AI is already being used in education, notably in the form of skill development tools and testing systems. As AI educational solutions improve, it is hoped that AI will be able to help bridge gaps in learning and teaching, allowing schools and teachers to accomplish more than ever before.AIcan improve efficiency, personalisation, and administrative responsibilities, giving teachers more time and freedom to focus on understanding and adaptability. The objective for AI in education is for them to work together for the best outcome for students by using the best features of machines and teachers. This was the motivation behind curating this exclusive course for teachers.

Teachers from all over India actively participated in the training on various topics covering the basics to advanced concepts inArtificial Intelligence. Eminent Industry veterans like Dr. Raja N. Moorthy, Member Advisory Board Kirusa Inc, Mr. Arpit Yadav, Senior Data Scientist INSOFE, Ms. Rupa Singh, CEO and Founder AI-Beehive, Dr. Dharmendra Singh Rajput, Associate Professor, VIT Vellore, Ms. Pradnya Paithankar, Senior Trainer and Consultant and Mr. Tushar Kute, Researcher and Senior Trainer, MITU Research, conducted the training sessions and engaged practical sessions using Industrial Case Studies which span over 5 weeks.

Distinguished Professor from the Indian Institute of Technology Bombay, Mumbai, Prof. Kannan Moudgalya, Erach and Meheroo Mehta Advanced Education Technology Chair, graced the occasion as the Chief Guest of the Valedictory Function. He shared his initiatives in upskilling students from humble backgrounds. His iconic project, 'Spoken Tutorial' is an educational multimedia platform that has won numerous awards, funded by the National Mission on Education through Information and Communication Technology (ICT) and launched by the Ministry of Human Resources and Development (MHRD), Government of India. Here, one can self-teach various Free and Open-Source Software. The self-paced, multilingual courses allow anybody with a computer and a desire to learn from anywhere, at any time, and in their preferred language. He believes that everyone deserves equal opportunity to reach the pyramid of success. He congratulated the teachers on their enthusiasm and urge in learning the new emerging technologies.He also commended & appreciated the endeavours by theMIT Centre for Future Skills Excellencefor upskilling & reskilling initiatives.

Mr. Sushant Gadankush, Founder & MD, InnoWise India, also graced the occasion. He presented an overview onRiYSALabs, a unique online platform that canprovide students with a rich, engaging experience and make it easy for the teachers to monitor their progress using live virtual machine views.He encouraged the education fraternity to understand the balance between educational organisations and technology. He appreciated the teachers' efforts in skilling up for a better teaching-learning experience in the new hi-tech classrooms.Dr. Vinnie Jauhari, Director of Education Advocacy at Microsoft Corporation India Limited sent her best wishes to the participants. She believes in Institutional excellence and setting global benchmarks in higher education, executive education and learning. She congratulated the teachers who have started their journey of upskilling towards emerging technologies.

Shri Tushar Kute shared his experience of training the teachers who had joined from all corners of India. He expressed his gratitude towards all the teachers for being participative, sincere and attentive throughout the training sessions. He said that this training would always be special to him as he had the good fortune of training the ones who were already working on the noble cause of nation-building. Dr. Asawari Bhave - Gudipudi, Dean, Faculty of Humanities & Social Sciences,MIT Art, Design and Technology Universityhas advised the education fraternity to explore Artificial Intelligence as it is an integral part of the lives and careers of the current and future generations.A few teacher participants also shared their remarkable experience from a total newbie in AI to a fairly knowledgeable AI enthusiast. For them, it was a total deviation from the regular classrooms of mathematics or science or languages to some challenging and exciting technology Gyan. From anxiety in the beginning to the accomplishment of becoming reasonably tech-savvy, their stories of an exciting journey said it all.

Prof. Suraj Bhoyar, Project Director,MIT Centre for Future Skills Excellenceshared the intent and summary of the 5-week, 2-credit course which was initiated exclusively for teachers looking to upskill themselves with Artificial Intelligence.MIT Centre for Future Skills Excellence (MIT FuSE)firmly believes in the potential of teachers in building the future of the nation and such training is an attempt to help teachers with their endeavour in keeping up with the National Education Policy (NEP) 2020's and guidelines by CBSE to mandate AI Training in Schools, Colleges to spread awareness on emerging technologies for students right from classromms.He promised more such short courses for tech enthusiasts in future. He also reiterated thatAI is not a futuristic vision, but rather something that is here today and being integrated with Education and deployed for better student-teacher interactions and go hand in hand with exponential technologies likeIoT,Data Analytics,Robotics,Cyber Security,Cloud Computing,Blockchain, etc.

