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Artificial intelligence (AI) used to be a fantasy, found only in science fiction. Today, it propels society forward in countless ways; even the phones in our pockets include multilingual translators, photo apps that recognize faces, and intelligent assistants that can understand spoken commands (thanks, Siri).

This is all made possible through the process of deep learning and Dr. Pablo Rivas, an assistant professor of computer science at Baylor, has literally written the book on the subject. (Were serious; check out Deep Learning for Beginners.)

I first fell in love with the field of AI and the principles that can explain human intelligence 20 years ago, when it was all beginning, says Rivas. Now, the industry is booming. And while the advances are incredible, they can also be a little disarming.

For example, AI has made it possible for individuals to receive personalized recommendations on products and services. This is concerning for those who feel devices are always listening. With that in mind, Rivas has been working with the IEEE Standards Association to study and design ethical practices for AI. Here, Rivas hopes to ensure consumers theyre being treated fairly.

AI is definitely changing society, and therefore we have to care for its responsible study and implementation, he explains.

[LEARN MORE about Rivas research in this Baylor Connections interview]

When it comes to teaching, Rivas recognizes the competitive nature of the industry and seeks to cultivate a collaborative, encouraging learning structure.

Unfortunately, there is a toxic culture among students and researchers in AI, and I believe our best work cannot flourish that way, says Rivas. I personally and intentionally make an effort to mentor students beyond simply directing their immediate research projects and help them ponder and brainstorm long-term plans that can benefit their careers. This has proven to be a stress-free exchange of ideas and knowledge in an environment that shows compassion for students.

Rivas and his colleagues have several projects in the works. Recently, theyve learned the National Science Foundation (NSF) will fund one of their studies using machine learning algorithms to observe different species spectral signatures and properties. In another venture, they are exploring quantum machine learning. Whatever the project is, Rivas has one goal: to make technology smarter and safer for all.

Sic em, Dr. Rivas!

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Meet Baylor's expert on artificial intelligence and deep learning - Baylor University

A team of researchers in Taiwan has developed software driven by machine learning to accurately identify specimensboth soldiers and workersof four different termite species. Their goal is to enable smartphone app termite ID for pest management pros and adapt the softwares use to additional species. Shown here is the setup used to capture images of termites via smartphone for training the identification program. (Image originally published in Huang et al 2021, Journal of Economic Entomology)

By Ed Ricciuti

Ed Ricciuti

In the not-too-distant future, a pest control technician prepares to deal with a termite infestation. His first order of business is to identify the species of termite, essential for effective management but not always an easy job nowadays because these little wood chewers are notoriously difficult to sort. For the tech of the future, however, its a snap. He pulls out his smart phone, clicks off a few pictures, and a souped-up version of machine learning fires back with an identification of the termite species at hand.

The scenario above is what a team of scientists in Taiwan hope will result from research, published in August in the Journal of Economic Entomology, that demonstrates how deep learning, in which a computer model identifies termite species, can be adapted for use via smartphone for pest control professionals and even homeowners. The system relies on what are called deep convolutional neural networks (DCNNs or CNNs) to extract and differentiate patterns within images and automatically identify termite species.

A team of researchers in Taiwan has developed software driven by machine learning to accurately identify specimensboth soldiers and workersof four different termite species. Their goal is to enable smartphone app termite ID for pest management pros and adapt the softwares use to additional species. Shown here are the four species, with both soldier and worker castes, used for training the identification program. (Image originally published in Huang et al 2021, Journal of Economic Entomology)

Says Jia-Hsin Huang, Ph.D., of the Institute of Information Science, Academia Sinica, in Taipei, Taiwan, The termite-CNN model developed in the paper opens a window to introduce the effective machine-learning algorithms to termite pest control.

