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In order to fulfill the rising food demand, great emphasis is being given to efficient farming through automation in the field and need-based resource management in farm operations to improve crop productivity. Though the scientific advances help us in understanding the crop, soil, weather, and in what conditions it can grow better, some aspects of agriculture can become more efficient by monitoring and predictive analysis for understanding the accurate status of crops, input requirements, and possible yield output. Artificial Intelligence (AI) and remote sensing can help us in this area. AI and advanced satellite imagery, along with weather forecasts provide a unique dataset that allows prediction of harvest, tracking the presence and spread of pests. This improves crop productivity, address food production issues without degrading environment, enhance farm incomes and can help tackle climate change effectively.

Written by Dr. Shivendra Bajaj, Executive Director, Federation of Seed Industry of India and Alliance for Agri Innovation

Dr Shivendra Bajaj

At present, there are several challenges that the agriculture sector is facing. Increase in production cost, lack of required water for irrigation, higher labor cost, fall in farm remuneration, frequent incidences of droughts and floods due to climate change are some of the major issues. To make it worse, the ongoing Covid-19 pandemic has disrupted the food production system and supply chains significantly.

India has seen rapid adoption of technological innovations in the agriculture sector in the recent past. Also, internet services are penetrating the rural areas at a fast pace. This sounds encouraging for the propagation of Artificial Intelligence-based tools for better farm management. One such example is AI tool being used to regulate water flow intelligently and prevent unrequired dampness in soil and thus rules out the possibility of pests that thrive under excessive moisture.

Precision choicesAI enables farmers to carry out farming activities with great precision as they can make informed choices on deciding the right crops and the right process to cultivate for better returns. Higher yields, healthier crops, effective pest control, soil monitoring are some of the major benefits. Artificial intelligence in agriculture involves sensors, robots, and drones to collect different kinds of information and perform tasks such as weeding, irrigation, spraying pesticides, and fertilizers without human intervention. The greenhouse emissions are decreased by 20 percent thanks to new ways of farming AI brings in. This is a significant contribution to the efforts being taken to cut carbon emissions.

At present, a major obstacle farmers face is a lack of timely and accurate data. Remote sensing, which gives valuable insights into various agronomic parameters, and AI facilitate farmers to micromanage standing crops by taking into account the weather conditions, market dynamics. These technological interventions capture green data from multiple sources, analyze it and turn into valuable insights to help stakeholder use fewer resources, manage farming activities accurately and efficiently.

The adoption of AI has so far been driven primarily from a commercial perspective thus we need to focus on how to benefit the agriculture sector at a large scale. NITI Aayog has sought to embrace AI to reduce our dependence on resource-intensive farm practices in order to boost agriculture value chain. There has been substantial rise of agricultural tech start-ups in the past few years. Yet, the adoption of AI has remained limited. NITI Aayog has narrowed down on some problem areas such as difficulty in access to data, high cost and low availability of computing infrastructure, lack of collaborative approach and public awareness.

Agriculture as a backboneThere is no doubt that agriculture is the backbone of India and supports the livelihood of over 50 percent of Indias population. Besides ensuring food security, the demand for healthy, chemical-free food from consumers, which is produced in a sustainable manner, all require the digital transformation in the agriculture sector. What we need to do is to ensure the availability of affordable hardware such as sensors and other communication devices, farm machinery, and equipment and software. Similarly, agriculture research institutes, universities must extend their support, build expertise and create awareness for the adoption of AI and remote sensing in agriculture. One such University in Andhra Pradesh, Acharya NG Ranga Agriculture University (ANGRAU) is using drones in the agriculture sector for various agri related activities. The university is now urging farmers to use drone technology to minimize the input cost on agriculture workers apart from effective practices of seed sowing and spraying pesticides. More colleges and universities should follow this suit.

We will have to start with building AI-enabling infrastructure that has region-specific, crop-specific databases. It will be followed up by capacity building and training of farmers on digital technologies including AI and remote sensing. These technologies may not be affordable to individual farmers so in such cases intermediaries such as FPOs or start-ups can address the problem. Using technology for efficient resources usage, we can achieve higher yields.

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India needs widespread adoption of Artificial Intelligence to improve crop productivity - hortidaily.com

A wide range of technology solutions purport to be "driven by AI," or artificial intelligence. But are they really? Not everything labeled AIis truly artificial intelligence. The technology, in reality, has not advanced nearly far enough to actually be "intelligent."

