Great Visuals with Data
What's Hot 🍜
Great Visuals w/ Data 🎞
Buzzword - Data Interoperability 📲
Uncharted Path 🛳
Thursday, April 4th
What's Hot 🍜
AXA and AWS Developing the First Global B2B Risk Management and Prevention Platform
Scott Gunter, CEO, AXA XL
"Globally clients are grappling with extreme weather, cyber-attacks and other shocks and disruptions. We believe that building resilience is more essential than ever. That is why we are excited to be working with AWS, combining their tech capabilities with AXA’s expertise in commercial insurance to develop the next generation of risk management insights and services to help clients unlock their full potential".
Kathrin Renz, VP of AWS Industries
“AXA is a customer-focused business, and we are excited to collaborate with them to develop new business models, using AWS capabilities, that will help companies around the world operate with greater confidence. AWS will help AXA support its business clients, creating secure, compliant new services and capabilities fueled by advanced technologies like generative AI. Amazon’s experience in building, operating, and scaling marketplaces will also help AXA tap into innovation from companies beyond the financial services industry, offering more ways to plan for uncertainty.” (Source: axa.com)
Great Visuals w/ Data 🎞
Amazon Web Services Introduces Pay As You Go Cloud Rendering Service For Deadline-Sensitive VFX and CG Work
Antony Passemard, general manager of creative tools at AWS, notes that VFX rendering might take “a few hours per frame on large complex frames for a movie, for example.” A full VFX-heavy scene, therefore, “could be many days of rendering, or many, many, many machines are working in parallel. Even when you’re not rendering, you’re still paying somewhat of an underlying infrastructure cost. With Deadline Cloud you only pay for when you render. When you have downtime in your production, it costs you zero.”
(Source: variety.com)
IBM watsonx Brings New Generative AI Capabilities to Masters Tournament Digital Platforms
Data-driven recaps of how each hole has played daily and throughout the 2024 Tournament (e.g., "The 14th hole has played difficult today, with 25% of shots resulting in bogies.").
Projections of how each hole might play, based on past and current performance data (e.g., "The 9th hole is projected to be the third most difficult hole today.").
Historical insights into how each hole has played, based on eight years of Tournament data – including more than 170,000 shots – and ball position on course (e.g., "Shots historically hit in this location have an 82% chance of resulting in a birdie."). (Source: newsroom.ibm.com)
Scientists Use NASA Data to Predict Solar Corona Before Eclipse
"We developed a software pipeline that took in the magnetic field maps, picked out all of the areas that should be energized, and then fine-tuned the amount of energy to add to those areas,” Emily Mason, a research scientist at Predictive Science.
(Source: science.nasa.gov)
Beyond the Buzzwords 📲
This week's buzzword was inspired by the following article, The Next Thing to Look For in AI Vendors: Interoperation
data interoperability
Data interoperability refers to the ways in which data is formatted that allow diverse datasets to be merged or aggregated in meaningful ways. It is a key aspect of the FAIR Data Principles, constituting the “I” in FAIR. (Source: nnlm.gov)
Findable.
Accessible.
Interoperable.
Reusable.
Uncharted Path 🛳
Exploring Location Data Using a Hexagon Grid
A comprehensive guide on how to use Uber’s H3 hexagon grid in data analysis
In this article, we will use Helsinki city bike data to demonstrate how one can utilise H3 hexagons to analyse spatial data. First, we provide an introduction to the H3 hexagon grid and its resolutions. Next, we delve into the main functionalities of the H3 library. Following that, we illustrate how a hexagon grid can enhance data analysis. Finally, we address some issues associated with hexagonal grids. All the notebooks used in this analysis can be found on this GitHub repository. All images in this article, unless otherwise noted, are by the author. (Source: towardsdatascience.com)