The Battle of the Boyne has arrived – a moment greeted with incredible significance by some people in the UK, and confusion by others. The event is a public holiday in Northern Ireland and marks one of the most controversial and important parts of British history. And it is marked by a message in people's iPhone calendar, which has the tendency to confuse some people who see it. For people in Northern Ireland, that fact is obvious: they have the day off, and the controversial celebrations are hard to miss. But for the rest of the UK, the only reminder might be a cryptic note inside their iPhone calendar.

A group of researchers from Switzerland's ETH Zurich – Swiss Federal Institute of Technology and Germany's University of Bamberg has developed a techno-economic simulation model based on a machine learning algorithm, which is aimed at optimizing configuration and profitability of residential solar-plus-storage power systems. In the paper, Economic assessment of photovoltaic battery systems based on household load profiles, the research team has created its model on the basis of real-world energy consumption data from 4,190 Swiss households, which were taken under current electricity rates and weather conditions in Zurich. The authors of the study stressed, however, that their algorithm is based only on a limited set of features, and on shorter measurement time-frames of smart-meter data. Several cost scenarios were presented in the research, the most optimistic of which envisages that the installation of a residential photovoltaic-battery (PVB) system, with a mean installed PV power of 4.4 kW and a mean battery size of 9.6 kWh, will be profitable for 99.9% of Swiss households. Under this scenario, the cost of residential solar is expected to be under €1,000 per kW of PV installed, while that of the battery will not exceed €250 per kWh.

Controversy arose not long ago when it became clear that Google had provided its TensorFlow APIs to the US Department of Defense's Project Maven. Google's assistance was needed to flag images from hours of drone footage for analyst review. There is clearly an ethical debate about the acceptability of appropriating ML for military uses. More broadly, there is also an important debate about the unintended consequences of ML that has not been weaponized but simply behaves unexpectedly, which results in damage or loss. Adding ML to a system inevitably introduces a black box and that generates risks.

The idea of model fusions is pretty simple: You combine the predictions of a bunch of separate classifiers into a single, uber-classifier prediction, in theory, better than the predictions of its individual constituents. As my colleague Teresa Álverez mentioned in a previous post, however, this doesn't typically lead to big gains in performance. In many cases, OptiML will find something as good or better than any combination you could try by hand. Why waste your time fiddling with combinations of models when you could spend it on doing things that will almost certainly have a more measurable impact on your model's performance, like feature engineering or better yet, acquiring more and better data? Part of the answer here is that looking at a number like "R squared" or "F1-score" is often an overly reductive view of performance.

Fractal Analytics, (http://www.fractalanalytics.com), a global leader in AI solutions that power human decision-making in Fortune 500 companies, today announced the launch of ai.now, a brand new conference focused on how Fortune 500 businesses can better leverage artificial intelligence for their organizations. The half-day event, which includes a number of sessions and roundtable discussions, will take place on Wednesday, July 18, 2018 at the One World Trade Center in New York City. Fractal Analytics' ai.now is the only industry event of its kind, designed to help Fortune 500 attendees from a wide-range of industries - CPG, financial services, healthcare, insurance, telecom, and retail - learn how to practically implement artificial intelligence for their business. Ford will explore how AI and automation will usher in a new era of man and machine collaboration, with a realistic view of what the future of work, and our place in it, will look like, from education to law, agriculture to healthcare, management and beyond. "As investments in AI, machine learning and data science surge among Fortune 500 companies, they are seeking ways to realize and maximize the full potential of these new technologies," said Pranay Agrawal, Co-founder & CEO, Fractal Analytics.

What if you could take your photos that were originally taken in low light and automatically remove the noise and artifacts? Have grainy or pixelated images in your photo library and want to fix them? This deep learning-based approach has learned to fix photos by simply looking at examples of corrupted photos only. The work was developed by researchers from NVIDIA, Aalto University, and MIT, and is being presented at the International Conference on Machine Learning in Stockholm, Sweden this week. Recent deep learning work in the field has focused on training a neural network to restore images by showing example pairs of noisy and clean images.

It's hard to imagine what scenes Florence Nightingale witnessed when she arrived at the British military hospitals during the wars in her time. Armed with only a notebook and lamp, Nightingale redefined the problem she was trying to solve. She did not believe soldiers were simply succumbing to their wounds and was determined to curb any avoidable deaths. By recording and visualizing data she uncovered that poor sanitary practices were the main culprit of fatality in hospitals. Put simply, she used data to save lives.

If you happen to take a stroll through building 1472 Broadway, New York, you might stumble across your reflection in a mirror. Given the fact that this is H & M's flagship store in Times Square, this hardly seems like an incident worth describing. For starters, you can talk to it – and it'll talk back. Ask it to take a selfie, for example, and it will happily oblige, capturing your graceful pose before immortalizing your beauty on the front cover of a virtual fashion magazine. You can, of course, choose to share your new-found modelling profession with the world, by sending your personalized cover out across social media.

There was much celebrating in America last month when the US Department of Energy unveiled Summit, the world's fastest supercomputer. Now the race is on to achieve the next significant milestone in processing power: exascale computing. This involves building a machine within the next few years that's capable of a billion billion calculations per second, or one exaflop, which would make it five times faster than Summit (see chart). Every person on Earth would have to do a calculation every second of every day for just over four years to match what an exascale machine will be able to do in a flash. This phenomenal power will enable researchers to run massively complex simulations that spark advances in many fields, from climate science to genomics, renewable energy, and artificial intelligence.

An Israeli startup behind one of the world's most expensive phones has unveiled its latest device, which it hopes will simultaneously revolutionise smartphones and deliver cryptocurrency to the masses. In contrast to its predecessor, the new device aims to reach as wide an audience as possible with a more modest price tag of $1,000. It is unique both in terms of its software and hardware, having ditched the monolithic slab design adopted by most modern-day smartphone manufacturers. A twist to this ubiquitous design means the introduction of a hidden screen that slides up from behind the Finney's main display to reveal a cold-storage cryptocurrency wallet. "We're blockchain enthusiasts," Nimrod May, Sirin Labs' chief marketing officer, tells The Independent.