The hardware and software comprising an artificial neural network is modeled after the way real neurons are arranged in the human brain. DCNNs mimic the processes in the cortex of the front lobe of the human brain that process angles, lines, and other features that combine to produce an image. Each artificial neuron handles a piece of the picture, so to speak, connecting with its counterparts to produce the full image captured by the eye. The algorithms used in deep learning systems can determine which features are associated with different species. The deep learning process depends on a classifier, an algorithm that the computer model uses to learn from data over which it has been trained, in this case for mobile applications. Once trained, the model can handle new data given to it.

A great number of images are required to train a model to distinguish different species. The Taiwan team employed smartphones to take 18,000 images of four termite pest species. Each image contained several termites so that, in the end, 24,000 individual images were used in development of the computer model. Senior author on the study Hou-Feng Li, Ph.D., of the National Chung Hsing University says, The dataset is a group of photos, and each one [is] associated with a species name. In this study, we focused on the pest species in Taiwan, and hence we only used the commonly encountered four pests to train the model. It is easy to understand, for application, not many people need a software/model/app to identify all termite pests around the world.

The termite researchers built their model on a framework, MobileNetV2, small enough to be used with smartphones. Describing the reasons for choosing that particular framework, Huang says, MobileNet is designed for mobile devices. We could expect the future application of this deep learning model installed in the mobile phones to be used by the pest management officers and ordinary citizens in the real world. The architecture of MobileNet is developed by Google, and the model is open-source. Anyone could implement this model freely and train their own prediction models. Because the parameters of the deep learning models are huge and usually difficult to fine-tune with small batches of images, we have released our model parameters for future studies. As a result, any pest control professionals or laymen could use our trained parameters as initial values to train their own classification models of other pest species. It will reduce much efforts to train a new classification model and improve the performance in prediction.

Deep learning is a natural for taxonomy and increasingly has been deployed to sort insects, but the research by the team from Taiwan is the first to do so with termites. Most classification of termites is based on their soldier or worker castes, as was done with this effort, because they are the castes most often encountered in the field. Even within a species, however, morphology and development of individuals can vary, influenced by factors such as age and environmental conditions. Reproductive adults, moreover, are often difficult to match with soldiers and workers. Termites from one geographic area, as well, may look different from their conspecifics elsewhere. There are, however, other clues to a termites identity than just its physical appearance. Behavior can be one. Two species used in the study, Coptotermes formosanus and Odontotermes formosanus, look alike, but soldiers of the former defend against ants while workers take on the foe for the latter.

Hou-Feng Li, Ph.D., professor of entomology at the National Chung Hsing University in Taichung City, Taiwan, is part of a team of researchers that has developed software driven by machine learning to accurately identify specimensboth soldiers and workersof four different termite species. (Photo courtesy of Hou-Feng Li, Ph.D.)

Jia-Hsin Huang, Ph.D. (left), and Huai-Kuang Tsai, Ph.D., of the Institute of Information Science, Academia Sinica, in Taipei, Taiwan, are part of a team of researchers that has developed software driven by machine learning to accurately identify specimensboth soldiers and workersof four different termite species. (Photo courtesy of Jia-Hsin Huang, Ph.D.)

There is no one-shot-cures-all for termite control, but it is not necessary to identify species. Control is tailored to the three different main groups of termites that are considered pests, determined by behavior and ecology: subterranean termites, drywood termites, and gardening and agriculture (or dampwood) termites.

To control subterranean termites in the urban environment, baiting and remedial treatment with liquid pesticide injections are the two most commonly used tools. However, current baiting tools are inadequate for controlling drywood termites. However, says Li, to deliver the knowledge and options of control methods of a specific pest to a homeowner or farmer is still very difficult. The bottleneck is correct and efficient pest identification.

Describing their objectives, the researchers write, Our main contributions include the curation of numerous smartphone-based images of termite pests and the successful implementation of deep learning models for termite classification. Our ultimate goal is providing the termite image classifiers for installing in regular smartphones under commercial operation systems. We also have released the full model trained on our complete dataset so other researchers might benefit by the pre-trained parameters to develop sophisticated models including other termite species or different CNN architectures in mobile devices. The immediate identification result will lead the user to biological information of the termite pest, control options, control products, and even professional pest management service in reachable distance. The high identification efficiency and accuracy will improve the integrated pest management of termites.

