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Abstract: Whats New for 2022? - Global competitiveness and key competitor percentage market shares. - Market presence across multiple geographies - Strong/Active/Niche/Trivial.

New York, May 11, 2022 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Global Artificial Intelligence (AI) Industry" - https://www.reportlinker.com/p05478480/?utm_source=GNW - Online interactive peer-to-peer collaborative bespoke updates - Access to our digital archives and MarketGlass Research Platform - Complimentary updates for one yearGlobal Artificial Intelligence (AI) Market to Reach US$341.4 Billion by the Year 2027

- Amid the COVID-19 crisis, the global market for Artificial Intelligence (AI) estimated at US$46.9 Billion in the year 2020, is projected to reach a revised size of US$341.4 Billion by 2027, growing at a CAGR of 32.8% over the analysis period 2020-2027.Services, one of the segments analyzed in the report, is projected to grow at a 32.6% CAGR to reach US$142.7 Billion by the end of the analysis period.After an early analysis of the business implications of the pandemic and its induced economic crisis, growth in the Software segment is readjusted to a revised 30.4% CAGR for the next 7-year period. This segment currently accounts for a 37.9% share of the global Artificial Intelligence (AI) market.

- The U.S. Accounts for Over 41.2% of Global Market Size in 2020, While China is Forecast to Grow at a 39.1% CAGR for the Period of 2020-2027

- The Artificial Intelligence (AI) market in the U.S. is estimated at US$19.3 Billion in the year 2020. The country currently accounts for a 41.22% share in the global market. China, the world second largest economy, is forecast to reach an estimated market size of US$64.7 Billion in the year 2027 trailing a CAGR of 39.1% through 2027. Among the other noteworthy geographic markets are Japan and Canada, each forecast to grow at 27.6% and 29% respectively over the 2020-2027 period. Within Europe, Germany is forecast to grow at approximately 31.2% CAGR while Rest of European market (as defined in the study) will reach US$64.7 Billion by the year 2027.

- Hardware Segment Corners a 19.9% Share in 2020

- In the global Hardware segment, USA, Canada, Japan, China and Europe will drive the 36.6% CAGR estimated for this segment. These regional markets accounting for a combined market size of US$7.7 Billion in the year 2020 will reach a projected size of US$68.5 Billion by the close of the analysis period. China will remain among the fastest growing in this cluster of regional markets. Led by countries such as Australia, India, and South Korea, the market in Asia-Pacific is forecast to reach US$46.7 Billion by the year 2027.

- Select Competitors (Total 865 Featured) AIBrain, Inc. Advanced Micro Devices, Inc. Amazon Web Services Baidu, Inc. Cisco Systems, Inc. eGain Corporation General Electric Company Google, Inc. Intel Corporation International Business Machines Corporation (IBM) Meta (Facebook company is now Meta) Micron Technology, Inc. Microsoft Corporation Nippon Telegraph and Telephone Corporation Nuance Communications, Inc. NVIDIA Corporation Omron Corporation Oracle Corporation Rockwell Automation, Inc. Salesforce.com, inc. Samsung Electronics Co., Ltd. SAP SE SAS Institute Inc. Siemens AG

Read the full report: https://www.reportlinker.com/p05478480/?utm_source=GNW

I. METHODOLOGY

II. EXECUTIVE SUMMARY

1. MARKET OVERVIEW Impact of Covid-19 and a Looming Global Recession With IMF Making an Upward Revision of Global GDP for 2022, Companies Remain Bullish About an Economic Comeback EXHIBIT 1: World Economic Growth Projections (Real GDP, Annual % Change) for 2020 through 2022 Artificial Intelligence Gains Significant Interest as Industries Expedite Digital Transformation Strategies A Peek into Application of AI in War Against the Pandemic Machine Learning Benefits Healthcare Organizations COVID-19-Led Budgetary Reticence Dampens Spending, but AI Enjoys Resilient Interest in Banking Sector Retailers Rely on AI to Stay Afloat & Embrace New Normal Emphasis on Technology Adoption Elicits AI Implementation in Manufacturing Industry Competition AI Marketplace Characterized by Intense Competition EXHIBIT 2: Artificial Intelligence (AI) - Global Key Competitors Percentage Market Share in 2022 (E) Growing Focus on AI by Leading Tech Companies with Huge Financial Resources AI Presents Compelling Opportunities for Established & Startup Companies Competitive Market Presence - Strong/Active/Niche/Trivial for 300 Players Worldwide in 2022 (E) Funding Landscape Remains Vibrant in the AI Technology Space EXHIBIT 3: Global AI Investment (in US$ Billion) for the Years 2015 through 2021 EXHIBIT 4: Distribution of Global Investment in AI by Region/ Country: 2021 EXHIBIT 5: Number of AI Startups with $1 Billion Valuations for the Years 2014-2020 EXHIBIT 6: AI Cumulative Funding (in US$ Billion) by Category (As of 2020) AI Applications and Major Startups Artificial Intelligence (AI): A Prelude Technologies Enabling AI Market Outlook Prominent Factors with Implications for Evolution & Future of Artificial Intelligence Advances in Real World AI Applications Bolster Growth Inherent Advantages of AI Technology to Accelerate Adoption in Varied Applications Banking Sector Shows Unwavering Interest in AI AI Reshapes the Future of Manufacturing Industry AI-based Services Segment Captures Major Share of Global AI Market Developed Markets Dominate, Asia-Pacific to Spearhead Future Growth Deep Learning and Digital Assistant Technologies Present Significant Growth Potential Major Challenges Faced in AI Implementation World Brands Recent Market Activity

