dec 2017 / greg goebel / follow "gvgoebel" on twitter

* This weblog provides an "online notebook" to provide comments on current events, interesting items I run across, and the occasional musing. It promotes no particular ideology. Remarks may be left on the site comment board; all sensible feedback is welcome.

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* LATIN AMERICA LOVES SKYRIDES: The use of aerial cable cars as mass-transit systems was discussed here in 2013. As discussed by an article from THE ECONOMIST, ("Subways In The sky", 26 October 2017), they're catching on big-time in Latin America.

Welcome to Ecatepec, a poor suburb of Mexico City. The citizens used to take the bus betweenfrom San Andres de La Canada, at the top of the hill, and Santa Clara Coatitla at the bottom; the trip took 80 minutes one way. Now they have Mexicable, an aerial cable-car line 4.9 kilometers (3 miles) long, to make the trip. Its 185 cars haul 18,000 people a day. The line makes five stops, taking less than 20 minutes to get from one end of the line to the other. A passenger named Nelly Hernandez, riding with her delighted little girl, says Mexicable is far superior to the bus, that the cable car ride is "super-quick and much less stressful,"

Aerial tramways do well in high-density urban areas; they are particularly attractive for the mountainous cities found in many locales of Latin America. Cable-car lines are relatively inexpensive and quick to build, and not so constrained by right-of-way issues because they have a small ground footprint -- using skyways, not roadways. The pioneer in Latin American aerial cable-car development was Medellin, the second city of Columbia, the exercise being an outgrowth of Colombia's long civil war. The countryside was at the mercy of FARC insurgents, so refugees crowded into Medellin's hillside districts. They overstressed the road networks; an aerial cable-car system seemed to be the most cost-effective and practical solution, with the first lines starting up in 2004.

Medellin aerial tramway

The idea caught on, with systems then set up in Cali, Colombia; Caracas, Venezuela; Rio de Janeiro, Brazil; Mexico City; and La Paz, Bolivia. The La Paz system is the highest and longest in the world. Aerial tramways are popular with the people not only because they are convenient, but because they are subsidized by governments. Mexicable charges seven pesos, about 37 cents, which is half the break-even price. The fact that there's a bit of fun and style to them helps as well. Their attractiveness isn't missed by politicians, who find the speed with which an aerial cable car line can be erected allowing them to be around to cut the opening ribbon.

It is still uncertain if aerial cable car systems are honestly cost-effective. A study of Medellin's system showed that crime fell and jobs increased in areas served by the cable cars -- but other investments had been made in those areas during that time. They do make citizens prouder of their communities. However, Rio's cable car system ended up being a bad example, its construction involving considerable graft and the state finally abandoning it, with the cable cars halted at last notice.

Rio was an exception. Other Latin American cities are charging forward. Bogota, Colombia's capital, will open its first line in 2018; in all, about 20 projects are in planning in the region. "Ole!"

ED: I remember my 2010 trip to Seattle, which went remarkably well -- it was pleasant and sunny in the spring, when it normally drizzles -- except for dealing with the traffic, the place being in the list of the ten cities in the USA with the worst traffic. The heavy population density of Seattle is part of the problem, but it's also due to the fact that in the old core city, the streets tend to be narrow -- and even more so because it's so hilly, as well as broken up by lakes and inlets.

I keep thinking that an aerial cable car network would be a great solution there, and after reading this article I'm more certain of it. There would be a star hub downtown, at the center of a network of hubs paralleling the city's freeways. It wouldn't be very fast -- but it's not fast to drive around either, and it can be very nerve-wracking. It would be relatively cheap to implement, with the technology being well-developed and unlikely to lead to major cost overruns. In addition, although Seattle is afflicted with the "Not In My Back Yard" mindset, people tend to like aerial cable cars.

There's a security issue, but less of one than in other mass transit. There would only be a few riders per gondola, and the gondolas could have security cameras. If anybody makes trouble, there's no place to go until the car reaches a station, and the cops will be waiting. There would be more problems with troublemakers at the stations.



* RESILIENT DESIGN: As discussed by an article from Climate Desk / WIRED.com ("Museums Are Ready for the Next Natural Disaster. Are You?" by Eleanor Cummins, 31 October 2017), the writing is increasingly on the wall that climate change is going to make big trouble for everyone -- but there's been a lot of complacency. Museums, with their precious inventories, are not ignoring the potential for trouble.

