Few things succeed in riling up the Internet faster, unleashing a unique cocktail of amazement and terror, than a new Boston Dynamics robot video. In the past, the tech company, owned by Japan's SoftBank Group, has released videos showing its robots climbing stairs, executing perfect back flips and opening doors with shocking facility. The company's latest YouTube submission: a 34-second clip of its boxy humanoid robot, Atlas, going for a jog in a grassy residential area on what appears to be a bright spring day. With its electronic appendages unleashing an animatronic whine that falls somewhere between an electronic knife and a Xerox machine, Atlas even stops to hop over a log before casually going on his bipedal way. In only a day, the video has racked up more than 900,000 page views, a testament to the powerful impression that Boston Dynamics's videos continually leave on viewers.
Boston Dynamics' Atlas robot has been untethered and set free, which, for anyone convinced of an imminent robot uprising, is a rather worrying development. A video posted on YouTube this week shows the humanoid robot running at a fair clip across open land before jumping over a log that it finds in its path. It's impressive stuff, and shows just how far bipedal robots have come in a short space of time. For proof, check out this amusing compilation video from a contest in 2015 showing a bunch of similarly designed robots looking as if they've spent a night drinking as they simply try to walk, let alone break into a jog à la Atlas. To be honest, it's not the most impressive thing we've seen Atlas do.
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. Boston Dynamics posted some awesome new videos this week; the first shows Atlas jogging (!): Jogging involves a flight phase, which means that the robot is spending time completely airborne during each gait cycle. It takes much more energy to do this relative to walking, which is more like a continuous controlled fall forward.
Engineering and robotics firm Boston Dynamics released two new videos Thursday showing off what their Atlas and SpotMini robots can do. In the first video, the humanoid Atlas robot goes for a swift jog through a yard and even leaps over a log. In the second, the dog-like SpotMini robot climbs up and down stairs. To steer properly, the robot was originally guided by an operator, the Verge reports. But now the robot has mapped the area and uses cameras to navigate autonomously.
Proving that robots even exercise better than you, Boston Dynamics released a new video today demonstrating its Atlas bot going for what looks to be an afternoon jog. Not to be confused with the robodogs likely plotting something nefarious at this very moment, Atlas is the same biped that seriously freaked out the internet when it did a backflip in November of last year. So, to recap: Boston Dynamics robots are opening doors, doing flips, and running across an uneven terrain. I guess there's no need to violently overthrow the humans when you can just embarrass us into going back to the gym. John Cho's new thriller'Searching' is so 2018 'Shitty Robots' creator Simone Giertz's TED Talk is a must-watch'Rick and Morty' co-creator Justin Roiland gets intimate with Jerry (exclusive video)
In May of last year, I needed surgery to fix my left knee in hopes that I could get back to running, one of my favorite activities. Now, you may be thinking "what the heck does this have to do with Data Science?", but during my recovery I found more parallels than you might think. I started to make the connection a few months into rehab, with all of the squats, icing, balancing drills, and massages. Getting back into running shape was so similar to the tasks I had to do on a daily basis to build a strong data science culture. Even more, I came to realize that data science today is much like preparing for and running a marathon (something few do, and even less do well).
Lactate threshold is considered an essential parameter when assessing performance of elite and recreational runners and prescribing training intensities in endurance sports. However, the measurement of blood lactate concentration requires expensive equipment and the extraction of blood samples, which are inconvenient for frequent monitoring. Furthermore, most recreational runners do not have access to routine assessment of their physical fitness by the aforementioned equipment so they are not able to calculate the lactate threshold without resorting to an expensive and specialized centre. Therefore, the main objective of this study is to create an intelligent system capable of estimating the lactate threshold of recreational athletes participating in endurance running sports. The solution here proposed is based on a machine learning system which models the lactate evolution using recurrent neural networks and includes the proposal of standardization of the temporal axis as well as a modification of the stratified sampling method. The results show that the proposed system accurately estimates the lactate threshold of 89.52% of the athletes and its correlation with the experimentally measured lactate threshold is very high (R=0,89). Moreover, its behaviour with the test dataset is as good as with the training set, meaning that the generalization power of the model is high. Therefore, in this study a machine learning based system is proposed as alternative to the traditional invasive lactate threshold measurement tests for recreational runners.
Getting around the Berlin marathon last weekend was at times a painful experience. Especially at an age where I ought to know better. As the song goes, things ain't what they used to be. Take a look at the bigger picture, however, and I'm doing well. Long distance running is one of the few physical activities where humans outperform the rest of the animal kingdom.
Those who run regularly or who have experienced the endorphin euphoria known as "runner's high", can experience the same heady feeling reading Joanna Goodman's "Robots in Law: How Artificial Intelligence is Transforming Legal Services" (Ark, 2016). The book provides a fulsome journey for the reader through the Artificial Intelligence (AI) legal landscape, explaining key concepts for the uninitiated and highlighting the most visible vendors and makers among other industry players. The running analogy has a special significance for those of us that are current or lapsed runners. Lisa started reading at the same time as beginning marathon training on her global travels. The hope was that like Nike running apps, "Robots" would provide her with the tools and insights she needed to understand the AI legal tech hype, and intelligently speak to the topic with fellow colleagues in legal innovation.
I've been writing marathon-related blog posts for about 2 years now, describing a range of studies on different aspects of marathon running, such as the influence of age, gender, and experience on performance and pacing, and focusing on race-records from a wide range of big-city marathons around the world. To date these studies have focused on analysing marathon data with a view to gaining insights into what has happened in the past; something that is often referred to as descriptive analytics in the world of data science. Recently I have turned my attention to the future, to use this marathon data to gain insights into what might happen in the future -- predictive analytics -- and, in particular to make predictions about the potential of runners to achieve new personal best (PB) finish-times. In fact, what began as a bit of data-fun in my spare-time, has now started to leak into my day-job, and this week I will present a scientific paper based on this prediction work. This is not so unusual. As a Professor in the area of artificial intelligence, machine learning, and recommender systems, a major part of my job involves publishing and presenting research ideas.