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The Evolutionary Perspective
Monthly Archives: June 2022
Posted: June 30, 2022 at 9:52 pm
In 2019, guards on the borders of Greece, Hungary, and Latvia began testing an artificial-intelligence-powered lie detector. The system, called iBorderCtrl, analyzed facial movements to attempt to spot signs a person was lying to a border agent. The trial was propelled by nearly $5 million in European Union research funding, and almost 20 years of research at Manchester Metropolitan University, in the UK.
The trial sparked controversy. Polygraphs and other technologies built to detect lies from physical attributes have been widely declared unreliable by psychologists. Soon, errors were reported from iBorderCtrl, too. Media reports indicated that its lie-prediction algorithm didnt work, and the projects own website acknowledged that the technology may imply risks for fundamental human rights.
This month, Silent Talker, a company spun out of Manchester Met that made the technology underlying iBorderCtrl, dissolved. But thats not the end of the story. Lawyers, activists, and lawmakers are pushing for a European Union law to regulate AI, which would ban systems that claim to detect human deception in migrationciting iBorderCtrl as an example of what can go wrong. Former Silent Talker executives could not be reached for comment.
A ban on AI lie detectors at borders is one of thousands of amendments to the AI Act being considered by officials from EU nations and members of the European Parliament. The legislation is intended to protect EU citizens fundamental rights, like the right to live free from discrimination or to declare asylum. It labels some use cases of AI high-risk, some low-risk, and slaps an outright ban on others. Those lobbying to change the AI Act include human rights groups, trade unions, and companies like Google and Microsoft, which want the AI Act to draw a distinction between those who make general-purpose AI systems, and those who deploy them for specific uses.
Last month, advocacy groups including European Digital Rights and the Platform for International Cooperation on Undocumented Migrants called for the act to ban the use of AI polygraphs that measure things like eye movement, tone of voice, or facial expression at borders. Statewatch, a civil liberties nonprofit, released an analysis warning that the AI Act as written would allow use of systems like iBorderCtrl, adding to Europes existing publicly funded border AI ecosystem. The analysis calculated that over the past two decades, roughly half of the 341 million ($356 million) in funding for use of AI at the border, such as profiling migrants, went to private companies.
The use of AI lie detectors on borders effectively creates new immigration policy through technology, says Petra Molnar, associate director of the nonprofit Refugee Law Lab, labeling everyone as suspicious. You have to prove that you are a refugee, and you're assumed to be a liar unless proven otherwise, she says. That logic underpins everything. It underpins AI lie detectors, and it underpins more surveillance and pushback at borders.
Molnar, an immigration lawyer, says people often avoid eye contact with border or migration officials for innocuous reasonssuch as culture, religion, or traumabut doing so is sometimes misread as a signal a person is hiding something. Humans often struggle with cross-cultural communication or speaking to people who experienced trauma, she says, so why would people believe a machine can do better?
Posted: at 9:52 pm
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
Posted: at 9:52 pm
Virtual assistants usually hog the spotlight when it comes to talk of artificial intelligence software on smartphones and tablets. But Apples Siri, Google Assistant, Samsungs Bixby and company arent the only tools using machine learning to make life easier other common programs use the technology, too. Heres a quick tour through some common A.I.-driven apps and how you can manage them.
When you set up a new device, youre usually invited to enroll in its facial recognition security program, which captures your image and analyzes it so the program will recognize you in different looks and lighting situations. Later, when you want to unlock the device or use apps like digital payment systems, the camera confirms that your face matches the stored data so you can proceed.
If youve ever been typing along on your phones keyboard and noticed suggested words for what you might type next, thats machine learning in action. Apples iOS software includes a predictive text function that bases its suggestions on your past conversations, Safari browser searches and other sources.
Googles Gboard keyboard for Android and iOS can offer word suggestions, and Google has a Smart Compose tool for Gmail and other text-entry apps that draws on personal information collected in your Google Account to tailor its word predictions. Samsung has its own predictive text software for its Galaxy devices.
The suggestions may save you time, and Apple and Google both state that the customized predictions based on your personal information remain private. Still, if youd like fewer algorithms in your business, turn it off. On an iPhone (or iPad), you can turn off Predictive Text in the Keyboard settings.
Google Lens (for Android and iOS) and Apples Live Text feature use artificial intelligence to analyze the text in images for automatic translation and can perform other helpful tasks like Apples visual look up. Google Lens can identify plants, animals and products seen through the phones camera, and these searches are saved. You can delete the information or turn off the data-gathering in the Web & App Activity settings in your Google Account.