"AI is often a sensationalized topic," said Neil Morelli, chief industrial and organizational psychologist for Codility, an assessment platform designed to identify the best tech talent. "That makes it easy to swing from one extreme reaction to another," he said.

"On the one hand, fear of AI's misuse, 'uncontrollability,' and 'black box' characteristics. And on the other hand, a gleeful, over-hyped optimism and adoption based on overpromising or misunderstanding AI's capabilities and limitations." Both can lead to negative outcomes, he said.

Much of the confusion that exists over what AI is, or isn't, is driven by the overly broad use of the term, fueled to a large degree by popular entertainment, the media and misinformation.

[Want to learn more about the future of work? Join us at theSHRM Annual Conference & Expo 2021, taking place Sept. 9-12 in Las Vegas and virtually.]

What Is AI, Really?

"Much of what is labeled as 'artificial intelligence'today is not,"said Peter Scott, the founding director of Next Wave Institute, a technology training and coaching firm. "This mislabeling is so common we call it 'AI-washing.' "

The boundaries have often shifted when it comes to AI, he said. "AI has been described as 'what we can't do yet,' because as soon as we learn how to do it, we stop calling it AI."

The ultimate goal of AI, Scott said, "is to create a machine that thinks like a human, and many people feel that anything short of that doesn't deserve the name." That's one extreme.

On the other hand, most of those in the field "will say that if it uses machine learning, especially if it uses deep learning, then it is AI," he said. Officially, "AI is a superset of machine learning, which leaves enough wiggle room for legions of advertisers to ply their trade, because the difference between the two is not well-defined."

Jeff Kiske, director of engineering, machine learning at Ripcord, agrees. Most of what is called AI today could better be referred to as "machine learning," he said. This, he added, is how he prefers to refer to "cutting-edge, data-driven technology." The term machine learning, noted Kiske, "implies that the computer has learned to model a phenomenon based on data. When companies tout their products as 'driven by machine learning,' I would expect a significantly higher level of sophistication."

Joshua A. Gerlick, a Fowler Fellow at Case Western Reserve University in Cleveland, said that AI "is an incredibly broad field of study that encompasses many technologies." At the risk of oversimplification, he said, "a common theme that differentiates a 'true' from a 'misleading' AI system is whether it learns from patterns and features in the data that it is analyzing."

This is the promise of many use-cases in HR for machine learning that actually don't rise to the level of true artificial intelligence.

Implications for HR

For example, Gerlick said: "Imagine a human resources department acquiring software that is 'powered by AI' to match newly hired employees with an experienced mentor within the organization. The software is programmed to find common keywords in both the profiles of the mentees and potential mentors, and a selection is obtained based upon the highest mutual match." While an algorithm is certainly facilitating the matching process within the software, Gerlick said, "it is absolutely not an AI-powered algorithm. This algorithm is simply replicating a process that any human could accomplish, and although it is fast, it does not make the matchmaking process more effective."

A truly AI-powered software platform, he said, would require some initial datalike profiles of previous mentee-mentor pairs and whether the outcomes were successful. It would then learn the factors that led to a successful pairing. "In fact, the software would be so sensitive that it might only be applicable to identifying successful mentee-mentor pairs at this one specific organization," Gerlick said. "In a roundabout way, it has 'learned' how to understand the unique culture of the organization and the archetypes of individuals who work within it. A human resources executive should find that the AI-powered software platform improves its effectiveness over timeand hopefully exceeds the success of its human counterparts, leaving them the time to undertake more complex initiatives."

Christen da Costa, founder of Gadgetreview.com, saidhe thinks the term "AI" is thrown around far too readily. "Most automation tools, for example, are not what I would call AI," he noted. "They take in information fed to them by the user and look for cases that match it. Over time they learn the user's preferences and become better, but that's algorithmic learning. While it can be an aspect of AI, it does not an AI make."

Does it matter? It can. When HR professionals are considering adopting new technology, it's important to not be confusedor swayedby lofty tech terms that tend to be thrown around far too frequently.

It's also important to not be overly enamored of, or potentially misled by, the lure of "artificial intelligence."

"Thoughtful readers and observers of AI in HR would be wise to remember that AI systems help perform manual, repetitious and laborious tasks in HR," Codility'sMorelli said. "However, the range and scope of these tasks are probably narrower than some vendors and providers lead people to believe."

