Would you like to read a story about why Shopify is making its chatbot marketing assistant free to merchants? Type'yes,' send a thumbs up emoji, er, well actually, just scroll down to learn more. Techopia may not yet be run by bots (deep down, we're paranoid luddites) but Shopify believes it can reduce price barriers to growing a business by giving all of its merchants its first employee for free. Kit, which Shopify acquired last spring, is an automated assistant that can set up marketing campaigns, re-engage with customers via email, refresh social channels and run reports, all through Facebook Messenger or text message conversations with merchants. Before today, businesses on Shopify had to pay $10 per month for Kit's services, but the Ottawa e-commerce giant announced today that the firm would make its virtual assistant free to all merchants on its platform.

In the first part of this series, we started off rather slowly but deliberately. The previous post laid out our goals, and started off with some basic building blocks for our machine learning workflows and pipelines we will eventually get to. If you have not yet read the first installment in this series, I suggest that you do so before moving on. This time around we pick up steam, and will be doing so with an implementation of the k-means clustering algorithm. We will discuss specific aspects of k-means as they come up while coding, but if you are interested in a superficial overview of what the algorithm is about, as well as how it relates to other clustering methods, you could check this out.

Introduce yourself to the basics of data science and leave armed with practical experience extracting value from big data. This course teaches the basic techniques of data science, including both SQL and NoSQL solutions for massive data management (e.g., MapReduce and contemporaries), algorithms for data mining (e.g., clustering and association rule mining), and basic statistical modelling (e.g., linear and non-linear regression).

The world's brightest minds in Artificial Intelligence (AI) and humanitarian action will meet with industry leaders and academia at the AI for Good Global Summit, 7-9 June 2017, to discuss how AI will assist global efforts to address poverty, hunger, education, healthcare and the protection of our environment. The event will in parallel explore means to ensure the safe, ethical development of AI, protecting against unintended consequences of advances in AI. The event is co-organized by ITU and the XPRIZE Foundation, in partnership with 20 other United Nations (UN) agencies, and with the participation of more than 70 leading companies and academic and research institutes. "Artificial Intelligence has the potential to accelerate progress towards a dignified life, in peace and prosperity, for all people," said UN Secretary-General António Guterres. "The time has arrived for all of us – governments, industry and civil society – to consider how AI will affect our future. The AI for Good Global Summit represents the beginnings of our efforts to ensure that AI charts a course that will benefit all of humanity."

Google taught DeepMind to play Atari games all on its own, but letting humans help may be faster, according to researchers from Microsoft and Germany. They invited folks of varying skills to play five Atari 2600 titles: Ms. Pac-Man, Space Invaders, Video Pinball, Q*Bert and Montezuma's Revenge. After watching 45 hours of human gameplay, the algorithm could beat its mentors at pinball, though it struggled at Montezuma's revenge -- just as Deepmind did. Unlike with DeepMind's trial-and-error methods (below), however, the human-aided AI learned to play the games in less time than other AI systems. "Current state-of-the-art approaches require millions of training samples," the paper states.

Andrew Ng is one of the foremost thinkers on the topic of artificial intelligence. He founded and led the "Google Brain" project which developed massive-scale deep learning algorithms. In 2011, he led the development of Stanford University's main Massive Open Online Course (MOOC) platform. His course on Machine Learning would eventually reach an "enrollment" of over 100,000 students. That experience led Ng to co-found Coursera, a MOOC that partners with some of the top universities in the world to offer high quality online courses. Today, Coursera is the largest MOOC platform in the world.

Here we bring you the most popular recent Machine Learning videos worth watching. This is the first video (Lecture 1 published 8 years ago) in the great series of Stanford machine learning lectures given by Andrew Ng. Originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control.

Aircraft fuel is without a doubt the biggest cost for any airline and often receives widespread attention, especially when airlines hedge their bets the wrong way. Cathay Pacific reported a HK$4.49 billion fuel-hedging loss in the first half of 2016, which has hurt the airline's profitability. The second biggest expense for an airline is human capital, and researchers from Hong Kong Polytechnic University and University of Nottingham Ningbo China Business School may have found a solution to ease some of Cathays financial woes through an unlikely source – Machine Learning and Data Science. The researchers say that a "poorly designed airline crew schedule can result in unreliable flight schedules, significantly jeopardizing airline operations and profitability if insufficient crew members are available or other glitches occur. For that reason, managing airline crew scheduling and costs are one of the most crucial topics for airlines because it yields enormous economic benefits and ranks as the second highest expenditure after fuel costs."

In this work we propose a novel architecture that augments the standard sequence-to-sequence attentional model in two orthogonal ways.

Chatbots are taking over the world. Over the past few years, virtual help agents have taken on surprisingly sensitive jobs in modern society: counseling Syrian refugees fleeing civil war, creating quiet spaces of contemplation for millions of Chinese living in densely populated cities, and helping Australians access national disability benefits. Bots have offered help, support, and companionship. But there's one line none of them have yet crossed: actually treating patients. That's just changed, with the release of a talk therapy chatbot that goes by … wait for it … Woebot.