Issue 114: CognitionX Data Science, AI and Machine Learning Briefing

Thanks to all those who attended #CogXmas2016 last night, and to our excellent panel members Kenneth Cukier, Alan Greenberg, Brandon Whitcher, and Alistair Moore. They gave us fantastic pieces of advice, profound insight, and heated debate on all things AI.

The question of the evening was whether IBM or DeepMind have provided the most important contribution to the AI space in recent years; whilst DeepMind are at the bleeding edge of AI research, IBM provide a low bar of entry that allow companies to rapidly incorporate AI into their business. Kenneth and Alan both had an opinion, but what do you think? Let us know in our poll and we will announce the results on Monday.


UntilTheBotsTakeOver Goldstaub

Feed your mind over lunch

Why are we still light years away from full AI?

Are we really approaching the singularity as fast as we think we are? It’s not hard to have that impression with the likes of Elon Musk, Stephen Hawking, leading university departments and research centers around the world and more being highly concerned with the potential risks brought about by AI and taking action now to avoid a doomsday scenario in the near future. They predict that by the year 2030 machines will develop consciousness through the application of human intelligence. Yet, the truth is, we are far from achieving true AI — something that is as reactive, dynamic, self-improving and powerful as human intelligence. Not in about 100 years, possibly centuries, millenniums and, perhaps, we might never get there at all. Here are some reasons.


50+ Data Science, Machine Learning Cheat Sheets

A cheat sheet or reference card is a compilation of mostly used commands to help you learn that language’s syntax at a faster rate. Here are the most important ones on data science and machine learning algorithms that have been brainstormed and captured in a few compact pages. Mastering Data Science involves understanding of statistics, mathematics, programming knowledge especially in R, Python & SQL and then deploying a combination of all these to derive insights using the business understanding and a human instinct that drives decisions.

Business Impact of AI

What DeepMind brings to Alphabet

The AI firm’s main value to Alphabet is as a new kind of algorithm factory. Google bought DeepMind for £400m ($660m) in January 2014. But why did it want to own a British artificial-intelligence (AI) company in the first place? Google was already on the cutting edge of machine learning and AI, its newly trendy cousin. What value could DeepMind provide?

150 Data Scientists and still no business value?

The data scientists were obsessed with fine tuning machine learning models rather than answering fresh questions, losing sight of the main purpose of their work: generating business value. In fact, when the author asked a room filled with 150 data scientists which of them had ever generated proven business value, no one raised their hand. Surprised?

Ethics question of the day

Evernote reminds: none of your data is private

Note-taking app Evernote said this week that some of its employees will be able to access users’ notes. People are outraged over the privacy policy update, which specifies that, as part of its machine learning strategy, Evernote employees may at times be peeking into users’ accounts in order to improve service. “While our computer systems do a pretty good job, sometimes a limited amount of human review is simply unavoidable in order to make sure everything is working exactly as it should,” according to the updated policy. But the update also called attention to the fact that some Evernote employees actually already have access to user notes for a variety of reasons, including “troubleshooting purposes or to maintain and improve the service.”

Case study

The impact of Virtualization on Machine Learning

This blogpost sheds some light on the impact of the hypervisor on Machine Learning applications. Originally, virtualization was developed to increase the efficiency and utilization of computing resources in typical business environments. The virtualization overhead can be justified in these environments because computing resources are normally underutilized. However, for Machine Learning tasks where computing resources can be pushed to the extreme, the impact of virtualization can be overwhelming and unpredictable.

Chat Bots yadda yadda yadda

Meet Gladys: the first robot used by a U.K. airport

Geneva airport has Leo. Amsterdam has Spencer. And now Glasgow has Gladys. She’ll keep younger passengers entertained by regaling them with a number of seasonal tales, sing and dance to various festive songs and generally interact with customers. “We’re always looking for new and innovative ways in which we can further enhance customer experience at Glasgow and believe the introduction of Gladys to be a first for a U.K. airport,” Glasgow airport’s Mark Johnston said.

Podcast founder on personal assistant bots that schedule your meetings

Dennis Mortensen, founder and CEO of, a personal assistant bot that handles meeting scheduling through email discusses: social considerations for personal assistants and the bots that stand in for them; what gender users tendentiously choose for their bot; his future goal, which is agents that carry over conversations from one channel or platform to another (like continuing an email conversation in Slack); how’s 40 human “trainers” review scheduling-related data and train the response algorithm, and how the scheduling bot
attempts to guide conversations in order to improve outcomes.

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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