Forget The Black Hole Picture- Check Out The Sweet Technology That Made It Possible by Maggie Koerth-Baker via @FiveThirtyEight

Forget the black hole picture. Check out the sweet technology that made it possible.
Astronomy isn’t the only field that deals with converting sparse data into images. From medicine to helping self-driving cars avoid potholes, the algorithms EHT researchers developed could have some pretty interesting applications.

This TEDxBeaconStreet presentation was also really interesting to listen to.

I agree with the linked article - there are many potential uses for the Algorithms the team developed.

WEBINAR: AI on Demand: Data Science in Operations - 30 April 2019

Data Science Central Webinar Series Event

AI on Demand: Data Science in Operations Join us for this latest DSC Webinar on April 30th, 2019 AI is right here, right now—and changing our lives. The ever-present need for business optimization, combined with a long history of applied statistics, explosive growth in data, and recent advances in cloud computing, has created a perfect storm of innovation. Join this latest Data Science Central webinar and learn how: Applying AI insights in real time to enable optimal business decisions Real-world examples of AI-powered recommender systems, continuous closed-loop learning and techniques for analysis of very wide time series data How to take advantage of TIBCO Speaker: Steven Hillion, Senior Director, Data Science -- TIBCO Software, Inc. Hosted by: Rafael Knuth, Contributing Editor -- Data Science Central   Title:  AI on Demand: Data Science in Operations Date:  Tuesday, April 30th, 2019 Time:  9:00 AM - 10:00 AM PDT   Space is limited so please register early: Reserve your Webinar seat now

Google has launched a new end-to-end AI platform by @fredericl via @TechCrunch

Google announced the beta launch of the company’s AI Platform. The platform brings together a variety of existing and new products that allow you to build a full data pipeline to pull in data, label it (with the help of a new built-in labelling service), and then use either existing classification, object recognition, or entity extraction models, or existing tools like AutoML or the Cloud Machine Learning Engine to train and deploy custom models.

This should make it far easier for companies to dip their foot into the use of ML and AI areas with minimum investment.

WEBINAR: How SDI Modernized Supply Chain Data - 25 April 2019

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Data quality issues - Who is responsible for resolving them? by Nicola Askham via @infomgmt

Your governance team will develop knowledge and expertise about the data your organization creates and uses, but they are not responsible for any cleansing that may be required.

Some great advice from Nicola which could form the centre of your own organisation's data quality and data stewardship strategy.

Steps organisations can take to quantify qualitative experiences by Anna Johansson via @infomgmt

Subjective experience tends to produce qualitative data if they produce data at all, which makes it notoriously hard to form the same calibre and a number of conclusions from those data.

I really like the three steps she suggests - numerical values for qualitative data is very important - it is efficient to create fields in the table or database, is a very efficient key for anything (be it a primary key or a foreign key) and can help you if you want to modularise code so that you can share it.

Morgan Stanley focuses on data quality to strengthen AI by Penny Crosman via @infomgmt

The bank is one of many to realize that artificial intelligence is only as good as the data fed into it.

They have it completely right - you absolutely cannot rely on answers if the underlying data is wrong in any way.

6 steps to creating data management as its own business department by Robert Vane via @infomgmt

Despite the repeated and justified calls from data governance and management practitioners to take a more holistic view, businesses generally still view data as a project.

Some great points and observations in this article which help to explain why this is really important.

Scientists rise up against statistical significance by Valentin Amrhein, Sander Greenland & Blake McShane via @nresearchnews

They suggest replacing p-values with confidence intervals, which are easier to interpret without special training.

I have to admit they are a pain to interpret sometimes and the confidence interval would make life easier.

In the Age of AI, Expect ‘Lifelong Learning’ to Stay Employed by Brandi Vincent via @Nextgov

Lifelong learning has always been a good thing for keeping your mind sharp. But in the age of AI, it may be necessary for your career too.

I think Brandi is exactly right and with new technologies being developed all of the time it will stay that way for everyone going forward.

Human or Machine? Two Paths for Deploying Analytics by Bill Franks via @Datafloq

While organizations pursue analytics that informs both human- and machine-based decisions, the differences in the requirements for each must also be understood.

Bill definitely has a point - both sets of audiences have different needs and requirements. Yes, I agree there are commonalities but there are also some differences that he explores in this article.

Checklist for debugging neural networks by @CeceliaShao via @Medium

Tangible steps you can take to identify and fix issues with training, generalization, and optimization for machine learning models

This is great and definitely worthy of a bookmark and some applause if you have a Medium account.

WEBINAR: Large Scale Data Management - Featuring Forrester - 10 April 2019

Data Science Central Webinar Series Event

Large Scale Data Management - Featuring Forrester Join us for the latest DSC Webinar on April 10th, 2019 Over the past few years, the evolution of technology for storing, processing and analyzing data has been absolutely staggering. The advent of cloud platforms from AWS, Microsoft Azure and Google Cloud gives every business the ability to swipe a credit card and have access to virtually any computing service... to handle just about any data initiative. Yet, why are so many organizations still struggling to drive meaningful ROI from their data investments? The answer starts with DataOps. Join this latest Data Science Central webinar to learn: How the emergence of new trends such as cloud, machine learning and AI have impacted the data management landscape What is DataOps and key considerations in implementing or transitioning data management to the cloud How to maximize cloud investments and adoption with new approaches to data preparation and data quality Featured Speakers: Noel Yuhanna, Principal Analyst -- Forrester Will Davis, Director of Product Marketing -- Trifacta Hosted by: Stephanie Glen, Editorial Director -- Data Science Central   Title:  Large Scale Data Management - Featuring Forrester Date:  Wednesday, April 10th, 2019 Time:  9 AM - 10 AM PDT   Space is limited so please register early: Reserve your Webinar seat now

How Artificial Intelligence Is Changing Science by Dan Falk via @QuantaMagazine

The latest AI algorithms are probing the evolution of galaxies, calculating quantum wave functions, discovering new chemical compounds and more. Is there anything that scientists do that can’t be automated?

This is a well written and well thought out article highlighting the amazing thinks that AI and ML can and could achieve in the search to advance our knowledge of the universe and general science. I'm really excited about what is likely to be discovered as we move forward.