Using Viso Suite, organizations can deliver object detection and counting, people detection, anomaly detection, vehicle identification, pose estimation, video analysis, surveillance, and safety monitoring, visual inspection and quality assessment, facial recognition, emotion analysis, and more.

27 Most Popular Computer Vision Companies & Startups (2024) - viso.ai

Visual AI can be your trusted travel companion when you&#x27;re exploring new places. It powers augmented reality apps that offer real-time information about landmarks, historical sites, and tourist attractions. Visual AI can also assist in language translation through image recognition, making ...

What is Visual AI? - And How It Is Reshaping The Future

The sensor technology is inspired ... the visual information in an extremely performing yet highly efficient way. ... Country: Israel | Funding: $106M Buildots specializes in transforming construction sites into a data-driven digital environment. It is leveraging artificial intelligence computer vision technology to address construction inefficiencies. ... Country: UK Hawk-Eye is a British company that develops ...

Top 10 Computer Vision startups

After a team walks through a site and compiles visual data, OnSiteIQ’s computer vision technology merges this data with a company’s floor plan. Workers and managers can then view their sites from anywhere and even travel back to previous project phases, making it easier to spot potential ...

35 Computer Vision Companies and Startups To Know | Built In

Derive insights from images with AutoML Vision, or use pre-trained Vision API models or create computer vision applications with Vertex AI Vision

Vision AI | Google Cloud

Here we will outline the top AI platforms for generating visual content and give you an idea of how to use them for web stories.

Top 5 AI platforms for generating visual content

Applitools is an AI-powered visual testing &amp; monitoring platform. Applitools Visual AI is easy to setup and integrates with all modern test frameworks.

AI-Powered Test Automation Platform - Applitools

AI art is becoming more popular and easy to create.

3 Free AI Art tools to make your visual content shine | by Heather ...

Every industry has a high demand for visual AI capabilities such as systems that can be used for automation to boost productivity, personalization to improve the customer experience, research to fasten learning and innovation, and more. In healthcare, providers use the technology, specifically imaging technologies, to automate the examination, detection, and diagnosis of health issues from devices such as X-rays and CT scans. Insurance companies ...

The Definition of Visual AI | Syte

There is a very good reason why companies in the phishing detection and threat […] Posted in: Anti-Phishing, Cybersecurity · Did We Just Accidentally Revolutionize Phishing Detection? - Reading Time: 5 minutes Visual-AI is the missing piece of the puzzle when it comes to tackling Phishing ...

What is Visual Artificial Intelligence? - VISUA

visualAI

visualai

Sounds (“Music”) and Images (“Art”) have been somewhat conquered by GenAI; it’s never been easier to create slop that’s “close enough” to the real thing. Creatives have taken notice, with 73% of them now believing that AI will significantly impact their industry. Fashion has up until now been somewhat disconnected from these conversations. Sure, it’s a creative sector, but it also has glamor… and a certain exclusivity that makes AI’s infinite abundance seem gauche in comparison. But times are changing, and even the Milanese are now asking if machines could craft the kind of cultural cachet luxury designers have spent lifetimes building.’ Three researchers — Page Moreau from the University of Wisconsin-Madison, Emanuela Prandelli from Bocconi University, and Martin Schreier from WU Vienna — recently added complexity to this debate with a research paper titled, “Generative Artificial Intelligence and Design Co-Creation in Luxury New Product Development: The Power of Discarded Ideas.” Their research explores whether generative AI can compete with human designers when it comes to luxury fashion… and how consumers may react to machine-made chic. Can AI Design Fashion Luxury? The researchers teamed up with AwayToMars, Missoni, and IBM Watson to create and sell t-shirts designed either by human designers or by AI. The AI, IBM Watson, was trained on designs previously discarded by AWTM, using machine learning to generate new design ideas based on recurring features and patterns. They conducted a controlled experiment with over 1,000 participants and analyzed real sales data of the t-shirts over 20 weeks. The results? Consumers preferred AI-designed t-shirts — but only if they didn’t know they were designed by AI. When the source was revealed, consumer enthusiasm waned. Sales data showed a whopping 127% increase for AI-designed shirts compared to human-designed ones. Turns out, when fashion is sold without disclaimers, AI might just have a better eye for the cutting edge. What Does This Mean for the Fashion Industry? AI-designed products can outsell human creations. In fact, AI-generated t-shirts outsold their human-designed counterparts by more than twofold in the study. This means that any sense of superiority capitalistically-inclined designers may have needed to be adjusted to a new reality. Consumer perception of AI is still a barrier. When consumers were informed that designs were AI-generated, their willingness to pay for the garments decreased (“Knowing that a luxury fashion item was designed by AI reduced consumers’ responses towards it.”) This reflects an ongoing distrust of AI’s creative capability. Some may be tempted to hide the use of AI in the face of these results, but consumers also value transparency, and laws are being proposed to improve that transparency. This no doubt will be the toughest nut to crack in the near future. AI can enhance brand identity. The AI-designed shirts more effectively embodied Missoni’s signature zigzag patterns and brand codes compared to the human designs. This can be well-received by customers and can reinforce a brand identity if used properly. “Trash” is more useful than we think. The AI drew inspiration from designs that had been previously discarded — transforming “waste” into wearable art. This highlights how AI’s capacity to learn from mass data can unlock the value of overlooked ideas. This is without a doubt my favorite part of the study. How Do We Move Forward? This is a lot to take in; enough to give fashion executives some vertigo. While they get their bearings, there are a few things the fashion community can push for. We need to implement mandatory AI disclosure in product descriptions. Transparency is crucial. Consumers should know if an item was designed by AI, even if it impacts their perception negatively. If 44% of participants disliked AI-designed fashion when informed, brands need to confront this distrust head-on to avoid backlash. “AI aversion” (as another study called it) suggests that transparency is necessary but should be handled with care. Brands might want to frame AI’s role in a positive light by focusing on efficiency, sustainability, or co-creation to mitigate negative perceptions. Fashion Houses need to expertly blend AI with humans. The AI designs from the study could have been reviewed and improved by designers, I’m sure. Allowing designers to steer AI’s outputs could preserve the “luxury touch” that customers associate with human creativity while benefiting from AI’s efficiency. This may even lead to better margins and perhaps (?) better pay for artisans who surely deserve it. Fashion houses need to establish strict, all-encompassing guidelines to future-proof their expertise. This includes establishing standards for what training data is used… and what data cannot/should not be used. AI in creative industries often relies on massive datasets, some of which are sourced unethically. Ensuring AI is trained ethically on licensed or voluntarily contributed data is key to gaining consumer trust. Nothing says luxury like ethics… right? Right? Above all, consumers need to be educated. Brands should invest in educating consumers on the strengths of AI for design — including sustainability and efficiency, where algorithms are likely well-placed to have an impact. Highlighting how AI uses discarded materials creatively, as seen with Missoni, could positively shift consumer sentiment. Where the Research Paper Falls Short While Moreau, Prandelli, and Schreier’s study brings some interesting insights to light, it is not without flaws. For one, all participants were business school students — hardly representative of the global, diverse consumer base of luxury fashion. It also didn’t explore whether consumers would prefer designs if AI had been marketed as an “assistant” rather than a “creator”. Furthermore, the research only touched upon visual identity, ignoring other sensory elements such as fabric texture and craftsmanship, which are crucial in luxury fashion. For future research, I’d love to see how AI fares when tasked with creating high-fashion couture pieces rather than simple t-shirts. How does it navigate avant-garde aesthetics or evolving cultural nuances? Probably not very well. Are Designers Doomed? Generative AI in luxury fashion is like a bold new fabric. One that can dazzle but also fray under scrutiny. If handled with care, AI could provide a much-needed spark of innovation while pushing the boundaries of design. The key lies in its careful integration, transparency, and cooperation with human experts. A future where AI complements, rather than replaces, human creativity isn’t just desirable… it might be our only shot at saving the soul of luxury. Good luck out there. Sounds (“Music”) and Images (“Art”) have been somewhat conquered by GenAI; it’s never been easier to create slop that’s “ close enough ” to the real thing. Creatives have taken notice, with 73% of them now believing that AI will significantly impact their industry . Sounds Images close enough 73% of them now believing that AI will significantly impact their industry Fashion has up until now been somewhat disconnected from these conversations. Sure, it’s a creative sector, but it also has glamor… and a certain exclusivity that makes AI’s infinite abundance seem gauche in comparison. But times are changing, and even the Milanese are now asking if machines could craft the kind of cultural cachet luxury designers have spent lifetimes building.’ Three researchers — Page Moreau from the University of Wisconsin-Madison, Emanuela Prandelli from Bocconi University, and Martin Schreier from WU Vienna — recently added complexity to this debate with a research paper titled, “ Generative Artificial Intelligence and Design Co-Creation in Luxury New Product Development: The Power of Discarded Ideas . ” Page Moreau Emanuela Prandelli Martin Schreier Generative Artificial Intelligence and Design Co-Creation in Luxury New Product Development: The Power of Discarded Ideas Generative Artificial Intelligence and Design Co-Creation in Luxury New Product Development: The Power of Discarded Ideas . Their research explores whether generative AI can compete with human designers when it comes to luxury fashion… and how consumers may react to machine-made chic. Can AI Design Fashion Luxury? The researchers teamed up with AwayToMars , Missoni , and IBM Watson to create and sell t-shirts designed either by human designers or by AI. The AI, IBM Watson, was trained on designs previously discarded by AWTM, using machine learning to generate new design ideas based on recurring features and patterns. They conducted a controlled experiment with over 1,000 participants and analyzed real sales data of the t-shirts over 20 weeks. AwayToMars Missoni The results? Consumers preferred AI-designed t-shirts — but only if they didn’t know they were designed by AI . When the source was revealed, consumer enthusiasm waned. Sales data showed a whopping 127% increase for AI-designed shirts compared to human-designed ones. but only if they didn’t know they were designed by AI Turns out, when fashion is sold without disclaimers, AI might just have a better eye for the cutting edge. What Does This Mean for the Fashion Industry? AI-designed products can outsell human creations. In fact, AI-generated t-shirts outsold their human-designed counterparts by more than twofold in the study. This means that any sense of superiority capitalistically-inclined designers may have needed to be adjusted to a new reality. AI-designed products can outsell human creations. In fact, AI-generated t-shirts outsold their human-designed counterparts by more than twofold in the study. This means that any sense of superiority capitalistically-inclined designers may have needed to be adjusted to a new reality. AI-designed products can outsell human creations. Consumer perception of AI is still a barrier. When consumers were informed that designs were AI-generated, their willingness to pay for the garments decreased (“Knowing that a luxury fashion item was designed by AI reduced consumers’ responses towards it.”) This reflects an ongoing distrust of AI’s creative capability. Some may be tempted to hide the use of AI in the face of these results, but consumers also value transparency, and laws are being proposed to improve that transparency. This no doubt will be the toughest nut to crack in the near future. Consumer perception of AI is still a barrier. When consumers were informed that designs were AI-generated, their willingness to pay for the garments decreased ( “Knowing that a luxury fashion item was designed by AI reduced consumers’ responses towards it.”) This reflects an ongoing distrust of AI’s creative capability. Some may be tempted to hide the use of AI in the face of these results, but consumers also value transparency, and laws are being proposed to improve that transparency. This no doubt will be the toughest nut to crack in the near future. Consumer perception of AI is still a barrier. “Knowing that a luxury fashion item was designed by AI reduced consumers’ responses towards it.”) AI can enhance brand identity. The AI-designed shirts more effectively embodied Missoni’s signature zigzag patterns and brand codes compared to the human designs. This can be well-received by customers and can reinforce a brand identity if used properly. AI can enhance brand identity. The AI-designed shirts more effectively embodied Missoni’s signature zigzag patterns and brand codes compared to the human designs. This can be well-received by customers and can reinforce a brand identity if used properly. AI can enhance brand identity. “Trash” is more useful than we think. The AI drew inspiration from designs that had been previously discarded — transforming “waste” into wearable art. This highlights how AI’s capacity to learn from mass data can unlock the value of overlooked ideas. This is without a doubt my favorite part of the study. “Trash” is more useful than we think. The AI drew inspiration from designs that had been previously discarded — transforming “waste” into wearable art. This highlights how AI’s capacity to learn from mass data can unlock the value of overlooked ideas. This is without a doubt my favorite part of the study. “Trash” is more useful than we think. How Do We Move Forward? This is a lot to take in; enough to give fashion executives some vertigo. While they get their bearings, there are a few things the fashion community can push for. We need to implement mandatory AI disclosure in product descriptions. We need to implement mandatory AI disclosure in product descriptions. Transparency is crucial. Consumers should know if an item was designed by AI, even if it impacts their perception negatively. If 44% of participants disliked AI-designed fashion when informed, brands need to confront this distrust head-on to avoid backlash. “AI aversion” ( as another study called it ) suggests that transparency is necessary but should be handled with care. as another study called it Brands might want to frame AI’s role in a positive light by focusing on efficiency, sustainability, or co-creation to mitigate negative perceptions. Fashion Houses need to expertly blend AI with humans. Fashion Houses need to expertly blend AI with humans. The AI designs from the study could have been reviewed and improved by designers, I’m sure. Allowing designers to steer AI’s outputs could preserve the “luxury touch” that customers associate with human creativity while benefiting from AI’s efficiency. This may even lead to better margins and perhaps (?) better pay for artisans who surely deserve it. Fashion houses need to establish strict, all-encompassing guidelines to future-proof their expertise. Fashion houses need to establish strict, all-encompassing guidelines to future-proof their expertise. This includes establishing standards for what training data is used… and what data cannot/should not be used. AI in creative industries often relies on massive datasets, some of which are sourced unethically. Ensuring AI is trained ethically on licensed or voluntarily contributed data is key to gaining consumer trust. Nothing says luxury like ethics… right? Right? Above all, consumers need to be educated. Above all, consumers need to be educated. Brands should invest in educating consumers on the strengths of AI for design — including sustainability and efficiency, where algorithms are likely well-placed to have an impact. Highlighting how AI uses discarded materials creatively, as seen with Missoni, could positively shift consumer sentiment. Where the Research Paper Falls Short While Moreau , Prandelli , and Schreier ’s study brings some interesting insights to light, it is not without flaws. For one, all participants were business school students — hardly representative of the global, diverse consumer base of luxury fashion. Moreau Prandelli Schreier It also didn’t explore whether consumers would prefer designs if AI had been marketed as an “assistant” rather than a “creator”. Furthermore, the research only touched upon visual identity, ignoring other sensory elements such as fabric texture and craftsmanship, which are crucial in luxury fashion. For future research, I’d love to see how AI fares when tasked with creating high-fashion couture pieces rather than simple t-shirts. How does it navigate avant-garde aesthetics or evolving cultural nuances? Probably not very well. Are Designers Doomed? Generative AI in luxury fashion is like a bold new fabric. One that can dazzle but also fray under scrutiny. If handled with care, AI could provide a much-needed spark of innovation while pushing the boundaries of design. The key lies in its careful integration, transparency, and cooperation with human experts. A future where AI complements, rather than replaces, human creativity isn’t just desirable… it might be our only shot at saving the soul of luxury. Good luck out there.

