Issue 95: CognitionX Data Science, AI and Machine Learning Briefing

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Stanford University has invited leading thinkers from several institutions to begin a 100-year effort to study and anticipate how the effects of artificial intelligence will ripple through every aspect of how people work, live and play.

It’s called the AI 100
and we’ll be watching along carefully.


Tabitha UntilTheBotsTakeOver Goldstaub

P.S Join us for breakfast on Wednesday with Mike Hyde (Director of Data & Insights Skype.) He’ll be sharing approaches and best practices to hiring high performance data science teams.


Open data aims to boost food security prospects

Rothamsted Research, a leading agricultural research institution, is attempting to make data from long-term experiments available to all.


How data science and rocket science will get humans to Mars

Large and complex data sets pose challenges for any organization about to embark on an analytics deployment. But NASA’s example of harnessing data to plan the most complicated of journeys — an expedition to Mars — proves that the challenges are not insurmountable. This article in Tech Crunch shows that with the right tools and, most importantly, a consistent and well-planned approach, data science doesn’t have to be as daunting as rocket science.

Chatbots yadda yadda ya

One Bot, Every Platform

Expand your bot’s reach from a single deployment to a cross-platform giant with a single click using the translation technology. You build it once and they customize it to work across platforms, giving you a single source to deploy, support and update.

Business Impact

Cognitive Computing is the new black

ZNet say terms like Data Mining, Predictive Analytics, and Advanced Analytics are considered too geeky or old for industry marketers and headline writers. IBM is pushing the term Cognitive Computing and looks like it could be taking hold.

Feed your mind over lunch

Machine learning is fundamentally a hard debugging problem.

S. Zayd Enam’s blog beautifully explains why Machine Learning is so hard.

The difficulty involves building an intuition for what tool should be leveraged to solve a problem. This requires being aware of available algorithms and models and the trade-offs and constraints of each one. By itself this skill is learned through exposure to these models (classes, textbooks and papers) but even more so by attempting to implement and test out these models yourself. Read on for advice and generally so you don’t feel so alone.

Case Study

How Quid uses deep learning with small data

The task at hand was to develop a classifier that can output a likelihood of any given sentence being “bad” or “good.” For this, they collected a few hundred examples of good and bad sentences, exploring some approaches to build a classifier to discriminate them. Read on to find out more

Product we want to love

Cooking with a little dash of AI

Watson makes suggestions that no human would ever make, like adding milk chocolate to a clam linguine or mayonnaise to a Bloody Mary.

Tools of the trade

How to Streamline the Data Science Workflow with SciKit-Learn [Video]

You can never learn too much. SciKit-Learn is an open source machine learning library for the Python programming language. By learning the ins and outs of pipelines and FeatureUnions it can help data scientists improve their workflow.

Isaac Laughlin, Data Science Instructor at Galvanize, presented at the Kaizen Data Conference on “SciKit-Learn: The Non-Modeling Parts”. You can follow along with this Github repository here

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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