Self-Driving Cars Could Mean the End of Parking Spaces, and That’s Great for Cities

As reported by Science Alert: We’re always being told how self-driving cars will reward us with almost unimaginable benefits when they finally hit the streets. Aside from the sheer convenience of being chauffeured everywhere by artificial intelligence (AI), there’s the safety factor of not having error-prone humans behind the wheel, not to mention how environmentally friendly driverless electric vehicles could be.

But there’s also another advantage to not driving our own vehicles around, and it’s one that could have a vast impact on the look, feel, and function of the cities that we live in: parking. Put simply, if we’re not driving our own vehicles to and from destinations any more, we won’t need to park idle vehicles on public streets or in car parks – something that could radically change the vibe of congested urban spaces."The biggest impact is going to be on parking. We aren’t going to need it, definitely not in the places we have it now," Alain L. Kornhauser, a researcher in autonomous vehicles at Princeton University, told Patrick Sisson at Curbed. "Having parking wedded or close to where people spend time, that’s going to be a thing of the past. If I go to a football game, my car doesn’t need to stay with me. If I’m at the office, it doesn’t need to be there. The current shopping center with the sea of parking around it, that’s dead."

While the extreme case of totally empty car parks and city streets with no stationary vehicles on them would probably require people to fully let go of personal car ownership – something many people won’t feel comfortable doing, in the near future at least – even moderate uptake of self-driving vehicles would constitute an improvement to clogged urban real estate jam-packed with stationary metal and rubber.

"An average vehicle in the US is parked for a staggering 95 percent of the time," Carlo Ratti, director of the Senseable City laboratory at the Massachusetts Institute of Technology (MIT), told Curbed. "Car sharing is already reducing the need for parking spaces: it has been estimated that every shared car removes between 10 and 30 privately owned cars from the street."

"Self-driving vehicles will reinforce this trend and promise to have a dramatic impact on urban life, because they will blur the distinction between private and public modes of transportation," he added. "‘Your’ car could give you a lift to work in the morning and then, rather than sitting idle in a parking lot, give a lift to someone else in your family – or, for that matter, to anyone else in your neighborhood, social-media community, or city."

If these kinds of predictions turn out to be accurate, cleared road areas no longer used for parked cars could be put to all sorts of uses, substantially adding to the footprint of city spaces, and letting people reclaim public territory once lost to machines.

"In this environment, you don’t need to park your car, it’ll park by itself (possibly while recharging), so you can think about recapturing the space from the front of one building to the front of another building," said Gerard Tierney of US-based architecture and design firm, Perkins+Will. "It does become a pedestrian-dominated environment, where these vehicles would need to take a more subsidiary role. We would see a huge increase in the amount of space given up to the public realm and a huge increase in the width of sidewalks, bike lanes, and space for any other kind of alternate transportation."

In all likelihood, we won’t see these kinds of changes happening in our cities for many years to come, with self-driving vehicles only just beginning to be considered by road and legal authorities. But it’s exciting to think of the tangential benefits this much-hyped technology could have in all sorts of fringe areas. We can’t wait to see what else the future of driving turns up.

Facebook can Map More of Earth in a Week than We Have in History

As reported by New Scientist: We just learned that Facebook’s artificial-intelligence software can probably map more in a week than humanity has mapped over our entire history.

In a blog post, the social network announced that its AI system took two weeks to build a map that covers 4 per cent of our planet. That’s 14 per cent of Earth’s land surface, with 21.6 million square kilometres of photographs taken from space, digested and traced into a digital representation of the roads, buildings and settlements they show. And Facebook says it can do it better and faster, potentially mapping the entire Earth in less than a week.

This is the starkest example I’ve seen so far of the most important phenomenon in technology – computers doing human work really fast. It’s going to change the way we work forever, and will have massive implications for how we acquire knowledge, cooperate on large projects and even understand the world.

The stated goal of Facebook’s data-science team is to build maps to help the social network plan how to deliver internet to people who are currently offline. It’s a dubious starting point, but whatever you think about Facebook’s internet colonialism, the company’s drones won’t be able to beam Wi-Fi to the disconnected until they know where they are.

Fast learner

The model was able to map 20 different countries after being trained on just 8000 human-labelled satellite photos from a single nation. This is mind-boggling – and Facebook’s data-science team wasn’t even trying to go fast.