When Hurricane Sandy hit the US Northeast in October 2012, the construction site of the Whitney Museum of American Art was flooded; had the facility been in service, it would have been devastated. The Rubin Museum of Art, a distance uptown, lost power. The facility had backup generators, since it needed to protect its artifacts, but they weren't intended for extended use. Executive director Patrick Sears says: "We thought if we do lose power, in the history of New York City, it would be for a day or two. No one really anticipated we could go without power for a week."

Sandy was a wake-up call. All along the Eastern Seaboard, from Miami to Manhattan, museums are taking extraordinary measures to protect their irreplaceable collections. In doing so, they are pioneering ideas and procedures for "resilient design" that may prove useful to vulnerable coastal communities everywhere.

John Stanley, chief operating officer of the Whitney, says that the museum was fortunate that Sandy happened when it did: construction was in its early phase, and the building's design could be modified to protect it from similar disasters in the future. Stanley says: "We searched the world for flood experts and engineers." The building designers got help from WTM Engineers of Hamburg, Germany, to come up with one of the most flood-hardened structures in NYC.

taking it easy at the Whitney

The Whitney is protected from any depth of storm surge that could be expected for the time being, thanks to its raised elevation and waterproofing via carefully selected materials. There's also a 150-meter (500-foot) emergency wall that can be put into place in seven hours, and a loading door that can withstand a bus thrown at it by a surge. Providing the storm reinforcement only added $10 million USD to a total final cost of $220 million USD. The Whitney hasn't been tested yet, but Stanley feels confident it will handle the next Big One unscathed.

When Hurricane Irma struck Saint Petersburg, Florida, in 2017, the Salvador Dali Museum was ready for trouble. The museum houses the biggest collection of Dali art in the world; loss of the collection to a storm would be a disaster. The Dalí is protected by walls 45 centimeters (18 inches) thick, built to stand up to a Category 5 storm, and fortified glass, which deal with Category 3 winds.

* The architectural features that protect the Whitney, Dalí, and their kin have begun to proliferate, thanks to consumer demand and new municipal standards. One of the prominent examples is the new residential American Copper Buildings of NYC, on Manhattan's eastern shore, on the East River near the United Nations complex.

There's a long waiting list of people wanting to take up residence in the apartment towers, mainly because the $650 million USD buildings, which were started before Sandy hit, meet or exceed the city's latest resilient design codes. The fact that the copper-clad buildings, offering 760 apartments, are stylish is another plus.

Connected by a three-story skybridge, the two towers have an elevated lobby, allowing them to stay above the storm surge level. They also have rooftop backup generators that can drive the elevators, a fridge, and one electrical outlet for a week. To be sure, if a big storm is coming, all the occupants will be ordered to evacuate, but they'll have something to come back to when the storm passes. Considering that a studio apartment in the towers goes for like $4,000 USD a month rent, the building's owners want to give potential occupants every reassurance that they're safe.

JDS Development Group, which owns American Copper Buildings, is one of the leaders in the movement towards resilient design; the movement is catching on. After Sandy, the Mayor's Office of Recovery & Resiliency set about studying the metro area's weather and climate vulnerabilities, and crafting solutions. The city is now implementing new building codes, with all new construction now held to these updated resiliency standards.

* That's great for new construction; more problematic for existing construction. It may be difficult to do much to protect old structures -- two-thirds of NYC's buildings were put up before 1960 -- and to the extent it can be done, the incremental expense is much greater than it would be if the structure were resilient from the outset. Raising an existing single-family home on stilts, as many thousands of East Coasters have done since Sandy, can cost more than $100,000 USD, on a house that's maybe only worth $400,000. Costs of local and Federal support programs have ballooned.

Fortunately, there's more to resilience than altering structures. The Rubin did not have the funds to fully update the building. Some money was put into improvements -- such as a stronger, waterproof roof -- but the museum has primarily focused on better training and communications. Patrick Sears says: "We're thinking about manual ways, simple ways, things you can buy on Amazon." One of his favorite investments is a crank-driven cellphone charger that doesn't require an electricity source.

With a little homework, anyone can devise a sensible disaster plan -- but but a 2015 Federal Emergency Management Agency survey showed only 39% of Americans have their own plan in place. The Rubin's disaster plan, in contrast, is 153 pages in length. That plan is focused on protecting the museum's collection; museums are not in a position to act as public shelters in an emergency. Making a city resilient against natural disasters, as they become more common, is going to require far-sighted efforts by city planners.