The short-term course on Artificial Intelligence was inaugurated on Sept. 27, 2021 with insights & blessings from the Top Global Artificial Intelligence Influencers and industry veterans, Mr. Utpal Chakraborty, TEDx speaker & Former Head of Artificial Intelligence, YES Bank Ltd., Dr. Anoop V.S., Senior Scientist (Research & Training) from IIITMK Kerala and Mr. Arpit Yadav, Senior Data Scientist from INSOFE. The valedictory ceremony concluded with a pledge to build smart and enterprising India from the teacher participants & educators. Prof. Vilas Khedekar, Prof. Ajita Deshmukh, & Dr. Priya Singh have taken efforts to curate the short-term course on AI in Education for teachers. Ms. Smruti Shelke fromMIT Centre for Future Skills Excellence (MIT FuSE)compered the ceremony along with Prof. Komal Gagare from MIT School of Education & Research.

MIT FuSEhas curated exclusive courses keeping up with the current job market requirements of experts inArtificial Intelligence & Machine Learning,Enterprise Resource Planning,Robotic Process Automation,Cloud Computing,Cyber Security&Blockchain Technology. Budding tech-enthusiasts can check theMIT FuSE Websitefor more information on career opportunities in various emerging technologies and guidance on pursuing the same.

About MIT-ADT University

MAEER's Trust which is known to set the strong precedence for the privatization of Engineering education in Maharashtra had taken a first mover's advantage by establishing the Maharashtra Institute of Technology (MIT-Pune), in 1983, which continues to remain the flagship institute of the group.

MIT Art, Design and Technology University,Punehas been established under the MIT Art, Design and Technology University Act, 2015 (Maharashtra Act No. XXXIX of 2015). The University commenced its operations successfully from27th June 2016. The University is a self-financed institution and empowered to award the degrees under section 22 of the University Grants Commission act, 1956. The University has a unique blend of Art, Design, and Technology as the core of its academics.

Recently, MIT Art, Design and Technology University,Punehas accomplished the following accolades:

1. Ranked 26th for ARIIA 2020 by the Ministry of Education, Govt. ofIndia.2. Received 5 Star rating for exemplary performance by the Ministry of Education's Innovation Council, Govt. ofIndia.3. Conferred with Best University Campus Award by ASSOCHAM,New Delhi4. Granted with Atal Incubation Centre under ATAL Innovation Mission, NITI Aayog, Govt. ofIndia

MIT Art, Design and Technology University has been taking a holistic approach towards imparting education wherein the students are being motivated to build a complete winning personality which is "physically fit, intellectually sharp, mentally alert and spiritually elevated". The students are being encouraged to participate in yoga, meditation, physical training, spiritual elevation, communication skills, and other personality development programmes. Currently, we have 7500+ students studying in various schools of higher education under the University viz. Engineering and Technology, Food Technology, Bioengineering, Arts, Design, Marine Engineering, Journalism and Broadcasting, Film and Television, Music (Hindustani Classical Vocal and Instrumental), Teacher Education, and Vedic Sciences.

Prof. Suraj Bhoyar Project Director MIT-FuSE, MIT-ADT University, Pune Mobile No.: 9028483286

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SOURCE MIT Art, Design & Technology University Pune

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MIT Centre for Future Skills Excellence Concludes the 5-Week, Short Term Course on Artificial Intelligence for Teachers - PR Newswire India

Non-fungible tokens (NFT) are becoming one of the most important trends in the crypto ecosystem. The first generation of NFTs has focused on key properties such as ownership representation, transfer, automation as well as building the core building blocks of the NFT market infrastructure.

The hype in the NFT market makes it relatively hard to distinguish signal versus noise when even the most simplistic form of NFTs are able to capture incredible value. But, as the space evolves, the value proposition of NFTs should go from static images or text to more dynamic and intelligent collectibles. Artificial intelligence (AI) is likely to have an impact in the next wave of NFTs.

Jesus Rodriguez is the CEO of IntoTheBlock, a market intelligence platform for crypto assets. He has held leadership roles at major technology companies and hedge funds. He is an active investor, speaker, author and guest lecturer at Columbia University in New York.

