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Will Artificial Intelligence and robotics usher in an era of sustainable precision agriculture? – Genetic Literacy Project

Across midwestern farms,if Girish Chowdhary has his way, farmers will someday release beagle-sized robots into their fields like a pack of hounds flushing pheasant. The robots, he says, will scurry in the cool shade beneath a wide diversity of plants, pulling weeds, planting cover crops, diagnosing plant infections, and gathering data to help farmers optimize their farms.

Chowdhary, a researcher at the University of Illinois, works surrounded by corn, one of the most productive monocultures in the world. In the United States, the corn industry was valued at $82.6 billion in 2021, but it like almost every other segment of the agricultural economy faces daunting problems, includingchanging weather patterns,environmental degradation, severelabor shortages, and therising costof key supplies, or inputs: herbicides, pesticides, and seed.

Agribusiness as a whole is betting that the world has reached the tipping point where desperate need caused by a growing population, the economic realities of conventional farming, and advancing technology converge to require something called precision agriculture, which aims to minimize inputs and the costs and environmental problems that go with them.

No segment of agriculture is without its passionate advocates of robotics and artificial intelligence as solutions to, basically, all the problems facing farmers today. The extent of their visions ranges from technology that overlays existing farm practices to a comprehensive rethinking of agriculture that eliminates tractors, soil, sunlight, weather, and even being outdoors as factors in farm life.

But the promises of precision agriculture still havent been met: Because most of the promised systems arent on the market, few final prices have been set and theres precious little real-world data proving whether they work.

The marketing around precision agriculture, that its going to have a huge impact, we dont have the data for that yet, says Emily Duncan, a researcher in the Department of Geography, Environment and Geomatics at the University of Guelph in Canada. Going back to the idea that we want to reduce the use of inputs, precision agriculture doesnt necessarily say were going to be using less overall.

Even so, Chowdhary, who is co-founder and chief technical officer of Earthsense, Inc., the company that makes those beagle-sized robots, is hopeful that the adoption of his robots will propel farmers well past precision agriculture, to think about the business of farming in a whole new way. Right now, he says, most farmers focus on yield, defining success as growing more on the same amount of land. The result: horizon-to-horizon, industrial monocultures saturated with chemicals and tended by massive and increasingly expensive machinery. With the help of his robots, Chowdhary foresees a future, instead, of smaller farms living more in harmony with nature, growing a diversity of higher value crops with fewer chemicals.

The biggest thing we can do is make it easier for farmers to focus on profit, and not just on yield, Chowdhary wrote in an email to Undark. Management tools that help reduce fertilizer and herbicide costs while improving the quality of land and keeping yield up will help farmers realize more profit through fundamentally more sustainable techniques.

Chowdharys robots may help farmers cut costs by, among other things, pulling weeds that compete with corn. For centuries, farmers tamed weeds with hoes and plows. World War II gave rise to the modern chemical industry, and the herbicides it produced made farmers perceive weeds as a non-issue, leaving the ground beneath crops like corn unnaturally bare and vastly increasing the yield per acre, revolutionizing the farm economy.

Nature is persistent, however, and inevitablyweeds evolvedthat resist herbicides. To compensate, suppliers blend powerful and increasingly expensive herbicidal cocktails and genetically modify seed to be chemically resistant. That agricultural arms race traps farmers in a cycle ofrising costs, threatens preciouswater resources, and only works until, as Iowa farmer Earl Slinker puts it, you go out and spray it one year and it doesnt do anything. The result is a smaller harvest, according to Slinker, which in the low-profit-margin business of farming can mean disaster.

The question that underlies all the theorizing is both economic and cultural: Are farmers going to buy in?

The challenge is demonstrating the benefits to farmers and making these things easy to adopt, says Madhu Khanna, who studies technology adoption at the University of Illinois Department of Agriculture and Consumer Economics. For most of these technologies, the benefits are uncertain.

In agriculture,the conventional wisdom is that the outcome of the race to the farm of the future will be determined by clear-eyed economic decision-making. If robotics and artificial intelligence make business sense, the market will develop. Farmers and growers are very smart about that, says Baskar Ganapathysubramanian of Iowa State Universitys Artificial Intelligence Institute for Resilient Agriculture. From hardware and software perspective, if theres a clear value proposition, he adds, theyre going to choose it.