2. FOCUS ON SELECT PLAYERS

3. MARKET TRENDS & DRIVERS Accelerating Pace of Digital Transformation to Benefit Demand for AI EXHIBIT 7: Digital Transformation by Industry: 2020 EXHIBIT 8: Industry Adoption of Artificial Intelligence (AI) by Function: 2020 Noteworthy Technological Trends to Watch-for in Artificial Intelligence Space Machine Learning and AI-Assisted Platforms Personalize Customer Experiences in Marketing Applications EXHIBIT 9: Ranking of Business Outcomes Realized through AI Application in Marketing Businesses to Gain from Application of AI in Predictive Marketing Analytics and Demand Forecasting Growing Role of AI in the Metaverse AI Hosting at Edge to Drive Growth EXHIBIT 10: Global Edge Computing Market in US$ Billion: 2020, 2024, and 2026 AI-enabled Analysis and Forecasts Aid Organizations Make Profitable Decisions AI-Powered Biometric Security Solutions Gain Momentum EXHIBIT 11: Global Biometrics Market in US$ Billion: 2016, 2020, and 2025 New and Improved Concepts in ML and AI take Stage IIoT & AI Convergence Brings in Improved Efficiencies EXHIBIT 12: Global Breakdown of Investments in Manufacturing IoT (in US$ Billion) for the Years 2016, 2018, 2020 and 2025 EXHIBIT 13: Industry 4.0 Technologies with Strongest Impact on Organizations: 2020 Increasing Adoption of AI Technology to Boost AI Chipsets Market Combination of Robotics and AI Set to Cause Significant Disruption in Various Industries AI Innovations Widen Prospects Blockchain & Artificial Intelligence (AI): A Powerful Combination Big Data Trends to Shape Future of Artificial Intelligence AI in Retail Market: Multi-Channel Retailing and e-Commerce Favor Segment Growth EXHIBIT 14: Digital Transformation in Retail Industry Promises Lucrative Growth Opportunities: Global Retail IT Spending (In US$ Billion) for the Years 2018, 2020, 2022 & 2024 AI for a Competitive Edge for Retail Organizations Online Retailers Eye on Artificial Intelligence to Boost Business in Post-COVID-19 Era AI & Analytics Help Retailers Survive Economic & Operational Implications of COVID-19 AI for Fashion Retail and Beauty AI for Grocery, Electronics, and Home & Furniture Ecommerce Attracts Strong Growth Detailed Insight into How e-commerce Makes use of AI EXHIBIT 15: Global B2C e-Commerce Market Reset & Trajectory - Growth Outlook (In %) For Years 2019 Through 2025 EXHIBIT 16: Retail M-Commerce Sales as % of Retail E-commerce Sales Worldwide for the Years 2016, 2018, 2020 & 2022 Financial Sector: AI and Machine Learning Offer Numerous Gains Fintech Deploys AI to Target Millennials AI in Media & Advertising: Targeting Customers with Right Marketing Content Possibilities Galore for AI in Digital Marketing AI-Enabled CRM Market: Promising Growth Opportunities in Store Artificial Intelligence Set to Transform Delivery of Healthcare Services AI to Play a Significant Role in Automation and Improving Clinical Outcomes EXHIBIT 17: Global Healthcare AI Market - Percentage Breakdown by Application for 2020 AI in Pharmaceutical Sector COVID-19 Spurs New Developments and Expedites AI Adoption in Healthcare Industry Artificial Intelligence Holds Potential to Accelerate Detection & Treatment of COVID-19 Rising Prevalence of Diabetes to Drive AI Adoption in Diabetes Management Market EXHIBIT 18: World Diabetes Prevalence (2000-2045P) Barriers Restraining AI Adoption in Healthcare Sector Automotive AI Market: Need to Enhance Customer Experience Propels Growth EXHIBIT 19: Automotive AI Market By Segment Demand Recovery in Automobile Sector Steers Growth Opportunities EXHIBIT 20: World Automobile Production in Million Units: 2008- 2022 Increasing Focus on Electric Vehicles and Autonomous Vehicles Provide the Perfect Platform to Shape Future Growth EXHIBIT 21: Global Autonomous Vehicle Sales (In Million) for Years 2020, 2025 & 2030 Automakers Focus on Integrating AI-Powered Driver Assist Features in Vehicles AI to Enhance Connectivity, Provide Infotainment and Enhance Safety in Vehicles AI for Smart Insurance Risk Assessment of Vehicles Artificial Intelligence Steps into Manufacturing Space to Transform Diverse Aspects Industrial AI to Influence Manufacturing in a Major Way Industrial IoT, Robotics and Big Data to Stimulate AI Implementations EXHIBIT 22: Global Investments on Industry 4.0 Technologies (in US$ Billion) for the Years 2017, 2020, & 2023 EXHIBIT 23: Global Predictive Maintenance by Market in US$ Billion for Years 2020, 2022, 2024, and 2026 AI as a Service Market: Obviating the Need to Make Huge Initial Investments AI in Education Market to Exhibit Strong Growth EXHIBIT 24: Global Market for AI in Healthcare Sector (2019): Percentage Breakdown of Revenues by End-Use - Higher Education and K-12 Sectors Focus on ITS, IAL and Chatbots Favors Market Growth Agriculture Sector: A Promising Market for AI Implementations AI Technologies Used in Agricultural Activities - A Review AI Poised to Create Smarter Agriculture Practices in Post- COVID-19 Period Food & Beverage Industry to Leverage AI Capabilities to Resolve Production Issues and Match Up to Customer Expectations AI Adoption Gains Acceptance in Modern Warfare Systems in the Defense Sector Energy & Utilities: Complex Landscape and High Risk of Malfunctions Enhances Need for AI-based Systems COVID-19 Raises Demand for AI Technologies in Oil & Gas Sector EXHIBIT 25: Top Technology Investments in Oil and Gas Sector: 2020 AI in Construction Sector: Need for Cost Reduction and Safety at Construction Sites Drive Focus onto the Use of AI-based Solutions