Ed Ricciutiis a journalist, author, and naturalist who has been writing for more than a half century. His latest book is calledBears in the Backyard: Big Animals, Sprawling Suburbs, and the New Urban Jungle (Countryman Press, June 2014).His assignments have taken him around the world. He specializes in nature, science, conservation issues, and law enforcement. A former curator at the New York Zoological Society, and now at the Wildlife Conservation Society, he may be the only man ever bitten by a coatimundi on Manhattans 57th Street.

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What's That Termite? Artificial Intelligence Might Have the Answer - Entomology Today

Quantiphi recognized in the IDC MarketScape: Worldwide Artificial Intelligence IT Services 2021 Vendor Assessment as a leader, an evaluation based on a comprehensive framework and a set of parameters expected to be most conducive to success in providing AI IT services in both the short term and the long term.

MARLBOROUGH, Mass., Sept. 28, 2021 /PRNewswire-PRWeb/ -- Quantiphi, an AI-first digital engineering company, today announced that it has been named a Leader in the IDC MarketScape: Worldwide Artificial Intelligence IT Services 2021 report. Quantiphi is one among the companies recognized for its AI strategies in the report, which covers a selection of vendors that are most conducive to success in providing AI services in both the short term and the long term

"As AI moves from a 'nice to have' capability to an essential component of the future enterprise, customers need partners with expertise in developing production-grade AI solutions and establishing the right organization, platform, governance, business process, and talent strategies to ensure sustainable AI adoption at scale," says Jennifer Hamel, research manager, Analytics and Intelligent Automation Services at IDC.

The report includes the perception of AI services buyers of both the key characteristics and the capabilities of these providers in the worldwide AI services market. According to the report, "Buyers rated Quantiphi highly for its ability to showcase and co-develop relevant use cases for AI solutions, deliver explainable and trustworthy AI decisions and outcomes, deliver the right value for fee paid, work with hardware and software product partners, and provide appropriate and high-quality resources for an engagement." The IDC MarketScape report also acknowledges Quantiphi's strategies around marketing, alliances, innovation and R&D, increasing revenue per employee, and employee retention.

"At Quantiphi we continue to reinforce our commitment towards democratizing data and AI led innovation. We are proud to be named as a Leader in the IDC MarketScape for AI IT services worldwide," said Asif Hasan, Co-Founder, Quantiphi. "This recognition validates our approach to building AI-First Digital Engineering solutions. With a broad catalog of industry-focused production grade AI-solutions, we continue to empower the innovation agenda within the enterprise."

Story continues

According to IDC analysis and buyer perception, Quantiphi is positioned in the Leader category in this 2021 IDC MarketScape for worldwide AI IT services. Quantiphi is an AI-first digital transformation engineering company that draws upon its portfolio of repeatable IP and accelerators and partnerships with major AI technology providers such as Google, AWS, and NVIDIA to assemble and scale AI solutions for clients in a variety of industries. Leveraging a talent pool of industry analysts, cloud/data engineers, and ML engineers, the company's engagement model takes clients from ideation (hack it) to pilot (prove it) to production applications (nail it) to a factory-based model for AI solutions (scale it).

Quantiphi aligns its solutions with the customer needs and offers the right combination of support and services, enabling customers to effectively transform their businesses with AI. Quantiphi has created an industrialized, IP-driven approach for building customized AI solutions using a repository of microagents that perform specialized tasks. Qognition.AI, Quantiphi's orchestration platform for MLOps, provides access to the repository as well as tools to manage the process of assembling, training, validating, and monitoring AI solutions at scale.

To read an excerpt of the report, click here.

