Blockchain in 5 industries by Alison McCauley via @oreillymedia

Blockchains go beyond finance. Here are five examples.

This is part of a series about Blockchain which can be found here. I recommend you read all three articles as it will give you a great understanding of Blockchain.

WEBINAR: How a Physics-Driven Analytics Platform Detects Reliability Threats - 26 February 2020

Data Science Central Webinar Series Event How a Physics-Driven Analytics Platform Detects Reliability Threats Join us for this latest DSC Webinar on February 26th, 2020 A physics-driven analytics platform aids in improvements to the reliability and efficiency of connected mechanical systems. The solution analyzes large quantities of time series data from IoT sensors to help identify issues affecting system performance in real-time as well as provide accurate data for predictive maintenance. Our presenter chose a time series database for its high ingest and storage of time series data as well as its ability to easily send this data into their systems for predictive analytics. During this latest Data Science Central webinar in association with IoT Central, learn how using a purpose-built time series database helps to continuously optimize reliability of their customers’ connected mechanical systems. Speaker: Jon Herlocker, President and CEO -- Tignis Hosted by: Rafael Knuth, Contributing Editor -- Data Science Central   Title:  How a Physics-Driven Analytics Platform Detects Reliability Threats Date:  Wednesday, February 26th, 2020 Time:  9:00 AM - 10:00 AM PDT   Space is limited so please register early: Reserve your Webinar seat now

AI Can Do Great Things—if It Doesn't Burn the Planet by/via @wired

OpenAI created an algorithm that successfully manipulates the pieces of a Rubik’s Cube using a robotic hand. But this accomplishment cost more than research time and effort—one estimate says it may have consumed about 2.8 gigawatt-hours of electricity, roughly equal to the output of three nuclear power plants for an hour. The computing power required for AI breakthroughs increased 300,000-fold from 2012 to 2018, creating an environmental impact that needs to be considered.

Something that we often don't consider but really should if we are serious about saving the planet.

Google just published 25 million free datasets by @tjwaterman99 via @TDataScience

What you need to know about the largest data repository in the world.

Useful for practice if nothing else.

WEBINAR: Organize a Winning AI Team from Design to Deployment - 19 February 2020

Data Science Central Webinar Series Event

Organize a Winning AI Team from Design to Deployment Join us for this latest DSC Webinar on February 19th, 2020 Designing, testing, and shipping an AI product is no small feat. How you organize your team — from roles and responsibilities to planning and process — can go a long way to ensuring AI success. In this latest Data Science Central webinar, we’ll talk about building a team that can deliver AI products to market using ML technology to deliver business value and impact the bottom line, be that customer delight, efficiency gains, or revenue growth. We’ll look at: How to design a winning team with skills and backgrounds that complement each other Defining the value derived from AI and building a roadmap to get there Using ML technology to deliver business value and make an impact Building a business case, designing a usable experience, and iterating a final product through customer insights Speaker: Alyssa Simpson Rochwerger, VP, AI Data Evangelism -- Figure Eight Hosted by: Rafael Knuth, Contributing Editor -- Data Science Central   Title:  Organize a Winning AI Team from Design to Deployment Date:  Wednesday, February 19th, 2020 Time:  9:00 AM - 10:00 AM PST   Space is limited so please register early: Reserve your Webinar seat now

Scale the value of analytics by Rita Sallam and Carlie Idoine.via @Gartner_inc

Manually identifying patterns in the quantities of data most organizations have is not just tedious; it’s inefficient. It’s also much too easy to find only what you were looking for—missing valuable, but unanticipated, insights. Augmented analytics uses ML and AI techniques to identify actionable insights.

Brief if you are not a subscriber but interesting to read.

5 Ways Julia Is Better Than Python by Emmett Boudreau via @TDataScience

Why Julia is better than Python for DS/ML

A nice short piece that should make you consider using Julia - even if it is not in your plans please at least install it and have a play.