Computer vision

01why it matters for a business

Wherever people look at things to make decisions, computer vision is a candidate: quality inspection, inventory counts, safety monitoring, document intake, property and damage assessment. The appeal is consistency and scale; a model looks at every item the same way, all shift long. Specialized models are fast and cheap to run once trained, while general multimodal models need little or no training but cost more per image and can be less precise.

Success depends more on data and setup than on algorithms. Lighting, camera placement, and a labeled set of real examples, including the rare failures you most care about, decide whether a vision system works in production. A model that scores well on clean sample images can struggle in a dim warehouse.

02what it looks like in practice

A food packaging plant checks that labels are present, straight, and showing the right lot code. Today inspectors sample a fraction of packages by hand. A camera over the line with a vision model checks every package, reads the lot code, compares it with the production schedule, and diverts mismatches. Inspectors now review the diverted packages and the borderline cases the model flags, and the examples they confirm are used to retrain the model as packaging changes.

03common mistakes

• Training on clean photos and deploying into messy real conditions.
• Too few examples of the rare defects that matter most.
• No plan for change. New products, packaging, or lighting require retraining.
• Choosing a general model for a high-volume, narrow task where a small specialized one is faster and cheaper.

04related terms

• Multimodal AIMultimodal AI is AI that can take in or produce more than one kind of data, such as text, images, audio, video, or documents, within the same model or system.
• On-premise AIOn-premise AI means running AI models and the systems around them on hardware in your own facilities or private data center, rather than calling a vendor's cloud service.
• Generative AI (GenAI)Generative AI is the category of artificial intelligence that creates new content, such as text, images, audio, video, or code, in response to a prompt, rather than only classifying or predicting from existing data.
• AI evaluations (evals)Evals are structured tests that measure how well an AI system performs on a defined task, using a set of real or realistic inputs with known good outcomes.

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