In 2015, a jovial three-foot-tall robot with pool noodles for arms set out on what seemed like a simple mission. Using the kindness of strangers, this machine, called "hitchBOT" would spend months hitchhiking across the continental United States. It made it just 300 miles. Two weeks into the road trip, HitchBOT was found abandoned in the streets of Philadelphia, its head severed and spaghetti arms ripped from its bucket-shaped body. "It was quite a setback, and we didn't really expect it," hitchBOT co-creator Frauke Zeller told CNN at the time.

AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner How is #blockchain meant to help create #autonomouscars? You might own an autonomous vehicle. It needs to talk to machines like traffic lights, parking meters, and other city infrastructure without any input from you. To do so, it needs to find, authenticate, communicate and transact with those machines. If the identity of these machines is not verified, it could be connecting to a parking meter that has been hacked and can steal my car's data.

Sure, it's 2020, but it seems the bug is back: Y2K. In fact, you might just be able to chalk up that inexplicable credit card rejection or parking meter fail to something that's so 2000. Twenty years ago, you may recall, there was a race against the calendar to update computer systems to correct what was deemed the Y2K or millennium bug. During the advent of computers, they were coded to store dates counting the years by the last two digits instead of all four, so when the year 2000 arrived requiring a full four-number shift, many systems would have jumped back to the year 1900, which many feared would wreak havoc across industries that had become dependent on the burgeoning network of interconnected computers. So, to keep the global network of computers from bringing all of plugged-in humanity to a screeching halt – or as the most doomsayers suggested – developers had two options: either rewrite the code to use four-digit years or use a temporary fix coined "windowing," according to New Scientist, Windowing allowed programmers to refer to dates from 00 to 20 as the 2000s instead of the 1900s.

We'll pass each frame of video through the pipeline, one frame at a time. The first step in the pipeline is to detect all possible parking spaces in a frame of video. Obviously we need to know which parts of the image are parking spaces before we can detect which parking spaces are unoccupied. The second step is to detect all the cars in each frame of video. This will let us track the movement of each car from frame to frame.

It's lunchtime, and the worker bees of Mountain View who aren't interested in their company's own catering are walking down East Middlefield Road in search of grub. It's a lovely esplanade, but all the trees and light poles make these pedestrians hard to spot. That makes things tricky for a human driver, and extra troublesome for a robot trying to learn to work the wheel. But on a large monitor inside Soroush Salehian and Mina Rezk's Mercedes Sprinter van, every meandering biped stands out against a sea of white. Those walking toward the van are blue, those moving away from it are red.

Urban planners talk about two visions of the future city: heaven and hell. Hell, in case it's not clear, is bad--cities built for technologies, big companies, and vehicles instead of the humans who actually live in them. And hell, in some ways, is here. Today's US cities are dominated by highways there were built by razing residential neighborhoods. It's all managed by public policies that incentivize commuting in your car.

Governments are already using data and analytics in a number of ways to help them become better informed and provide superior services for their citizens. For both central and local governments, an increasing number of back end processing and citizen engagement opportunities are emerging for smart use of artificial intelligence and its many subfields. The biggest area for potential quick wins will be the vast processing that occurs in various administration tasks. This includes improving awareness of patterns in data, to create new theses and models. Bringing together data from different areas and using algorithms that learn, can create new insights.