We are already seeing manifestations of NFT-AI convergence in the form of generative art. However, the potential is much bigger. Injecting AI capabilities into the lifecycle of NFTs opens the door to forms of intelligent ownership that we havent seen before.

Today, NFTs remain mostly digital manifestations of the offline word in areas such as art or collectibles. While compelling, that vision is quite limited. A more intriguing way to think about NFTs is as digital ownership primitives. Ownership representations have much wider applications than collectibles. While in the physical world ownership is mostly represented as static records, in the digital on-chain world ownership can be programmable, composable and, of course, intelligent.

With intelligent digital ownership the possibilities are endless. Lets illustrate this in the context of collectibles that remain one the best-known applications of NFTs.

Read more: Jesus Rodriguez - When DeFi Becomes Intelligent

Imagine digital-art NFTs that could converse in natural language answering questions to explain the inspiration behind their creation and adapt those answers to a specific conversation context. We could also envision NFTs that could adapt to your feelings, mood and provide an experience that is constantly fulfilling. What about intelligent NFT wallets that, as they interact with a website, could decide which ownership rights to present in order to improve the experience for a given user?

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Echoing William Gibsons famous quote, The future is already here, its just not very evenly distributed, we should think about the intersection of intelligent digital ownership as something that is possible with todays AI and NFT technologies. NFTs are likely to evolve as a digital ownership primitive and intelligence should definitely be part of it.

To understand how intelligent NFTs can be enabled with todays technologies, we should understand what AI disciplines have intersection points with the current generation of NFTs. The digital representation of NFTs relies on digital formats such as images, video, text or audio. These representations map brilliantly to different AI sub-disciplines.

Podcast: How Erick Calderon Turned NFT Squiggles Into a $6M Funding Round

Deep learning is the area of AI that relies on deep neural networks as a way to generalize knowledge from datasets. Although the ideas behind deep learning have been around since the 1970s, they have seen an explosion in the last decade with a number of frameworks and platforms that have catalyzed its mainstream adoption. There are some key areas of deep learning that can be incredibly influential to enable intelligence capabilities in NFTs:

Computer vision: NFTs today are mostly about images and videos and, therefore, a perfect fit to leverage the advancements in computer vision. In recent years, techniques such as convolutional neural networks (CNN), generative adversarial neural networks (GAN) and, more recently, transformers have pushed the boundaries of computer vision. Image generation, object recognition, scene understanding are some of the computer vision techniques that can be applied in the next wave of NFT technologies. Generative art seems like a clear domain to combine computer vision and NFTs.

Natural language understanding: Language is a fundamental form to express cognition, and that includes forms of ownership. Natural language understanding (NLU) has been at the center of some of the most important breakthroughs in deep learning in the last decade. Techniques such as transformers powering models such as GPT-3 have reached new milestones in NLU. Areas such as question answering, summarization and sentiment analysis could be relevant to new forms of NFTs. The idea of superposing language understanding to existing forms of NFTs seems like a trivial mechanism to enrich the interactivity and user experience in NFTs.

Read more: Jesus Rodriguez - 3 Factors That Make Quant Trading in Crypto Unique

Speech recognition: Speech intelligence can be considered the third area of deep learning that can have an immediate impact in NFTs. Techniques such as CNNs and recurrent neural networks (RNN) have advanced the speech intelligence space in the last few years. Capabilities such as speech recognition or tone analysis could power interesting forms of NFTs. Not surprisingly, audio-NFTs seem like the perfect scenario for speech intelligence methods.

The advancements in language, vision and speech intelligence expand the horizon of NFTs. The value unlocked at the intersection of AI and NFTs will impact not one but many dimensions of the NFT ecosystem. In todays NFT ecosystem, there are three fundamental categories that can be immediately reimagined by incorporating AI capabilities:

AI-generated NFTs: This seems to be the most obvious dimension of the NFT ecosystem to benefit from recent advancements in AI technologies. Leveraging deep learning methods in areas such as computer vision, language and speech can enrich the experience for NFT creators to levels we havent seen before. Today, we can see manifestations of this trend in areas such as generative art but they remain relatively constrained both in terms of the AI methods used as well as in the use cases they tackle.

In the near future, we should see the value of AI-generated NFTs to expand beyond generative art into more generic NFT utility categories providing a natural vehicle for leveraging the latest deep learning techniques. An example of this value proposition can be seen in digital artists like Refik Anadol who are already experimenting with cutting edge deep learning methods for the creation of NFTs. Anadols studio have been a pioneer in using techniques such as GANs, and even dabbling into quantum computing, trained models in hundreds of millions images and audio clips to create astonishing visuals. NFTs have been one of the recent delivery mechanisms explored by Anadol.

