DLD Special: CognitionX Tuesday Briefing

Yesterday, during DLD Conference, Demis Hassabis, founder and CEO of
DeepMind, discussed AlphaGo beating Go-champion with machine learning, and what the collaboration between humans and machines will be bringing in the future. He highlighted the importance of staying creative and follow our own intuitions when working with AI, forecasting 10 exciting years ahead of us.

We also hosted our own panel on “Fixing Education for the A.I. age” where our panelists discussed the shifts in education that will push towards a computational knowledge
economy. Conrad Wolfram introduced us to the need to teach students the skills to solve problems with the support of computers, instead of learning how to do specific things (particularly in maths) ourselves. Jurgen Schmidhuber instead observed how we are only thinking of short-term improvements in the education system, while we should be looking decades ahead. Rose Luckin focused on the need to move away from “stop & test” methods, and instead adopt continuous assessment. Her take on the introduction of AI in the classrooms shows that the purpose of involving new technologies in teaching will not eliminate the human interaction with students but enhance their possibility to improve the environments and their methods. Esther Wojcicki discussed the current testing system, affirming that it costs UK Govt £1bn per year and it is not helping students getting more brilliant: in reality, it stifles creativity.

I discussed an initiative called Project Placed which streamlines the process for universities to provide experience based education by match making their students with companies to complete their degree.

We have created a WhatsApp group to bring together people who would like to keep discussing and hopefully make some change in the Education space. Please email me tabitha@cognitionx.io with your mobile number if you’d like to join us.

Deal of the day

Fraugster, a startup that uses AI to detect payment fraud, raises $5M

Fraugster, a German and Israeli startup that has developed AI technology to help eliminate payment fraud, has raised $5 million in funding. Fraugster says it’s already handling almost $15 billion in transaction volume for “several thousand” international merchants and payment service providers, including (and most notably) Visa. Its AI-powered fraud detection technology learns from each transaction in real-time and claims to be able to anticipate fraudulent attacks even before they happen.

China’s Kuang-Chi takes first stake in UK tech

Kuang-Chi Group, a Chinese technology company best-known for making a huge high-altitude helium balloon, has made its first major investment in the UK, shrugging off investor concerns about Brexit. It recently announced it was creating a second fund to invest $250m in companies focusing on the internet of things, AI and virtual reality.


Riding on its AI research, Baidu Opens AR lab

Baidu, search engine firm, unveiled its AR Lab, a spin-off from its Institute for Deep LearningBaidu plans to use its new Beijing lab, as well as technology from its AI research – image recognition, object detection, and more – to build smartphone-based AR applications. It signals a future in which your phone’s camera and AR – popularized by Pokemon Go – could become one method of navigating the internet.

Chat Bots, yadda yadda yadda

Savings chatbot Digit debuts on Messenger

Digit wants to make saving easier and more conversational. Its algorithm already assesses a user’s income and spending patterns to siphon a small and safe amount from the user’s checking account into a savings account every few days, money which can later be used to meet short-term cash needs such as paying bills and paying for holidays. Now, Digit is working on a new product, an AI-powered “financial goal program.”


Interview: CEO of CES 2017 on the Future of Connectivity

CES 2017 emphasised that everything is eventually going to be digitally connected, and the promise of the Internet of Things is now real, especially in automotive and residential design. The most high-profile marketplaces at CES 2017 were Self-Driving Technology and Smart Homes, occupying a disproportionate chunk of the 2.8 million square feet of exhibit space. However, the driverless cars really stole the show, positioned equally as both autonomous vehicles and online hubs integrated with AI platforms.


Jason Brownlee’s Machine Learning Bookshelf

In this guide, you will discover the top books on Machine Learning according to Jason Brownlee. There are many reasons to want to read machine learning books. For this reason, he grouped and listed machine learning books in a number of different ways, for example by type, topic, and publisher.

Future of transportation

Nissan to put self-driving cars on the streets of London

The Japanese carmaker is the first mass-market brand to launch semi-autonomous vehicles to the public in Europe, with the Qashqai SUV that can steer in a motorway lane coming later this year. An electric Nissan
LEAF car, decked out with special radar, laser and camera systems, will ferry selected passengers around a fixed route in one London borough, Nissan said, although the technology inside the vehicle is not expected to be available in vehicles until at least 2020.

Data visualization

Split-second data mapping

Todd Mostak, a former researcher at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), is using GPUs to develop an analytic database and visualisation platform called MapD, which is the fastest of its kind in the world, according to Mostak: the database-analytics platform queries and maps billions of data points in milliseconds.

MapD is essentially a form of a commonly used database-management system that’s modified to run on GPUs instead of the central processing units (CPUs) that power most traditional database-management systems.


Rules of Machine Learning: best practices for ML Engineering

This document is intended to help those with a basic knowledge of machine learning get the benefit of best practices in machine learning from around Google. It presents a style for machine learning, similar to the Google C++ Style Guide and other popular guides to practical programming. If you have taken a class in machine learning, or built or worked on a machine­learned model, then you have the necessary background to read this document.

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

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