Luke Arrigoni started Arricor in 2012 to help large companies make sense of their data. Since then, he and the team have taught organizations like Goldman Sachs, AT&T, and Thomson Reuters about the principles of AI. His secret? Focus on the business problem and the right technology approach becomes obvious.
Listen and learn…
- How UPS uses AI to automatically assign the right tax code for packages
- What responsibility AI developers have for the decisions their algorithms make
- How to clean dirty data to make it ready for AI model training
- When to use neural nets vs. gradient-boosted trees
- Which tasks are good candidates for classifier models vs. NLP
- Which job skills are future-proof… and which are likely to be replaced by automation
References in this episode:
- Fish from Mozart Data on AI and the Future of Work
- Airflow for data pipeline automation