Read more: Designer Eric Hu on Generative Butterflies and the Politics of NFTs

NFTs embedded-AI: We can use AI to generate NFTs but that doesnt mean that they will be intelligent. But what if they could? Natively embedding AI capabilities into NFT is another market dimension that can be unlocked by the intersection of these two fascinating technology trends. Imagine NFTs that incorporate language and speech capabilities to establish a dialog with users, answer questions about its meaning or interact with a specific environment. Platforms such as Alethea AI or Fetch.ai are starting to scratch the surface here.

AI-first NFT infrastructures: The value of deep learning methods for NFTs wont only be reflected at the individual NFT level but across the entire ecosystem. Incorporating AI capabilities in building blocks such as NFT marketplaces, oracles or NFT data platforms can prepare the foundation to gradually enable intelligence across the entire lifecycle of NFTs. Imagine NFT data APIs or oracles that provide intelligent indicators extracted from on-chain datasets or NFT marketplaces that use computer vision methods to make smart recommendations to users. Data and intelligence APIs are going to become an important component of the NFT market.

AI is changing the landscape of all software and NFTs are not the exception. By incorporating NFT capabilities, the NFTs can evolve from basic ownership primitives to intelligent, self-evolving forms, or ownership that enable richer digital experiences and higher utility for NFT creators and consumers. The era of intelligent NFTs does not require any futuristic technical breakthroughs. The recent advancements in computer vision, natural language understanding or speech analysis combined with the flexibility of NFT technologies already offered a great landscape for experimentation to bring intelligence to the NFT ecosystem.

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The Coming Convergence of NFTs and Artificial Intelligence - Yahoo Finance

What does it mean to be first in developing applications of artificial intelligence (AI), and does it matter? In a recent interview, the former Chief Software Officer of the U.S. Air Force Nicolas Chaillan stated that he resigned in part because he believed that, We have no competing chance against China in fifteen to twenty years. Right now, its already a done deal; it is already over. He reasoned that a failure of the U.S. Department of Defense (DoD) to follow through on stated intentions to build up in AI and cyber means many departments within DoD still operate at what Chaillan considers a kindergarten level. Those are strong words, but Chaillans overall assessment misses the markthe United States becoming an AI also-ran is not a foregone conclusion. Leadership in AI is not necessarily achieved by the first adopter.

Much of the debate over military AI leadership and U.S. technological competition with China hinges on the assumption that there is a significant first-mover advantage when it comes to these technologies, meaning the first to develop them could reap substantial economic and military effects. However, fear of pronounced AI first-mover advantages instead reflects how AI is prone to overhyping where incredibly high expectations of capabilities surpass the reality of what is possible. Overhyping can obscure real progressand generate an inappropriate perception of an AI arms racethat misrepresents the competition going on among countries. Any possible first-mover advantage for AI would be unsustainable.

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Robots and Artificial Intelligence

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AI is a general-purpose, enabling technology not dissimilar to electricity. Moreover, the private sector drives its development, rather than the defense sector. While technologies that are singularly applicable to military contexts diffuse more slowly, those that are multi-use like AI have the added prodding of market incentives to speed up their spread.

Renewing America

Ideas and initiatives for renewing Americas economic strength.

Even when compared to other private sector-driven technologies, AI could spread even faster, since most AI research and development is open source with an unprecedented exchange of code and talent between tech companies and academia. This is a relatively new phenomenon in tech - there is no business need to make closed infrastructure solutions, because within a few months everything will be totally different, which has led to actors releasing even the most cutting-edge, proprietary AI. In 2015, Google opened up its sourcing framework TensorFlow. Facebook followed suit just a few years later with Caffe2 and PyTorch. OpenAI published GPT-2 in 2019, a large language processing model. The culture of keeping this work open-source and collaborative is widespread. A survey of AI and machine learning researchers showed that a majority believed that both a high-level and detailed description of methods, the results, and the actual algorithms should always be published, absent compelling risks from openness.

What does it mean to be competitive, if not a leader, in AI if AI techniques themselves will spread quickly, leading to a similar nature and quality level across the board? The competitive advantage for countries will lie in a states ability to successfully leverage AI. Renewing America through military AI leadership will not succeed if focused purely on acquiring a technical edge, as opposed to organizational capacity and integration.