The growth numbers suggest farmers are open to the potential benefits of advanced technology. Overall, farmers spent almost $25 billion on tractors and other farm equipment in 2020. While Covid-19 slowed the adoption of robotics, farms worldwide are expected to incorporate the technology into their operations faster than the industrial market increases of 19.3 percent and 12.3 percent, respectively, over five years. The global research firm MarketsandMarkets estimates that spending on robots will go from nearly $5 billion in 2021 to almost $12 billion in 2026. One result of that optimism, according to CropLife, a U.S. agribusiness publication, is that the third quarter of 2021 saw more venture capital investment in agriculture technology startups than ever: more than $4 billion.

So few people have experience with farming, says Joe Anderson, an agricultural historian and professor at Mount Royal University in Calgary. They assume theres more stasis than there has been. There are lots of innovations. There have been lots of changes.

The tractors dragging huge implements across fertile fields feature technology that has outpaced even the most advanced automobiles. Many are steered by GPS, following paths mapped out over years of planting and harvest, rendering the farmer in the air-conditioned, video-equipped cab not much more than a passenger.

You put your first pass and the next ones will follow right along, says Slinker, who farms 500 acres outside Grundy Center, Iowa. I just put on a little Keith Jarrett and sit back and travel across the field.

In the autumn, harvesting machinery guides itself along those same tracks, sensing and recording the productivity of every square foot of field. That data can be used to calculate how much of which hybrid seed should be planted next year, determine how heavily it should be fertilized to reach its fullest potential, and identify small patches of ground that arent productive enough to be profitably planted.

When I stop and think about an autonomous tractor, that seems like a really big leap, Sarah Schinkel, who leads John Deeres technology stack innovation group, said at the National Farm Machinery Showin February, but when I stop and think about it and how much automation is already a part of our equipment, maybe its not that big of a leap.

Deere is doing a limited release of its first fully autonomous tractor this year, with greater availability in 2023 and beyond. In contrast to the small-robot vision of researchers like Chowdhary, its a remake of the companys popular Model 8R tractor, which weighs 14 tons. It fits neatly into the existing agribusiness model, but even with that adoption advantage no one expects a fast transition. Farm equipment has an amazingly long lifespan, at least compared to consumer products like cars. Modern tractors routinely operate for 4,000 hours, and a well-maintained model can last 10,000 or approximately 25 years.

Even though you may think youd be interested in getting some new robotic equipment, says Scott Swinton, a distinguished professor in Michigan State Universitys Department of Agriculture, Food, and Resource Economics, a lot depends on where you are in the depreciation and use cycles for the equipment you have. So we see a lot slower adoption than you do in genetics or chemicals.

And there is another thing: Critics note that robotics, even if widely adopted, wont address some of the underlying inadequacies of conventional agriculture.

When we think about this global challenge of feeding everyone our current system is not set up to do that, says Duncan. The fix isnt to throw more tech at it. Its to question the system.

The Midwestern corn-and-soybeans row-crop sector is just a fraction of all of agriculture, which in the U.S. was valued at over $205 billion in 2020. Much of that is what farmers refer to as horticultural crops fruit, vegetables, and other produce.

The important distinction is between field crops that are highly mechanized like corn and horticultural crops that require special treatment, says Swinton. They are higher value and can tolerate higher investments in equipment. Its equipment that does weeding in vegetable crops, some robotic harvesting of, say, asparagus or broccoli, some robotic pickers of tree fruits. These are all in areas where you need somewhat skilled labor, and labor can be hard to get.

The problem is, the planting and harvesting of horticultural crops that is handled so easily by people flummoxes robots. George Kantor, a research professor in Carnegie Mellons Robotics Institute, says it will be necessary to change farms to suit robots. Consider, he suggests, the unremarkable act of picking an apple. What a human laborer can accomplish almost without a thought is nearly impossible for a machine. Locating each piece of fruit, gauging its ripeness, and reaching through a tangle of leaves and branches to gently pluck it from the tree its easier, he says, to train the tree than it is to train the robot. In the case of apples, that means sculpting the orchard into what he calls fruiting walls.

Their tree canopy is trained to be essentially a two-dimensional object, Kantor says. Its a wall with a bunch of apples hanging off of it. We dont have anything that can harvest your grandfathers apple tree, that can reach inside the canopy and pick an apple. But these fruiting walls, its a much easier problem.

Where the agricultural labor shortage is most intense, robotics are gaining ground the fastest. Robert Hagevoort, an extension dairy specialist and professor at New Mexico State University, says the nature of dairy farming makes its labor crisis among the worst in agricultures sectors. Cows need to be milked twice a day, he says, every day, creating a lifestyle that is a tough sell to young people choosing a career. The labor shortage is contributing to the decrease in the number of dairy farms.