The company says it has now improved the process to the point where it could do the same mapping in a few hours. Assuming it had the photographs, it could map Earth in about six days. That’s something that humanity still hasn’t managed to do.

“We processed 14.6 billion images with our convolutional neural nets, typically running on thousands of servers simultaneously,” said Facebook in its blog post.

Using its AI, Facebook aped how humans make maps in the 21st century. One way they are now being made is a project called Open Street Map, which uses volunteer labour to trace satellite photographs by hand, picking out roads and houses. The resulting maps have been used all over the world, often for disaster response – with the system able to build maps of an entirely unmapped region in a few days. Facebook’s AI can do the same in seconds.

Mapping on the scale that its AI system has demonstrated would take decades for a human team of any size – and it is more data than people or their organisations are built to handle.

The power of AI

Facebook’s map-making AI is just one of probably thousands of narrow AIs – ones that are trained to focus on a single task – churning through human tasks around the planet right now, faster and on larger scales than we ever could. The CERN particle physics laboratory near Geneva, Switzerland, is using deep learning to find patterns in the mass of its collision data; pharmaceutical companies are using it to find new drug ideas in data sets that no human could plumb.

Nvidia’s Alison Lowndes, who helps organisations build deep-learning systems, says she now works with everyone: governments, doctors, researchers, parents, retailers and even, mysteriously, meatpackers.

What’s exciting is that all neural networks can scale like Facebook’s mapping AI. Have a narrow AI that can spot the signs of cancer in a scan? Good: if you have the data, you can now search for cancer in every human on Earth in a few hours. An AI that knows how to spot a crash in the markets? Great: it can watch all 20 of the world’s major stock exchanges at the same time, as well as the share prices of individual companies.

The real power of narrow AI isn’t in what it can do, because its performance is almost never as good as that of a human would be. The maps that Facebook’s AI produces are nowhere near as good as those that come out of a company such as custom map developer Mapbox.

But the smart systems being built in labs at Google, Facebook and Microsoft are powerful because they run on computers. What the future of human work looks like will be determined by whether it is better to do an average-quality job 50 million times a second or a human-quality job once every few minutes.

Make no mistake, AI is here – and it’s real and powerful. But humans are still in total control. We’re just all about to get some extremely clever help.

SpaceX Doesn't Expect a Successful Drone Ship Recovery After Tomorrow's Planned Satellite Launch

As reported by The Verge: SpaceX is gearing up for its next Falcon 9 rocket launch this Wednesday, which will send a telecommunications satellite into orbit for the company SES. It will also attempt to land its rocket after launch, but doesn't expect a successful recovery.

This mission will also take a different path than originally intended. The launch was supposed to take place in September, but was then repeatedly delayed after one of SpaceX’s rockets exploded on June 28th. Those delays prompted SpaceX to change the flight path for the launch to help get the satellite into its intended orbit much faster, according to SES.

DELAYS PROMPTED SPACEX TO CHANGE THE FLIGHT PATH FOR THE LAUNCH

The SES-9 satellite that SpaceX is launching is meant to go into geostationary orbit — a circular path more than 22,000 miles above the equator. To get there, SpaceX will boost the satellite part of the way into space; then the satellite will propel itself the rest of the way to its intended orbit. Compared to the rocket, the satellite travels very slowly, and may take months to get to where it’s going, according to Space News. And with all the launch delays, SES is eager to get the satellite operational as soon as possible. So SpaceX has agreed to drop off SES-9 at a higher orbit than originally planned, cutting down the satellite’s solo trip.

Rocket recovery will be much more difficult since this satellite is very heavy and going to a very high orbit. The Falcon 9 will need to go a lot faster. That means the rocket will need extra fuel for the actual launch. But it also means there will be less room for the fuel needed to perform a landing afterward. "Given this mission’s unique GTO profile, a successful landing is not expected," SpaceX said in a statement.