4. GLOBAL MARKET PERSPECTIVE Table 1: World Recent Past, Current & Future Analysis for Artificial Intelligence (AI) by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2020 through 2027 and % CAGR

Table 2: World Historic Review for Artificial Intelligence (AI) by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 3: World 12-Year Perspective for Artificial Intelligence (AI) by Geographic Region - Percentage Breakdown of Value Revenues for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets for Years 2015, 2021 & 2027

Table 4: World Recent Past, Current & Future Analysis for Services by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2020 through 2027 and % CAGR

Table 5: World Historic Review for Services by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 6: World 12-Year Perspective for Services by Geographic Region - Percentage Breakdown of Value Revenues for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World for Years 2015, 2021 & 2027

Table 7: World Recent Past, Current & Future Analysis for Software by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2020 through 2027 and % CAGR

Table 8: World Historic Review for Software by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 9: World 12-Year Perspective for Software by Geographic Region - Percentage Breakdown of Value Revenues for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World for Years 2015, 2021 & 2027

Table 10: World Recent Past, Current & Future Analysis for Hardware by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2020 through 2027 and % CAGR

Table 11: World Historic Review for Hardware by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 12: World 12-Year Perspective for Hardware by Geographic Region - Percentage Breakdown of Value Revenues for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World for Years 2015, 2021 & 2027

Table 13: World Recent Past, Current & Future Analysis for Computer Vision by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2020 through 2027 and % CAGR

Table 14: World Historic Review for Computer Vision by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 15: World 12-Year Perspective for Computer Vision by Geographic Region - Percentage Breakdown of Value Revenues for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World for Years 2015, 2021 & 2027

Table 16: World Recent Past, Current & Future Analysis for Machine Learning by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2020 through 2027 and % CAGR

Table 17: World Historic Review for Machine Learning by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 18: World 12-Year Perspective for Machine Learning by Geographic Region - Percentage Breakdown of Value Revenues for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World for Years 2015, 2021 & 2027

Table 19: World Recent Past, Current & Future Analysis for Context Aware Computing by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2020 through 2027 and % CAGR

Table 20: World Historic Review for Context Aware Computing by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 21: World 12-Year Perspective for Context Aware Computing by Geographic Region - Percentage Breakdown of Value Revenues for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World for Years 2015, 2021 & 2027

Table 22: World Recent Past, Current & Future Analysis for Natural Language Processing by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2020 through 2027 and % CAGR

Table 23: World Historic Review for Natural Language Processing by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 24: World 12-Year Perspective for Natural Language Processing by Geographic Region - Percentage Breakdown of Value Revenues for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World for Years 2015, 2021 & 2027

Table 25: World Recent Past, Current & Future Analysis for Advertising & Media by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2020 through 2027 and % CAGR

Table 26: World Historic Review for Advertising & Media by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 27: World 12-Year Perspective for Advertising & Media by Geographic Region - Percentage Breakdown of Value Revenues for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World for Years 2015, 2021 & 2027

Table 28: World Recent Past, Current & Future Analysis for BFSI by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2020 through 2027 and % CAGR

Table 29: World Historic Review for BFSI by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 30: World 12-Year Perspective for BFSI by Geographic Region - Percentage Breakdown of Value Revenues for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World for Years 2015, 2021 & 2027

Table 31: World Recent Past, Current & Future Analysis for Healthcare by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2020 through 2027 and % CAGR

Table 32: World Historic Review for Healthcare by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 33: World 12-Year Perspective for Healthcare by Geographic Region - Percentage Breakdown of Value Revenues for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World for Years 2015, 2021 & 2027

Table 34: World Recent Past, Current & Future Analysis for Retail by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2020 through 2027 and % CAGR

Table 35: World Historic Review for Retail by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 36: World 12-Year Perspective for Retail by Geographic Region - Percentage Breakdown of Value Revenues for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World for Years 2015, 2021 & 2027

Table 37: World Recent Past, Current & Future Analysis for Automotive & Transportation by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2020 through 2027 and % CAGR

Table 38: World Historic Review for Automotive & Transportation by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 39: World 12-Year Perspective for Automotive & Transportation by Geographic Region - Percentage Breakdown of Value Revenues for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World for Years 2015, 2021 & 2027

Table 40: World Recent Past, Current & Future Analysis for Manufacturing by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2020 through 2027 and % CAGR

Table 41: World Historic Review for Manufacturing by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 42: World 12-Year Perspective for Manufacturing by Geographic Region - Percentage Breakdown of Value Revenues for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World for Years 2015, 2021 & 2027

Table 43: World Recent Past, Current & Future Analysis for Agriculture by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2020 through 2027 and % CAGR

Table 44: World Historic Review for Agriculture by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 45: World 12-Year Perspective for Agriculture by Geographic Region - Percentage Breakdown of Value Revenues for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World for Years 2015, 2021 & 2027

Table 46: World Recent Past, Current & Future Analysis for Other End-Uses by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2020 through 2027 and % CAGR

Table 47: World Historic Review for Other End-Uses by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 48: World 12-Year Perspective for Other End-Uses by Geographic Region - Percentage Breakdown of Value Revenues for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World for Years 2015, 2021 & 2027

