{"id":2262,"date":"2020-02-17T02:45:32","date_gmt":"2020-02-16T23:45:32","guid":{"rendered":"http:\/\/kusuaks7\/?p=1867"},"modified":"2021-05-11T14:32:34","modified_gmt":"2021-05-11T14:32:34","slug":"roundup-of-machine-learning-forecasts-and-market-estimates-2020","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/ai-ml\/roundup-of-machine-learning-forecasts-and-market-estimates-2020\/","title":{"rendered":"Roundup Of Machine Learning Forecasts And Market Estimates, 2020"},"content":{"rendered":"<p style=\"text-align: center;\">IDC predicts spending on AI systems will reach $97.9B in 2023, more than two and one-half times the\u00a0&#8230; [+]\u00a0<span style=\"background-color: rgba(0, 0, 0, 0.05);\">\u00a0<\/span><small style=\"background-color: rgba(0, 0, 0, 0.05);\">ISTOCK<\/small><\/p>\n<ul>\n<li>75% of Netflix users select films recommended to them by the company\u2019s machine learning algorithms.<\/li>\n<li>The global machine learning market was valued at $1.58B in 2017 and is expected to reach $20.83B in 2024,\u00a0<a title=\"http:\/\/zmrblog.com\/2017\/6066\/machine-learning-market-to-record-lucrative-growth-revenue-to-surge-to-us20-83-billion-by-2024\/\" href=\"http:\/\/zmrblog.com\/2017\/6066\/machine-learning-market-to-record-lucrative-growth-revenue-to-surge-to-us20-83-billion-by-2024\/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:http:\/\/zmrblog.com\/2017\/6066\/machine-learning-market-to-record-lucrative-growth-revenue-to-surge-to-us20-83-billion-by-2024\/\">growing at a CAGR of 44.06% between 2017 and 2024<\/a>.<\/li>\n<li>Projected to grow at a Compound Annual Growth Rate (CAGR) of 42.8% from 2018 to 2024,\u00a0<a title=\"https:\/\/www.marketresearchfuture.com\/reports\/machine-learning-market-2494\" href=\"https:\/\/www.marketresearchfuture.com\/reports\/machine-learning-market-2494\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/www.marketresearchfuture.com\/reports\/machine-learning-market-2494\">the global Machine Learning (ML) market will worth $30.6B in four years<\/a>.<\/li>\n<li>Tractica predicts annual global\u00a0<a title=\"https:\/\/www.tractica.com\/newsroom\/press-releases\/artificial-intelligence-software-market-to-reach-126-0-billion-in-annual-worldwide-revenue-by-2025\/\" href=\"https:\/\/www.tractica.com\/newsroom\/press-releases\/artificial-intelligence-software-market-to-reach-126-0-billion-in-annual-worldwide-revenue-by-2025\/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/www.tractica.com\/newsroom\/press-releases\/artificial-intelligence-software-market-to-reach-126-0-billion-in-annual-worldwide-revenue-by-2025\/\">AI software revenue will grow from $10.1B in 2018 to $126.0B by 2025<\/a>, achieving a CAGR of 43.41%.<\/li>\n<\/ul>\n<p>Machine learning\u2019s growing adoption in business across industries reflects how effective its algorithms, frameworks and techniques are at solving complex problems quickly. Open jobs requiring\u00a0<a title=\"https:\/\/www.tensorflow.org\/\" href=\"https:\/\/www.tensorflow.org\/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/www.tensorflow.org\/\">TensorFlow<\/a>\u00a0experience is a useful way to quantify how prevalent machine learning is becoming in business today.\u00a0<a title=\"https:\/\/www.linkedin.com\/jobs\/tensorflow-jobs\/\" href=\"https:\/\/www.linkedin.com\/jobs\/tensorflow-jobs\/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/www.linkedin.com\/jobs\/tensorflow-jobs\/\">There are 4,134 open positions in the U.S. on LinkedIn that require TensorFlow expertise<\/a>\u00a0and\u00a0<a title=\"https:\/\/www.linkedin.com\/jobs\/search\/?geoId=92000000&amp;keywords=tensorflow&amp;location=Worldwide\" href=\"https:\/\/www.linkedin.com\/jobs\/search\/?geoId=92000000&amp;keywords=tensorflow&amp;location=Worldwide\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/www.linkedin.com\/jobs\/search\/?geoId=92000000&amp;keywords=tensorflow&amp;location=Worldwide\">12,172 open positions worldwide as of today<\/a>. Open jobs on LinkedIn requesting machine learning expertise in the U.S. further reflect its growing dominance in all businesses. There are\u00a0<a title=\"https:\/\/www.linkedin.com\/jobs\/search\/?geoId=103644278&amp;keywords=machine%20learning&amp;location=United%20States\" href=\"https:\/\/www.linkedin.com\/jobs\/search\/?geoId=103644278&amp;keywords=machine%20learning&amp;location=United%20States\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/www.linkedin.com\/jobs\/search\/?geoId=103644278&amp;keywords=machine%20learning&amp;location=United%20States\">44,864 jobs in the U.S. today according to LinkedIn<\/a>\u00a0that list machine learning as a required skill,\u00a0<a title=\"https:\/\/www.linkedin.com\/jobs\/search\/?geoId=92000000&amp;keywords=machine%20learning&amp;location=Worldwide\" href=\"https:\/\/www.linkedin.com\/jobs\/search\/?geoId=92000000&amp;keywords=machine%20learning&amp;location=Worldwide\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/www.linkedin.com\/jobs\/search\/?geoId=92000000&amp;keywords=machine%20learning&amp;location=Worldwide\">and 98,371 worldwide<\/a>.<\/p>\n<p>Senior management teams at enterprises who are Gartner clients initiated over 18,000 search queries last year on machine learning, with the majority from banking and financial institution clients, followed by government, services and manufacturing. One of the best reports published last year is from\u00a0<a title=\"https:\/\/hai.stanford.edu\/\" href=\"https:\/\/hai.stanford.edu\/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/hai.stanford.edu\/\">Stanford University\u2019s Institute for Human-Centered Artificial Intelligence<\/a>, the\u00a0<a title=\"https:\/\/hai.stanford.edu\/sites\/g\/files\/sbiybj10986\/f\/ai_index_2019_report.pdf\" href=\"https:\/\/hai.stanford.edu\/sites\/g\/files\/sbiybj10986\/f\/ai_index_2019_report.pdf\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/hai.stanford.edu\/sites\/g\/files\/sbiybj10986\/f\/ai_index_2019_report.pdf\">Artificial Intelligence Index Report 2019<\/a>, (PDF, 291 PP., no opt-in)<\/p>\n<p>Key takeaways from the series of machine learning market forecasts and market estimates from the last year include the following:<\/p>\n<ul>\n<li><strong>The global machine learning market is projected to grow from $7.3B in 2020 to $30.6B in 2024, attaining a CAGR of 43%.