The rocket will attempt to land on one of its drone ships at sea, a feat that requires less fuel than a ground landing. SpaceX is also using its new updated Falcon 9 for the launch — a vehicle that's supposed to make rocket recovery easier. But SpaceX has tried three times before to land the Falcon 9 at sea, and it failed to stick the landing each time. (It did get pretty close on its last attempt.) A floating drone ship on a choppy sea is a much more difficult landing target than a large expanse of unmoving land.SpaceX has been trying for more than a year to land its rockets after launching them into space. It's part of the company's plan to reuse its vehicles in order to bring down launch costs. So far, SpaceX has only been able to land the Falcon 9 on solid ground — as it did in December. But CEO Elon Musk has said that not all missions are suited for ground landings. Demonstrating a successful sea landing would prove that SpaceX is capable of mastering multiple types of rocket recovery, potentially allowing them to reuse even more rockets in the future.

The Falcon 9 is scheduled to take off at 6:46PM ET tomorrow from Cape Canaveral, Florida. Check back here tomorrow to watch the launch live.

Mars in 3 day? NASA Researchers are Working on a Photonic Propulsion System

As reported by Science Alert: Despite how far we've come in space exploration, one thing still holding us back from interstellar travel is our slow spacecraft. While we're able to propel particles to close to the speed of light in the lab, we're struggling to even accelerate spacecraft to beyond 3 percent of that. With our current technology, it's estimated it'll take humans around five months to reach Mars.

But NASA scientist Philip Lubin is working on a system where lasers propel spacecraft with giant sails to the Red Planet in as little as three days. Much likeBill Nye's much-hyped solar sail, this 'photonic propulsion' system relies on the momentum of photons - particles of light - to move forward. But instead of photons from the Sun's rays, Lubin's design would be given a push by giant Earth-based lasers.

It sounds pretty far-fetched, but in a video for NASA 360, Lubin explains that the technology is very much readily available, and that the system could easily be scaled up.

"There are recent advances that take this from science fiction to science reality,"Lubin explains. "There is no known reason why we can not do this."

But let's step back for a second and investigate how the system works. Right now, when we launch spacecraft, the thrust comes from burning a chemical, such as rocket fuel. Not only does this fuel source weigh down spacecraft, it's also an incredibly inefficient system when compared to electromagnetic acceleration, which is the use of light or other electromagnetic radiation to accelerate objects.

"Electromagnetic acceleration is only limited by the speed of light while chemical systems are limited to the energy of chemical processes," writes Lubin in a paper on the technology.

But while electromagnetic acceleration in the lab is relatively straightforward, it requires a lot of complicated and expensive equipment - such as the ring of superconducting magnets that make up the Large Hadron Collider - and hasn't been easy to scale up to the size required for space travel.

One candidate propulsion system, known as the 'impossible' EM Drive, has received a whole lot of attention for allegedly achieving electromagnetic acceleration, but NASA scientists still haven't been able to figure out how it works, or prove that it wasn't an experimental anomaly. 

Photonic propulsion, on the other hand, works in theory, regardless of the scale, making it a more viable candidate.

NASA 360

So how do photons work to propel something as big as a spacecraft? Despite not having any mass, particles of light have both energy and momentum, and when they reflect off an object, that momentum is transferred into a little push. With a large, reflective sail, it's possible to generate enough momentum to gradually accelerate a spacecraft.

While Lubin and his team haven't yet tried out their system, their calculations show that photonic propulsion could get a 100-kg robotic craft to Mars in just three days. 

A larger craft, like the kind humans might travel in, would take around a month to get there - one-fifth of the time it would take the Space Launch System (SLS), the world's most powerful rocket currently being developed to take us to Mars.

Lubin also explains that in the 10 minutes it will take to get the SLS into orbit, photonic propulsion could propel a spacecraft to an unheard-of 30 percent the speed of light - and it would also use a similar amount of chemical energy (50 to 100 gigawatts) to do so.

But the real benefit of photonic propulsion comes over longer distances, where the spacecraft has more time to speed up, and could eventually take us outside our Solar System and to neighboring stars.

To be clear, the system isn't designed to send humans across interstellar distances - first of all, robots are far better equipped for that mission, and secondly, we'd be far too heavy. Instead, Lubin proposes wafer-thin spacecraft that can get close to the speed of light.

But sending our own artificial intelligence to these distant solar systems - especially ones that potentially harbor habitable planets - would still be huge.

"The human factor of exploring the nearest stars and exoplanets would be a profound voyage for humanity, one whose non-scientific implications would be enormous," writes Lubin. "It is time to begin this inevitable journey beyond our home."

