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Nintendo announces Super Mario Maker 2 for Switch, so goodbye forever

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Nintendo has ruined my life, and all our lives, by announcing Super Mario Maker 2, the sequel to the level-constructing game on Wii U that produced thousands of devious levels for those who think the “real” games aren’t hard enough. Gamers have been asking for this basically since the Switch was first rumored.

Mario Maker 2 looks like it’s been updated in a number of helpful ways apart from being on a console that will actually be supported long-term. The interface needed some sprucing up for the lower precision players will have using their fingers instead of a stylus on the touchscreen.

No doubt this will be a huge draw for Nintendo’s Switch Online service, which will likely not only allow you to share your levels and try those of others, but — if Nintendo listened to its player base — compete with ghosts and other multiplayer features. Here’s hoping we can build whole worlds, but let’s not get greedy. But we definitely have slopes now!

Honestly I could play NES and SNES-era Mario games forever on repeat, and the re-releases of other Marios on Switch has made the newer ones even more accessible. Probably between those and Mario Maker I’ll never leave the house again.

Details are truly scant for now except that the game will come out in June of this year, just in time for summer to arrive — and be shut out with blackout curtains so glare doesn’t get on my greasy Switch. I’ll update this post if any new information becomes available.

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Opportunity Mars Rover goes to its last rest after extraordinary 14-year mission

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Opportunity, one of two rovers sent to Mars in 2004, is officially offline for good, NASA and JPL officials announced today at a special press conference. “I declare the Opportunity mission as complete, and with it the Mars Exploration Rover mission as complete,” said NASA’s Thomas Zurbuchen.

The cause of Opportunity’s demise was a planet-scale sandstorm that obscured its solar panels too completely, and for too long, for its onboard power supply to survive and keep even its most elementary components running. It last communicated on June 10, 2018, but could easily have lasted a few months more as its batteries ran down — a sad picture to be sure. Even a rover designed for the harsh Martian climate can’t handle being trapped under a cake of dust at -100 degrees celsius for long.

The team has been trying to reach it for months, employing a variety of increasingly desperate techniques to get the rover to at least respond; even if its memory had been wiped clean or instruments knocked out, it could be reprogrammed and refreshed to continue service if only they could set up a bit of radio rapport. But every attempt, from ordinary contact methods to “sweep and beep” ploys, was met with silence. The final transmission from mission control was last night.

Spirit and Opportunity, known together as the Mars Exploration Rovers mission, were launched individually in the summer of 2003 and touched down in January of 2004 — 15 years ago! — in different regions of the planet.

Each was equipped with a panoramic camera, a macro camera, spectrometers for identifying rocks and minerals, and a little drill for taking samples. The goal was to operate for 90 days, traveling about 40 meters each day and ultimately covering about a kilometer. Both exceeded those goals by incredible amounts.

Spirit ended up traveling about 7.7 kilometers and lasting about 7 years. But Opportunity outshone its twin, going some 45 kilometers over 14 years — well over a marathon.

And of course both rovers contributed immensely to our knowledge of the Red Planet. It was experiments by these guys that really established a past when Mars not only had water, but bio-friendly liquid water that might have supported life.

Opportunity did a lot of science but always had time for a selfie, such as this one at the edge of Erebus Crater.

It’s always sad when a hard-working craft or robot finally shuts down for good, especially when it’s one that’s been as successful as “Oppy.” The Cassini probe went out in a blaze of glory, and Kepler has quietly gone to sleep. But ultimately these platforms are instruments of science and we should celebrate their extraordinary success as well as mourn their inevitable final days.

“Spirit and Opportunity may be gone, but they leave us a legacy — a new paradigm for solar system exploration,” said JPL head Michael Watkins. “That legacy continues not just in the Curiosity rover, which is currently operating healthily after about 2,300 days on the surface of Mars. But also in our new 2020 rover, which is under construction here at the Jet Propulsion Laboratory.”

“But Spirit and Opportunity did something more than that,” he continued. “They energized the public about the spirit of robotic Mars exploration. The infectious energy and electricity that this mission created was obvious to the public.”

Mars of course is not suddenly without a tenant. The Insight lander touched down last year and has been meticulously setting up its little laboratory and testing its systems. And the Mars 2020 rover is well on its way to launch. It’s a popular planet.

Perhaps some day we’ll scoop up these faithful servants and put them in a Martian museum. For now let’s look forward to the next mission.

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Xnor’s saltine-sized, solar-powered AI hardware redefines the edge

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“If AI is so easy, why isn’t there any in this room?” asks Ali Farhadi, founder and CEO of Xnor, gesturing around the conference room overlooking Lake Union in Seattle. And it’s true — despite a handful of displays, phones, and other gadgets, the only things really capable of doing any kind of AI-type work are the phones each of us have set on the table. Yet we are always hearing about how AI is so accessible now, so flexible, so ubiquitous.