* UNDERSTANDING AI (3): As discussed by an article from SCIENCEMAG.org ("How AI Detectives Are Cracking Open The Black Box Of Deep Learning" by Paul Voosen, 6 July 2017), big tech firms are now very interested in artificial intelligence, pumping vast sums into research on AI.

Welcome to Uber's headquarters in San Francisco, California. There Jason Yosinski, an Uber researcher, probes into a deep neural network (DNN), an electronic system modeled on the brain. This AI was trained, using a vast store of labeled images to recognize a wide range of objects, from zebras and fire trucks to seat belts. With the DNN filtering an image of Yosinski and the author from a webcam, Yosinski is able to find a neuron in the network that apparently had learned to recognize the outlines of faces. Yosinski says: "This responds to your face and my face. It responds to different size faces, different color faces."

The strange thing is that nobody ever tried to teach the DNN to recognize faces. How it managed to do so is not clear. According to Yosinski: "We build amazing models, but we don't quite understand them. And every year, this gap is going to get a bit larger."

For decades, AI technology remained a largely academic exercise, early efforts to go commercial ending in disappointment. Now "deep learning" provided by DNNs is being put to use in one profession after another, and having a particularly profound influence in the sciences. DNNs can determine the best way to synthesize elaborate molecules, to sort out the effects of specific genes from genomes, to search images of deep space for interesting cosmic objects. However, DNNs pose a puzzle, in that nobody knows how they really work.

Sure, the architecture of a DNN is understood in detail; there's no mystery at all about how its elements work. The difficulty is that, given training from a huge stockpile of examples, there's little comprehension of how inputs get to specific outputs. Nobody has a good handle on properly sorting out exactly what the DNN is doing, as it mangles and tangles input data to get to the desired output data.

Of course, people who are using DNNs to solve particular problems may not care much about this "interpretability problem"; they know how a DNN works, it gets the results they want, and they don't care about exactly how it gets from here to there. So what if they don't know why the DNN does what it does? They run the DNN through test sets, and have confidence in it to the extent that the test sets are thorough and the DNN handles them competently. That isn't really different from any other software validation -- given elaborate software, we can only have confidence in it to the extent it's been tested thoroughly, and has been put to a lot of use.

However, those working on neural networks in both industry and academia regard the interpretability problem as a major issue. Given a bug in elaborate software, it can be traced down and fixed; given a bug in a DNN, all that can be done at present is add relevant training and hope the bug goes away. When Maya Gupta, a machine-learning researcher at Google in Mountain View CA, joined the company in 2012, she asked AI engineers about their concerns with the systems they were working on. They usually told her: "I'm not sure what it's doing. I'm not sure I can trust it."

Rich Caruana, a computer scientist at Microsoft Research in Redmond WA, had first-hand experience in that weakening of trust. In the 1990s, he was a graduate student at Carnegie Mellon University in Pittsburgh, Pennsylvania, a hotbed of AI research. There, he he joined a team trying to see whether machine learning could help with the treatment of pneumonia patients. It's usually best for them to stay at home, since they could pick up other infections in a hospital -- but some patients, particularly those with complicating factors like asthma, need to be hospitalized soonest.

Caruana ran a data set of symptoms and outcomes provided by 78 hospitals through a neural net, and it appeared to work well. However, a simpler, more transparent model using the same data suggested that asthmatic patients be sent home, which was the wrong answer. He had no way of knowing if his neural net had picked up on the same bad answer. Carauna says: "Fear of a neural net is completely justified. What really terrifies me is what else did the neural net learn that's equally wrong?"

AI geeks aren't the only ones worried about the interpretability problem. A directive issued by the European Union stated that, in 2018, companies deploying algorithms that substantially influence the public must by create "explanations" for their models' internal logic. The Defense Advanced Research Projects Agency, the Pentagon's blue-sky research office, is pumping $70 million USD into a new program named "Explainable AI", for interpreting the deep learning that flies drones and obtains intelligence through data-mining operations. [TO BE CONTINUED]



* ONCE & FUTURE EARTH (17): Following the Big Thwack, the metal core had separated from the peridotite-rich mantle, with partial meltings of the peridotite producing basalt -- forming the Earth's initial crust, and incidentally producing its early atmosphere and oceans. That initial crust trapped heat from the mantle below, resulting in melting of the bottom of the crust. This melt was affected by the presence of water, generating a new material with different properties from the peridotite from which it came -- richer in silicon, enhanced in sodium and potassium, incorporating water and dozens of trace elements.

