EDGE AI with Machine Vision and Visual AI

5 Main Types of AI the enterprises can leverage to drive insights and generate business value.

• Text AI
• Visual AI
• Interactive AI
• Analytic AI
• Functional AI.

Computer Vision with Edge revolutionising the Industries and enabling next Generation Solutions with real time applications.

IBM offers a concise definition:

Computer vision is a field of artificial intelligence (AI) that enables computers and systems to derive meaningful information from digital images, videos, and other visual inputs—and take actions or make recommendations based on that information.

If AI enables computers to think, computer vision enables them to see, observe, and understand and Visual AI enables them to act.

Visual AI is a discipline of computer science that enables machines to identify, understand, and act on imagery and visual data just like a human vision system.

It combines several technologies, including computer vision,?natural language processing(NLP), different content formats (video, photos, and extended reality), and deep learning to derive insights and create business value from images.

Visual AI Use Cases?

• Document classification?which enables access to information by combining computer vision and NLP to classify, extract, and enrich documents.
• Product detection and search?which identifies products within images and searches a given catalog for exact matches.
• Visual search?which is a search engine that looks for information through the input of an image and/or displays visual search results.

Visual AI Benefits for Retailers and eCommerce Brands

• Seamless product discovery?with the use, for example, of a?visual search engine?that delivers accurate and relevant search results, and a Pinterest-like tool that allows shoppers to view similar items to an image of interest.
• Better customer experience?with?relationship-building recommendation engines?that suggest products like a family or friend, based on visual data and real-time context. Applications include “Complete the Look,” “Similar Products,” and “Customers Also Bought.”
• More efficient backend processes?using insights generated from search query data and user interactions with on-site image assets that inform business decisions,?inventory management, and trend and demand forecasts.

Edge AI + Vision refers to the practical use of artificial intelligence and computer vision in machines that perceive and understand their environment through visual and other means

Edge AI means AI processing that occurs locally, whether on a chip, device, or on premise.?Hybrid approaches where some processing happens locally and some in the cloud.?Edge devices that process all sorts of sensor data: images, audio, vibration, radar, lidar, and the like.

Machine vision gives industrial equipment the ability to “see” what it is doing and make rapid decisions based on what it sees.

"The shift from machines that can automate simple tasks to autonomous machines that can ‘see’ to optimize elements for extended periods will drive new levels of industrial innovation,”

The most common uses of machine vision are visual inspection and defect detection, positioning and measuring parts, and identifying, sorting, and tracking products.

Detecting visual anomalies is one thing. Uncovering revenue opportunities is when it gets really interesting

Towards reliable and safe AI?

ML reliability

Increasing ML reliability through systematic testing is at the core of what Lakera does. Here is a read through some of this year’s articles to make sure you are proactively making your AI as reliable and safe as possible

Why ML testing is crucial for reliable computer vision.

Bias in ML

“Data is a reflection of the inequalities that exist in the world”. While this might be true, developers have great potential to curb bias in their computer vision systems.

Establishing whether bias is present in a computer vision system is key to understanding how it will perform in operation. Bias manifests itself in numerous ways, from data collection and annotation to the features that a system uses for prediction.

The computer vision bias trilogy: Data representativity

The computer vision bias trilogy: Shortcut learning

The computer vision bias trilogy: Drift and monitoring

Fairness and ethics

Fairness assessment that checks for bias against protected categories in computer vision systems.

These tests allow, for example, to test the following:

• Is my pedestrian detector fair as defined by Equality of Opportunity?
• Is my model ISO/IEC 24027 compliant?
• Does my model discriminate against a given demographic?

MLTest implements the core metrics described in ISO/IEC 24027.

Synthetic data for computer vision (CV) for training artificial intelligence algorithms is poised to gain widespread adoption by government and commercial customers to reduce cost and time of acquiring real sensor data?

Gartner estimates that by 2030, synthetic data will completely overshadow real data in AI models.

Gartner's Innovation Insight for Synthetic Data stated, "We believe that synthetic data is important for the future of AI because it solves one of the most pervasive and critical challenges that AI systems face today — the lack of domain-specific, well-labeled, high-volume data at a reasonable cost."?

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