Features

hello-sunshine:-we-test-mclaren’s-drop-top-hybrid-artura-spider

Hello sunshine: We test McLaren’s drop-top hybrid Artura Spider

orange express —

The addition of a retractable roof makes this Artura the one to pick.

An orange McLaren Artura Spider drives on a twisy road

Enlarge / The introduction of model year 2025 brings a retractable hard-top option for the McLaren Artura, plus a host of other upgrades.

McLaren

MONACO—The idea of an “entry-level” supercar might sound like a contradiction in terms, but every car company’s range has to start somewhere, and in McLaren’s case, that’s the Artura. When Ars first tested this mid-engined plug-in hybrid in 2022, It was only available as a coupe. But for those who prefer things al fresco, the British automaker has now given you that option with the addition of the Artura Spider.

The Artura represented a step forward for McLaren. There’s a brand-new carbon fiber chassis tub, an advanced electronic architecture (with a handful of domain controllers that replace the dozens of individual ECUs you might find in some of its other models), and a highly capable hybrid powertrain that combines a twin-turbo V6 gasoline engine with an axial flux electric motor.

More power, faster shifts

For model year 2025 and the launch of the $273,800 Spider version, the engineering team at McLaren have given it a spruce-up, despite only being a couple of years old. Overall power output has increased by 19 hp (14 kW) thanks to new engine maps for the V6, which now has a bit more surge from 4,000 rpm all the way to the 8,500 rpm redline. Our test car was fitted with the new sports exhaust, which isn’t obnoxiously loud. It makes some interesting noises as you lift the throttle in the middle of the rev range, but like most turbo engines, it’s not particularly mellifluous.

  • The new engine map means the upper half of third gear will give you a real shove toward the horizon.

    McLaren

  • The Artura Spider’s buttresses are made from a lightweight and clear polymer, so they do their job aerodynamically without completely obscuring your view over your shoulder.

    McLaren

  • The Artura Spider is covered in vents and exhausts to channel air into and out of various parts of the car.

    McLaren

  • You could have your Artura Spider painted in a more somber color. But Orange with carbon fiber looks pretty great to me.

  • If you look closely, you can see the transmission hiding behind the diffuser.

    Jonathan Gitlin

Combined with the 94 hp (70 kW) electric motor, that gives the Artura Spider a healthy 680 hp (507 kW), which helps compensate for the added 134 lbs (62 kg) due to the car’s retractable hard top. There are stiffer engine mounts and new throttle maps, and the dual-clutch transmission shifts 25 percent faster than what we saw in the car that launched two years ago. (These upgrades are carried over to the Artura coupe as well, and the good news for existing owners is that the engine remapping can be applied to their cars, too, with a visit to a McLaren dealer.)

Despite the hybrid system—which uses a 7.4 kWh traction battery—and the roof mechanism, the Artura Spider remains a remarkably light car by 2024 standards, with a curb weight of 3,439 lbs (1,559 kg), which makes it lighter than any comparable car on the market.

In fact, picking a comparable car is a little tricky. Ferrari will sell you a convertible hybrid in the shape of the 296 GTS, but you’ll need another $100,000 or more to get behind the wheel of one of those, which in truth is more of a competitor for the (not-hybrid) 750S, McLaren’s middle model. Any other mid-engined drop-top will be propelled by dino juice alone.

What modes do you want today?

It's easy to drive around town and a lot of fun to drive on a twisty road.

Enlarge / It’s easy to drive around town and a lot of fun to drive on a twisty road.

McLaren

You can drive it using just the electric motor for up to 11 miles if you keep the powertrain in E-mode and start with a fully charged battery. In fact, when you start the car, it begins in this mode by default. Outside of E-mode, the Artura will use spare power from the engine to top up the battery as you drive, and it’s very easy to set a target state of charge if you want to save some battery power for later, for example. Plugged into a Level 2 charger, it should take about 2.5 hours to reach 80 percent.

The car is light enough that 94 hp is more than adequate for the 20 mph or 30 km/h zones you’re sure to encounter whether you’re driving this supercar through a rural village or past camera-wielding car-spotters in the city. Electric mode is serious, and the car won’t fire up the engine until you switch to Comfort (or Sport, or Track) with the control on the right side of the main instrument display.