III. MARKET ANALYSIS

UNITED STATES Artificial Intelligence (AI) Market Presence - Strong/Active/ Niche/Trivial - Key Competitors in the United States for 2022 (E) Artificial Intelligence Market: An Overview Healthcare: A Promising Application Market for AI Technology Funding for AI Startups Continues to Grow EXHIBIT 26: Top Funded AI Startups in the US: 2021 Market Analytics Table 49: USA Recent Past, Current & Future Analysis for Artificial Intelligence (AI) by Component - Services, Software and Hardware - Independent Analysis of Annual Revenues in US$ Million for the Years 2020 through 2027 and % CAGR

Table 50: USA Historic Review for Artificial Intelligence (AI) by Component - Services, Software and Hardware Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 51: USA 12-Year Perspective for Artificial Intelligence (AI) by Component - Percentage Breakdown of Value Revenues for Services, Software and Hardware for the Years 2015, 2021 & 2027

Table 52: USA Recent Past, Current & Future Analysis for Artificial Intelligence (AI) by Technology - Computer Vision, Machine Learning, Context Aware Computing and Natural Language Processing - Independent Analysis of Annual Revenues in US$ Million for the Years 2020 through 2027 and % CAGR

Table 53: USA Historic Review for Artificial Intelligence (AI) by Technology - Computer Vision, Machine Learning, Context Aware Computing and Natural Language Processing Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 54: USA 12-Year Perspective for Artificial Intelligence (AI) by Technology - Percentage Breakdown of Value Revenues for Computer Vision, Machine Learning, Context Aware Computing and Natural Language Processing for the Years 2015, 2021 & 2027

Table 55: USA Recent Past, Current & Future Analysis for Artificial Intelligence (AI) by End-Use - Advertising & Media, BFSI, Healthcare, Retail, Automotive & Transportation, Manufacturing, Agriculture and Other End-Uses - Independent Analysis of Annual Revenues in US$ Million for the Years 2020 through 2027 and % CAGR

Table 56: USA Historic Review for Artificial Intelligence (AI) by End-Use - Advertising & Media, BFSI, Healthcare, Retail, Automotive & Transportation, Manufacturing, Agriculture and Other End-Uses Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 57: USA 12-Year Perspective for Artificial Intelligence (AI) by End-Use - Percentage Breakdown of Value Revenues for Advertising & Media, BFSI, Healthcare, Retail, Automotive & Transportation, Manufacturing, Agriculture and Other End-Uses for the Years 2015, 2021 & 2027

CANADA Market Overview Top-Tier Canadian Cities Primed for AI Growth Market Analytics Table 58: Canada Recent Past, Current & Future Analysis for Artificial Intelligence (AI) by Component - Services, Software and Hardware - Independent Analysis of Annual Revenues in US$ Million for the Years 2020 through 2027 and % CAGR

Table 59: Canada Historic Review for Artificial Intelligence (AI) by Component - Services, Software and Hardware Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 60: Canada 12-Year Perspective for Artificial Intelligence (AI) by Component - Percentage Breakdown of Value Revenues for Services, Software and Hardware for the Years 2015, 2021 & 2027

Table 61: Canada Recent Past, Current & Future Analysis for Artificial Intelligence (AI) by Technology - Computer Vision, Machine Learning, Context Aware Computing and Natural Language Processing - Independent Analysis of Annual Revenues in US$ Million for the Years 2020 through 2027 and % CAGR

Table 62: Canada Historic Review for Artificial Intelligence (AI) by Technology - Computer Vision, Machine Learning, Context Aware Computing and Natural Language Processing Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 63: Canada 12-Year Perspective for Artificial Intelligence (AI) by Technology - Percentage Breakdown of Value Revenues for Computer Vision, Machine Learning, Context Aware Computing and Natural Language Processing for the Years 2015, 2021 & 2027

Table 64: Canada Recent Past, Current & Future Analysis for Artificial Intelligence (AI) by End-Use - Advertising & Media, BFSI, Healthcare, Retail, Automotive & Transportation, Manufacturing, Agriculture and Other End-Uses - Independent Analysis of Annual Revenues in US$ Million for the Years 2020 through 2027 and % CAGR

Table 65: Canada Historic Review for Artificial Intelligence (AI) by End-Use - Advertising & Media, BFSI, Healthcare, Retail, Automotive & Transportation, Manufacturing, Agriculture and Other End-Uses Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 66: Canada 12-Year Perspective for Artificial Intelligence (AI) by End-Use - Percentage Breakdown of Value Revenues for Advertising & Media, BFSI, Healthcare, Retail, Automotive & Transportation, Manufacturing, Agriculture and Other End-Uses for the Years 2015, 2021 & 2027

JAPAN Artificial Intelligence (AI) Market Presence - Strong/Active/ Niche/Trivial - Key Competitors in Japan for 2022 (E) Market Analytics Table 67: Japan Recent Past, Current & Future Analysis for Artificial Intelligence (AI) by Component - Services, Software and Hardware - Independent Analysis of Annual Revenues in US$ Million for the Years 2020 through 2027 and % CAGR

Table 68: Japan Historic Review for Artificial Intelligence (AI) by Component - Services, Software and Hardware Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 69: Japan 12-Year Perspective for Artificial Intelligence (AI) by Component - Percentage Breakdown of Value Revenues for Services, Software and Hardware for the Years 2015, 2021 & 2027

Table 70: Japan Recent Past, Current & Future Analysis for Artificial Intelligence (AI) by Technology - Computer Vision, Machine Learning, Context Aware Computing and Natural Language Processing - Independent Analysis of Annual Revenues in US$ Million for the Years 2020 through 2027 and % CAGR

Table 71: Japan Historic Review for Artificial Intelligence (AI) by Technology - Computer Vision, Machine Learning, Context Aware Computing and Natural Language Processing Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 72: Japan 12-Year Perspective for Artificial Intelligence (AI) by Technology - Percentage Breakdown of Value Revenues for Computer Vision, Machine Learning, Context Aware Computing and Natural Language Processing for the Years 2015, 2021 & 2027

