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Barnes & Noble hopes book lovers give Nook another look with new $50 tablet

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Barnes & Noble Nook 7-inch tablet

With its Kindle Fire 7, Amazon has created a cheap entryway into its ecosystem of Kindle e-books and Prime shopping. For $50 — or sometimes as low as $30 — the Fire is as much a marketing vehicle as it is a tablet, made all the more transparent by the company tacking on “special offers” as a standard feature unless you pay more to remove them.

It’s a strategy that’s worked extremely well, with book-selling rival Barnes & Noble continually attempting to reproduce it with its Nook devices. Unfortunately for B&N, Nook tablets have never generated the heat of the Fire, leading to all kinds of issues for the company. Nonetheless, the bookseller has kept the Nooks coming, launching its first $49.99 Fire competitor a couple of years ago, and introducing a new version yesterday.

Like the Fire 7, the latest Nook is a 7-inch Android slate with 1,024×600 IPS display, 7 hours of battery life, and easy access to Barnes & Noble’s e-book offerings. It offers two advantages over its Amazon competition: twice the built-in storage for the price (16GB versus 8GB) and no built-in ads. To get an equivalent Fire 7 version would set you back $84.99.

On the other hand, the 7-inch Nook obviously doesn’t have Amazon’s Alexa virtual assistant enabled as the latest Fire tablets do. It also doesn’t offer the additional color choices of the Fire 7, though hardcore book readers might not be concerned with carrying around a canary yellow tablet.

As proven time and time again, the Nook is never going to be a major player in the tablet market, which has already peaked anyway. But for those who are looking for an alternative to Amazon’s dominance, Barnes & Noble hopes they (finally) take a long look at the Nook as the device to read their e-books.

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Programming a robot to teach itself how to move

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Enlarge / The robotic train.

Oliveri et. al.

One of the most impressive developments in recent years has been the production of AI systems that can teach themselves to master the rules of a larger system. Notable successes have included experiments with chess and Starcraft. Given that self-teaching capability, it’s tempting to think that computer-controlled systems should be able to teach themselves everything they need to know to operate. Obviously, for a complex system like a self-driving car, we’re not there yet. But it should be much easier with a simpler system, right?

Maybe not. A group of researchers in Amsterdam attempted to take a very simple mobile robot and create a system that would learn to optimize its movement through a learn-by-doing process. While the system the researchers developed was flexible and could be effective, it ran into trouble due to some basic features of the real world, like friction.

Roving robots

The robots in the study were incredibly simple and were formed from a varying number of identical units. Each had an on-board controller, battery, and motion sensor. A pump controlled a piece of inflatable tubing that connected a unit to a neighboring unit. When inflated, the tubing generated a force that pushed the two units apart. When deflated, the tubing would pull the units back together.

Linking these units together created a self-propelled train. Given the proper series of inflation and deflation, individual units could drag and push each other in a coordinated manner, providing a directional movement that pushed the system along like an inchworm. It would be relatively simple to figure out the optimal series of commands sent to the pump that controls the inflation—simple, but not especially interesting. So the researchers behind the new work decided to see if the system could optimize its own movement.

Each unit was allowed to act independently and was given a simple set of rules. Inflation/deflation was set to cycle every two seconds, with the only adjustable parameter being when, within that 2-second window, the pump would turn on (it would stay on for less than a second). Each unit in the chain would choose a start time at random, use it for a few cycles, and then use the system’s on-board sensor to determine how far the robot moved. The start time was chosen randomly during the learning period, and a refinement period followed, during which areas around the best-performing times were sampled.

Critically, each unit in the chain operated completely independently, without knowing what the other units were up to. The coordination needed for forward motion emerged spontaneously.

The researchers started by linking two robots and an inert block into a train and placing the system on a circular track. It only took about 80 seconds for some of the trains to reach the maximum speed possible, a stately pace of just over two millimeters per second. There’s no way for this hardware to go faster, as confirmed by simulations in a model system.

Not so fast

But problems were immediately apparent. Some of the systems got stuck in a local minimum, optimizing a speed that was only a quarter that of the maximum possible. Things went poorly in a different way when the team added a third robot to the train.

Here again, the system took only a few minutes to approach the maximum speed seen in simulations. But once they reached that speed, most systems seemed to start slowing down. That shouldn’t be possible, as the units always saved the cycle start time associated with the maximum velocity they reached. Since they should never intentionally choose a lower velocity, there’s no reason they should slow down, right?

