Tag Archives: week

#435512 Russian Humanoid Robot to Pilot Soyuz ...

Skybot F-850 will spend a week on the ISS charming astronauts with its sense of humor Continue reading

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#435505 This Week’s Awesome Stories From ...

AUGMENTED REALITY
This Is the Computer You’ll Wear on Your Face in 10 Years
Mark Sullivan | Fast Company
“[Snap’s new Spectacles 3] foreshadow a device that many of us may wear as our primary personal computing device in about 10 years. Based on what I’ve learned by talking AR with technologists in companies big and small, here is what such a device might look like and do.”

ROBOTICS
These Robo-Shorts Are the Precursor to a True Robotic Exoskeleton
Devin Coldewey | TechCrunch
“The whole idea, then, is to leave behind the idea of an exosuit as a big mechanical thing for heavy industry or work, and bring in the idea that one could help an elderly person stand up from a chair, or someone recovering from an accident walk farther without fatigue.”

ENVIRONMENT
Artificial Tree Promises to Suck Up as Much Air Pollution as a Small Forest
Luke Dormehl | Digital Trends
“The company has developed an artificial tree that it claims is capable of sucking up the equivalent amount of air pollution as 368 living trees. That’s not only a saving on growing time, but also on the space needed to accommodate them.”

FUTURE
The Anthropocene Is a Joke
Peter Brannen | The Atlantic
“Unless we fast learn how to endure on this planet, and on a scale far beyond anything we’ve yet proved ourselves capable of, the detritus of civilization will be quickly devoured by the maw of deep time.”

ARTIFICIAL INTELLIGENCE
DeepMind’s Losses and the Future of Artificial Intelligence
Gary Marcus | Wired
“Still, the rising magnitude of DeepMind’s losses is worth considering: $154 million in 2016, $341 million in 2017, $572 million in 2018. In my view, there are three central questions: Is DeepMind on the right track scientifically? Are investments of this magnitude sound from Alphabet’s perspective? And how will the losses affect AI in general?”

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#435494 Driverless Electric Trucks Are Coming, ...

Self-driving and electric cars just don’t stop making headlines lately. Amazon invested in self-driving startup Aurora earlier this year. Waymo, Daimler, GM, along with startups like Zoox, have all launched or are planning to launch driverless taxis, many of them all-electric. People are even yanking driverless cars from their timeless natural habitat—roads—to try to teach them to navigate forests and deserts.

The future of driving, it would appear, is upon us.

But an equally important vehicle that often gets left out of the conversation is trucks; their relevance to our day-to-day lives may not be as visible as that of cars, but their impact is more profound than most of us realize.

Two recent developments in trucking point to a future of self-driving, electric semis hauling goods across the country, and likely doing so more quickly, cheaply, and safely than trucks do today.

Self-Driving in Texas
Last week, Kodiak Robotics announced it’s beginning its first commercial deliveries using self-driving trucks on a route from Dallas to Houston. The two cities sit about 240 miles apart, connected primarily by interstate 45. Kodiak is aiming to expand its reach far beyond the heart of Texas (if Dallas and Houston can be considered the heart, that is) to the state’s most far-flung cities, including El Paso to the west and Laredo to the south.

If self-driving trucks are going to be constrained to staying within state lines (and given that the laws regulating them differ by state, they will be for the foreseeable future), Texas is a pretty ideal option. It’s huge (thousands of miles of highway run both east-west and north-south), it’s warm (better than cold for driverless tech components like sensors), its proximity to Mexico means constant movement of both raw materials and manufactured goods (basically, you can’t have too many trucks in Texas), and most crucially, it’s lax on laws (driverless vehicles have been permitted there since 2017).

Spoiler, though—the trucks won’t be fully unmanned. They’ll have safety drivers to guide them onto and off of the highway, and to be there in case of any unexpected glitches.

California Goes (Even More) Electric
According to some top executives in the rideshare industry, automation is just one key component of the future of driving. Another is electricity replacing gas, and it’s not just carmakers that are plugging into the trend.

This week, Daimler Trucks North America announced completion of its first electric semis for customers Penske and NFI, to be used in the companies’ southern California operations. Scheduled to start operating later this month, the trucks will essentially be guinea pigs for testing integration of electric trucks into large-scale fleets; intel gleaned from the trucks’ performance will impact the design of later models.

Design-wise, the trucks aren’t much different from any other semi you’ve seen lumbering down the highway recently. Their range is about 250 miles—not bad if you think about how much more weight a semi is pulling than a passenger sedan—and they’ve been dubbed eCascadia, an electrified version of Freightliner’s heavy-duty Cascadia truck.

Batteries have a long way to go before they can store enough energy to make electric trucks truly viable (not to mention setting up a national charging infrastructure), but Daimler’s announcement is an important step towards an electrically-driven future.

