and Context Aware Processing), Application, End-User Industry, and Geography - Global Forecast to 2022" report to their offering. Growing usage of big data in healthcare industry and imbalance between health workforce and patients is expected to drive the growth of the AI in healthcare market The artificial intelligence (AI) in healthcare market was valued at USD 667.1 million in 2016 and is expected to reach USD 7,988.8 million by 2022, at a CAGR of 52.68% between 2017 and 2022. The growth of this market is driven by the growing usage of Big Data in healthcare industry, ability of AI to improve patient outcomes, imbalance between health workforce and patients, reducing the healthcare costs, growing importance on precision medicine, cross-industry partnerships, and significant increase in venture capital investments in AI in healthcare domain. However, reluctance among medical practitioners to adopt AI-based technologies and ambiguous regulatory guidelines for medical software are the major factors restraining the growth of the AI in healthcare market Hardware-which includes GPUs, DSPs, FPGAs, and neuromorphic chips-is expected to grow at the highest rate in the offering segment of AI in healthcare. The GPU, DSP, and FPGA are widely used to implement the deep learning algorithm. In terms of throughput, GPU is faster than FPGA; whereas in the case of power efficiency, FPGA is better than GPU. Keeping these factors in mind, the AI providers choose the hardware accordingly. Owing the factors of Natural Language Processing (NLP) to handle structured and unstructured data are driving the NLP market in AI in healthcare market For more information about this report visit http://www.researchandmarkets.com/research/w2c8rd/artificial


"This has been a transformative year for Incedo, not only as demonstrated by financial performance, but also in terms of partnerships and acquisitions, and client growth and retention," said Tejinderpal Miglani, CEO, Incedo. "In addition, our strong focus on new technologies and verticals has enabled us to develop innovative and custom solutions to meet client needs. We're excited to continue our pattern of incredible growth, while retaining the entrepreneurial attributes that keep us nimble and adaptable to the fast-changing technology industry." Pursuing strategic partnerships and acquisitions is a key component to Incedo's global growth strategy. The organization focuses on engaging smaller firms to ensure its entrepreneurial culture remains intact. As part of this strategy, within the past year Incedo closed a number of deals to offer innovative solutions and services to its customers across verticals. It acquired Syslogic to offer broad-based communication engineering solutions and services to the telecom service provider markets, and entered into numerous partnerships - including with UiPath and Splice Machine - to develop and deliver solutions within the key areas of data management, robotic process automation and cognitive and machine learning-based automation. Client focus is Incedo's top priority. The firm is dedicated to consistently building expertise in each vertical, delivering high-quality solutions for clients, and doing whatever it takes to ensure clients are successful and achieve their desired results. This client-centric model has led to significant growth and retention rates over the last year across all business units, with the firm acquiring 27 new clients and retaining over 90% of its current clients for new projects. Additionally, the firm won and worked on 180 active projects, and achieved double-digit client growth in every business unit: Incedo invests heavily in emerging technologies that will drive its clients' businesses and will position it at the forefront of the industry. The areas where Incedo currently sees the most potential and promise for growth, and has thus focused the majority of its innovation and R&D efforts over the last year, include chatbots, artificial intelligence (AI), Natural Language Processing (NLP) and advanced analytics. Incedo has invested in these areas by hiring experts across verticals to build a deep bench of leadership in these technologies, partnering with other labs and acquiring assets to provide strong solutions to customers, and launching several dedicated R&D labs, including the Incedo Innovation Lab. The labs analyze and test innovative ideas for clients, build prototypes, and develop solution Accelerators and Frameworks to help customers reduce implementation time and cost. For example, the company developed a framework that enables clients to launch a new chatbot with greater ease and less time. Incedo has also built dedicated practice areas in related horizontal offerings including IoT and robotic process automation (RPA). "Our strong focus on emerging technologies is designed to meet clients' needs, so they can remain innovators and leaders in their industries, and better meet their own customer demands," continued Miglani. "We'll continue to invest in these areas and other technologies as they continue to emerge and evolve. It is this constant forward-looking and customer-focused approach, combined with strategic partnerships and acquisitions, that have facilitated our growth, and we look forward to broadening our engagements and deepening our current relationships in 2017." Incedo is a technology solutions provider specializing in Data, Information Management, Business Intelligence, Analytics, and Emerging Technologies. Incedo has deep-rooted industry expertise in financial services, life sciences and communication engineering. Headquartered in the Bay Area, Incedo has offices across North America and India. Its young, agile team consists of industry practitioners who understand the business needs of their clients. Incedo works with four of the top ten life sciences and pharmaceutical companies, one of the top telecommunications companies in the world and some of the globe's largest financial services firms. Since its inception in 2011, Incedo has experienced growth of over 600% and has been recognized as one of the top 'Ten Emerging Analytics Start-ups to Watch' by Analytics India Magazine and was named to CRN's 2016 Solution Provider 500 list, recognizing the top channel partners in North America.


