censiCLICK gets behind the hype of Amazon Sidewalk and gets to the truth.
In this video, Jin-Sung Kim, PhD explains how some quantum algorithms can outperform their classical counterpart and shows us how to implement Grover’s Algorithm.
Engadget sat down with Calista Redmond from RISC-V International, Krste Asanović from SiFive, Ted Marena of Western Digital, and Art Swift from Esperanto Technologies to talk about RISC-V.
This is an open-standard processor architecture that hopes to open up chip design to many companies from startups to major firms. RISC-V enables companies to design their own chips without paying license fees to the big industry players like Intel and ARM, and provides a structure for companies to produce their own custom processors, micro-controllers and chips.
The Linux Experiment has been trying to degoogle his life as much as possible lately, but there are 2 main holdouts: YouTube, and Android.
In this video, he takes a look at the /e/ project.
It might seem paradoxical to want to “sell” open source, but lo these many years into the open source revolution, many organizations have yet to get the message.
Press the play button below to listen here or visit the show page.
Listen in as James Genus helps makes sense of it all, giving us the insights we need to convince the laggards to get onboard.
ABOUT: Microsoft Principal Cloud Solution Architect James Genus Jr. has been working in the technology field for over 20 years. Working across multiple industries including manufacturing, construction, research and technology, open source solutions have been trusted companions helping to solve technical and business challenges.
LINKS: OpenSource.net, Open Source Blog (Microsoft), Open Source Initiative
CREDITS: Louis Berman (Host); James Genus (Guest); Dan Phillipson / PremiumBeat (Music); Anne Lamb (Intro/Outro); East Coast Studio (Editing)
MORE: visit https://azure-success.com for additional episodes, plus transcripts, and more ways to listen to the show. As to your comments and suggestions, please feel free to email your host, Louis Berman, at [email protected]
Here’s a great round up of 5 open source tools that every data scientist will find useful.
If you’re already a data scientist, then you likely use some or all of these. If you’re learning, then here’s your study list. 😉
There was once a time when folks pondered whether or not open source would be a viable business model.
Today, that sounds comical, a there are numerous open-source tech companies today, some of which have gone beyond $100 million (or even $1 billion) in their annual revenue including RedHat, MongoDB, Cloudera, MuleSoft, Hashicorp, Databricks (Spark) and Confluent (Kafka).
Why do tech companies open source their products?
“Open-source is an enabler of innovation, giving organisations access to a global pool of talent and the tools to develop secure, reliable and scalable software – fast. The organisations that are most effectively speeding up business transformation are those who have turned to open-source software development to succeed in a fast-changing, digital world,” told Maneesh Sharma, General Manager of Github India in an interview with Analytics India Magazine.
Databricks, the company behind the commercial development of Apache Spark, is placing its machine learning lifecycle project MLflow under the stewardship of the Linux Foundation.
MLflow provides a programmatic way to deal with all the pieces of a machine learning project through all its phases — construction, training, fine-tuning, deployment, management, and revision. It tracks and manages the the datasets, model instances, model parameters, and algorithms used in machine learning projects, so they can be versioned, stored in a central repository, and repackaged easily for reuse by other data scientists.
Here’s an interesting and thoughtful look the role open source plays in AI development.
Think about it: just about every major AI tool is open source. TensorFlow being the most obvious example.
The biggest advantage of open-source AI systems is that they involve no licensing fee for using open-source AI systems. And that’s especially useful for people having little or no experience in IT infrastructure. But what’s free is not always free. Although open-source AI comes without any license fee, it has other hidden costs like commercial software such as training, implementation, and maintenance costs. But, open-source AI can also reduce these costs.
You’re probably stuck at home right about now and, if you’re like me, you have a couple Raspberry Pi’s laying around.
TJ FREE shares some cool project ideas to put them (and your lockdown time) to good use.