Would You Still Love a $500 T-Shirt If You Knew AI Designed It?

Sal jy steeds van 'n $500 T-hemp hou as jy geweet het dat KI dit ontwerp het?

AI ዲዛይኑን ካወቁ አሁንም የ500 ዶላር ቲሸርት ይወዳሉ?

¿Aún te gustaría una camiseta de 500 dólares si supieras que fue diseñada por una IA?

اگر می دانستید که هوش مصنوعی آن را طراحی کرده است، آیا هنوز عاشق یک تی شرت 500 دلاری هستید؟

यदि आपको पता हो कि 500 डॉलर की टी-शर्ट को AI ने डिज़ाइन किया है, तो क्या आप उसे अब भी पसंद करेंगे?

Ti piacerebbe ancora una maglietta da 500 dollari se sapessi che è stata progettata dall'intelligenza artificiale?

AI がデザインした 500 ドルの T シャツをまだ気に入っていますか?

კიდევ გიყვართ 500 დოლარიანი მაისური, თუ იცოდით, რომ ხელოვნური ინტელექტის დიზაინია?

AI가 디자인했다고 해도 500달러짜리 티셔츠를 여전히 좋아할까요?

ایا تاسو به بیا هم د 500 ډالرو ټي شرټ خوښ کړئ که تاسو پوهیږئ چې AI یې ډیزاین کړی؟

Xana Wa Ha Swi Rhandza Xikipa Xa R1 000 Loko U Swi Tiva Leswaku AI Yi Xi Endlile?

Bạn có còn thích áo phông giá 500 đô la không nếu biết nó được thiết kế bởi AI?

The Top AI Tools for Web Development

AI technology is firmly carving out a niche in defense technology. News reports refer to partnership agreements between large defense technology corporations such as Northrop Grumman, Lockheed Martin, Raytheon, and startups in Artificial Intelligence/Machine Learning (AI/ML), Image Intelligence (IMINT) Recognition, Battlefield Awareness, Multi‐Domain Battle Management Team (MBMT) Mobility Platform, Tactical Augmented Reality (TAR), Radio Frequency and Wireless Surveillance (RFWS) Systems. At the same time, defense startups like Palantir and Anduril, which have relied on the use of AI since their inception, have now emerged as major game-changing market players. Undoubtedly, this trend will continue in the future and we will soon see significant changes in the direction of enhanced national security and deployed defense technologies. As proof, Project Maven, announced in 2017 according to various sources, is being tested in combat use in military conflicts in Ukraine and Israel. A wearable mobile platform with a high-performance computing module within the Soldier-as-Sensor concept is expected to complement the Battlefield Management System (BMS) and Joint All-Domain Command and Control (JADC2) ecosystems. At least a few defense agencies periodically publish innovation requests for these and related research and technology areas in the prototype stage. Today's highly mobile battlefield threats and higher demands on the cognitive capabilities of operators are driving the need to act simultaneously on multiple fronts. Everyone is required to have accurate Tactical Situational Awareness, Friend-or-Foe Identification, Synthetic Day/Night Vision, BLOS Communications, Sensors, Signal Intelligence, RF Wideband Digital Beamforming, and be an element for sharing this information in peer-to-peer networks. Until recently, some technological limitations have prevented the creation of a convenient mobile platform with dimensions that would allow a person to wear an electronic device for extended periods. For several years, Microsoft tried to adapt its HoloLense 2 device for the IVAS defense project. However, immersive technologies (AR/MR) designed for entertainment, education and training are not well-suited for the military. At Furtherium, my team and I initially created the concept of AI-powered Tactical Situational Awareness Mobility Platform primarily for the military, security forces, rescuers, and firefighters. The key advantages of this technology are its compact design, fast performance, integration of all important functions, and affordable price. And a special feature is the application of several AI technologies on a local device. I founded Furtherium in 2021 and started developing an integrated product ARHUDFM (Augmented Reality Head-Up Display Fullface Mask) - Hardware modules, SaaS and Healthcare modules. Several detection and recognition functions cover visual information in the visible and infrared spectrum, sound and radio frequency waves over a very wide range, passive radar signatures and an EOD scanner for explosive detection. The core of the technology is not only pre-trained Machine Learning models, but also neural network generative AI models that are trained together with each user. For this purpose, advanced CPU and NPU technologies are used in each device. You could say this is the next step in using generative AI in mobile platforms, focused not only on defense applications, with huge dual-use potential. Fine-tuning a local model on a user's device still looks unfamiliar right now, but that's the future. In addition to the key goals of detection and recognition, other basic functions are Omnichannel Messenger, Media Capture and Exchange, Ballistic Fire Assistant and Spotter, Multi-Domain Operations, RC Universal Controller for drones and robots. You don't have to look into the future to understand the trends, just open YouTube and see for yourself the training programs at the U.S. Marine Corps and Army. I'd like to share the new YouTube video we created that demonstrates the capabilities of my startup's mobile platform, controlled primarily by a voice assistant that adapts to each user. https://www.youtube.com/watch?v=kIUt2jbYHMs&embedable=true It seems natural and logical now, but we didn't come to this immediately, it took several years of research into behavioral capabilities, cognitive constraints, tactical documentation and the capabilities of different electronic component architectures. The technical requirements of the military are very different from civilian applications. This applies not only to Hardware, but also to Software, including how generative AI models should be used, what criteria they are required to meet. Many features must be considered when designing for military applications: Night Vision and Augmented Reality without outward illumination, protection against ionizing radiation, main and backup power system, battery life, active cooling, shock wave and fragments exposure, encryption and hopping frequencies for data exchange, reduced traffic while maintaining data quality. It would be wrong to think about the protection of the device and not provide for the protection of the user. For this purpose, several options are created simultaneously for hearing protection and protection against concussion, eye and face protection, respiratory protection, built-in drinking system, automatic tourniquets to stop acute bleeding. Sensors in the user's arms, legs, back, and chest allow health monitoring during exercises, training, and tactical missions. SAM (Serviceable Addressable Market) for this technology estimates exceed $31B and include markets in over 32 NATO countries and its allies. Amid the Research, Development, Test & Evaluation budget of the U.S. DoD alone of $148B this looks promising. Military innovation budgets are on the rise, that's another trend today. My startup's project involves integrating products from companies such as Palantir, Anduril, Teledyne FLIR, Shield AI, Epirus, Elbit Systems, Raytheon, Northrop Grumman, and Thales Group. Combining non-competing systems creates unique synergies. I am also pleased to announce that we are close to raising funds. Investors are expected to include companies with expertise in the Defense and Security area. More Details Furtherium, Inc. | Website | Wiki | Community | Pitch Deck AI technology is firmly carving out a niche in defense technology. News reports refer to partnership agreements between large defense technology corporations such as Northrop Grumman, Lockheed Martin, Raytheon, and startups in Artificial Intelligence/Machine Learning (AI/ML), Image Intelligence (IMINT) Recognition, Battlefield Awareness, Multi‐Domain Battle Management Team (MBMT) Mobility Platform, Tactical Augmented Reality (TAR), Radio Frequency and Wireless Surveillance (RFWS) Systems. At the same time, defense startups like Palantir and Anduril, which have relied on the use of AI since their inception, have now emerged as major game-changing market players. Undoubtedly, this trend will continue in the future and we will soon see significant changes in the direction of enhanced national security and deployed defense technologies. As proof, Project Maven, announced in 2017 according to various sources, is being tested in combat use in military conflicts in Ukraine and Israel. A wearable mobile platform with a high-performance computing module within the Soldier-as-Sensor concept is expected to complement the Battlefield Management System (BMS) and Joint All-Domain Command and Control (JADC2) ecosystems. At least a few defense agencies periodically publish innovation requests for these and related research and technology areas in the prototype stage. Today's highly mobile battlefield threats and higher demands on the cognitive capabilities of operators are driving the need to act simultaneously on multiple fronts. Everyone is required to have accurate Tactical Situational Awareness, Friend-or-Foe Identification, Synthetic Day/Night Vision, BLOS Communications, Sensors, Signal Intelligence, RF Wideband Digital Beamforming, and be an element for sharing this information in peer-to-peer networks. Until recently, some technological limitations have prevented the creation of a convenient mobile platform with dimensions that would allow a person to wear an electronic device for extended periods. For several years, Microsoft tried to adapt its HoloLense 2 device for the IVAS defense project. However, immersive technologies (AR/MR) designed for entertainment, education and training are not well-suited for the military. At Furtherium, my team and I initially created the concept of AI-powered Tactical Situational Awareness Mobility Platform primarily for the military, security forces, rescuers, and firefighters. The key advantages of this technology are its compact design, fast performance, integration of all important functions, and affordable price. And a special feature is the application of several AI technologies on a local device. I founded Furtherium in 2021 and started developing an integrated product ARHUDFM (Augmented Reality Head-Up Display Fullface Mask) - Hardware modules, SaaS and Healthcare modules. Several detection and recognition functions cover visual information in the visible and infrared spectrum, sound and radio frequency waves over a very wide range, passive radar signatures and an EOD scanner for explosive detection. The core of the technology is not only pre-trained Machine Learning models, but also neural network generative AI models that are trained together with each user. For this purpose, advanced CPU and NPU technologies are used in each device. You could say this is the next step in using generative AI in mobile platforms, focused not only on defense applications, with huge dual-use potential. Fine-tuning a local model on a user's device still looks unfamiliar right now, but that's the future. In addition to the key goals of detection and recognition, other basic functions are Omnichannel Messenger, Media Capture and Exchange, Ballistic Fire Assistant and Spotter, Multi-Domain Operations, RC Universal Controller for drones and robots. You don't have to look into the future to understand the trends, just open YouTube and see for yourself the training programs at the U.S. Marine Corps and Army. I'd like to share the new YouTube video we created that demonstrates the capabilities of my startup's mobile platform, controlled primarily by a voice assistant that adapts to each user. the new YouTube video https://www.youtube.com/watch?v=kIUt2jbYHMs&embedable=true https://www.youtube.com/watch?v=kIUt2jbYHMs&embedable=true It seems natural and logical now, but we didn't come to this immediately, it took several years of research into behavioral capabilities, cognitive constraints, tactical documentation and the capabilities of different electronic component architectures. The technical requirements of the military are very different from civilian applications. This applies not only to Hardware, but also to Software, including how generative AI models should be used, what criteria they are required to meet. Many features must be considered when designing for military applications: Night Vision and Augmented Reality without outward illumination, protection against ionizing radiation, main and backup power system, battery life, active cooling, shock wave and fragments exposure, encryption and hopping frequencies for data exchange, reduced traffic while maintaining data quality. It would be wrong to think about the protection of the device and not provide for the protection of the user. For this purpose, several options are created simultaneously for hearing protection and protection against concussion, eye and face protection, respiratory protection, built-in drinking system, automatic tourniquets to stop acute bleeding. Sensors in the user's arms, legs, back, and chest allow health monitoring during exercises, training, and tactical missions. SAM (Serviceable Addressable Market) for this technology estimates exceed $31B and include markets in over 32 NATO countries and its allies. Amid the Research, Development, Test & Evaluation budget of the U.S. DoD alone of $148B this looks promising. Military innovation budgets are on the rise, that's another trend today. My startup's project involves integrating products from companies such as Palantir, Anduril, Teledyne FLIR, Shield AI, Epirus, Elbit Systems, Raytheon, Northrop Grumman, and Thales Group. Combining non-competing systems creates unique synergies. I am also pleased to announce that we are close to raising funds. Investors are expected to include companies with expertise in the Defense and Security area. More Details Furtherium, Inc. | Website | Wiki | Community | Pitch Deck Website Wiki Community Pitch Deck

