#AI-based facial recognition (fr) system

[third-eyeai]

[intozitech]

Intelligent Crime Surveillance using Facial Recognition System

Think about a world where technology helps the police keep everyone safe by always watching out. The amazing skills of face recognition technology are making this happen. In this article, we'll talk about how smart technology is making crime surveillance better, exploring how facial recognition is changing the way we identify and capture criminals

Exploring Facial Recognition’s Role in Crime Prevention

Criminal activity has become more complex over time, thus the methods used to stop it have to advance even more quickly. In this endeavour, facial recognition technology has become an important compact, providing an accurate and effective method of identifying suspects.

Now let us look at some benefits of Face Recognition System (FRS):

Accurate Identification: Face recognition systems can accurately identify individuals based on their facial features. This helps law enforcement agencies quickly and reliably identify suspects, missing persons, or individuals of interest.

Real-time Monitoring: AI-powered face recognition can analyse live video streams in real-time. This enables law enforcement to detect and respond to suspicious activities as they happen, enhancing situational awareness and proactive crime prevention.

Enhanced Investigations: Face recognition systems provide valuable evidence during investigations. They can help track the movements of suspects, establish timelines, and identify potential accomplices, aiding law enforcement in building stronger cases.

Crime Deterrence: The presence of face recognition technology can act as a deterrent for potential criminals, as they are aware that their activities are being monitored and their identities can be easily established.

Rapid Alerts: When integrated with monitoring systems, face recognition can trigger alerts whenever a person of interest is detected. This enables law enforcement to respond quickly and take appropriate actions.

Reduced Human Error: Automated face recognition reduces the chances of human error in identifying individuals. Traditional methods of identification can sometimes be subjective and prone to mistakes, but AI algorithms are consistent in their analysis.

Utilizing Surveillance Cameras for crime monitoring

Police departments around the world are using lots of surveillance cameras to create a strong network of cameras that keep a close watch. These cameras are placed in public areas and record what is happening in real-time. This footage is then used in facial recognition systems. When these things work together, it changes regular watching into a way to actively detect and prevent crime.

Smart Interceptor Vehicle on the chase

When cameras watch the streets, another kind of technology gets to work too. Smart interceptor vehicles equipped with facial recognition capabilities receive alerts when suspects enter their area. These swift-reaction units drive these vehicles as they rush to the site, cutting down on response times and raising the chance of a successful capture.

Technology and Law Enforcement Collaborating for impact

Even though technology is getting better at watching out for criminals, we should not forget that human knowledge is still important. Police officers are good at looking at the pictures from face recognition and using that to make smart choices. When they work together with technology, it acts as a powerful team that can catch even the cleverest criminals.

Conclusion: A Safer Tomorrow with Intelligent Surveillance

It is good to see things getting better for keeping people safe, even though there are a lot of problems in the world. Our face recognition technology shows how new ideas can help with watching out for criminals. We are getting closer to a place where we all feel safer and more protected. This happens as we keep working on and making these kinds of technologies better.

[hannahmatthewswestminster]

bloom exhibition, trevor paglen

a hugely inspiring and knowledge-full exhibition exploring invisible acts of surveillance, bringing them to visibility. what was instantly evident is the octopus surveillance cameras that livestream the exhibition online, allowing the online user to show their webcam on one of four screens positioned throughout the exhibition. many different aspects dealt with different technological advances that remain hidden from the public sphere and understanding. from facial recognition systems, to computer algorithms and the training of these technologies. what struck me is the face sculpture in the middle of the exhibition, a face that doesn’t exist but enabled the development of FRS. I read up on the exhibition before I attended so I understood the pieces within it, which was helpful as there was no text, so it allowed the me to feel the full impact of the exhibition

the exhibition is a series of photographs of flowers and plants in bloom. they’re then imported into a suite of artificial intelligence algorithms developed in the studio that try to dissect those images into their component parts, textures, objects, regions and represents those difference in the images by assigning different colours to them

we are living in a moment where we are seeing things like artificial intelligence which to a large extent is about automating the interpretation of images, AI is about building applications online that are evaluating you, trying to recognise you, looking at things like facial recognition… it looks at things like your behaviours, and what that says about you, whilst working out how that information be monetised

the sculpture at the centre of the exhibition is something called the standard head, and that is a model of the head that has never existed but that was a mathematical abstraction that was meant to represent a standard head to create a ground truth upon which facial recognition was built

when we’re looking at things like facial recognition, computer vision, artificial intelligence, we’re seeing the establishment of norms, classifications, categories and those always have a politics to them. those classifications are being made by people who have economic or political interests. “the question that becomes is what kind of judgements are built into technical systems, why are they made that way, who are they benefitting and at who’s expense do they come... i am somebody who does not believe that technologies are ever neutral, i don’t think that can exist”

there’s a piece in the exhibition called “we…” this is looking at the specific data set that is used to teach AI how to read handwriting. the piece is based on a data set from the late 1980’s where what the researchers did was have high school kids write out the preamble to the constitution of the united states, and it felt highly ironic to me that this text, this declaration of public sovereignty, would then be used to create a means by which to ultimately surveil people

the geography of seeing this changing, the fact that we’re not really travelling, we’re engaging with other people, looking at things primarily through online media, how does that change what an art exhibition is 