This new material was lighter than its parent basalt -- only about 2.7 times denser than water -- and so forced its way to the surface, to become granite rock. Granite hosts four different mineral species:

This matrix is easily observed in any slab of polished granite. There are also dispersions of tiny grains of minerals, for example zircons. As noted, the zircons found in the Jack Hills deposits have been dated to over 4 billion years old; some of them incorporate quartz, a marker of granite, and so may be remnants of the oldest granite on the Earth.

Granite requires a good deal of heat to be formed, with the heat proportional to the size of the rocky world from which it arose. The smaller rocky worlds of the Solar System -- Mercury, the Moon, Mars -- couldn't produce such heat, and so they are generally lacking in granite. It played a much more significant role on Earth, creating great land masses and high mountains. Incidentally, like icebergs, most of the mass of a granitic mountain range is underground: while the peaks of the US Rocky Mountain range may exceed 4 kilometers in height, the roots of the mountains go 60 kilometers deep, or deeper.

At first, the elements of the new granite crust were small and isolated islands. How larger elements emerged is unclear: possibly asteroid impacts, much more common then, left scars that encouraged the emergence of more granite to the surface. In any case, the engine of plate tectonics then began to assemble the separate granite elements, rafting on the seafloor conveyor belts of basalt, into continents. By three billion years ago, the continents had emerged, though not in the configuration they are today.

Also by that time, single-celled life had emerged on Earth. The oldest undisputed fossils of microorganisms are about 3.4 billion years old; there are older candidates, but they remain more or less disputed. Exactly how these microorganisms arose is a matter under intense study. Given that it involves biochemistry, it's not useful to discuss it here. What can be said is that elementary building blocks of life were commonplace; and there was no shortage of sites on or under the ocean floor where volcanic venting could provide the energy to support intensive chemical activity.

Much is made today of the complexity of even the most humble single-celled organism that exists today, leading to the claim that life arising from nonlife -- "abiogenesis" -- is obviously impossible. Since the definition of "impossible" is "can't happen", and it did happen, then obviously it wasn't impossible. True, we don't have solid handle on how it did happen, but those doing research in abiogenesis are confident they are making progress towards credible theories of how life started. They do agree that a single-celled organism could not have emerged from nonlife in a single step, instead envisioning a process of "chemical evolution" -- in which there was a sequence of "proto-life" systems, the first being very inefficient, with each subsequent generation being more efficient, and devouring the generation that came before it. Different variants of proto-life may have teamed up, the whole being more than the sum of the parts. [TO BE CONTINUED]



* SCIENCE NOTES: As discussed by an article from AAAS SCIENCE Online ("Watch These Tiny Parrots Reveal How Dinosaurs May Have Learned To Fly" by Ryan Cross, 17 May 2017), there's long been a discussion among paleontologists and evolutionary biologists as to how birds acquired the trick of flying. There are many animals that can glide -- flying squirrels as the stereotypical example -- but it is uncertain that gliding, by itself, could lead to true flight. One alternative concept is that flight began as a boost to running.

Now a group of researchers has come up with a new idea, after training four Pacific parrotlets (Forpus coelestis) -- small, colorful parrots about 13 centimeters (5 inches) long -- to jump and fly for millet seed rewards. The researchers built a cage with with perches that also measured the birds' leg forces, and surrounded the cages with high-speed cameras to study the birds' wing beats as they moved between branches.

Pacific parrotlets

For short jumps, the parrotlets primarily used their legs, using the wings only for controlling touchdown. For longer jumps, they relied mostly on their wings. The researchers used the parrotlet data to build a software model to see how four feathered dinosaurs, which were obviously capable of gliding, might have obtained propulsion as well from their feathered arms. The model showed a distinction between the four:

The suggestion is that Archaeopteryx and Microraptor acquired an edge over other tree-foraging competitors by using jumping and wing flapping to minimize energy expenditure while foraging for food in their trees, hopping from branch to branch.

* As discussed by an article from NATURE.com ("Ant Colonies Flow Like Fluid To Build Tall Towers" by Laura Castells, 12 July 2017), to deal with streams or water currents, fire ants will hook together to form towers or rafts. Given the tower configuration, the question arises: how do the ants on the bottom of the tower keep from being crushed by the load of all the other ants above them? Researchers have now discovered how: the tower isn't static, with the ants circulating around in it, as if particles in a fluid, each bearing the load and yielding.