<\/strong>\u00a0AI-based processors, integrated memory and networking systems are projected to contribute a large percentage of market growth. Source:\u00a0<a title=\"https:\/\/www.marketresearchfuture.com\/reports\/machine-learning-market-2494\" href=\"https:\/\/www.marketresearchfuture.com\/reports\/machine-learning-market-2494\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/www.marketresearchfuture.com\/reports\/machine-learning-market-2494\">Market Research Future, Machine Learning Market Forecast Report \u2013 Global Forecast to 2024<\/a>.<\/li>\n<\/ul>\n<p style=\"text-align: center;\"><img decoding=\"async\" style=\"width: 700px; height: 341px;\" src=\"https:\/\/specials-images.forbesimg.com\/imageserve\/5e24a603f133f400076a2fb2\/960x0.jpg?fit=scale\" alt=\"Roundup of Machine Learning Forecasts And Market Estimates, 2020\" data-height=\"344\" data-width=\"707\" \/><\/p>\n<p style=\"text-align: center;\"><small>SOURCE: MARKET RESEARCH FUTURE, MACHINE LEARNING MARKET FORECAST REPORT \u2013 GLOBAL FORECAST TO 2024.<\/small><\/p>\n<ul>\n<li><strong>One in ten enterprises now use ten or more AI applications; chatbots, process optimization and fraud analysis lead a recent survey\u2019s top use cases.<\/strong>\u00a0\u00a0Prevalent applications include consumer\/market segmentation (15%), Computer-assisted diagnostics (14%), call center virtual assistants (12%), sentiment analysis\/opinion mining (12%), face detection\/recognition (11%), and HR applications (e.g.resume screening) (10%). Source:\u00a0<a title=\"https:\/\/www.mmcventures.com\/wp-content\/uploads\/2019\/02\/The-State-of-AI-2019-Divergence.pdf\" href=\"https:\/\/www.mmcventures.com\/wp-content\/uploads\/2019\/02\/The-State-of-AI-2019-Divergence.pdf\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/www.mmcventures.com\/wp-content\/uploads\/2019\/02\/The-State-of-AI-2019-Divergence.pdf\">MMC Ventures, The State of AI Divergence, 2019<\/a>\u00a0(PDF, 151 pp., no opt-in).<\/li>\n<\/ul>\n<p style=\"text-align: center;\"><img fetchpriority=\"high\" decoding=\"async\" class=\"alignnone\" style=\"width: 700px; height: 397px;\" src=\"https:\/\/specials-images.forbesimg.com\/imageserve\/5e24b367f133f400076a305a\/960x0.jpg?fit=scale\" alt=\"THE STATE OF AI DIVERGENCE, 2019 (PDF, 151 PP., NO OPT-IN\" width=\"960\" height=\"545\" data-height=\"436\" data-width=\"768\" \/><\/p>\n<p style=\"text-align: center;\"><small>SOURCE: MMC VENTURES, THE STATE OF AI DIVERGENCE, 2019 (PDF, 151 PP., NO OPT-IN).<\/small><\/p>\n<ul>\n<li><strong>$28.5B was invested in machine learning applications in the first calendar quarter of 2019, leading all other AI investment categories.<\/strong>\u00a0In total over $82B was invested in all AI categories shown in the chart below, with machine learning platforms and applications combining for over half of all AI investments at $42.9B. Source:\u00a0<a title=\"https:\/\/www.statista.com\/chart\/17966\/worldwide-artificial-intelligence-funding\/\" href=\"https:\/\/www.statista.com\/chart\/17966\/worldwide-artificial-intelligence-funding\/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/www.statista.com\/chart\/17966\/worldwide-artificial-intelligence-funding\/\">Statista, Machine Learning Tops AI Dollars, May 10, 2019.<\/a><\/li>\n<\/ul>\n<p style=\"text-align: center;\"><img decoding=\"async\" style=\"width: 700px; height: 495px;\" src=\"https:\/\/specials-images.forbesimg.com\/imageserve\/5e24a63b8b6cf300071c7432\/960x0.jpg?fit=scale\" alt=\"Roundup of Machine Learning Forecasts And Market Estimates, 2020\" data-height=\"674\" data-width=\"953\" \/><\/p>\n<p style=\"text-align: center;\"><small>SOURCE: STATISTA, MACHINE LEARNING TOPS AI DOLLARS, MAY 10, 2019.<\/small><\/p>\n<ul>\n<li><strong>83% of IT leaders say AI &amp; ML is transforming customer engagement, and 69% say it is transforming their business.<\/strong>\u00a079% believe that AI will help their organization identify external and internal security threats. The following graphic summarizes key findings from a recent Salesforce Research study. Source:\u00a0<a title=\"https:\/\/www.salesforce.com\/research\/market\/\" href=\"https:\/\/www.salesforce.com\/research\/market\/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/www.salesforce.com\/research\/market\/\">Enterprise Technology Trends, Salesforce Research<\/a>\u00a0(opt-in).<\/li>\n<\/ul>\n<p style=\"text-align: center;\"><img decoding=\"async\" class=\"alignnone\" style=\"width: 700px; height: 1158px;\" src=\"https:\/\/specials-images.forbesimg.com\/imageserve\/5e24b859a854780006cb375b\/960x0.jpg?fit=scale\" alt=\"TRENDS, SALESFORCE RESEARCH (OPT-IN)\" width=\"960\" height=\"1587\" data-height=\"1768\" data-width=\"1069\" \/><\/p>\n<p style=\"text-align: center;\"><small>SOURCE: ENTERPRISETECHNOLOGY TRENDS, SALESFORCE RESEARCH (OPT-IN).<\/small><\/p>\n<ul>\n<li><strong>Reducing company costs (38%), generating customer insights &amp; intelligence (37%), and improving customer experiences are the three most popular ML use cases.<\/strong>\u00a0Algorithmia\u2019s recent machine learning survey found the top five uses cases for ML in companies with 10,000 employees or more are reducing company costs, process automation for internal organization, improving customer experience, generating customer insights and intelligence and detecting fraud. Source: Algorithmia,\u00a0<a title=\"https:\/\/info.algorithmia.com\/hubfs\/2019\/Whitepapers\/The-State-of-Enterprise-ML-2020\/Algorithmia_2020_State_of_Enterprise_ML.pdf?hsLang=en-us\" href=\"https:\/\/info.algorithmia.com\/hubfs\/2019\/Whitepapers\/The-State-of-Enterprise-ML-2020\/Algorithmia_2020_State_of_Enterprise_ML.pdf?hsLang=en-us\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/info.algorithmia.com\/hubfs\/2019\/Whitepapers\/The-State-of-Enterprise-ML-2020\/Algorithmia_2020_State_of_Enterprise_ML.pdf?hsLang=en-us\">2020 state of enterprise machine learning<\/a>, Nov., 2019 (PDF, 29 PP., no opt-in).<\/li>\n<\/ul>\n<p style=\"text-align: center;\"><img decoding=\"async\" class=\"alignnone\" style=\"width: 700px; height: 540px;\" src=\"https:\/\/specials-images.forbesimg.com\/imageserve\/5e24a67d8b6cf300071c744d\/960x0.jpg?fit=scale\" alt=\"ALGORITHMIA, 2020 STATE OF ENTERPRISE MACHINE LEARNING\" width=\"960\" height=\"740\" data-height=\"578\" data-width=\"749\" \/><\/p>\n<p style=\"text-align: center;\"><small>ALGORITHMIA, 2020 STATE OF ENTERPRISE MACHINE LEARNING, NOV., 2019 (PDF, 29 PP., NO OPT-IN).<\/small><\/p>\n<ul>\n<li><strong>Achieving price optimization by persona is now possible using machine learning, factoring in brand and channel preferences, previous purchase history, and price sensitivity.