Table 73: Japan Recent Past, Current & Future Analysis for Artificial Intelligence (AI) by End-Use - Advertising & Media, BFSI, Healthcare, Retail, Automotive & Transportation, Manufacturing, Agriculture and Other End-Uses - Independent Analysis of Annual Revenues in US$ Million for the Years 2020 through 2027 and % CAGR

Table 74: Japan Historic Review for Artificial Intelligence (AI) by End-Use - Advertising & Media, BFSI, Healthcare, Retail, Automotive & Transportation, Manufacturing, Agriculture and Other End-Uses Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 75: Japan 12-Year Perspective for Artificial Intelligence (AI) by End-Use - Percentage Breakdown of Value Revenues for Advertising & Media, BFSI, Healthcare, Retail, Automotive & Transportation, Manufacturing, Agriculture and Other End-Uses for the Years 2015, 2021 & 2027

CHINA Artificial Intelligence (AI) Market Presence - Strong/Active/ Niche/Trivial - Key Competitors in China for 2022 (E) Market Overview China Continues Investments in AI Startups EXHIBIT 27: Chinese AI Market: Funding for AI Startups (in $ Billion): 2016-2020

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Global Artificial Intelligence (AI) Market to Reach US$341.4 Billion by the Year 2027 - Yahoo Finance

Artificial Intelligence/Machine Learning and the Future of National Security

AI is a once-in-a lifetime commercial and defense game changer

By Steve Blank

Hundreds of billions in public and private capital is being invested in AI and Machine Learning companies. The number of patents filed in 2021 is more than 30 times higher than in 2015 as companies and countries across the world have realized that AI and Machine Learning will be a major disruptor and potentially change the balance of military power.

Until recently, the hype exceeded reality. Today, however, advances in AI in several important areas (here, here, here, here and here) equal and even surpass human capabilities.

If you havent paid attention, nows the time.

AI and the DoD

The Department of Defense has thought that AI is such a foundational set of technologies that they started a dedicated organization -- the JAIC -- to enable and implement artificial intelligence across the Department. They provide the infrastructure, tools, and technical expertise for DoD users to successfully build and deploy their AI-accelerated projects.

Some specific defense-related AI applications are listed later in this document.

Were in the Middle of a Revolution

Imagine its 1950, and youre a visitor who traveled back in time from today. Your job is to explain the impact computers will have on business, defense and society to people who are using manual calculators and slide rules. You succeed in convincing one company and a government to adopt computers and learn to code much faster than their competitors /adversaries. And they figure out how they could digitally enable their business supply chain, customer interactions, etc. Think about the competitive edge theyd have by today in business or as a nation. Theyd steamroll everyone.

Thats where we are today with Artificial Intelligence and Machine Learning. These technologies will transform businesses and government agencies. Today, 100s of billions of dollars in private capital have been invested in 1,000s of AI startups. The U.S. Department of Defense has created a dedicated organization to ensure its deployment.

But What Is It?

Compared to the classic computing weve had for the last 75 years, AI has led to new types of applications, e.g. facial recognition; new types of algorithms, e.g. machine learning; new types of computer architectures, e.g. neural nets; new hardware, e.g. GPUs; new types of software developers, e.g. data scientists; all under the overarching theme of artificial intelligence. The sum of these feels like buzzword bingo. But they herald a sea change in what computers are capable of doing, how they do it, and what hardware and software is needed to do it.

This brief will attempt to describe all of it.

New Words to Define Old Things

One of the reasons the world of AI/ML is confusing is that its created its own language and vocabulary. It uses new words to define programming steps, job descriptions, development tools, etc. But once you understand how the new world maps onto the classic computing world, it starts to make sense. So first a short list of some key definitions.

AI/ML - a shorthand for Artificial Intelligence/Machine Learning

Artificial Intelligence (AI) - a catchall term used to describe Intelligent machines which can solve problems, make/suggest decisions and perform tasks that have traditionally required humans to do. AI is not a single thing, but a constellation of different technologies.

Machine Learning (ML) - a subfield of artificial intelligence. Humans combine data with algorithms (see here for a list) to train a model using that data. This trained model can then make predications on new data (is this picture a cat, a dog or a person?) or decision-making processes (like understanding text and images) without being explicitly programmed to do so.

Machine learning algorithms - computer programs that adjust themselves to perform better as they are exposed to more data.

The learning part of machine learning means these programs change how they process data over time. In other words, a machine-learning algorithm can adjust its own settings, given feedback on its previous performance in making predictions about a collection of data (images, text, etc.).

Deep Learning/Neural Nets a subfield of machine learning. Neural networks make up the backbone of deep learning. (The deep in deep learning refers to the depth of layers in a neural network.) Neural nets are effective at a variety of tasks (e.g., image classification, speech recognition). A deep learning neural net algorithm is given massive volumes of data, and a task to perform - such as classification. The resulting model is capable of solving complex tasks such as recognizing objects within an image and translating speech in real time. In reality, the neural net is a logical concept that gets mapped onto a physical set of specialized processors. See here.)

Data Science a new field of computer science. Broadly it encompasses data systems and processes aimed at maintaining data sets and deriving meaning out of them. In the context of AI, its the practice of people who are doing machine learning.

Data Scientists - responsible for extracting insights that help businesses make decisions. They explore and analyze data using machine learning platforms to create models about customers, processes, risks, or whatever theyre trying to predict.

Whats Different? Why is Machine Learning Possible Now?

To understand why AI/Machine Learning can do these things, lets compare them to computers before AI came on the scene. (Warning simplified examples below.)

