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London, June 15, 2020 (GLOBE NEWSWIRE) -- The automotive artificial intelligence market is expected to grow at a CAGR of 39.8% from 2019 to reach $15.9 billion by 2027.

Several established automotive organizations across the globe are increasingly struggling with the rising cost of operations, dissatisfied customers, declining sales, and unidentified competition. Advanced capabilities of AI, coupled with rising consumer expectations, have pushed the automotive industry into adopting artificial intelligence. Several organizations are investing heavily in order to reap the profits in highly dynamic and competitive market environments. The global artificial intelligence in automotive market is expected to witness strong growth over the coming years due to the growing demand for autonomous vehicles, adoption of advanced automotive solutions, growing adoption of artificial intelligence for traffic management, and government initiatives and investments towards connected and autonomous vehicles.

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The increasing volume of data gathered through IoT devices, coupled with the widespread availability of high-speed broadband networks and the emergence of 5G technologies is driving the need for faster data processing. Apart from this, widening the implementation of computer vision technologies across vehicles and shifting consumer preferences for premium vehicles to improve the driving experience while enhancing the vehicle and pedestrian safety are some of the key factors anticipated to drive the growth of artificial intelligence in automotive market in the near future. However, lack of infrastructure coupled with the high procurement operating cost is expected to challenge the growth of the artificial intelligence in automotive market growth during the forecast period.

The global market for artificial intelligence in automotive industry is expected to grow at a CAGR of 39.8% from 2019 to reach $15.9 billion by 2027. The market is witnessing consistent growth owing to the increasing demand for smart IoT devices in automotive, surging demand for connected vehicles, and adoption of advanced driver assistance systems. Apart from this, surging adoption of AI-based solutions and services among the automotive industry is also contributing to the overall growth of artificial intelligence in automotive market. While developed economies offer technological growth opportunities through the proliferation of advanced technologies, the ongoing digital transformation initiatives across emerging economies such as Asia-Pacific and Latin America are likely to offer high growth opportunities for vendors operating in the market.

The global artificial intelligence in automotive market is mainly segmented by components (hardware, software, services), by technology (machine learning, computer vision, natural language processing, context-aware computing), by process (signal recognition, image recognition, voice recognition, data mining), by application (semi-autonomous driving, human-machine interface), and region.

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Based on components, the artificial intelligence in automotive market is segmented into hardware, software, and services. The software segment dominated the artificial intelligence in automotive market in 2019 in terms of market share. This is mainly attributed to the growing usage of learning analytics, growing acceptance of in-car assistants driven by machine learning techniques and an increase in demand for autonomous platforms for automotive industry. However, the services segment is slated to grow at the fastest CAGR during the forecast period and will emerge as the major segment in terms of market share by 2027. This growth is mainly driven by the surging demand for AI-based cloud services for autonomous vehicles, over-the-air (OTA) software services, traffic and mapping services, shared mobility services, remote maintenance services, technical support & training services, maintenance & support services, integration services, performance measurement services, and consulting services.

Based on technology, the artificial intelligence in automotive market is segmented into machine learning, computer vision, natural language processing, and context-aware computing. The machine learning technology segment held the largest share of the overall automotive artificial intelligence market in 2019, owing to the demand for signal diagnosing, image recognition, speech recognition, data mining, and an increase in unstructured data generated by the automotive industry. However, the computer vision technology is slated to grow at the fastest CAGR during the forecast period, due to a widening implementation of computer vision in semi-autonomous vehicles to tackle distracted/ drowsy driving and surging use of LIDAR sensors and cameras to avoid vehicle collisions.

Based on process, the overall artificial intelligence in automotive market is segmented into signal recognition, image recognition, voice recognition, and data mining. The signal recognition segment dominated the artificial intelligence in the automotive market in 2019 and is also estimated to continue its dominance over the forecast period. The growth in this market segment is attributed to the increasing growth of automotive safety systems, rising consumer preference for signal recognition in autonomous vehicles, and government regulations pertaining to the safety rating of a vehicle to reduce road collisions. However, the image recognition process is slated to grow at the fastest CAGR during the forecast period, due to growing demand for advanced driver assistance systems (ADAS) such as road signs detection and pedestrian protection systems.