<\/strong>\u00a0Brands, retailers, and manufacturers are saying that cloud-based price optimization and management apps are easier to use and more powerful based on rapid advances in AI and machine learning algorithms than ever before. The combination of easier to use, more powerful apps and the need to better manage and optimize omnichannel pricing is fueling rapid innovation in this area. The following example is from Microsoft Azure\u2019s Interactive Pricing Analytics Pre-Configured Solution (PCS). Source:\u00a0<a title=\"https:\/\/github.com\/Azure\/cortana-intelligence-price-analytics\/blob\/master\/User%20Guide\/UserGuide.md\" href=\"https:\/\/github.com\/Azure\/cortana-intelligence-price-analytics\/blob\/master\/User%20Guide\/UserGuide.md\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/github.com\/Azure\/cortana-intelligence-price-analytics\/blob\/master\/User%20Guide\/UserGuide.md\">Azure Cortana Interactive Pricing Analytics Pre-Configured Solution<\/a>.<\/li>\n<\/ul>\n<p style=\"text-align: center;\"><img decoding=\"async\" style=\"width: 700px; height: 415px;\" src=\"https:\/\/specials-images.forbesimg.com\/imageserve\/5e24a6aea854780006cb3654\/960x0.jpg?fit=scale\" alt=\"Roundup of Machine Learning Forecasts And Market Estimates, 2020\" data-height=\"920\" data-width=\"1553\" \/><\/p>\n<p style=\"text-align: center;\"><small>SOURCE: AZURE CORTANA INTERACTIVE PRICING ANALYTICS PRE-CONFIGURED SOLUTION.<\/small><\/p>\n<ul>\n<li><strong>As of Q2, 2019 AI startups brought in $7.4Bn in funding, the single highest amount ever in a quarter, according to CB Insights.<\/strong>\u00a0There were 488 artificial intelligence deals, the second-highest number in a given quarter. Despite this, for the first time, the United States made up less than half of all AI startup funding deals. Source:\u00a0<a title=\"https:\/\/www.statista.com\/chart\/18878\/artificial-intelligence-startup-funding\/\" href=\"https:\/\/www.statista.com\/chart\/18878\/artificial-intelligence-startup-funding\/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/www.statista.com\/chart\/18878\/artificial-intelligence-startup-funding\/\">Statistica, AI Startup Funding Reaches Record High, July 31, 2019<\/a><\/li>\n<\/ul>\n<p style=\"text-align: center;\"><img decoding=\"async\" style=\"width: 700px; height: 499px;\" src=\"https:\/\/specials-images.forbesimg.com\/imageserve\/5e24a6d3f133f400076a2fc0\/960x0.jpg?fit=scale\" alt=\"Roundup of Machine Learning Forecasts And Market Estimates, 2020\" data-height=\"684\" data-width=\"960\" \/><\/p>\n<p style=\"text-align: center;\"><small>SOURCE: STATISTICA, AI STARTUP FUNDING REACHES RECORD HIGH, JULY 31, 2019<\/small><\/p>\n<ul>\n<li><strong>IBM was the largest owner of active machine learning and artificial intelligence (AI) patent families worldwide with 5,570 families owned as of July, 2019.\u00a0<\/strong>In 2018, the company had claimed the leading position from Microsoft now ranked second with 5,330 active families owned. Samsung ranked third with slightly above five thousand patent families. Source:\u00a0<a title=\"https:\/\/www.statista.com\/statistics\/1032627\/worldwide-machine-learning-and-ai-patent-owners-trend\/\" href=\"https:\/\/www.statista.com\/statistics\/1032627\/worldwide-machine-learning-and-ai-patent-owners-trend\/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/www.statista.com\/statistics\/1032627\/worldwide-machine-learning-and-ai-patent-owners-trend\/\">Statistica, Companies with the most machine learning &amp; AI patents worldwide 2010-2019<\/a>.<\/li>\n<\/ul>\n<p style=\"text-align: center;\"><img decoding=\"async\" style=\"width: 700px; height: 368px;\" src=\"https:\/\/specials-images.forbesimg.com\/imageserve\/5e24a704f133f400076a2fc9\/960x0.jpg?fit=scale\" alt=\"Roundup of Machine Learning Forecasts And Market Estimates, 2020\" data-height=\"730\" data-width=\"1390\" \/><\/p>\n<p style=\"text-align: center;\"><small>SOURCE: STATISTICA, COMPANIES WITH THE MOST MACHINE LEARNING &amp; AI PATENTS WORLDWIDE 2010-2019.<\/small><\/p>\n<ul>\n<li><strong>IDC predicts spending on AI systems will reach $97.9B in 2023, more than two and one-half times the $37.5B that will be spent in 2019.<\/strong>\u00a0The compound annual growth rate (CAGR) for the 2018-2023 forecast period will be 28.4%. Spending on AI systems will be led by the retail and banking industries, each of which will invest more than $5B in 2019. Nearly half of the retail spending will go toward automated customer service agents and expert shopping advisors &amp; product recommendation systems. Source:<a title=\"https:\/\/www.idc.com\/getdoc.jsp?containerId=prUS45481219\" href=\"https:\/\/www.idc.com\/getdoc.jsp?containerId=prUS45481219\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/www.idc.com\/getdoc.jsp?containerId=prUS45481219\">\u00a0IDC, Worldwide Spending on Artificial Intelligence Systems Will Be Nearly $98 Billion in 2023, According to New IDC Spending Guide.<\/a><\/li>\n<\/ul>\n<p style=\"text-align: center;\"><img decoding=\"async\" style=\"width: 700px; height: 362px;\" src=\"https:\/\/specials-images.forbesimg.com\/imageserve\/5e24a72e8b6cf300071c7460\/960x0.jpg?fit=scale\" alt=\"Roundup of Machine Learning Forecasts And Market Estimates, 2020\" data-height=\"503\" data-width=\"971\" \/><\/p>\n<p style=\"text-align: center;\"><small>SOURCE: IDC, WORLDWIDE SPENDING ON ARTIFICIAL INTELLIGENCE SYSTEMS WILL BE NEARLY $98 BILLION IN 2023, ACCORDING TO NEW IDC SPENDING GUIDE.<\/small><\/p>\n<ul>\n<li><strong>Microsoft and SAS are the two fastest-growing AI software platforms according to IDC.<\/strong>\u00a0According to IDC, growth in this market continues to be driven by increases in general-purpose AI software platforms and conversational AI software platforms being used to develop predictive and prescriptive applications that offer advice and recommendations as well as digital assistants and conversational user interfaces. Source:\u00a0<a title=\"https:\/\/www.ibm.com\/downloads\/cas\/PQ29DALE\" href=\"https:\/\/www.ibm.com\/downloads\/cas\/PQ29DALE\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/www.ibm.com\/downloads\/cas\/PQ29DALE\">IDC Worldwide Artificial Intelligence Software Platforms Market Shares, 2018: Steady Growth \u2014 Moving Toward Production, courtesy of IBM.