Fire ants (Solenopsis invicta) have sticky pads on their feet that help them to link to each other. Researchers had already figured out how they made rafts: the ants joined to each other at their feet to form air pockets, making up a relatively uniform matrix of such air pockets to distribute the mass of the collective. A team co-led by Craig Tovey -- a modeling mathematician at the Georgia Institute of Technology in Atlanta -- then went on to investigation how they formed into towers.

In the lab, the researchers used high-speed cameras to observe how the ants assembled around a slippery teflon rod, and tagged half the colony with a radioactive tracer to observe the movements of the ants in the tower. It turned out they use a trial-&-error method, rebuilding weaker parts of the tower that collapse until they finally have a sound structure.

Each individual ant can support three other ants; when an ant is overloaded, it lets go and drops down the tower, until it emerges from the base at the bottom. The resulting tower is a bell-shaped dynamic structure with resemblance to a fluid, with the ants ending up carrying equal loads. According to Tovey: "The ants are circulating like a water fountain, in reverse."

The dynamic nature of such ant structures is not news, but nobody had ever observed it carefully. The researchers were able to predict the shape and growth rate of the towers using mathematical models. They already knew that fire ants form rafts using "swarm intelligence", each ant following a few of simple rules on its own, with no central direction; the rules can be used as the basis of a mathematical model for the formation of the rafts. The researchers were surprised to find that the ants used the same rules for the formation of the towers. Tovey says: "The next step is to figure out how they build bridges."

* As discussed by an article from THE NEW YORK TIMES ("Ladybugs Pack Wings and Engineering Secrets in Tidy Origami Packages" by Joanna Klein, 18 May 2017), the ladybug is an endearing insect, and it also knows a few tricks. One of the most intriguing are its hind wings, which are four times its size. On landing after a flight, it folds the wings neatly and packs them away under its protective hard-shell forewings, the "elytra", which is normally decorated with polka dots. That's trickier than it sounds. Imagine trying to fold two large tents, with poles that do not detach, that are stuck to your back beneath a plastic case and you have no hands to help you. A ladybug does it many times in a day.

Kazuya Saito, an aerospace engineer at the University of Tokyo, works on deployable structures like large sails and solar power systems for spacecrafts. He decided to conduct a study on how the ladybug -- in Japanese, "tentou mushi" -- manages to pack away its wings. Saito, commented: "Ladybugs seem to be better at flying than other beetles because they repeat takeoff and landing many times in a day. I thought their wing should have excellent transformation system."

The difficulty in the study was figuring out what happened underneath the elytra. Through microsurgery, Saito and colleagues swapped out the ladybug elytra with transparent plastic replacments, then observed the transformation with a high-speed camera, supported by high-resolution X-ray images.

The study revealed that, on landing, the ladybug closes its elytra and aligns them backward. Vertical movements of the abdomen pull the wings under the elytra, with tiny structures on the elytra and abdomen helping keep the wings in place through friction. The wings fold in and over, then tuck into a Z shape. The veins on the wings, springy like a tape measure, bend into a cylindrical shape, elastic under pressure. When the ladybug wants to take off again, it pops open the elytra, and the wings spring out spontaneously.

Saito finds the process fascinating, and is impressed with its effectiveness: "The beetles can fold their wing without any mistakes from the first folding."



* CONTINUING EVOLUTION: Genomics has now become a facet of "big science", with ever more ambitious analysis efforts sorting through mountains of genomics data. As a case in point, as discussed by an article from NATURE.com ("Massive Genetic Study Shows How Humans Are Evolving" by Bruno Martin, 6 September 2017), a study of the genomes of 215,000 people gave clues as to how humans are evolving over a few generations.

The study checked US and UK databases to see which mutations were associated with different age groups. According to Hakhamanesh Mostafavi, an evolutionary biologist at Columbia University in New York City who led the study: "If a genetic variant influences survival, its frequency should change with the age of the surviving individuals."

It's simple, if cold-blooded: if people have mutations that cause them to die at relatively young ages, that mutation gets scarcer as people get older. The researchers scanned for more than 8 million common mutations, and found two that appear to become less common with age:

Of course, if the subjects with such genes died after reproductive age, there's no reason to think those genes would be less common in the next generation. However, as the researchers point out, they could only find two troublesome genes; if bad actors weren't being weeded out by natural selection, they would have expected to see many more.

