1.
USA’s Venezuela takeover comes with bonus exposure to Chinese AI surveillance tech
Think tank the Australian Strategic Policy Institute (ASPI) has warned that Venezuela is poised to adopt Chinese AI systems to enhance surveillance systems that already rely on Middle Kingdom tech, and called for US Secretary of State Marco Rubio to do something about it. ASPI outlined the Venezuelan situation in a recent report [PDF], titled Warning signals: Venezuela and the risk of Chinese AI-enabled digital authoritarianism. The document explains that Venezuela’s government built a surveillance state a decade ago, largely using technology from Chinese companies.
2.
AI risks make some insurers wary of corporate liability
3.
This awesome weather-based Raspberry Pi project lets you know if rain is on the way
One of the trickiest arts when living in the UK is trying to predict whether it will rain or not. Precipitation chance percentages mean next to nothing, as predicted rain fails to appear and supposed dry days soak everyone looking for a nice day out. As such, someone made their own cloud tracker with a Raspberry Pi to give them at least an educated guess as to how the weather will be.
4.
OpenAI Introduces Triage Framework and Case Studies to Report Model Misalignment
OpenAI has released a disclosure framework for model misalignment during its lifecycle. Employees can flag potential issues, prompting technical staff to label incidents. The initial case studies outline unexpected model behaviours, providing insights into deviations from expected parameters. Community reactions show both approval and scepticism regarding transparency and corporate narratives.
5.
80%+ of companies don’t have a plan for Gen AI use cases — insights from 700+ data leaders
I spent last week in the heart of San Francisco at Snowflake Summit 2024. With a packed house of over 20,000 attendees across 400 sessions, it was an incredible week full of insights and learnings. At the Summit, we surveyed over 700 data leaders to get the latest scoop on what’s happening in the data world. They shared their top priorities, current AI journeys, and much more. In this issue of Metadata Weekly, I’m breaking down these “hot off the press” survey results with my co-author Austin Kronz, Director of Data Strategy at Atlan.
6.
3-step framework for scaling data quality in the age of generative AI
I’ve found that data quality isn’t really about cleanliness or completeness or accuracy. Instead, it’s about trust. A recent survey showed that though nearly every data team is diving headfirst into AI applications, 68% of companies aren’t confident in the data behind these applications. Imagine this: someone looks at a dashboard and says, "That number doesn’t look right." Diagnosing it is a huge challenge.
7.
A Message for the Humans of Data & AI
Hey Humans of Data & AI 👋 Over the past few months, we’ve been listening closely. We heard you talk about the overload of content, the endless marketing spin, and how hard it is to find something genuinely useful in the data and AI space. That matters because when noise drowns out real voices, the community loses what makes it special. So we went back to the drawing board. Today, we’re reintroducing Metadata Weekly, a project rebuilt on one simple belief: the data and AI community deserves a space that puts humans first. For the Humans of Data & AI.
8.
National AI Rules Should Not Become a Privilege for Incumbents
The Washington Examiner published two related reports on September 15. OpenAI lobbyists are backing regulatory provisions in the bipartisan FRONTIER Act, H.R. 9925, introduced in July by Reps. Jay Obernolte and Lori Trahan. Rep. Josh Gottheimer, who co-chairs the House work on the subject, is pressing a different method. He wants mandatory government review of the most advanced models and has said third-party audits alone do not meet the moment. The live argument in Washington is now the machinery.
9.
Being an engineering manager at Amazon
Gilad Naor shared his journey from a startup to Amazon and then Meta, highlighting the distinct approach to management at Amazon. One standout aspect is how meetings begin with a quiet reading period, allowing everyone to absorb the material before discussion. This method stems from a belief that intentions don’t matter if the outcome isn’t effective; mechanisms must be in place to ensure quality decision-making. He emphasizes the importance of clear communication, avoiding vague terms, and providing specific feedback to foster growth.
At Amazon, there’s no one-size-fits-all process; teams have the autonomy to choose their methods and tech stacks, promoting ownership among developers. This principle extends to managers, who are accountable for their projects from conception to execution. Recently, under CEO Andy Jassy, Amazon introduced a new leadership principle focused on being a people-first employer, addressing past variances in management styles. Gilad reflects on the value of technical talks at Amazon, where senior engineers share insights, enriching the learning experience for everyone involved.
10.
Why sprints are taking the joy out of building software
To sprint is to run as fast as you can over a short distance. And what happens after you finish a sprint? You need to catch your breath and rest (maybe even vomit a little if you are out of shape). Imagine a 100m runner doing 26 sprints, one-after-the-other, no breaks: And then, start another one… That’s how most software teams feel! Stop right there. Let’s break that “Sprints are at the very heart of scrum and agile methodologies” myth. Sprints ARE NOT at the heart of Agile! Think about your last sprint.