NEWS RELEASE

UNIVERSITY OF WATERLOO

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Professional sports teams pour millions of dollars into data analytics, using advanced tracking systems to study every sprint, pass, and decision on the field. The results of that analysis, however, are industry secrets, making many sports difficult for researchers to study. 

Now, two University of Waterloo researchers, Dr. David Radke and Kyle Tilbury, are using AI to level the playing field. 

By tapping into Google Research Football’s reinforcement learning environment, the researchers developed a system that can simulate and record unlimited soccer matches. To get things started, they generated and saved data from 3,000 simulated soccer games, resulting in a rich and complex dataset of passes, goals, and player movements for researchers to study. 

“While researchers have access to a lot of data about episodic sports like baseball, continuous invasion-game sports like soccer and hockey are much more difficult to analyze,” said Radke, a recent Waterloo PhD graduate in computer science and currently a senior research scientist for the NHL’s Chicago Blackhawks. 

“While the AI-generated players might not exactly play like Lionel Messi, the simulated datasets they generate are still useful for developing sports analysis tools.”

Datasets like the ones generated by the team are particularly useful for researchers, enthusiastic fans, and smaller research teams that may not have extensive access to proprietary sports data.

“Enabling researchers to have this data will open up all kinds of opportunities,” said Tilbury, a Waterloo PhD student in computer science who equally co-authored the research. “It’s a democratization of access to this kind of sports analytics data. 

While datasets like the one generated by the team are particularly interesting for sports enthusiasts, they have larger implications for AI research as well. 

“At its core, invasion-game sports analytics is about understanding complex multiagent systems,” Radke said. “The better we are at modelling the complexity of human behaviour in a sporting situation, the more useful that is for AI research. In turn, more advanced multiagent systems will help us better understand invasion-game sports.” 

Radke and the team believe the future of sports analytics relies on progress in the space of tracking data. They therefore hope researchers interested in sports without access to tracking data will utilize their datasets and repository to gain experience working with this type of data.

The study, “Simulating tracking data to advance sports analytics research,” appeared in the proceedings of the 24th International Conference on Autonomous Agents and Multiagent Systems.

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Artificial intelligence (AI) promises to be a game changer for many industries. And chatbots such as OpenAI’s ChatGPT are at the forefront of the innovation, making it easy for companies to do more with less and automate repetitive tasks with ease.

The problem, however, is that expectations can become overblown, potentially setting investors up for disappointment. With generative AI in its early innings, there’s still a lot to be proven. Will it lead to job losses and radically change day-to-day operations, as many people are both anticipating and fearing? That’s still up for debate.

Even OpenAI CEO Sam Altman thinks that expectations may be getting too high. He has warned investors in the past about getting their hopes up, and he recently reiterated his concerns.

Is an AI bubble already here?

Worries of an AI bubble aren’t new, as valuations haven’t been skyrocketing for many stocks. And even Altman believes the market may indeed be in one. While he does believe AI will be transformative and incredibly important for innovation, he does worry that expectations are getting a bit too ambitious.

“Are we in a phase where investors as a whole are overexcited about AI?,” he said. “My opinion is yes. Is AI the most important thing to happen in a very long time? My opinion is also yes.”

A few years ago, when OpenAI was in the midst of developing GPT-4, Altman said that “people are begging to be disappointed.” And while the chatbot has been improved since then, it may not be up to the level that many people were expecting by now. But that hasn’t stopped valuations from becoming incredibly inflated.

Many AI stocks are trading at excessive valuations

Finding overvalued AI stocks isn’t hard right now. Perhaps the best example is Palantir Technologies (PLTR -1.42%). While the data analytics company has benefited from enhancing its platform to use AI, its valuation has gotten excessive. At a market cap of around $370 billion, it’s now one of the most valuable companies in the world, worth more than blue chip stocks such as Coca-Cola, Wells Fargo, and T-Mobile US.

While Palantir’s business has been growing at a fast rate of around 50% year over year, it still has generated a fairly modest $3.4 billion in revenue over the trailing 12 months, putting it at a price-to-sales multiple of around 110. And its price-to-earnings (P/E) multiple of 520 is gargantuan.

Palantir is perhaps the most recognizable example of a company whose valuation has taken off to obscene levels due to AI, but there are other cases as well.