On the left side is another control to switch the chassis settings between Comfort, Sport, and Track. For road driving, comfort never felt wrong-footed, and I really would leave track for the actual track. The same goes for the Track powertrain setting; for the open road, Sport is the best-sounding, and comfort is well-judged for everyday use and will kill the V6 when it’s not needed. Sport and Track instead use the electric motor—mounted inside the case of the eight-speed transmission—to fill in torque where needed, similar to an F1 or LMDh race car.

Hello sunshine: We test McLaren’s drop-top hybrid Artura Spider Read More »

mod-easy:-a-retro-e-bike-with-a-sidecar-perfect-for-indiana-jones-cosplay

Mod Easy: A retro e-bike with a sidecar perfect for Indiana Jones cosplay

Pure fun —

It’s not the most practical option for passengers, but my son had a blast.

The Mod Easy Sidecar

Enlarge / The Mod Easy Sidecar

As some Ars readers may recall, I reviewed The Maven Cargo e-bike earlier this year as a complete newb to e-bikes. For my second foray into the world of e-bikes, I took an entirely different path.

The stylish Maven was designed with utility in mind—it’s safe, user-friendly, and practical for accomplishing all the daily transportation needs of a busy family. The second bike, the $4,299 Mod Easy Sidecar 3, is on the other end of the spectrum. Just a cursory glance makes it clear: This bike is built for pure, head-turning fun.

The Mod Easy 3 is a retro-style Class 2 bike—complete with a sidecar that looks like it’s straight out of Indiana Jones and the Last Crusade. Nailing this look wasn’t the initial goal of Mod Bike founder Dor Korngold. In an interview with Ars, Korngold said the Mod Easy was the first bike he designed for himself. “It started with me wanting to have this classic cruiser,” he said, but he didn’t have a sketch or final design in mind at the outset. Instead, the design was based on what parts he had in his garage.

The first step was adding a wooden battery compartment to an old Electra frame he had painted. The battery compartment “looked vintage from the beginning,” he said, but the final look came together gradually as he added the sidecar and some of the other motorcycle-style features. Today, the Mod Easy is a sleek bike reminiscent of World War II-era motorcycles and comes in a chic matte finish.

An early version of the Mod Easy bike.

Enlarge / An early version of the Mod Easy bike.

Dor Korngold

When I showed my 5-year-old son a picture of the bike and sidecar, he was instantly enamored and insisted I review it. How could I refuse? He thoroughly enjoyed riding with me on the Maven, but riding in the sidecar turned out to be some next-level fun. He will readily tell you he gives it a five out of five-star rating. But in case you want a more thorough review, my thoughts are below. I’ll start with some general impressions and then discuss specific features of the bike and experience.

The Mod Easy Sidecar 3 at a glance

General impressions

  • The Mod Easy Sidecar 3.

  • Just the bike, which is sold at $3,299

    Beth Mole

  • The Mod Easy Sidecar 3.

    Beth Mole

Again, this is a stylish, fun bike. The bike alone is an effortless and smooth ride. Although it has the heft of an e-bike at 77 pounds (without the sidecar), it never felt unwieldy to me as a 5-foot-4-inch rider. The torque sensors are beautifully integrated into the riding experience, allowing the motor to feel like a gentle, natural assist to pedaling rather than an on-off boost. Of course, with my limited experience, I can’t comment on how these torque sensors compare to other torque sensors, but I have no complaints, and they’re an improvement over my experience with cadence sensors.

You may remember from my review of the Maven that the entrance to a bike path in my area has a switchback path with three tight turns on a hill. With the Maven’s cadence sensors, I struggled to go through the U-turns smoothly, especially going uphill, even after weeks of practice. With the Mod Easy’s torque sensors (and non-cargo length), I glided through them perfectly on the first try. Overall, the bike handles and corners nicely. The wide-set handlebars give the driving experience a relaxed, cruising feel, while the cushy saddle invites you to sink in and stay awhile. The sidecar, meanwhile, was a fun, head-turning feature, but it presents some practical aspects to consider.

Below, I’ll go through key features, starting with the headlining one: the sidecar.

Mod Easy: A retro e-bike with a sidecar perfect for Indiana Jones cosplay Read More »

may-contain-nuts:-precautionary-allergen-labels-lead-to-consumer-confusion

May contain nuts: Precautionary allergen labels lead to consumer confusion

can i eat this or not? —

Some labels suggest allergen cross-contamination that might not exist.