Keep on Truckin’
Obviously, it’s more exciting to think about hailing one of those cute little Waymo cars with no steering wheel to shuttle you across town than it is to think about that 12-pack of toilet paper you ordered on Amazon cruising down the highway in a semi while the safety driver takes a snooze. But pushing driverless and electric tech in the trucking industry makes sense for a few big reasons.

Trucks mostly run long routes on interstate highways—with no pedestrians, stoplights, or other city-street obstacles to contend with, highway driving is much easier to automate. What glitches there are to be smoothed out may as well be smoothed out with cargo on board rather than people. And though you wouldn’t know it amid the frantic shouts of ‘a robot could take your job!’, the US is actually in the midst of a massive shortage of truck drivers—60,000 short as of earlier this year, to be exact.

As Todd Spencer, president of the Owner-Operator Independent Drivers Association, put it, “Trucking is an absolutely essential, critical industry to the nation, to everybody in it.” Alas, trucks get far less love than cars, but come on—probably 90 percent of the things you ate, bought, or used today were at some point moved by a truck.

Adding driverless and electric tech into that equation, then, should yield positive outcomes on all sides, whether we’re talking about cheaper 12-packs of toilet paper, fewer traffic fatalities due to human error, a less-strained labor force, a stronger economy… or something pretty cool to see as you cruise down the highway in your (driverless, electric, futuristic) car.

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#435474 Watch China’s New Hybrid AI Chip Power ...

When I lived in Beijing back in the 90s, a man walking his bike was nothing to look at. But today, I did a serious double-take at a video of a bike walking his man.

No kidding.

The bike itself looks overloaded but otherwise completely normal. Underneath its simplicity, however, is a hybrid computer chip that combines brain-inspired circuits with machine learning processes into a computing behemoth. Thanks to its smart chip, the bike self-balances as it gingerly rolls down a paved track before smoothly gaining speed into a jogging pace while navigating dexterously around obstacles. It can even respond to simple voice commands such as “speed up,” “left,” or “straight.”

Far from a circus trick, the bike is a real-world demo of the AI community’s latest attempt at fashioning specialized hardware to keep up with the challenges of machine learning algorithms. The Tianjic (天机*) chip isn’t just your standard neuromorphic chip. Rather, it has the architecture of a brain-like chip, but can also run deep learning algorithms—a match made in heaven that basically mashes together neuro-inspired hardware and software.

The study shows that China is readily nipping at the heels of Google, Facebook, NVIDIA, and other tech behemoths investing in developing new AI chip designs—hell, with billions in government investment it may have already had a head start. A sweeping AI plan from 2017 looks to catch up with the US on AI technology and application by 2020. By 2030, China’s aiming to be the global leader—and a champion for building general AI that matches humans in intellectual competence.

The country’s ambition is reflected in the team’s parting words.

“Our study is expected to stimulate AGI [artificial general intelligence] development by paving the way to more generalized hardware platforms,” said the authors, led by Dr. Luping Shi at Tsinghua University.

A Hardware Conundrum
Shi’s autonomous bike isn’t the first robotic two-wheeler. Back in 2015, the famed research nonprofit SRI International in Menlo Park, California teamed up with Yamaha to engineer MOTOBOT, a humanoid robot capable of driving a motorcycle. Powered by state-of-the-art robotic hardware and machine learning, MOTOBOT eventually raced MotoGPTM world champion Valentino Rossi in a nail-biting match-off.

However, the technological core of MOTOBOT and Shi’s bike vastly differ, and that difference reflects two pathways towards more powerful AI. One, exemplified by MOTOBOT, is software—developing brain-like algorithms with increasingly efficient architecture, efficacy, and speed. That sounds great, but deep neural nets demand so many computational resources that general-purpose chips can’t keep up.

As Shi told China Science Daily: “CPUs and other chips are driven by miniaturization technologies based on physics. Transistors might shrink to nanoscale-level in 10, 20 years. But what then?” As more transistors are squeezed onto these chips, efficient cooling becomes a limiting factor in computational speed. Tax them too much, and they melt.

For AI processes to continue, we need better hardware. An increasingly popular idea is to build neuromorphic chips, which resemble the brain from the ground up. IBM’s TrueNorth, for example, contains a massively parallel architecture nothing like the traditional Von Neumann structure of classic CPUs and GPUs. Similar to biological brains, TrueNorth’s memory is stored within “synapses” between physical “neurons” etched onto the chip, which dramatically cuts down on energy consumption.

But even these chips are limited. Because computation is tethered to hardware architecture, most chips resemble just one specific type of brain-inspired network called spiking neural networks (SNNs). Without doubt, neuromorphic chips are highly efficient setups with dynamics similar to biological networks. They also don’t play nicely with deep learning and other software-based AI.

Brain-AI Hybrid Core
Shi’s new Tianjic chip brought the two incompatibilities together onto a single piece of brainy hardware.

First was to bridge the deep learning and SNN divide. The two have very different computation philosophies and memory organizations, the team said. The biggest difference, however, is that artificial neural networks transform multidimensional data—image pixels, for example—into a single, continuous, multi-bit 0 and 1 stream. In contrast, neurons in SNNs activate using something called “binary spikes” that code for specific activation events in time.

