Time is the most valuable and finite asset in the world. Our weekly dose of "Monday Morsels" centre's on optimising our time to impact our lives and business. We have no intent on the theme, just purely lessons that can consistently be used. This week we cover quantum computing, how reality is not what it seems, what's on the other side of fear, and which artificial intelligence platforms will reign. Richard Feynman is undoubtedly the most famous physicist since Einstein and the man that first embedded my intrigue for science & technology.

The skies above are growing increasingly crowded with satellites zipping across Earth's upper atmosphere, relaying signals for everything from the picture on your television to the map on your phone. While this constant communication makes the world go round, all of that orbiting technology brings with it a problem, in the form of space junk – the debris from rocket launches and defunct satellites which hangs on in space. Lapping the Earth at thousands of miles per hour means even smallest chunks of metal or flecks of paint can cause significant damage if they run into the path of a satellite. But scientists at the University of Surrey are gearing up to test technologies which could target potentially hazardous space junk and remove it from orbit before it can cause any damage. The first experiment will use a net to capture a target in the form of a small CubeSat launched from the main satellite.

A system of nested dichotomies is a method of decomposing a multi-class problem into a collection of binary problems. Such a system recursively applies binary splits to divide the set of classes into two subsets, and trains a binary classifier for each split. Although ensembles of nested dichotomies with random structure have been shown to perform well in practice, using a more sophisticated class subset selection method can be used to improve classification accuracy. We investigate an approach to this problem called random-pair selection, and evaluate its effectiveness compared to other published methods of subset selection. We show that our method outperforms other methods in many cases when forming ensembles of nested dichotomies, and is at least on par in all other cases.

Do you believe that companies live and die by their understanding of how customers use and grow with their products? Do you love the idea of rapidly iterating on new features as experiments against thousands of users to find the ones which move the needle and hit customer engagement out of the ballpark? Would you like to drive real change at the world's hottest SaaS company, and the best place to work in Australia? Atlassian is looking for an analytical mind to work in our Product Growth team to analyze the way customers use our our amazing suite of products – JIRA, Confluence, BitBucket, Bamboo, HipChat... We use real data science to point the way, then design experiments to improve customer engagement and growth; we work with Product Managers, Designers and Engineers to build a magical experience for customers. It's not for the faint of heart -- you'll need to slice and dice large-scale data, looking for key trends and patterns.

In a recent project, we found which of 700000 property parcels in Adelaide, Australia, contain swimming pools. We used a combination of crowdsourcing and supervised machine learning in order to harness the inherent ability of humans to identify objects in imagery and the speed of machines, which can perform this task much faster than humans, once trained sufficiently. Our initial approach consisted of training a random forest classifier with a set of crowdsourced labels, then using the machine classifications to present to the crowd only the parcels that were likely to contain swimming pools. Since only a small percentage of the parcels actually contain pools, the efficiency gain of this approach is huge compared to a pure crowdsourcing campaign. At first glance, identifying a pool in a high-resolution satellite image might appear to be a simple task for a human and a machine alike.

With machine learning at the centre of our business platforms, we've been exploring the future of AI. Like many people, I start most mornings digging myself out of an inbox that often feels like more trouble than it's worth thanks to the seemingly infinite amount of newsletters I receive. And although these newsletters cross verticals as varied as marketing, technology, advertising and the agency world, lately each one has a headline that includes artificial intelligence. Not surprisingly, all of us are curious to know how this growing field of technology is going to infiltrate our professional and personal lives. Across every industry, people are afraid that AI is going to steal jobs.

In the back kitchen of Mountain View's newest pizzeria, Marta works tirelessly, spreading marinara sauce on uncooked pies. She doesn't complain, takes no breaks, and has never needed a sick day. Marta is one of two robots working at Zume Pizza, a secretive food delivery startup trying to make a more profitable pizza through machines. It's also created special delivery trucks that will finish cooking pizzas during the journey to hungry customers if approved by the Santa Clara County Department of Environmental Health. Right now Zume is only feeding people in Mountain View, California, but it has ambitions to dominate the 9.7 billion pizza delivery industry.

Like many people, I start most mornings digging myself out of an inbox that often feels like more trouble than it's worth thanks to the seemingly infinite amount of newsletters I receive. And although these newsletters cross verticals as varied as marketing, technology, advertising and the agency world, lately each one has a headline that includes artificial intelligence. Not surprisingly, all of us are curious to know how this growing field of technology is going to infiltrate our professional and personal lives. Across every industry, people are afraid that AI is going to steal jobs. As a marketer I don't think we have any urgent concerns over job security.

Hi PK and all, I'm organising this Bothaton and am working around the clock to lock in the venue, generate more awarness and get more local or remote participants from Australia as well as some sponsors. There will be more info coming soon, drafting a page on http://devpost.com/ha..., planning leads catch up over this weekend and a Hangout/webinar a few days in prior the next weekend. If you or anyone is interested in a project leadership or some other involvement, please do not hesitate to msg me!:)

Artificial intelligence in Korea is currently prospering. The media is regularly reporting AI-enabled products such as smart advisors, personal robots, autonomous cars, and human-level intelligence machines. The IT industry is investing in deep learning and AI to maintain the global competitive edge in their services and products. The Ministry of Science, ICT, and Future Planning (MSIP) has launched new funding programs in AI and cognitive science to implement the government’s newly adopted endeavor of building a “Creative Economy” and “Software Centric Society”. However, AI was not always flourishing as it is now. Similar to the history of AI worldwide, AI research and industry in Korea have faced both the ups and downs in its history.