“the octopus piece is a response to trying to think about this dislocatedness. throughout the gallery there’s dozens of video cameras, all of those cameras are then streaming onto a web platform so that you as a viewer remotely can go and look at it and you can pick what camera you want to see the exhibition through. there’s another thing that happens, when you go and you’re looking at the exhibition online you can give the octopus apparatus consent to use your webcam. when you do that your webcam will take your image and will stream it onto one of four monitors located in the exhibition, if you’re somebody in the exhibition and you look up, you will see the faces looking down on you of people who are looking at the exhibition online. these multiple dialectics of seeing, of remoteness and of presence, and the different kinds of mediation and abstractions that happen in those relationships for me was the point of this exhibition”

“i’m pointing out the degree to which our interactions with each other have become part of a vast planetary machine that is designed to extract as much value as possible from us and from the most intimate parts of our lives”

i absolutely loved this exhibition, it was so beautiful to see the work of an inspiration in the flesh 

an interesting ideal watching the technology within my phone, identify the face of the sculpture within the exhibition, the face structure that actually enabled the development of facial recognition systems; the very systems being used within my device to identify the face. i screenshot this dialect occurring

[ainerdofficial]

Facial Recognition

Face recognition systems are a powerful tool for identifying and verifying a person, and there are many different types of face recognition software. The selected features of an image are compared with faces in a database and a face is checked. Widespread use has been observed in a variety of industries including healthcare, education, healthcare, business and the military. Face recognition systems (FRS) are automated or semi-automatic technologies that analyze individual features by extracting facial patterns from video or still images. FRLscreate data that can be used to uniquely identify certain persons. Data protection will play a key role in the development of facial recognition technologies for use in medicine, healthcare and the military. Today, facial recognition systems are powered by deep learning, a form of AI that works by transmitting input through stacked layers of simulated neurons to process information. The type of problems the system is likely to encounter can be trained in advance so that the model can learn how to correctly identify patterns in the data. Face recognition systems are able to identify people from images or videos based on the analysis of individual facial features. Although the data and algorithm differ for each brand, the system potentially draws millions of samples from Alearna to draw conclusions about how the faces fit together, such as their age, gender, ethnicity, and other characteristics. #ai #aitechnologies Read the full article

[khalilhumam]

The Brookings glossary of AI and emerging technologies

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The Brookings glossary of AI and emerging technologies

By John R. Allen, Darrell M. West

Algorithms:

According to author Pedro Domingos, algorithms are “a sequence of instructions telling a computer what to do.”[1] These software-based coding rules started with simple and routine tasks, but now have advanced into more complex formulations, such as providing driving instructions for autonomous vehicles, identifying possible malignancies in X-rays and CT scans, and assigning students to public schools. Algorithms are widely used in finance, retail, communications, national defense, and many other areas.

Artificial Intelligence (AI):

Indian engineers Shukla Shubhendu and Jaiswal Vijay define AI as “machines that respond to stimulation consistent with traditional responses from humans, given the human capacity for contemplation, judgment, and intention.”[2] This definition emphasizes several qualities that separate AI from mechanical devices or traditional computer software, specifically intentionality, intelligence, and adaptability. AI-based computer systems can learn from data, text, or images and make intentional and intelligent decisions based on that analysis.

Augmented Reality (AR):

Augmented reality puts people in realistic situations that are augmented by computer-generated video, audio, or sensory information. This kind of system allows people to interact with actual and artificial features, be monitored for their reactions, or be trained on the best ways to deal with various stimuli.

Big Data:

Extremely large data sets that are statistically analyzed to gain detailed insights. The data can involve billions of records and require substantial computer-processing power. Data sets are sometimes linked together to see how patterns in one domain affect other areas. Data can be structured into fixed fields or unstructured as free-flowing information. The analysis of big data sets can reveal patterns, trends, or underlying relationships that were not previously apparent to researchers.

Chatbots:

Automated tools for answering human questions. Chatbots are being used in retail, finance, government agencies, nonprofits, and other organizations to respond to frequently asked questions or routine inquiries.

Cloud Computing:

Data storage and processing used to take place on personal computers or local servers controlled by individual users. In recent years, however, storage and processing have migrated to digital servers hosted at data centers operated by internet platforms, and people can store information and process data without being in close proximity to the data center. Cloud computing offers convenience, reliability, and the ability to scale applications quickly.

Computer Vision (CV):

Computers that develop knowledge based on digital pictures or videos.[3] For example, cameras in automated retail outlets that are connected to CV systems can observe what products shoppers picked up, identify the specific items and their prices, and charge consumers’ credit card or mobile payment system without involving a cash register or sales clerk. CV also is being deployed to analyze satellite images, human faces, and video imagery.

