TL;DR version

Image recognition allows machines to see, understand, and interpret visual media, identifying objects, people, text, and actions by assigning numerical value (RGB) to pixels (picture elements).

Image recognition process consists of 4 steps:

1. Data Gathering
2. Model Training
3. Prediction
4. Testing and Deployment

While AI demonstrates remarkable prowess in swiftly analyzing extensive visual data with minimal error, continuous training remains essential for contextual understanding, especially in intricate scenarios like compliance and prevention.

Tasks in image recognition span a spectrum from objective to subjective, with AI excelling in objective tasks while benefiting from human intervention in subjective ones. The impact of error significantly influences the reliability and efficacy of AI systems across various tasks.

How does AI Image Recognition work?

To understand AI Image Recognition, let's start with defining what an "image" is.

An image is composed of tiny elements known as pixels (picture elements), each assigned a numerical value representing its light intensity or levels of red, green, and blue (RGB). AI Image Recognition enables machines to recognize patterns in images using said numerical data. It replicates the human ability to perceive images, identify objects and patterns within them, and respond accordingly.

To train AI for this task, we provide AI models with vast amounts of labeled images. This process helps them learn to recognize similar patterns effectively and make predictions based on past data.

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The Process

1. Data Gathering

• Collect a vast data set of images
• Carefully analyze and label images with features or characteristics; for instance, an image of a package needs to be identified as "package".  The different packages would need to be labeled with relevant class “tags” or a “bounding box” to denote the location

1. Training

• Like a human brain, machines need to "see" many different examples to accurately recognize an image
• During the training process, the network learns to distinguish between different object categories (e.g. "package" and "no package")

1. Prediction

• Once trained, the AI can now analyze new images and videos, comparing them to the original training data to make predictions.
• With continuous machine learning, the AI can assign classifications to images or indicate the presence of specific elements.
• The system then converts those into predictions that can be put into action. For example, you can train your AI to identify the presence of a human element in an image as "non-compliant" to follow data security procedures.

1. Test. Test. Test.

• Continuous testing is needed to see how the model will perform when given new scenarios
• A dedicated development team has to continuously evaluate the model’s performance and make any necessary adjustments.

The model’s performance is measured using metrics such as accuracy, precision, and recall. This will be discussed in the next series.

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Are there limits to AI capabilities in image recognition?

In the realm of image recognition, artificial intelligence (AI) has advanced significantly, enabling machines to interpret visual media with remarkable accuracy.

However, while AI excels in analyzing vast amounts of visual data quickly and with less error, just like humans, they need to be continuously trained to better understand context and changes in rules/ environments, particularly in complex scenarios like compliance and prevention.

Would you trust AI to decide when your avocado is ripe and ready?

Ah, the avocado – a versatile fruit that keeps us guessing: when is it truly ripe? The answer isn't simple. It all depends on the context: what you plan to do with it, what's your threshold for sweetness, and more.

Wait, hold on- what does that have to do with AI??? Well, think of this: when you pose that question to AI, what do you think its answer would be?

That brings us to the spectrum of objective to subjective tasks in AI. 🥑🤖

Objective tasks can be executed perfectly by AI, while subjective tasks benefit from human intervention with AI support. Let's explore these concepts further by examining the different types of tasks and the varying impacts of error.

What are objective and subjective tasks?

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When considering image recognition tasks, we can view them along a spectrum ranging from objective to subjective.

1. Objective: These involve clear criteria and yield yes or no answers. They pertain to tasks where the evaluation is based on measurable attributes or binary classification.

Examples:

• Identifying whether an image contains a cat.
• Determining the presence of a specific object, such as a car or a tree, in an image.

1. Subjective / Qualitative: These involve nuanced analysis and interpretation. They pertain to tasks where evaluation relies on human judgment and may vary depending on context.

Examples:

• Assessing the quality of medical images to diagnose severity of a skin condition from dermatological images
• Analyzing whether parking is deemed compliant or not based on predetermined city guidelines

AI generally finds objective tasks easier than subjective ones due to their clear criteria. Objective tasks involve binary answers based on measurable attributes, allowing AI to handle them more easily. Conversely, subjective tasks require more training and human intervention for nuanced analysis. This underscores the importance of tailored approaches in AI training and intervention across the spectrum of tasks.

How critical is your impact of error in your AI task?

As we know, AI, while powerful, is not perfect and can make mistakes, including in the field of image recognition. In our previous article, we explained the difference between objective and subjective tasks, and how AI, lacking human experience, is prone to miss nuance on a variety of subjective tasks that’s why human intervention and continuous training is necessary in more challenging tasks. This imperfection in AI leads us to the important discussion of the degree of error in AI tasks.

In the realm of image recognition, the consequences of errors can vary significantly depending on the context and application. The impact of error plays a crucial role in determining the reliability and effectiveness of AI systems deployed for various tasks. This is a crucial factor when considering to build vs buy AI tools, which we will discuss later

Low Impact of Error:

There are tasks with low impact of error, where inaccuracies may lead to minor inconveniences or have minimal consequences. Examples:

1. In social media tagging, inaccuracies in automatically tagging people in uploaded images may result in misidentifications. These errors typically have minimal impact on user experience.
2. In virtual try-on applications, inaccuracies in overlaying virtual clothing or accessories onto user images may result in imperfect fit or placement, but these errors do not significantly affect user experience.

Medium Impact of Error:

Tasks with medium error impacts may seem minor initially but can escalate to significant costs later. Examples:

1. Errors in delivery verification can lead to incorrect deliveries, resulting in customer dissatisfaction and potential losses for businesses.
2. Parking compliance errors can lead to parking violations, fines, and disruptions to traffic flow or public safety.

Over time, these errors erode trust and require costly interventions. So, while initially minor, they can have major consequences if not addressed promptly.

High Impact of Error:

On the extreme end of the spectrum, tasks with a high impact of error are those where inaccuracies can lead to severe consequences, posing risks to safety, security, or health. Examples:

1. In autonomous vehicles, errors in object detection systems can result in accidents or collisions, endangering the lives of passengers, pedestrians, and other road users.
2. In medical imaging diagnosis, misinterpretation of images can lead to incorrect diagnoses and inappropriate treatments, compromising patient safety and well-being.

The varying impacts of errors in image recognition tasks emphasize the need for continuous training and human guidance, particularly in scenarios with higher potential consequences. Tasks with higher error impacts necessitate more rigorous training and increased human oversight to ensure accuracy and mitigate risks effectively.

The Need for Continuous Training in Image Recognition

In image recognition, a regular continuous (supervised) training loop is crucial to prevent errors. Continuous training helps the AI model adapt and improve over time, reducing the likelihood of mistakes. By updating the model with new data and refining its algorithms regularly, we enhance its accuracy and reliability, crucial for safety and effectiveness in various applications.

References:

1. Entrepreneur. (2017, April 1). Can Artificial Intelligence Identify Pictures Better Than Humans? Entrepreneur. Retrieved from https://www.entrepreneur.com/science-technology/can-artificial-intelligence-identify-pictures-better-than/283990
2. Veryfi. (2023, April 5). Artificial Intelligence vs. Intelligent Automation. Veryfi. Retrieved from https://www.veryfi.com/technology/artificial-intelligence-vs-intelligent-automation/
3. MIT News. (2023, December 15). Image Recognition Accuracy Measured with Minimum Viewing Time Metric. MIT News. https://news.mit.edu/2023/image-recognition-accuracy-minimum-viewing-time-metric-1215
4. Viso.ai. (n.d.). Image Recognition. Viso.ai. Retrieved from https://viso.ai/computer-vision/image-recognition/