AI's Influence is Going to Go Deeper Than Just the Corporate Sector

AI এর প্রভাব শুধু কর্পোরেট সেক্টরের চেয়ে গভীরে যেতে চলেছে৷

Der Einfluss der KI wird über den Unternehmenssektor hinausgehen

La influencia de la IA va a llegar más allá del sector corporativo

L’influence de l’IA va s’étendre au-delà du secteur privé

एआई का प्रभाव सिर्फ कॉर्पोरेट क्षेत्र तक ही सीमित नहीं रहेगा

AIの影響は企業部門だけにとどまらない

AI의 영향력은 기업 부문에만 국한되지 않고 더욱 깊어질 것입니다.

A influência da IA vai além do setor corporativo

Влияние ИИ выйдет за рамки корпоративного сектора

Yapay Zekanın Etkisi Sadece Kurumsal Sektörle Sınırlı Kalmayacak

Ảnh hưởng của AI sẽ không chỉ giới hạn trong lĩnh vực doanh nghiệp

人工智能的影响将不仅仅局限于企业领域

The AI Renaissance: How Machines are Transforming Art, Music, and Literature

Authors:
(1) Gonzalo J. Aniano Porcile, LinkedIn;
(2) Jack Gindi, LinkedIn;
(3) Shivansh Mundra, LinkedIn;
(4) James R. Verbus, LinkedIn;
(5) Hany Farid, LinkedIn and University of California, Berkeley. Table of Links Abstract and Intro
Data sets
Model
Results
Discussion, Acknowledgements, and References Abstract AI-based image generation has continued to rapidly improve, producing increasingly more realistic images with fewer obvious visual flaws. AI-generated images are being used to create fake online profiles which in turn are being used for spam, fraud, and disinformation campaigns. As the general problem of detecting any type of manipulated or synthesized content is receiving increasing attention, here we focus on a more narrow task of distinguishing a real face from an AI-generated face. This is particularly applicable when tackling inauthentic online accounts with a fake user profile photo. We show that by focusing on only faces, a more resilient and general-purpose artifact can be detected that allows for the detection of AI-generated faces from a variety of GAN- and diffusion-based synthesis engines, and across image resolutions (as low as 128 × 128 pixels) and qualities. 1. Introduction The past three decades have seen remarkable advances in the statistical modeling of natural images. The simplest power-spectral model [20] captures the 1/ω frequency magnitude fall-off typical of natural images, Figure 1(a). Because this model does not incorporate any phase information, it is unable to capture detailed structural information. By early 2000, new statistical models were able to capture the natural statistics of both magnitude and (some) phase [25], leading to breakthroughs in modeling basic texture patterns, Figure 1(b). While able to capture repeating patterns, these models are not able to capture the geometric properties of objects, faces, or complex scenes. Starting in 2017, and powered by large data sets of natural images, advances in deep learning, and powerful GPU clusters, generative models began to capture detailed properties of human faces and objects [16, 18]. Trained on a large number of images from a single category (faces, cars, cats, etc.), these generative adversarial networks (GANs) capture highly detailed properties of, for example, faces, Figure 1(c), but are constrained to only a single category. Most recently, diffusion-based models [2,26] have combined generative image models with linguistic prompts allowing for the synthesis of images from descriptive text prompts like “a beekeeper painting a self portrait”, Figure 1(d). Traditionally, the development of generative image models were driven by two primary goals: (1) understand the fundamental statistical properties of natural images; and (2) use the resulting synthesized images for everything from computer graphics rendering to human psychophysics and data augmentation in classic computer vision tasks. Today, however, generative AI has found more nefarious use cases ranging from spam to fraud and additional fuel for disinformation campaigns. Detecting manipulated or synthesized images is particularly challenging when working on large-scale networks with hundreds of millions of users. This challenge is made even more significant when the average user struggles to distinguish a real from a fake face [24]. Because we are concerned with the use of generative AI in creating fake online user accounts, we seek to develop fast and reliable techniques that can distinguish real from AI-generated faces. We next place our work in context of related techniques. 1.1. Related Work Because we will focus specifically on AI-generated faces, we will review related work also focused on, or applicable to, distinguishing real from fake faces. There are two broad categories of approaches to detecting AI-generated content [10]. In the first, hypothesis-driven approaches, specific artifacts in AI-generated faces are exploited such as inconsistencies in bilateral facial symmetry in the form of corneal reflections [13] and pupil shape [15], or inconsistencies in head pose and the spatial layout of facial features (eyes, tip of nose, corners of mouth, chin, etc.) [23, 33, 34]. The benefit of these approaches is that they learn explicit, semanticlevel anomalies. The drawback is that over time synthesis engines appear to be – either implicitly or explicitly – correcting for these artifacts. Other non-face specific artifacts include spatial frequency or noise anomalies [5,8,12,21,35], but these artifacts tend to be vulnerable to simple laundering attacks (e.g., transcoding, additive noise, image resizing). In the second, data-driven approaches, machine learning is used to learn how to distinguish between real and AIgenerated images [11, 29, 32]. These models often perform well when analyzing images consistent with their training, but then struggle with out-of-domain images and/or are vulnerable to laundering attacks because the model latches onto low-level artifacts [9]. We attempt to leverage the best of both of these approaches. By training our model on a range of synthesis engines (GAN and diffusion), we seek to avoid latching onto a specific low-level artifact that do not generalize or may be vulnerable to simple laundering attacks. By focusing on only detecting AI-generated faces (and not arbitrary synthetic images), we show that our model seems to have captured a semantic-level artifact distinct to AI-generated faces which is highly desirable for our specific application of finding potentially fraudulent user accounts. We also show that our model is resilient to detecting AI-generated faces not previously seen in training, and is resilient across a large range of image resolutions and qualities. This paper is available on arxiv under CC 4.0 license. Authors: (1) Gonzalo J. Aniano Porcile, LinkedIn; (2) Jack Gindi, LinkedIn; (3) Shivansh Mundra, LinkedIn; (4) James R. Verbus, LinkedIn; (5) Hany Farid, LinkedIn and University of California, Berkeley. Authors: Authors: (1) Gonzalo J. Aniano Porcile, LinkedIn; (2) Jack Gindi, LinkedIn; (3) Shivansh Mundra, LinkedIn; (4) James R. Verbus, LinkedIn; (5) Hany Farid, LinkedIn and University of California, Berkeley. Table of Links Abstract and Intro Data sets Model Results Discussion, Acknowledgements, and References Abstract and Intro Abstract and Intro Data sets Data sets Model Model Results Results Discussion, Acknowledgements, and References Discussion, Acknowledgements, and References Abstract AI-based image generation has continued to rapidly improve, producing increasingly more realistic images with fewer obvious visual flaws. AI-generated images are being used to create fake online profiles which in turn are being used for spam, fraud, and disinformation campaigns. As the general problem of detecting any type of manipulated or synthesized content is receiving increasing attention, here we focus on a more narrow task of distinguishing a real face from an AI-generated face. This is particularly applicable when tackling inauthentic online accounts with a fake user profile photo. We show that by focusing on only faces, a more resilient and general-purpose artifact can be detected that allows for the detection of AI-generated faces from a variety of GAN- and diffusion-based synthesis engines, and across image resolutions (as low as 128 × 128 pixels) and qualities. AI-based image generation has continued to rapidly improve, producing increasingly more realistic images with fewer obvious visual flaws. AI-generated images are being used to create fake online profiles which in turn are being used for spam, fraud, and disinformation campaigns. As the general problem of detecting any type of manipulated or synthesized content is receiving increasing attention, here we focus on a more narrow task of distinguishing a real face from an AI-generated face. This is particularly applicable when tackling inauthentic online accounts with a fake user profile photo. We show that by focusing on only faces, a more resilient and general-purpose artifact can be detected that allows for the detection of AI-generated faces from a variety of GAN- and diffusion-based synthesis engines, and across image resolutions (as low as 128 × 128 pixels) and qualities. 1. Introduction The past three decades have seen remarkable advances in the statistical modeling of natural images. The simplest power-spectral model [20] captures the 1/ω frequency magnitude fall-off typical of natural images, Figure 1(a). Because this model does not incorporate any phase information, it is unable to capture detailed structural information. By early 2000, new statistical models were able to capture the natural statistics of both magnitude and (some) phase [25], leading to breakthroughs in modeling basic texture patterns, Figure 1(b). While able to capture repeating patterns, these models are not able to capture the geometric properties of objects, faces, or complex scenes. Starting in 2017, and powered by large data sets of natural images, advances in deep learning, and powerful GPU clusters, generative models began to capture detailed properties of human faces and objects [16, 18]. Trained on a large number of images from a single category (faces, cars, cats, etc.), these generative adversarial networks (GANs) capture highly detailed properties of, for example, faces, Figure 1(c), but are constrained to only a single category. Most recently, diffusion-based models [2,26] have combined generative image models with linguistic prompts allowing for the synthesis of images from descriptive text prompts like “a beekeeper painting a self portrait”, Figure 1(d). Traditionally, the development of generative image models were driven by two primary goals: (1) understand the fundamental statistical properties of natural images; and (2) use the resulting synthesized images for everything from computer graphics rendering to human psychophysics and data augmentation in classic computer vision tasks. Today, however, generative AI has found more nefarious use cases ranging from spam to fraud and additional fuel for disinformation campaigns. Detecting manipulated or synthesized images is particularly challenging when working on large-scale networks with hundreds of millions of users. This challenge is made even more significant when the average user struggles to distinguish a real from a fake face [24]. Because we are concerned with the use of generative AI in creating fake online user accounts, we seek to develop fast and reliable techniques that can distinguish real from AI-generated faces. We next place our work in context of related techniques. 1.1. Related Work Because we will focus specifically on AI-generated faces, we will review related work also focused on, or applicable to, distinguishing real from fake faces. There are two broad categories of approaches to detecting AI-generated content [10]. In the first, hypothesis-driven approaches, specific artifacts in AI-generated faces are exploited such as inconsistencies in bilateral facial symmetry in the form of corneal reflections [13] and pupil shape [15], or inconsistencies in head pose and the spatial layout of facial features (eyes, tip of nose, corners of mouth, chin, etc.) [23, 33, 34]. The benefit of these approaches is that they learn explicit, semanticlevel anomalies. The drawback is that over time synthesis engines appear to be – either implicitly or explicitly – correcting for these artifacts. Other non-face specific artifacts include spatial frequency or noise anomalies [5,8,12,21,35], but these artifacts tend to be vulnerable to simple laundering attacks (e.g., transcoding, additive noise, image resizing). In the second, data-driven approaches, machine learning is used to learn how to distinguish between real and AIgenerated images [11, 29, 32]. These models often perform well when analyzing images consistent with their training, but then struggle with out-of-domain images and/or are vulnerable to laundering attacks because the model latches onto low-level artifacts [9]. We attempt to leverage the best of both of these approaches. By training our model on a range of synthesis engines (GAN and diffusion), we seek to avoid latching onto a specific low-level artifact that do not generalize or may be vulnerable to simple laundering attacks. By focusing on only detecting AI-generated faces (and not arbitrary synthetic images), we show that our model seems to have captured a semantic-level artifact distinct to AI-generated faces which is highly desirable for our specific application of finding potentially fraudulent user accounts. We also show that our model is resilient to detecting AI-generated faces not previously seen in training, and is resilient across a large range of image resolutions and qualities. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Finding AI-Generated Faces in the Wild: Abstract and Intro