<\/a><\/li>\n<\/ul>\n<p style=\"text-align: center;\"><img decoding=\"async\" style=\"width: 700px; height: 494px;\" src=\"https:\/\/specials-images.forbesimg.com\/imageserve\/5e24a75ea854780006cb3670\/960x0.jpg?fit=scale\" alt=\"Roundup of Machine Learning Forecasts And Market Estimates, 2020\" data-height=\"501\" data-width=\"709\" \/><\/p>\n<p style=\"text-align: center;\"><small>SOURCE: IDC WORLDWIDE ARTIFICIAL INTELLIGENCE SOFTWARE PLATFORMS MARKET SHARES, 2018: STEADY GROWTH \u2014 MOVING TOWARD PRODUCTION, COURTESY OF IBM.<\/small><\/p>\n<ul>\n<li><strong>PwC predicts the market for AI-related semiconductors to grow from a current $6B in revenues to more than $30B by 2022.<\/strong>\u00a0Although <a href=\"https:\/\/www.experfy.com\/blog\/ai-ml\/ways-ai-improving-new-product-development\/\">AI-driven use cases<\/a> are expected to find their way across every industry segment over time, their adoption will be determined by the size of investment in the technology, the pace of its development and the speed at which its benefits are realized. Source:\u00a0<a title=\"https:\/\/www.pwc.com\/gx\/en\/industries\/tmt\/publications\/assets\/pwc-semiconductor-report-2019.pdf\" href=\"https:\/\/www.pwc.com\/gx\/en\/industries\/tmt\/publications\/assets\/pwc-semiconductor-report-2019.pdf\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/www.pwc.com\/gx\/en\/industries\/tmt\/publications\/assets\/pwc-semiconductor-report-2019.pdf\">PwC,\u00a0Opportunities for the global semiconductor market; Growing market share by embracing AI<\/a>\u00a0(PDF, 18 pp., no opt-in).<\/li>\n<\/ul>\n<p style=\"text-align: center;\"><img decoding=\"async\" style=\"width: 700px; height: 812px;\" src=\"https:\/\/specials-images.forbesimg.com\/imageserve\/5e24af76a854780006cb3707\/960x0.jpg?fit=scale\" alt=\"Roundup of Machine Learning Forecasts And Market Estimates, 2020\" data-height=\"731\" data-width=\"630\" \/><\/p>\n<p style=\"text-align: center;\"><small>SOURCE: PWC, OPPORTUNITIES FOR THE GLOBAL SEMICONDUCTOR MARKET; GROWING MARKET SHARE BY EMBRACING AI (PDF, 18 PP., NO OPT-IN)<\/small><\/p>\n<ul>\n<li><strong>McKinsey predicts that AI-related semiconductors will see growth of about 18% annually over the next few years\u2014five times greater than the rate for semiconductors used in non-AI applications.<\/strong>\u00a0By 2025, AI-related semiconductors could account for almost 20% of all demand, which would translate into approximately $67B in revenue. Opportunities will emerge at both data centers and the edge. If this growth materializes as expected, semiconductor companies will be positioned to capture more value from the AI technology stack than they have obtained with previous innovations\u2014about 40% 50% of the total. Source:\u00a0<a title=\"https:\/\/www.mckinsey.com\/industries\/semiconductors\/our-insights\/artificial-intelligence-hardware-new-opportunities-for-semiconductor-companies\" href=\"https:\/\/www.mckinsey.com\/industries\/semiconductors\/our-insights\/artificial-intelligence-hardware-new-opportunities-for-semiconductor-companies\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/www.mckinsey.com\/industries\/semiconductors\/our-insights\/artificial-intelligence-hardware-new-opportunities-for-semiconductor-companies\">McKinsey, Artificial-intelligence hardware: New opportunities for semiconductor companies. January, 2019<\/a>.<\/li>\n<\/ul>\n<p style=\"text-align: center;\"><img decoding=\"async\" style=\"width: 700px; height: 508px;\" src=\"https:\/\/specials-images.forbesimg.com\/imageserve\/5e24a78fa854780006cb3686\/960x0.jpg?fit=scale\" alt=\"Roundup of Machine Learning Forecasts And Market Estimates, 2020\" data-height=\"543\" data-width=\"748\" \/><\/p>\n<p style=\"text-align: center;\"><small>SOURCE: MCKINSEY, ARTIFICIAL-INTELLIGENCE HARDWARE: NEW OPPORTUNITIES FOR SEMICONDUCTOR COMPANIES. JANUARY, 2019.<\/small><\/p>\n<ul>\n<li><strong>Machine learning-based algorithms are the foundation of the next generation of logistics technologies, with the most significant gains being made with advanced resource scheduling systems.<\/strong>\u00a0Machine learning and AI-based techniques are the foundation of a broad spectrum of next-generation logistics and supply chain technologies now under development. The most significant gains are being made where machine learning can contribute to solving complex constraint, cost and delivery problems companies face today. McKinsey predicts machine learning\u2019s most significant contributions will be in providing supply chain operators with more significant insights into how supply chain performance can be improved, anticipating anomalies in logistics costs and performance before they occur. Machine learning is also providing insights into where automation can deliver the most significant scale advantages. Source: McKinsey &amp; Company,\u00a0<a title=\"https:\/\/www.mckinsey.com\/industries\/travel-transport-and-logistics\/our-insights\/automation-in-logistics-big-opportunity-bigger-uncertainty\" href=\"https:\/\/www.mckinsey.com\/industries\/travel-transport-and-logistics\/our-insights\/automation-in-logistics-big-opportunity-bigger-uncertainty\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/www.mckinsey.com\/industries\/travel-transport-and-logistics\/our-insights\/automation-in-logistics-big-opportunity-bigger-uncertainty\">Automation in logistics: Big opportunity, bigger uncertainty<\/a>, April 2019. By Ashutosh Dekhne, Greg Hastings, John Murnane, and Florian Neuhaus<\/li>\n<\/ul>\n<p style=\"text-align: center;\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone\" style=\"width: 700px; height: 493px;\" src=\"https:\/\/specials-images.forbesimg.com\/imageserve\/5e24a7c58b6cf300071c7463\/960x0.jpg?fit=scale\" alt=\"BIG OPPORTUNITY, BIGGER UNCERTAINTY, APRIL 2019. BY ASHUTOSH DEKHNE, GREG HASTINGS, JOHN MURNANE, AND FLORIAN NEUHAUS\" width=\"960\" height=\"676\" data-height=\"574\" data-width=\"815\" \/><\/p>\n<p style=\"text-align: center;\"><small>SOURCE: MCKINSEY &amp; COMPANY, AUTOMATION IN LOGISTICS: BIG OPPORTUNITY, BIGGER UNCERTAINTY, APRIL 2019. BY ASHUTOSH DEKHNE, GREG HASTINGS, JOHN MURNANE, AND FLORIAN NEUHAUS<\/small><\/p>\n<ul>\n<li><strong>Machine learning algorithms are being relied on to create propensity models by persona, providing invaluable insights that predicting which customers will act on a bundling or pricing offer<\/strong>. By definition\u00a0<a