About IDC MarketScape

About IDC MarketScape: IDC MarketScape vendor assessment model is designed to provide an overview of the competitive fitness of ICT (information and communications technology) suppliers in a given market. The research methodology utilizes a rigorous scoring methodology based on both qualitative and quantitative criteria that results in a single graphical illustration of each vendor's position within a given market. IDC MarketScape provides a clear framework in which the product and service offerings, capabilities and strategies, and current and future market success factors of IT and telecommunications vendors can be meaningfully compared. The framework also provides technology buyers with a 360-degree assessment of the strengths and weaknesses of current and prospective vendors.

About Quantiphi

Quantiphi is an award-winning AI-first digital transformation engineering company driven by the desire to solve transformational problems at the heart of business. Quantiphi solves the toughest and complex business problems by combining deep industry experience, disciplined cloud and data engineering practices, and cutting-edge artificial intelligence research to achieve quantifiable business impact at unprecedented speed. We are passionate about our customers and obsessed with problem-solving to make products smarter, customer experiences frictionless, processes autonomous and businesses safer by detecting risks, threats and anomalies. For more on Quantiphi's capabilities, visit http://www.quantiphi.com.

Media Contact

Jhon Alexander, Quantiphi, 8044849813, jhon.alexander@quantiphi.com

SOURCE Quantiphi

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Quantiphi Named a Leader in the IDC MarketScape Worldwide Artificial Intelligence IT Services, 2021 - Yahoo Finance

Artificial intelligence was once just a buzzword, but today its impact on our daily lives is far greater than ever before.

Let us imagine that you have decided to find an online casino available in your area with great bonuses. You type a query into Google and discover Tonybet. Why were we given this answer? Because the intelligence of the system is artificial intelligence, which makes decisions to improve your user experience based on your preferences and your online behavior.

Artificial Intelligence has a huge impact on many other industries as well.

In medicine, search, languages, automobiles, and, of course, advertising, artificial intelligence has driven innovation. Heres how we assess the impact of artificial intelligence on our daily lives.

In this sphere, AI memory is valued, as well as the ability to generate and compare vast amounts of information.

For several years now, everyone has heard about IBM Watson and DeepMind Health (a Google development) smart assistants that not only give advice to doctors but also figure out the genetic predisposition to pathologies. For example, IBM Watson already identifies and develops therapy plans for 13 types of malignant neoplasms: from cervical cancer to colon cancer.

Artificial intelligence even comes to the aid of patients. Telemedicine applications that collect data from fitness bracelets and other sensors, as well as questionnaires that establish the exact symptoms and diseases of patients, are becoming increasingly popular. For example, AI can recognize tuberculosis and disorders of internal organs, including the brain.

Some of the apps parse human speech and respond verbally, while others prefer written communication. The apps get the necessary information and then make recommendations on what to do next, or send the data to a therapist. The most popular intelligent assistants are Yours.MD and Ada, which can be downloaded from the App Store or Google Play.

Of particular importance are systems that can develop new medicines. According to Pfizers top manager, it takes an average of 12 years to develop and bring a new medicine to market. AI will make it possible to create the molecular structure and simulate the drug, which will increase its quality and reduce the time it takes to produce new drugs. Atomwise and Berg Health are pioneering supercomputers that solve this problem.

Large industrial companies in countries such as Japan, China, the US, Germany, and Switzerland are investing in new technologies. The trend today is toward fewer jobs involving intellectual labor and more computers.

Such jobs will suffer in the coming decades:

Robotization soon will also affect such professions as secretaries, cashiers, truck drivers, and waiters. An example of successful AI implementation was the H&H line plant. The technology, which tracks the gaze of workers, helped save 400 hours of training for trainees in 1 year and reduce the likelihood of accidents.

MIT Technology Review reported that Andrew Eun, a robotics and machine learning researcher, is developing a new project called Landing.AI. It aims to set up a manufacturing mechanism in factories and plants. His first partner is Foxconn, which manufactures Apple gadgets.

Soon, the field of education will be developing rapidly in two directions adaptive learning and proctoring.