AI-জেনারেটেড ফেস ইন দ্য ওয়াইল্ড খোঁজা: অ্যাবস্ট্রাক্ট এবং ইন্ট্রো

KI-generierte Gesichter in freier Wildbahn finden: Zusammenfassung und Einführung

Encontrar rostros generados por IA en la naturaleza: resumen e introducción

Trouver des visages générés par l'IA dans la nature : résumé et introduction

जंगल में एआई-जनरेटेड चेहरे खोजना: सार और परिचय

AI が生成した顔の発見: 概要と導入

야생에서 AI가 생성한 얼굴 찾기: 초록 및 소개

Encontrando rostos gerados por IA na natureza: resumo e introdução

Поиск лиц, созданных искусственным интеллектом, в дикой природе: краткое содержание и введение

Vahşi Doğada Yapay Zeka Tarafından Üretilen Yüzleri Bulma: Soyut ve Giriş

Tìm kiếm khuôn mặt do AI tạo ra trong thế giới hoang dã: Tóm tắt và giới thiệu

在野外寻找人工智能生成的面孔：摘要和简介

Authors:
(1) Gonzalo J. Aniano Porcile, LinkedIn;
(2) Jack Gindi, LinkedIn;
(3) Shivansh Mundra, LinkedIn;
(4) James R. Verbus, LinkedIn;
(5) Hany Farid, LinkedIn and University of California, Berkeley. Table of Links Abstract and Intro
Data sets
Model
Results
Discussion, Acknowledgements, and References 4. Results Our baseline training and evaluation performance is shown in Table 2. The evaluation is broken down based on whether the evaluation images contain a face or not (training images contained only faces) and whether the images were generated with the same (in-engine) or different (out-of-engine) synthesis engines as those used in training (see Section 2.6). In order to provide a direct comparison of the true positive rate[8] (TPR) for the training and evaluation, we adjust the final classification threshold to yield a false positive rate[9] (FPR) of 0.5%. With a fixed FPR of 0.5%, AI-generated faces are correctly classified in training and evaluation at a rate of 98%. Across different synthesis engines (StyleGAN 1,2,3, Stable Diffusion 1,2, and DALL-E 2) used for training, TPR was varied somewhat from a low of 93.3% for Stable Diffusion 1 to a high of 99.5% for StyleGAN 2, and 98.9% for StyleGAN1, 99.9% for StyleGAN3, 94.9% for Stable Diffusion 2, and 99.2% for DALL-E 2. For faces generated by synthesis engines not used in training (out-of-engine), TPR drops to 84.5% at the same FPR, showing good but not perfect out-of-domain generalization. Across the different synthesis engines not used in training, TPR varied widely with a low of 19.4% for Midjourney to a high of 99.5% for EG3D, and 95.4% for generated.photos. Our classifier generalizes well in some cases, and fails in others. This limitation, however, can likely be mitigated by incorporating these out-of-engine images into the initial training. In a particularly striking result, non-faces – generated by the same synthesis engines as used in training – are all incorrectly classified. This is most likely because some of our real images contain non-faces (see Section 2.1) while all of the AI-generated images contain faces. Since we are only interested in detecting fake faces used to create an account, we don’t see this as a major limitation. This result also suggests that our classifier has latched onto a specific property of an AI-generated face and not some low-level artifact from the underlying synthesis (e.g., a noise fingerprint [8]). In Section 4.1, we provide additional evidence to support this hypothesis. The above baseline results are based on training and evaluating images at a resolution of 512×512 pixels. Shown in Figure 3(a) (solid blue) is the TPR when the training images are down scaled to a lower resolution (256, 128, 64, and 32) and then up scaled back up to 512 for classification. With the same FPR of 0.5%, TPR for classifying an AI-generated face drops fairly quickly from a baseline of 98.0%. The true positive rate, however, improves significantly when the model is trained on images at a resolution of N × N (N = 32, 64, 128, or 256) and then evaluated against the same TPR seen in training, Figure 3(a) (dashed red). As before, the false positive rate is fixed at 0.5%. Here we see that TPR at a resolution of 128 × 128 remains relatively high (91.9%) and only degrades at the lowest resolution of 32×32 (44.1%). The ability to detect AI-generated faces at even relatively low resolutions suggests that our model has not latched onto a low-level artifact that would not survive this level of down-sampling. Shown in Figure 3(b) is the TPR of the classifier, trained on uncompressed PNG and JPEG images of varying quality, evaluated against images across a range of JPEG qualities (ranging from the highest quality of 100 to the lowest quality of 20). Here we see that TPR for identifying an AIgenerated face (FPR is 0.5%) degrades slowly with a TPR of 94.3% at quality 80 and a TPR of 88.0% at a quality of 60. Again, the ability to detect AI-generated faces in the presence of JPEG compression artifacts suggests that our model has not latched onto a low-level artifact. 4.1. Explainability As shown in Section 4, our classifier is highly capable of distinguishing AI faces generated from a range of different synthesis engines. This classifier, however, is limited to only faces, Table 2. That is, when presented with non-face images from the same synthesis engines as used in training, the classifier completely fails to classify them as AI-generated. We posit that our classifier may have learned a semanticlevel artifact. This claim is partly supported by the fact that our classifier remains highly accurate even at resolutions as low as 128×128 pixels, Figure 3(a), and remains reasonably accurate even in the face of fairly aggressive JPEG compression, Figure 3(b). Here we provide further evidence to support this claim that we have learned a structural- or semantic-level artifact. It is well established that while general-purpose object recognition in the human visual system is highly robust to object orientation, pose, and perspective distortion, face recognition and processing are less robust to even a simple inversion [27]. This effect is delightfully illustrated in the classic Margaret Thatcher illusion [31]. The faces in the top row of Figure 4 are inverted versions of those in the bottom row. In the version on the right, the eyes and mouth are inverted relative to the face. This grotesque feature cocktail is obvious in the upright face but not in the inverted face. We wondered if our classifier would struggle to classify vertically inverted faces. The same 10,000 validation images (Section 2.6) were inverted and re-classified. With the same fixed FPR of 0.5%, TPR dropped by 20 percentage points from 98.0% to 77.7%. By comparison, flipping the validation images about just the vertical axis (i.e., left-right flip) yields no change in the TPR of 98.0% with the same 0.5% FPR. This pair of results, combined with the robustness to resolution and compression quality, suggest that our model has not latched onto a low-level artifact, and may have instead discovered a structural or semantic property that distinguishes AI-generated faces from real faces. We further explore the nature of our classifier using the method of integrated gradients [28]. This method attributes the predictions made by a deep network to its input features. Because this method can be applied without any changes to the trained model, it allows us to compute the relevance of each input image pixel with respect to the model’s decision. Shown in Figure 5(a) is the unsigned magnitude of the normalized (into the range [0, 1]) integrated gradients averaged over 100 StyleGAN 2 images (because the StyleGANgenerated faces are all aligned, the averaged gradient is consistent with facial features across all images). Shown in Figure 5(b)-(e) are representative images and their normalized integrated gradients for an image generated by DALL-2, Midjourney, Stable Diffusion 1, and Stable Diffusion 2. In all cases, we see that the most relevant pixels, corresponding to larger gradients, are primarily focused around the facial region and other areas of skin. 4.2. Comparison Because it focused specifically on detecting GANgenerated faces, the work of [23] is most directly related to ours. In this work, the authors show that a low-dimensional linear model captures the common facial alignment of StyleGAN-generated faces. Evaluated against 3,000 StyleGAN faces, their model correctly classifies 99.5% of the GAN faces with 1% of real faces incorrectly classified as AI. By comparison, we achieve a similar TPR, but with a lower 0.5% FPR. Unlike our approach, however, which generalizes to other GAN faces like generated.photos, TPR for this earlier work drops to 86.0% (with the same 1% FPR). Furthermore, this earlier work fails to detect diffusion-based faces because these faces simply do not contain the same alignment artifact as StyleGAN faces. By comparison, our technique generalizes across GAN- and diffusion-generated faces and to synthesis engines not seen in training. We also evaluated a recent state-of-the-art model that exploits the presence of Fourier artifacts in AI-generated images [8]. On our evaluation dataset of real and in-engine AIgenerated faces this model correctly classifies only 23.8% of the AI-generated faces at the same FPR of 0.5%. This TPR is considerably lower than our model’s TPR of 98.0% and also lower than 90% TPR reported in [8]. We hypothesize that this discrepancy is due to the more diverse and challenging in-the-wild real images of our dataset. This paper is available on arxiv under CC 4.0 license. [8] True positive