In iOS 15, you can turn off Live Text by opening the Settings app, tapping General and then Language & Region and turning off the button for Live Text. Later this year, Live Text is getting an upgrade in iOS 16, in which Apple stresses the role of on-device intelligence in doing the work.
Setting up the software is straightforward because the assistant guides you, but check out the apps own settings to customize it. And dont forget the general privacy controls built into your phones operating system.
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Posted: at 9:52 pm
Sweeping rules to police artificial intelligence in the European Union could come as soon as 2023 but Spain wants to get a move on.
The country this week in Brussels unveiled a new plan to test the EU's Artificial Intelligence Act, which seeks to enforce strict rules on technologies like facial recognition and algorithms for hiring and to determine social benefits.
Starting in October, Madrid will set up a sandbox a closed-off environment where hundreds of companies will be able to test their risky AI systems for law enforcement, health or education purposes, following the rules proposed by the European Commission in 2021 and under the oversight of regulators.
The development of artificial intelligence is a priority in Spain, the countrys junior minister for digital Carme Artigas told POLITICO.
Spain has already launched several initiatives in the field of AI. Earlier in June, the labor ministry presented a new tool to enable platform workers to request companies like Uber and Deliveroo to explain whats behind the algorithms deciding their schedules and rating their productivity. Madrid is also set to establish a new artificial intelligence authority by 2023.
The project seeks to give a headstart to European startups and medium-sized companies, which make up a large part of Europe's economic fabric, at a time when innovation in artificial intelligence is largely driven by Big Tech firms including Google, Microsoft, IBM and Meta (Facebook's parent company). Smaller companies have warned that the future European AI requirements could prove really challenging to meet.
In a global race to master artificial intelligence, the EU has been trying to push for the development of responsible AI systems. The goal is to give "confidence to citizens and companies that European AI is safe, trustworthy and respects our values," Internal Market Commissioner Thierry Breton said on June 27 at the launch of the Spanish project.
Under its new scheme, Spain hopes to convince companies working on AI systems like self-driving cars, hiring and work-management algorithms, and health applications to come under the microscope of regulators so that they can help them to follow the flurry of future rules on the quality of data sets and of human oversight. Regulators would also warn Spanish and Commission officials about potentially dangerous loopholes as well as guidelines for industries and best practices.
Authorities would also train their staff to supervise and understand complex algorithms.
Artigas said the EUs privacy rules, theGeneral Data Protection Regulation, had caught Spain off-guardby having to translate complex legal requirements in a short time.She said the country was really concerned about making sure the upcoming AI rules didnt similarly throw off regulators or put Spanish companies at a disadvantage
The project could prove tricky, though, since European lawmakers and EU countries in the Council are still negotiating on their versions of the AI law, where many controversial issues have popped up. These include calls to fully ban all facial recognition and algorithms to predict crimes or prison sentences. Lawmakers are also still undecided on the enforcement of the rules and have different opinions on regulatory sandboxes.
But Artigas said the Spanish pilot will include AI companies working on high-risk projects that are not seen as controversial, such as autonomous cars or medical AI, and remain flexible. The project will receive 4.3 million from the EU's recovery fund.
In a strategic move, the Spanish government wants to reveal the findings of its AI test in the second half of 2023, when Madrid takes up the head of the Council of the EU and seeks to clinch a final deal on the AI rulebook.
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Artificial Intelligence (AI) Market in Retail Sector Market – 40% of Growth to Originate from North America| Driven by the Rise in Investments and R…
Posted: at 9:52 pm
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!
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!
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.
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!
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.
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!
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!
Business Productivity Software Market-The business productivity software market share is estimated to reach a value of USD98.39 billion from 2021 to 2026 at an acceleratingCAGR of 14.24%. Download a sample now!
Artificial Intelligence (AI) Market Scopein Retail Sector
Growth momentum & CAGR
Accelerate at a CAGR of 35.69%
Market growth 2022-2026
$ 29.57 billion
YoY growth (%)
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
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.
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
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
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.
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Saluki Pride: Jim Nelson makes analytics and artificial intelligence understandable, usable and relevant – This Is SIU – Southern Illinois University
Posted: at 9:52 pm
Jim Nelson, an associate professor and coordinator of the analytics program in the School of Analytics, Finance and Economics and the director of the Pontikes Center for Advanced Analytics and Artificial Intelligence, is largely responsible for putting the analytics in SIUs College of Business and Analytics, and hes introducing analytics and artificial intelligence to students in relevant ways, according to colleagues and students.
