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What can we learn from a new documentary on Elon Musk?

The Guardian

You could be forgiven for believing that we've already achieved the era of autonomous vehicles. Tesla, the electric car manufacturer run by Elon Musk, refers to a version of its Autopilot software as "Full Self Driving". The company released a (misleadingly edited) video of an autonomous vehicle navigating city streets, its drivers' hands on their lap – a style replicated by enthusiasts. Musk has repeatedly assured in speeches and interviews that autonomous vehicles were one to two years away – or, as he put it in 2015, a "solved problem" because "we know what to do and we'll be there in a few years." But the existing Autopilot technology has not yet realized those promises and, as a new New York Times documentary illustrates, the gap in expectation and reality has led to several deadly crashes.


Hyundai's luxury Genesis brand opens US orders for its first EV

Engadget

Hyundai's Genesis brand is now taking orders for its first electric vehicle, the GV60. The EV, which follows the G80 hybrid, starts at $58,890 and comes with three years of 30-minute charging sessions at Electrify America stations at no extra cost. US sales will be limited at the outset, however. To begin with, the GV60 will only be available for purchase at select retailers in California, Connecticut, New Jersey and New York. The EV will be available in two dual-motor trims, Advanced AWD and Performance AWD.


Insurance Companies Using AI to Build Safety Systems, Optimize Rates - AI Trends

#artificialintelligence

INSHUR is aimed at helping rideshare drivers using Uber or Lyft, and limousine drivers, to find competitive rates for auto insurance. Founded in 2016, the company is based in New York City, is backed by Munich Re Digital Partners, and launched in the UK in 2018. INSHUR has signed up over 40,000 drivers. The company supports liability and physical damage policies with minimum limits of insurance as required by the NYC Taxi & Limousine Commission (TLC) for limousines, which is also compatible with requirements for ride sharing services.


Funding obscured: The family office behind Elon Musk's $44 billion Twitter buyout

The Japan Times

The small family office that is managing the wealth of the world's richest person and is helping put together the largest-ever acquisition to be carried out by one person is shrouded in secrecy. On Monday, Musk clinched a deal to buy Twitter Inc. for $44 billion in a seminal moment for one of the world's most influential public forums. Musk -- who is also the chief executive of electric car maker Tesla Inc. and aerospace company SpaceX -- revealed in a regulatory filing last week that the social media company should reach out to its family office as a point of contact regarding his proposed acquisition. Yet little is known about the Austin, Texas-based family office that manages Musk's assets. The office is called Excession and the man who helped build it is Jared Birchall, a former Morgan Stanley banker who has advised Musk on his interactions with Wall Street for several years, according to regulatory filings and legal documents.


How the Metaverse Could Change Work

#artificialintelligence

Imagine a world where you could have a beachside conversation with your colleagues, take meeting notes while floating around a space station, or teleport from your office in London to New York, all without taking a step outside your front door. Feeling under pressure with too many meetings scheduled today? Then why not send your AI-enabled digital twin instead to take the load off your shoulders? These examples offer but a glimpse into the future vision of work promised by "the metaverse," a term originally coined by author Neal Stephenson in 1992 to describe a future world of virtual reality. While defying precise definition, the metaverse is generally regarded as a network of 3-D virtual worlds where people can interact, do business, and forge social connections through their virtual "avatars."


It's How AI is Preventing Accidents and Protecting Drivers

#artificialintelligence

While many people think that Tesla was the first car company to come up with the idea, manufacturers have actually been toying with the concept of autonomous vehicles since the 1930s. In 1939, an exhibit at the New York World's Fair called "Futurama" envisioned a world 20 years into the future in which an automated highway system would guide autonomous vehicles. As with other technological advancements, the idea of self-driving cars would have to wait until our technology had caught up to our drive for innovation. Self-driving vehicles are going to have a significant impact on all aspects of our lives. They have the potential to clean up our air (especially if the cars are electric) and make our commutes more enjoyable.


Improving short-term bike sharing demand forecast through an irregular convolutional neural network