Connected Vehicles:

Cars, trucks, and buses that communicate directly with one another and with highway infrastructure. This capacity speeds navigation, raises human safety, and takes advantage of the experiences of other vehicles on the road to improve the driving experience.

Data Analytics:

The analysis of data to gather substantive insights. Researchers use statistical techniques to find trends or patterns in the data, which give them a better understanding of a range of different topics. Data analytic approaches are used in many businesses and organizations to track day-to-day activities and improve operational efficiency.

Data Mining:

Techniques that analyze large amounts of information to gain insights, spot trends, or uncover substantive patterns. These approaches are used to help businesses and organizations improve their processes or identify associations that shed light on relevant questions.

Deepfakes:

Digital images and audio that are artificially altered or manipulated by AI and/or deep learning to make someone do or say something he or she did not actually do or say. Pictures or videos can be edited to put someone in a compromising position or to have someone make a controversial statement, even though the person did not actually do or say what is shown. Increasingly, it is becoming difficult to distinguish artificially manufactured material from actual videos and images.

Deep Learning:

A subset of machine learning that relies on neural networks with many layers of neurons. In so doing, deep learning employs statistics to spot underlying trends or data patterns and applies that knowledge to other layers of analysis. Some have labeled this as a way to “learn by example” and a technique that “perform[s] classification tasks directly from images, text, or sound” and then applies that knowledge independently.[4] Deep learning requires extensive computing power and labeled data, and is used in medical research, automated vehicles, electronics, and manufacturing, among other areas.

Digital Sovereigns:

The speed, scope, and timing of technology innovation today is often decided not by government officials but by coders, software designers, and corporate executives. Digital sovereigns set the rules of the road and terms of service for consumers. What they decide, directly or indirectly, has far-reaching consequences for those using their software or platform. The power of business decisionmakers raises important governance questions regarding who should decide on matters affecting society as a whole and the role that policymakers, consumers, and ethicists should play in digital innovation.

Distributed Collaboration:

Connecting frontline people with others who have differing skills and getting them to work together to solve problems. Distributed collaboration differs from current governance paradigms that emphasize hierarchical, top-down decisionmaking by those who do not always have relevant knowledge about the issues being addressed. The new model takes advantage of the fact that a range of skills are needed to resolve technology issues, and those skills are located in different subject areas and organizational parts. Rather than keeping AI expertise in isolation, distributed collaboration brings together software and product designers, engineers, ethicists, social scientists, and policymakers to draw on their respective expertise and integrate their knowledge to solve pressing problems.

Dual-Use Technologies:

Many technologies can be used in a good or ill manner. The very same facial recognition system could be used to find missing children or provide a means for mass surveillance. It is not the technology per se that raises ethical issues but how the technology is put to use. The dual-use nature of technologies makes regulation difficult because it raises the question of how to gain the benefits of technology innovation while avoiding its detrimental features.

Facial Recognition (FR):

A technology for identifying specific people based on pictures or videos. It operates by analyzing features such as the structure of the face, the distance between the eyes, and the angles between a person’s eyes, nose, and mouth. It is controversial because of worries about privacy invasion, malicious applications, or abuse by government or corporate entities. In addition, there have been well-documented biases by race and gender with some facial recognition algorithms.

5G Networks:

These are fifth-generation wireless telecommunications networks that have been deployed in major cities and feature faster speeds and enhanced capabilities for transmitting data and images. As such, 5G networks enable new digital products and services, such as video streaming, autonomous vehicles, and automated factories and homes that require a fast broadband.

Hyperwar:

High-tech military situations in which robots, sensors, AI, and autonomous systems play important roles and command decisions have to unfold at speeds heretofore unseen in warfare. Because of the acceleration of the pace and scope of conflict, countries will have to conduct simultaneous operations in every warfare domain and national leaders will need to accelerate technology innovation to build a safe and stable future.[5]

Machine Learning (ML):

According to Dorian Pyle and Cristina San Jose of the McKinsey Quarterly, machine learning is “based on algorithms that can learn from data without relying on rules-based programming.”[6] ML represents a way to classify data, pictures, text, or objects without detailed instruction and to learn in the process so that new pictures or objects can be accurately identified based on that learned information. ML furthermore can be used to estimate continuous variables (such as estimating home sales prices) or to play games. Many of its insights come by examining prior data and learning how to improve understanding.

Natural Language Processing (NLP):

The analysis of textual information to make sense of its meaning and intentions. NLP software can take a large amount of text and see how words are linked together to assess positive or negative sentiment, relationships, associations, and meaning. For example, researchers can study medical records to see which patient symptoms appear to be most related to particular illnesses.

Neural Networks:

Researchers use computer software to “perform some task by analyzing training examples” and by grouping data based on common similarities.[7] Similar to the neural nodes of a brain, neural networks learn in layers and build complex concepts out of simpler ones. They break up tasks, identify objects at a number of different levels, and apply that knowledge to other activities. These kinds of systems allow computers to learn and adapt to changing circumstances, similar to the way a brain functions. Deep learning and many of the most prominent recent applications of machine learning operate through neural networks (e.g., driverless cars, deepfakes, and AlphaGo game playing).