rate (TPR) is the fraction of AI-generated photos that are correctly classified. [9] False positive rate (FPR) is the fraction of real photos that are incorrectly classified. Authors: (1) Gonzalo J. Aniano Porcile, LinkedIn; (2) Jack Gindi, LinkedIn; (3) Shivansh Mundra, LinkedIn; (4) James R. Verbus, LinkedIn; (5) Hany Farid, LinkedIn and University of California, Berkeley. Authors: Authors: (1) Gonzalo J. Aniano Porcile, LinkedIn; (2) Jack Gindi, LinkedIn; (3) Shivansh Mundra, LinkedIn; (4) James R. Verbus, LinkedIn; (5) Hany Farid, LinkedIn and University of California, Berkeley. Table of Links Abstract and Intro Data sets Model Results Discussion, Acknowledgements, and References Abstract and Intro Abstract and Intro Data sets Data sets Model Model Results Results Discussion, Acknowledgements, and References Discussion, Acknowledgements, and References 4. Results Our baseline training and evaluation performance is shown in Table 2. The evaluation is broken down based on whether the evaluation images contain a face or not (training images contained only faces) and whether the images were generated with the same (in-engine) or different (out-of-engine) synthesis engines as those used in training (see Section 2.6). In order to provide a direct comparison of the true positive rate[8] (TPR) for the training and evaluation, we adjust the final classification threshold to yield a false positive rate[9] (FPR) of 0.5%. With a fixed FPR of 0.5%, AI-generated faces are correctly classified in training and evaluation at a rate of 98%. Across different synthesis engines (StyleGAN 1,2,3, Stable Diffusion 1,2, and DALL-E 2) used for training, TPR was varied somewhat from a low of 93.3% for Stable Diffusion 1 to a high of 99.5% for StyleGAN 2, and 98.9% for StyleGAN1, 99.9% for StyleGAN3, 94.9% for Stable Diffusion 2, and 99.2% for DALL-E 2. For faces generated by synthesis engines not used in training (out-of-engine), TPR drops to 84.5% at the same FPR, showing good but not perfect out-of-domain generalization. Across the different synthesis engines not used in training, TPR varied widely with a low of 19.4% for Midjourney to a high of 99.5% for EG3D, and 95.4% for generated.photos. Our classifier generalizes well in some cases, and fails in others. This limitation, however, can likely be mitigated by incorporating these out-of-engine images into the initial training. In a particularly striking result, non-faces – generated by the same synthesis engines as used in training – are all incorrectly classified. This is most likely because some of our real images contain non-faces (see Section 2.1) while all of the AI-generated images contain faces. Since we are only interested in detecting fake faces used to create an account, we don’t see this as a major limitation. This result also suggests that our classifier has latched onto a specific property of an AI-generated face and not some low-level artifact from the underlying synthesis (e.g., a noise fingerprint [8]). In Section 4.1, we provide additional evidence to support this hypothesis. The above baseline results are based on training and evaluating images at a resolution of 512×512 pixels. Shown in Figure 3(a) (solid blue) is the TPR when the training images are down scaled to a lower resolution (256, 128, 64, and 32) and then up scaled back up to 512 for classification. With the same FPR of 0.5%, TPR for classifying an AI-generated face drops fairly quickly from a baseline of 98.0%. The true positive rate, however, improves significantly when the model is trained on images at a resolution of N × N (N = 32, 64, 128, or 256) and then evaluated against the same TPR seen in training, Figure 3(a) (dashed red). As before, the false positive rate is fixed at 0.5%. Here we see that TPR at a resolution of 128 × 128 remains relatively high (91.9%) and only degrades at the lowest resolution of 32×32 (44.1%). The ability to detect AI-generated faces at even relatively low resolutions suggests that our model has not latched onto a low-level artifact that would not survive this level of down-sampling. Shown in Figure 3(b) is the TPR of the classifier, trained on uncompressed PNG and JPEG images of varying quality, evaluated against images across a range of JPEG qualities (ranging from the highest quality of 100 to the lowest quality of 20). Here we see that TPR for identifying an AIgenerated face (FPR is 0.5%) degrades slowly with a TPR of 94.3% at quality 80 and a TPR of 88.0% at a quality of 60. Again, the ability to detect AI-generated faces in the presence of JPEG compression artifacts suggests that our model has not latched onto a low-level artifact. 4.1. Explainability As shown in Section 4, our classifier is highly capable of distinguishing AI faces generated from a range of different synthesis engines. This classifier, however, is limited to only faces, Table 2. That is, when presented with non-face images from the same synthesis engines as used in training, the classifier completely fails to classify them as AI-generated. We posit that our classifier may have learned a semanticlevel artifact. This claim is partly supported by the fact that our classifier remains highly accurate even at resolutions as low as 128×128 pixels, Figure 3(a), and remains reasonably accurate even in the face of fairly aggressive JPEG compression, Figure 3(b). Here we provide further evidence to support this claim that we have learned a structural- or semantic-level artifact. It is well established that while general-purpose object recognition in the human visual system is highly robust to object orientation, pose, and perspective distortion, face recognition and processing are less robust to even a simple inversion [27]. This effect is delightfully illustrated in the classic Margaret Thatcher illusion [31]. The faces in the top row of Figure 4 are inverted versions of those in the bottom row. In the version on the right, the eyes and mouth are inverted relative to the face. This grotesque feature cocktail is obvious in the upright face but not in the inverted face. We wondered if our classifier would struggle to classify vertically inverted faces. The same 10,000 validation images (Section 2.6) were inverted and re-classified. With the same fixed FPR of 0.5%, TPR dropped by 20 percentage points from 98.0% to 77.7%. By comparison, flipping the validation images about just the vertical axis (i.e., left-right flip) yields no change in the TPR of 98.0% with the same 0.5% FPR. This pair of results, combined with the robustness to resolution and compression quality, suggest that our model has not latched onto a low-level artifact, and may have instead discovered a structural or semantic property that distinguishes AI-generated faces from real faces. We further explore the nature of our classifier using the method of integrated gradients [28]. This method attributes the predictions made by a deep network to its input features. Because this method can be applied without any changes to the trained model, it allows us to compute the relevance of each input image pixel with respect to the model’s decision. Shown in Figure 5(a) is the unsigned magnitude of the normalized (into the range [0, 1]) integrated gradients averaged over 100 StyleGAN 2 images (because the StyleGANgenerated faces are all aligned, the averaged gradient is consistent with facial features across all images). Shown in Figure 5(b)-(e) are representative images and their normalized integrated gradients for an image generated by DALL-2, Midjourney, Stable Diffusion 1, and Stable Diffusion 2. In all cases, we see that the most relevant pixels, corresponding to larger gradients, are primarily focused around the facial region and other areas of skin. 4.2. Comparison Because it focused specifically on detecting GANgenerated faces, the work of [23] is most directly related to ours. In this work, the authors show that a low-dimensional linear model captures the common facial alignment of StyleGAN-generated faces. Evaluated against 3,000 StyleGAN faces, their model correctly classifies 99.5% of the GAN faces with 1% of real faces incorrectly classified as AI. By comparison, we achieve a similar TPR, but with a lower 0.5% FPR. Unlike our approach, however, which generalizes to other GAN faces like generated.photos, TPR for this earlier work drops to 86.0% (with the same 1% FPR). Furthermore, this earlier work fails to detect diffusion-based faces because these faces simply do not contain the same alignment artifact as StyleGAN faces. By comparison, our technique generalizes across GAN- and diffusion-generated faces and to synthesis engines not seen in training. We also evaluated a recent state-of-the-art model that exploits the presence of Fourier artifacts in AI-generated images [8]. On our evaluation dataset of real and in-engine AIgenerated faces this model correctly classifies only 23.8% of the AI-generated faces at the same FPR of 0.5%. This TPR is considerably lower than our model’s TPR of 98.0% and also lower than 90% TPR reported in [8]. We hypothesize that this discrepancy is due to the more diverse and challenging in-the-wild real images of our dataset. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv [8] True positive rate (TPR) is the fraction of AI-generated photos that are correctly classified. [9] False positive rate (FPR) is the fraction of real photos that are incorrectly classified.

