Category Archives: Artificial Intelligence
Google is using artificial intelligence to make information more useful – TechSpot
In brief: This week, Google unveiled plans for a way to search that combines images and text to give more context to search queries. The method can use a smartphone's camera in combination with AI, attempting to intuitively refine and expand search results.
At its Search On event this week, Google revealed details about how it plans to use a technology it calls Multitask Unified Model (MUM), which should intelligently figure out what a user is searching for based on images and text, as well as give users more ways to search for things.
While Google didn't give a specific date, its blog post stated the feature should roll out "in the coming months." Users will be able to point at something with a phone camera, tap an icon which Google calls Lens, and ask Google something related to what they're looking at. The blog post theorizes scenarios like taking a picture of a bicycle part you don't know the name of and asking Google how to fix it, or taking a picture of a pattern and trying to find socks with the same pattern.
Google initially introduced MUM back in May where it theorized more scenarios in which the AI might help expand and refine searches. If a user asks about climbing Mt. Fuji for instance, MUM might bring up results with information about the weather, what gear one might need, the mountain's height, and so-on.
A user should also be able to use MUM to take a picture of a piece of equipment or clothing and ask if it's suitable for climbing Mt. Fuji. MUM should additionally be able to deliver information it learns from sources in many different languages other than the one the user searched in.
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Google is using artificial intelligence to make information more useful - TechSpot
This is what companies can do to build more inclusive AI – World Economic Forum
Conversations around responsible artificial intelligence (AI) are heating up as the ethical implications of its use are increasingly felt in our daily lives and society. With AI influencing life-changing decisions around mortgage loans, healthcare, parole and more, an ethical approach to AI development isnt just a nice-to-have its a requirement.
In theory, companies want to produce AI thats inclusive, responsible and ethical both in service of their customers and to maintain their brand reputation; in practice, they often struggle with the specifics.
Creating AI thats inclusive requires a full shift in mindset throughout the entirety of the development process. It involves considering the full weight of every crucial decision in the build process. At a minimum, a full revamp in strategies around data, the AI model (programmes that represent the rules, numbers and any other algorithm-specific data structures required to make predictions for a specific task) and beyond will be needed.
Its the responsibility of the people who build AI solutions to ensure that their AI is inclusive and provides a net-positive benefit to society. To accomplish this, there are several essential steps to take during the AI life cycle:
1. Data: At the data stage, organizations collect, clean, annotate, and validate data for their machine learning models. At this phase of the AI life cycle theres maximum opportunity to incorporate an inclusive approach, as the data serves as the foundation of the model. Here are two factors to consider:
Without representative data, you cant hope to create an inclusive product. Spend the majority of the time on your project making sure youve got the data right or partner with an external data provider who can ensure the data is representative of the group for whom your model is built.
The most lucrative use cases of AI until 2025
Image: Statista
2. Model: While perhaps less weighty than the data element, there are critical opportunities during the building of the model through which to incorporate inclusive practices.
Strategizing and delivering on the right objectives (for instance, a KPI that measures bias) will take you a long way toward building a responsible end product.
3. Post-deployment: Some teams feel their work is mostly done after they deploy their model, but the opposite is true: this is only the beginning of the models life cycle. Models need significant maintenance and retraining to stay at the same performance level and this cant be an afterthought: letting performance dip could have serious ethical implications under certain use cases. Incorporate the following best practices as part of your post-deployment infrastructure:
The above isnt an exact blueprint, but offers a starting point for transitioning inclusive AI from a theoretical discussion to an action plan for your organization. If you approach AI creation with an inclusive lens, youll ideally find many additional steps to take throughout the development life cycle. Its a mission-critical endeavour: for AI to work well, it needs to work well for everyone.
Written by
Mark Brayan , Chief Executive Officer, Appen
The views expressed in this article are those of the author alone and not the World Economic Forum.
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This is what companies can do to build more inclusive AI - World Economic Forum
Meet Baylor’s expert on artificial intelligence and deep learning – Baylor University
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
The United States must lead the way on artificial intelligence standards | TheHill – The Hill
Nearly three years ago, Chinese President Xi Jinpingannouncedbefore the Chinese Communist Partys policy-making elite that artificial intelligence is a vital driving force for a new round of technological revolution and industrial transformation, and accelerating AI development is a strategic issue to decide whether we can grasp opportunities. This statement set in motion a plan that was announced just a year earlier, wherein the CCP laid out itsvisionto become the world leader in AI by 2030. As with most sectors in which the CCP is interested, the goal is to dominate this all-important field and supplant the U.S. en route to creating a domestic industry worth billions of dollars.