Classic Computers

For the last 75 years computers (well call these classic computers) have both shrunk to pocket size (iPhones) and grown to the size of warehouses (cloud data centers), yet they all continued to operate essentially the same way.

Classic Computers - Programming

Classic computers are designed to do anything a human explicitly tells them to do. People (programmers) write software code (programming) to develop applications, thinking a priori about all the rules, logic and knowledge that need to be built in to an application so that it can deliver a specific result. These rules are explicitly coded into a program using a software language (Python, JavaScript, C#, Rust, ).

Classic Computers - Compiling

The code is then compiled using software to translate the programmers source code into a version that can be run on a target computer/browser/phone. For most of todays programs, the computer used to develop and compile the code does not have to be that much faster than the one that will run it.

Classic Computers - Running/Executing Programs

Once a program is coded and compiled, it can be deployed and run (executed) on a desktop computer, phone, in a browser window, a data center cluster, in special hardware, etc. Programs/applications can be games, social media, office applications, missile guidance systems, bitcoin mining, or even operating systems e.g. Linux, Windows, IOS. These programs run on the same type of classic computer architectures they were programmed in.

Classic Computers Software Updates, New Features

For programs written for classic computers, software developers receive bug reports, monitor for security breaches, and send out regular software updates that fix bugs, increase performance and at times add new features.

Classic Computers- Hardware

The CPUs (Central Processing Units) that write and run these Classic Computer applications all have the same basic design (architecture). The CPUs are designed to handle a wide range oftasks quickly in a serial fashion. These CPUs range from Intel X86 chips, and the ARM cores on Apple M1 SoC, to thez15 in IBM mainframes.

Machine Learning

In contrast to programming on classic computing with fixed rules, machine learning is just like it sounds we can train/teach a computer to learn by example by feeding it lots and lots of examples. (For images a rule of thumb is that a machine learning algorithm needs at least 5,000 labeled examples of each category in order to produce an AI model with decent performance.) Once it is trained, the computer runs on its own and can make predictions and/or complex decisions.

Just as traditional programming has three steps - first coding a program, next compiling it and then running it - machine learning also has three steps: training (teaching), pruning and inference (predicting by itself.)

Machine Learning - Training

Unlike programing classic computers with explicit rules, training is the process of teaching a computer to perform a task e.g. recognize faces, signals, understand text, etc. (Now you know why you're asked to click on images of traffic lights, cross walks, stop signs, and buses or type the text of scanned image in ReCaptcha.) Humans provide massive volumes of training data (the more data, the better the models performance) and select the appropriate algorithm to find the best optimized outcome.

(See the detailed machine learning pipeline later in this section for the gory details.)

By running an algorithm selected by a data scientist on a set of training data, the Machine Learning system generates the rules embedded in a trained model. The system learns from examples (training data), rather than being explicitly programmed. (See the Types of Machine Learning section for more detail.) This self-correction is pretty cool. An input to a neural net results in a guess about what that input is. The neural net then takes its guess and compares it to a ground-truth about the data, effectively asking an expert Did I get this right? The difference between the networks guess and the ground truth is itserror. The network measures that error, and walks the error back over its model, adjusting weights to the extent that they contributed to the error.)

Just to make the point again: The algorithms combined with the training data - not external human computer programmers - create the rules that the AI uses. The resulting model is capable of solving complex tasks such as recognizing objects its never seen before, translating text or speech, or controlling a drone swarm.

(Instead of building a model from scratch you can now buy, for common machine learning tasks, pretrained models from others and here, much like chip designers buying IP Cores.)

Machine Learning Training - Hardware

Training a machine learning model is a very computationally intensive task. AI hardware must be able to perform thousands of multiplications and additions in a mathematical process called matrix multiplication. It requires specialized chips to run fast. (See the AI hardware section for details.)

Machine Learning - Simplification via pruning, quantization, distillation

Just like classic computer code needs to be compiled and optimized before it is deployed on its target hardware, the machine learning models are simplified and modified(pruned) touse less computingpower, energy, and memory before theyre deployed to run on their hardware.

Read more here:
Artificial Intelligence/Machine Learning and the Future of National Security - smallwarsjournal

Seven years have passed since world leaders met in New York and agreed 17 Sustainable Development Goals (SDGs) to resolve major challenges including poverty, hunger, inequality, climate change and health.

The pandemic undoubtedly diverted attention from some of these issues in the past couple of years. But even before COVID-19, the UN was warning that progress to meet the SDGs was not advancing at the speed or on the scale needed. Meeting them by 2030 will be tough.

Yet I remain optimistic. The pandemic demonstrated like nothing else the power of working collaboratively, across borders, for the benefit of society. It concentrated minds, funding and policy to accelerate research into virus detection, disease treatments, vaccines and manufacturing platforms.

It was a truly remarkable effort from the global community to develop effective vaccines within a year of the virus first being detected, and these and other treatments have dramatically reduced the viruss fatality rate. This can be attributed to the brilliance, perseverance and creativity of scientists across the world. But they were not working alone: Artificial intelligence (AI) also played a key part.

The US company Moderna was among the first to release an effective COVID-19 vaccine. One reason it was able to make this breakthrough so quickly was the use of AI to speed up development. Modernas Chief Data and Artificial Intelligence Officer Dave Johnson explains that AI algorithms and robotic automation helped them move from manually producing around 30 mRNAs (a molecule fundamental to the vaccine) each month, to being able to produce around 1,000 a month.

Moderna is also using artificial intelligence to help their mRNA sequence design. Its co-founder Noubar Afeyan recently predicted during a visit to Imperial College London that immune medicine will see large advances in the coming years, and we can look forward to a future where medicine is more pre-emptive than reactionary.

