AI Research Lab
AI Research Lab at /ailab collects lightweight machine-learning utilities that belong in a browser tab: token counting compatible with common GPT-style tokenizers, vector comparison, rough training cost estimation, model scoring helpers, and embedding projection with PCA for visualization. Use it to sanity-check prompt length before an API call, compare cosine similarity between two embeddings you already have, or sketch how high-dimensional vectors separate in 2D, without claiming to train production models here. Work stays client-side like other UtilBloom labs, which suits draft prompts and experimental vectors you would rather not upload to random demo sites. Pair with Coding Studio when preprocessing needs Python, or catalog tools like /ai/token-counter when you want a minimal entry point into the same toolkit.
What you can do
- Token counting
Estimate token totals for prompts and completions using GPT-compatible counting rules so you can budget context windows before paying for API usage.
- Vector comparison
Measure similarity between numeric vectors you supply, helpful when evaluating embedding models or duplicate detection prototypes.
- Training cost estimator
Rough-order arithmetic on parameters, steps, and hardware assumptions to frame budgets; not a substitute for cloud provider billing dashboards.
- Model scoring helpers
Explore simple metrics and comparisons when you are teaching or prototyping, without standing up a full MLOps stack.
- PCA embedding projection
Reduce high-dimensional embedding lists to 2D or 3D plots for slides and intuition, acknowledging PCA’s linear limitations.
- Research-oriented layout
Keep related AI micro-tools one click apart instead of scattering them across disconnected catalog pages during a literature review day.
Worked examples
Prompt length before API call
Multi-section system + user prompt pasted from a draft doc
Count tokens, compare against model context limits, and trim redundant examples before you send the prompt to your provider’s endpoint.
Cosine similarity smoke test
Two 384-dimensional embedding arrays exported from a local model
Verify near-duplicate paragraphs score higher than unrelated text baselines before you trust retrieval rankings in production code.
PCA plot of class embeddings
CSV of embeddings labeled by category
Project to 2D, inspect overlap clusters, and remember nonlinear manifolds may need t-SNE or UMAP elsewhere for faithful geometry.
How it compares
**Weights & Biases**, **MLflow**, and cloud **SageMaker** consoles track experiments, artifacts, and GPU clusters AI Research Lab does not host. **OpenAI’s** or **Anthropic’s** own dashboards show billing-accurate usage. This lab occupies the **exploratory, pre-production** niche inside UtilBloom: token math, vector toys, and PCA plots beside PDF and code tools on one site. For heavy training, export scripts to [/codelab](/codelab) or your GPU provider. For a single tokenizer without the full lab chrome, [/ai/token-counter](/ai/token-counter) remains linked from catalog descriptions.
Questions
Does AI Research Lab call OpenAI automatically?
Core panels operate on data you paste unless a specific feature documents an external API. Token counting is local estimation, not a chat completion request.
Are training cost numbers guaranteed?
No. They are educational arithmetic using assumptions you provide. Always reconcile with your cloud invoice and hardware utilization metrics.
Why might token counts differ from my API bill?
Providers update tokenizers and may count tool schemas or image tokens differently. Use UtilBloom counts for drafting, then verify in the provider console.
Can PCA replace t-SNE for publication figures?
PCA is linear and fast but can mislead on curved manifolds. Use it for slides and intuition; choose nonlinear methods when reviewers expect them.
Can I upload a private corpus for embedding?
You paste or load what the browser can hold in memory. There is no UtilBloom cloud corpus storage tied to this lab.
How does this relate to Graph Lab or Data Lab?
Graph Lab plots functions; Data Lab profiles spreadsheets. AI Research Lab focuses on vectors, tokens, and rough ML budgeting, not general charting or CAS math.