Based on application, the artificial intelligence in automotive market is majorly segmented into semi-autonomous driving and human-machine interface. The human-machine interface segment dominated the artificial intelligence in automotive market in 2019. This is attributed to the increasing demand for interactive technologies in vehicles, connected systems, and smart convenient features.

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Geographically, the global artificial intelligence in automotive market is segmented into five regions, namely, North America, Europe, Asia-Pacific, Latin America, and the Middle East & Africa with a further analysis of major countries in these regions. North America accounted for the largest share of global artificial intelligence in automotive market in 2019, followed by Europe and the Asia-Pacific region. The largest share of this region is mainly attributed to the presence of developed economies, such as the United States and Canada, focusing on enhancing the existing solutions in the automotive industry, and the existence of major players in this market along with a high willingness to adopt advanced technologies. Apart from this, the growing demand for enhanced user experience, rising living standards, growing adoption of autonomous vehicles and availability of high-end infrastructure, increasing R&D expenditure, and various government initiatives supporting AI research are contributing to the growth in this region.

On the other hand, Asia-Pacific region is projected to grow at the highest CAGR during the forecast period. This growth is attributed to an increase in demand for premium vehicles, growing investments in AI technology for improved productivity, and increasing adoption of AI-based solutions and services in the automotive industry. Apart from this, developing internet & connectivity infrastructure, growing adoption of intelligent solutions and increasing digitalization, and increasing investments by the major players in this region are contributing to the growth in the Asia Pacific AI in automotive market.

Some of the key players operating in the global artificial intelligence in automotive market are Google LLC (U.S.), IBM Corporation (U.S.), Intel Corporation (U.S.), Microsoft Corporation (U.S.), Nvidia Corporation (U.S.), Tesla, Inc. (U.S.), Xilinx, Inc. (U.S.), Micron Technology, Inc. (U.S.), Ford Motor Company (U.S.), General Motors Company (U.S.), Harman International Industries Inc. (South Korea), Honda Motor Co., Ltd. (Japan), Audi AG (Germany), and Qualcomm Technologies, Inc. (U.S.), among others.

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Scope of the Report:

Automotive AI Market, by Component

Automotive AI Market, by Technology

Automotive AI Market, by Process

Automotive AI Market, by Application

Automotive AI Market, by Geography

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Automotive Artificial Intelligence Market Worth $15.9 Billion by 2027, Growing at a CAGR of 39.8% from 2019- Global Market Opportunity Analysis and...

Jun 11th 2020

IT WILL BE as if the world had created a second China, made not of billions of people and millions of factories, but of algorithms and humming computers. PwC, a professional-services firm, predicts that artificial intelligence (AI) will add $16trn to the global economy by 2030. The total of all activityfrom banks and biotech to shops and constructionin the worlds second-largest economy was just $13trn in 2018.

PwCs claim is no outlier. Rival prognosticators at McKinsey put the figure at $13trn. Others go for qualitative drama, rather than quantitative. Sundar Pichai, Googles boss, has described developments in AI as more profound than fire or electricity. Other forecasts see similarly large changes, but less happy ones. Clever computers capable of doing the jobs of radiologists, lorry drivers or warehouse workers might cause a wave of unemployment.

Yet lately doubts have been creeping in about whether todays AI technology is really as world-changing as it seems. It is running up against limits of one kind or another, and has failed to deliver on some of its proponents more grandiose promises.

There is no question that AIor, to be precise, machine learning, one of its sub-fieldshas made much progress. Computers have become dramatically better at many things they previously struggled with. The excitement began to build in academia in the early 2010s, when new machine-learning techniques led to rapid improvements in tasks such as recognising pictures and manipulating language. From there it spread to business, starting with the internet giants. With vast computing resources and oceans of data, they were well placed to adopt the technology. Modern AI techniques now power search engines and voice assistants, suggest email replies, power the facial-recognition systems that unlock smartphones and police national borders, and underpin the algorithms that try to identify unwelcome posts on social media.