title=\"https:\/\/www.oreilly.com\/library\/view\/predictive-analytics-with\/9781484212004\/9781484212011_Ch07.xhtml\" href=\"https:\/\/www.oreilly.com\/library\/view\/predictive-analytics-with\/9781484212004\/9781484212011_Ch07.xhtml\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/www.oreilly.com\/library\/view\/predictive-analytics-with\/9781484212004\/9781484212011_Ch07.xhtml\">propensity models<\/a>\u00a0rely on predictive analytics including machine learning to predict the probability a given customer will act on a bundling or pricing offer, e-mail campaign or other call-to-action leading to a purchase, upsell or cross-sell. Propensity models have proven to be very effective at increasing customer retention and reducing churn. Every business excelling at omnichannel today rely on propensity models to better predict how customers\u2019 preferences and past behavior will lead to future purchases. The following is a dashboard that shows how propensity models work. Source: customer propensities dashboard is from\u00a0<a title=\"https:\/\/community.tibco.com\/wiki\/tibco-cpg-retail-solutions\" href=\"https:\/\/community.tibco.com\/wiki\/tibco-cpg-retail-solutions\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/community.tibco.com\/wiki\/tibco-cpg-retail-solutions\">TIBCO<\/a>.<\/li>\n<\/ul>\n<p style=\"text-align: center;\"><img decoding=\"async\" style=\"width: 700px; height: 346px;\" src=\"https:\/\/specials-images.forbesimg.com\/imageserve\/5e24a7eba854780006cb368e\/960x0.jpg?fit=scale\" alt=\"Roundup of Machine Learning Forecasts And Market Estimates, 2020\" data-height=\"566\" data-width=\"1147\" \/><\/p>\n<p style=\"text-align: center;\"><small>TIBCO<\/small><\/p>\n<ul>\n<li><strong>71% of today\u2019s organizations reporting they spend more on machine learning for cybersecurity than they did two years ago.<\/strong>\u00a026% and 28% of U.S. and Japanese IT professionals believe their organizations could be doing more. Additionally, 84% of respondents believe cyber-criminals are also using AI and ML to launch their attacks. When considered together, these figures indicate a strong belief that AI\/ML-based cybersecurity is no longer simply nice to have; it\u2019s crucial to stop modern cyberattacks.\u00a0\u00a0Source:\u00a0<a title=\"https:\/\/www-cdn.webroot.com\/6015\/4999\/4566\/Webroot_AI_ML_Survey_US-2019.pdf\" href=\"https:\/\/www-cdn.webroot.com\/6015\/4999\/4566\/Webroot_AI_ML_Survey_US-2019.pdf\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/www-cdn.webroot.com\/6015\/4999\/4566\/Webroot_AI_ML_Survey_US-2019.pdf\">Webroot, Knowledge Gaps: AI and Machine Learning in Cybersecurity Perspectives from the U.S. and Japanese IT Professionals<\/a>\u00a0(PDF, 9 pp., no opt-in)<\/li>\n<\/ul>\n<p style=\"text-align: center;\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone\" style=\"width: 700px; height: 383px;\" src=\"https:\/\/specials-images.forbesimg.com\/imageserve\/5e24a8188b6cf300071c7488\/960x0.jpg?fit=scale\" alt=\"AI AND MACHINE LEARNING IN CYBERSECURITY PERSPECTIVES FROM THE U.S. AND JAPANESE IT PROFESSIONALS\" width=\"960\" height=\"525\" data-height=\"525\" data-width=\"960\" \/><\/p>\n<p style=\"text-align: center;\"><small>SOURCE: WEBROOT, KNOWLEDGE GAPS: AI AND MACHINE LEARNING IN CYBERSECURITY PERSPECTIVES FROM THE U.S. AND JAPANESE IT PROFESSIONALS (PDF, 9 PP., NO OPT-IN)<\/small><\/p>\n<ul>\n<li><strong>Credit unions will adopt machine learning in 2020 to automate routine tasks and free up human underwriters to focus on providing more personalized services, including improvements in inquiry resolution &amp; dispute and fraud management.<\/strong>\u00a0Credit unions are built on an annuity-based business model that delivers successively higher profitability the longer a member is retained. Credit unions will capitalize on ML by driving up loan approvals with no added risk and automating more of the loan approval process. By the end of 2020, according to a Fannie Mae survey of mortgage lenders, 71% of credit unions plan to investigate, test, or fully implement AI\/ML solutions \u2013 up from just 40% in 2018. AI and ML will also be adopted across credit unions to improve inquiry resolution &amp; dispute and fraud management while improving multichannel customer experiences. Providing real-time, relevant responses to customers to expedite inquiries and dispute resolutions using AI and ML is going to become commonplace in 2020. AI and ML is predicted to make a significant contribution to automating anomaly detection and borrower default risk assessment as the graphic below from\u00a0<a title=\"https:\/\/www.fanniemae.com\/resources\/file\/research\/mlss\/pdf\/mlss-artificial-intelligence-100418.pdf\" href=\"https:\/\/www.fanniemae.com\/resources\/file\/research\/mlss\/pdf\/mlss-artificial-intelligence-100418.pdf\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/www.fanniemae.com\/resources\/file\/research\/mlss\/pdf\/mlss-artificial-intelligence-100418.pdf\">Fannie Mae\u2019s Mortgage Lender Sentiment Survey\u00ae How Will Artificial Intelligence Shape Mortgage Lending? Q3 2018 Topic Analysis<\/a>\u00a0illustrates:<\/li>\n<\/ul>\n<p style=\"text-align: center;\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone\" style=\"width: 700px; height: 391px;\" src=\"https:\/\/specials-images.forbesimg.com\/imageserve\/5e24a8518b6cf300071c7494\/960x0.jpg?fit=scale\" alt=\"FANNIE MAE\u2019S MORTGAGE LENDER SENTIMENT SURVEY\u00ae HOW WILL ARTIFICIAL INTELLIGENCE SHAPE MORTGAGE LENDING? Q3 2018 TOPIC ANALYSIS\" width=\"960\" height=\"536\" data-height=\"536\" data-width=\"960\" \/><\/p>\n<p style=\"text-align: center;\"><small>FANNIE MAE\u2019S MORTGAGE LENDER SENTIMENT SURVEY\u00ae HOW WILL ARTIFICIAL INTELLIGENCE SHAPE MORTGAGE LENDING? Q3 2018 TOPIC ANALYSIS<\/small><\/p>\n<ul>\n<li><strong>AI and machine learning will thwart compromised hardware finding its way into organizations\u2019 supply chains<\/strong>. Rising demand for electronic components will expand the market for counterfeit components and cloned products, increasing the threat of compromised hardware finding its way into organizations\u2019 supply chains. The vectors for hardware supply-chain attacks are expanding as market demand for more and cheaper chips, and components drive a booming business for hardware counterfeiters and cloners. This expansion is likely to create greater opportunities for compromise by both nation-state and cybercriminal threat actors. Source: <a title=\"https:\/\/www.boozallen.com\/content\/dam\/boozallen_site\/ccg\/pdf\/publications\/top-9-cybersecurity-trends-for-2020.pdf\" href=\"https:\/\/www.boozallen.com\/content\/dam\/boozallen_site\/ccg\/pdf\/publications\/top-9-cybersecurity-trends-for-2020.pdf\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/www.boozallen.com\/content\/dam\/boozallen_site\/ccg\/pdf\/publications\/top-9-cybersecurity-trends-for-2020.pdf\">2020 Cybersecurity Threats Trends Outlook; Booz, Allen, Hamilton, 2019.