Quantum Computing:

Quantum computers have tremendous capacity for storing and processing information because their storage processes are not in the form of a zero or one, as is the case with traditional computers. Rather, they take advantage of superposition—the fact that electrons can be in two places at once—to create “quantum bits” that store multiple values in each point.[8] That capability dramatically increases storage capacity and decreases processing times, thereby improving the scope of data, textual, or image analysis.

Singularity:

Futurist Ray Kurzweil describes a singularity as a “machine-based superintelligence [that is] greater than human intelligence.”[9] It combines advanced computing power with artificial intelligence, machine learning, and data analytics to create super-powered entities. There are extensive (and unresolved) debates regarding whether humanity will face a computing singularity in the next 50, 100, or 250 years.

Social Credit Systems:

The ubiquity of people’s online activities enables technology that tracks behavior and rates people based on their online actions. As an illustration, some organizations have piloted systems that compile data on social media activities, personal infractions, and behaviors such as paying taxes on time. They use that data to rate people for creditworthiness, travel, school enrollment, and government positions.[10] These systems are problematic from an ethical standpoint because they lack transparency and can be used to penalize political opponents.

Supervised Learning:

According to Science magazine, supervised learning is “a type of machine learning in which the algorithm compares its outputs with the correct outputs during training. In unsupervised learning, the algorithm merely looks for patterns in a set of data.”[11] Supervised learning allows ML and AI to improve information processing and become more accurate.

Techlash:

The backlash against emerging technologies that has developed among many individuals. People worry about a host of problems related to technology innovation, such as privacy invasions, mass surveillance, widening income inequality, and possible job losses. Figuring out how to assuage understandable human fears is a major societal challenge going forward.

Virtual Reality (VR):

Virtual reality uses headsets equipped with projection visors to put people in realistic-seeming situations that are completely generated by computers. People can see, hear, and experience many types of environments and interact with them. By simulating actual settings, VR can train people how to deal with various situations, vary the features that are observed, and monitor how people respond to differing stimuli.

[1] Pedro Domingos, The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World (New York: Basic Books, 2018). [2] Shukla Shubhendu and Jaiswal Vijay, “Applicability of Artificial Intelligence in Different Fields of Life,” International Journal of Scientific Engineering and Research, vol. 1, no. 1 (September 2013), pp. 28–35. [3] Jason Brownlee, “A Gentle Introduction to Computer Vision,” Machine Learning Mastery, July 5, 2019. [4] Math Works, “What Is Deep Learning?” undated. [5] John R. Allen and Amir Husain, “Hyperwar and Shifts in Global Power in the AI Century,” in Amir Husain and others, Hyperwar: Conflict and Competition in the AI Century (Austin, TX: SparkCognition Press, 2018), p. 15. [6] Dorian Pyle and Cristina San Jose, “An Executive’s Guide to Machine Learning,” McKinsey Quarterly, June, 2015. [7] Larry Hardesty, “Explained:  Neural Networks,” MIT News, April 14, 2017. [8] Cade Metz, “In Quantum Computing Race, Yale Professors Battle Tech Giants,” New York Times, November 14, 2017, p. B3. [9] Quoted in Tom Wheeler, From Gutenberg to Google: The History of Our Future (Brookings, 2019), p. 226. Also see Ray Kurzweil, The Singularity Is Near: Where Humans Transcend Biology (London: Penguin Books, 2006). [10] Jack Karsten and Darrell M. West, “China’s Social Credit System Spreads to More Daily Transactions,” TechTank (blog), Brookings, June 18, 2018. [11] Matthew Hutson, “AI Glossary:  Artificial Intelligence, in So Many Words,” Science, July 7, 2017.

[latestdubainews]

“LipSecure works in conjunction with the vendor’s existing FR [facial recognition] system and prompts the user to repeat a random sequence of digits that appear on their screen. Liopa’s AI-based Visual Speech Recognition (VSR) technology, checks if the digits have been spoken/mimed correctly and thus determines if there is a ‘live’ person present.”

Read the rest of Liopa uses lip reading to add security to face recognition systems at NFC World.

from NFC World via Dubai Movers, House Movers in Dubai, Office Movers In Dubai

[enterinit]

