Issue 97: CognitionX Data Science, AI and Machine Learning Briefing

Share the daily briefing with friends

Building a data science team – “It’s like an Opera you have to think what skills you need in the round” – Mike Hyde told our community at breakfast this morning. We discussed the skills Skype looked for and advises you make your own Hex…

Read more advice from Mike on
the CognitionX blog.


Tabitha UntilTheBotsTakeOver Goldstaub

p.s tip to promote happiness at work: share the daily briefing with your colleagues:

Reactions to AI

Google Facebook and Microsoft remaking around AI

duh…I hear you say, but I’m sharing this link because Wired’s article is a wonderful read.

Also interesting to remember these big companies are working to retrain their employees in data science. Google runs internal classes in the art of deep learning, and Facebook offers machine learning instruction to all engineers inside the company alongside a formal program that allows employees to become full-time AI researchers. Will you do the same?

Tools of the trade

All a buzz at the LTA Accelerate NLP Conference: Industrial-Strength Natural Language Processing in Python

spaCy performs large-scale information extraction tasks. It’s written from the ground up in memory-managed Cython. Independent research has confirmed that spaCy is the fastest in the world. If your application needs to process entire web dumps, spaCy is the library you want to be using.

Ethics Questions

The humanism of AI will eventually be what brings us together

Professor Manuela Veloso, head of the machine learning department at Carnegie Mellon University, envisions a future in which humans and intelligent systems are inseparable, bound together in a continual exchange of information and goals that she calls “symbiotic autonomy.” In Veloso’s future, it will be hard to distinguish human agency from automated assistance — but neither people nor software will be much use without the other.

Politics and AI

Exclusive Interview: How Jared Kushner Won Trump The White House

I wasn’t surprised to read about how Kushner built an 100-person data hub. I was interested to read how the team turned to machine learning, installing digital marketing companies on a trading floor to make them compete for business. Ineffective ads were killed in minutes, while successful ones scaled. The campaign was sending more than 100,000 uniquely tweaked ads to targeted voters each day. Kushner said “We played Moneyball, asking ourselves which states will get the best ROI for the electoral vote,”

Business Impact

Inside a Moneymaking Machine Like No Other

What can we learn from The Medallion Fund? An employees-only offering for the quants at Renaissance Technologies, the blackest box in all of finance. Employees have more and better data. They’ve found more signals on which to base their predictions and have better models for allocating capital.

Coding Experiments

Machine Learning Basics with Naive Bayes

This blog post requires just basic mathematics knowledge and an interest in data science and machine learning. Lewis Gavin talks about Naive Bayes as a classifier and explaining in simple terms how it works and when you might use it.

Future of Health

DeepMind Health – Partnership with the Royal Free London NHS Foundation Trust

Official Statement and video from Deep Mind “Lives could be saved and patient safety dramatically improved thanks to a landmark partnership announced today between the Royal Free London NHS Foundation Trust and British technology company DeepMind.” helps you understand what they are actually offering a little better than the press articles.

Share with your less data savvy friends

What Neural Networks, Artificial Intelligence, and Machine Learning Actually Do

This Life Hacker article may be useful to share with those who want to understand AI, machine learning, and neural networks in really really simple terms.

Please share any other articles that you’ve found or written that are good for our less data savvy friends.

I’ve been making some changes based on Feedback. Would love to hear from more of you. Please do click to share your thoughts

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