The history of AI is often told as the story of machines getting smarter over time. What's lost is the human element in the narrative, how intelligent machines are designed, trained, and powered by human minds and bodies. In this six-part series, we explore that human history of AI--how innovators, thinkers, workers, and sometimes hucksters have created algorithms that can replicate human thought and behavior (or at least appear to). While it can be exciting to be swept up by the idea of super-intelligent computers that have no need for human input, the true history of smart machines shows that our AI is only as good as we are. In the 1970s, Dr. Geoffrey Franglen of St. George's Hospital Medical School in London began writing an algorithm to screen student applications for admission.

We don't see a lot of inflatable robots, which is really too bad, because they're weirdly excellent at being exactly the sort of robot that everybody seems to want: They're cheap, being made of mostly fabric and rubber, and they're very easy to fix. Relative to most other robots, they're extraordinarily lightweight, and they pack down to a small fraction of their size once deflated. Despite their squishiness, they can be strong and unexpectedly fast, since they're essentially hydraulic in nature. And perhaps most importantly, in most cases they're passively safe, since they don't have much in the way of rigid components or the inertia that comes with them. The downside of inflatable robots is that in general they're not very good at precise, repeatable control, precisely because they're so floppy.

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. The 2019 IEEE International Conference on Soft Robotics (RoboSoft) takes place in Seoul, South Korea, next week, and the organizers put together this preview video stuffed full of--what else?--soft robots. Single-stream recycling is currently an extremely labor intensive process due to the need for manual object sorting.

Alphabet's subsidiary Wing announced this week that it has officially launched a commercial drone delivery service "to a limited set of eligible homes in the suburbs of Crace, Palmerston and Franklin," which are just north of Canberra, in Australia. Wing's drones are able to drop a variety of small products, including coffee, food, and pharmacy items, shuttling them from local stores to customers' backyards within minutes. We've been skeptical about whether this kind of drone delivery makes sense for a long, long time, and while this is certainly a major milestone for Wing, I'm still not totally convinced that the use-cases that Wing is pushing here are going to be sustainable long term. I've still got a bunch of questions about these things. For example, does the drone have any kind of in-flight sense and avoid?

Robots are well-known for being very good at some very specific things. They're often defined by words like "precision" and "repeatability" and "speed," because if you want a robot to be uniquely useful, it's usually going to have to leverage one or more of those characteristics in a way that makes it better at some specific task than humans are. Robots have been doing this for decades, typically in places like industrial settings, but things are starting to change, and roboticists are beginning to look towards other applications in more unconstrained, dynamic environments, like non-industrial settings. Such environments (our homes, for example) are the kinds of places that we really, really want robots to be useful in. We want them doing our chores so that we don't have to, ideally without causing catastrophic damage or injury at the same time.

AI can reveal how many cigarettes a person has smoked based on the DNA contained in a single drop of their blood, or scrutinize Islamic State propaganda to discover whether violent videos are radicalizing potential recruits. Because AI is such a powerful tool, Microsoft president Brad Smith told the crowd at Columbia University's recent Data Science Day that tech companies and universities performing AI research must also help ensure the ethical use of such technologies. AI is now an invisible but inextricable part of life for hundreds of millions of people. The rise of machine learning algorithms combined with cloud computing services has put massive computer power at the fingertips of companies and customers worldwide. These trends have also enabled the rise of data science that applies AI methods to constantly analyze information from online services and Internet-connected devices. In his talk, Smith emphasized the need for policies and laws that hold these systems and machines accountable to humans.

The history of AI is often told as the story of machines getting smarter over time. What's lost is the human element in the narrative, how intelligent machines are designed, trained, and powered by human minds and bodies. In this six-part series, we explore that human history of AI--how innovators, thinkers, workers, and sometimes hucksters have created algorithms that can replicate human thought and behavior (or at least appear to). While it can be exciting to be swept up by the idea of super-intelligent computers that have no need for human input, the true history of smart machines shows that our AI is only as good as we are. At 10:30pm on 29 October 1969, a graduate student at UCLA sent a two-letter message from an SDS Sigma 7 computer to another machine a few hundred miles away at the Stanford Research Institute in Menlo Park. The student had meant to send "LOGIN," but the packet switching network supporting the transmission of the message, the ARPANET, crashed before the whole message could be typed out.

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. Dorabot has brilliant realized that machine learning requires machine teaching. Isn't it time to get a smart robot to teach your dumb robots to be less stupid?

In an earlier post today, we distilled half a dozen DARPA-dense docs into an easy-to-follow overview of the DARPA Subterranean Challenge (SubT), a new competition that will task teams of humans and robots to explore complex underground environments. In this post, we have an interview with SubT program manager Timothy Chung, whom we met late last year at DARPA's D60 Conference. "I think for many of the technologies we're seeking to advance--it's one of those, aim for the moon, even if you miss you hit the stars type of an approach," he told us about the new challenge. "So we envision some component technologies being immediately operationally of value, but we've set the bar ambitiously high enough for it to be DARPA-worthy and also provide a vision for how that kind of impact could be magnified if and when we're successful." IEEE Spectrum: What are the SubT courses going to be like?

As part of the very first event in the DARPA Subterranean Challenge (SubT), the organizers have invited nine teams (and their robots) to Edgar Experimental Mine in Idaho Springs, Colo., for a sort of test run called the SubT Integration Exercise, or STIX. These nine teams have already demonstrated their systems to DARPA, showing that they can navigate autonomously over rough terrain, locate objects, and respond to an e-stop command if they go berserk. For the teams, this will be an opportunity to test out their robots in an actual tunnel system, and at the same time DARPA itself will be able to make sure all of their testing infrastructure and whatnot works, well in advance of the Tunnel Circuit Challenge itself, which will take place in August. Our detailed post on SubT and interview with DARPA program manager Timothy Chung cover all of this stuff, along with the guidelines that teams have to follow when designing and deploying their systems, but all that information doesn't necessarily give a sense of what kind of hardware teams will likely be deploying at SubT. Fortunately, many of the teams participating in STIX have posted pictures or videos of their robots, so we've put together this article to introduce each team and have a look at what they'll be working with.