Lubin and his team last year received a proof-of-concept grant from NASA to show that photonic propulsion could be used for space travel, so we should start seeing some real-life results soon. Let's hope that the reality lives up to the hype, because we're pretty excited.

Find out more in the NASA 360 video:

AT&T and Intel Test drones’ Capabilities on LTE Beyond Line of Sight and at Higher Altitudes

As reported by Venture Beat: U.S. telecom giant AT&T is partnering with chip maker Intel to test and optimize how drones perform on LTE connections beyond line of sight, at higher altitudes, or when faced with external interference.

Intel is demoing an LTE-connected drone at Mobile World Congress (MWC) in Barcelona this week. The partnership is designed to show how a network that has primarily been designed to connect devices (such as smartphones) on the ground can be reappropriated for unmanned aerial vehicles (UAVs).

Above: Drone Controller

Many drones can be connected by smartphones, with onboard cameras giving the operator a real-time view of the UAV in flight. That, in conjunction with other sensors required to control the drone, means that a constant connection is required. And this is why Intel is teaming up with AT&T’s Internet of Things (IoT) team and its Foundry innovation center to see how drones can best work on an LTE network.

Drones have exploded in popularity in recent years, leading to increasing concerns over security and questions about how the vehicles could or should be regulated. And this is one of the areas the two companies are looking to address, including examining how signals between a smartphone and a drone may interfere with airplanes and other manned aircraft. They’ll also explore usability and functionality in “beyond line-of-sight” situations, a scenario the Federal Aviation Administration (FAA) is currently considering.

It’s worth noting here that the partnership is less about helping consumers navigate their toys around public thoroughfares than it is about exploring legitimate business scenarios. While the likes of Amazon are currently looking into “delivery by drone” initiatives, there are also many other use-cases across numerous industries that could see UAVs taken to the next level.

“AT&T and Intel will be testing how the network can enable the most exciting business use cases for drones,” said Chris Penrose, senior vice president of IoT Solutions at AT&T, in a press release. “Our LTE network is uniquely positioned to connect industries like delivery, agriculture, construction and insurance. We’re using the network to transfer important information, images and video quickly and efficiently — far beyond the boundaries of short-range connectivity.”

For AT&T, the move makes sense as the broader telecom industry increasingly embraces the so-called Internet of Things. As for Intel, it has been pushing heavily into the drone realm of late — recently acquiring Ascending Technologies, a drone maker that specializes in collision-avoidance technology.

“Intel believes UAVs have great potential, from inspections, precision agriculture to deliveries of consumer goods and providing emergency disaster relief,” added Anil Nanduri, vice president of the New Technology Group at Intel. “We want to grow this market through our collaborations and by integrating new technologies and compute to UAVs.”

Higher Accuracy GPS Could Boost Self-Driving Cars and Other Tech

As reported by Gizmag: Researchers have found a better way to crunch the data that GPS-enabled devices use to determine their location. The result could provide a level of accuracy down to the centimeter that's needed in things like autonomous vehicles and other precision tech.

We've seen other efforts to improve GPS location to centimeter-level accuracy using what's called "differential GPS," that makes use of ground-based reference points in addition to satellite GPS data. This latest effort from the University of California - Riverside (UCR) seems similar in that it's basically a software-based approach.

What's perhaps most revolutionary about the advance is not just the improved level of accuracy, but just how efficiently centimeter-accurate positioning is established.

"Achieving this level of accuracy with computational loads that are suitable for real-time applications on low-power processors will not only advance the capabilities of highly specialized navigation systems – like those used in driverless cars and precision agriculture – but it will also improve location services accessed through mobile phones and other personal devices, without increasing their cost," said UCR professor Jay Farrell, who led the research.

He claims that until now, achieving such accuracy has been computationally expensive, but the new approach returns a highly accurate position with several orders of magnitude fewer computations. That could make this level of GPS more practical in a number of emerging real world applications."To fulfill both the automation and safety needs of driverless cars, some applications need to know not only which lane a car is in, but also where it is in that lane – and need to know it continuously at high rates and high bandwidth for the duration of the trip," said Farrell.

Two members of the team that conducted the research have since moved on to work at Qualcomm and Google. The research was published recently in IEEE's Transactions on Control Systems Technology.

Is Tesla About To Unleash A Black Swan On The Energy Industry?