There is no AI system that understands, perceives, learns, pattern-matches or adapts on its own, he said. "Instead, it needs human-labeled and curated data as a starting point. For this reason, users and evaluators should apply more scrutiny to the training data used to teach AI systems," he said, "especially the data's origin, development and characteristics."

"When skeptical over whether a technology is truly 'powered by AI,' consider asking a few simple questions," Gerlick suggested:

If the answers to those questions are yes, he said, "then artificial intelligence might be lending a helping hand."

Lin Grensing-Pophal is a freelance writer in Chippewa Falls, Wis.

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Not All AI Is Really AI: What You Need to Know - SHRM

Getafe players celebrate their first goal during the Spanish La Liga soccer match between FC ... [+] Barcelona and Getafe at the Camp Nou stadium in Barcelona, Spain, Thursday, April 22, 2021. Getafe is one of the teams that use Zone 7, an artificial intelligence platform to predict injury risks in players. (AP Photo/Joan Monfort)

For about two years, Tal Brown and Eyal Eliakim worked together at Salesforce.com Inc. CRM in the cloud-computing software companys Tel Aviv, Israel office. Brown was a product manager and Eliakim was a data analyst, with both men focusing on developing what would become Salesforces Einstein artificial intelligence system.

In 2017, Brown and Eliakim struck out on their own and formed Zone7, an artificial intelligence and machine learning platform focused on helping sports organizations predict the risk of injuries in players and improve their performance.

The company recently secured $8 million in a Series A funding round to help expand its reach in sports as well as other industries. Blumberg Capital, a San Francisco early-stage venture capital firm, led the round. Other investors included Resolute Ventures, UpWest, PLG Ventures and Joyance Partners, each of whom had also participated in Zone7s seed round through which the company raised $2.5 million in early 2019.

So far, Zone7 has worked with more 50 teams in numerous leagues in North America and Europe, including the English Premier League, Major League Soccer, National Football, La Liga, Serie A and Premiership Rugby.

Industries like sports whose success relies on healthy, high-performing individuals have only scratched the surface when it comes to using data to improve human performance, said Bruce Taragin, Blumberg Capitals managing director. Zone7 has the team and technology to take the data play in sports, among other industries, to a new level.

Zone7s system analyzes data that teams collect on athletes when theyre practicing, playing in games, working out and even sleeping. It then produces reports on each players fitness and other variables, forecasts their injury risk and suggests ways in which so-called operators such as coaches and medical personnel can help.

What we typically find is organizations already have a ton of data, Brown said. The need for a central brain to analyze all the data in context is really growing.

He added: Without Zone7, the job of the operator is really hard. Were like the cloud engine that will collect everything and analyze it in context.

Brown noted that some artificial intelligence systems such as those used in stock trading or on Amazon.com are intended to replace humans and eliminate any subjectivity when making decisions. But thats not the case with Zone7, according to Brown.

Were not creating an auto-pilot to get rid of coaches, he said. Thats exactly the opposite of what were trying to do. Were trying to put out the best instrumentation in front of the most elite coaches and others out there.

Zone7 plans on using the Series A funds to increase its staff from 10 to 25, hiring data scientists and engineers to improve the platform and sales and business development employees to complete deals with more organizations and leagues.

Although sports remains the focus of Zone7 and its largest vertical by far, the company is also looking to expand into other sectors, including health care, oil and gas companies and the military. Employees in those industries are also subject to injury risk or exhaustion and are valuable to their companies, so Brown envisions a future where employers and governments use Zone7 to make sure people are performing at their highest levels.

Brown, Zone7s chief executive, and Eliakim, its chief technology officer, are both former members of the Israel Defense Forces Intelligence Corps, where they worked on cybersecurity and big data projects. They now want to use their experience with big data, artificial intelligence and machine learning to help as many people as possible.

Im really passionate about this problem (of injuries and missed time) and the potential impact it can have on a persons well-being, Brown said. Sure, were dealing with a lot of athletes right now. But the potential of impacting a lot of others, whether its doctors or line workers or first responders, that to me is a personal motivation to work on this problem.

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Zone7 Raises $8 Million In Series A Round To Expand Artificial Intelligence Platform For Predicting Sports Injury Risks - Forbes

Global Artificial Intelligence (AI) Chips Market 2021-2025 The analyst has been monitoring the artificial intelligence (AI) chips market and it is poised to grow by $ 73. 49 billion during 2021-2025, progressing at a CAGR of over 51% during the forecast period.