Windows 10 Insider Preview Build 18272 released

Windows 10 Insider Preview Build 18272 released. Sign-in Options Redesigned for Windows Hello Security shouldn’t be intimidating, and we aspire to help you eliminate passwords. That’s why we want to make sure you can easily set up your first line of defense securing your device with Windows Hello. Your feedback that the previous design was cluttered, and confusing is what drove us to simplify the Sign-in options Settings. We believe this update will help you choose the safest and fastest sign-in option for your needs, whether that’s using a PIN or a biometric factor like fingerprint or facial recognition. Ultimately, we want to help you make the right decision for your situation. By condensing each sign-in option into a list that explains individual properties and next step for setup, we hope you feel more informed the next time you decide how you want to sign in using enhanced security features with Windows Hello. Input Improvements Expanding SwiftKey’s Typing Intelligence to More Languages: In RS5 we started working on bringing SwiftKey’s AI technology to Windows, and we’re excited to announce that we’re expanding support to the following languages: English (Canada) – en-CA English (India) – en-IN French (Canada) – fr-CA French (Belgium) – fr-BE French (Switzerland) – fr-CH Portuguese (Portugal) – pt-PT German (Switzerland) – de-CH Spanish (United States) – es-US If you’re a touch keyboard user that writes in any of these languages, please try out the text predictions and shapewriting in this build and let us know how it feels for you. If you’d like to compare how the keyboard feels with and without this change, check the options for supported languages under Settings > Devices > Typing > Suggestions and Autocorrections. Using a physical keyboard? Hardware keyboard text suggestions are also supported with the above languages now. Text suggestions for the hardware keyboard is a learning tool we originally introduced in RS4, and expanded language support for with RS5. If you’d like to try it out in one of the supported languages, you can do so by enabling the “Show text suggestions as I type” feature under Settings > Devices > Typing. Indic Phonetic keyboards now available for PC: Do you write in an Indic language? We’ve heard your feedback, and in addition to the Indic Traditional INSCRIPT keyboards already available, with today’s build we’re adding Indic Phonetic keyboards for Hindi, Bangla, Tamil, Marathi, Punjabi, Gujarati, Odia, Telugu, Kannada and Malayalam! How does a phonetic keyboard work? Basically it’s a convenient way of typing that leverages the English QWERTY keyboard – as you type, we use transliteration to suggest possible Indic text candidates. For example, if you typed “namaste” using the Hindi Phonetic keyboard we would suggest नमस्ते. If your native language is one of the above languages or you have some knowledge of Indic languages, please try this out with either the hardware or touch keyboard and let us know how it feels. Here are the steps to get started: Select Start > Settings > Time & Language-> Language from the navigation menu. Select the + icon labeled (or skip ahead to step 4 if your preferred Indic language was already added). Type the name of an Indic language into the search box and select it – for example “Hindi”. Click the Next button and install the Indic language on your device, which will return you to the Language page. Now back on the Language page, click the one you just added, and then click the Options button. This will take you to that language’s options page. Select the + icon labeled . Enable the Phonetic keyboard, for example – now the languages options page will look something like this: Click input indicator on the taskbar (or press the Windows key + Space) and select the Indic Phonetic keyboard. Time to type something! Note: The dictionaries used for the Indic Phonetic keyboards need to be downloaded. Right after adding the keyboard, please connect to Internet – you may need to wait a bit for the dictionary download to finish. Accessibility Improvements Narrator Caps Lock ON alert while typing: Narrator will now alert you when you are accidentally typing with Caps Lock turned on. The setting is on by default. To adjust this setting, visit Narrator Settings (Ctrl + Win + N), then navigate to the “Change how much content you hear” heading and review the combo box for “Change when you receive Caps Lock warnings while typing”. It will work even if you have character echo option (“Hear characters as you type”) disabled, and it will work in edit fields as well as password field. By default, Narrator will always alert you regardless of presses of the Shift key as you type alphabetic characters. But you can choose to only be alerted when you press the Shift key together with an alphabetic character, or you can disable the feature altogether. In this initial release, the sound effect you hear will sound like the standard Narrator error sound effect which we are working on improving. We look forward to your feedback on this new feature! Other Updates for Insiders Snip & Sketch (Version 10.1809.2964.0): Thank you to everyone who’s shared feedback so far! It’s been great seeing all of your comments, and we’re making progress on addressing them with our latest app update that’s in the process of rolling out to Insiders in the Fast ring. The update brings several improvements, including: Adding some emphasis. Planning to paste your screenshot somewhere? Does that place have the same color background as the background of your screenshot? When you update to the latest app version you’ll find a new option to add a border to your screenshots – giving them that little bit extra needed to stand out. Printing! Sometimes you want to keep a physical copy of your screenshots – we hear you that it took too many steps before, and have added a button in the toolbar so you can print directly from the app (you can also print to PDF!) Refining our save options: If you have unsaved changes and go to close a window or open a file that will now pop a confirmation. The default proposed file name will now include a timestamp. We’ve heard your feedback and saving now also supports jpg and gif file types. Also, we have a new settings page! We’re replacing our old the old About flyout with a full settings page where you’ll find the aforementioned option to add a border, an option for updates made to your snip to copy automatically to your clipboard, and more! Have an opinion as to what’s next? We have a few things on our list already and would love to hear your thoughts! Head to the Feedback Hub to see some of the things we’re already tracking and share your voice. Sticky Notes 3.1 (Version 3.1.32): Thank you for sharing your feedback on 3.0 release with us. We think you are going to love this update! We tested the waters with the Charcoal Note and you all seemed keen to go even darker! Introducing full-on Dark Mode support in Sticky Notes.  We support Windows Dark Mode as well as the ability to set your color mode in Sticky Notes to suit your mood independent of your system settings. Everyone seemed excited about Sticky Notes syncing, so we spent some time making it even faster! Check it out and let us know how it feels. General changes, improvements, and fixes We’re rolling out a feature where if you right click on a group name or tile folder in Start, you will now have an option to unpin it. Currently 50% of Insiders will have this option. We fixed an issue where Task Manager settings wouldn’t persist after closing and reopening Task Manager in recent flights. We fixed an issue where only users in Vietnam could use the new Vietnamese Telex and Number key-based keyboards. Now anyone can use them – please try it out if you’re familiar with the language! We fixed an issue where doing an upwards search with wrap enabled in Notepad wouldn’t find the last word in the text. We fixed an issue that could result in Settings crashing when navigating to Data Usage. We fixed an issue where starting the PIN removal process in Settings then clicking Cancel when prompted for your password would crash Settings. We fixed an issue where twinui.dll would crash on some devices in the last few builds after selecting a wireless display to project to from the Connect flyout. We fixed an issue where enhancements selected under Speaker Properties > Enhancements wouldn’t persist on upgrade. We fixed an issue resulting in FLAC metadata being cut short in File Explorer and other locations. The “Forget” option for Wi-Fi profiles is now available for non-admin users. Ctrl + Mouse Wheel Scroll to zoom in text is now supported in Command Prompt, PowerShell and WSL. When using dark theme (Settings > Personalization > Colors) your scrollbars in Command Prompt, PowerShell and WSL will now become dark too. We fixed an issue with the console that could cause graphical glitches in the embedded terminal of VS Code. The options to change your default app mode and enable/disable transparency have moved to the top of Colors Settings so it’s easier for people to find. Known issues Task View fails to show the + button under New Desktop after creating 2 Virtual Desktops. Some users will notice the update status cycling between Getting Things Ready, Downloading, and Installing. This is often accompanied with error 0x8024200d caused by a failed express package download. If you have a have a large number of OTF fonts, or OTF fonts that support the extended East Asian character set, you may experience some unexpectedly missing text across the system. We’re working on a fix. If you encounter this issue, navigating to the Fonts folder (c:\windows\fonts) may resolve it. PDFs opened in Microsoft Edge may not display correctly (small, instead of using the entire space). We’re investigating a race condition resulting in blue screens if your PC is set up to dual boot. If you’re impacted the workaround is to disable dual boot for now, we’ll let you know when the fix flights. The hyperlink colors need to be refined in Dark Mode in Sticky Notes if the Insights are enabled. Settings page will crash after changing the account password, we recommend using the CTRL + ALT + DEL method to change the password. WSL will not work on this build. If you require WSL to work, you can rollback to Build 18267. Known issues for Developers If you install any of the recent builds from the Fast ring and switch to the Slow ring – optional content such as enabling developer mode will fail. You will have to remain in the Fast ring to add/install/enable optional content. This is because optional content will only install on builds approved for specific rings. Read the full article

