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Artificial Intelligence

A collection of 59 issues

Generative AI x Knowledge Graphs

Data wrangling tasks include data acquisition, data ingestion, data extraction, data cleaning, data transformation, exploratory data analysis, and data pipelining for ML tasks. Data variety, dimensionality, complexity, duplication, provenance can further complicate this data wrangling process.

Joyce's picks - readings in machine learning /AI - July 21, 2023

Meta LLaMA — original blog (link) Meta and Microsoft Introduce the Next Generation of Llama (link) Most outsourced coders in India will be gone in 2 years due to A.I., Stability AI boss predicts (CNBC) One senator’s big idea for AI (Politico) Unstructured, which offers tools to prep enterprise

Joyce’s picks — readings in machine learning /AI

July 14, 2023 Nvidia deepens bets on AI in drug discovery with Recursion investment (Reuters) New York City’s AI Hiring Bias Law Creates Hurdles for Companies (Bloomberg Law) Removing bias in AI: Wells Fargo shares open toolkit for explainability (Venturebeat) This amazing new AI tool turns a scribble into

Bigger is not better in machine learning

I heard an interview today featuring the Chief Scientist of Mosaic ML. One of the things Jonathan Frankle said is that bigger is not better. He, of course, is talking about machine model size (e.g., number of parameters). I posit that we can agree that bigger models may not

Security Robots

I recently learned that a Santa Fe high school is testing autonomous robots with multiple cameras to monitor the surrounding areas of the school to assist school security team. The autonomous robots learn the routes of the school, capture videos of the surroundings, and provide the footage to school'

Machine Learning and Audio Data

I was excited to see WSJ featuring Shazam. The 6-minute video explains how Shazam works, albeit in a simple manner. How Shazam Makes Unique Audio Fingerprints to Identify SongsMore than 23,000 songs are identified each minute by Shazam and the app has been used over 70 billion times. But

AI Generative Models are eating the world

Originally posted on September 21, 2022 My Berkeley co-instructor David Steier and I have been talking about large transformer and/or generative models for a while. We have seen many students using these large models and apply them in very interesting and creative ways to enable various meaningful use cases

Thoughts on Generative AI, June 2023

This week I had the opportunity to speak at Women in Data Science event hosted by 6sense and the Generative AI event hosted by CFA Institute of New York. I also had the opportunity to meet the some of the new Berkeley SkyDeck startups as an advisor. Some high level

Defensibility in AI Applications

I have seen many AI startups in the past few months, and we need to talk more about defensibility. While it is great to see rapid innovation in ML and AI, I urge entrepreneurs and startups to consider defensibility further. What is defensibility? Defensibility is demonstrating high barrier to entry