New York, July 22, 2021 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Global Artificial Intelligence (AI) Chips Market 2021-2025" - https://www.reportlinker.com/p05006367/?utm_source=GNW Our report on the artificial intelligence (AI) chips market provides a holistic analysis, market size and forecast, trends, growth drivers, and challenges, as well as vendor analysis covering around 25 vendors.The report offers an up-to-date analysis regarding the current global market scenario, latest trends and drivers, and the overall market environment. The market is driven by the increasing adoption of AI chips in data centers, increased focus on developing AI chips for smartphones, and the development of AI chips in autonomous vehicles. In addition, the increasing adoption of AI chips in data centers is anticipated to boost the growth of the market as well.The artificial intelligence (AI) chips market analysis includes the product segment and geographic landscape.

The artificial intelligence (AI) chips market is segmented as below:By Product ASICs GPUs CPUs FPGAs

By Geography North America Europe APAC South America MEA

This study identifies the convergence of AI and IoT as one of the prime reasons driving the artificial intelligence (AI) chips market growth during the next few years. Also, increasing investments in ai start-ups and advances in the quantum computing market will lead to sizable demand in the market.

The analyst presents a detailed picture of the market by the way of study, synthesis, and summation of data from multiple sources by an analysis of key parameters. Our report on artificial intelligence (AI) chips market covers the following areas: Artificial intelligence (AI) chips market sizing Artificial intelligence (AI) chips market forecast Artificial intelligence (AI) chips market industry analysis

This robust vendor analysis is designed to help clients improve their market position, and in line with this, this report provides a detailed analysis of several leading artificial intelligence (AI) chips market vendors that include Alphabet Inc., Broadcom Inc., Intel Corp., NVIDIA Corp., Qualcomm Inc., Advanced Micro Devices Inc., Huawei Investment and Holding Co. Ltd., International Business Machines Corp., Samsung Electronics Co. Ltd., and Taiwan Semiconductor Manufacturing Co. Ltd. Also, the artificial intelligence (AI) chips market analysis report includes information on upcoming trends and challenges that will influence market growth. This is to help companies strategize and leverage all forthcoming growth opportunities.The study was conducted using an objective combination of primary and secondary information including inputs from key participants in the industry. The report contains a comprehensive market and vendor landscape in addition to an analysis of the key vendors.

The analyst presents a detailed picture of the market by the way of study, synthesis, and summation of data from multiple sources by an analysis of key parameters such as profit, pricing, competition, and promotions. It presents various market facets by identifying the key industry influencers. The data presented is comprehensive, reliable, and a result of extensive research - both primary and secondary. Technavios market research reports provide a complete competitive landscape and an in-depth vendor selection methodology and analysis using qualitative and quantitative research to forecast the accurate market growth.Read the full report: https://www.reportlinker.com/p05006367/?utm_source=GNW

About ReportlinkerReportLinker is an award-winning market research solution. Reportlinker finds and organizes the latest industry data so you get all the market research you need - instantly, in one place.

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The Global Artificial Intelligence (AI) Chips Market is expected to grow by $ 73.49 billion during 2021-2025, progressing at a CAGR of over 51% during...

DUBLIN, July 20, 2021 /PRNewswire/ -- The "Global Artificial Intelligence (AI) in Operating Room Market: Focus on Offering, Technology, Indication, Application, End User, Unmet Demand, Cost-Benefit Analysis, and Over 16 Countries' Data - Analysis and Forecast, 2021-2030" report has been added to ResearchAndMarkets.com's offering.

Global AI in Operating Room Market to Reach $2,951.5 Million by 2030

The purpose of the study is to enable the reader to gain a holistic view of the global AI in the operating room market by each of the aforementioned segments.

The report constitutes an in-depth analysis of the global AI in the operating room market, including a thorough analysis of the applications. The study also provides market and business-related information on various products, applications, technologies, and end users. The report considers software solutions and hardware solutions integrated with AI.

Expert Quote

"I think these are exciting times. Not considering the buzz around AI, ultimately, it is an enabler to do things at scale and quickly. It needs to serve a higher purpose that provides surgeons or other stakeholders in the healthcare ecosystem with value. The real value that a company provides with AI is the key component. This technology can be leveraged to tackle the disparity in the world of surgery".