[third-eyeai]

[third-eyeai]

Corporate Spaces: Leveraging Facial Recognition Technology for Enhanced Security

In the ever-evolving landscape of corporate security, traditional access control methods are giving way to innovative solutions that offer enhanced efficiency and reliability. One such technology making waves in the corporate world is facial recognition. By harnessing the power of facial recognition technology, corporate buildings can bolster their security measures while streamlining access management processes for employees and visitors alike.

Enhancing Access Control

At the heart of any corporate security strategy lies access control, the cornerstone of protecting valuable assets and sensitive information. Facial recognition technology revolutionizes access control by replacing inconvenient keycards and PIN codes with a touchless, biometric solution. Employees no longer need to fumble for access cards or remember complex passcodes; instead, their unique facial features serve as the key to unlock doors and secure areas within the building.

Streamlining Visitor Management

In addition to managing employee access, facial recognition technology plays a crucial role in visitor management. Gone are the days of paper sign-in sheets and manual registration processes. With facial recognition systems in place, visitors can quickly and accurately register their arrival, allowing security personnel to monitor and track their movements throughout the building. This not only enhances security but also creates a seamless and welcoming experience for guests.

Real-Time Monitoring and Response

Facial recognition cameras strategically positioned at building entrances and key access points provide real-time monitoring of employee and visitor activities. Security personnel can instantly identify individuals entering the premises, flagging any unauthorized access attempts or suspicious behavior. In the event of a security incident or emergency, facial recognition technology enables swift and targeted response measures, ensuring the safety and well-being of everyone within the corporate space.

Data-driven Insights and Compliance

Facial recognition systems generate valuable data insights that go beyond just access control. By analyzing patterns of employee and visitor movements, organizations can identify areas of congestion, optimize space utilization, and enhance operational efficiency. Moreover, facial recognition technology helps organizations maintain compliance with regulatory requirements by providing detailed audit trails of access events and security incidents.