The CCPs interest in AI is understandable. This burgeoning technological field has the potential to revolutionize society as a whole, and Beijing wants to ensure it does not lag behind its American and European rivals as it did in the earliest days of the computer revolution. Furthermore, potential applications for AI technology include everything from agricultural development to weapons. For China specifically, AI has the potential to bolster its massive domestic surveillance system and internet censorship efforts. Put simply, AI could be the CCPs key to finally surpassing the U.S. and maintaining its iron grip on its own people.
As is the case with any budding industry, those who get in first will set the standard for everyone else. Thanks to our economic prowess and technological innovation, it has been American companies like Apple and Microsoft that have blazed the trail in technological standards for the rest of the world since the earliest days of the personal computer. Our track record of establishing global standards has been beneficial to both domestic and international economic development. American innovators have generally seen technological advancement as a rising tide that lifts all boats, while the CCP has made clear in its past behavior that only those who bend the knee to Beijing will share in its bounty.
In order to continue our impressive track record in setting global technological standards, it is important we ensure American small and medium-sized businesses have a seat at the table. Many of most remarkable AI technological breakthroughs are being developed by smaller innovators who simply do not have the financial resources of a CCP-backed megacorporation. Accordingly, we are proud to haveintroducedthe Leadership in Global Tech Standards Act of 2021, legislation that would provide small businesses throughout the country with the financial backing they need to participate in setting global AI standards. This bipartisan legislation, which is also being co-sponsored by Reps. Jason CrowJason CrowOvernight Defense & National Security Presented by AM General Afghan evacuation still frustrates Bipartisan momentum builds for war on terror memorial Democrats face full legislative plate and rising tensions MORE (D-Colo.) and Jerry McNerneyGerlad (Jerry) Mark McNerneyHouse passes host of bills to strengthen cybersecurity in wake of attacks In defense of misinformation House Democrats want to silence opposing views, not 'fake news' MORE (D-Calif.), will help increase U.S. global competitiveness and prevent the CCP from dominating the AI space.
Our bill is a direct response to the National Security Commission on AIs finalreportto Congress released earlier this year. Included in the report was a recommendation to establish a grant program for small and medium-sized American AI companies that will help them conduct relevant research, develop requisite skills and expertise, prepare standards proposals, and attend technical standards setting meetings. Sens. Rob PortmanRobert (Rob) Jones PortmanHillicon Valley Presented by Ericsson Bill would give some groups 24 hours to report ransomware payments Senators roll out bill giving organizations 24 hours to report ransomware attack payments House passes bill to end crack and powder cocaine sentencing disparity MORE (R-Ohio) and Gary PetersGary PetersHillicon Valley Presented by Ericsson Bill would give some groups 24 hours to report ransomware payments Senators roll out bill giving organizations 24 hours to report ransomware attack payments Hillicon Valley Presented by Xerox Officials want action on cyberattacks MORE (D-Mich.) were the first to offer a bill creating such a grant program. Our bill builds on that legislation by including certain safeguards and requirements that will help ensure the money is well-spent.
While we are proud to champion this bill, we recognize our country is quickly running out of time to counter Chinas influence in setting AI standards. According to the National Institute of Standards and Technology (NIST), China is outpacing the U.S. in setting standards for certain areas of telecommunication. China has also repeatedly stated the importance of setting AI standards now in order to expand its domestic market in this space:
Artificial intelligence industry competitiveness develops into the international first phalanx. We shall make the initial establishment of artificial intelligence technology standards, service system and industrial ecological chain, cultivate a number of the world's leading artificial intelligence backbone enterprises, make the scale of artificial intelligence core industry more than 150 billion yuan, and drive the scale of related industries more than 1 trillion yuan, the Chinese State Councilwrotein its 2017 AI development plan.
AI is a powerful tool with the potential to progress humankinds technological journey or suppress billions under the thumb of the CCP. Whoever sets this technologys standards will determine the course it takes in the future. America must be at the forefront, just as it always has been.
Scott Franklin represents Floridas 15th District and Jay Obernolte represents Californias 8th District and is ranking member of the Science Subcommittee on Investigations and Oversight.
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The United States must lead the way on artificial intelligence standards | TheHill - The Hill
What’s That Termite? Artificial Intelligence Might Have the Answer – Entomology Today
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 Named a Leader in the IDC MarketScape Worldwide Artificial Intelligence IT Services, 2021 – Yahoo Finance
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."
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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
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Quantiphi Named a Leader in the IDC MarketScape Worldwide Artificial Intelligence IT Services, 2021 - Yahoo Finance
CHINA: THE BRAINS BEHIND ADVANCES IN ARTIFICIAL INTELLIGENCE – Asia Media International
NATALIA FALCHI WRITES Chinas technological advancement and dedication to innovative Artificial Intelligence systems is growing rapidly and robustly. Not only does this affect China socially, economically, and politically, but it affects the entire globe.