Perhaps the highest-profile display of the technologys potential came in 2016, when a system built by DeepMind, a London-based AI firm owned by Alphabet, Googles corporate parent, beat one of the worlds best players at Go, an ancient Asian board game. The match was watched by tens of millions; the breakthrough came years, even decades, earlier than AI gurus had expected.

As Mr Pichais comparison with electricity and fire suggests, machine learning is a general-purpose technologyone capable of affecting entire economies. It excels at recognising patterns in data, and that is useful everywhere. Ornithologists use it to classify birdsong; astronomers to hunt for planets in glimmers of starlight; banks to assess credit risk and prevent fraud. In the Netherlands, the authorities use it to monitor social-welfare payments. In China AI-powered facial recognition lets customers buy groceriesand helps run the repressive mass-surveillance system the country has built in Xinjiang, a Muslim-majority region.

AIs heralds say further transformations are still to come, for better and for worse. In 2016 Geoffrey Hinton, a computer scientist who has made fundamental contributions to modern AI, remarked that its quite obvious that we should stop training radiologists, on the grounds that computers will soon be able to do everything they do, only cheaper and faster. Developers of self-driving cars, meanwhile, predict that robotaxis will revolutionise transport. Eric Schmidt, a former chairman of Google (and a former board member of The Economists parent company) hopes that AI could accelerate research, helping human scientists keep up with a deluge of papers and data.

In January a group of researchers published a paper in Cell describing an AI system that had predicted antibacterial function from molecular structure. Of 100 candidate molecules selected by the system for further analysis, one proved to be a potent new antibiotic. The covid-19 pandemic has thrust such medical applications firmly into the spotlight. An AI firm called BlueDot claims it spotted signs of a novel virus in reports from Chinese hospitals as early as December. Researchers have been scrambling to try to apply AI to everything from drug discovery to interpreting medical scans and predicting how the virus might evolve.

This is not the first wave of AI-related excitement (see timeline in next article). The field began in the mid-1950s when researchers hoped that building human-level intelligence would take a few yearsa couple of decades at most. That early optimism had fizzled by the 1970s. A second wave began in the 1980s. Once again the fields grandest promises went unmet. As reality replaced the hype, the booms gave way to painful busts known as AI winters. Research funding dried up, and the fields reputation suffered.

Many of the grandest claims made about AI have once again failed to become reality

Modern AI technology has been far more successful. Billions of people use it every day, mostly without noticing, inside their smartphones and internet services. Yet despite this success, the fact remains that many of the grandest claims made about AI have once again failed to become reality, and confidence is wavering as researchers start to wonder whether the technology has hit a wall. Self-driving cars have become more capable, but remain perpetually on the cusp of being safe enough to deploy on everyday streets. Efforts to incorporate AI into medical diagnosis are, similarly, taking longer than expected: despite Dr Hintons prediction, there remains a global shortage of human radiologists.

Surveying the field of medical AI in 2019, Eric Topol, a cardiologist and AI enthusiast, wrote that the state of AI hype has far exceeded the state of AI science, especially when it pertains to validation and readiness for implementation in patient care. Despite a plethora of ideas, covid-19 is mostly being fought with old weapons that are already to hand. Contacttracing has been done with shoe leather and telephone calls. Clinical trials focus on existing drugs. Plastic screens and paint on the pavement enforce low-tech distancing advice.

The same consultants who predict that AI will have a world-altering impact also report that real managers in real companies are finding AI hard to implement, and that enthusiasm for it is cooling. Svetlana Sicular of Gartner, a research firm, says that 2020 could be the year AI falls onto the downslope of her firms well-publicised hype cycle. Investors are beginning to wake up to bandwagon-jumping: a survey of European AI startups by MMC, a venture-capital fund, found that 40% did not seem to be using any AI at all. I think theres definitely a strong element of investor marketing, says one analyst delicately.

This Technology Quarterly will investigate why enthusiasm is stalling. It will argue that although modern AI techniques are powerful, they are also limited, and they can be troublesome and difficult to deploy. Those hoping to make use of AIs potential must confront two sets of problems.