Finding AI-Generated Faces in the Wild: Results

বন্য এ এআই-উত্পন্ন মুখগুলি সন্ধান করা: ফলাফল

KI-generierte Gesichter in freier Wildbahn finden: Ergebnisse

Encontrar rostros generados por IA en la naturaleza: resultados

Trouver des visages générés par l'IA dans la nature : résultats

जंगल में एआई-जनरेटेड चेहरे खोजना: परिणाम

AI が生成した顔の実例の発見: 結果

야생에서 AI가 생성한 얼굴 찾기: 결과

Encontrando rostos gerados por IA na natureza: resultados

Поиск лиц, созданных искусственным интеллектом, в дикой природе: результаты

Vahşi Doğada Yapay Zekanın Oluşturduğu Yüzleri Bulma: Sonuçlar

Tìm kiếm khuôn mặt do AI tạo ra trong tự nhiên: Kết quả

在野外寻找人工智能生成的面孔：结果

When I was diagnosed with autism at age 28, I finally got an explanation for so many of the struggles I’d faced in school and my professional life as a writer and website designer. My doctor explained that some people go undiagnosed due to learning to mask specific indicators they are autistic throughout their lifetime. Since I worked behind a computer, I typically avoided situations where I might have to make small talk. I could plan my own schedule and take breaks when I felt overwhelmed. I set up my home office workspace how I wanted and reduced distractions. However, I found myself struggling when I had to venture outside the norm for a team meeting or professional conference, which ultimately led to my diagnosis. Now, I’m proud of the coping strategies and tools I’ve used to grow in my field since I was diagnosed. One of the biggest aids has, to my surprise, been the rise in AI. What a Neurodevelopmental Diagnosis Means for Design and Development Professionals When my psychologist first suggested I might be neurodivergent, I felt unsure of the potential diagnosis. However, as I learned more about the condition, it was a relief to have a word for the struggles I’d faced trying to match emotions and fit into a nine-to-five job at a company. I learned one in every 36 children is now diagnosed with ASD. The numbers are steadily growing. Other neurodivergent conditions that might impact web design work include ADHD, dyslexia, Tourette syndrome and OCD. According to the AAPC, about 80% of women remain undiagnosed with neurodivergent conditions until after the age of 18. So what does all this mean for a website designer? Here are some of the most common neurodivergent traits that can impact design, development and other technology professionals. Communication differences: A neurodivergent person often communicates differently than a neurotypical person. I must be aware of this and ask for clarification if a client is vague.
Need for additional flexibility: Many people with neurodivergent conditions do better in a flexible work atmosphere, which many companies have started to offer.
Repetitive behaviors: People with conditions such as OCD may complete the same task repeatedly, causing projects to take longer.
Obsessive focus: I and many other neurodivergent workers can get obsessed with research on a topic. Those with ASD often are very detail-oriented. I have to remind myself to focus on the project and not spend three days researching how people respond to the color red.
Distress at small changes: When a client tells someone with executive functioning challenges to do a design one way and then changes their mind halfway through, that person may get stressed and want to quit working on the project.
Sensitivity to the environment: Many neurodivergent people can’t focus if there is a lot of light or sound. For me, using headphones helps when I work around others.
Difficulty paying attention: While medication can help the brain focus on one task, a difficulty focusing on routine tasks is an extremely common trait of ADHD.
Disorganization: ADHD designers may need to devise systems to keep them organized, such as planning books and reminders.
Impulse control: People who face impulsivity might make big promises that they then struggle to complete by the deadline.
Forgetting essential things: Alarms and reminders are the best friend of someone who struggles with executive functioning challenges.
Unfocused: Designers with ADHD often struggle with focus and should create project management lists. If they skip to another part of the project, they will know where they left off and can return to the same task. Researchers have conducted neurological imaging on autistic brains and found ASD people have a brain structure that gears them to have talent in visual arts, music, math and programming. Once I accepted that my condition and the things that make my brain different from an average person’s are also what gives me a creative edge as a designer, I could focus on solutions, and AI has been a large part of this. How AI Is Changing Things for Neurodivergent Developers and Designers Those with a neurodivergent disorder are lucky to live in a time when artificial intelligence (AI) can fill the gap in many areas, such as communication and analysis. I may think something makes sense that a neurotypical person fails to understand. A machine can analyze word patterns and help me fix anything confusing. Here are some of the ways neurodivergent professionals can utilize AI to create more manageable and even enjoyable work experiences. Better Training Neurodivergent brains may struggle to learn through traditional educational methods. AI is proficient at taking a complex concept and breaking it down. People can ask ChatGPT to explain a topic in simpler terms. Schools must offer more than just book studies — they must also provide hands-on training. New Communication Methods One struggle for me has been learning to communicate with my peers. While my mind thinks concretely, many people share broad concepts or subtle jokes. More than once, I’ve misunderstood what a client wants for a project. I’ve since learned to ask detailed questions to get to the heart of the website’s goal. While about 80% of people check their phones first thing in the morning, mine can overstimulate my brain if I start looking at messages and moving elements too early or too long. AI allows me to tap into the power of chatbots to respond to clients without feeling overwhelmed with all the chatter. Allowing for Breaks Business owners use AI for everything from sending automated responses to creating newsletters to generating artwork. As a web designer, my focus has to be on the most critical tasks or I feel stressed. AI can take over sending out marketing emails, responding to repetitive questions and creating some content to save me time. The extra time allows me to stop and go into a bubble when needed, helping me adapt to situations where I feel burdened. I can adjust my schedule and still meet project deadlines. The future of AI is intriguing. Researchers are using robots to find new ways to interact with autistic children. The robots consider the person’s unique sensory challenges and typical social cues and utilize the training robots to show them appropriate facial responses and respond accordingly. Better Organization One of my biggest mistakes is underestimating how long a project will take. I might think I can finish a multilingual website in two days, but it takes two weeks. Rather than give clients an immediate answer, I run the task through models. The computer takes the data and breaks up big tasks into smaller ones. I can use that information to estimate a completion date. I add a few extra days as a buffer in case I have times when I am unable to focus or need a break to continue my work. The Unique Qualities That Benefit Me as a Web Designer An adult diagnosis of neurodivergence can be shocking at first. However, once I realized the many benefits that come with a neurodivergent brain, I better understood why I chose a creative field. My unique ways of looking at the world around me also help me develop designs my clients love that no other designer thought of. AI is helping me work innovatively as a neurodivergent designer. As technology advances, I’m confident I’ll find even more ways to utilize it in my work and help other professionals like myself succeed. When I was diagnosed with autism at age 28, I finally got an explanation for so many of the struggles I’d faced in school and my professional life as a writer and website designer. My doctor explained that some people go undiagnosed due to learning to mask specific indicators they are autistic throughout their lifetime. Since I worked behind a computer, I typically avoided situations where I might have to make small talk. I could plan my own schedule and take breaks when I felt overwhelmed. I set up my home office workspace how I wanted and reduced distractions. However, I found myself struggling when I had to venture outside the norm for a team meeting or professional conference, which ultimately led to my diagnosis. Now, I’m proud of the coping strategies and tools I’ve used to grow in my field since I was diagnosed. One of the biggest aids has, to my surprise, been the rise in AI. What a Neurodevelopmental Diagnosis Means for Design and Development Professionals When my psychologist first suggested I might be neurodivergent, I felt unsure of the potential diagnosis. However, as I learned more about the condition, it was a relief to have a word for the struggles I’d faced trying to match emotions and fit into a nine-to-five job at a company. I learned one in every 36 children is now diagnosed with ASD. The numbers are steadily growing. Other neurodivergent conditions that might impact web design work include ADHD, dyslexia, Tourette syndrome and OCD. According to the AAPC, about 80% of women remain undiagnosed with neurodivergent conditions until after the age of 18. one in every 36 children one in every 36 children 80% of women remain undiagnosed 80% of women remain undiagnosed So what does all this mean for a website designer? Here are some of the most common neurodivergent traits that can impact design, development and other technology professionals. Communication differences: A neurodivergent person often communicates differently than a neurotypical person. I must be aware of this and ask for clarification if a client is vague. Need for additional flexibility: Many people with neurodivergent conditions do better in a flexible work atmosphere, which many companies have started to offer. Repetitive behaviors: People with conditions such as OCD may complete the same task repeatedly, causing projects to take longer. Obsessive focus: I and many other neurodivergent workers can get obsessed with research on a topic. Those with ASD often are very detail-oriented. I have to remind myself to focus on the project and not spend three days researching how people respond to the color red. Distress at small changes: When a client tells someone with executive functioning challenges to do a design one way and then changes their mind halfway through, that person may get stressed and want to quit working on the project. Sensitivity to the environment: Many neurodivergent people can’t focus if there is a lot of light or sound. For me, using headphones helps when I work around others. Difficulty paying attention: While medication can help the brain focus on one task, a difficulty focusing on routine tasks is an extremely common trait of ADHD. Disorganization: ADHD designers may need to devise systems to keep them organized, such as planning books and reminders. Impulse control: People who face impulsivity might make big promises that they then struggle to complete by the deadline. Forgetting essential things: Alarms and reminders are the best friend of someone who struggles with executive functioning challenges. Unfocused: Designers with ADHD often struggle with focus and should create project management lists. If they skip to another part of the project, they will know where they left off and can return to the same task. Communication differences: A neurodivergent person often communicates differently than a neurotypical person. I must be aware of this and ask for clarification if a client is vague. Communication differences: Need for additional flexibility: Many people with neurodivergent conditions do better in a flexible work atmosphere, which many companies have started to offer. Need for additional flexibility: Repetitive behaviors: People with conditions such as OCD may complete the same task repeatedly, causing projects to take longer. Repetitive behaviors: Obsessive focus: I and many other neurodivergent workers can get obsessed with research on a topic. Those with ASD often are very detail-oriented. I have to remind myself to focus on the project and not spend three days researching how people respond to the color red. Obsessive focus: Distress at small changes: When a client tells someone with executive functioning challenges to do a design one way and then changes their mind halfway through, that person may get stressed and want to quit working on the project. Distress at small changes: Sensitivity to the environment: Many neurodivergent people can’t focus if there is a lot of light or sound. For me, using headphones helps when I work around others. Sensitivity to the environment: Difficulty paying attention: While medication can help the brain focus on one task, a difficulty focusing on routine tasks is an extremely common trait of ADHD. Difficulty paying attention: Disorganization: ADHD designers may need to devise systems to keep them organized, such as planning books and reminders. Disorganization: Impulse control: People who face impulsivity might make big promises that they then struggle to complete by the deadline. Impulse control: Forgetting essential things: Alarms and reminders are the best friend of someone who struggles with executive functioning challenges. Forgetting essential things: Unfocused: Designers with ADHD often struggle with focus and should create project management lists. If they skip to another part of the project, they will know where they left off and can return to the same task. Unfocused: Researchers have conducted neurological imaging on autistic brains and found ASD people have a brain structure that gears them to have talent in visual arts , music, math and programming. Once I accepted that my condition and the things that make my brain different from an average person’s are also what gives me a creative edge as a designer, I could focus on solutions, and AI has been a large part of this. gears them to have talent in visual arts gears them to have talent in visual arts How AI Is Changing Things for Neurodivergent Developers and Designers Those with a neurodivergent disorder are lucky to live in a time when artificial intelligence (AI) can fill the gap in many areas, such as communication and analysis. I may think something makes sense that a neurotypical person fails to understand. A machine can analyze word patterns and help me fix anything confusing. Here are some of the ways neurodivergent professionals can utilize AI to create more manageable and even enjoyable work experiences. Better Training Neurodivergent brains may struggle to learn through traditional educational methods. AI is proficient at taking a complex concept and breaking it down. People can ask ChatGPT to explain a topic in simpler terms. Schools must offer more than just book studies — they must also provide hands-on training. struggle to learn through traditional struggle to learn through traditional New Communication Methods One struggle for me has been learning to communicate with my peers. While my mind thinks concretely, many people share broad concepts or subtle jokes. More than once, I’ve misunderstood what a client wants for a project. I’ve since learned to ask detailed questions to get to the heart of the website’s goal. While about 80% of people check their phones first thing in the morning, mine can overstimulate my brain if I start looking at messages and moving elements too early or too long. AI allows me to tap into the power of chatbots to respond to clients without feeling overwhelmed with all the chatter. 80% of people check their phones 80% of people check their phones Allowing for Breaks Business owners use AI for everything from sending automated responses to creating newsletters to generating artwork. As a web designer, my focus has to be on the most critical tasks or I feel stressed. AI can take over sending out marketing emails, responding to repetitive questions and creating some content to save me time. The extra time allows me to stop and go into a bubble when needed, helping me adapt to situations where I feel burdened. I can adjust my schedule and still meet project deadlines. The future of AI is intriguing. Researchers are using robots to find new ways to interact with autistic children. The robots consider the person’s unique sensory challenges and typical social cues and utilize the training robots to show them appropriate facial responses and respond accordingly. new ways to interact with autistic new ways to interact with autistic Better Organization One of my biggest mistakes is underestimating how long a project will take. I might think I can finish a multilingual website in two days, but it takes two weeks. Rather than give clients an immediate answer, I run the task through models. The computer takes the data and breaks up big tasks into smaller ones. I can use that information to estimate a completion date. I add a few extra days as a buffer in case I have times when I am unable to focus or need a break to continue my work. The Unique Qualities That Benefit Me as a Web Designer An adult diagnosis of neurodivergence can be shocking at first. However, once I realized the many benefits that come with a neurodivergent brain, I better understood why I chose a creative field. My unique ways of looking at the world around me also help me develop designs my clients love that no other designer thought of. AI is helping me work innovatively as a neurodivergent designer. As technology advances, I’m confident I’ll find even more ways to utilize it in my work and help other professionals like myself succeed.