Confused? Yeah, it’s hard to wrap my head around it too. That’s because SNNs act very similarly to our neural networks and nothing like computers. A particular neuron needs to generate an electrical signal (a “spike”) large enough to transfer down to the next one; little blips in signals don’t count. The way they transmit data also heavily depends on how they’re connected, or the network topology. The takeaway: SNNs work pretty differently than deep learning.

Shi’s team first recreated this firing quirk in the language of computers—0s and 1s—so that the coding mechanism would become compatible with deep learning algorithms. They then carefully aligned the step-by-step building blocks of the two models, which allowed them to tease out similarities into a common ground to further build on. “On the basis of this unified abstraction, we built a cross-paradigm neuron scheme,” they said.

In general, the design allowed both computational approaches to share the synapses, where neurons connect and store data, and the dendrites, the outgoing branches of the neurons. In contrast, the neuron body, where signals integrate, was left reconfigurable for each type of computation, as were the input branches. Each building block was combined into a single unified functional core (FCore), which acts like a deep learning/SNN converter depending on its specific setup. Translation: the chip can do both types of previously incompatible computation.

The Chip
Using nanoscale fabrication, the team arranged 156 FCores, containing roughly 40,000 neurons and 10 million synapses, onto a chip less than a fifth of an inch in length and width. Initial tests showcased the chip’s versatility, in that it can run both SNNs and deep learning algorithms such as the popular convolutional neural network (CNNs) often used in machine vision.

Compared to IBM TrueNorth, the density of Tianjic’s cores increased by 20 percent, speeding up performance ten times and increasing bandwidth at least 100-fold, the team said. When pitted against GPUs, the current hardware darling of machine learning, the chip increased processing throughput up to 100 times, while using just a sliver (1/10,000) of energy.

Although these stats are great, real-life performance is even better as a demo. Here’s where the authors gave their Tianjic brain a body. The team combined one chip with multiple specialized networks to process vision, balance, voice commands, and decision-making in real time. Object detection and target tracking, for example, relied on a deep neural net CNN, whereas voice commands and balance data were recognized using an SNN. The inputs were then integrated inside a neural state machine, which churned out decisions to downstream output modules—for example, controlling the handle bar to turn left.

Thanks to the chip’s brain-like architecture and bilingual ability, Tianjic “allowed all of the neural network models to operate in parallel and realized seamless communication across the models,” the team said. The result is an autonomous bike that rolls after its human, balances across speed bumps, avoids crashing into roadblocks, and answers to voice commands.

General AI?
“It’s a wonderful demonstration and quite impressive,” said the editorial team at Nature, which published the study on its cover last week.

However, they cautioned, when comparing Tianjic with state-of-the-art chips designed for a single problem toe-to-toe on that particular problem, Tianjic falls behind. But building these jack-of-all-trades hybrid chips is definitely worth the effort. Compared to today’s limited AI, what people really want is artificial general intelligence, which will require new architectures that aren’t designed to solve one particular problem.

Until people start to explore, innovate, and play around with different designs, it’s not clear how we can further progress in the pursuit of general AI. A self-driving bike might not be much to look at, but its hybrid brain is a pretty neat place to start.

*The name, in Chinese, means “heavenly machine,” “unknowable mystery of nature,” or “confidentiality.” Go figure.

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#435462 Where Death Ends and Cyborgs Begin, With ...

Transhumanism is a growing movement but also one of the most controversial. Though there are many varying offshoots within the movement, the general core idea is the same: evolve and enhance human beings by integrating biology with technology.

We recently sat down with one of the most influential and vocal transhumanists, author and futurist Zoltan Istvan, on the latest episode of Singularity University Radio’s podcast series: The Feedback Loop, to discuss his ideas on technological implants, religion, transhumanism, and death.

Although Zoltan’s origin story is rooted deeply in his time as a reporter for National Geographic, much of his rise to prominence has been a result of his contributions to a variety of media outlets, including an appearance on the Joe Rogan podcast. Additionally, many of you may know him from his novel, The Transhumanist Wager, and his 2016 presidential campaign, where he drove around the United States in a bus that had been remodeled into the shape of a coffin.

Although Zoltan had no illusions about actually winning the presidency, he had hoped that the “immortality bus” and his campaign might help inject more science, technology, and longevity research into the political discourse, or at the very least spark a more serious conversation around the future of our species.

Only time will tell if his efforts paid off, but in the meantime, you can hear Zoltan discuss religion, transhumanism, implants, the existential motivation of death, and the need for new governmental policies in Episode 7 of The Feedback Loop. To listen in each week you can find us on your favorite podcasting platforms, such as Spotify, Apple, or Google, and you can find links to other podcasting platforms and Singularity Hub’s text-to-speech articles here. You can also find our past episodes with other thought leaders like Douglas Rushkoff and Annaka Harris below.

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