Issue 73: CognitionX Data Science, AI and Machine Learning Briefing

Always wanted to know why Elon Musk thinks we live in a simulation? Well I asked him…or at least his Bot.

Every day there is a new celebrity bot ready to chat. Now it’s your turn! Just enter your Twitter username into this web app and get your bot to talk to my bot.

Tabitha @TabithaBotGold Goldstaub

Ethics Questions Of The Day
Artificial Intelligence could be humanity’s greatest disaster

Professor Hawking has been one of the most high-profile sceptics about AI. He was one of more than 1,000 other experts and researchers to sign an open letter warning of the perils of artificially intelligent weapons last year. Last night he was speaking at the opening of a new Cambridge centre that will seek to address the potential dangers and conundrums of AI.

Interesting to note that he believes “Intelligence is the ability to adapt to change” and things are certainly changing.

Chatbots yadda yadda yadda
How Google is planning to make conversational interfaces easier to build

They have been investing in the core machine learning technologies that enable natural language interfaces for years. Each week I add an acquisition to the Deal Of The Day section. In this blog Scott Huffman, VP of Engineering gives some insights into how they might do this.

Product We Love
Stripe uses ML to tackle e-commerce fraud

Stripe, the startup that lets websites and mobile apps implement payment services through its API and a few lines of code, is today adding in another new feature as it continues to build out its platform with more tools. It is now going to help prevent fraud on Stripe transactions, through a new service called Radar.

Data Visualisation
Social Out, AI In: CB Insights Analysed Early-Stage Startup Descriptions To See Where Tech Is Headed Next

Vicki Boykis uses a tag cloud to compare 2013 and 2016 Strata Conference to identify technology trends. A quick glance at the titles of the talks confirms things have really moved on.


How far has the technology come in 3 years?

Vicki Boykis uses a tag cloud to compare 2013 and 2016 Strata Conference to identify technology trends. A quick glance at the titles of the talks confirms things have really moved on.


Education, Advice and Training We Rate
Advantages of over-investing in early database design

In this FirstRound interview, Barrett, CEO of Expensify explains how startups fall into damaging defaults when it comes to database architecture and deconstructs the specific mistakes to avoid. He shares the three key steps to follow to set yourself up for both technological and business-model scale and success. Lastly, he shares how to course-correct midstream if you need to change your database architecture.

TensorFlow on Android

Justin Francis from Oreilly steps you through how he learned to get his custom classifier working on an Android device—getting the custom graph to work was a lot of work and was not documented anywhere. A lot of searching in TensorFlow’s GitHub forums was necessary; hope this spares you some of that trouble.

7 Ways to Introduce AI into Your Organisation

Harvard Business Review looks at the buy build discussion that rages when trying to bring AI into your company. Some good advice in here…Some Build, Some Buy

Business Impact of AI
LinkedIn updates endorsements to make them actually relevant

LinkedIn is utilising its machine learning to assess which endorsements matter to you when viewing someone’s profile. Endorsements will be prioritised by a variety of factors, including mutual connections, colleagues, and people who are knowledgeable about the skill.

Tools of the Trade
Open source free tool to help clean dirty data

To reduce data-cleaning mistakes, ActiveClean takes humans out of the two most error-prone steps of data cleaning: finding dirty data and updating the model. The tool uses machine learning to analyse a model’s structure to determine what errors are most likely to throw it off and then it cleans enough data to create “reasonably accurate” models.

Microsoft introduces two new data science utilities on GitHub

Today, Microsoft introduced two new data science utilities on GitHub to help boost productivity; Interactive Data Exploration, Analysis, and Reporting (IDEAR) and Automated Modeling and Reporting (AMAR). Both IDEAR and AMAR run in CRAN-R are accessible via GitHub.

Ready …steady… ML…

Published in

Leave a reply

Thank you! Your subscription has been confirmed. You'll hear from us soon.

Log in with your credentials


Forgot your details?

Create Account