The first is practical. The machine-learning revolution has been built on three things: improved algorithms, more powerful computers on which to run them, andthanks to the gradual digitisation of societymore data from which they can learn. Yet data are not always readily available. It is hard to use AI to monitor covid-19 transmission without a comprehensive database of everyones movements, for instance. Even when data do exist, they can contain hidden assumptions that can trip the unwary. The newest AI systems demand for computing power can be expensive. Large organisations always take time to integrate new technologies: think of electricity in the 20th century or the cloud in the 21st. None of this necessarily reduces AIs potential, but it has the effect of slowing its adoption.

The second set of problems runs deeper, and concerns the algorithms themselves. Machine learning uses thousands or millions of examples to train a software model (the structure of which is loosely based on the neural architecture of the brain). The resulting systems can do some tasks, such as recognising images or speech, far more reliably than those programmed the traditional way with hand-crafted rules, but they are not intelligent in the way that most people understand the term. They are powerful pattern-recognition tools, but lack many cognitive abilities that biological brains take for granted. They struggle with reasoning, generalising from the rules they discover, and with the general-purpose savoir faire that researchers, for want of a more precise description, dub common sense. The result is an artificial idiot savant that can excel at well-bounded tasks, but can get things very wrong if faced with unexpected input.

Without another breakthrough, these drawbacks put fundamental limits on what AI can and cannot do. Self-driving cars, which must navigate an ever-changing world, are already delayed, and may never arrive at all. Systems that deal with language, like chatbots and personal assistants, are built on statistical approaches that generate a shallow appearance of understanding, without the reality. That will limit how useful they can become. Existential worries about clever computers making radiologists or lorry drivers obsoletelet alone, as some doom-mongers suggest, posing a threat to humanitys survivalseem overblown. Predictions of a Chinese-economy-worth of extra GDP look implausible.

Todays AI summer is different from previous ones. It is brighter and warmer, because the technology has been so widely deployed. Another full-blown winter is unlikely. But an autumnal breeze is picking up.

This article appeared in the Technology Quarterly section of the print edition under the headline "Reality check"

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An understanding of AIs limitations is starting to sink in - The Economist

LONDON--(BUSINESS WIRE)--Technavio has been monitoring the artificial intelligence-as-a-service (AIaaS) market and it is poised to grow by USD 15.14 billion during 2020-2024, progressing at a CAGR of over 48% during the forecast period. The report offers an up-to-date analysis regarding the current market scenario, latest trends and drivers, and the overall market environment.

Technavio suggests three forecast scenarios (optimistic, probable, and pessimistic) considering the impact of COVID-19. Request for Technavio's latest reports on directly and indirectly impacted markets. Market estimates include pre- and post-COVID-19 impact on the Artificial Intelligence-as-a-Service (AIaaS) Market Download free sample report

The market is concentrated, and the degree of concentration will accelerate during the forecast period. Alphabet Inc., Amazon.com Inc., Apple Inc., Intel Corp., International Business Machines Corp., Microsoft Corp., Oracle Corp., Salesforce.com Inc., SAP SE, and SAS Institute Inc. are some of the major market participants. To make the most of the opportunities, market vendors should focus more on the growth prospects in the fast-growing segments, while maintaining their positions in the slow-growing segments.

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Growing adoption of cloud based solutions has been instrumental in driving the growth of the market.

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Artificial Intelligence-as-a-Service (AIaaS) Market 2020-2024: Segmentation

Artificial Intelligence-as-a-Service (AIaaS) Market is segmented as below:

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Artificial Intelligence-as-a-Service (AIaaS) Market 2020-2024: Scope

Technavio presents a detailed picture of the market by the way of study, synthesis, and summation of data from multiple sources. The artificial intelligence-as-a-service (AIaaS) market report covers the following areas:

This study identifies the increasing adoption of AI in predictive analysis as one of the prime reasons driving the artificial intelligence-as-a-service (AIaaS) market growth during the next few years.