Microsoft, for instance, is trading close to 40 times its trailing earnings, which is a higher-than-typical valuation for the tech stock. But with its Copilot assistant and AI-powered personal computers, expectations are also elevated that Microsoft’s growth may soar due to AI in the future. In its most recent quarter, it grew at a rate of 18%, which is solid but arguably not worth such a high P/E multiple, suggesting that expectations are sky high for Microsoft as well.

Investors should tread carefully with AI stocks

AI has the potential to truly change businesses in meaningful ways, and many tech companies are ramping up spending and investments in anticipation of that. But that doesn’t mean that the payoff will line up with investor expectations. And if that doesn’t happen, it can make a stock vulnerable to a sizable sell-off in the future.

Whether it’s Palantir, Microsoft, or another AI stock, it’s always important to consider a stock’s valuation when buying it. Even if the business may be performing well, that doesn’t mean it’s a good investment at any price. Valuation matters, which is why when it comes to AI, investors should consider Altman’s warnings carefully.

InsightAce Analytic Pvt. Ltd. announces the release of a market assessment report on the “Global AI in Livestock Welfare Monitoring Market Size, Share & Trends Analysis Report By Component (Software [Data Management Platforms, Behavior Analytics Software, AI & Machine Learning Models, Health Monitoring Algorithms], Hardware [Cameras, Sensors, Microphones, Gateways, RFID Tags], and Services [Maintenance & Support, Installation & Integration Services, Training & Consulting]), Type (Wearable Sensor-Based Systems, Thermal Imaging Systems, Vision-Based Systems, Integrated Multi-Sensor Platforms, and Audio-Based Monitoring Systems), Livestock Type (Swine, Poultry, Cattle, Sheep & Goats, and Others), Application (Health Monitoring, Environmental Monitoring, Behavior Analysis, Stress & Pain Detection, Feeding Pattern Monitoring, and Breeding Management), Deployment Mode (On-Premise, Cloud-Based, and Hybrid), Technology (Machine Learning, Edge AI, Computer Vision, IoT & Smart Sensors, and Data Analytics), End-user (Animal Welfare Organizations, Commercial Livestock Farms, Veterinary Clinics & Hospitals, Research Institutes & Universities, and Government & Regulatory Bodies),-Market Outlook And Industry Analysis 2034”

The Global AI in Livestock Welfare Monitoring Market is valued at US$ 2.3 Bn in 2024 and it is expected to reach US$ 11.8 Bn by the year 2034, with a CAGR of 18.4% during the forecast period of 2025-2034.

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AI in livestock welfare monitoring seeks to use intelligent technologies to enhance animal health, behaviour tracking, and environmental factors. It uses sensors, cameras, and algorithms to monitor livestock continuously without the need for human intervention.

This technique helps farmers identify early signs of illness, stress, or discomfort, allowing them to take precise action to prevent the spread of disease and boost productivity. The market for AI in livestock welfare management is growing quickly due to the need for efficient livestock management and technological advancements.

The need for sustainable agricultural methods, the growing demand for food as a result of the world’s population, and technological improvements are some of the main causes driving the growth of AI in livestock welfare management. AI helps address these needs by increasing efficiency and productivity, which results in higher outputs with less input.

Additionally, governments and the corporate sector are investing more in smart agricultural solutions as they recognize the potential of AI to transform agriculture and promote food security. This will boost the growth of AI in the livestock welfare management market in the coming years.

List of Prominent Players in the AI in Livestock Welfare Monitoring Market:

• Merck Animal Health

• Afimilk

• Connecterra

• DeLaval

• Vence (acquired by Merck)

• Gallagher Animal Management

• HerdDogg

• Lely

• Allflex

• PrecisionAG (formerly PrecisionHawk)

• Stellapps

• Zoetis

• Tri-Scan (acquired by Zoetis)

• AgriWebb

• Cainthus

• Nedap

• Silent Herdsman (acquired by Afimilk)

• Halo (livestock monitoring Al)

• SmartBow (by Allflex)

• Cargill (livestock Al division)

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Market Dynamics:

Drivers-

The market for AI in livestock welfare management is anticipated to grow in the future due to the rising demand for livestock products. Livestock products are a variety of goods derived from animals bred for agricultural purposes, including meat, dairy, eggs, and other commodities. Large amounts of data from sensors, drones, and satellite photos may be gathered, analysed, and interpreted by farmers thanks to artificial intelligence (AI) technologies.

