By Bobby Mukherjee
Hi readers. I just finished this article in the New Yorker on the famous science fiction author William Gibson. The article talks about how some of Gibson’s best sci-fi ideas actually come from the present time, as opposed to looking years into the future.
Instead of thinking about “beam me up, Scotty” and flying cars and other unrealized futures, he looks at the pragmatic. This is very much what you’ll find here.
There are a lot of forecasts for the future that (validly) bring us down. We too are futurists, but not the dystopian kind. Not Terminator…
Researchers trained an algorithm to predict knee pain better than the decades-old standard
By Melanie Ehrenkranz
Emma Pierson, a senior researcher at Microsoft Research New England, said that her medical collaborator on a recent study shared an accurate, though not very reassuring fact about pain. “We don’t understand it very well.”
Pierson is a computer scientist developing machine learning solutions to inequality and healthcare. A research paper she published in January alongside other researchers explores pain disparities in underserved populations, specifically looking at osteoarthritis in the knee and how it disproportionately affects people of color. …
RAPIDS Series 2: Step-by-step Python configuration for more efficient computing
By Filip Velkov
Have you ever found yourself desperately needing more GPU power to run your deep learning models, but your local configuration simply can’t carry out? If so, then this article will show you how to use cloud GPUs as if they were part of your personal machine.
The usage of GPU computational power in data science—especially in scenarios where someone is training and using deep learning models—is becoming increasingly appealing to our fellow data scientists and engineers. …
A voyage — and invitation — into NVIDIA’s GPU-accelerated ecosystem
By Juan Medina, Team Lead in Data Science & ML at Loka.com
More and more disruptive companies are looking at data science to leverage large-scale datasets and generate precious insights. While data science is incredibly valuable to businesses, it can also be an arduous, costly process.
Building and training models can take months, and productionizing models can compound that time exponentially. This can drastically impact budget, speed to production, and time to market.
No matter the industry, startups and innovation arms of big brands want to train machine learning models…
Spoiler alert: This is not the usual post about Flutter 😉
By: Alfredo Rinaudo, Senior Mobile Developer at Loka, Inc.
Well, it’s time to tell you (or at least I will try to) why, after 2 and a half years of developing with it, I chose Flutter as my main framework.
I first came across Flutter by way of an old coworker. One day…
By Fernando Escobar, Data Lead at Loka, Inc.
Hi, I’m Fer. And for the better part of the last decade, I’ve specialized in data analysis and data engineering for Fortune 500s and Series-A Startups. Here’s what I see coming our way in 2021.
When I think about how I’d define data, many things come to mind. It’s facts, it’s observations, it’s behaviors, all put together to be later referenced and analyzed.
When data is given a reason, a purpose, and starts answering questions, then it becomes information we can put to use.
Information can be descriptive (what happened?), and it…
By Melanie Ehrenkranz
We know that training powerful machine learning models takes a lot of time and massive computing power—and these things inherently cost a lot of money and take a toll on our planet. For computer science students interested in learning how to build off these models and progressing innovation in these spaces, the time and capital required are demoralizing roadblocks.
But if companies like Facebook and Google publish their pretrained models, the path toward developing their own deep learning models becomes pretty clear.
Daniel Larremore, an assistant professor in the Department of Computer Science at the University of…
By Melanie Ehrenkranz
Much of our digital lives are in the cloud, a seemingly abstract digital space. But it’s not abstract—while the data is intangible, the storage centers where this data lives are very real in the physical world. They have a carbon footprint, a truth opening the door to new sustainability solutions.
“So far a huge amount of effort has gone toward trying to figure out how we design better algorithms to predict who should get a loan, or who should get bail, and is it ethical to have machines making these decisions,” Daniel Larremore, an assistant professor in…
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