May contain nuts: Precautionary allergen labels lead to consumer confusion

TopMicrobialStock, Getty Images

When Ina Chung, a Colorado mother, first fed packaged foods to her infant, she was careful to read the labels. Her daughter was allergic to peanuts, dairy, and eggs, so products containing those ingredients were out. So were foods with labels that said they may contain the allergens.

Chung felt like this last category suggested a clear risk that wasn’t worth taking. “I had heard that the ingredient labels were regulated. And so I thought that that included those statements,” said Chung. “Which was not true.”

Precautionary allergen labels like those that say “processed in a facility that uses milk” or “may contain fish” are meant to address the potential for cross-contact. For instance, a granola bar that doesn’t list peanuts as an ingredient could still say they may be included. And in the United States, these warnings are not regulated; companies can use whatever precautionary phrasing they choose on any product. Some don’t bother with any labels, even in facilities where unintended allergens slip in; others list allergens that may pose little risk. Robert Earl, vice president of regulatory affairs at Food Allergy Research & Education, or FARE, a nonprofit advocacy, research, and education group, has even seen such labels that include all nine common food allergens. “I would bet my bottom dollar not all of those allergens are even in the facility,” he said.

So what are the roughly 20 million people with food allergies in the US supposed to do with these warnings? Should they eat the granola bar or not?

Recognizing this uncertainty, food safety experts, allergy advocates, policymakers, and food producers are discussing how to demystify precautionary allergen labels. One widely considered solution is to restrict warnings to cases where visual or analytical tests demonstrate that there is enough allergen to actually trigger a reaction. Experts say the costs to the food industry are minimal, and some food producers across the globe, including in Canada, Australia, Thailand, and the United States, already voluntarily take this approach. But in the US, where there are no clear guidelines to follow, consumers are still left wondering what each individual precautionary allergen label even means.

Pull a packaged food off an American store shelf and the ingredients label should say if the product intentionally contains one of nine recognized allergens. That’s because in 2004, Congress granted the Food and Drug Administration the power to regulate labeling of eight major food allergens—eggs, fish, milk, crustaceans, peanuts, tree nuts, soybeans, and wheat. In 2021, sesame was added to the list.

But the language often gets murkier further down the label, where companies may include precautionary allergen labels, also called advisory statements, to address the fact that allergens can unintentionally wind up in foods at many stages of production. Perhaps wheat grows near a field of rye destined for bread, for instance, or peanuts get lodged in processing equipment that later pumps out chocolate chip cookies. Candy manufacturers, in particular, struggle to keep milk out of dark chocolate.

The FDA offers no labeling guidance beyond declaring that “advisory statements should not be used as a substitute for adhering to current good manufacturing practices and must be truthful and not misleading.”

Companies can choose when to use these warnings, which vary widely. For example, a 2017 survey conducted by the FDA and the Illinois Institute of Technology of 78 dark chocolate products found that almost two-thirds contained an advisory statement for peanuts; of those, only about four actually contained the allergen. Meanwhile, of 18 bars that carried no advisory statement for peanuts specifically, three contained the allergen. (One product that was positive for peanuts did warn more generally of nuts, but the researchers noted that this term is ambiguous.) Another product that tested positive included a nut warning on one lot but not on another. Individual companies also select their own precautionary label phrasing.

For consumers, the inconsistency can be confusing, said Ruchi Gupta, a pediatrician and director of the Center for Food Allergy & Asthma Research at Northwestern University’s Feinberg School of Medicine in Chicago. In 2019, Gupta and colleagues asked around 3,000 US adults who have allergies or care for someone who does about how different precautionary allergen label phrases make a difference when they are considering whether to buy a particular food. About 80 percent never purchase products with a may contain warning. Less than half avoid products with labels suggesting that it was manufactured in a facility that also processes an allergen, even though numerous studies show that the wording of a precautionary allergen label has no bearing on risk level. “People are making their own decisions on what sounds safe,” said Gupta.

When Chung learned that advisory labels were unregulated, she experimented with ignoring them when her then-toddler really wanted a particular food. When her daughter developed a couple of hives after eating a cereal labeled may contain peanuts, Chung went back to heeding warnings of peanut cross-contact but continued ignoring the rest.

“A lot of families just make up their own rules,” she said. “There’s no way to really know exactly what you’re getting.”

May contain nuts: Precautionary allergen labels lead to consumer confusion Read More »

neutrinos:-the-inscrutable-“ghost-particles”-driving-scientists-crazy

Neutrinos: The inscrutable “ghost particles” driving scientists crazy

ghostly experiments —

They hold the keys to new physics. If only we could understand them.