That leaves the question of how the weeding was performed. The authors suggest that for men, it might be that those who live longer can have more children, but they don't believe that's the whole story. There are two other possibilities:

The researchers also discovered that certain clusters of genetic mutations -- none of which represented much of a threat by themselves, but did so as a group -- were found less often in people with long lives. That included predispositions to predispositions to asthma, high body mass index, and high cholesterol. More surprising was that sets of mutations that delay puberty and childbearing are more common in long-lived people.

According to Jonathan Pritchard -- a geneticist at Stanford University in California -- the link between longevity and late fertility has been spotted before, but those studies were confounded by the effects of wealth and education, since people with high levels of both tend to have children later in life. The genetic evidence uncovered in this study does hint to an evolutionary trade-off between fertility and longevity, a correlation that had previously examined in other animals. Pritchard commented: "To actually find this in humans is really pretty cool."



* ROBOSHUTTLES: As discussed by an article from WIRED.com ("Self-Driving Shuttle Buses Might Be the Future of Transportation" by Aarian Marshall, 10 November 2017), in early November a collaboration of organizations -- multinational transportation company Keolis, French manufacturer Navya, and the American Automobile Association -- launched a small driverless vehicle in Las Vegas, carrying eight people in a loop around the Fremont Street Entertainment District. It had an attendant to keep an eye on things.

Only hours after beginning service, it was in an accident -- because it couldn't understand clueless humans. The vehicle spotted a truck backing out of an alley and obediently stopped; there was a vehicle behind it, so it couldn't back up, and it just sat there as the truck backed into it. It might have honked a warning, but the peculiarities of the truck's movements kept the robot vehicle from recognizing it as a threat. The shuttle was back in service the next day.

It wasn't really practical transportation, mostly being a Vegas amusement. John Moreno, a spokesman for the AAA, says: "It's a fun, short experience, similar to an attraction you'd ride at a theme park."

Nonetheless, robotic shuttle vehicles are on the leading edge of autonomous vehicle technology. The Vegas robot shuttle is something of an innovation in the USA, but such vehicles are becoming established in Europe and Asia. Navya shuttles have been in operation in Switzerland and Singapore since the fall of 2016. London's Heathrow Airport has transported passengers in autonomous "pods" since 2011, while the Australian Intellibus completed a three-month pilot in 2016.

The Vegas experiment is not the only one in the USA, either. Navya started running self-driving shuttles on the University of Michigan campus before one showed up in Vegas; while another company, TransDev, is working to put its electric minibus on the streets of a planned community in Florida, and EasyMile showed off its own little transporters in Arlington, Texas, during the summer.

The companies see these experiments as not only a way to get experience, but also show off the technology to the public. Local governments tend to be enthusiastic as well, getting good press by encouraging innovative technology. However, they're more than just publicity stunts, all involved believing the technology has potential -- to provide transport on college campuses, in retirement communities, in the suburbs. Maurice Bell, Keolis North America's head of mobility, says: "Most transit authorities are looking for opportunities to answer the 'first-mile, last mile' question." -- bridging the gap between transit hubs and people's final destinations.

Navya shuttlebus

According to Susan Shaheen, a civil engineer who studies mobility innovation at UC Berkeley: "Automated shuttles have the ability to reduce operational expenditures by lowering per mile costs, reducing labor expenditures, and offering a variety of flexible and on-demand public transportation services when paired with advanced algorithms and smartphone apps."

It is doubtful that such shuttles would be useful in central urban areas; they're slow, running at about a sprint, and simply create traffic congestion. The full-size autonomous bus is better suited to the high-density urban traffic environment, consolidating a good number of passengers and fast enough to keep up with other vehicles. Automated shuttles do have a role to play in the overall transport network, however, and they will also assist in the development of technologies useful for other elements of the network. The future whole will be more than the sum of its parts.



* UNDERSTANDING AI (2): Many of the basic concepts in AI go back to the middle of the last century. In the 1950s, researchers like Frank Rosenblatt, Bernard Widrow, and Marcian Hoff came up with models, based on mathematical procedures, for how the brain's neurons got things done. However, it takes a lot of neurons to get anything particularly useful done, and the field made little practical progress for decades.