arXiv.org Artificial Intelligence

As an important task for the management of bike sharing systems, accurate forecast of travel demand could facilitate dispatch and relocation of bicycles to improve user satisfaction. In recent years, many deep learning algorithms have been introduced to improve bicycle usage forecast. A typical practice is to integrate convolutional (CNN) and recurrent neural network (RNN) to capture spatial-temporal dependency in historical travel demand. For typical CNN, the convolution operation is conducted through a kernel that moves across a "matrix-format" city to extract features over spatially adjacent urban areas. This practice assumes that areas close to each other could provide useful information that improves prediction accuracy. However, bicycle usage in neighboring areas might not always be similar, given spatial variations in built environment characteristics and travel behavior that affect cycling activities. Yet, areas that are far apart can be relatively more similar in temporal usage patterns. To utilize the hidden linkage among these distant urban areas, the study proposes an irregular convolutional Long-Short Term Memory model (IrConv+LSTM) to improve short-term bike sharing demand forecast. The model modifies traditional CNN with irregular convolutional architecture to extract dependency among "semantic neighbors". The proposed model is evaluated with a set of benchmark models in five study sites, which include one dockless bike sharing system in Singapore, and four station-based systems in Chicago, Washington, D.C., New York, and London. We find that IrConv+LSTM outperforms other benchmark models in the five cities. The model also achieves superior performance in areas with varying levels of bicycle usage and during peak periods. The findings suggest that "thinking beyond spatial neighbors" can further improve short-term travel demand prediction of urban bike sharing systems.


Hitting the Books: What autonomous vehicles mean for tomorrow's workforce

Engadget

In the face of daily pandemic-induced upheavals, the notion of "business as usual" can often seem a quaint and distant notion to today's workforce. But even before we all got stuck in never-ending Zoom meetings, the logistics and transportation sectors (like much of America's economy) were already subtly shifting in the face of continuing advances in robotics, machine learning and autonomous navigation technologies. In their new book, The Work of the Future: Building Better Jobs in an Age of Intelligent Machines, an interdisciplinary team of MIT researchers (leveraging insights gleaned from MIT's multi-year Task Force on the Work of the Future) exam the disconnect between improvements in technology and the benefits derived by workers from those advancements. It's not that America is rife with "low-skill workers" as New York's new mayor seems to believe, but rather that the nation is saturated with low-wage, low-quality positions -- positions which are excluded from the ever-increasing perks and paychecks enjoyed by knowledge workers. The excerpt below examines the impact vehicular automation will have on rank and file employees, rather than the Musks of the world.


New York Times ad warns against Tesla's "Full Self-Driving" – TechCrunch

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A full page advertisement in Sunday's New York Times took aim at Tesla's "Full Self-Driving" software, calling it "the worst software ever sold by a Fortune 500 company" and offering $10,000, the same price as the software itself to the first person who could name "another commercial product from a Fortune 500 company that has a critical malfunction every 8 minutes." The ad was taken out by The Dawn Project, a recently founded organization aiming to ban unsafe software from safety critical systems that can be targeted by military-style hackers, as part of a campaign to remove Tesla Full Self-Driving (FSD) from public roads until it has "1,000 times fewer critical malfunctions." The founder of the advocacy group, Dan O'Dowd, is also the CEO of Green Hill Software, a company that builds operating systems and programming tools for embedded safety and security systems. At CES, the company said BMW's iX vehicle is using its real-time OS and other safety software, and it also announced the availability of its new over-the-air software product and data services for automotive electronic systems. Despite the potential competitive bias of The Dawn Project's founder, Tesla's FSD beta software, an advanced driver assistance system that Tesla owners can access to handle some driving function on city streets, has come under scrutiny in recent months after a series of YouTube videos that showed flaws in the system went viral.


Demographic Confounding Causes Extreme Instances of Lifestyle Politics on Facebook

arXiv.org Artificial Intelligence

Lifestyle politics emerge when activities that have no substantive relevance to ideology become politically aligned and polarized. Homophily and social influence are able generate these fault lines on their own; however, social identities from demographics may serve as coordinating mechanisms through which lifestyle politics are mobilized are spread. Using a dataset of 137,661,886 observations from 299,327 Facebook interests aggregated across users of different racial/ethnic, education, age, gender, and income demographics, we find that the most extreme instances of lifestyle politics are those which are highly confounded by demographics such as race/ethnicity (e.g., Black artists and performers). After adjusting political alignment for demographic effects, lifestyle politics decreased by 27.36% toward the political "center" and demographically confounded interests were no longer among the most polarized interests. Instead, after demographic deconfounding, we found that the most liberal interests included electric cars, Planned Parenthood, and liberal satire while the most conservative interests included the Republican Party and conservative commentators. We validate our measures of political alignment and lifestyle politics using the General Social Survey and find similar demographic entanglements with lifestyle politics existed before social media such as Facebook were ubiquitous, giving us strong confidence that our results are not due to echo chambers or filter bubbles. Likewise, since demographic characteristics exist prior to ideological values, we argue that the demographic confounding we observe is causally responsible for the extreme instances of lifestyle politics that we find among the aggregated interests. We conclude our paper by relating our results to Simpson's paradox, cultural omnivorousness, and network autocorrelation.