How AI Has Helped Me Work Smarter as a Neurodivergent Web Designer

AI has taken over social media. you have a tool that can dig deep into audience insights, predict the next big content hit, craft posts and visuals on its own, spot consumer trends, and decode public sentiment. Picture this: There is no doubt to say that AI is the backbone of every social media platform out there. It doesn't only help in analyzing what content pops up in your feed, but also cleaning up comments for a better browsing experience, and suggesting new content and friends you might enjoy. But, it poses a challenge for marketers, finding that spot where content appeals to both humans and AI algorithms. When it comes to what “The Algorithm" wants, we're left guessing. And, All too often, we switch from cheap tricks to increased engagement without creating any lasting value for our audiences. Interestingly, AI can lead to more genuine marketing on social media. It helps us understand the vast amount of data from social media platforms. This way, we can create content that truly matters to our audience. AI analyzes posts, comments, and interactions to let us provide what people want and address their interests better. Plus, AI saves a lot of time and money by automating and scaling these processes. That's why top social media marketers are using AI to: Automate repetitive tasks, saving time and money. Expand social media strategies across different platforms. Create posts and campaigns based on data that actually works. You can do this too, no matter your budget or company size. The rest of this post shows you how. How AI is Being Used in Social Media So how exactly can you use artificial intelligence to take your social media marketing game to a whole new level? Let's look at some of the best ways to do this. AI-Powered Content Curation One of the most challenging aspects of social media marketing is coming up with fresh content ideas that can drive engagement for your brand. Instead of copying your competitors or throwing a bunch of content ideas at the wall and hoping one sticks, AI analyzes your data and creates targeted messages based on what your audience has liked in the past. This way, you can be sure that the content you share will resonate with your social media audience and get the reaction you want. AI also helps in improving the quality of your content in terms of tone and grammar. Note: AI-enabled tools even analyze your social media data and determine which keywords, images, hashtags, and colors have a stronger impact on your audience based on past results. In addition, AI-powered content curation tools automatically create social media posts using a URL or information you provide that suits your brand voice. These tools also share the posts on different social platforms. Did you know that Elon Musk says xAI will open-source Grok this week? Automation of Social Network Management Your business is likely on more than one social media platform. And, while consistently publishing quality content, keeping up with the latest trends/updates on every platform can be quite a hassle. However, with the help of AI-powered social media management tools, it is easy to grow your influence and connect with your audience. These tools also plan out your social media posts in advance, track all your profiles, and generate appropriate responses for consumers after analyzing their messages. In simple form, AI will not only plan your campaigns. but also fill content gaps and keep your followers engaged with your brand voice. This tool can recycle content you've previously published, such as blog posts or infographics, and turn them into upload-ready social media posts with hashtags and relevant links. Advertising Campaigns on Social Networks How to get ahead of your competitors and get the most out of advertising on social networks? Several companies are using paid advertising on social media. If you aim to stand out and increase your campaign's impact, it's time to start creating better, smarter ads. AI-powered tools like Sprinklr help you create compelling, personalized social media ads and monitor and optimize your campaigns to maximize ROAS. Such tools offer recommendations to help your campaigns perform better. From comparing ad performance to planning out strategies, AI reduces your overall advertising costs. The copywriting AI tool analyzes your brand language and creates social media ad copy that will increase engagement and conversions for your business. Ensure that your social media ad copy matches your landing page to maintain consistency and communicate with users. Personalization Using Chatbots More followers and clients mean more money, but it also means more time spent serving them. Luckily for you, highly effective customer support and personalized audience experiences are some of the benefits that help your brand grow with the help of artificial intelligence. Hiring enough customer service staff to provide 24/7 customer support may not be feasible. Investing in AI-powered chatbots will respond to audience messages immediately, regardless of the time of day. You can even combine chatbots with your existing live chat software to provide your audience with 24/7 customer service support. With the help of machine learning, your AI chatbots will be able to provide relevant answers to various problems, questions, or queries that your audience may have. Such tools allow you to deliver engaging, seamless, and personalized customer experiences using bots across a variety of social media platforms. Competitor Analysis and Social Listening It's not enough to just know who your competitors are. If you want to get an advantage over them, you need to analyze their social media strategies, find out their weaknesses, and determine what they think. The manually searching and analyzing competitor pages can take weeks or months of grueling work., And, if you succeed in your analysis, there is a high probability that it will contain errors. But with the help of AI, you can easily track your competitors and analyze their social media strategies, reach, engagement rates, conversion rates, brand sentiment, and more. Various AI-powered competitive analysis tools monitor your competitors' social marketing efforts, from user interactions to posts and campaigns. It helps in knowing which parts require a more effective marketing strategy to achieve success. Artificial intelligence tools track mentions of your business, as well as specific hashtags and keywords related to you on social media. Analyzing this information will help you determine whether the public opinion of your brand is negative, positive, or neutral. You will be able to identify potential problems before they become more serious. Emerging AI Technologies in Social Media AI is about to take social media on a futuristic ride. We're not just talking about minor upgrades here - AI is changing the way we scroll, post, and consume content online. Different technologies like synthetic media, AI-created content, and virtual influencers are expanding the limits of what we can do online. Let's take a look at what insanely innovative experiences are coming next… Deepfakes You've probably seen those mind-bending "deepfake" videos going viral where celebrities or politicians seem to be saying and doing things they never actually did. Now creators can create stunningly realistic footage that pushes the boundaries of imagination thanks to artificial intelligence. Deepfakes also open up a world of exciting opportunities for creative self-expression and next-level storytelling through synthetic media. As the technology continues evolving, we'll see deepfakes enabling Hollywood-level visual effects that bring our wildest visions to life. Virtual Influencers This one's unquestionably innovative - companies are using AI to generate incredibly human-looking virtual influencers to advertise products on social media. We're talking lifelike computer avatars with their names, personalities, and millions of followers! Now, imagine the creative potential here. Brands can craft highly engaging, endlessly customizable digital personas to connect with audiences in new ways. Virtual influencers offer limitless brand ambassadorship and storytelling opportunities. AI Content CreatorsImagine telling an AI to whip up your next social media caption, graphic, or video ad...and having it generate something cleverly crafted in seconds. Using large language models and other AI tools, brands can augment human creativity. For content creators, this AI assistance will prove to be an amazing productivity multiplier and source of innovative inspiration. While not replacing humans, AI can elevate our creative visions to new heights while saving tons of time. Feed Personalization Of course, the biggest AI impact on social is automated feed curation. It further deeply understands our preferences to serve up a perfectly personalized experience tailored to each user's interests. This allows for a more rewarding, endlessly engaging social media journey of discovery and relevance. While some worry about filtered bubbles, smarter AI will also ensure we're exposed to a balanced variety of perspectives selected just for us. Conclusion There's no doubt to say that AI is completely changing the world of social media as we know it. From automated content creation to hyper-personalized feeds to wildly realistic synthetic media, this technology is rapidly transforming how we connect, share, and experience online. Artificial intelligence (AI) is making it easier for social media platforms to sort through vast amounts of data. It helps them spot the latest trends, understand what users like, block harmful content, and do much more. AI has a promising future in this space because it makes things better for users and helps brands cater to them more effectively. In social media marketing, AI is crucial because it allows companies to track how well they're doing and pinpoint potential customers.