This idea isnt newthe 2018 National Defense Strategy said that when it comes to adopting and deploying emerging technologies like AI, Success no longer goes to the country that develops a new technology first, but rather to the one that better integrates it and adapts its way of fighting. AI integration leadership will not only improve operations in DoD and beyond in the US government, but it will also enhance US economic competitiveness by setting a model and serving as a catalyst for broader innovation. But success will require both a significant mindset shift within DoD, as well as an elevation of the value of data.

Algorithms are continuously evolving, being tested against new data and updated and verified accordinglyto stay competitive in the 21st century DoD must operate and move in a similar way. In parallel with how tech companies are developing new algorithmswhatever state manages to adopt the latest open-source model, train and benchmark against their own data and models, and discard the losing model while implementing the more efficient one, will be the one to win military AI leadership.

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Robots and Artificial Intelligence

Defense Technology

U.S. Department of Defense

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Successful military AI leadership will also require U.S. data leadership. As Andrew Ng explains, data is food for AI, and with models and algorithms being open sourcedata will become the differentiating factor. Labeling, standardization, and sharing of data across DoD, therefore, is a critical precursor to AI integration and adoption. As it currently stands, DoD has access to large, diverse streams of datahowever much of it is unlabeled, uncleaned, unconsolidated, and further complicated by security restrictions. When it comes to creating algorithms, it has been estimated that up to 80% of the time spent is allocated to processing the data needed for training them. Google released a paper in 2021 that discussed how data cascadescompounding events causing negative, downstream effects from data issues are pervasive. Moreover, DoD already has a competitive advantage when it comes to processing dataan existing cadre of data scientists, analysts, and more with particular knowledge of how to assemble high-quality data in their domain, it just needs to use them.

The United States has the capacity to become the world leader in AIbut it needs to take the necessary steps to revitalize its ability to adopt innovations in order to do so.

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US Leadership in Artificial Intelligence is Still Possible - Council on Foreign Relations

Abstract: Global Artificial Intelligence (AI) Market to Reach US$291. 5 Billion by the Year 2026 . Artificial Intelligence (AI) is emerging as one of the promising technologies, against the backdrop of fast paced digitalization and rapidly evolving technology landscape globally.

New York, Oct. 27, 2021 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Global Artificial Intelligence (AI) Industry" - https://www.reportlinker.com/p05478480/?utm_source=GNW AI technology is associated with making machines and related processes intelligent through the use of advanced computer programming solutions. The AI technology market is poised to grow at a robust pace driven by its increasing adoption in an expanding range of applications in varied industries. The growing need to analyze and interpret burgeoning volumes of data and the escalating demand for advanced AI solutions to improve customer services are expected to fuel growth in the AI market. With significant improvements being seen in data storage capacity, computing power and parallel processing capabilities, the adoption of AI technology in various end-use sectors is on the rise. The rising adoption of cloud-based services and applications, rapid growth of big data, and the increasing need for intelligent virtual assistants are also contributing to the rapid growth of AI market. The advent of face, image, and voice recognition technologies is further favoring growth in the global market.

Amid the COVID-19 crisis, the global market for Artificial Intelligence (AI) estimated at US$47.1 Billion in the year 2020, is projected to reach a revised size of US$291.5 Billion by 2026, growing at a CAGR of 34.3% over the analysis period. Services, one of the segments analyzed in the report, is projected to grow at a 34.1% CAGR to reach US$154.8 Billion by the end of the analysis period. After a thorough analysis of the business implications of the pandemic and its induced economic crisis, growth in the Software segment is readjusted to a revised 31.7% CAGR for the next 7-year period. This segment currently accounts for a 37.9% share of the global Artificial Intelligence (AI) market. The increasing penetration of chatbots or virtual assistants for providing customer assistance in various end-use industries including e-commerce and banking is expected to further enhance demand for AI-based software and systems.