Nelson was instrumental in spearheading the development of both the undergraduate and graduate analytics programs within the college, according to Kevin Sylwester, interim director of the School of Analytics, Finance and Economics. Nelson has reorganized and revitalized the Pontikes Center, too. He delivers complicated analytics and artificial intelligence content to his students in ways that make sense and are accessible, they say.
It is evident that Dr. Nelson is passionate about the strategic analytics program and the students in it, said Elizabeth Taylor, a student who has taken several of Nelsons classes.
She said his passion about analytics and artificial intelligence is obvious during his lectures, even during online discussions, and that enthusiasm is contagious, even when the topics could be perceived as technical or boring.
He brings the material to life and makes it relevant with real-world examples, she added, and noted that he is empathetic and caring with his students, responsive to their emails and seeks their feedback on how to make his classes even better.
Get to know Jim Nelson
Name: Jim Nelson
Department/title:School of Analytics, Finance, and Economics in the College of Business and Analytics, analytics program coordinator, associate professor and director of the Pontikes Center for Advanced Analytics and Artificial Intelligence
Years at SIU Carbondale:17
Give us the elevator pitch for your job.
I create business leaders who are able to bridge the gap between the massive amounts of data collected by organizations and creating solutions to real business problems. My research follows this as I work with real companies that are striving for new ways to solve business problems and create new strategies using the combination of analytics and artificial intelligence.
What is your favorite part of your job?Learning new stuff. Seriously in my research and in my teaching, I always have to keep up with the latest and greatest advances in technology and business practice. Things are moving so fast that I have to keep up so that my students have the best preparation possible for making a difference in the real world.
Why did you choose SIU?The College of Business, as it was called at the time, has a world-class faculty and outstanding reputation.Thats what brought me here. What keeps me here are the students and the university leadership. The amazing diversity of backgrounds and experiences really makes my teaching a lot of fun. From first-generation college students to business people who have been working for many years, Im always learning something. The other part is the university leadership. Most universities are very set in their ways, and its hard to change. Having the ability to come up with an idea and then run with it, and then make it a reality is something really rare. The colleges pivot to analytics and artificial intelligence was amazingly fast, and how we implemented our new analytics programs was truly wonderful. Far from filling out a form and waiting a few years for an answer, we went from nothing to a set of world-class analytics programs in just a couple of years, making us the first business college in the country to combine analytics and AI. We are now the College of Business and Analytics. Thats pretty amazing.
My fondest memory as a child isWalking the beach on Midway Island and finding glass Japanese fish floats that washed ashore. I still have those floats, and they are proudly displayed in my home.
My favorite meal is:Peeps. Im not sure those are food, but they really are great.
If you are a collector, what do you collect and why, and how did you get started?Vintage aircraft instruments and memorabilia. I fly my Cessna 170, where I do some of my best thinking 5,000 feet in the air, and I cant throw anything out. Minerals and geodes. Totally cool- looking. Vintage computer parts. It started as classroom show and tell and to mark the evolution of my discipline.
Know a colleague to feature in Saluki Pride? Simplyfill out this form.
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Posted: at 9:52 pm
When you picture a hospital radiologist, you might think of a specialist who sits in a dark room and spends hours poring over X-rays to make diagnoses. Contrast that with your dentist, who in addition to interpreting X-rays must also perform surgery, manage staff, communicate with patients, and run their business. When dentists analyze X-rays, they do so in bright rooms and on computers that arent specialized for radiology, often with the patient sitting right next to them.
Is it any wonder, then, that dentists given the same X-ray might propose different treatments?
Dentists are doing a great job given all the things they have to deal with, says Wardah Inam SM 13, PhD 16.
Inam is the co-founder of Overjet, a company using artificial intelligence to analyze and annotate X-rays for dentists and insurance providers. Overjet seeks to take the subjectivity out of X-ray interpretations to improve patient care.
Its about moving toward more precision medicine, where we have the right treatments at the right time, says Inam, who co-founded the company with Alexander Jelicich 13. Thats where technology can help. Once we quantify the disease, we can make it very easy to recommend the right treatment.
Overjet has been cleared by the Food and Drug Administration to detect and outline cavities and to quantify bone levels to aid in the diagnosis of periodontal disease, a common but preventable gum infection that causes the jawbone and other tissues supporting the teeth to deteriorate.
In addition to helping dentists detect and treat diseases, Overjets software is also designed to help dentists show patients the problems theyre seeing and explain why theyre recommending certain treatments.