How AI is Shaping the Future of Social Media

Why We Need to Think About the Full Stack of AI Artificial Intelligence is an emerging technology, with significantly more end users than the usual techno-shift. As discussed in both and , this is one of the fastest growing technologies without a mature bedrock of security and reliability support. Security Threats to High Open Source Impact Large Language Models A Tale of Two LLMs: Open Source vs the US Military's LLM Trials In this, we’ll talk about the larger scope of understanding and protecting artificial intelligence pipelines, whether it’s a large language or a large model. anything With models like ChatGPT representing its cognitive capabilities in the way the end user engages with them, a lot of people talk about AI think of it like a magical thinking head, this bot that can talk, but not quite walk. It Gets More Interesting, The More You Know To ensure the overall health and well-being of AI, we must recognize that it requires more than just a brain. Like the human body, AI relies on multiple interconnected components. Just as our lungs provide vital oxygen to sustain life, the carbon consumption of AI represents its energy source. Equally crucial is AI's immune system—cybersecurity—protecting against threats and vulnerabilities. In this series, we will explore the comprehensive nature of AI, focusing on the critical role of cybersecurity and the importance of holistic treatment for addressing security concerns. The AI Ecosystem: Beyond the Cognitive "Talking Head" AI, with models like ChatGPT at its forefront, possesses remarkable cognitive abilities. These models excel in generating human-like responses and assisting with various tasks. However, AI is not solely reliant on its cognitive prowess. To function effectively, AI requires a stable and secure ecosystem that sustains its operations. This ecosystem includes data sources, computational infrastructure, energy consumption, and cybersecurity measures. Carbon Consumption: AI's Lungs Similar to how our lungs enable the exchange of oxygen and carbon dioxide, the carbon consumption of AI represents its energy source. As AI continues to evolve and become more prevalent, addressing its energy demands and environmental impact becomes crucial. Striving for energy efficiency and sustainable practices is essential for the long-term viability of AI technology. “A main problem to tackle in reducing AI’s climate impact is to quantify its energy consumption and carbon emission, and to make this information transparent”, says Payal Dhar in a recent . Nature Machine Intelligence journal article To be clear, it’s not always about making the technology more efficient. Sometimes, it’s about making humans less stupid about wasting compute. Shifting developer mindsets to massive state-space feature engineering means getting people to understand why they need to use (guesstimate your feature space before you compute it, and how many different types of hay are in it). It’s why you have to statistically consider (how you want to investigate that haystack with best-of-n sampling, helping to decide if executing with the you have/want to spend is worth the byte flips). HyperLogLog Goodheart’s Law compute resources Cybersecurity: AI's Immune System Just as our immune system protects our body from infections, AI relies on robust cybersecurity measures to defend against threats. The interconnectedness of AI systems with various networks and data sources exposes them to vulnerabilities and potential attacks. Ensuring the security of AI requires comprehensive protection mechanisms, including data privacy, secure infrastructure, and resilient defenses against malicious actors. AI Security Groups and Resources OWASP Foundation AI/ML Security work being done: The OWASP foundation more broadly aims to improve the security of software through its community-led open source software projects. They have an and broadly light on technical implementation. provides developer centered information about the top known cybersecurity risks for open source machine learning, with a description, example attack scenario, and a suggestion of how to prevent it. AI Security Guide OWASP Project Machine Learning Security Top 10 The LFAI Security Committee LFAI supports AI and Data open source projects through incubation, education, and best practices. AI Security Regulatory Organizations focus on common sense regulation and standards around AI/ML that are compliant to exclusively EU regulation. Similarly, the is an independent working group to advance AI Risk aimed at security governance. The was similarly established solely for regulation, and only scoped to cloud services. efforts are also largely regulatory advisements. ETSI’s Securing Artificial Intelligence (SAI) AIRS group CSA Artificial Intelligence group IEE’s AI/ML Central Nervous System Infections: Addressing Security Concerns with Large Language (and Large Models) Anything In the context of AI, security concerns around the data and model processing around large langauge (and large anything models) can be likened to a central nervous system infection, affecting the core functionality of the entire system. While traditional cybersecurity threats often focus on breaches, data leaks, and unauthorized access, the challenges posed by Large Language Models (LLMs) and other generative technologies extend beyond conventional threats. Understanding these distinctive aspects is crucial for effectively addressing security concerns in this domain. Hallucinations One of the notable challenges associated with LLMs is the problem of hallucinations or the generation of false information. Due to their ability to generate human-like responses based on training data, LLMs may inadvertently produce content that lacks factual accuracy or even fabricate entirely false narratives. This presents a unique security risk as it can lead to the dissemination of misinformation, manipulation of public opinion, and potential damage to the credibility of AI systems. Mitigating this risk requires a combination of careful training data curation, robust fact-checking mechanisms, and ongoing validation processes to ensure the veracity of the generated content. . This is a security problem when downloading open source package registries at the moment Internal Drift Another significant concern is internal drift within LLMs and generative models. Over time, these models can exhibit a shift in behavior, generating responses that deviate from the intended purpose or ethical guidelines. This phenomenon poses a challenge to maintaining control and predictability in AI systems, as it introduces an element of uncertainty that can be exploited by adversaries. Addressing internal drift requires continuous monitoring and adaptation, establishing feedback loops between developers and the models, and implementing techniques such as reinforcement learning to reinforce desired behavior and align the outputs with ethical standards. Proactive threat detection involves actively monitoring the behavior of LLMs and generative models to identify potential vulnerabilities or anomalies in their output. Secure development practices entail incorporating robust security measures throughout the entire development lifecycle, including secure coding practices, vulnerability testing, and adherence to established industry standards. A Holistic Treatment for AI Security To ensure the longevity and trustworthiness of AI, we’ll have to integrate security considerations throughout the AI development lifecycle, establishing strong partnerships with cybersecurity experts, and fostering a culture of security awareness and education. Implementing ethical guidelines, robust encryption practices, and continuous monitoring. . It’s All About Attention I’ve spent a lot of my life thinking about how humans interact with computers and computers that interact with humans. I used to study how we could design better Boeing Airline cockpits by getting real pilots into a 4D flight simulator of potentially life-threatening conditions to see where they looked, how they moved, and what they said to prevent a crash - . Later, I worked at the National Institutes of Health on , where we timed simple visual feedback to real-time brain networks to improve clinical psychiatric outcomes without any pill-based intervention. back or not real-time fMRI neurofeedback Just attention. Attention is powerful. It’s probably the most powerful thing to learn about cybersecurity. When threat modeling any technical stack, you have to be able to think about where are people paying attention. That’s your most exploitable surface area. aren’t Emerging technologies are always a really interesting place to observe people’s attention. There hasn’t really been an emerging technology that so many people have had an opinion on before, because this time it’s just a face-to-deep technology that’s genuinely easy to start using. That’s beautiful. Attention and awareness are different things though. We need to understand how the whole thing works. You Should Be Paying Attention to Chip Security AI Chips: The Heart of the AI Body AI-specific chips, often referred to as neural processing units (NPUs) or graphics processing units (GPUs), serve as the vital heart of the AI ecosystem. Much like the human heart, these specialized hardware components play a crucial role in enabling the efficient and optimal functioning of AI systems. In this series, we delve into the significance of chip technology and its pivotal role in ensuring the security and robustness of the AI body. The Powerhouse Organs: AI-specific chips are specifically designed to accelerate AI computations, enabling exceptional performance, efficiency, and speed. These chips are tailored to meet the unique requirements of AI workloads, such as training deep neural networks and running complex AI algorithms. Just like the heart pumps blood to supply oxygen and nutrients to the body, AI-specific chips empower AI systems to process and analyze vast amounts of data with remarkable speed and efficiency. The Importance of Chip Technology: Advancements in chip architectures and designs have led to significant breakthroughs in AI capabilities. Parallel processing, specialized matrix calculations, and optimized memory access are just a few examples of the technological advancements that AI-specific chips have brought to the field. These advancements push the boundaries of AI performance, enabling AI systems to tackle increasingly complex tasks and deliver accurate results. Additionally, chip technology plays a vital role in addressing energy consumption concerns. As AI deployment expands, the energy efficiency of AI systems becomes a crucial factor. Advancements in chip technology contribute to reducing energy consumption, making AI more sustainable and environmentally friendly. The Role of Secure Chip Technology: As AI becomes more prevalent and critical in various sectors, ensuring the security of AI-specific chips becomes paramount. The article " " has some great key points to understand, but I’ll boil them down to two that you need to understand today: Security Becomes Much Bigger Issue For AI/ML Chips, Tools The rapid development of AI and machine learning chips has led to increased potential for security threats, including intellectual property theft and attacks that can corrupt data or lead to ransomware (this can be supply chain and supplier threats) The need for standards that include both hardware and software. It suggests using multiple AI algorithms and diverse sets of training data to reduce the risk of attacks. If you are optimizing - yes, I said optimising, not just running, an AI system, you will be building a system that maximizes the compute over cost. (GPUs) and Intel (CPUs and VPUs), and with (TPU) and that’s just the ones who have been on the market the longest - emerging tech means emerging market. You can do this with hyperscalers (kind, of) but what if you are building one from scratch? You’ve got NVidia Google Tensor Processing Unit But, oof, do we have some these days, as seen in the “Blessings of Scale” from . computational power Compute Trends Across Three Eras of Machine Learning Securing the Evolving AI Body: Just as the heart is vital for maintaining overall health, ensuring the security of AI-specific chips is crucial for the resilience and trustworthiness of the AI body. Secure chip technology must be integrated into every stage of the AI development process, from chip design and manufacturing to deployment and maintenance. Collaborative efforts among chip manufacturers, AI developers, and cybersecurity experts are essential to identify and address potential vulnerabilities in chip architectures and mitigate emerging threats. By prioritizing chip security, we can ensure that the AI body remains robust, efficient, and secure. Where do you want to see this go next? Town Hall mode is on this Hackernoon article, so feel free to comment anywhere on this article where you have a question, comment or better idea than myself. The author will absolutely read it.

Securing AI: Concerns & Immune Systems for Emerging Technologies

Great relationships with friends and family depend on mutual understanding, and the same can be said for apps on the internet nowadays. They understand us very well now and have become our daily companions. Apps that use personalized website recommendation systems in particular have come a long way. The systems use algorithms and machine learning techniques to suggest websites and content that are tailored to the individual user. They have become an integral part of the modern internet, with many websites and apps incorporating them to improve the user experience. The first personalized recommendation systems appeared in the late 1990s, with the launch of Amazon's "Customers Who Bought This Item Also Bought" feature. This system used data on past purchases and browsing history to recommend products to customers. Since then, personalized recommendation systems have become increasingly sophisticated, using a wide range of data sources and machine learning techniques to make recommendations. Collaborative filtering One key development in personalized recommendation systems has been the use of collaborative filtering. This approach involves analyzing the preferences and behaviors of a group of users, and using that data to make recommendations to individual users. For example, if a group of users who have similar interests and tastes have all liked a particular website, the recommendation system might suggest that website to a new user who exhibits similar behavior. Content-based recommendation systems Another important development has been the use of content-based recommendation systems. These systems analyze the content of a website or piece of content, and use that information to make recommendations to users. For example, a content-based recommendation system might analyze the text of an article and use the keywords to recommend similar articles to the reader. In recent years, personalized recommendation systems have also started to incorporate more data sources, such as social media activity and location data. This has allowed them to make even more personalized recommendations, as they can take into account a wider range of factors that might influence a user's preferences. Overall, personalized website recommendation systems have come a long way in a short amount of time. They have become an essential tool for improving the user experience on the internet, and are likely to continue to evolve and become even more sophisticated in the future. The Golden Geese of Personalization Systems Traditionally websites have personalized their user experience with inhouse recommendation systems requiring expensive coding expertise. However, more affordable SaaS solutions are now available as website plugins that are easy to install and they more or less deliver the same types of recommendations. is one of the original onsite personalization solutions that’s still used today. For example, a very common segment is based on the rule of . If a cart gets abandoned, a rule can be set to offer a recommendation or a discount. Rules-based segmentation whether or not a user abandons a cart came next, and again it's still going strong. Marketplaces like Amazon track user behavior (what users view or buy) and then when the user clicks a product the website recommends other “Related Items” and/or “Best Sellers in this Category.” Behavioral recommendations Most websites using personalization today still use these user behavior recommendations, including Amazon. are a more recent development, the result of Amazon (and other big companies) refining their algorithms in recent years. Now machine learning can predict what want after just a handful of visits to the site. Predictive recommendations individual visitors By paying attention to our personal browsing history in real-time, companies with AI-driven recommendations systems can make recommendations to shoppers in real time that are . On the Nike website the “You Might Also Like” recommendation box shows items that become more personalized as visitors continue to frequent the site. truly 1-to-1 The more the visitor views, adds to cart, or buys, the more accurately the recommendation engine becomes at predicting what that visitor wants to see. It’s at this point that a website begins to make more cross-sells because the products being recommended really resonate with individual shoppers. The next frontier: specialized cross selling tools for specific niches Drilling down into a specific niche with a tool optimized to do the job better is the next step. In the fashion industry websites and ecommerce platforms use personalized recommendation systems to suggest products and styles to their users. These systems can be tailored to the fashion industry by using data sources and machine learning techniques that are specific to fashion. For example, a fashion recommendation system might use data on the user's past purchases and browsing history to . It might also analyze the user's social media activity and location data to get a better understanding of their personal style and the types of products they are likely to be interested in. In addition to using traditional recommendation system techniques, such as collaborative filtering and content-based recommendations, a fashion recommendation system might also use Visual AI image recognition and natural language processing to analyze the user's interactions with the website and thus make more accurate recommendations to the shopper. Conclusion Overall, personalized website recommendation systems have come a long way in a short amount of time. They have become an essential tool for improving the user experience on the internet, and are likely to continue to evolve and become even more sophisticated in the future. Furthermore, a specialized recommendation system for fashion can be a powerful tool for improving the user experience on a fashion website or e-commerce platform, and can help drive sales by suggesting products and styles that are likely to be of interest to the user. Also published on Rosetta AI.

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