EmtelliSuite's deep learning-based apps can help healthcare providers improve patient care, boost departmental efficiency and publish faster Emtelligent Software Ltd. today announced EmtelliSuiteTM, a family of clinically-focused apps combining the best ideas from machine learning with expert guidance from practicing medical professionals. The fast, reliable, easy-to-use software can help providers resolve information overload, administrators ease the transition to new payment models and researchers increase their output. Developed by radiologist Tim O’Connell, M. Eng, MD, and Professor Anoop Sarkar, PhD, who collaborated with interface designers from leading entertainment products on the functionality, EmtelliSuite represents a custom-built NLP solution for one of the biggest problems affecting healthcare today. O’Connell, Emtelligent CEO, comments: "Several years into the widespread implementation of Electronic Medical Records, users are now being overwhelmed by data. The EMRs contain so much information, we are often searching for the proverbial needle in the haystack. This makes us inefficient, and can cause 'data fatigue' for the user which puts patients at risk of receiving suboptimal care. Over the past two years, Emtelligent has developed apps that can quickly 'bolt on' to any EMR and can make it easier, faster, and safer for care providers, administrators, and researchers to do their jobs." O’Connell adds, “Our apps are designed from the ground-up with care providers in mind. As a practicing radiologist, I have a first-hand understanding of the pressure providers are under, and we’ve developed EmtelliSuite’s apps — in particular the Clinical Helper which turns volumes of patient data into succinct, searchable summaries — to help relieve that strain and reduce the risk to patients.” Sarkar, Emtelligent CTO, affirms, “We’ve done this the hard way, by developing algorithms that perform natural language understanding of medical reports. Our deep-learning models can detect multiple features from unstructured medical text such as knowing when a patient has appendicitis, rather than that appendicitis simply appeared somewhere in the report, which is what other NLP engines do.” Tested since virtually day one in clinical settings, EmtelliSuite is available now. The apps — Clinical Helper, EmtelliSearch, Category Search, Comparison Search and Emtelligent Quality Assurance — are available either bundled or separately. Also, Emtelligent’s deep learning-based NLP engine is available as an API for organizations wishing to develop their own apps or process large batches of reports. Emtelligent is a proudly Canadian company founded in 2015 by four friends. It aims to make the jobs of healthcare professionals easier and their patients’ lives better by offering the world’s most accurate and practical medical NLP solutions. Learn more or schedule a demonstration at www.emtelligent.ai, or call 1-877-GO-EMTEL (1-877-463-6835).


