Day: October 16, 2023

• A Tesla-owning couple said they received a £17,374 bill, or $20,698, after their vehicle broke down.
• One of the owners, Johnny Bacigalupo, told Edinburgh Live that the bill was “absolutely obscene.” 
• They were told by a Tesla customer support rep that the battery was “damaged due to water ingress.”

A Tesla owner said he was “flabbergasted” when he and his partner were hit with a hefty bill to fix their electric vehicle. 

Johnny Bacigalupo and Rob Hussey told Scottish news outlet Edinburgh Live they were given a £17,374, or $20,698, bill to fix their Tesla after its battery was damaged by rain last week. 

“I honestly can’t believe that this has happened. When I first got the call I thought we would get a bill for £500 or £1,000,” Bacigalupo told Edinburgh Live. “When they said over 17 grand – it’s absolutely obscene. My heart missed a beat, honestly.”

Elon Musk said in 2019 that it could cost between $5,000 and $10,000 to replace a Tesla battery, J.D. Power reported, noting that the figures are different in 2023. Recurrent, which reports on EV battery health, said battery replacement could cost anywhere from $5,000 to $20,000.

After being unable to start their vehicle and arranging to have it delivered to Tesla Edinburgh by a collection firm, Bacigalupo and Hussey received a call on Wednesday informing them that the battery was “damaged due to water ingress.”

The 8-year warranty didn’t cover this, and they were asked if they wanted to proceed with a repair costing $20,698.

“Did I wish to proceed?? I was flabbergasted and couldn’t really find my words,” Bacigalupo said, who told the Tesla representative that the couple wasn’t at fault. 

The outlet said it verified the bill via correspondence between Tesla and the couple and that it had seen an email from Tesla customer relations, which says it’s investigating the complaint. 

A similar incident occurred last year when a Canadian Tesla owner was told it would cost $26,000 to get a replacement battery for his vehicle, Fox Business reported.

The owner, Mario Zelaya, shared his experience in a TikTok video and said he was locked out of his Tesla Model S after the battery died. Zelaya said he eventually sold his Tesla after he spent $30 getting replacement ownership papers, which were locked in the vehicle. 

EV batteries can deteriorate at various rates depending on numerous factors, including how they were charged and the environment in which the vehicle was driven.

Tesla Europe didn’t immediately respond to Insider’s request for comment. 

“You won’t believe what’s coming,” warned the title of a January 2023 video from the Inside China Auto YouTube channel. “Europe’s premium car makers aren’t ready for this,” warned another video from the same channel, uploaded in July.

Produced by Shanghai-based automotive journalist Mark Rainford, a former communications executive for Mercedes-Benz, the channel is one of several by China-based Western commentators agog at what they are seeing—and driving.

The channels tell salivating viewers that the tech-heavy yet keenly priced Chinese electric vehicles that have appeared on China’s domestic market since the end of the global pandemic will soon wipe the floor with their Western counterparts.

Auto executives in Europe, America, and Japan “didn’t believe China’s car companies could grow so fast,” Rainford told me. “That’s an easy mistake to make from outside the country. You see a lot of stories about China—they don’t hit home until you live here and experience it.”

Rainford worked at Mercedes-Benz for eight years—in the UK, Germany, and latterly China—and has lived in China, in two stints, for five years. He started his YouTube channel to cater to the growing interest in Chinese cars from overseas. His most popular video—“Think You Know Chinese Cars? Think Again. You Won’t Believe What’s Coming”—has had more than 800,000 views. It’s an 84-minute wander through the 11 immense halls of the Guangzhou Auto Show, previewing the automotive near future.

He highlighted cars from 42 brands, almost all of which are largely unknown outside China. Some of the eye-popping EVs he featured would be considered concept cars at a Western auto show, but many are already on the road in China.