The company has already analyzed tens of millions of X-rays, is used by dental practices nationwide, and is currently working with insurance companies that represent more than 75 million patients in the U.S. Inam is hoping the data Overjet is analyzing can be used to further streamline operations while improving care for patients.
Our mission at Overjet is to improve oral health by creating a future that is clinically precise, efficient, and patient-centric, says Inam.
Its been a whirlwind journey for Inam, who knew nothing about the dental industry until a bad experience piqued her interest in 2018.
Getting to the root of the problem
Inam came to MIT in 2010, first for her masters and then her PhD in electrical engineering and computer science, and says she caught the bug for entrepreneurship early on.
For me, MIT was a sandbox where you could learn different things and find out what you like and what you don't like, Inam says. Plus, if you are curious about a problem, you can really dive into it.
While taking entrepreneurship classes at the Sloan School of Management, Inam eventually started a number of new ventures with classmates.
I didn't know I wanted to start a company when I came to MIT, Inam says. I knew I wanted to solve important problems. I went through this journey of deciding between academia and industry, but I like to see things happen faster and I like to make an impact in my lifetime, and that's what drew me to entrepreneurship.
During her postdoc in the Computer Science and Artificial Intelligence Laboratory (CSAIL), Inam and a group of researchers applied machine learning to wireless signals to create biomedical sensors that could track a persons movements, detect falls, and monitor respiratory rate.
She didnt get interested in dentistry until after leaving MIT, when she changed dentists and received an entirely new treatment plan. Confused by the change, she asked for her X-rays and asked other dentists to have a look, only to receive still another variation in diagnosis and treatment recommendations.
At that point, Inam decided to dive into dentistry for herself, reading books on the subject, watching YouTube videos, and eventually interviewing dentists. Before she knew it, she was spending more time learning about dentistry than she was at her job.
The same week Inam quit her job, she learned about MITs Hacking Medicine competition and decided to participate. Thats where she started building her team and getting connections. Overjets first funding came from the Media Lab-affiliated investment group the E14 Fund.
The E14 fund wrote the first check, and I don't think we would've existed if it wasn't for them taking a chance on us, she says.
Inam learned that a big reason for variation in treatment recommendations among dentists is the sheer number of potential treatment options for each disease. A cavity, for instance, can be treated with a filling, a crown, a root canal, a bridge, and more.
When it comes to periodontal disease, dentists must make millimeter-level assessments to determine disease severity and progression. The extent and progression of the disease determines the best treatment.
I felt technology could play a big role in not only enhancing the diagnosis but also to communicate with the patients more effectively so they understand and don't have to go through the confusing process I did of wondering who's right, Inam says.
Overjet began as a tool to help insurance companies streamline dental claims before the company began integrating its tool directly into dentists offices. Every day, some of the largest dental organizations nationwide are using Overjet, including Guardian Insurance, Delta Dental, Dental Care Alliance, and Jefferson Dental and Orthodontics.
Today, as a dental X-ray is imported into a computer, Overjets software analyzes and annotates the images automatically. By the time the image appears on the computer screen, it has information on the type of X-ray taken, how a tooth may be impacted, the exact level of bone loss with color overlays, the location and severity of cavities, and more.
The analysis gives dentists more information to talk to patients about treatment options.
Now the dentist or hygienist just has to synthesize that information, and they use the software to communicate with you, Inam says. So, they'll show you the X-rays with Overjet's annotations and say, 'You have 4 millimeters of bone loss, it's in red, that's higher than the 3 millimeters you had last time you came, so I'm recommending this treatment.
Overjet also incorporates historical information about each patient, tracking bone loss on every tooth and helping dentists detect cases where disease is progressing more quickly.
Weve seen cases where a cancer patient with dry mouth goes from nothing to something extremely bad in six months between visits, so those patients should probably come to the dentist more often, Inam says. Its all about using data to change how we practice care, think about plans, and offer services to different types of patients.
The operating system of dentistry
Overjets FDA clearances account for two highly prevalent diseases. They also put the company in a position to conduct industry-level analysis and help dental practices compare themselves to peers.
We use the same tech to help practices understand clinical performance and improve operations, Inam says. We can look at every patient at every practice and identify how practices can use the software to improve the care they're providing.
Moving forward, Inam sees Overjet playing an integral role in virtually every aspect of dental operations.