Integration and Automation

The true power of facial recognition technology lies in its ability to seamlessly integrate with other building systems and technologies. From lighting and HVAC controls to elevator operations and occupancy sensors, facial recognition serves as the linchpin of a fully automated and intelligent building ecosystem. By centralizing access control and security functions, organizations can unlock new levels of efficiency and convenience in managing corporate spaces.

Conclusion

As corporate security threats continue to evolve, organizations must stay ahead of the curve by embracing cutting-edge technologies like facial recognition. By leveraging the power of biometric identification, corporate buildings can fortify their defenses, streamline access management processes, and create safer and more secure environments for employees, visitors, and assets alike. With facial recognition technology at the helm, the future of corporate security is brighter than ever.

[third-eyeai]

Revolutionizing Canteen Management with AI-powered Facial Recognition

In the dynamic landscape of canteen management, the adoption of innovative technologies is essential for staying ahead of the curve. Among these technologies, AI-powered Facial Recognition (FR) Systems have emerged as a game-changer, offering a seamless and secure authentication solution for users.

Boosting Efficiency and User Experience

Gone are the days of fumbling with ID cards or tickets. FR Systems provide a seamless experience, eliminating the need for physical items. This streamlines the entry process, reducing wait times and improving user satisfaction.

Data-Driven Decisions for Optimized Operations

Beyond authentication, FR Technology unlocks a treasure chest of valuable data. By analyzing users’ preferences and consumption habits, canteen managers can gain insights to optimize dish selection, inventory management, and overall operations. This data-driven approach ensures resources are allocated efficiently and menus cater to customer preferences.

Scalability for All Sizes

Whether it’s a cozy cafeteria or a bustling corporate dining facility, FR Technology adapts seamlessly. It streamlines authentication and optimizes workflows, enhancing operational efficiency and resource allocation regardless of the size or scale of the canteen.

Personalized Recommendations for Increased Engagement

One of the most exciting features of FR Technology is its ability to personalize the user experience. By analyzing past orders, the system can offer tailored recommendations and promotions. This fosters customer engagement and loyalty, encouraging repeat visits and satisfaction.

Benefits of AI-Powered Facial Recognition

Streamlined Authentication: Eliminates the need for physical items, boosting convenience.

Data-Driven Optimization: Provides valuable insights to optimize operations and menus.

Personalized Experiences: Recommends dishes and promotions based on individual preferences.

Scalable Solution: Adapts seamlessly to canteens of all sizes.

Transforming the Dining Experience

AI-powered Facial Recognition Systems represent a significant shift in canteen management. They offer a multitude of benefits, including streamlined processes, data-driven decision making, and personalized experiences. Embracing these innovative solutions isn’t just about keeping up with the times; it’s a leap towards a more efficient, engaging, and enjoyable dining experience for everyone.

This version emphasizes the user experience benefits and data-driven decision making aspects while avoiding the phrase “enhancing security.

[khalilhumam]

Black Lives Matter forces an IBM U-turn on facial recognition

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Black Lives Matter forces an IBM U-turn on facial recognition

A system which worsens discrimination.

Facial recognition technology analyses the structure of faces to allow identification of individuals. Image used with permission. Source: Pikrepo

In the midst of the recent Black Lives Matter protests, which raised questions about policing and racism in the United States and elsewhere, technology company IBM announced its withdrawal from the general-purpose facial recognition market. Could this be a turning point in the use of such technologies—fraught with drawbacks— by security services? The news has gone almost unnoticed. On one side of the story lies the bungled COVID-19 health crisis. On the other, protests and anger ignited by shocking police violence, that revived the Black Lives Matter movement worldwide. Caught between the two, IBM declared on June 8, its decision to abandon the marketing of facial recognition software, having been driven by the protest actions into honouring its ethical charter. “Technology can increase transparency and help the police to protect communities, but must not promote discrimination or racial injustice”, Arving Krishna, the company’s CEO notably commented. The announcement has garnered few reactions, except from a few political economy analysts, technophiles, and activists. And yet it could indicate major changes in the influence which technology exerts on our lives.

A controversial technology

Facial recognition software permits individuals to be identified from photos or videos in an automated way. To achieve this, it relies on two pillars: a reference dataset of pre-recorded images, and large processing capacity. Both are areas which have seen phenomenal progress recently, owing to innovations in Big Data and in Artificial Intelligence (AI). Scaling-up facial recognition massively has thus become a possibility. For several years, examples have mushroomed worldwide. As far back as February 2005, the police department of Los Angeles was using a system developed by General Electric and Hamilton Pacific. It’s a practice which has since become generalised, and accelerated. In 2019, China had a total of 200 million video surveillance cameras on its soil. An even denser network is under preparation in Russia. Not to mention the initiatives of cities like Nice, which is currently testing [fr] the technology, or London, where cameras analyse the faces of passers-by (without informing them) with the aim of locating people being sought by the authorities. Authorities justify such automated surveillance by security imperatives: in late 2016, the International Criminal Organization, Interpol, claimed the identification of “over 650 felons, fugitives, persons of interest or missing persons […]”. It is all done in the name of the struggle against criminality, terrorism, or more recently the spread of coronavirus. But as with other advanced technologies, facial recognition is a double-edged sword. The progress it brings is accompanied by threats, particularly to civil liberties. Several digital rights organizations have been alerting the public opinion to potential threats and abuses enabled by facial recognition. Among them, the Electronic Frontier Foundation (EFF) and La Quadrature du Net. The latter is co-ordinating a campaign called Technopolice [fr], an initiative which logs and exposes automatic surveillance plans in France, and calls for systematic resistance.

