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Machine learning is one of the artificial intelligence models frequently used for modeling, prediction, and management of swine farming. Machine learning models mainly include algorithms of a decision tree, clustering, a support vector machine, and the Markov chain model focused on disease detection, behaviour recognition for postural classification, and sound detection of animals. The researchers from North Carolina State University and Smithfield Premium Genetics* demonstrated the application of machine learning algorithms to estimate body weight in growing pigs from feeding behaviour and feed intake data.

Feed intake, feeder occupation time, and body weight information were collected from 655 pigs of 3 breeds (Duroc, Landrace, and Large White) from 75 to 166 days of age. 2 machine learning algorithms (long short-term memory network and random forest) were selected to forecast the body weight of pigs using 4 scenarios. Long short-term memory was used to accurately predict time series data due to its ability in learning and storing long term patterns in a sequence-dependent order and random forest approach was used as a representative algorithm in the machine learning space. The scenarios included an individually informed predictive scenario, an individually and group informed predictive scenario, a breed-specific individually and group informed predictive scenario, and a group informed predictive scenario. 4 models each implemented with 3 algorithms were constructed and trained by different subsets of data collected along the grow-finish period to predict the body weight of individuals or groups of pigs.

Overall, as pigs matured and gained weight, daily feed intake increased, while the daily number of visits and daily occupation time decreased. Overall, the individually informed predictive scenario achieved better predictive performances than the individually and group informed predictive scenarios in terms of correlation, accuracy, sensitivity, and specificity. The greatest correlation was 0.87, and the highest accuracy was 0.89 for the individually informed prediction, while they were 0.84 and 0.85 for the individually and group informed predictions, respectively. The effect of the addition of feeding behaviour and feed intake data varied across algorithms and scenarios from a small to moderate improvement in predictive performance.

This study demonstrated various roles of feeding behaviour and feed intake data in diverse predictive scenarios. The information collected from the period closest to the finishing stage was useful to achieve the best predictive performance across predictions. Artificial intelligence has the potential to connect feeding behaviour dynamics to body growth and to provide a promising picture of the feeding behaviour data involvement in the group-housed pigs body weight prediction. Artificial intelligence and machine learning can be used as management tools for swine farmers to evaluate and rank individual pigs to adjust feeding strategies during the growth period and to avoid sorting losses at the finishing stage while reducing labor and costs.

Some technologies and tools have been developed for data collection, data processing, and modeling algorithms to evaluate pigs feeding behaviour and feed intake. These technologies demonstrated great potential to enhance the swine industry efficiency on decision making. A standard database or method for data cleaning and selection is however required to minimise the time and costs of data processing.

* He Y, Tiezzi F, Howard J, Maltecca C. Predicting body weight in growing pigs from feeding behavior data using machine learning algorithms. Comput Electron Agric. 2021;184:106085. doi:10.1016/j.compag.2021.106085

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Artificial intelligence: a new paradigm in the swine industry - Pig Progress

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NEW YORK, June 29, 2022 /PRNewswire/ --The "Artificial Intelligence (AI) Market in Retail Sector Market - Competitive Analysis, Drivers, Trends, Challenges &Five Force Analysis" report has been added to Technavio's offering.The artificial intelligence (AI) market in the retail sector market value is anticipated to grow by USD 29.57 billion, at a CAGR of 35.69% from 2021to 2026.

Technavio has announced its latest market research report titled Artificial Intelligence (AI) Market in Retail Sector Market by Application and Geography - Forecast and Analysis 2022-2026

40% of the market's growth will originate from North America during the forecast period. US andCanada are the key markets forartificial intelligence (AI)in the retail sectorin North America. Market growth in this region will be fasterthan the growth of the market in South America and MEA.The significant increase in theinvestments in the technology and theearly adoption of AI will facilitate theartificial intelligence (AI) market growth in the retail sector in North America over the forecast period.

For more information on region segment Get a sample now!

Market Dynamics

The key factordriving the global artificial intelligence (AI) market growth inthe retail sector is the rise in investments and R&D in AI startups. Many governments have come up with formal AI frameworks and strategies, such as the US executive order on American leadership in AI, China's Next Generation Artificial Intelligence Development Plan, and AI Made in Germany, all of which are aimed at driving economic and technological growth.

However,the key challenge to the global artificial intelligence market growth in the retail sector is theprivacy issues associated with AI deployment. By using advanced data mining techniques, data is gathered on several parameters such as the customer's buying habits, customers' online behavior, and payment information.