These “digital bling” cars, as Oxford-based Ade Thomas, founder of the five-year-old World EV Day, calls them—some with navigation on autopilot (NOA) systems, a precursor to full-on autonomous driving; others with face-recognition cameras that monitor driver fatigue; more equipped with multiple high-res dashboard screens pimped with generative AI and streaming video—are not inferior, unsafe copycats, as mainstream Asian and Western automakers have often urged us to believe, they are standards-compliant, road-going smartphones.

This “iPhone on wheels” epithet has been used by Tesla for many years, with traditional auto brands—led, so the caricature goes, by sensible German men in suits on eye-watering remuneration packages—reportedly flailing in Elon Musk’s wake.

In 2016, Oskarina Fuentes got a tip from a friend that seemed too good to be true. Her life in Venezuela had become a struggle: Inflation had hit 800 percent under President Nicolás Maduro, and the 26-year-old Fuentes had no stable job and was balancing multiple side hustles to survive.

Her friend told her about Appen, an Australian data services company that was looking for crowdsourced workers to tag training data for artificial intelligence algorithms. Most internet users will have done some form of data labeling: identifying images of traffic lights and buses for online captchas. But the algorithms powering new bots that can pass legal exams, create fantastical imagery in seconds, or remove harmful content on social media are trained on datasets—images, video, and text—labeled by gig economy workers in some of the world’s cheapest labor markets.

Appen’s clients have included Amazon, Facebook, Google, and Microsoft, and the company’s 1 million contributors are just a part of a vast, hidden industry. The global data collection and labeling market was valued at $2.22 billion in 2022 and is expected to grow to $17.1 billion by 2030, according to consulting firm Grand View Research. As Venezuela slid into an economic catastrophe, many college-educated Venezuelans like Fuentes and her friends joined crowdsourcing platforms like Appen.

For a while, it was a lifeline: Appen meant Fuentes could work from home at any hour of the day. But then the blackouts started—power cutting out for days on end. Left in the dark, Fuentes was unable to pick up tasks. “I couldn’t take it anymore,” she says, speaking in Spanish. “In Venezuela, you don’t live, you survive.” Fuentes and her family migrated to Colombia. Today she shares an apartment with her mother, her grandmother, her uncles, and her dog in the Antioquia region.

Appen is still her sole source of income. Pay ranges from 2.2 cents to 50 cents per task, Fuentes says. Typically, an hour and a half of work will bring in $1. When there are enough tasks to work a full week, she earns approximately $280 per month, almost meeting Colombia’s minimum wage of $285. But filling out a week with tasks is rare, she says. Down days, which have become increasingly common, will bring in no more than $1 to $2. Fuentes works on a laptop from her bed, glued to her computer for over 18 hours a day to get the first pick of tasks that could arrive at any time. Given Appen’s international clients, days begin when the tasks come out, which can mean 2 am starts.

It’s a pattern that’s being repeated across the developing world. Labeling hot spots in east Africa, Venezuela, India, the Philippines, and even refugee camps in Kenya and Lebanon’s Shatila camps offer cheap labor. Workers pick up microtasks for a few cents each on platforms like Appen, Clickworker, and Scale AI, or sign onto short-term contracts in physical data centers like Sama’s 3,000-person office in Nairobi, Kenya, which was the subject of a Time investigation into the exploitation of content moderators. The AI boom in these places is no coincidence, says Florian Schmidt, author of Digital Labour Markets in the Platform Economy. “The industry can flexibly move to wherever the wages are lowest,” he says, and can do it far quicker than, for example, textile manufacturers.

Meta’s flirtation with innovation has been, at best, poorly panning out and, at worst, delightfully creepy. The latter best describes the case of the tech company’s new foray into AI with machine-generated personas.

News broke late last month that Meta would begin adding some new AI-generated personas to platforms like Instagram and Facebook in the coming weeks, and those personas are now live. Take, for example, Billie who is based on the likeness of model and socialite Kendall Jenner. According to her Instagram bio, Billie is “your local ride or die” based in New York City. A gray disclaimer tag indicated that the account is not, in fact, a human clone of Jenner but is “AI managed by Meta.” Billie’s feed is chock full of AI-generated imagery of photoshoot sets, brick oven pizza, a cheugy fall spread, and even an introductory Reel with a caption touting that “chatting with me is like having an older sister you can talk to, but who can’t steal your clothes.”