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Artificial Intelligence-as-a-Service (AIaaS) Market 2020-2024: Key Highlights

Table of Contents:

Executive Summary

Market Landscape

Market Sizing

Five Forces Analysis

Market Segmentation by End-user

Customer Landscape

Geographic Landscape

Drivers, Challenges, and Trends

Vendor Landscape

Vendor Analysis

Appendix

About Us

Technavio is a leading global technology research and advisory company. Their research and analysis focus on emerging market trends and provides actionable insights to help businesses identify market opportunities and develop effective strategies to optimize their market positions. With over 500 specialized analysts, Technavios report library consists of more than 17,000 reports and counting, covering 800 technologies, spanning across 50 countries. Their client base consists of enterprises of all sizes, including more than 100 Fortune 500 companies. This growing client base relies on Technavios comprehensive coverage, extensive research, and actionable market insights to identify opportunities in existing and potential markets and assess their competitive positions within changing market scenarios.

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COVID-19 Impact and Recovery Analysis- Artificial Intelligence-as-a-Service (AIaaS) Market 2020-2024 | Growing Adoption of Cloud Based Solutions to...

Pune, June 15, 2020 (GLOBE NEWSWIRE) -- The global IoT market size is projected to reach USD 1,102.6 billion by 2026, exhibiting a CAGR of 24.7% during the forecast period. The outbreak of the COVID-19 pandemic is expected to create ample innovation opportunities in the market, finds Fortune Business Insights in its report, titled Internet of Things (IoT) Market Size, Share and Industry Analysis By Platform (Device Management, Application Management, Network Management), By Software & Services (Software Solution, Services), By End-Use Industry (BFSI, Retail, Governments, Healthcare, Others) And Regional Forecast, 2019 2026.

IoT is swiftly emerging as viable option solution of tracking, monitoring, and ultimately preventing the spread of the coronavirus infection around the world. Many companies are bringing out innovative products to aid frontline healthcare workers to effectively fight the contagion. For example, the Shanghai Public Health Clinical Center is utilizing connected thermometers developed by the Californian health startup, VivaLNKs, to monitor temperatures of COVID-19 patients and protect workers from getting infected. Canada-based Visionstate designed its IoT buttons product for hospitals in Vancouver, to be used for sending alerts to officials regarding maintenance and sanitation issues. These novelty solutions powered by IoT are bolstering response of healthcare systems in many countries to the COVID-19 pandemic, benefiting the Internet of Things market growth in the process.

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The emergence of COVID-19 has brought the world to a standstill. We understand that this health crisis has brought an unprecedented impact on businesses across industries. However, this too shall pass. Rising support from governments and several companies can help in the fight against this highly contagious disease. There are some industries that are struggling and some are thriving. Overall, almost every sector is anticipated to be impacted by the pandemic.

We are taking continuous efforts to help your business sustain and grow during COVID-19 pandemics. Based on our experience and expertise, we will offer you an impact analysis of coronavirus outbreak across industries to help you prepare for the future.

Click here to get the short-term and long-term impact of COVID-19 on this market.

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As per the report findings, the market value was at USD 190.0 billion in 2018. The other highlights of the report include:

Market Driver

Increasing Applicability of IoT in Agriculture to Augment Market Growth

Adoption of smart farming methods is opening a new avenue of growth for Internet of Things. The Food and Agriculture (FAO) opines that application of IoT in agriculture could prove instrumental in meeting the escalating food demand worldwide by making farming activities more efficient. The lucrative opportunities generated by this sector have prompted many start-ups to explore and advance the integration of IoT in agricultural productivity. Some of them are:

Regional Insights

Evolving Connectivity Infrastructure in Asia Pacific to Ensure Its Leading Position

Among regions, Asia Pacific is set to command the Internet of Things market share during the forecast period on account of rapid improvement in internet connectivity and growing usage of smartphones. China, which accounts for 64% of the global cellular connections, is likely to present plenty of attractive opportunities for market players. In 2018, the regions market size was at USD 74.5 billion.

Europe and North America are anticipated to showcase steady growth in the coming years owing to massive investments in 5G technology and active adoption of Internet of Things for manufacturing activities. Moreover, the two continents are home to some of the biggest technological companies in the world that are aggressively innovating in AI and IoT applications.

