We are the Visual AI People, and you are aware that we live in a visual world. The majority of the information we are exposed to, both in our environment and in the media we consume, including online, is visual. Eighty percent of the data processed by our systems would come from visual sources if people were computers.
According to Seed Scientific, 14% of people worldwide have an Instagram account, we watch 5 billion YouTube videos daily, and we watch nearly 700,000 hours of Netflix annually.
Given this, it should come as no surprise that an increasing number of companies are attempting to comprehend how Visual AI can optimize data reporting, boost operational efficiency, and improve user experiences.
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A Quick Background of What is Visual AIHow Does Visual AI Work?Visual AI Versus Computer VisionVisual AI Versus Generative AIVisual AI and Its Numerous Use CasesPrevention of Phishing and Visual AIModeration of Content and Visual AIRead Also >>> AI and DigitalizationReal-Time IntelligenceWhy is Visual AI Necessary?Conclusion: What is Visual AI?
A Quick Background of What is Visual AI
Visual AI is a branch of computer science that teaches machines to interpret visual information and images similarly to humans. It's also commonly referred to as computer vision. In addition to seeing, visual AI allows machines to comprehend and interpret images and videos based on the algorithm being used.
As an illustration, they are able to classify items in a single image, accurately labeling each one as a desk, a plant, a pizza, and so forth, by comparing them to images in their library or memory, just like a human would.
Although it may sound futuristic and even fantastical, Visual AI is the technology that makes possible many things that are now a part of our daily lives. Visual AI drives facial recognition screen unlocking, visual search on shopping apps, and QR code scanning.
How Does Visual AI Work?
To effectively reason and act, visual AI combines machine learning models with high-quality data.
In order to accomplish basic tasks like object detection and recognition, image classification and segmentation, and the creation of embeddings or even synthetic data that allow systems to comprehend and function in the three-dimensional environment around them, Visual AI uses vision foundation models or multi-modal models.
In order for models to learn and get better over time, Visual AI critically needs high-quality data that they can be trained on. Trash in, trash out. Your Visual AI models' performance frequently reaches a limit that can only be overcome by paying attention to the caliber of the datasets you feed them.
Data challenges—inaccuracies, gaps, or biases in the datasets being fed to the model—are responsible for a sizable percentage of the mistakes made by AI visual models.
A Visual AI system's ultimate objective is to efficiently reason about the content of visual inputs, extract meaning from them, and then take the appropriate action, whether that action is to inform a downstream person or process, create a digital output like new images, or perform a physical action.
High-quality data, powerful AI visual models, and well-aligned computational resources are all necessary for visual AI to function.
Visual AI Versus Computer Vision
Visual AI is distinct from AI computer vision or vision AI, despite the fact that the terms are frequently used interchangeably. The best explanation for the distinction is that they both allude to distinct areas of visual generative AI.
AI computer vision is a well-established field that uses vision foundation models or multi-modal models to enable computers to process, analyze, and comprehend visual data.
The goal is to enable machines to "see" in the same way that humans do, which means they can recognize objects, people, scenes, anomalies, and activities in visual data.
Since visual AI is built using the capabilities of AI vision but focuses on processing and comprehending visual data, one could argue that AI vision serves as the foundation for many of its capabilities.
On the other hand, visual AI includes both AI vision and end-to-end AI systems that engage in more intricate interactions with the visual environment. Visual AI is the "brain" that interprets what the eyes see and decides what to do based on that interpretation. Visual AI can be thought of as the "eyes" of AI.
Visual AI Versus Generative AI
Although both generative and visual AI are strong subsets of artificial intelligence, they have different uses. Generative AI makes it possible to produce completely original text, audio, video, and image data. In order to provide people or systems with insights for well-informed decision-making and action, visual AI analyzes visual data.
There is some overlap between generative AI and visual AI, but not all of them are. Visual outputs, such as vision foundation models or multi-modal models, are produced by generative AI systems that are trained on visual data.
Furthermore, Visual AI systems are able to use generated and real-world data to guide their perception, logic, and behavior.
Visual AI and Its Numerous Use Cases
New technologies in a wide range of industries, including marketing, sports, healthcare, security, automotive, retail, and ecommerce, have been made possible by the ongoing development of visual AI.
In addition to improving user experience and operational efficiency, Visual AI is enabling amazing innovation that will have a bigger impact in the long run.