Now the neural approach underlies most of the AI activities of major tech companies, from Google and Amazon to Facebook and Microsoft. In the mid-2000s, graphics processor unit company Nvidia concluded that their chips were well-suited for running neural networks, and began making it easier to use its hardware for AI applications. With faster and more elaborate neural networks available, AI actually started to amount to something.

A neural net is not programmed as such; it is instead trained, typically being fed a set of tagged samples of what the neural net is supposed to recognize. In 2009, AI researcher Fei-Fei Li published a database named ImageNet, which contained more than 3 million images with labels of what they were about.

She thought that if these algorithms had more examples of the world to find patterns between, it could help them understand more complex ideas. She started an ImageNet competition in 2010, and by 2012 researcher Geoff Hinton used those millions of images to train a neural network to beat all other applications by more than 10% accuracy. Hinton also moved on to "deep" neural networks, with neural layers stacked on top of each other, capable of "deep learning". Today, deep neural networks are almost synonymous with AI.

The tech industry was impressed, and the AI boom began. Researchers who had been working on deep learning for decades became superstars. By 2015, Google had more than a thousand projects that involved some sort of machine learning.

Along with the boom in AI, there's been a boom in hysteria over the technology. Won't the technology keep on improving until it results in superintelligences that will overthrow us, even exterminate us? AI researchers consider such scenarios silly. Even if we were to build a general superintelligence that outstripped humans, it would have no incentive to become a threat. Humans were designed by evolution to get by in the world, and were not put together according to a formal specification; machines are designed by humans, using formal specifications, to serve humans. As Yann Lecun, head of Facebook's AI research, commented:


Behavior like becoming violent when we feel threatened, being jealous, wanting exclusive access to resources, preferring our next of kin to strangers ETC were built into us by evolution for the survival of the species. Intelligent machines will not have these basic behaviors unless we explicitly build these behaviors into them.


We're not remotely close to building an AI that could even in principle compete with a human, and there's no reason to build one that could try to. After all, a scientific pocket calculator is "superintelligent", able to perform calculations with ease that would stymie even a math savant, but we don't feel threatened by pocket calculators. As Andrew Ng, a senior AI researcher at Google, likes to say: "The reason I say that I don't worry about AI turning evil is the same reason I don't worry about overpopulation on Mars,"

That isn't saying AI poses no hazards. AI could, by data mining, undermine the privacy of citizens; keep close tabs on citizens for an authoritarian government; accumulate monopoly power unto corporations; and be corrupted by malware. There is also the subtler problem of taking the results generated by an AI system at face value, failing to realize those results may be affected by biases, possibly ones not known to the people who set up the system. AI researchers don't worry about overpopulation on Mars; they've got too much in the here and now to worry about. [TO BE CONTINUED]



* ANOTHER MONTH: In ridiculous news for last month, one Mike Hughes, a 61-year-old California limousine driver, planned to launch himself from the Mojave desert to altitude in a homebuilt rocket so he could obtain proof that the Earth is flat. The authorities, fearing the exercise might end badly, told him he couldn't fly in his rocket over public land.

There's been a resurgence in Flat Eartherism (FE) over the last few years, somewhat in harmony with the current spirit of the era. This has led to the question as to if FEs honestly believe the Earth is flat. That's naive: of course they do, people won't tilt at windmills unless they believe they're evil giants. The trick is that such folk have no interest in whether something is true or false, instead believing whatever they want. Their dishonesty is as evident as that of a deliberate liar; it's just at a deeper level.

That leads to the next question of: so why? That's not an easy question to answer, because it's trying to unravel broken thinking. There's a certain obvious conceit to it, along the lines of physics cranks who think they've refuted Einstein, even though they clearly know little or nothing about physics: "I'm smarter than EINSTEIN!"

It's a kind of showing off, an expression of defiance. Arguing with them is futile; they're passive-aggressive, they intend to provoke, they start barking contests so they can out-bark the opposition. People who care about their credibility, who want to be regarded as grown-ups, do not play such games.

* In more widespread absurdity, talk-show host Jimmy Kimmel sent a video team out on the streets of Los Angeles to ask people: "Do you think Hillary Clinton should be impeached?" A number of people bit hard on that one: "Absolutely!" "She needs to be locked up for her crimes!" BENGHAZI! EMAILS! URANIUM1! LOCK HER UP!