Key Questions Answered in this Report:

In addition, the report provides:

Key Topics Covered:

1 Product Definition

2 Scope of Research

3 Research Methodology

4 Impact of COVID-19 on the Global AI in Operating Room Market4.1 Impact on Facilities4.2 Impact on AI Adoption in Operating Rooms4.3 Impact on Market Size4.4 COVID-19 Recovery Timeline4.5 Entry Barriers and Opportunities

5 Industry Analysis5.1 Technology Landscape5.1.1 Key Trends5.2 Value Chain Analysis5.3 Cost-Benefit Analysis5.4 End-User Perceptions5.5 Funding Scenario5.6 Regulatory Framework and Government Initiatives5.6.1 Regulations in North America5.6.1.1 U.S.5.6.1.1.1 Connected Devices5.6.1.1.2 Software-as-a-Medical Device (SaMD)5.6.1.1.2.1 General Considerations for SaMDs5.6.2 Regulations in Europe5.6.3 Regulations in Japan5.6.4 Regulations in China5.7 Patent Analysis5.7.1 Awaited Technological Developments5.7.2 Patent Filing Trend5.8 Product Benchmarking

6 Competitive Landscape6.1 Market Share Analysis6.2 Key Strategies and Developments6.3 Business Model Analysis6.4 Pricing Analysis6.5 Competitive Benchmarking

7 Global AI in Operating Room Market Scenario7.1 Assumptions and Limitations7.2 Global AI in Operating Room Market Assessment7.3 Key Findings and Opportunity Assessment7.4 Global AI in Operating Room Market Size and Forecast7.5 Market Dynamics7.5.1 Impact Analysis7.5.2 Market Growth Promoting Factors7.5.2.1 Growth in Funding for AI7.5.2.2 Growing Adoption of AI-Enabled Technologies in Healthcare Settings7.5.2.3 Advancement in Robotics and Medical Visualization Technologies7.5.2.4 Benefits of Artificial Intelligence-Enabled Surgeries Over Conventional Surgeries7.5.3 Market Growth Restraining Factors7.5.3.1 Lack of a Well-Defined Regulatory Framework in Regions7.5.3.2 Limited Studies and Data on the Efficiency of AI in Operating Rooms7.5.4 Market Growth Opportunities7.5.4.1 Leverage AI to Enhance Remote Surgical Capabilities7.5.4.2 Leveraging Business Synergies for Capability and Portfolio Enhancement7.5.5 Current Surgical Challenges7.5.6 Capitalizing on Unmet Demand

8 Global AI in Operating Room Market (by Offering)8.1 Key Findings and Opportunity Assessment8.2 Hardware8.3 Software-as-a-Service (SaaS)

9 Global AI in Operating Room Market (by Technology)9.1 Key Findings and Opportunity Assessment9.2 Machine Learning (ML) and Deep Learning9.3 Natural Language Processing (NLP)

10 Global AI in Operating Room Market (by Indication)10.1 Key Findings and Opportunity Assessment10.2 Cardiology10.3 Orthopedics10.4 Urology10.5 Gastroenterology10.6 Neurology

11 Global AI in Operating Room Market (by Application)11.1 Key Findings and Opportunity Assessment11.2 Training11.3 Diagnosis11.4 Surgical Planning and Rehabilitation11.4.1 Pre-Operative11.4.2 Intra-Operative11.4.3 Post-Operative11.5 Outcomes and Risk Analysis11.6 Integration and Connectivity11.7 Others (Instrument Tracking and Traceability, Scheduling, Anesthesia Management)

12 Global AI in Operating Room Market (by End User)12.1 Opportunity Assessment12.2 Hospitals12.3 Others (Ambulatory Surgical Centers, Private, Standalone, and Specialized Facilities)

13 Global AI in Operating Room Market (by Region)

14 Case Studies14.1 Enabling the Future Operating Room with AI14.2 Role of M&As in the Future of AI in Operating Room14.3 Role of AI in Minimally Invasive Surgeries

15 Company Profiles

For more information about this report visit https://www.researchandmarkets.com/r/555a1

Media Contact: Research and Markets Laura Wood, Senior Manager [emailprotected]

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Global Operating Room Artificial Intelligence (AI) Market Report 2021-2030 with Case Studies to Assess the Key Strategies Adopted by Some of the...