In industries as old as civilization itself, there’s an awful lot at stake. That’s why it’s crucial that banking and insurance embrace data science and adopt data-driven attitudes and strategies.

Banking and insurance have been relatively slow in embracing big data, the age of algorithms, machine learning, and the looming ubiquity of IoT.

But it’s not too late! Both industries have enormous potential in this area, not only to improve current offerings, but to be completely innovative and cutting-edge.

Download the ebook from Dataiku

Web logs are probably the most common form of big data out there. That makes it pretty important to know how to extract as much information from them as possible, thanks to machine learning.

Data science and Machine Learning algorithms can provide a way of extricating value from web logs. The objective is to find correlations or indicators in the data, which are not necessarily visible to the human eye, in order to resolve a defined problem.

When the learning process is over, we look at the performance of the models created by the algorithms. Similarly, we look at what the model has learned from the data.

Read the whole article from the Dataiku blog

Spoiler: machine learning can do pretty much anything but it needs a lot of data to do it better than humans.

The media is sometimes guilty of painting an unrealistic picture of AI. As transformative as it is, AI isn’t magic or science fiction. It relies on big amounts of data and talented data scientists. 

Read why on the Harvard Business Review

Fresh research has revealed that those organizations leading the pack when it comes to data science (those using many different data sources and developing highly qualified data science teams) are 4x more likely to create revenue growth that surpasses shareholder expectations. 

That’s not all - many smaller companies appear to be driving data science innovation.

Read the full article by Bernard Marr

If you don’t know the difference between supervised and unsupervised learning, you need to read this article. 

It explains the basics of data science lingo including ML, algorithms, models, and DL.

Understanding specialized terminology means understanding your data science team and that means everyone getting along a whole lot more effectively! 

Read the article by The Huffington Post

Halloween has come early in a series of two projects, Haunted Faces and Haunted Places, masterminded by MIT Media Lab team. 

They have trained a ‘Nightmare Machine’ to generate frightening faces and haunted buildings using algorithms. 

Imagine disfigured features, eyes and mouths that spurt blood, haunted houses, and settings resembling toxic wastelands.

If the results weren’t disturbing enough, the team are asking viewers to rate the scariness of the images in order to improve the AI behind the ‘Nightmare Machine’. 

Check out the 'Nightmare Machine’ here (if you dare!) or read more about it here

As always, we didn’t have enough room to put all the stuff we’re reading in our newsletter - so here’s the rest.

- Big Data Survey

- What I’ve learned about neural network quantization by Pete Warden
- Predictive learning vs. representation learning by Harvard Intelligent Probabilistic Systems
- [Presentation] Automating Data analysis Pipelines
- Zero to One — A Ton of Awe-Inspiring Deep Learning Demos with Code for Beginners by Sam Putnam

- Watch 35 Years of the World’s Economy Evolving as a Living Organism by howmuch
- [Data viz] Alone Time by Nathan Yau

- [Paper] Two Decades of Recommender Systems at Amazon.com
- A generative model for music track playlists by IHeartRadio

- What tomorrow’s business leaders need to know about Machine Learning?by Bill Schmarzo
- [Video] China’s All-Seeing Surveillance State Is Reading Its Citizens’ Faces by the Wall Street Journal

“Machine learning is like a deep-fat fryer. If you’ve never deep-fried something before, you think to yourself: ‘This is amazing! I bet this would work on anything!’ And it kind of does.”

In our case, the deep-fryer is a toolbox of statistical techniques. The names keep changing—it used to be unsupervised learning, now it’s called big data or deep learning or AI. Next year it will be called something else. But the core ideas don’t change. You train a computer on lots of data, and it learns to recognize structure.

So what’s your data being fried in? These algorithms train on large collections that you know nothing about. Sites like Google operate on a scale hundreds of times bigger than anything in the humanities. Any irregularities in that training data end up infused into in the classifier.

Read the talk by Idle words or watch it here.

“The nature of machine learning — how it works, what makes it good, and how it’s delivered — ensures that [its] strategic prioritization will significantly change the tech industry before even a fraction of machine learning’s value is unleashed.”
A shorter essay on how ML and deep learning should be leveraged by companies.

“As buzzwords become ubiquitous they become easier to tune out.

We’ve finely honed this defense mechanism, for good purpose. It’s better to focus on what’s in front of us than the flavor of the week. CRISPR might change our lives, but knowing how it works doesn’t help you. VR could eat all media, but it’s hardware requirements keep it many years away from common use.

But please: do not ignore machine learning.

Yes, machine learning will help us build wonderful applications. But that isn’t why I think you should pay attention to it.

You should pay attention to machine learning because it has been prioritized by the companies which drive the technology industry, namely Google, Facebook, and Amazon. The nature of machine learning — how it works, what makes it good, and how it’s delivered — ensures that this strategic prioritization will significantly change the tech industry before even a fraction of machine learning’s value is unleashed.

To understand the impact of machine learning, let’s first explore it’s nature.”

Read the whole essay by Drew Breunig on Medium

“Meet the classified artificial brain being developed by US intelligence programs”

A long exposé about the fairly secret project Sentient - an artificial brain developed by the National Reconnaissance Office.

Check out the article by the Verge