If we can catch disease early and delay it, at a minimum, we could have a lot more impact at a lot less cost, he said. This is a great example of how AI can free up time for scientists to accelerate discovery and dedicate efforts to solving big challenges.

We are also seeing examples of AI technology driving improvements in other areas of healthcare, such as disease screening for cancer and malaria. Researchers from Google Health, DeepMind, the NHS, Northwestern University and colleagues at Imperial have designed and trained an AI model to spot breast cancer from X-ray images.

The computer algorithm, which was trained using mammography images from almost 29,000 women, was shown to be as effective as human radiologists in spotting cancer. At a time when health services around the world are stretched as they deal with long backlogs of patients following the pandemic, this sort of technology can help ease bottlenecks and improve treatment.

For malaria, a handheld lab-on-a-chip molecular diagnostics systems developed with AI could revolutionize how the disease is detected in remote parts of Africa. The project, which is led by the Digital Diagnostics for Africa Network, brings together collaborators such as MinoHealth AI Labs in Ghana and Imperial College Londons Global Development Hub. This technology could help pave the way for universal health coverage and push us towards achieving SDG3.

There are numerous other examples of how advances in AI could support our understanding of climate change (SDG13), enable our transition to sustainable transport systems (SDG11), and accelerate agri-tech to help farmers end food poverty and malnutrition (SDG2) among many benefits to the other SDGs too.

For example, the Alan Turing Institute, the UKs national centre for data science and artificial intelligence, are using machine learning to better understand the complex interactions between climate and Arctic sea ice.

With an expanding global population, we face challenges around food demand and production not only how to reduce malnourishment but the impact on the planet too, such as deforestation, emissions and biodiversity loss. To meet these needs, the use of artificial intelligence in agriculture is growing rapidly and is enabling farmers to enhance crop production, direct machinery to carry out tasks autonomously, and identify pest infestations before they occur.

Smart sensing technology is also helping farmers use fertilizer more effectively and reduce environmental damage. An exciting research project, funded by the EPSRC, Innovate UK and Cytiva, will help growers optimize timing and amount of fertilizer to use on their crops, taking into account factors like the weather and soil condition. This will reduce the expense and damaging effects of over-fertilizing soil.

Developing sustainable and smart transport systems will also be vital as cities and countries look to reduce the impact of air pollution and improve infrastructure. In the last decade, AI has powered a revolution in transport and mobility, from autonomous vehicles to ride-sharing apps and route-planners. AI is also being used to make public transport systems more efficient, reduce traffic congestion and pollution, and improve safety.

Despite its benefits to research and medicine, integrating AI into society and innovation is not always smooth sailing. Recent controversies on facial recognition, automated decision-making and COVID-related tracking, have led to some caution and suspicion. We need to ensure that AI is employed in ways that are trusted, transparent and inclusive. We need to make sure that there is an internationally coordinated, collaborative approach, just as there was in the pandemic.

The World Economic Forums Global AI Action Alliance brings together more than 100 leading companies, governments, international organizations, non-profits and academics united in a commitment to maximize AI's societal benefits while minimizing its risks.

Artificial intelligence (AI) is impacting all aspects of society homes, businesses, schools and even public spaces. But as the technology rapidly advances, multistakeholder collaboration is required to optimize accountability, transparency, privacy and impartiality.

The World Economic Forum's Platform for Shaping the Future of Technology Governance: Artificial Intelligence and Machine Learning is bringing together diverse perspectives to drive innovation and create trust.

Contact us for more information on how to get involved.

It is imperative that we put good processes and practices in place to ensure AI is developed in a positive and ethical way to see it adopted and used to its fullest by citizens and governments.

We must now work together to ensure that artificial intelligence can accelerate progress of the Sustainable Development Goals and help us get back on track to reaching them by 2030.

Written by

Alice Gast, President, Imperial College London

The views expressed in this article are those of the author alone and not the World Economic Forum.

Read more here:
Why artificial intelligence is vital in the race to meet the SDGs - World Economic Forum

I last sat down in 2005," Geoffrey Hinton often says, and it was a mistake." In the 17 years since, Hinton has never sat down; his severe back problems prevent him from doing so. He travels only by train or car, so he can sprawl across the seats. He cannot fly commercial, since airlines insist on being seated for take-off or landing. He eats like a monk on the altar", using a foam cushion to kneel at a table. With his trademark wry British humour, he talks of his back being a long-standing problem". In these 17 years, Hinton, working from the University of Toronto, has also transformed artificial intelligence (AI). He rescued neural networks back from an AI winter, invented deep learning, tutored a bevy of geniuses now at the bleeding edge of AI, and won the fabled Turing Award while he was at it.

I first came across the legend of Hinton in a fabulous book by Cade Metz called Genius Makers, where he detailed the lives of those who shaped AI, foremost among them being Hinton. After studying psychology at Cambridge and AI at the University of Edinburgh, Hinton went back to something which had fascinated him even as a child: How the human brain stored memories, and how it worked. He was one of the first researchers who started working on mimicking the human brain with computer hardware and software, thus constructing a newer and purer form of AI, which we now call deep learning. He started doing this in the 1980s, along with an intrepid bunch of students. His PhD thesis, titled Deep Neural Networks for Acoustic Modelling in Speech Recognition, demonstrated how deep neural networks outclassed older machine learning models like Hidden Markovs and Gaussian Mixtures at identifying speech patterns. He literally invented backpropagation, which was reportedly one of the concepts that inspired Googles BackRub search algorithm, the core of its exemplary service.