Fortunately, someone on the team noticed that the systems weren’t experiencing a uniform slowdown. Instead, they came to a near-halt at specific locations on the track, suggesting that they were running into issues with friction at those points. Even though the robots kept performing the actions associated with the maximum speed elsewhere on the track, they were doing so in a location where a different series of actions might power through the friction more effectively.

To fix this issue, the researchers did some reprogramming. Originally, the system just looked for the maximum velocity and stored that and the inflation cycle start time associated with it. After the switch, the system always saved the most recent velocity but only updated the start time if the stored velocity was slower than the more recent one. If the system hit a rough spot and slowed down dramatically, it could find an optimal means to power through and then re-optimize for the optimum speed afterward.

This adjustment got the four-car system to move at an average speed of two millimeters per second. Not quite as good as the three-car train, but quite close to it.

More twists

The misadventures between expectations and reality did not end there. To test whether the system could learn to recover from failure, the researchers blocked the release valve in one of the units, forcing it into an always-inflated state. The algorithm re-optimized, but the researchers found that it worked even better when the pump still turned on and off, even if the pump wasn’t pushing any air. Apparently, the vibrations helped limit the friction that might otherwise bog the whole system down.

The refinement system, which tried start times close to the maximum, also turned out to be problematic once a train got long enough. With a seven-car example, the system would regularly reach the maximum speed but quickly slow back down. Apparently, the slight variations tested during refinement could be tolerated when a train was small, but they put too many cars out of sync once the train got long enough.

Still, the overall system was pretty effective, even if used on a simple system. It took two simple properties and turned them into a self-learning system that could respond to environmental changes like friction. The system was scalable in that it worked well for systems with a variety of train lengths. And it was robust to damage, such as when the researchers blocked a valve. In a different experiment, the researchers cut the train in half, and both halves re-optimized their speeds.

While simple, the system provides some insights into how we might think about self-teaching systems. And the experiment reminds us that the real world will throw even the best self-teaching system a few curves.

PNAS, 2021. DOI: 10.1073/pnas.2017015118  (About DOIs).

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A new book, Amazon Unbound, reveals Jeff Bezos’ envy of SpaceX

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Enlarge / Jeff Bezos announces Blue Moon, a lunar landing vehicle for the Moon, during a Blue Origin event in Washington, DC, May 9, 2019.

By as early as the fall of 2016, Amazon founder Jeff Bezos had already started to worry deeply about the progress—or lack thereof—being made by his rocket company, Blue Origin.

Although the business had begun to successfully launch its suborbital vehicle, New Shepard, Bezos watched with increasing envy as SpaceX landed its much larger Falcon 9 rocket on ocean-based drone ships. He saw, too, this surging new-space competitor winning launch contract after contract from NASA and the US Department of Defense.

And so, in response, Bezos invited a succession of executives from Blue Origin to his office in Seattle for one-on-one lunches. During these meetings, the executives complained about poor internal communication, long meetings, and questionable spending decisions. One engineer described the company as a Potemkin village—with a dysfunctional culture concealed beneath an industrious façade.

This anecdote is recounted in Amazon Unbound, a new book about the rise of Bezos and Amazon over the last decade. Authored by Brad Stone, the book is being published today, and much of the book deals with Bezos’ much more valuable retail business. But there is a chapter devoted to Blue Origin that reveals a business in distress.

After the fall 2016 meetings, Bezos informed company President Rob Meyerson that he would hire a chief executive officer of Blue Origin for the first time. According to Stone’s book, this process included an inquiry to SpaceX’s president and chief operating officer, Gwynne Shotwell. Shotwell, who had worked for SpaceX almost from the beginning of its founding in 2002, quickly turned down the opportunity. (A source confirmed this to Ars).

Following a yearlong search, Bezos selected Bob Smith, a senior manager at Honeywell Aerospace. Smith was hired to lead Blue Origin through a transition from its startup phase, with just a few hundred employees, to become a major player in the space business. Most of all, Bezos wanted to start winning government contracts like SpaceX.

ULA dispute

The book also delves into the 2014 decision by United Launch Alliance to purchase BE-4 rocket engines from Blue Origin for its Vulcan rocket. Significant fallout ensued a few years later when Blue Origin announced it would build the large New Glenn rocket that would compete with Vulcan.

“Executives from the two companies stopped talking; tensions were so high that they walked past one another in the halls of the annual Space Symposium that year without acknowledging one another,” Stone writes. “Blue later disputed the notion that its execs stopped talking to counterparts at ULA. Nevertheless, the story ULA execs eventually heard from employees at Blue, Sowers said, was that Bezos was frustrated hat the government was funding Elon Musk’s space dreams and wanted to get in on the action.”

