AI – Friday, August 23, 2024: Notable and Interesting News, Articles, and Papers

A selection of the most important recent news, articles, and papers about AI.

News, Articles, and Analyses

NEA led a $100M round into Fei-Fei Li’s new AI startup, now valued at over $1B | TechCrunch

https://techcrunch.com/2024/08/14/nea-led-a-100m-round-into-fei-fei-lis-new-ai-startup-now-valued-at-over-1b/

Author: Marina Temkin

(Wednesday, August 14, 2024) “World Labs, a stealthy startup founded by renowned Stanford University AI professor Fei-Fei Li, has raised two rounds of financing two months apart,”

The State of Generative AI in the Enterprise: Moving from potential to performance

https://www2.deloitte.com/us/en/pages/consulting/articles/state-of-generative-ai-in-enterprise.html

(Thursday, August 15, 2024) “New Q3 insights: Explore the latest findings from the Deloitte AI Institute‘s survey series tracking Generative AI adoption, successes, and challenges throughout 2024.”

Artificial Intelligence: Vogue publisher and OpenAI strike deal

https://www.bbc.com/news/articles/cpqjvl9z9w1o

Author: João da Silva

(Tuesday, August 20, 2024) “The multi-year deal is the latest such agreement to be struck between OpenAI and a major media company.”

NVIDIA’s First SLM Helps Bring Digital Humans to Life | NVIDIA Blog

https://blogs.nvidia.com/blog/ai-decoded-gamescom-ace-nemotron-instruct/

Author: Ike Nnoli

(Wednesday, August 21, 2024) “Announced at Gamescom, ‘Mecha BREAK’ is the first game to showcase ACE technology, including NVIDIA Nemotron-4 4B, for quicker, more relevant responses.”

Resist the seductive power of AI in military decision-making – The Japan Times

https://www.japantimes.co.jp/commentary/2024/08/21/world/ai-in-military-decisionmaking/

Author: Brad Glosserman

(Wednesday, August 21, 2024) “The maturation of AI and the creation of large learning models have driven the war-gaming industry — and it is an industry — to new heights of fever and frenzy.”

Gen AI discussions must prioritize sustainability (opinion)

https://www.insidehighered.com/opinion/views/2024/08/22/gen-ai-discussions-must-prioritize-sustainability-opinion

Author: Susanne Hall

(Thursday, August 22, 2024) “Faculty lack information about generative AI’s environmental impacts, and universities should prioritize sustainable computing, Susanne Hall writes.”

Learn how AI transformers actually work – IBM Research

https://research.ibm.com/blog/how-ai-transformers-work

(Thursday, August 22, 2024) “This web-based tool lets you explore the neural network architecture that started the modern AI boom.”

Technical Papers, Articles, and Preprints

[2408.11876] From Glucose Patterns to Health Outcomes: A Generalizable Foundation Model for Continuous Glucose Monitor Data Analysis

https://arxiv.org/abs/2408.11876

Authors: Lutsker, Guy; Sapir, Gal; Godneva, Anastasia; Shilo, Smadar; Greenfield, Jerry R; Samocha-Bonet, Dorit; Mannor, Shie; Meirom, Eli; Chechik, Gal; Rossman, Hagai; and Segal, Eran

(Tuesday, August 20, 2024) “Recent advances in self-supervised learning enabled novel medical AI models, known as foundation models (FMs) that offer great potential for characterizing health from diverse biomedical data. Continuous glucose monitoring (CGM) provides rich, temporal data on glycemic patterns, but its full potential for predicting broader health outcomes remains underutilized. Here, we present GluFormer, a generative foundation model on biomedical temporal data based on a transformer architecture, and trained on over 10 million CGM measurements from 10,812 non-diabetic individuals. We tokenized the CGM training data and trained GluFormer using next token prediction in a generative, autoregressive manner. We demonstrate that GluFormer generalizes effectively to 15 different external datasets, including 4936 individuals across 5 different geographical regions, 6 different CGM devices, and several metabolic disorders, including normoglycemic, prediabetic, and diabetic populations, as well as those with gestational diabetes and obesity. GluFormer produces embeddings which outperform traditional CGM analysis tools, and achieves high Pearson correlations in predicting clinical parameters such as HbA1c, liver-related parameters, blood lipids, and sleep-related indices. Notably, GluFormer can also predict onset of future health outcomes even 4 years in advance.”

[2408.12308] Deep Learning with CNNs: A Compact Holistic Tutorial with Focus on Supervised Regression (Preprint)

https://arxiv.org/abs/2408.12308

Authors: Tejeda, Yansel Gonzalez and Mayer, Helmut A.

(Thursday, August 22, 2024) “In this tutorial, we present a compact and holistic discussion of Deep Learning with a focus on Convolutional Neural Networks (CNNs) and supervised regression. While there are numerous books and articles on the individual topics we cover, comprehensive and detailed tutorials that address Deep Learning from a foundational yet rigorous and accessible perspective are rare. Most resources on CNNs are either too advanced, focusing on cutting-edge architectures, or too narrow, addressing only specific applications like image classification. This tutorial not only summarizes the most relevant concepts but also provides an in-depth exploration of each, offering a complete yet agile set of ideas. Moreover, we highlight the powerful synergy between learning theory, statistic, and machine learning, which together underpin the Deep Learning and CNN frameworks. We aim for this tutorial to serve as an optimal resource for students, professors, and anyone interested in understanding the foundations of Deep Learning.”