I have a old friend in Birmingham, Alabama, and I had to report to him that the one person who didn't bite was a Southern boy. He nibbled at the bait a bit, then said hey wait: "She's not in office!" Hillary C is last year's news; in weeks, she'll the year after last's news.

* In the current Real Fake News, US President Donald Trump went on a Far East tour. It was nothing unprecedented, with Trump's griping about unfair trade with China, South Korea, and Japan somewhat overshadowed by continued bluster over North Korean missile tests. As crises go, this one is becoming tiresome.

At home, the main issue preoccupying the White House remained the Republican push for a tax cut bill -- but there was an overriding distraction from a widespread frenzy over sexual misconduct by the great and powerful. The most relevant focus was Judge Roy Moore, running in Alabama for a US Senate seat in a special election. Moore, who is very far to the Right, once had a habit of picking up high-school girls, and it caught up with him in a big way. Nobody's betting on it, but there is a chance a Democrat might even win the election, if enough Republican Alabamans decided not to vote.

Moore's problems are only part of a huge wave of sexual misconduct accusations being thrown around. In some cases, they seem only too justified, with Hollywood producer Harvey Weinstein being pilloried for well-known habits of sexual predation -- actresses saying they had to barricade their hotel room doors to keep him out. Indeed, TV sitcoms were making jokes about Weinstein's behavior well before the current furor.

In other cases, it's not easy to see if there's substance to the accusations. It does seem that the dust-up traces back to the presidential campaign and Trump's unfortunate comments about his regard, or lack thereof, for women. The fact that he then won the election aroused considerable female anger.

Other than that, it was quiet through the month, with the GOP in the Senate carefully keeping the tax cut exercise under cover. Well for them that they should, since the general perception is that it's a tax cut for the rich, with some small sops thrown out to placate the lower orders. Worse, it promises to ramp up the Federal budget deficit ferociously. Nobody feels too confident on betting whether the bill will pass or not -- but Trump didn't help his case by insisting that the bill also kill the "individual mandate" to buy health insurance that is needed to keep ObamaCare afloat.

Since the GOP got run through the mill in trying to kill ObamaCare earlier in the year, it seems foolish to have added that provision into the tax cut bill. The only way it makes sense is on the basis that the Senate didn't want to cross Trump -- not out of fear so much, they just want to make sure that, should the bill fail, Trump can't throw all the blame on Congress: "Hey, we did what you wanted!"

Trump, after insisting on the anti-ObamaCare provision, then stated he might not insist on it after all. However, everyone's long got used to Trump's clumsy smoke-&-mirrors, and knows not to pay any attention to what he says, instead watching what he does, which is confusing enough.

Indeed, one gets so used to the nonsense the president says as to automatically tune it out. Late in the month, Trump was in prime form. First, on 27 November he called Senator Liz Warren "Pocahontas" again -- but this time, at a commemoration of Navaho code-talkers of World War II. Yeah, the reaction is: "So what else is new?" Warren's tough-minded and can give as good as she gets. However, what might be greeted with a roll of the eyes and a shrug when coming from a dim-witted relative carries a lot more significance when it comes from the President of the United States.

On 28 November, he tweeted:


Meeting with 'Chuck and Nancy' [Democratic leaders Schumer and Pelosi in the Senate and House respectively] today about keeping government open and working. Problem is they want illegal immigrants flooding into our Country unchecked, are weak on Crime and want to substantially RAISE Taxes. I don't see a deal!


To no surprise, Schumer and Pelosi responded that they weren't going to the meeting. Why should they, if Trump ruled out a deal with them? The president then petulantly sent out a photo of him sitting with a sullen demeanor in a White House meeting room, an empty chair on each side of him. On top of that, the next day Trump retweeted anti-Muslim videos produced by British trolls, leading to a protest from the British government. In response, Trump told the British government off.

Again, so what else is new? CNN's Stephen Collinson commented that such behavior raised questions about the president's competence. Questions? There is no doubt any longer that Trump is unqualified for the job. Unfortunately, given there's no prospect at present of removing him, we have to accept that he's going to be in the White House for three more years.

That hardly makes things easier to swallow, and nobody has to be Left of center to dislike the taste. Bill Kristol, a traditional moderate conservative pundit, tweeted on 21 November:


The GOP tax bill's bringing out my inner socialist. The sex scandals are bringing out my inner feminist. Donald Trump and Roy Moore are bringing out my inner liberal. WHAT IS HAPPENING?


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