In some places, he says, some of those producers with land they bought by the acre for agriculture end up selling it by the square foot for real estate development.

Robotics have offered a lifeline to some dairy farmers. But contrary to the idealized vision of smaller, more local, family farms, robotics have nudged dairy toward larger operations.

If you went into farming because you wanted to do your own thing and be by yourself like my father did, says Christopher Wolf, professor of agricultural economics at Cornell University, thats not the job anymore. Its a different skill set. Youre going to be part of a management team.

Wolf grew up in Wisconsin at a time when 150 cows was a large herd, but still manageable by a single large family. Adding robots to dairy farming creates the same potential economies of scale that have industrialized row crops like corn and soybeans. A single robotic milker can care for over 60 cows, and the second milker is cheaper than the first, and the third cheaper than the second. In advanced milking parlors dozens of milkers can be linked together and managed by only a few technicians working predictable eight-hour shifts and having barely any contact with the cows.

If youre set up that way you can also take a vacation, says Wolf. I knew dairy farmers growing up who hadnt taken a vacation in 20 years.

At the farthest reaches of robotic farming are the developers who are completely abandoning almost every aspect of traditional farming. Iron Ox, a California start-up that just received a $53 million infusion of capital from Bill Gates Breakthrough Energy Ventures fund, grows high-value fresh produce in completely controlled, indoor environments.

Most approaches to automating parts of agriculture are one robot that does one operation, says Brandon Alexander, CEO of the company. The reason that hasnt succeeded is at the end of the day plants are complex things. If youre really going to automate it, you have to design the entire process from the ground-up for automation.

That will likely happen first in an agricultural sector with few traditions to change, a very small installed technical base to replace, and a high rate of potential return which is a pretty apt description of the embryonic cannabis industry. Legal cannabis is already the U.S.sfifth most valuable crop, and producers are adopting new technology in ways traditional farmers are not.

Theres not a strong bias looking backwards at how the crop is produced, says Kantor. The other thing of course is we talk about high value crops. Grapes are high value crops, leafy greens are high value crops, but cannabis is in a whole other league. Its going to drive a lot of interesting technologies.

A study by the University of Illinois estimatesthat the cost of seed, fertilizer, herbicides, and other farming inputs for corn and soybean production are going to rise over 30 percent between 2020 and the 2022 planting season. The study predicts per acre return roughly the equivalent of gross profit for corn will drop from $378 to $61 per acre in 2022.

From a farmers perspective they know they need help, says Alexander. The average grower recognizes that something pretty drastic needs to change if were going to feed a growing population.

But according to Terry Griffin, a cropping systems economist at Kansas State University, economists too often assume farmers will behave like businesses, when they often behave more like consumers. Different people measure value differently, Griffin says. Some farm management goes to having the greatest net return. Some might want the newest equipment or the best environmental metrics. For every individual its a different value proposition.

Khanna cites another factor that is often forgotten: consumer perceptions. If consumers start to demand, for example, more crops produced without todays heavy application of chemicals, it could drive adoption of robotics.

We underestimate consumers, she says, in reference to the role they can play in creating this market. As there is more demand for sustainably produced agricultural products, there will be a greater shift toward documenting what farmers are doing. Policies will do that too, but a lot of the change is going to be driven by consumer and market pressures.

I dont think there will be one model of agriculture in the future, but there is a push to move away from the industrial model of farming, says Hermione Dace, a policy analyst at the Tony Blair Institute for Global Change in London. Traditional farming will still exist, but there will be less of it. Robotics will help traditional farmers apply inputs more precisely and reduce the environmental impact of farming as well as saving cost.

Nidhi Kalra, a senior information scientist at the Rand Corporation, a public policy think tank, says the current moment in agriculture recalls theGartner Hype Cycle, a formulation of the adoption of new technology which is basically that new tech comes in, dreams are vastly overinflated, those technologies crash and people say its garbage, and then you come out of the valley and the tech starts doing useful things in the world.

If shes right, todays excited anticipation of agricultures robotic utopia-to-come will inevitably give way to disillusionment as seemingly world-changing ideas amount to very little.

Kantor believes there have already been three or four robotic waves. In the 1950s, Walt Disney created Tomorrowland, the first really vivid demonstration of what very human robots might one day do. It generated a lot of excitement, but what came out of that period were industrial robots, bolted to factory floors and accomplishing a single rote task. Roughly every decade since then theres been some new technology that opened wider possibilities. He cites the personal computer, ATMs, and shopping kiosks.