To know about other drivers & challenges along with market trends Request a sample now!

Company Profiles

The artificial intelligence (AI) market in the retail sector market is fragmented and the vendors are deploying growth strategies such aspricing and marketing strategies andproduct differentiationto compete in the market.

Story continues

Some of the companies covered in this report are Accenture Plc, Amazon.com Inc., BloomReach Inc., Capgemini SE, Daisy Intelligence Corp., Element AI Inc., Evolv Technology Solutions Inc., Inbenta Technologies Inc., Infosys Ltd., Intel Corp., International Business Machines Corp., Mad Street Den Inc., Microsoft Corp., NVIDIA Corp., Oracle Corp., Plexure Group Ltd., Salesforce.com Inc., SAP SE, Symphony Retail Solutions, and Trax Technology Solutions Pte. Ltd., etc.

To know about all major vendor offerings Click here for sample report!

Competitive Analysis

The competitive scenario provided in the artificial intelligence (AI) market in retail sector market report analyzes, evaluates, and positions companies based on various performance indicators. Some of the factors considered for this analysis include the financial performance of companies over the past few years, growth strategies, product innovations, new product launches, investments, growth in market share, etc.

Segmentation Analysis

By Application, the market is classified assales and marketing, in-store, PPP, and logistics management.

ByGeography, the market is classified as North America, APAC, Europe, the Middle East and Africa, and South America.

To know about the contribution of each segment - Request a sample report!

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Product Information Management Market-The product information management market share is expected to increase toUSD7.40 billion from 2021 to 2026,and the market's growth momentum will accelerate at a CAGR of 12.17%.Download a sample now!

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Artificial Intelligence (AI) Market Scopein Retail Sector

Report Coverage

Details

Page number

120

Base year

2021

Forecast period

2022-2026

Growth momentum & CAGR

Accelerate at a CAGR of 35.69%

Market growth 2022-2026

$ 29.57 billion

Market structure

Fragmented

YoY growth (%)

31.45

Regional analysis

North America, APAC, Europe, Middle East and Africa, and South America

Performing market contribution

North America at 40%

Key consumer countries

US, Canada, China, Japan, and UK

Competitive landscape

Leading companies, Competitive strategies, Consumer engagement scope

Key companies profiled

Accenture Plc, Amazon.com Inc., BloomReach Inc., Capgemini SE, Daisy Intelligence Corp., Element AI Inc., Evolv Technology Solutions Inc., Inbenta Technologies Inc., Infosys Ltd., Intel Corp., International Business Machines Corp., Mad Street Den Inc., Microsoft Corp., NVIDIA Corp., Oracle Corp., Plexure Group Ltd., Salesforce.com Inc., SAP SE, Symphony Retail Solutions, and Trax Technology Solutions Pte. Ltd.

Market dynamics

Parent market analysis, Market growth inducers and obstacles, Fast-growing and slow-growing segment analysis, COVID 19 impact and recovery analysis and future consumer dynamics, Market condition analysis for forecast period

Customization purview

If our report has not included the data that you are looking for, you can reach out to our analysts and get segments customized.

Table of Content

1 Executive Summary

2 Market Landscape

3 Market Sizing

4 Five Forces Analysis

5 Market Segmentation by Application

6 Customer Landscape

7 Geographic Landscape

8 Drivers, Challenges, and Trends

9 Vendor Landscape

10 Vendor Analysis

11 Appendix

About Us

Technavio is a leading global technology research and advisory company. Their research and analysis focus on emerging market trends and provides actionable insights to help businesses identify market opportunities and develop effective strategies to optimize their market positions. With over 500 specialized analysts, Technavio's report library consists of more than 17,000 reports and counting, covering 800 technologies, spanning across 50 countries. Their client base consists of enterprises of all sizes, including more than 100 Fortune 500 companies. This growing client base relies on Technavio's comprehensive coverage, extensive research, and actionable market insights to identify opportunities in existing and potential markets and assess their competitive positions within changing market scenarios.

Contact

Technavio ResearchJesse MaidaMedia & Marketing ExecutiveUS: +1 844 364 1100UK: +44 203 893 3200Email: media@technavio.comWebsite: http://www.technavio.com/

Technavio (PRNewsfoto/Technavio)

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CHANDLER, Ariz. Microchip Technology Inc. in Chandler, Ariz., is introducing the SAMA7G54 Arm Cortex A7-based microprocessor that runs as fast as 1 GHz for low-power stereo vision applications with accurate depth perception.

