Machine learning — Is the emperor wearing clothes? by Cassie Kozyrkov via @Medium

Cassie Kozyrkov, chief decision intelligence engineer at Google, offers a "behind-the-scenes look at how machine learning works."

This was really interesting and made me think about everything in a bit more detail.

9 Developments In AI That You Really Need to Know by John Welsh via @Forbes

Speakers at the World Summit AI offer up nine bits of advice for people working in AI.

This was really interesting reading and definitely worth a read.

Convolutional Neural Net in Tensorflow by Stephen Barter via @Medium

Here's a look at the fundamentals of convolutional neural nets and how you can create one yourself to classify handwritten digits.

This is a great guide and I think it is well worth a subscription to see what else the author has written on Medium - so much in this article to learn from.

The Main Approaches to Natural Language Processing Tasks by Matthew Mayo via @kdnuggets

Let's have a look at the main approaches to NLP tasks that we have at our disposal. We will then have a look at the concrete NLP tasks we can tackle with said approaches.

Good lists of approaches with examples that are useful for both the learner and the more experienced practitioner to keep on hand to remind you or them all.

8 ways agile methodologies can improve a firm’s culture by Greg Robinson via @infomgmt

Agile project management is becoming hugely popular. It's no wonder. Agile teams are proving that traditional project management strategies fall short. Startups and large corporations are adopting agile principles to stay competitive.

Seems Agile is the way to go if you want to have a more cohesive team that works better together and is happier while they are doing it. 

Amazon's gender-biased algorithm is not alone by Cathy O'Neil via @infomgmt

Internet giant Amazon recently ran into a problem that eloquently illustrates the pitfalls of big data: It tried to automate hiring with a machine learning algorithm, but upon testing it realised that it merely perpetuated the tech industry’s bias against women

I agree with Cathy here - Amazon should be congratulated for a) testing it properly and b) doing something about it when it was clear there was a problem. It cannot be acceptable to just use the excuse (for that is what it actually is) that you didn't know so cannot be liable. It really makes me mad when we all know that bias is a risk and we should all do the due diligence to test properly to make sure that we ensure it is no longer there. Please recognise bias as a risk and test carefully for it by using someone who is not on your team so they have fresh eyes.

The neural history of natural language processing by Sebastian Ruder via @_aylien

Here's a review of the last 15 years of natural language processing (NLP) research.

I love this and think it is worth a read if only to remind yourself on how far we have already come and that judging from the pace of change great things are always possible and coming at some point in the future.

12 trends impacting the future of data management jobs by David Weldon via @infomgmt

Technologies such as artificial intelligence, the Internet of Things and augmented reality are changing how employees work and what skills employers need. Here are 12 top trends that will reshape the workforce over the next five years.

Some interesting thoughts. I think the most important thing I can suggest is that you read and keep up with new technologies and trends so that you understand them so that you are ready to move into them whenever you are able to. After all you might be able to save your employer money, improve processes and get valuable skills all at the same time.

IoT analytics guide: What to expect from Internet of Things data by Bob Violino via @NetworkWorld

Data capture, data governance, and availability of services are among the biggest challenges IT will face in creating an IoT analytics environment.

Interesting article that definitely highlights so of the challenges that are involved in IOT data and reporting off it. This is definitely a new data source with it's own challenges and will need you to rethink the kind of validation needed in order to make important decisions based upon it.

5 Data Science Projects That Will Get You Hired in 2018 by John Sullivan via @kdnuggets

A portfolio of real-world projects is the best way to break into data science. This article highlights the 5 types of projects that will help land you a job and improve your career.

As one of the comments on the article points out these are skills that you need to be able to show. My suggestion is that you use Kaggle to provide a project or at least the data for it., do the things in this as part of a project, and store the code and results on Github so that it can easily be seen.

A Concise Explanation of Learning Algorithms with the Mitchell Paradigm by Matthew Mayo via @kdnuggets

A single quote from Tom Mitchell can shed light on both the abstract concept and concrete implementations of machine learning algorithms:

A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E. 
- Tom Mitchell, "Machine Learning"

I really like this thoughtful and clear article discussing the quote form Tom.

5 mistakes even the best organizations make with product and customer data by Grant Emison via @infomgmt

CIOs are responsible for the lifeblood of the enterprise, its information, and their purview reaches every corner of the organisation. Mistakes can lead to a loss in productivity, a damaged corporate reputation, security breaches, lawsuits and more.

I have to agree with Grant's last point. We can read it, work hard and try to not make any mistakes, but the chances are we WILL make a mistake - the important thing is to LEARN from the mistake so we don't keep making the same one over and over again.

How DeepMind's biggest AI project is fixing bad Android batteries by Matt Burgess via @WiredUK

Google's Android Pie operating system uses DeepMind's AI in a bid to improve your phone's battery life. But is it making any difference?

This sounds great and of course over time it will get even better.

Can We Make Artificial Intelligence Accountable? by @ctowersclark via @forbes

The ability to open the black box is the holy grail of AI—particularly for industries like law, healthcare, and finance that handle sensitive customer data. IBM may have an answer.

I love the sound of this bias detection software as it's one of those things that you have to watch out for but find it hard to find in your own model - I usually recommend someone else not connected to your area check for bias as they are fresh eyes but if you can use code it will a) improve the detection and b) give some kind of audit trail to show that you don't have bias and did everything possible to ensure there was none.

6 Steps To Write Any Machine Learning Algorithm From Scratch: Perceptron Case Study by John Sullivan, via @kdnuggets

Writing a machine learning algorithm from scratch is an extremely rewarding learning experience. We highlight 6 steps in this process.

Great article with very clear steps to follow - I don't think I will be brave enough to do that yet - I need a time with more free time and the courage to work through it all. It does however seem to be a great set of steps to work from - worth a bookmark I think.

Why customer data research is more important than ever by Megan Harris via @infomgmt

The rise of social media and advancements in marketing software has resulted in an increase in purpose-driven marketing tactics that have changed the way companies interact with consumers forever.

Interesting article. As computing power has increased and the data a company holds on us increases they are doing more and more sophisticated data analyses in order to increase sales to existing customers. After all it costs less to sell to an existing customer than it does to get a new customer via marketing, special offers etc.

Building the ideal data quality team starts with these roles by Wilfried Lemahieu, Seppe vanden Broucke and Bart Baesens via @infomgmt

Poor data quality impacts organisations in many ways. At the operational level, it has an impact on customer satisfaction, increases operational expenses and will lead to lowered employee job satisfaction.

Great list of job roles and a blueprint of roles that we could all aim for if we understand each one of them.

Meet the little-known group inside of Google that's fighting terrorists and trolls all across the web by Julie Bort via @BIUK

Here's a look at the team at Alphabet that interviews ISIS defectors, protects news and political websites from distributed denial of service (DDoS) attacks, and combats radicalism.

This is a great initiative and something that is not widely known - you certainly don't see it publicised on the main news channels.

What If.you could inspect a machine learning model, with no coding required? by/via @GoogleAI

Building effective machine learning systems means asking a lot of questions. It's not enough to train a model and walk away. Instead, good practitioners act as detectives, probing to understand their model better.

Kudos to them - they really are doing great things - I can only hope that one day I could be good enough to join them.