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If Your API Isn’t Fresh, Your Agents Aren’t Either. (Sponsored)
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In the agentic era, outdated retrieval breaks workflows. This API Benchmark Report from You.com shows how each major search API performs to reveal which can best answer real-world, time-sensitive queries.
What’s inside:
Head-to-head benchmarks comparing You.com, Google SerpAPI, Exa, and Tavily across accuracy, latency, and cost
Critical performance data to identify which APIs best handle time-sensitive queries
A data-driven analysis of the Latency vs. Accuracy trade-off to help you select the best retrieval layer for enterprise agents
Curious who performed best?
Disclaimer: The details in this post have been derived from the details shared online by the OpenAI Engineering Team. All credit for the technical details goes to the OpenAI Engineering Team. The links to the original articles and sources are present in the references section at the end of the post. We’ve attempted to analyze the details and provide our input about them. If you find any inaccuracies or omissions, please leave a comment, and we will do our best to fix them.
Imagine teaching a computer to recognize objects not by showing it millions of labeled photos, but by letting it browse the internet and learn from how people naturally describe images. That’s exactly what OpenAI’s CLIP does, and it represents a fundamental shift in how we teach machines to understand visual content.
CLIP (Contrastive Language-Image Pre-training) is a neural network that connects vision and language. Released in January 2021, it can classify images into any categories you want without being specifically trained for that task. Just tell it what you’re looking for in plain English, and it can recognize it. This “zero-shot” capability makes CLIP different from almost every computer vision system that came before it.
In this article, we will look at how CLIP works and the problems it tries to solve.
The Problem CLIP Solves
Traditional computer vision followed a rigid formula. If you want a model to distinguish cats from dogs, you need thousands of labeled photos. For different car models, you need another expensive dataset. For reference, ImageNet, one of the most famous image datasets, required over 25,000 workers to label 14 million images.
This approach created three major problems:
First, datasets were expensive and time-consuming to build.
Second, models became narrow specialists. An ImageNet model could recognize 1,000 categories, but adapting it to new tasks required collecting more data and retraining.
Third, models could “cheat” by optimizing for specific benchmarks.
For example, a model achieving 76% accuracy on ImageNet might drop to 37% on sketches of the same objects, or plummet to 2.7% on slightly modified images. Models learned ImageNet’s quirks rather than truly understanding visual concepts.
CLIP’s approach is radically different. Instead of training on carefully labeled datasets, it learns from 400 million image-text pairs collected from across the internet. These pairs are everywhere online: Instagram photos with captions, news articles with images, product listings with descriptions, and Wikipedia entries with pictures. People naturally write text that describes, explains, or comments on images, creating an enormous source of training data.
However, CLIP doesn’t try to predict specific category labels. Instead, it learns to match images with their corresponding text descriptions. During training, CLIP sees an image and a huge batch of text snippets (32,768 at a time). Its job is to determine which text snippet best matches the image.
Think of it as a massive matching game. For example, we show the system a photo of a golden retriever playing in a park. Among 32,768 text options, only one is correct: maybe “a golden retriever playing fetch in the park.” The other 32,767 options might include “a black cat sleeping,” “a mountain landscape at sunset,” “a person eating pizza,” and thousands of other descriptions. To consistently pick the right match across millions of such examples, CLIP must learn what objects, scenes, actions, and attributes look like and how they correspond to language.
By solving this matching task over and over with incredibly diverse internet data, CLIP develops a deep understanding of visual concepts and their linguistic descriptions. For example, it might learn that furry, four-legged animals with wagging tails correspond to words like “dog” and “puppy”. It might learn that orange and pink skies over water relate to “sunset” and “beach.” In other words, it builds a rich mental model connecting the visual and linguistic worlds.
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