The SAMA7G54 includes a MIPI CSI-2 camera interface and a traditional parallel camera interface for high-performing yet low-power artificial intelligence (AI) solutions that can be deployed at the edge, where power consumption is at a premium.

AI solutions often require advanced imaging and audio capabilities which typically are found only on multi-core microprocessors that also consume much more power.

When coupled with Microchip's MCP16502 Power Management IC (PMIC), this microprocessor enables embedded designers to fine-tune their applications for best power consumption vs. performance, while also optimizing for low overall system cost.

Related: Embedded computing sensor and signal processing meets the SWaP test

The MCP16502 is supported by Microchip's mainline Linux distribution for the SAMA7G54, allowing for easy entry and exit from available low-power modes, as well as support for dynamic voltage and frequency scaling.

For audio applications, the device has audio features such as four I2S digital audio ports, an eight-microphone array interface, an S/PDIF transmitter and receiver, as well as a stereo four-channel audio sample rate converter. It has several microphone inputs for source localization for smart speaker or video conferencing systems.

The SAMA7G54 also integrates Arm TrustZone technology with secure boot, and secure key storage and cryptography with acceleration. The SAMA7G54-EK Evaluation Kit (CPN: EV21H18A) features connectors and expansion headers for easy customization and quick access to embedded features.

For more information contact Microchip online at http://www.microchipdirect.com.

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Arm Cortex microprocessor for artificial intelligence (AI), imaging, and audio introduced by Microchip - Military & Aerospace Electronics

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MONTREAL--(BUSINESS WIRE)--On July 19th, Behavox will host a conference to share the next generation of artificial intelligence in Compliance and Security with clients, regulators, and industry leaders.

The Behavox AI in Compliance and Security Conference will be held at the company HQ in Montreal. With this exclusive in-person conference, Behavox is relaunching its pre-COVID tradition of inviting customers, regulators, AI industry leaders, and partners to its Montreal HQ to deep dive into workshops and keynote speeches on compliance, security, and artificial intelligence.

Were extremely excited to relaunch our tradition of inviting clients to our offices in order to learn directly from the engineers and data scientists behind our groundbreaking innovations, said Chief Customer Intelligence Officer Fahreen Kurji. Attendees at the conference will get to enjoy keynote presentations as well as Innovation Paddocks where you can test drive our latest innovations and also spend time networking with other industry leaders and regulators.

Keynote presentations will cover:

The conference will also feature Innovation Paddocks where guests will be able to learn more from the engineers and data scientists behind Behavox innovations. At this conference, Behavox will demonstrate its revolutionary new product - Behavox Quantum. There will be test drives and numerous workshops covering everything from infrastructure for cloud orchestration to the AI engine at the core of Behavox Quantum.

Whats in it for participants?

Behavox Quantum has been rigorously tested and benchmarked against existing solutions in the market and it outperformed competition by at least 3,000x using new AI risk policies, providing a holistic security program to catch malicious, immoral, and illegal actors, eliminating fraud and protecting your digital headquarters.

Attendees at the July 19th conference will include C-suite executives from top global banks, financial institutions, and corporations with many prospects and clients sending entire delegations to the conference. Justin Trudeau, Canadian Prime Minister, will give the commencement speech at the conference in recognition/ celebration of the world leading AI innovations coming out of Canada.

This is a unique opportunity to test drive the product and meet the team behind the innovations as well as network with top industry professionals. Register here for the Behavox AI in Compliance and Security Conference.

About Behavox Ltd.

Behavox provides a suite of security products that help compliance, HR, and security teams protect their company and colleagues from business risks.

Through AI-powered analysis of all corporate communications, including email, instant messaging, voice, and video conferencing platforms, Behavox helps organizations identify illegal, immoral, and malicious behavior in the workplace.

Founded in 2014, Behavox is headquartered in Montreal and has offices in New York City, London, Seattle, Singapore, and Tokyo.

More information about the company is available at https://www.behavox.com/.

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Deep Dive Into Advanced AI and Machine Learning at The Behavox Artificial Intelligence in Compliance and Security Conference - Business Wire

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Explanation methods that help users understand and trust machine-learning models often describe how much certain features used in the model contribute to its prediction. For example, if a model predicts a patients risk of developing cardiac disease, a physician might want to know how strongly the patients heart rate data influences that prediction.