In 2017, Chinas ambition to become more advanced in the realm of Artificial Intelligence intensified dramatically. The State Council of the Peoples Republic of China (also known as the Central Peoples Government) published a New Generation AI Development Plan (here you can find the original document translated to English). Essentially, this document set up a multifaceted strategy to meet the government and societys technological development goals. Their primary goal was to develop the same level of Artificial Intelligence capacity as competing nations, including the United States. But the first goal included advancing the AI industry to be worth at least 150 billion RMB (Ren Min Bi) by 2020.
The documents second goal was to become the dominant country in Artificial Intelligence breakthroughs and discoveries, and to grow the AI industry value to at least 400 billion RMB (Ren Min Bi) by 2025. Lastly, the document presented its goal of becoming the most significant AI superpower in the world, with a worth of at least 1000 billion RMB (Ren Min Bi) by the year 2030.
These goals, laid out in the New Generation AI Development Plan in 2017, have served as a catalyst for further investment in AI technologies and have gained the attention of other contesting nations, including the United States.
What does this mean for jobs and our economy? We humans can easily be replaced by robotic technology and artificial intelligence that do not require health benefits, salaries, HR departments, and other social and employee services and costs.
One may well be excited by technological breakthroughs and innovations concerning Artificial Intelligence, and the ways in which that could improve industry sectors productivity and innovation. On the contrary, one could also be horrified by the jobs, industries, and societies that Artificial Intelligence could radically change if not destroy. How will the world adapt to such major changes in the near future- and to Chinas lead in the AI industry? Who will win, and who will lose?
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CHINA: THE BRAINS BEHIND ADVANCES IN ARTIFICIAL INTELLIGENCE - Asia Media International
Artificial intelligence could help to predict the next virus to jump from animals to humans – BBC Science Focus Magazine
Reader Q&A:How do viruses jump from animals to humans?
Every animal species hosts unique viruses that have specifically adapted to infect it. Over time, some of these have jumped to humans these are known as zoonotic viruses.
As our populations grow, we move into wilder areas, which brings us into more frequent contact with animals we dont normally have contact with. Viruses can jump from animals to humans in the same way that they can pass between humans, through close contact with body fluids like mucus, blood, faeces or urine.
Because every virus has evolved to target a particular species, its rare for a virus to be able to jump to another species. When this does happen, its by chance, and it usually requires a large amount of contact with the virus.
Initially, the virus is usually not well-suited to the new host and doesnt spread easily. Over time, however, it can evolve in the new host to produce variants that are better adapted.
When viruses jump to a new host, a process called zoonosis, they often cause more severe disease. This is because viruses and their initial hosts have evolved together, and so the species has had time to build up resistance. A new host species, on the other hand, might not have evolved the ability to tackle the virus. For example, when we come into contact with bats and their viruses, we may develop rabies orEbola virus disease, while the bats themselves are less affected.
Its likely that bats were the original source of three recently emerged coronaviruses: SARS-CoV (2003), MERS-CoV (2012) and SARS-CoV-2, the cause of the2019-20 coronavirus outbreak. All of these jumped from bats to humans via an intermediate animal; in the case of SARS-CoV-2, this may have been pangolins, but more research is needed.
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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
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
Artificial Intelligence in Automotive Claims on Fast Track During Pandemic – Autobody News
The use of artificial intelligence (AI) in our daily lives was predicted in Hollywood movies decades ago and began to come true with Siri, Alexa and smartphones.
According to a white paper released recently by Mitchell International, parent company of NAGS, artificial intelligence use in automotive claims is growing fast as a result of the COVID-19 pandemic, which made a transition to digital essential to decrease the spread of the virus from human to human.
As insurers embrace AI and its ability to improve the claims process, they are devoting a larger portion of their technology budgets to AI-enabled solutions. In fact, according to one report, 87% of carriers are now spending in excess of $5 million annually on these technologies, which is more than in the banking and retail sectors, Mitchell reported.
Although new to the auto insurance industry, the science behind AI has existed for more than 50 years. Conceived in 1956, when President Dwight D. Eisenhower authorized construction of the nations interstate highway system, it is uncertain if AI pioneer John McCarthy imagined a future where AI would drive vehicles down Eisenhowers highway system.
Interest in AI grew until the 1980s when scientists moved from hard-coded algorithms to machine learning: what would make it possible for AI to generate predictions based on data and learned experiences.
By 2012, according to Mitchell, deep-learning algorithms powered Google Street View, Apples Siri and other applications.
With the new wave of deep learning techniques, such as convolutional neural networks, AI has the potential to live up to its promise of mimicking the perception, reasoning, learning and problem solving of the human mind. In this evolution, insurance will shift from...
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Artificial Intelligence in Automotive Claims on Fast Track During Pandemic - Autobody News