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Competitive Landscape

Collaborative Innovation by Market Leaders to Stir Competitive Spirits

The competitive landscape of the IoT market is dominated by the presence and operations of tech behemoths such as Microsoft and Oracle. These companies are making speedy advancements in the Internet of Things and other AI-based technologies through innovation-centric collaborations with smaller players and new entrants.

Industry Developments:

List of Key Players Profiled in the Internet of Things Market Report:

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Detailed Table of Content

TOC Continued..!!!

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Internet of Things (IoT) Market to Hit $1,102.6 Billion by 2026; Advancements in Artificial Intelligence-based Technologies to Feed Market Growth:...

'); $("#expandable-weather-block .modal-body #mrd-alert"+ alertCount).append(""+val.title+""); // if (window.location.hostname == "www.kmov.com" || window.location.hostname == "www.kctv5.com" || window.location.hostname == "www.azfamily.com" || window.location.hostname == "www.kptv.com" || window.location.hostname == "www.fox5vegas.com" || window.location.hostname == "www.wfsb.com") { if (val.poly != "" && val.polyimg != "") { $("#expandable-weather-block .modal-body #mrd-alert"+ alertCount).append('"+val.ihtml+""); $("#expandable-weather-block .weather-index-alerts").show(); $("#expandable-weather-block .modal-body h2").css({"font-family":"'Fira Sans', sans-serif", "font-weight":"500", "padding-bottom":"10px"}); $("#expandable-weather-block .modal-body p").css({"font-size":"14px", "line-height":"24px"}); $("#expandable-weather-block .modal-body span.wxalertnum").css({"float":"left", "width":"40px", "height":"40px", "color":"#ffffff", "line-height":"40px", "background-color":"#888888", "border-radius":"40px", "text-align":"center", "margin-right":"12px"}); $("#expandable-weather-block .modal-body b").css("font-size", "18px"); $("#expandable-weather-block .modal-body li").css({"font-size":"14px", "line-height":"18px", "margin-bottom":"10px"}); $("#expandable-weather-block .modal-body ul").css({"margin-bottom":"24px"}); $("#expandable-weather-block .modal-body pre").css({"margin-bottom":"24px"}); $("#expandable-weather-block .modal-body img").css({"width":"100%", "margin-bottom":"20px", "borderWidth":"1px", "border-style":"solid", "border-color":"#aaaaaa"}); $("#expandable-weather-block .modal-body #mrd-alert"+ alertCount).css({"borderWidth":"0", "border-bottom-width":"1px", "border-style":"dashed", "border-color":"#aaaaaa", "padding-bottom":"10px", "margin-bottom":"40px"}); }); } function parseAlertJSON(json) { console.log(json); alertCount = 0; if (Object.keys(json.alerts).length > 0) { $("#mrd-wx-alerts .modal-body ").empty(); } $.each(json.alerts, function(key, val) { alertCount++; $("#mrd-wx-alerts .alert_count").text(alertCount); $("#mrd-wx-alerts .modal-body ").append(''); $("#mrd-wx-alerts .modal-body #mrd-alert"+ alertCount).append(""+val.title+""); // if (window.location.hostname == "www.kmov.com" || window.location.hostname == "www.kctv5.com" || window.location.hostname == "www.azfamily.com" || window.location.hostname == "www.kptv.com" || window.location.hostname == "www.fox5vegas.com" || window.location.hostname == "www.wfsb.com") { if (val.poly != "" && val.polyimg != "") { $("#mrd-wx-alerts .modal-body #mrd-alert"+ alertCount).append(''); } else if (val.fips != "" && val.fipsimg != "") { // $("#mrd-wx-alerts .modal-body #mrd-alert"+ alertCount).append(''); } // } //val.instr = val.instr.replace(/[W_]+/g," "); $("#mrd-wx-alerts .modal-body #mrd-alert"+ alertCount).append(val.dhtml+"

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3000 Bar using artificial intelligence technology to check temperatures of patrons - WSMV Nashville