Adaptive learning is designed to solve the problem of different performances of pupils and students. The fact is that one person learns material much faster and more successfully than another. Therefore, AI will monitor the level of knowledge of the student and adapt the order of blocks of courses to his abilities or inform the teacher how well the student has mastered the material. An example of such a system could be the Third Space Learning platform, which is currently under development.

Proctoring represents the control of pupils and students during the passing of control and examination tests. Whereas in the past, students were in the crosshairs of a webcam, now AI comes to the rescue. It monitors how often a student takes his or her eyes off the computer screen, whether he or she changes tabs on a browser, and whether there are no extra voices in the room. As soon as the AI notices an irregularity, it immediately alerts the human lecturer.

But can the machine replace an ordinary teacher? Rosa Lukin, a professor at University College London, denies it. It is worth finding a compromise, she says. After all, the goal is not to replace teachers with machines, but to improve education. The human teacher is certainly indispensable here.

The opinion that farming and livestock are backward and old-fashioned industries is a thing of the past. Today, the intensive growth of the global AI market in the agricultural industry is caused by the following factors: introduction of the data management system, automation of irrigation, increase in productivity of crops through the introduction of learning methods and increase in the number of people on the planet. At the same time, the growth of the AI market is limited by the high cost of collecting information about agricultural lands.

The widespread introduction of robotics in agriculture is represented by such developments:

Energias Market Research predicts that the AI market in the agricultural industry will grow by 24.3% by 2024. It will actively develop in the U.S. and the Asia-Pacific region. Agworld, Farmlogs, Cropx, Microsoft, AGCO, and others are among the central players in the smart agribusiness market.

The goal of implementing AI in this area is to fight traffic jams. Such systems are already working successfully in major cities in Europe, North America, and Asia.

Collecting information from traffic lights, analysis of traffic density, traffic accidents, weather data, and other factors that create traffic jams these are the functions of the computer. As a result, the intelligent system monitors the roads online, predicts what the traffic will be, and according to this, switches the traffic lights.

It monitors not only the traffic on the road, but also helps the drivers. For example, if necessary, the system calls for a tow truck. This solution will not be able to get rid of traffic jams completely, but it is quite possible to speed up the traffic by several times.

Probably, progress will be notified if we see widespread use of unmanned cars, which are vehicles that can move without human intervention. They are being developed by Google, AKTIV, Tesla Motors, and some others.

Of course, everyone has heard of the smart house, which in the future will be a typical example of AI. The largest manufacturers are Yamaha, Siemens, Abb, Beckhoff, and Legrand.

Such developments simplify human life to the maximum. For example, such a system will open the curtains in the morning, wake up the owners and make coffee. In the future, the functionality of the smart house will be expanded up to the fact that the closet will automatically steam clothes and the refrigerator will order food. Such a solution will optimize costs related to power, ventilation, heating, adjusting to a convenient schedule.

Vacuum cleaners that can not only do the cleaning but also move objects and charge themselves remain popular as well.

Another example of a domestic AI application is automated translators. While in the past machine translation left much to be desired, today the situation has changed dramatically. Google Translate demonstrates this: the algorithm is based on the fact that the computer perceives not individual words, but a complete sentence. It allows to obtain high-quality text, so soon, this method will become the basis for automatic translation.

Human-like androids are used not only for household chores but also for communication. Iron friend will not let you die of boredom, and sometimes it becomes a full member of the family. So, one lucky man in China managed to marry a robot. He turned out to be engineer Zheng Jiajia, who made his own bride.

Undoubtedly, the future of humanity is intertwined with robots, because every year more and more applications of artificial intelligence are developed. Most likely, it will surpass the abilities of humans, but at the same time greatly improve the quality of their lives. The main thing here is to find reasonable limits before AI learns to reproduce itself. According to Elon Musk, it is worth taking a proactive stance and already now limiting the use of AI, at least in the military industry.

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The Future Is Already Here: Areas Where Artificial Intelligence Is Already in Use and This Is Just the Beginning - Analytics Insight