But if those features are so complex or convoluted that the user cant understand them, does the explanation method do any good?

MIT researchers are striving to improve the interpretability of features so decision makers will be more comfortable using the outputs of machine-learning models. Drawing on years of field work, they developed a taxonomy to help developers craft features that will be easier for their target audience to understand.

We found that out in the real world, even though we were using state-of-the-art ways of explaining machine-learning models, there is still a lot of confusion stemming from the features, not from the model itself, says Alexandra Zytek, an electrical engineering and computer science PhD student and lead author of a paper introducing the taxonomy.

To build the taxonomy, the researchers defined properties that make features interpretable for five types of users, from artificial intelligence experts to the people affected by a machine-learning models prediction. They also offer instructions for how model creators can transform features into formats that will be easier for a layperson to comprehend.

They hope their work will inspire model builders to consider using interpretable features from the beginning of the development process, rather than trying to work backward and focus on explainability after the fact.

MIT co-authors include Dongyu Liu, a postdoc; visiting professor Laure Berti-quille, research director at IRD; and senior author Kalyan Veeramachaneni, principal research scientist in the Laboratory for Information and Decision Systems (LIDS) and leader of the Data to AI group. They are joined by Ignacio Arnaldo, a principal data scientist at Corelight. The research is published in the June edition of the Association for Computing Machinery Special Interest Group on Knowledge Discovery and Data Minings peer-reviewed Explorations Newsletter.

Real-world lessons

Features are input variables that are fed to machine-learning models; they are usually drawn from the columns in a dataset. Data scientists typically select and handcraft features for the model, and they mainly focus on ensuring features are developed to improve model accuracy, not on whether a decision-maker can understand them, Veeramachaneni explains.

For several years, he and his team have worked with decision makers to identify machine-learning usability challenges. These domain experts, most of whom lack machine-learning knowledge, often dont trust models because they dont understand the features that influence predictions.

For one project, they partnered with clinicians in a hospital ICU who used machine learning to predict the risk a patient will face complications after cardiac surgery. Some features were presented as aggregated values, like the trend of a patients heart rate over time. While features coded this way were model ready (the model could process the data), clinicians didnt understand how they were computed. They would rather see how these aggregated features relate to original values, so they could identify anomalies in a patients heart rate, Liu says.

By contrast, a group of learning scientists preferred features that were aggregated. Instead of having a feature like number of posts a student made on discussion forums they would rather have related features grouped together and labeled with terms they understood, like participation.

With interpretability, one size doesnt fit all. When you go from area to area, there are different needs. And interpretability itself has many levels, Veeramachaneni says.

The idea that one size doesnt fit all is key to the researchers taxonomy. They define properties that can make features more or less interpretable for different decision makers and outline which properties are likely most important to specific users.

For instance, machine-learning developers might focus on having features that are compatible with the model and predictive, meaning they are expected to improve the models performance.

On the other hand, decision makers with no machine-learning experience might be better served by features that are human-worded, meaning they are described in a way that is natural for users, and understandable, meaning they refer to real-world metrics users can reason about.

The taxonomy says, if you are making interpretable features, to what level are they interpretable? You may not need all levels, depending on the type of domain experts you are working with, Zytek says.

Putting interpretability first

The researchers also outline feature engineering techniques a developer can employ to make features more interpretable for a specific audience.

Feature engineering is a process in which data scientists transform data into a format machine-learning models can process, using techniques like aggregating data or normalizing values. Most models also cant process categorical data unless they are converted to a numerical code. These transformations are often nearly impossible for laypeople to unpack.

Creating interpretable features might involve undoing some of that encoding, Zytek says. For instance, a common feature engineering technique organizes spans of data so they all contain the same number of years. To make these features more interpretable, one could group age ranges using human terms, like infant, toddler, child, and teen. Or rather than using a transformed feature like average pulse rate, an interpretable feature might simply be the actual pulse rate data, Liu adds.

In a lot of domains, the tradeoff between interpretable features and model accuracy is actually very small. When we were working with child welfare screeners, for example, we retrained the model using only features that met our definitions for interpretability, and the performance decrease was almost negligible, Zytek says.

Building off this work, the researchers are developing a system that enables a model developer to handle complicated feature transformations in a more efficient manner, to create human-centered explanations for machine-learning models. This new system will also convert algorithms designed to explain model-ready datasets into formats that can be understood by decision makers.

Originally posted here:
Building explainability into the components of machine-learning models - MIT News

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