Limited reliability

The most prominent harm of using facial recognition tools is bias. These tools identify and verify people based on exposure to sample data: so-called training data sets. If these are incomplete or lacking in relevance, the tool will make poor interpretations. It’s called learning bias. To demonstrate such bias, last month, Twitter users tested an AI that reconstructs portraits from pixelated images,and published their anomalous results online. There were significantly more failures with Afro-American or Hispanic subjects, such as Barack Obama and Alexandria Ocasio-Cortez. The dataset, which consisted almost solely of portraits of white people, had wrongly nudged the reconstructions in the direction of the most likely typical profiles.

This Image of a White Barack Obama Is AI’s Racial Bias Problem In a Nutshell https://t.co/88Sl9H0lEp — adafruit industries (@adafruit) June 27, 2020

When using AI to identify people from images rather than to enhance them, the analytical process will probably show a similar bias.

Imaginons que vous décidiez d’évaluer la dangerosité, le risque de criminalité d’une personne, à partir de paramètres tels que l’âge, le lieu de résidence, la couleur de peau, le diplôme le plus élevé… et que pour entraîner votre logiciel, vous utilisiez les données fournies par les centres de détention, les prisons. Alors il est fort probable que votre logiciel minimise fortement les risques pour les personnes blanches et l’augmente pour les autres.

Suppose you decided to assess the level of risk of a person’s criminality, based on parameters like age, home address, skin colour, highest academic qualification… and, to train your software, you used data supplied by detention centres, or prisons. Then it’s highly likely your software will seriously downplay the risks for white people, and elevate them for others. Source: https://intelligence-artificielle.agency/les-biais/ [fr]

The facts speak for themselves. In London real-time facial recognition shows an 81% error rate; in Detroit, an African-American man spoke up about his wrongful arrest because of a faulty identification.

Disputed legitimacy

Not only is facial recognition fallible, it worsens discrimination, as confirmed by an inquiry by the investigative site ProPublica back in 2016.

Google Photos tagged two African Americans as “gorillas” […] a recruitment aid tool used by Amazon disadvantaged women Source: « IA : la reconnaissance faciale est-elle raciste ? » Orange Digital Society Forum [fr]

See For police forces, often the object of accusations of discrimination, facial recognition is an extra, highly inflammable element. The death of George Floyd in an incident of police violence, on May 25, in Minneapolis, sparked a wave of demonstrations in the United States initially, then across the world. They started off denouncing discrimination targeting ethnic minorities. But an escalation of violence led the demonstrators to demand the de-militarisation of law enforcement agencies, chanting the slogan “Defund the police”. By extension, the tools of widespread surveillance have also come under scrutiny, as well as the private companies which provide them. So, now, under pressure from Black Lives Matter activists, the company IBM has announced its partial withdrawal from the facial recognition market. It is no accident that Big Blue [IBM’s nickname] has been the first to react. The firm has a long, sometimes shameful history, which it had to learn to come to terms with. Back in 1934, it collaborated with the Nazi regime via its German subsidiary. Much later, in 2013, it was implicated in the case of the PRISM surveillance programme, exposed by the whistleblower Edward Snowden. Perhaps for this reason, it has been able to finesse its role in the current conflict between a security-driven state and human rights activists. Of course, it’s possible to come up with a far more rational argument for IBM’s strategy, eager to protect itself from future judicial proceedings and their financial cost. Nonetheless, the reorientation of its activity is real enough, and it has sparked an initiative which is being followed by other industry giants. Microsoft declared on June 12 that it would refuse to sell its facial recognition technology to police agencies; Amazon, under peer pressure, ended up declaring a moratorium on its Rekognition tool.

A step towards reform?

The need for a statutory framework has become obvious. In his announcement, Arvind Krishna, IBM CEO, called on the US Congress to “open a national dialogue to see whether, and how, facial recognition technologies are to be used by law enforcement agencies.” This call has been heard. Members of Congress introduced on June 25 a bill to ban the use of facial recognition by the police. A day earlier, the ban was endorsed in the city of Boston by its City Council. No doubt this is just the start of a long political and legal battle, to bind the use of facial recognition into a procedure respectful of citizens. But for the first time, human rights defence movements seem in a position to influence the Big Tech companies and the political establishment, in the direction of the shared aspiration of a technology profitable for everyone.

Written by Rémy Vuong Translated by Adam Long · · View original post [fr] · comments (0) Donate · Share this: twitter facebook reddit