The U.S. Market is Estimated at $28.9 Billion in 2021, While China is Forecast to Reach $53.6 Billion by 2026

The Artificial Intelligence (AI) market in the U.S. is estimated at US$28.9 Billion in the year 2021. The country currently accounts for a 41.4% share in the global market. China, the world`s second largest economy, is forecast to reach an estimated market size of US$53.6 Billion in the year 2026 trailing a CAGR of 40.9% through the analysis period. Among the other noteworthy geographic markets are Japan and Canada, each forecast to grow at 28.8% and 30.2% respectively over the analysis period. Within Europe, Germany is forecast to grow at approximately 32.5% CAGR while Rest of European market (as defined in the study) will reach US$70.9 Billion by the end of the analysis period. The dominant share of the US is mainly attributed to the widespread adoption of AI technology in several end-use industries including media, e-commerce and manufacturing. Increased funding for developing and advancing AI technology and applications, and a robust technical adoption base are also favoring growth. Europe, is the second largest regional AI market. Europe is expected to witness a significant increase in the deployments of cloud-based AI solutions, driven by the growing consumer demand for on-demand and faster access to data and relatively easy document control. Europe`s AI market is likely to benefit from the European Commission`s plans to invest 20 billion for AI research during the period 2018-2020 in order to fuel R&D initiatives for businesses and government. Growth in Asia-Pacific including China is propelled by the increasing adoption of natural language processing (NLP) and deep learning technologies in sectors such as marketing, finance, law, and agriculture. The market also benefits from the rapid pace of improvements being seen in computing power, data storage capacity and processing capabilities, which facilitate adoption of AI technology in sectors such as healthcare and automotive.

Hardware Segment to Reach $71.2 Billion by 2026

The constant decline in hardware costs is fueling growth in the hardware segment. By type of hardware, processor captures the largest share of the AI chipsets market, due mainly to the rising demand for high computing processors for running AI algorithms in servers and for the development of edge devices. In the global Hardware segment, USA, Canada, Japan, China and Europe will drive the 38.2% CAGR estimated for this segment. These regional markets accounting for a combined market size of US$7.8 Billion in the year 2020 will reach a projected size of US$74.8 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$9.8 Billion by the year 2026. Select Competitors (Total 300 Featured)

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Accenture

AIBrain, Inc.

Amazon Web Services

Baidu, Inc.

BIGO Technology

ByteDance Ltd

Cisco Systems, Inc.

CloudMinds

Dell Technologies

eGain Corporation

Esri

Facebook, Inc.

General Electric Company

Google, Inc.

Habana Labs Ltd

Inspur

Intel Corporation

International Business Machines Corporation (IBM)

IPsoft Inc

Micron Technology, Inc.

Microsoft Corporation

Mobileye, an Intel Company

NetEase Fuxi Lab

NetEase, Inc

Next IT Corporation

NICE inContact

Nuance Communications, Inc.

NVIDIA Corporation

Omron Robotics and Safety Technologies, Inc

Oracle Corporation

Rockwell Automation, Inc.

Salesforce.com, inc.

Samsung Electronics Co., Ltd.

SAP SE

SAS Institute Inc.

Siemens AG

Smartron India Private Limited

The Hewlett-Packard Company

Trifo

Xilinx, Inc.

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 2020: A Year of Disruption & Transformation As the Race between the Virus & Vaccines Intensifies, Where is the World Economy Headed in 2021? EXHIBIT 1: World Economic Growth Projections (Real GDP, Annual % Change) for 2020 through 2022 Artificial Intelligence Gains Interest during COVID-19 Pandemic Artificial Intelligence Makes Significant Contribution in War against COVID-19 Machine Learning Benefits Healthcare Organizations AI-Powered Sentiment Analysis Scales & Shapes Vaccination Programs in US Industrial and Commercial Applications Take a Hit as COVID-19 Evolves Into an Economic Crisis EXHIBIT 2: Global PMI Index Points for the Years 2018, 2019 & 2020 EXHIBIT 3: Business Confidence Index (BCI) Points for 3Q 2019, 4Q 2019, 1Q 2020, & 2Q 2020 COVID-19-Led Budgetary Reticence Dampens Spending, but AI Enjoys Resilient Interest in Banking Sector Retailers Rely on AI during COVID-19 to Stay Afloat & Embrace New Normal Emphasis on Technology Adoption Elicits AI Implementation in Manufacturing Industry AI & Machine Learning to Redefine Manufacturing Operations Artificial Intelligence (AI): A Prelude Technologies Enabling AI Outlook 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 Competition AI Marketplace Characterized by Intense Competition EXHIBIT 4: Global Artificial Intelligence Market by Leading Vendors for 2020 Growing Focus on AI by Leading Tech Companies with Huge Financial Resources Investments in AI Startups on Rise EXHIBIT 5: Global AI Startup Funding (in US$ Million) for the Years 2014 through Q12020 EXHIBIT 6: Number of AI Startups with $1 Billion Valuations for the Years 2014-2020 EXHIBIT 7: AI Cumulative Funding (in US$ Billion) by Category (As of 2020) AI Applications and Major Startups Select Companies Raising AI Investments in 2020 EXHIBIT 8: Total Number of Investments in AI by Investor Type: April 2021 World Brands Recent Market Activity