Now were in a self-driving car wave and agriculture wave, and its going to recede, he says. I like to think of it as tides, waves washing up on the beach, and theres a lot of excitement and then the waves recede, and one or two things are left behind and are useful.

It ultimately will come down to what farmers choose. On his farm in Iowa, Slinker thinks of himself as pretty typical. Hes not on the cutting edge of technology, but he adopts what makes sense to him and what he has seen work for farmers he knows. But he will keep some things, too, even when its not completely rational.

And so, along with the modern equipment he uses to operate his farm, he holds onto an old tractor that belonged to his father. That tractor may not be part of the billion-dollar calculations being made on his behalf by people who spend more time in research labs and conference rooms than they do on the farm, but it should be. Its handy for hauling small loads without putting hours on his bigger, more expensive tractors. And it reminds Slinker, he says, of why he got into farming in the first place, and thats something hed like to preserve.

Tom Johnson writes about technology, business, and whiskey in Louisville, Kentucky. He has written or co-written dozens of historical and military documentaries, and been published in Los Angeles, Newsday, Vineyard & Winery Management, Bourbon+, and other publications. Check out Tom Johnsons personal website at http://www.excellentproj.com

A version of this article was posted at Undark and is used here with permission. Check out Undark on Twitter @undarkmag

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Will Artificial Intelligence and robotics usher in an era of sustainable precision agriculture? - Genetic Literacy Project

U.S. warns of discrimination in using artificial intelligence to screen job candidates – NPR

Assistant Attorney General for Civil Rights Kristen Clarke speaks at a news conference on Aug. 5, 2021. The federal government said Thursday that artificial intelligence technology to screen new job candidates or monitor their productivity can unfairly discriminate against people with disabilities. Andrew Harnik/AP hide caption

Assistant Attorney General for Civil Rights Kristen Clarke speaks at a news conference on Aug. 5, 2021. The federal government said Thursday that artificial intelligence technology to screen new job candidates or monitor their productivity can unfairly discriminate against people with disabilities.

The federal government said Thursday that artificial intelligence technology to screen new job candidates or monitor worker productivity can unfairly discriminate against people with disabilities, sending a warning to employers that the commonly used hiring tools could violate civil rights laws.

The U.S. Justice Department and the Equal Employment Opportunity Commission jointly issued guidance to employers to take care before using popular algorithmic tools meant to streamline the work of evaluating employees and job prospects but which could also potentially run afoul of the Americans with Disabilities Act.

"We are sounding an alarm regarding the dangers tied to blind reliance on AI and other technologies that we are seeing increasingly used by employers," Assistant Attorney General Kristen Clarke of the department's Civil Rights Division told reporters Thursday. "The use of AI is compounding the longstanding discrimination that jobseekers with disabilities face."

Among the examples given of popular work-related AI tools were resume scanners, employee monitoring software that ranks workers based on keystrokes, game-like online tests to assess job skills and video interviewing software that measures a person's speech patterns or facial expressions.

Such technology could potentially screen out people with speech impediments, severe arthritis that slows typing or a range of other physical or mental impairments, the officials said.

Tools built to automatically analyze workplace behavior can also overlook on-the-job accommodations such as a quiet workstation for someone with post-traumatic stress disorder or more frequent breaks for a pregnancy-related disability that enable employees to modify their work conditions to perform their jobs successfully.

Experts have long warned that AI-based recruitment tools while often pitched as a way of eliminating human bias can actually entrench bias if they're taking cues from industries where racial and gender disparities are already prevalent.

The move to crack down on the harms they can bring to people with disabilities reflects a broader push by President Joe Biden's administration to foster positive advancements in AI technology while reining in opaque and largely unregulated AI tools that are being used to make important decisions about people's lives.

"We totally recognize that there's enormous potential to streamline things," said Charlotte Burrows, chair of the EEOC, which is responsible for enforcing laws against workplace discrimination. "But we cannot let these tools become a high-tech path to discrimination."

A scholar who has researched bias in AI hiring tools said holding employers accountable for the tools they use is a "great first step," but added that more work is needed to rein in the vendors that make these tools. Doing so would likely be a job for another agency, such as the Federal Trade Commission, said Ifeoma Ajunwa, a University of North Carolina law professor and founding director of its AI Decision-Making Research Program.

"There is now a recognition of how these tools, which are usually deployed as an anti-bias intervention, might actually result in more bias while also obfuscating it," Ajunwa said.

A Utah company that runs one of the best-known AI-based hiring tools video interviewing service HireVue said Thursday that it welcomes the new effort to educate workers, employers and vendors and highlighted its own work in studying how autistic applicants perform on its skills assessments.