<\/a><\/li>\n<\/ul>\n<h4><u>Machine Learning sources:<\/u><\/h4>\n<ul>\n<li>Algorithma,\u00a0<a title=\"https:\/\/info.algorithmia.com\/hubfs\/2019\/Whitepapers\/The-State-of-Enterprise-ML-2020\/Algorithmia_2020_State_of_Enterprise_ML.pdf?hsLang=en-us\" href=\"https:\/\/info.algorithmia.com\/hubfs\/2019\/Whitepapers\/The-State-of-Enterprise-ML-2020\/Algorithmia_2020_State_of_Enterprise_ML.pdf?hsLang=en-us\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/info.algorithmia.com\/hubfs\/2019\/Whitepapers\/The-State-of-Enterprise-ML-2020\/Algorithmia_2020_State_of_Enterprise_ML.pdf?hsLang=en-us\">2020 state of enterprise machine learning<\/a>, Nov., 2019 (PDF, 29 PP., no opt-in)<\/li>\n<li>Accenture,\u00a0<a title=\"https:\/\/www.accenture.com\/t20180822T093440Z__w__\/us-en\/_acnmedia\/PDF-84\/Accenture-Machine-Leaning-Insurance.pdf\" href=\"https:\/\/www.accenture.com\/t20180822T093440Z__w__\/us-en\/_acnmedia\/PDF-84\/Accenture-Machine-Leaning-Insurance.pdf\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/www.accenture.com\/t20180822T093440Z__w__\/us-en\/_acnmedia\/PDF-84\/Accenture-Machine-Leaning-Insurance.pdf\">Machine Learning In Insurance<\/a>\u00a0(PDF, 14 pp., no opt-in)<\/li>\n<li>Ark Invest\u00a0<a title=\"https:\/\/research.ark-invest.com\/hubfs\/1_Download_Files_ARK-Invest\/White_Papers\/Big-Ideas-2019-ARKInvest.pdf?hsCtaTracking=389fa33c-10c9-4345-8c9e-e457b82977f8%7C7114deb8-15db-4540-81d7-4a5f7de51e66\" href=\"https:\/\/research.ark-invest.com\/hubfs\/1_Download_Files_ARK-Invest\/White_Papers\/Big-Ideas-2019-ARKInvest.pdf?hsCtaTracking=389fa33c-10c9-4345-8c9e-e457b82977f8%7C7114deb8-15db-4540-81d7-4a5f7de51e66\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/research.ark-invest.com\/hubfs\/1_Download_Files_ARK-Invest\/White_Papers\/Big-Ideas-2019-ARKInvest.pdf?hsCtaTracking=389fa33c-10c9-4345-8c9e-e457b82977f8%7C7114deb8-15db-4540-81d7-4a5f7de51e66\">Big Ideas 2019, Innovation is the Key To Growth<\/a>\u00a0(PDF, 94 pp., no opt-in)<\/li>\n<li><a title=\"https:\/\/www.gao.gov\/assets\/700\/690910.pdf\" href=\"https:\/\/www.gao.gov\/assets\/700\/690910.pdf\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/www.gao.gov\/assets\/700\/690910.pdf\">Artificial Intelligence: Emerging Opportunities, Challenges and Implications<\/a>. U.S. Government Accountability Office, March 2018 (PDF, 100 pp., no opt-in)<\/li>\n<li><a title=\"https:\/\/pulse.microsoft.com\/uploads\/prod\/2018\/10\/WE_AI_Report_2018.pdf\" href=\"https:\/\/pulse.microsoft.com\/uploads\/prod\/2018\/10\/WE_AI_Report_2018.pdf\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/pulse.microsoft.com\/uploads\/prod\/2018\/10\/WE_AI_Report_2018.pdf\">Artificial Intelligence in Europe: How 277 Major Companies Benefit from AI Outlook for 2019 and Beyond<\/a>\u00a0by Ernst &amp; Young (PDF, 41 pp., no opt-in)<\/li>\n<li>Artificial Intelligence Index<a title=\"http:\/\/cdn.aiindex.org\/2018\/AI%20Index%202018%20Annual%20Report.pdf\" href=\"http:\/\/cdn.aiindex.org\/2018\/AI%20Index%202018%20Annual%20Report.pdf\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:http:\/\/cdn.aiindex.org\/2018\/AI%20Index%202018%20Annual%20Report.pdf\">, 2018 Annual Report<\/a>\u00a0(PDF, 94 pp., no opt-in)<\/li>\n<li>Boston Consulting Group,\u00a0<a title=\"https:\/\/www.bcg.com\/capabilities\/technology-digital\/AI-at-scale.aspx\" href=\"https:\/\/www.bcg.com\/capabilities\/technology-digital\/AI-at-scale.aspx\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/www.bcg.com\/capabilities\/technology-digital\/AI-at-scale.aspx\">AI at Scale: The Next Frontier in Digital Transformation<\/a><\/li>\n<li>Capgemini,\u00a0<a title=\"https:\/\/www.capgemini.com\/research\/accelerating-automotives-ai-transformation\/\" href=\"https:\/\/www.capgemini.com\/research\/accelerating-automotives-ai-transformation\/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/www.capgemini.com\/research\/accelerating-automotives-ai-transformation\/\">Accelerating Automotive\u2019s AI transformation: How driving AI enterprise-wide can turbo-charge organizational value, March<\/a>\u00a02019.\u00a0<a title=\"https:\/\/www.capgemini.com\/wp-content\/uploads\/2019\/03\/30-min-%E2%80%93-Report-2.pdf\" href=\"https:\/\/www.capgemini.com\/wp-content\/uploads\/2019\/03\/30-min-%E2%80%93-Report-2.pdf\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/www.capgemini.com\/wp-content\/uploads\/2019\/03\/30-min-%E2%80%93-Report-2.pdf\">PDF of the study is available here<\/a>\u00a0(PDF, 36 pp.., no opt-in)<\/li>\n<li>Chamakkala, Vipin,\u00a0<a title=\"https:\/\/medium.com\/work-bench\/todays-ai-software-infrastructure-landscape-and-trends-shaping-the-market-460d0c1c26d2\" href=\"https:\/\/medium.com\/work-bench\/todays-ai-software-infrastructure-landscape-and-trends-shaping-the-market-460d0c1c26d2\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/medium.com\/work-bench\/todays-ai-software-infrastructure-landscape-and-trends-shaping-the-market-460d0c1c26d2\">Today\u2019s AI Software Infrastructure Landscape (And Trends Shaping The Market)<\/a>\u00a0Medium. May 7, 2018<\/li>\n<li>Deloitte,\u00a0<a title=\"https:\/\/www2.deloitte.com\/content\/dam\/insights\/us\/articles\/4780_State-of-AI-in-the-enterprise\/DI_State-of-AI-in-the-enterprise-2nd-ed.pdf\" href=\"https:\/\/www2.deloitte.com\/content\/dam\/insights\/us\/articles\/4780_State-of-AI-in-the-enterprise\/DI_State-of-AI-in-the-enterprise-2nd-ed.pdf\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/www2.deloitte.com\/content\/dam\/insights\/us\/articles\/4780_State-of-AI-in-the-enterprise\/DI_State-of-AI-in-the-enterprise-2nd-ed.pdf\">State of AI in the Enterprise, 2nd Edition, Early adopters combine bullish enthusiasm with strategic investments<\/a>\u00a0(PDF, 28 pp., no opt-in)<\/li>\n<li>Forbes,\u00a0<a title=\"https:\/\/www.forbes.com\/sites\/louiscolumbus\/2018\/12\/26\/10-ways-machine-learning-is-revolutionizing-sales\/#80394543fd10\" href=\"https:\/\/www.forbes.com\/sites\/louiscolumbus\/2018\/12\/26\/10-ways-machine-learning-is-revolutionizing-sales\/#80394543fd10\" target=\"_self\" rel=\"noopener