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A collection of 33 issues

Google's Gemini

Google launched Gemini today. Gemini 1.0 is a multimodal model that has been trained on image, audio, video and text data and is intended to be generalizable across different data modalities (e.g., you give an image, it can return text-based description of the image). Gemini comes in three

OpenAI Board Should Resign

What happened at OpenAI was crazy on many levels. While I am an outsider to OpenAI's inner workings, I will go out on a limb and say that the people who made the decision to oust Sam Altman probably do not have the right experience for making this

Evaluating Gen AI Applications - Part I

When I worked in mergers & acquisitions way back, one of the things to do was generating project code names to help preserve the confidentiality of these highly sensitive projects. I remember that we have gone through all kinds of names, drawing inspiration from mountains, park names, bird names, car

Joyce’s picks: readings in ML/AI , September 12, 2023

“TSMixer: An all-MLP architecture for time series forecasting” (Google) “Attention Is All You Need” (a major classic!) “Language to rewards for robotic skill synthesis” (Google) “Consistent Collaborative Filtering via Tensor Decomposition Authors” (Apple) “Speeding Up Text-To-Speech Diffusion Models by Distillation” (Nvidia) “To Copy Rather Than Memorize: A Vertical Learning Paradigm

Building a cyber defense fortress

Cyber attacks are getting out of hand, and every organization is vulnerable. Just looking at recent news: Clorox had to take certain operations offline due to cyber attack (Reuters) Discord went offline because over 700K user information were stolen (link) Prince George's County public school system was hacked

Joyce’s picks: readings in ML/AI, July 28, 2023

“Aided by A.I. Language Models, Google’s Robots Are Getting Smart” (NYTimes) “AI search of Neanderthal proteins resurrects ‘extinct’ antibiotics” (Nature) “SK Hynix and Samsung’s early bet on AI memory chips pays off” (FT) “KNN-Diffusion: Image Generation via Large-Scale Retrieval” (Link) “Consistency Models” (Link) “SelfCheckGPT: Zero-Resource Black-Box Hallucination

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