LONDON, May 29, 2017 /PRNewswire/ -- Today's advanced call centers and virtual digital assistants make it clear that artificial intelligence (AI) systems, which essentially use data and algorithms to mimic the cognitive functions of the human mind, including the ability to learn and solve problems independently, are rapidly making their way into all facets of society. Not surprisingly, the automotive industry has seen the promise of such technology, and is among the industries at the forefront of using AI to augment human actions and to mimic the actions of humans, while also harnessing the advanced reaction times and pinpoint precision of machine-based systems. Both semi-autonomous and the fully autonomous vehicles of the future will rely heavily on AI systems. Download the full report: https://www.reportbuyer.com/product/4919825/ But AI is not restricted to autonomous driving. Suppliers and automakers realize that the AI engines can also be used to make life in the car more convenient, for both the driver and the passengers. Using natural language processing (NLP) and machine learning techniques make it possible to create in-car assistants that can respond to voice commands and infer what actions to take, without needing a direct command. Both types of systems are on their way into vehicles, although their respective introductions may be made gradually, due to safety concerns, as well as a desire to ensure that the systems work as naturally and as smoothly as possible. Tractica believes that while the market for AI systems is clearly in its infancy, a future of strong growth is virtually assured. Tractica forecasts that the market for automotive AI hardware, software, and services will grow from $404 million in 2016 to $14.0 billion by 2025. This Tractica report provides detailed market forecasts for AI hardware, software, and services in the automotive market during the period from 2016 through 2025. The technologies covered include machine learning, deep learning, NLP, computer vision, machine reasoning, and strong AI. The forecast covers 15 key use cases for automotive AI, segmented by world region. Profiles are also included for 30 key participants in the emerging automotive AI market ecosystem. Key Market Forecasts - Automotive Artificial Intelligence Software Revenue by Region, World Markets: 2016-2025 - Automotive Artificial Intelligence Hardware Revenue by Region, World Markets: 2016-2025 - Automotive Artificial Intelligence Services Revenue by Region, World Markets: 2016-2025 - Automotive Artificial Intelligence Software Revenue by Use Case, World Markets: 2016-2025 - Automotive Artificial Intelligence-Driven Hardware Revenue by Product Category, World Markets: 2016-2015 - Automotive Artificial Intelligence-Driven Services Revenue by Service Category, World Markets: 2016-2015 - Automotive Artificial Intelligence-Driven Cloud Services Revenue by Region, World Markets: 2016-2025 Technologies - Artificial Neural Networks - Biometrics - Clustering Algorithms - Decision Trees - Deep Learning - Naïve Bayes Classifiers - Natural Language Processing - Personalization - Supervised Learning Technologies - Unsupervised Learning Technologies Application Markets - Automotive Human Machine Interaction - Autonomous Driving - Customer Service - Fleet Management - Localization and Mapping - Network and Data Security - Personalized Services - Predictive Maintenance - Sensor Data Fusion Geographies - North America - Europe - Asia Pacific - Latin America Download the full report: https://www.reportbuyer.com/product/4919825/ About Reportbuyer Reportbuyer is a leading industry intelligence solution that provides all market research reports from top publishers http://www.reportbuyer.com For more information: Sarah Smith Research Advisor at Reportbuyer.com  Email: query@reportbuyer.com   Tel: +44 208 816 85 48 Website: www.reportbuyer.com To view the original version on PR Newswire, visit:http://www.prnewswire.com/news-releases/artificial-intelligence-for-automotive-applications-software-hardware-and-services-for-autonomous-driving-personalized-services-predictive-maintenance-localization-and-mapping-sensor-data-fusion-and-other-use-cases-market-300465069.html


News Article | May 25, 2017
Site: www.techradar.com

A strategic technology trend is one with substantial disruptive potential that is just beginning to break out of an emerging state into broader impact and use or which are rapidly growing trends with a high degree of volatility reaching tipping points over the next five years, according to Gartner. Artificial intelligence and advanced machine learning are composed of many technologies and techniques (e.g., deep learning, neural networks, natural-language processing [NLP]). The more advanced techniques move beyond traditional rule-based algorithms to create systems that understand, learn, predict, adapt and potentially operate autonomously. This is what makes smart machines appear "intelligent." Intelligent apps such as VPAs perform some of the functions of a human assistant making everyday tasks easier (by prioritizing emails, for example), and its users more effective. Over the next 10 years, virtually every app, application and service will incorporate some level of AI. As intelligent things, such as drones, autonomous vehicles and smart appliances, permeate the environment, Gartner anticipates a shift from stand-alone intelligent things to a collaborative intelligent things model. The landscape of immersive consumer and business content and applications will evolve dramatically through 2021, says Gartner. VR and AR capabilities will merge with the digital mesh to form a more seamless system of devices capable of orchestrating a flow of information that comes to the user as hyperpersonalized and relevant apps and services. A digital twin is a dynamic software model of a physical thing or system that relies on sensor data to understand its state, respond to changes, improve operations and add value. Within three to five years, hundreds of millions of things will be represented by digital twins. Blockchain and distributed-ledger concepts are gaining traction because they hold the promise to transform industry operating models. While the current hype is around the financial services industry, there are many possible applications including music distribution, identity verification, title registry and supply chain. The current focus for conversational interfaces is focused on chatbots and microphone-enabled devices (e.g., speakers, smartphones, tablets, PCs, automobiles). However, the digital mesh encompasses an expanding set of endpoints people use to access applications and information, or interact with people, social communities, governments and businesses. In the mesh app and service architecture, mobile apps, web apps, desktop apps and IoT apps link to a broad mesh of back-end services to create what users view as an "application." The architecture encapsulates services and exposes APIs at multiple levels and across organizational boundaries balancing the demand for agility and scalability of services with composition and reuse of services. Digital technology platforms provide the basic building blocks for a digital business and are a critical enabler to become a digital business. Every organization will have some mix of these five digital technology platforms. The platforms provide the basic building blocks for a digital business and are a critical enabler to become a digital business. The intelligent digital mesh and related digital technology platforms and application architectures create an ever-more-complex world for security. The IoT edge is a new frontier for many IT security professionals creating new vulnerability areas and often requiring new remediation tools and processes that must be factored into IoT platform efforts.

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