Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next few months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. We wrote about HAMR back in February, and it's now grown some cute lil' footpads that allow it to walk on water, and also sink (on purpose). In nature, cockroaches can survive underwater for up to 30 minutes.

The facial recognition software used by London's Metropolitan Police is producing a 98 per cent false positive rate. This means the system is regularly getting it wrong, with only two out of every 100 'matches' pinpointing the correct person. Despite this success rate, London's police commissioner Cressida Dick says she is'completely comfortable' to continue using the system in the capital. So far, the programme has resulted in only two accurate identifications – and neither pinpointed a wanted criminal. One was an individual from an out-of-date watch list, while the other was a person with a mental condition who regularly contacts public figures, but is not wanted for arrest.

There are smart speakers, which connect wirelessly to other devices, and then there's the new era of smart speakers, designed to offer services through voice-controlled virtual assistants. Sonos, for a long time, was all about the former, having been a pioneer of high-quality, WiFi-connected speaker systems. Now it's entered the next era with products like Sonos One and Sonos Beam, which work with Amazon's Alexa and other virtual assistants. But Sonos' partners are also rivals, and Sonos' reliance on companies like Amazon, Google, and Apple makes it vulnerable, as it revealed in filing for an initial public offering Friday. To cite one example: Amazon can disable Alexa on Sonos devices any time, with "limited notice," according to the filing.

Daimler will soon take its Mercedes-Benz self-driving cars to the public streets of Beijing. It's the first non-Chinese company to win a license to test level 4 self-driving vehicles there. Level 4 is the second-highest tier of autonomous driving, in which cars can operate without human input in select conditions. Eventually, you might be able to take a nap while these types of vehicles ferry you around. The test vehicles use technology from Daimler's partner Baidu Apollo, and they had to go through rigorous closed-course testing in Beijing and Hebei before Chinese authorities granted the license.

Let me give you a brief note on the purpose of this article and then will discuss the technical part. I see plenty of Python based tutorials on how to start coding deep learning models, train them and deploy into production. Some way or the other my research always ended up considering Python for any machine learning practices. Most of the time, it was due to simplicity and the fact that majority of machine learning libraries are released in Python. Python is interesting, but it was never my primary choice.

Amazon Prime Day is here. The bizarre holiday – which has been running for years and is mostly the concoction of Amazon – offers both great deals and strange offers. Amazon promises the day will be the biggest ever. That is undoubtedly true, but it's important to make sure you don't end up with the biggest buyer's remorse ever, too. It is, really, just a sale.

IBM's Watson artificial intelligence platform compiling highlights at the Wimbledon tennis tournament in which players display a heightened sense of emotion. IBM's Watson artificial intelligence (AI) platform is analyzing players' emotions at the Wimbledon tennis tournament to compile highlights in which players display a heightened sense of emotion. In addition to recognizing emotions, Watson is also analyzing crowd noise, players' movements, and match data. IBM's Sam Seddon says the company is using machine learning to pinpoint scenes after exciting play when athletes show their emotions. "If you've got the visual element from the player, and you know that it's a tight pressure point in the match, then those are the points that you are going to really target in on in the highlights package," he says.

Our brain and all its magnificent capabilities is powered by less than 20 watts. Stop to think about that for a second. As I write this blog my laptop is using about 80 watts, yet at only a fourth of the power, our brain outperforms state-of-the-art supercomputers by several orders of magnitude when it comes to energy efficiency and volume. For this reason it shouldn't be surprising that scientists around the world are seeking inspiration from the human brain as a promising avenue towards the development of next generation AI computing systems and while the IT industry has made significant progress in the past several years, particularly in using machine learning for computer vision and speech recognition, current technology is hitting a wall when it comes to deep neural networks matching the power efficiency of their biological counterpart, but this could be about to change. As reported last week in Nature Communications, my colleagues and I at IBM Research and collaborators at EPFL and the New Jersey Institute of Technology have developed and experimentally tested an artificial synapse architecture using 1 million devices -- a significant step towards realizing large-scale and energy efficient neuromorphic computing technology.

"Artificial Intelligence (AI) will replace almost half of the jobs in America in the next decade" – How often have you heard such phrases? To enable fully functioning AI working in both blue and white-collar jobs by 2030, educators are creating a new generation with IT-savvy skills to keep graduates employable in this imagined future landscape. A study by McKinsey Global Institute (2018) suggests that AI won't necessarily lead to massive unemployment as innovation-focused AI adopters would likely increase their employment to achieve growth. Gartner also reported that AI is expected to create 2.3 million jobs by 2020 and Capgemini stated that 83% of companies using AI said that the technology has led to job creation in their organization. As has been said of the past, "the stone age didn't end because we ran out of stones", same could be said about the nature of employment; "the boring desk job didn't end because we ran out of routine tasks".

Artificial Intelligence can seem like it is improving at an often alarming rate but the reality is often less glamorous.