2. FOCUS ON SELECT PLAYERS

3. MARKET TRENDS & DRIVERS AI Breakthroughs with Significant Potential to Radically Transform Future Machine Learning and AI-Assisted Platforms to Personalize Customer Experiences in Marketing Applications EXHIBIT 9: Ranking of Business Outcomes Realized through AI Application in Marketing Ecommerce Attracts Strong Growth Detailed Insight into how e-commerce makes use of AI 3x Faster Acceleration in E-Commerce Induced by the Pandemic Brings Out Automated Fulfilment of E-Commerce Orders as a Major Growth Driver EXHIBIT 10: Global B2C e-Commerce Market Reset & Trajectory - Growth Outlook (In %) For Years 2019 through 2025 EXHIBIT 11: Retail M-Commerce Sales as % of Retail E-commerce Sales Worldwide for the Years 2016, 2018, 2020 & 2022 AI Hosting at Edge to Drive Growth EXHIBIT 12: 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 13: 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 14: Global Breakdown of Investments in Manufacturing IoT (in US$ Billion) for the Years 2016, 2018, 2020 and 2025 Increasing Adoption of AI Technology to Boost AI Chipsets Market Combination of Robotics and AI Set to Cause Significant Disruption in Various Industries AI in Customer-Centric Operations Gain Momentum AI Innovations Widen Prospects Blockchain & Artificial Intelligence (AI): A Powerful Combination Notable Trends in the Artificial Intelligence Market Big Data Trends to Shape Future of Artificial Intelligence AI Exudes Potential to Mitigate Adversities Amid COVID-19 AI in Retail Market: Multi-Channel Retailing and e-Commerce Favor Segment Growth 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 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 COVID-19 Impacts Advertising Industry, Affects AI Investments EXHIBIT 15: COVID-19 Impact on Global Ad Spending: March 2020 Possibilities Galore for AI in Digital Marketing Marketing Functions Where AI is Yet Impossible to Deploy AI-Enabled CRM Market: Promising Growth Opportunities in Store Artificial Intelligence to Transform Delivery of Healthcare Services Healthcare AI Market to Experience Remarkable Expansion EXHIBIT 16: Global Healthcare AI Market - Percentage Breakdown by Application for 2020 EXHIBIT 17: Worldwide Current & Required Healthcare Spending as % of GDP AI in Medical Diagnostics and Pharmaceutical Sectors COVID-19 Spurs New Developments and Expedites AI Adoption in Healthcare Industry Developments Impacting AI in Healthcare Domain Artificial Intelligence Holds Potential to Accelerate Detection & Treatment of COVID-19 Detecting Personalized Therapeutic Targets 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 and Increasing Focus on Autonomous Vehicles Propels Growth EXHIBIT 19: Automotive AI Market By Segment Slowdown in Automobile Production Hit AI Investments in Auto Sector EXHIBIT 20: Automobile Production % YoY Change Across Select Countries: 2020 Vs 2019 EXHIBIT 21: Reduction in Automotive Demand in 2020 (In Million Vehicles) EXHIBIT 22: World Automobile Production in Million Units: 2008 -2022 COVID-19 Outbreak to Speed up Digitalization & Automation in Automotive Sector Driverless Cars: The Ultimate Future of AI in Auto industry 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 IoT, Robotics and Big Data to Stimulate AI Implementations EXHIBIT 23: Global Investments on Industry 4.0 Technologies (in US$ Billion) for the Years 2017, 2020, & 2023 AI Moves from Factory Floor to Supply Chain and Beyond Machine Learning: Growing Role in Smart Manufacturing 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 AI in Construction Sector: Need for Cost Reduction and Safety at Construction Sites Drive Focus onto the Use of AI-based Solutions AI Contributing in Sustaining Critical Infrastructure Amid COVID-19

4. GLOBAL MARKET PERSPECTIVE Table 1: World 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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

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Global Artificial Intelligence (AI) Market to Reach US$291.5 Billion by the Year 2026 - Yahoo Finance