noreferrer\" data-ga-track=\"InternalLink:https:\/\/www.forbes.com\/sites\/louiscolumbus\/2018\/12\/26\/10-ways-machine-learning-is-revolutionizing-sales\/#80394543fd10\">10 Ways Machine Learning Is Revolutionizing Sales,<\/a>\u00a0December 26, 2018<\/li>\n<li>Forbes,\u00a0<a title=\"https:\/\/www.forbes.com\/sites\/louiscolumbus\/2018\/12\/16\/how-china-is-dominating-artificial-intelligence\/#7db8159a2b2f\" href=\"https:\/\/www.forbes.com\/sites\/louiscolumbus\/2018\/12\/16\/how-china-is-dominating-artificial-intelligence\/#7db8159a2b2f\" target=\"_self\" rel=\"noopener noreferrer\" data-ga-track=\"InternalLink:https:\/\/www.forbes.com\/sites\/louiscolumbus\/2018\/12\/16\/how-china-is-dominating-artificial-intelligence\/#7db8159a2b2f\">How China Is Dominating Artificial Intelligence<\/a>, December 16, 2018<\/li>\n<li>Forbes,\u00a0<a title=\"https:\/\/www.forbes.com\/sites\/louiscolumbus\/2019\/04\/28\/how-to-improve-supply-chains-with-machine-learning-10-proven-ways\/#578a92e83f3c\" href=\"https:\/\/www.forbes.com\/sites\/louiscolumbus\/2019\/04\/28\/how-to-improve-supply-chains-with-machine-learning-10-proven-ways\/#578a92e83f3c\" target=\"_self\" rel=\"noopener noreferrer\" data-ga-track=\"InternalLink:https:\/\/www.forbes.com\/sites\/louiscolumbus\/2019\/04\/28\/how-to-improve-supply-chains-with-machine-learning-10-proven-ways\/#578a92e83f3c\">How To Improve Supply Chains With Machine Learning: 10 Proven Ways,<\/a>\u00a0April 28, 2019<\/li>\n<li>Forbes,\u00a0<a title=\"https:\/\/www.forbes.com\/sites\/louiscolumbus\/2019\/01\/06\/microsoft-leads-the-ai-patent-race-going-into-2019\/#474d9fd44de9\" href=\"https:\/\/www.forbes.com\/sites\/louiscolumbus\/2019\/01\/06\/microsoft-leads-the-ai-patent-race-going-into-2019\/#474d9fd44de9\" target=\"_self\" rel=\"noopener noreferrer\" data-ga-track=\"InternalLink:https:\/\/www.forbes.com\/sites\/louiscolumbus\/2019\/01\/06\/microsoft-leads-the-ai-patent-race-going-into-2019\/#474d9fd44de9\">Microsoft Leads The AI Patent Race Going Into 2019<\/a>, January 6, 2019<\/li>\n<li><a title=\"https:\/\/www.ibm.com\/downloads\/cas\/MK85Y8V3\" href=\"https:\/\/www.ibm.com\/downloads\/cas\/MK85Y8V3\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/www.ibm.com\/downloads\/cas\/MK85Y8V3\">IDC Worldwide Artificial Intelligence Market Shares, 2018: Steady Growth \u2014 POCs Poised to Enter Full-Blown Production<\/a><\/li>\n<li><a title=\"https:\/\/www.idc.com\/getdoc.jsp?containerId=prUS44291818\" href=\"https:\/\/www.idc.com\/getdoc.jsp?containerId=prUS44291818\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/www.idc.com\/getdoc.jsp?containerId=prUS44291818\">IDC Worldwide Spending on Cognitive and Artificial Intelligence Systems Forecast to Reach $77.6 Billion in 2022, According to New IDC Spending Guide.<\/a><\/li>\n<li>The Economist,\u00a0<a title=\"https:\/\/eiuperspectives.economist.com\/sites\/default\/files\/Risks_and_rewards_2018.2.7.pdf\" href=\"https:\/\/eiuperspectives.economist.com\/sites\/default\/files\/Risks_and_rewards_2018.2.7.pdf\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/eiuperspectives.economist.com\/sites\/default\/files\/Risks_and_rewards_2018.2.7.pdf\">Risks and Rewards, Scenarios around the economic impact of machine learning<\/a>\u00a0(PDF, 80 pp., no opt-in)<\/li>\n<li>McKinsey,\u00a0<a title=\"https:\/\/www.mckinsey.com\/business-functions\/mckinsey-analytics\/our-insights\/an-executives-guide-to-ai\" href=\"https:\/\/www.mckinsey.com\/business-functions\/mckinsey-analytics\/our-insights\/an-executives-guide-to-ai\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/www.mckinsey.com\/business-functions\/mckinsey-analytics\/our-insights\/an-executives-guide-to-ai\">An Executive\u2019s Guide to AI<\/a><\/li>\n<li>McKinsey Global Institute,\u00a0<a title=\"https:\/\/www.mckinsey.com\/featured-insights\/artificial-intelligence\/tackling-europes-gap-in-digital-and-ai\" href=\"https:\/\/www.mckinsey.com\/featured-insights\/artificial-intelligence\/tackling-europes-gap-in-digital-and-ai\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/www.mckinsey.com\/featured-insights\/artificial-intelligence\/tackling-europes-gap-in-digital-and-ai\">Tackling Europe\u2019s gap in digital and AI<\/a>, February 2019 Discussion paper<\/li>\n<li>McKinsey Global Institute,\u00a0<a title=\"https:\/\/www.mckinsey.com\/featured-insights\/artificial-intelligence\/applying-artificial-intelligence-for-social-good\" href=\"https:\/\/www.mckinsey.com\/featured-insights\/artificial-intelligence\/applying-artificial-intelligence-for-social-good\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/www.mckinsey.com\/featured-insights\/artificial-intelligence\/applying-artificial-intelligence-for-social-good\">Applying artificial intelligence for social good<\/a>, November, 20-8 discussion paper<\/li>\n<li>McKinsey Global Institute,\u00a0<a title=\"https:\/\/www.mckinsey.com\/~\/media\/McKinsey\/Featured%20Insights\/Artificial%20Intelligence\/Tackling%20Europes%20gap%20in%20digital%20and%20AI\/MGI-Tackling-Europes-gap-in-digital-and-AI-Feb-2019-vF.ashx\" href=\"https:\/\/www.mckinsey.com\/~\/media\/McKinsey\/Featured%20Insights\/Artificial%20Intelligence\/Tackling%20Europes%20gap%20in%20digital%20and%20AI\/MGI-Tackling-Europes-gap-in-digital-and-AI-Feb-2019-vF.ashx\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/www.mckinsey.com\/~\/media\/McKinsey\/Featured%20Insights\/Artificial%20Intelligence\/Tackling%20Europes%20gap%20in%20digital%20and%20AI\/MGI-Tackling-Europes-gap-in-digital-and-AI-Feb-2019-vF.ashx\">Notes from the AI Frontier: Tackling Europe\u2019s Gap In Digital and AI<\/a>\u00a0(PDF, 60 pp., no opt-in)<\/li>\n<li>McKinsey Global Institute,\u00a0<a title=\"https:\/\/www.mckinsey.com\/featured-insights\/artificial-intelligence\/notes-from-the-ai-frontier-applications-and-value-of-deep-learning\" href=\"https:\/\/www.mckinsey.com\/featured-insights\/artificial-intelligence\/notes-from-the-ai-frontier-applications-and-value-of-deep-learning\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/www.mckinsey.com\/featured-insights\/artificial-intelligence\/notes-from-the-ai-frontier-applications-and-value-of-deep-learning\">Notes from the AI frontier: Applications and value of deep learning<\/a>, April 2018<\/li>\n<li>McKinsey Global Institute,\u00a0<a