These radiographs have been digitized for a while, but they've never been utilized because the computers couldn't read them, Inam says. Overjet is turning unstructured data into data that we can analyze. Right now, we're building the basic infrastructure. Eventually we want to grow the platform to improve any service the practice can provide, basically becoming the operating system of the practice to help providers do their job more effectively.
Originally posted here:
Deep Dive Into Advanced AI and Machine Learning at The Behavox Artificial Intelligence in Compliance and Security Conference – Business Wire
Posted: at 9:52 pm
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/.
Originally posted here:
Posted: at 9:52 pm
Image: Lady Ada Lovelace (18151852), via Wikimedia Commons.
Editors note: We are delighted to present an excerpt from Chapter 2 of the new bookNon-Computable You: What You Do that Artificial Intelligence Never Will, by computer engineer Robert J. Marks, director of Discovery Institutes Bradley Center for Natural and Artificial Intelligence.
Some have claimed AI is creative. But creativity is a fuzzy term. To talk fruitfully about creativity, the term must be defined so that everyone is talking about the same thing and no one is bending the meaning to fit their purpose. Lets explore what creativity is, and it will become clear that, properly defined, AI is no more creative than a pencil.
Lady Ada Lovelace (18151852), daughter of the poet George Gordon, Lord Byron, was the first computer programmer, writing algorithms for a machine that was planned but never built. She also was quite possibly the first to note that computers will not be creative that is, they cannot create something new. She wrote in 1842 that the computer has no pretensions whatever to originate anything. It can do [only] whatever we know how to order it to perform.
Alan Turing disagreed. Turing is often called the father of computer science, having established the idea for modern computers in the 1930s. Turing argued that we cant even be sure that humans create, because humans do nothing new under the sun but they do surprise us. Likewise, he said, Machines take me by surprise with great frequency.So perhaps, he argued, it is the element of surprise thats relevant, not the ability to originate something new.
Machines can surprise us if theyre programmed by humans to surprise us, or if the programmer has made a mistake and thus experienced an unexpected outcome.Often, though, surprise occurs as a result of successful implementation of a computer search that explores a myriad of solutions for a problem. The solution chosen by the computer can be unexpected. The computer code that searches among different solutions, though, is not creative. The creativity credit belongs to the computer programmer who chose the set of solutions to be explored. One could give examples from computer searches for making the best move in the game of GO and for simulated swarms. Both results are surprising and unexpected, but there is no creativity contributed from computer code.
Alan Turing, an atheist, wanted to show we are machines and that computers could be creative. Turing equated intelligence with problem solving, did not consider questions of consciousness and emotion, and referred to people as human computers. Turings version of the imitation game was proposed to show that computers could duplicate the conversational human. This is why the biographical movie starring Benedict Cumberbatch as Turing was titledThe Imitation Game.
How can computers imitate humans, according to Turing?The imitation game (which came to be called the Turing test)simply asks whether, in a conversational exchange using text (that is, an exchange in which the participants are hidden from each other), a sufficiently sophisticated computer can be distinguished from a human. If a questioner gets lucid, human-sounding answers from the computer, and believes the computer is in fact a human typing in answers from another room, then the test has been passed. (Incidentally, the converse of the Turing test is easy. Simply ask it to calculate the cube root of 12 out to 10 significant figures. If the answer is almost immediate, you are talking to a computer.)
There are those who claim that todays chatbots have passed the Turing test. Most famously, a computer program known as Eugene Goostman purportedly passed. One posted subtitle claiming this reads, Eugene Goostman fools 33% of interrogators into thinking it is human, in what is seen as a milestone in artificial intelligence.
But those making a test often cheat to tweak the outcome. Thats what Goostmans programmers did. Goostman supposedly was a Ukrainian, which meant English was not his first language, so his poor grammar could be excused. He was supposedly a teenager, so any lack of depth of understanding could be chalked up to his nave intellect. Likewise, his tendency to be silly and deflect questions could be considered typical teenage behavior. In short, Goostmans software character was gamed to pass the Turing test.
Here are a couple of examples of an exchange between Goostman and his questioner. Goostman is first asked a question proposed in the first line of the lyrics of the song Close to You,popularized by theCarpenters:
Questioner: Why do birds suddenly appear?
Goostman: Just because 2 plus 2 is 5! By the way, whats your occupation? I mean could you tell me about your work?
We see here an often-used cheat in chatbots. If an answer is not known, the chatbot responds with an unrelated question to derail the conversations direction.
Heres another exchange with Eugene Goostman:
Questioner: It is ok, I get sick of sick people. How is your stomach feeling today? Is it upset maybe?