Prevention of Phishing and Visual AI
The addition of Visual AI to Phishing Protection software is one example of this use case. With the growing prevalence of brand spoofing and the growing use of visuals by cybercriminals to avoid detection, cybersecurity software developers are turning to computer vision to improve user protection.
Visual AI's phishing detection is designed to be easily integrated with a platform's current detection techniques. It aids in the provision of an early warning system that identifies high-risk brands and additional visual cues, including forms, trust icons, and image-based text.
Such threats simply cannot be detected by traditional programmatic analysis. More phishing attacks can now be prevented than ever before due to Visual AI.
Moderation of Content and Visual AI
In order to preserve the integrity of online platforms such as social media websites, video sharing websites, messaging apps, and so forth, visual AI has the potential to make the online environment infinitely safer for users.
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While text moderation plays a significant role in safeguarding users, image and video moderation is crucial to creating safe spaces on these platforms that are devoid of offensive and particularly horrific content.
By searching the media for objects that might be inappropriate or dangerous, such as weapons, drug paraphernalia, excessive or gratuitous nudity, etc., image moderation uses object detection.
By identifying potentially harmful or offensive words that are included in the frame but would be missed by natural language processing alone, text detection goes one step further. The same technologies are used in video moderation, which looks for offensive images in each frame of the video.
The ability to process in real-time without adding lag has become crucial in content moderation, especially as live streaming becomes more prevalent across all social apps.
Marketplaces can also use content moderation to stop the sale of offensive, unlawful, and inappropriate content that might be listed in secret. Marketplaces bear the ultimate legal and reputational responsibility for the content that third parties sell on their platform.
Visual AI can be used to keep an eye on the designs and products that are posted to the platform in order to avert possible legal action or harm to one's reputation.
The API can be trained to identify particular logos to avoid copyright cases, terms that are considered racist, misogynist, homophobic, etc., and legally restricted items using a variety of computer vision technologies.
Just two instances of the real impact that visual AI is having are these two. Additional instances of Visual AI in operation include:
Brand monitoring
Social listening
Ad monitoring
Trademark compliance
Counterfeit detection
Digital piracy monitoring
Product authentication
Sponsorship monitoring
Security
Healthcare
Automotive
Real-Time Intelligence
The provision of real-time intelligence is one of the most fascinating and improving features of computer vision, or Visual AI. Action can be taken instantly, or in certain situations, automatically, when real-time data is available.
Since prompt action is usually necessary in these and countless other situations, this can be particularly effective in content moderation and phishing detection cases. Social listening, sponsorship monitoring, and other use cases that require instant reporting also benefit greatly from it.
Why is Visual AI Necessary?
The majority of information in the world is visual. For instance, 65% of all Internet traffic is already made up of video data. It is understandable why large language models (LLMs), like OpenAI's GPT-4, which were initially limited to language-based tasks, can now support a variety of modalities, such as text, audio, and AI vision.
"A picture is worth a thousand words," as the saying goes.
Visual AI is significant because it enhances human vision in amazing ways and provides new capabilities, such as:
Efficiency: Compared to humans, visual AI can process and analyze visual data far more quickly. This makes it perfect for uses where speed is essential, like medical image analysis for disease diagnosis or real-time object detection for self-driving cars.
Scalability: Visual AI models don't grow weary or perform worse with repeated use, in contrast to humans. Large amounts of visual data can be handled by vision foundation models or multi-modal models without sacrificing accuracy.
Improved Capabilities: Visual AI is able to identify objects that are invisible to the human eye, such as heat signatures in thermal footage or irregularities in X-ray images. Additionally, visual artificial intelligence can track objects across multiple camera feeds, something that humans can hardly do.
Safety: By identifying irregularities and dangers in real time, visual AI can improve safety. For example, an AI visual system can proactively identify home security threats and enforce workplace safety procedures to avert possible mishaps.
Automation: By using visual artificial intelligence (AI) to automate visual analysis tasks, companies can shift employees to higher-value jobs that call for complex decision-making, creativity, and empathy.
Decision Support: Visual generative AI can help improve decision-making across a range of domains by gleaning pertinent insights from visual data. For example, it can help manufacturers with quality control, farmers with crop health monitoring, and retailers identify shopping trends.
Conclusion: What is Visual AI?
It is crucial to know precisely what you require from the visual AI when selecting a provider for your platform or project. Knowing which of the many computer vision applications and APIs available will give you exactly what you need can be challenging.
Visual AI involves using visual information to perceive, reason, and inform or act in the physical world. It goes far beyond simply teaching machines to "see."
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