“Our journey with AIs is just beginning, and it isn’t purely about building AIs that only answer questions. We’ve been creating AIs that have more personality, opinions, and interests, and are a bit more fun to interact with,” Meta wrote in a release. “And because interacting with them should feel like talking to familiar people, we did something to build on this even further. We partnered with cultural icons and influencers to play and embody some of these AIs.”

Billie is one of 28 AI influencers that Meta has added to Facebook, Instagram, Messenger, and WhatsApp, all with a unique personality and a likeness based on some celebrity or public figure. The AI influencers began posting towards the end of September, and users can direct message these influencers and have a conversation with a chatbot, or browse their feeds for AI-generated content. These profiles may also feature an occasional video or selfie from the celebrities playing these AI characters, who shot content in a studio. Along the way, the faceless corporation tries its best to be hip and relatable with half-hearted attempts at sparking memes and generating social media challenges.

Meta is not breaking any new ground with these AI personas as virtual influencers were popularized by the likes of Lil Miquela, an artificial teenager who has a music career and has modeled for Calvin Klein. What Meta is doing is begging you to spend time on Instagram and use its in-house AI chatbot to spill your guts and ask for recipes. Whether or not people want to meme on the AI or genuinely interact with it, people will be using it, at least for a while. And numbers are numbers.

The AI influencers are one of Meta’s recent attempts to chase an ill-conceived trend that’s already beginning to trend downward along with a healthy dash of corporate cringe. Meta also recently revealed AI-generated stickers for Instagram and Facebook that are able to embody Elon Musk with boobs and school children holding guns.

Here are the 12 worst AI personas Meta unveiled in no particular order.

You can add supernova spotting to the laundry list of accolades attributed to artificial intelligence. This week, a collaboration of astronomers led by Northwestern University said they have developed the world’s first AI-assisted, fully automatic supernova detection, identification, and classification system. The powerful new tool, which the scientists say could “significantly streamline” large studies of exploding stars in the future, has already detected its first supernova.

Prior to this tool, astronomers involved in its creation say, supernova detection relied on a combination of automated systems and human verification methods. Ordinarily, robotic telescopes gaze out into sections of the sky in search of new potential supernova sources that weren’t there before. Once a candidate is spotted, the process is then usually handed over to humans operating telescopes with spectrographs to collect the source’s spectrum, or dispersed light. The automated tool, dubbed the “Bright Transient Survey Bot” (BTSbot),” aims to remove that human middle-man from the process.

Researchers fed the BTSbot machine learning algorithm 1.4 million images from 16,000 astronomical sources. Those images included past evidence of supernovae, glaring galaxies, and temporarily flaring stars. Equipped with that training set, the AI model was able to identify a new supernova candidate and automatically request its spectrum reading from a robotic telescope at the Palomar Observatory in California. The system eventually identified the supernova candidate as a “stellar explosion” in which a white dwarf star fully exploded, and it automatically shared its findings with the astronomical community. In other words, the AI system identified and shared the new discovery all on its own—great news to the humans involved.

“The simulated performance was excellent, but you never really know how that translates to the real-world until you actually try it,” Northwestern graduate student Nabeel Rehemtulla said in a statement. “We felt a huge wave of relief.”

Astronomers responsible for building BTSbot believe all that extra time saved trying to manually detect and identify supernovas will give human scientists more time to analyze their observations and consider new ideas.

“Ultimately, removing humans from the loop provides more time for the research team to analyze their observations and develop new hypotheses to explain the origin of the cosmic explosions that we observe,” Northwestern Assistant Professor of Physics and Astronomy Adam Miller said.

Of course, astronomers don’t necessarily need fully automated AI systems to snap snazzy supernova images. Keep reading for a series of some of the most interesting supernova discoveries in recent memory. If you’re feeling particularly sentimental, you can pour one out for human supernova detectors while you’re at it.