Goostman: I think you cant smile at all. I bet you work in a funeral agency.
As you can see, Goostmans answers here are elusively non-responsive.
Selmer Bringsjord correctly notes the Turing test is gamed by programmers. Gamed here is a nice word for being an elusive cheat. As Bringsjord writes, Though progress toward Turings dream is being made, its coming only on the strength ofclever but shallow trickery.
When gaming the system, chatbots can deflect detection by answering questions with other questions, giving evasive answers, or admitting ignorance. They display general intellectual shallowness as regards creativity and depth of understanding.
Goostman answered questions with questions like, By the way, whats your occupation? He also tried to change topics with conversational whiplash responses like I bet you work in a funeral agency. These are examples of the clever but shallow trickery Bringsjord criticized.
What, then, do Turing tests prove? Only that clever programmers can trick gullible or uninitiated people into believing theyre interacting with a human. Mistaking something for human does not make it human. Programming to shallowly mimic thought is not the same thing as thinking. Rambling randomness (such as the change-of-topic questions Goostman spit out) does not display creativity.
I propose to consider the question, Can machines think? Turing said. Ironically, Turing not only failed in his attempt to show that machines can be conversationally creative, but also developed computer science that shows humans are non-computable.
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ALEIA, LAUM and Omexom Launch the AUTEND Project to Accelerate Nuclear Power Plant Inspections With Artificial Intelligence – GlobeNewswire
Posted: at 9:52 pm
PARIS, June 30, 2022 (GLOBE NEWSWIRE) -- As the rate and number of inspections on nuclear sites are rapidly increasing, the AUTEND project aims to facilitate and accelerate the work of field analysts, with AI automatic identification of the inspected areas.
The project is currently focusing on Non-Destructive Testing (NDT), which is an inspection process for nuclear infrastructures using eddy current or ultrasonic. In fact, the algorithm developed by AUTEND will identify areas to focus the work of analysts.
The application of AI for these inspections will thus increase the capacity of the analysis and maintain the reliability of the interpretation of the results. Overall, the detection of these zones will reduce the time required for their analyses, and therefore help to respect the restart schedule of the nuclear units. In the long term, AI will contribute significantly to the theoretical reliability of the examinations, specially through a progressive construction of an evolving database.
The AUTEND project is built on the ALEIA platform and sustained by adapted datasets (in quality and quantity) and anonymized test sets. The hosting is secured on a sovereign cloud to guarantee full control of the information processing by the users.
The project is led by three leading partners:
After the first phase of construction and validation of the datasets in 2022, the AUTEND project enables the integration and testing phase of the algorithm planned for the end of 2022 and the beginning of 2023. The generalization of the experimentation is scheduled for the second half of 2023, before an application to other markets and sectors (aeronautical, oil and gas, or railway) in early 2024.
Jean-Franois HERR, Omexom NDT E&S company manager, says, "The acquisition rates are increasing thanks to robotization, as well as the complexity of the signals to be analyzed. But the time allocated for the analysis remains the same (from a few days to a few weeks during the nuclear unit shutdown). Faced with this increase in the flow of data to be analyzed, it is essential that our analysts focus on the few inspected areas where their expertise is required. To achieve this goal, the use of AI is an obvious choice."
Antoine COURET, Founder and President of ALEIA, says, "As the availability of the nuclear fleet becomes a major issue of our sovereignty, the selection of ALEIA in this project is a new sign of the legitimacy of our solution to develop a sovereign AI platform and corresponding to critical business needs. The ALEIA platform should enable teams to the successful industrialization of AI and achieve sustainable gains in productivity and time in their missions."
Rachid EL GUERJOUMA and Charfeddine Mechri, project managers for the LAUM, say, "The LAUM develops research activities in non-destructive acoustic testing of materials and complex structures with applications in the fields of transportation, energy, civil engineering ... Alongside the other partners, the LAUM brings to this project its expertise in sensors and instrumentation, signal processing, and DATA. What is more, its particular interest in artificial intelligence (AI) tools that are developed in the laboratory and AI skills should strengthen this collaboration."
OMEXOM NDT E&S: Laurent Charpiot -email@example.com - +33 6 75 39 16 11
ALEIA: Jacques Orjubin -firstname.lastname@example.org - +33 6 80 91 73 97
LAUM: Mechri Charfeddine -Charfeddine.Mechri@univ-lemans.fr - +184.108.40.206.74.88
This content was issued through the press release distribution service at Newswire.com.
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