12 New Code Interpreter Uses (Image to 3D, Book Scans, Multiple Datasets, Error Analysis ... )

Code Interpreter’s biggest practical payoff is turning messy inputs—images, long documents, spreadsheets, and multiple datasets—into structured outputs (3D plots, extracted quotes, anomaly flags, downloadable files, even PowerPoint-ready visuals) while still admitting when evidence is weak. The most striking demonstrations pair “creative” transformations with reliability stress tests: it can analyze Anna Karenina at ~340,000 words, but it can also fabricate quotes when the file isn’t actually provided.

A first example shows an image-to-3D workflow inside ChatGPT: after uploading an image, the system analyzes pixel RGB values and generates a 3D surface map. The result isn’t perfect immediately, but iterative adjustments eventually produce a usable plot, including reflective details that hint at how the model is interpreting the scene.

The documentary-style power shows up next with Anna Karenina. The workflow uploads a Word document (~340,000 words), then asks for all mentions of England and an analysis of the tone toward the country. It returns seven legitimate quotes and attaches sentiment/tone analysis to each passage—something that’s far beyond Ctrl+F searching because it requires passage-level interpretation. The reliability lesson arrives when the same request is made in a separate window without uploading the text: the system still produces “quotes,” but later checks reveal those quotes are made up. The takeaway is blunt: factual extraction depends on having the underlying data available; otherwise, hallucinations can look convincing.

From there, the transcript shifts into data analytics and error detection. With four uploaded datasets (sugar consumption, murder rate, inequality index, and population aged 20–39), it keeps track across multiple files and generates “surprising correlations,” explicitly distinguishing correlation from causation. One example finds a moderate positive correlation (~0.4) between murder rates and the Gini inequality index, then offers a plausible social-science explanation for why inequality might relate to violence.

For anomaly hunting, the system is tested on a large population-density CSV/Excel file. By changing just two values among roughly 36,000 data points (Isle of Man in 1975 and Liechtenstein in another year), it successfully flags those entries as implausible year-to-year percent changes—especially when prompted with a specific instruction to look for suspicious percent changes rather than vague “anything strange.” It also notes alternative explanations (migration, territorial changes), showing how it can generate hypotheses alongside detected outliers.

Creative and operational outputs keep stacking up: it can generate ASCII art from images, overlay a sonnet onto a dystopian scene using edge detection, and extract structured polling data from “unclean” RealClearPolitics formatting into a new downloadable file with candidate averages and trend analysis. It even produces PowerPoint slides directly from code-interpreter-generated visuals.

Finally, the transcript compares math performance with Wolfram on differential equations and reports that both can be correct, though code interpreter is described as more consistently accurate on a harder differential-equation prompt where Wolfram returns an incorrect value. The overall message is practical: Code Interpreter can be a powerful analyst and transformer across modalities, but accuracy hinges on data grounding, prompt specificity, and verification when stakes are high.