Furthermore, improvements in machine learning techniques are driving the AI in livestock welfare management market. The behavior and health of livestock may now be predicted with greater accuracy due to these advancements. Businesses are focusing on developing user-friendly solutions that meet the needs of farmers.

Challenges:

There are many obstacles in the way of integrating AI in livestock welfare management. A primary obstacle is the high upfront cost of AI systems, which small and medium-sized farms may find unaffordable. Additionally, farmers must learn how to utilize advanced AI technology, which requires training and skill development.

Furthermore, because these systems frequently gather and handle vast volumes of sensitive data, worries regarding data security and privacy surface. To fully utilize AI in livestock welfare management, two more issues that must be resolved are technological dependability and the requirement for a strong infrastructure to support AI applications.

Regional Trends:

The region’s strong infrastructure and cutting-edge agricultural technology allowed North America to maintain its leading position in the AI in livestock welfare management market in 2024. The incorporation of AI into different livestock farming operations is further fueled by the fact that North American farmers are frequently early adopters of technology that promises more profitability and efficiency. Further supporting the adoption of AI technologies is the region’s significant emphasis on precision and sustainable agriculture.

The AI in livestock welfare management market in Asia Pacific is growing in strength as corporate parties and governments work to modernize livestock welfare management. Asia Pacific nations such as China, Japan, and India choose cost-effective aluminium solutions designed for intensive animal husbandry. The demand for cloud-based, mobile-enabled Al platforms that function well in a variety of infrastructure configurations is also rising in these locations.

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Recent Developments:

• In October 2024, Merck Animal Health officially introduced SenseHub Cow Calf, a remote livestock monitoring system designed for cow/calf operations. The solution automatically detects estrus, identifies optimal insemination times, tracks activity and rumination using ear-mounted accelerometers, and delivers insights via cloud-based dashboards to improve breeding efficiency and reduce labor.

Segmentation of AI in Livestock Welfare Monitoring Market-

By Component-

• Software

o Data Management Platforms

o Behavior Analytics Software

o AI & Machine Learning Models

o Health Monitoring Algorithms

• Hardware

o Cameras

o Sensors

o Microphones

o Gateways

o RFID Tags

• Services

o Maintenance & Support

o Installation & Integration Services

o Training & Consulting

By Type –

• Wearable Sensor-Based Systems

• Thermal Imaging Systems

• Vision-Based Systems

• Integrated Multi-Sensor Platforms

• Audio-Based Monitoring Systems

By Livestock Type-

• Swine

• Poultry

• Cattle

• Sheep & Goats

• Others

By Application-

• Health Monitoring

• Environmental Monitoring

• Behavior Analysis

• Stress & Pain Detection

• Feeding Pattern Monitoring

• Breeding Management

By Deployment Type-

• On-Premise

• Cloud-Based

• Hybrid

By Technology-

• Machine Learning

• Edge AI

• Computer Vision

• IoT & Smart Sensors

• Data Analytics

By End-use-

• Animal Welfare Organizations

• Commercial Livestock Farms

• Veterinary Clinics & Hospitals

• Research Institutes & Universities

• Government & Regulatory Bodies

By Region-

North America-

• The US

• Canada

Europe-

• Germany

• The UK

• France

• Italy

• Spain

• Rest of Europe

Asia-Pacific-

• China

• Japan

• India

• South Korea

• South East Asia

• Rest of Asia Pacific

Latin America-

• Brazil

• Argentina

• Mexico

• Rest of Latin America

Middle East & Africa-

• GCC Countries

• South Africa

• Rest of Middle East and Africa

Read Overview Report- https://www.insightaceanalytic.com/report/ai-in-livestock-welfare-monitoring-market/3149

About Us:

InsightAce Analytic is a market research and consulting firm that enables clients to make strategic decisions. Our qualitative and quantitative market intelligence solutions inform the need for market and competitive intelligence to expand businesses. We help clients gain competitive advantage by identifying untapped markets, exploring new and competing technologies, segmenting potential markets and repositioning products. Our expertise is in providing syndicated and custom market intelligence reports with an in-depth analysis with key market insights in a timely and cost-effective manner.

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InsightAce Analytic Pvt. Ltd.

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This release was published on openPR.