Ben chats with Shayne Longpre and Robert Mahari of the Data Provenance Initiative about what GenAI means for the data commons. They discuss the decline of public datasets, the complexities of fair use in AI training, the challenges researchers face in accessing data, potential applications for synthetic data, and the evolving legal landscape surrounding AI and copyright.
In this episode, Ben interviews Jannis Kallinikos, a professor at Luiss University in Rome, Italy about his new book Data Rules: Reinventing the Market Economy, coauthored with Cristina Alaimo. They discuss the social impact of data, explore the idea that data filters how we see the world and interact with each other, and highlight the need for social accountability in data tracking and surveillance.
With the ever-increasing importance of data, we’re always looking for expert voices that can expand our view of what data and our reliance on data means for software development and society as a whole. More and more of our lives are becoming data-driven. Is that a good thing?
On this episode: Stack Overflow senior data scientist Michael Geden tells Ryan and Ben about how data scientists evaluate large language models (LLMs) and their output. They cover the challenges involved in evaluating LLMs, how LLMs are being used to evaluate other LLMs, the importance of data validating, the need for human raters, and more needs and tradeoffs involved in selecting and fine-tuning LLMs.
If you’re building experimental GenAI features that haven’t proven their product market fit, you don’t want to commit to a model that runs up costs without a return on that investment.
AI systems obey the golden rule: garbage in, garbage out, Want good results, feed it good data.
If we can make operational data easier to manage and easier to access through simple, standardized APIs, everyone can transform their companies into sustainable data-driven organizations.
Tim Tutt, CEO and cofounder of Night Shift Development, tells the home team about his work in deploying large-scale search and discovery analytics, why he’s working to help nontechnical users understand and utilize their business data, and how GenAI is teaching people to ask better questions.
Machine learning uses data structures that don't always resemble the ones used in standard computing. You'll need to process your data first if you want efficient machine learning.
Bigeye cofounders Kyle Kirwan (CEO) and Egor Gryaznov (CTO) join the home team to discuss their data observability platform, what it’s like to go from coworkers to cofounders, and the surprising value of boring technology.
Distributed work may hold the key to creating forward thinking metropolises.
When APIs send data, chances are they send it as JSON objects. Here's a primer on why JSON is how networked applications send data.
As with any good joke, the most important part is the resulting data.
As May is Mental Health Awareness Month, we wanted to see what developers are doing to decrease that stress and prioritize their own wellness. Earlier this year, we surveyed over 800 developers to see if they are happy at work and what they are doing to maintain or improve mental health.
When the Log4j security issue was disclosed, developers came looking for answers. We took a look at our site data around it.
At this point, most software engineers see the value of testing their software regularly. But are you testing your data engineering as well?
Get your data here! Get your fresh hot 2021 Developer Survey data!
Here's three areas where data visualization can make your engineering life easier.
The center of gravity for software is shifting. Tik Tok is a Chinese-born, consumer-facing, global hit. Meanwhile, developers are fleeing San Francisco as the pandemic rages on.
Stack Overflow traffic during DEF CON shows what technologies are trending in the hacker community
We chat with the CEO of Count about a new programming language his startup has created for data analysis.