"We agree with the EEOC and DOJ that employers should have accommodations for candidates with disabilities, including the ability to request an alternate path by which to be assessed," said the statement from HireVue CEO Anthony Reynold.

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U.S. warns of discrimination in using artificial intelligence to screen job candidates - NPR

Trending Today, Airing on Fox Business, Features The Security Oracle, Experts in the Security and Artificial Intelligence Space – PR Newswire

ORLANDO, Fla., May 13, 2022 /PRNewswire/ -- Coming soon to Trending Today, airing on Fox Business, The Security Oracle, a leader in the security and artificial intelligence space, reveals how its revolutionary robots are changing the world of security, one robot at a time. Watch to learn how The Security Oracle's robotic appliances meet the market demand for early alerts and mitigation of all-hazard threats. The Security Oracle's AI technology deploys non-lethal, non-permanent blinding lights and disorienting sounds to stun and confuse potential attacks and criminals at close or long distance, providing critical time for security and law enforcement to respond in person.

"One of the most urgent, and important, problems we need to solve in America today is violent crime and attacks on our critical infrastructure.When dealing with active shootings or attacks on critical infrastructure, current security solutions aren't fast enough!" says Charles L Butler, Jr., CEO of The Security Oracle.

While the best average response time from a human to an active shooting is five minutes, The Security Oracle's artificial intelligence robots can respond in a fraction of the time. Nearly 70% percent of lives lost during an active shooting situation occur within the first five minutes. A quick response time is critical. The Security Oracle's robots close this response time gap down to seconds, not only keeping critical infrastructure online, but also saving precious lives.

"The Security Oracle is solving wicked problems of active shooters and threats against critical infrastructure, assets and people around the globe," says Vontella Kay Kimball, President of The Security Oracle.

Over the last five years, The Security Oracle has delivered five deployments of Robots in the Sky, as game-changer appliances, having over 200,000 hours of operational excellence and amazing uptime performance, protecting America's power grid from attack. "If the ability to defend the asset is a vital component of your overall critical infrastructure protection strategy, then the 'TSO RCADS' offers a unique and reliable solution," says Al Perotti, CPP, power grid Physical Security Director and early adopter of TSO's Remotely Controlled Active Defense and Denial System (RCADS).

"TSO's disruptive robotic solution, (RCADS), is the enabling AI technology that makes it possible for Robots in the Sky and TSO's family of robotic appliances to empower organizations to dynamically reconfigure access control and emergency communications systems to adapt to new security threats in sub-second response time," says Anna M. Wang, Wang & Associates, Security & Compliance Consulting.

Robotics is the new technological revolution. Per a recent Oxford study, "84% of security guards will be replaced by AI security technology and robots!" This is the future of security! The Security Oracle has been referred to as a Unicorn in Waiting given their global portfolio of 9 Pioneering Patents/1 Pending Patent and 163 registered trademarks in 35 countries across 4 continents: Robots in the Sky - Robots on the Move - Robots on the Seas Robots on the Rails - RCADS

Is The Security Oracle a Unicorn in Waiting?

About Trending Today:Trending Today is an award-winning business show that features entrepreneurs, companies, and trendsetters that are transforming their respective industries. Trending Today guests share their stories and commitment to building their brands, inspiring entrepreneurship and the American dream. Trending Today airs on Fox Business. Learn more at http://www.TrendingToday.com.

About The Security Oracle:The Security Oracle is a Visionary Team founded to be a catalyst for change in Public Safety and Homeland Security. They develop market-disrupting, purpose-built, artificial intelligence robotic security appliances to help protect the future and save lives. CEO, Charles L. Butler, Jr. explains how the "MISSION of TSO is to fuse the 2000 year-old philosophy of Sun Tzu, SPEED-SURPRISE-MANEUVER, with 21st century AI, to transform the Global Security Market."

TSO's transformative technology is patented in 9 countries and 1 pending patent.Learn more at http://www.TheSecurityOracle.com.

About the Author:Michelle Layne is the resident writer for Trending Today. She loves telling and following inspiring stories about entrepreneurs who are revolutionizing their industries and chasing their dreams. She has a Master of Science in Industrial Organizational Psychology from SNHU, and a Master of Arts in Homeland Security from Northeastern University.

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Trending Today, Airing on Fox Business, Features The Security Oracle, Experts in the Security and Artificial Intelligence Space - PR Newswire

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

ReportLinker

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 – smallwarsjournal

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.

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Artificial Intelligence/Machine Learning and the Future of National Security - smallwarsjournal