As reported by Forbes: Clients often ask me about what might be a “black swan” (an event that comes as a surprise to conventional wisdom, has a significant impact and is often rationalized after the fact) for the oil industry. Lately I’ve been thinking how a dramatic breakthrough in battery technology could be such an event. The implications for the petroleum industry might be broader than you think.

In a recent Forbes blog, tech contributor Mark Rogowsky reported some interesting details of the new Tesla Model 3 scheduled to make its debut on March 31. The Model 3 will be about the same size as a BMW 3 Series, with a price point of $35,000 and a range of 200 miles.

To be viable in the mass market for vehicles, an alternative vehicle needs to be competitive with the internal combustion engine (ICE) in three ways. It must have:

1. a price point of about $25,000
2. a record of reliability, and
3. a driving range of at least 300 miles, typical of mid-size sedans.

So the Model 3 is getting closer, but it’s not quite there. Yet.

A useful rule-of-thumb for automobile makers in model selection and pricing strategy is a price point that is 1.25x personal income. If we use GDP per capita as a proxy for income, then Tesla’s current mass market (at a price of $35,000) would be countries with income levels of about $28,000 per year (on a purchasing power parity basis). The chart below shows a market size of about 675 million (age 15 – 64) in 2015. This market grows to over 700 million by 2025. The market in advanced economies is fairly flat, but the emerging world nearly quadruples from 15 million to almost 60 million in the next ten years. If over the next 10 years Tesla can compress the price of its Model 3 (or equivalent) to $25,000, its market potential expands to almost 750 million drivers.

Today, there are over one billion ICE passenger vehicles on the roads compared to just over one million electric. Electric vehicles sales have increased almost 80% per annum since 2010 and were about 400,000 (equivalent to two weeks of VW’s current sales) of the 85 million vehicles sold globally in 2015.

Driving range is important to compete with an ICE vehicle, which are fueled through a fully depreciated distribution system. For the alternative to be successful, it needs to avoid (or minimize) investments in a network of refueling infrastructure (e.g., charge stalls in store parking lots) to keep down the effective cost of operating the vehicle. Avoiding this infrastructure investment is critical for electric vehicles to compete in advanced economies.

The initial version of the Tesla Model 3 looks to fall short of these mass market parameters. However, it does not take much of a stretch of the imagination for Tesla to close these competitive gaps as it builds actual road and manufacturing experience and production of the Model 3 ramps up.

How long might it take for these vehicles to significantly penetrate the global fleet? Once the technology breakthroughs are made, it will quickly disperse to other automobile manufacturers (similar to the spread of hydraulic fracturing in tight oil).

Technological innovation in automobiles tend to follow the rule of “7 in 10”, shorthand for a technology (e.g., air bags, anti-lock brakes, seatbelts) deployed in 70% of new car sales within 10 years. So if next generation electric vehicles can make the technology leap, then by 2025 they could account for about 70 million car sales annually. Over that pivotal decade, electric vehicles would grow to about 7% of the global car fleet. That might seem small, but without such a technology leap the share would be closer to 2%.

So what is the black swan event? It is not only that such a mass market electric vehicle could threaten oil demand growth, but that the underlying technological innovations necessary to be competitive with ICEs threaten the entire hydrocarbon industry – not just oil, but natural gas and coal as well. My hypothesis is that if a battery is capable of storing enough energy to move a $25,000 mid-size sedan 300 miles, then it would be cheap and small enough to mount another battery in the garage. If the garage battery is charged by roof-mounted solar and/or wind, it removes the need for incremental electricity generation from central power plants fired by natural gas or coal.

A more cost-effective battery (increasingly likely as electric vehicles penetrate deeper into the vehicle fleet) opens the possibility of even greater battery capacity and of households removing themselves from a central power grid. Not depending on a central power grid would be important to consumers in emerging economies who are looking for power reliability in addition to personal mobility.

Former Saudi Oil Minister Zaki Yamani is credited with the quip, “The Stone Age didn’t end because the world ran out of stones – and the same will be true with oil.” I usually conclude these black swan discussions with clients by saying that I won’t see the end of the oil industry, my kids won’t see the end, but my grandkids just might. If Tesla can meet its Model 3 aspirations in the next few years, my observation might be realized much earlier than I used to think. There’s a new Tesla dealership near my house; I think I’ll take a look.