At the time, Bezos was telling colleagues that he wanted to “get paid to practice” with launching and landing the New Glenn rocket.

As the book makes clear, in seeking to compete with SpaceX, Bezos made a mistake with the hiring of Smith as CEO. In filling out his leadership team, Smith brought in people from companies not known for disruption but rather traditional space practices. Many of his senior hires came from Raytheon, Boeing, Lockheed Martin, Northrop Grumman, the aerospace division of Rolls-Royce, and other legacy companies. These leaders, alongside Smith, built a culture of caution rather than deliberate risk taking in order to move more quickly.

Partly because of this slow development pace, Blue Origin has in some ways become even less competitive with SpaceX since Bezos’ meetings in fall 2016. At the time both companies, led by billionaires, seemed on the cusp of a great space race. But whereas SpaceX has launched 100 rockets to orbit since then, more than 1,500 of its own satellites, and several crews of NASA astronauts, Blue Origin has only flown New Shepard about a dozen times, without any people on board. (A first crewed flight is likely to finally occur in July).

And what about those government contracts? Blue Origin has been largely shut out. When it came to the latest round of national security launch contracts, United Launch Alliance and SpaceX won the business, with Blue left on the sidelines. And last month, a Blue Origin-led bid to land humans on the Moon for NASA lost out to SpaceX for a high-profile and lucrative contract.

Bezos has also had to set aside some of his personal ambitions with New Glenn, because the oft-delayed booster will not launch any time soon. Amazon recently announced that it will turn to United Launch Alliance for the first nine launches of its Project Kuiper satellite Internet project.

It’s a shame that Amazon Unbound does not bring the Blue Origin story up to the present day. I would be interested to know which Blue Origin executives are lunching with Bezos now and what they are saying. Even more intriguingly, it would be fun to know what Bezos is saying to them about the rocket company’s ongoing troubles.

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FDA authorizes Pfizer’s COVID-19 vaccine for 12- to 15-year-olds

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Enlarge / An illustration picture shows vials with COVID-19 Vaccine stickers attached, with the logo of US pharmaceutical company Pfizer, on November 17, 2020.

The US Food and Drug Administration has authorized the use of the Pfizer-BioNTech COVID-19 vaccine in adolescents between the ages of 12 to 15, the agency announced Monday evening.

In the announcement, acting FDA Commissioner Janet Woodcock called the authorization “a significant step in the fight against the COVID-19 pandemic” that will bring the country “closer to returning to a sense of normalcy and to ending the pandemic.”

Peter Marks, director of the FDA’s Center for Biologics Evaluation and Research, echoed that sentiment. He called the ability to vaccinate children and teens “a critical step” in the fight against COVID-19.

Both Marks and Woodcock emphasized the agency’s rigorous data review that led to the authorization.

“With science guiding our evaluation and decision-making process, the FDA can assure the public and medical community that the available data meet our rigorous standards to support the emergency use of this vaccine in the adolescent population 12 years of age and older,” Marks said.

The authorization of the Pfizer-BioNTech vaccine for the adolescent group was widely expected. It follows an announcement from the two companies on March 31 which declared that the vaccine completely protected 12- to 15-year-olds against COVID-19 in a small Phase III clinical trial involving 2,260-adolescents.

Trial data

In the trial, 1,131 adolescents received the vaccine while the other 1,129 received a placebo. The FDA focused on those who had no evidence of being infected by the pandemic coronavirus prior to the trial, leaving the agency with 1,005 vaccinated adolescents and 978 adolescents given a placebo. The FDA reported 16 cases in the trial, all of them in the placebo group. “The vaccine was 100% effective in preventing COVID-19,” the agency announced. Moreover, in a smaller sampling, those in the vaccinated group appeared to produce neutralizing antibodies at higher levels than those seen earlier in people ages 16 to 25, Pfizer noted in March.

The vaccine also appeared to be tolerated by the adolescents. The most commonly reported side effects included pain at the injection site, tiredness, headache, chills, muscle pain, fever and joint pain, all of which tended to occur within one to three days after vaccination.

Like in older age groups, the FDA says that people with a history of severe allergic reactions, including anaphylaxis, should not get the vaccine.

Now that the FDA has granted authorization, a committee of independent advisors for the Centers for Disease Control and Prevention will review the data on the vaccine in this age group and vote on policy recommendations for use. The committee—the Advisory Committee on Immunization Practices—has already set a meeting for Wednesday, May 12, to vote on their recommendations. If the CDC accepts the committee’s recommendations—which it likely will—vaccinations could become available for adolescents as early as Thursday.

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