title=\"https:\/\/www.mckinsey.com\/featured-insights\/artificial-intelligence\/visualizing-the-uses-and-potential-impact-of-ai-and-other-analytics\" href=\"https:\/\/www.mckinsey.com\/featured-insights\/artificial-intelligence\/visualizing-the-uses-and-potential-impact-of-ai-and-other-analytics\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/www.mckinsey.com\/featured-insights\/artificial-intelligence\/visualizing-the-uses-and-potential-impact-of-ai-and-other-analytics\">Visualizing the uses and potential impact of AI and other analytics<\/a>, April 2018<\/li>\n<li>MIT Sloan Management Review,\u00a0<a title=\"https:\/\/sloanreview.mit.edu\/projects\/artificial-intelligence-in-business-gets-real\/\" href=\"https:\/\/sloanreview.mit.edu\/projects\/artificial-intelligence-in-business-gets-real\/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/sloanreview.mit.edu\/projects\/artificial-intelligence-in-business-gets-real\/\">Artificial Intelligence in Business Gets Real: Pioneering Companies Aim for AI at Scale<\/a>, September 17, 2018,\u00a0<a title=\"https:\/\/sloanreview.mit.edu\/projects\/artificial-intelligence-in-business-gets-real\/?switch_view=PDF\" href=\"https:\/\/sloanreview.mit.edu\/projects\/artificial-intelligence-in-business-gets-real\/?switch_view=PDF\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/sloanreview.mit.edu\/projects\/artificial-intelligence-in-business-gets-real\/?switch_view=PDF\">PDF available here<\/a>.<\/li>\n<li>Stanford University,\u00a0<a title=\"https:\/\/hai.stanford.edu\/sites\/g\/files\/sbiybj10986\/f\/ai_index_2019_report.pdf\" href=\"https:\/\/hai.stanford.edu\/sites\/g\/files\/sbiybj10986\/f\/ai_index_2019_report.pdf\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/hai.stanford.edu\/sites\/g\/files\/sbiybj10986\/f\/ai_index_2019_report.pdf\">Artificial Intelligence Index Report 2019<\/a>, (PDF, 291 PP., no opt-in)<\/li>\n<li>Statista,\u00a0<a title=\"https:\/\/www.statista.com\/study\/50485\/artificial-intelligence\/\" href=\"https:\/\/www.statista.com\/study\/50485\/artificial-intelligence\/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/www.statista.com\/study\/50485\/artificial-intelligence\/\">In-Depth: Artificial Intelligence 2019<\/a>, February 2019<\/li>\n<li><a title=\"https:\/\/www.statista.com\/chart\/17966\/worldwide-artificial-intelligence-funding\/\" href=\"https:\/\/www.statista.com\/chart\/17966\/worldwide-artificial-intelligence-funding\/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/www.statista.com\/chart\/17966\/worldwide-artificial-intelligence-funding\/\">Statista, Machine Learning Tops AI Dollars, May 10, 2019.<\/a><\/li>\n<li>Tractica,\u00a0<a title=\"https:\/\/www.bastagroup.nl\/wp-content\/uploads\/2019\/01\/Artificial-Intelligence-10-Predictions-for-2019.pdf\" href=\"https:\/\/www.bastagroup.nl\/wp-content\/uploads\/2019\/01\/Artificial-Intelligence-10-Predictions-for-2019.pdf\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/www.bastagroup.nl\/wp-content\/uploads\/2019\/01\/Artificial-Intelligence-10-Predictions-for-2019.pdf\">Artificial Intelligence: 10 Predictions for 2019<\/a>\u00a0(PDF, 12 pp., no opt-in)<\/li>\n<li>U.S. Government Accountability Office,<a title=\"https:\/\/www.gao.gov\/assets\/700\/690910.pdf\" href=\"https:\/\/www.gao.gov\/assets\/700\/690910.pdf\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:https:\/\/www.gao.gov\/assets\/700\/690910.pdf\">\u00a0AI technology Assessment, Emerging Opportunities, Challenges, and Implications<\/a>\u00a0(PDF, 100 pp., no opt-in)<\/li>\n<li>World Economic Forum,\u00a0<a title=\"http:\/\/www3.weforum.org\/docs\/WEF_40065_White_Paper_How_to_Prevent_Discriminatory_Outcomes_in_Machine_Learning.pdf\" href=\"http:\/\/www3.weforum.org\/docs\/WEF_40065_White_Paper_How_to_Prevent_Discriminatory_Outcomes_in_Machine_Learning.pdf\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" data-ga-track=\"ExternalLink:http:\/\/www3.weforum.org\/docs\/WEF_40065_White_Paper_How_to_Prevent_Discriminatory_Outcomes_in_Machine_Learning.pdf\">How to Prevent Discriminatory Outcomes in Machine Learning<\/a>\u00a0(PDF, 30 pp., no opt-in)<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Machine learning&rsquo;s growing adoption in business across industries reflects how effective its algorithms, frameworks and techniques are at solving complex problems quickly. Machine learning and AI-based techniques are the foundation of a broad spectrum of next-generation logistics and supply chain technologies now under development. Learn from this article about the Key takeaways from the series of machine learning market forecasts and market estimates from the last year from different sources.<\/p>\n","protected":false},"author":138,"featured_media":3695,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[183],"tags":[92],"ppma_author":[2679],"class_list":["post-2262","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-ml","tag-machine-learning"],"authors":[{"term_id":2679,"user_id":138,"is_guest":0,"slug":"louis-columbus","display_name":"Louis Columbus","avatar_url":"https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2020\/04\/medium_65812956-54d2-4b01-9e05-0d23abb284c7-150x150.jpg","user_url":"https:\/\/softwarestrategiesblog.com\/","last_name":"Columbus","first_name":"Louis","job_title":"","description":"Louis Columbus is currently serving as Principal, IQMS. He is Marketing and Product Management Leader, Forbes Columnist, Software Expertise in Analytics, Cloud, CPQ &amp; ERP Solutions. He teaches MBA courses in international business, global competitive strategies, international market research, and capstone courses in strategic planning and market research. He has taught at California State University, Fullerton: University of California, Irvine; Marymount University, and Webster University."}],"_links":{"self":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/2262","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/users\/138"}],"replies":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/comments?post=2262"}],"version-history":[{"count":2,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/2262\/revisions"}],"predecessor-version":[{"id":11146,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/2262\/revisions\/11146"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media\/3695"}],"wp:attachment":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media?parent=2262"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/categories?post=2262"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/tags?post=2262"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/ppma_author?post=2262"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}