And in many cases even those devices that can aren’t employing machine learning techniques themselves, but rather sending data off to the cloud where it can be done more efficiently. Because the processes that make up “AI” are often resource-intensive, sucking up CPU time and battery power.

That’s the problem Xnor aimed to solve, or at least mitigate, when it spun off from the Allen Institute for Artificial Intelligence in 2017. Its breakthrough was to make the execution of deep learning models on edge devices so efficient that a $5 Raspberry Pi Zero could perform state of the art computer vision processes nearly well as a supercomputer.

The team achieved that, and Xnor’s hyper-efficient ML models are now integrated into a variety of devices and businesses. As a follow-up, the team set their sights higher — or lower, depending on your perspective.

Answering his own question on the dearth of AI-enabled devices, Farhadi pointed to the battery pack in the demo gadget they made to show off the Pi Zero platform, Farhadi explained: “This thing right here. Power.”

Power was the bottleneck they overcame to get AI onto CPU- and power-limited devices like phones and the Pi Zero. So the team came up with a crazy goal: Why not make an AI platform that doesn’t need a battery at all? Less than a year later, they’d done it.

That thing right there performs a serious computer vision task in real time: It can detect in a fraction of a second whether and where a person, or car, or bird, or whatever, is in its field of view, and relay that information wirelessly. And it does this using the kind of power usually associated with solar-powered calculators.

The device Farhadi and hardware engineering head Saman Naderiparizi showed me is very simple — and necessarily so. A tiny camera with a 320×240 resolution, an FPGA loaded with the object recognition model, a bit of memory to handle the image and camera software, and a small solar cell. A very simple wireless setup lets it send and receive data at a very modest rate.

“This thing has no power. It’s a two dollar computer with an uber-crappy camera, and it can run state of the art object recognition,” enthused Farhadi, clearly more than pleased with what the Xnor team has created.

For reference, this video from the company’s debut shows the kind of work it’s doing inside:

As long as the cell is in any kind of significant light, it will power the image processor and object recognition algorithm. It needs about a hundred millivolts coming in to work, though at lower levels it could just snap images less often.

It can run on that current alone, but of course it’s impractical to not have some kind of energy storage; to that end this demo device has a supercapacitor that stores enough energy to keep it going all night, or just when its light source is obscured.

As a demonstration of its efficiency, let’s say you did decide to equip it with, say, a watch battery. Naderiparizi said it could probably run on that at one frame per second for more than 30 years.

Not a product

Of course the breakthrough isn’t really that there’s now a solar-powered smart camera. That could be useful, sure, but it’s not really what’s worth crowing about here. It’s the fact that a sophisticated deep learning model can run on a computer that costs pennies and uses less power than your phone does when it’s asleep.

“This isn’t a product,” Farhadi said of the tiny hardware platform. “It’s an enabler.”

The energy necessary for performing inference processes such as facial recognition, natural language processing, and so on put hard limits on what can be done with them. A smart light bulb that turns on when you ask it to isn’t really a smart light bulb. It’s a board in a light bulb enclosure that relays your voice to a hub and probably a datacenter somewhere, which analyzes what you say and returns a result, turning the light on.

That’s not only convoluted, but it introduces latency and a whole spectrum of places where the process could break or be attacked. And meanwhile it requires a constant source of power or a battery!

On the other hand, imagine a camera you stick into a house plant’s pot, or stick to a wall, or set on top of the bookcase, or anything. This camera requires no more power than some light shining on it; it can recognize voice commands and analyze imagery without touching the cloud at all; it can’t really be hacked because it barely has an input at all; and its components cost maybe $10.

Only one of these things can be truly ubiquitous. Only the latter can scale to billions of devices without requiring immense investment in infrastructure.

And honestly, the latter sounds like a better bet for a ton of applications where there’s a question of privacy or latency. Would you rather have a baby monitor that streams its images to a cloud server where it’s monitored for movement? Or a baby monitor that absent an internet connection can still tell you if the kid is up and about? If they both work pretty well, the latter seems like the obvious choice. And that’s the case for numerous consumer applications.

Amazingly, the power cost of the platform isn’t anywhere near bottoming out. The FPGA used to do the computing on this demo unit isn’t particularly efficient for the processing power it provides. If they had a custom chip baked, they could get another order of magnitude or two out of it, lowering the work cost for inference to the level of microjoules. The size is more limited by the optics of the camera and the size of the antenna, which must have certain dimensions to transmit and receive radio signals.

And again, this isn’t about selling a million of these particular little widgets. As Xnor has done already with its clients, the platform and software that runs on it can be customized for individual projects or hardware. One even wanted a model to run on MIPS — so now it does.

By drastically lowering the power and space required to run a self-contained inference engine, entirely new product categories can be created. Will they be creepy? Probably. But at least they won’t have to phone home.

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