Nicolay Gerold

4. Being Prepared for Errors

Source:https://pair.withgoogle.com/guidebook/patterns

Errors are inevitable in any system, AI included. Planning for these hiccups from the start is crucial. Consider the potential errors, their impacts, and how you’ll address them. Will you offer manual overrides? How about direct access to customer support? These are... See more

Boosting annotator efficiency with Large Language Models

In July and August, we released LLM-assisted recipes for data annotation and prompt engineering:

• NER: ner.llm.correct, ner.llm.fetch


• Spancat: spans.llm.correct, spans.llm.fetch


• Textcat: textcat.llm.correct, textcat.llm.fetch


• Prompt engineering and terms: ab.llm.tournament terms.llm.fetch

Prodigy's... See more

LLM agents rely on their Long-Term Memory (LTM) to store and retrieve crucial memories. Conversations, thoughts, plans, actions, observations, skills, and behaviors are all stored within a vector database. The LTM enables pre-processing and post-processing of memories, ensuring optimal retrieval. By considering factors such as context, recency,... See more

Whenever we are fasting we are putting ourself on the edge of stress and alertness due to increased epinephrine and cortisol. If you are already highly stressed this can have averse effects.

One of the first things Data Scientists learn as they run predictions is to avoid the use of loops. That’s because most ML libraries support vectorized inference, combining many inputs into a batch and more efficiently calculating the results. This specialized technique combines framework-level features with specialized hardware like GPUs, making... See more

ETL

The part of the system I'm most proud of, and on which I spent the most effort, is the ETL process.

We had a series of shell scripts for each data source we ingested (there were many), which would pull the data and put it in an s3 bucket.

Then, early in the morning, a cron job would spin up an EC2 instance, which would pull in the latest ETL code... See more

When our internal states of stress or calm match (mismatch) the demands on us, we tend to interpret these as good (bad). Alertness vs. stressors as a driver of emotions.

When you are very alert, everything is a stimulus, you are very unlikely to suppress an action. You should suppress all incoming stimuli except the one you want to focus on. Best situation for learning is silence. If you are tired, some background noise can increase alertness.