I get very excited when we discover a way of making neural networks betterand when thats closely related to how the brain works," says Hinton. By mimicking the brain, he sought to get rid of traditional machine learning techniques, where humans would label pictures, words and objects; instead, his work copied the brains self-learning techniques. He and his team built artificial neurons from interconnected layers of software modelled after the columns of neurons in the brains cortex. These neural nets can gather information, react to it, build an understanding of what something looks or sounds like" (bit.ly/3LRJwWo ). The AI community did not trust this new approach; Hinton told Sky News that it was an idea that almost no one on Earth believed in at that pointit was pretty much a dead idea, even among AI researchers".

Well, that sentiment has changed. Deep Learning has been harnessed by Google, Meta, Microsoft, DeepMind, Baidu and almost every other tech firm to build driverless cars, predict protein folding and beating humans at Go. Of Hintons students, Yann LeCun now leads Metas AI efforts, Yoshua Bengio is doing seminal work at University of Montreal, Ilya Sutskevar co-founded OpenAI, famous for GPT-3. Hinton himself works part time for Google, the result of a frenzied bidding war between Google, Microsoft and Baidu, where he auctioned his company (and his services) to Google for $44 millionthe stuff of legend in itself. Deep learning is now considered one of the most exciting developments in AI. It is regarded as the surest bet that AI will achieve artificial general intelligence, or AGI. As Hinton put it: We ceased to be the lunatic fringe. Were now the lunatic core."

Hinton comes from a formidably intellectual and academic family. His mother used to tell him to be an academic or be a failure". His great-great grandfather was George Boole, who invented Boolean logic and algebra, the foundation of modern computers. Georges wife Mary was a well-known teacher of algebra and logic. Marys uncle was George Everest, and as the Surveyor General of India, had the worlds highest peak named after him. Geoffreys great grandfather, a renowned mathematician, created the concept of the fourth dimension, and first drew the tesseract, and his cousin, Joan, a nuclear physicist was one of the few women to work on the Manhattan Project. His father, Howard Hinton, a formidable entomologist and a fellow of the Royal Society, often told him, Work really hard and maybe when youre twice as old as me, youll be half as good." Geoffrey did work hard, became the godfather of deep learning, a Turing Award winner and a fellow of the Royal Society. And he is not sitting on his laurels.

Jaspreet Bindra is founder of Tech Whisperer Ltd, a digital transformation and technology advisory practice.

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Artificial Intelligence And What It Owes A Man Who Never Sits Down | Mint - Mint

During one of the many live collaboration panels of MUSCs 2022 Innovation Week, an interesting discussion ensued, mirroring a common debate in health care and that is: How does artificial intelligence (AI) fit in?

Last week, as several clinicians and key members of the Clemson-MUSC AI Hub which was formed in 2021 were on hand at the Gazes Cardiac Research Institute, it became quickly evident that AI is gaining traction throughout the world of heath care. But equally evident was the fact that theres still some skepticism from the mainstream when it comes to the best ways to use it.

For congenital cardiologist G. Hamilton Baker, M.D., associate professor of pediatrics, AI remains a tremendous untapped resource.

AI is such a blanket term, he said in an interview right after the formation of the Clemson-MUSC AI Hub last year. Were leveraging data science and wrangling those giant databases with appropriately applied machine learning methods.

Baker has been utilizing AI in his work for several years now, working on a number of different AI+Biomedical projects ranging from congenital heart disease to diabetic eye disease.

I feel very strongly about education on AI. The goal is to teach clinicians how to understand and utilize AI. We arent asking people to learn how to code, we simply want them to learn how AI can work for them, Baker said.

At the Gazes, the topic quickly centered on AI and bias. Some clinicians believe the most elegant aspect of AI is that it removes unintended biases by letting the computers which are inherently without bias because theyre metal and silicone do the data crunching and leaving the treatment to the physicians.

When two clinicians might disagree on something, AI can help uncover unknown biases and dispel others, said MUSC Public Health Sciences assistant professor Paul Heider, Ph.D. AI just looks at the data and makes decisions that are based on that alone.

However, others argued that those AI programs were written by humans, and those inadvertent biases almost certainly were sprinkled in.

Trustworthiness is a key word that we need to be focusing on here, said Brian Dean, Ph.D., chairman of the Division of Computer Science at Clemson University. Because the AI system is becoming less of a smart sensor that provides input to the medical decision-making process and more of a teammate. So we have to be super careful because, after all, AI was trained based on human expert opinion, which is biased.

Dean agreed that AI is an extremely valuable tool for the medical field, cautioning all to simply be judicious with its use.

Jihad Obeid, M.D., co-director of the Biomedical Informatics Center at MUSC, agreed. If you use it as a decision aid, rather than a decision-maker, he said, AI can be a real asset.

Regardless of the differences of opinion in the room, panel members agreed that AI has unlimited potential for researchers and clinicians alike.

When it comes to AI in health care, its so tempting to talk about the hype, all the big stuff it can do, Baker said. But the truth of the matter is there are plenty of easy, smart projects where AI could really make a significant difference, and we just need more people on board.

According to MUSC provost Lisa K. Saladin, PT, Ph.D., MUSC is already using AI to develop techniques that can help to diagnose and treat a range of ills, including cancer, Alzheimers disease, substance abuse, child abuse, epilepsy, aphasia, inflammatory skin conditions and cardiac issues.

Baker said that clinicians who are interested in implementing AI into their research or practice should look into the AI Hub, as it offers a host of resources, including funding for AI. During this years Innovation Week, the Clemson-MUSC AI Hub gave out $100,000 worth of grants to five worthy projects.

We want people to know about this, he said. I know there are lots of people out there who could really use our help. We want to accelerate the adoption of AI for those who are interested."

More here:
Can Artificial Intelligence remove unintended bias from health care? Clinicians optimistic, but wary - Medical University of South Carolina