The Super-Kamiokande neutrino detector at the Kamioka Observatory in Japan.

Enlarge / The Super-Kamiokande neutrino detector at the Kamioka Observatory in Japan.

Kamioka Observatory, ICRR (Institute for Cosmic Ray Research), the University of Tokyo

Somehow, neutrinos went from just another random particle to becoming tiny monsters that require multi-billion-dollar facilities to understand. And there’s just enough mystery surrounding them that we feel compelled to build those facilities since neutrinos might just tear apart the entire particle physics community at the seams.

It started out innocently enough. Nobody asked for or predicted the existence of neutrinos, but there they were in our early particle experiments. Occasionally, heavy atomic nuclei spontaneously—and for no good reason—transform themselves, with either a neutron converting into a proton or vice-versa. As a result of this process, known as beta decay, the nucleus also emits an electron or its antimatter partner, the positron.

There was just one small problem: Nothing added up. The electrons never came out of the nucleus with the same energy; it was a little different every time. Some physicists argued that our conceptions of the conservation of energy only held on average, but that didn’t feel so good to say out loud, so others argued that perhaps there was another, hidden particle participating in the transformations. Something, they argued, had to sap energy away from the electron in a random way to explain this.

Eventually, that little particle got a name, the neutrino, an Italian-ish word meaning “little neutral one.” Whatever the neutrino was, it didn’t carry any electric charge and only participated in the weak nuclear force, so we only saw neutrinos at work in radioactive decay processes. But even with the multitude of decays with energies great and small happening all across the Universe every single second, the elusive nature of neutrinos meant we could only occasionally, rarely, weakly see them.

But see them we did (although it took 25 years), and for a while, we could just pretend that nothing was wrong. The neutrino was just another particle the Universe didn’t strictly need to give us but somehow stubbornly insisted on giving us anyway.

And then we discovered there wasn’t just one neutrino but three of them. For reasons the cosmos has yet to divulge to us, it likes to organize its particles into groups of three, known as generations. Take a nice, stable, regular fundamental particle, like an electron or an up or down quark—those particles represent the first generation. The other two generations share the same properties (like spin and electric charge) but have a heavier mass.

For the electron, we have its generational sibling, the muon, which is just like the electron but 200 times heavier, and the tau, which is also just like the electron but 3,500 times heavier (that’s heavier than a proton). For the down quark, we have its siblings, the “strange” and “bottom” quarks. And we call the heavier versions of the up quark the “charm” and “top” quarks. Why does the Universe do this? Why three generations with these masses? As I said, the cosmos has chosen not to reveal that to us (yet).

So there are three generations of neutrinos, named for the kinds of interactions they participate in. Some nuclear reactions involve only the first generation of particles (which are the most common by far), the up and down quarks, and the electrons. Here, electron-neutrinos are involved. When muons play around, muon-neutrinos come out, too. And no points will be awarded for guessing the name of the neutrinos associated with tau particle interactions.

All this is… fine. Aside from the burning mystery of the existence of particle generations in the first place, it would be a bit greedy for one neutrino to participate in all possible reactions. So it has to share the job with two other generations. It seemed odd, but it all worked.

And then we discovered that neutrinos had mass, and the whole thing blew up.

Neutrinos: The inscrutable “ghost particles” driving scientists crazy Read More »

brompton-c-line-electric-review:-fun-and-foldable,-fits-better-than-you’d-think

Brompton C Line Electric review: Fun and foldable, fits better than you’d think

Brompton C Line Electric Review —

A motor evens out its natural disadvantages, but there’s still a learning curve.

What can I say? It was tough putting the Brompton C Line Electric through its paces. Finding just the right context for it. Grueling work.

Enlarge / What can I say? It was tough putting the Brompton C Line Electric through its paces. Finding just the right context for it. Grueling work.

Kevin Purdy

There’s never been a better time to ride a weird bike.

That’s especially true if you live in a city where you can regularly see kids being dropped off at schools from cargo bikes with buckets, child seats, and full rain covers. Further out from the urban core, fat-tire e-bikes share space on trails with three-wheelers, retro-style cruisers, and slick roadies. And folding bikes, once an obscurity, are showing up in more places, especially as they’ve gone electric.

So when I got to try out the Brompton Electric C Line (in a six-speed model), I felt far less intimidated riding, folding, and stashing the little guy wherever I went than I might have been a few years back. A few folks recognized the distinctively small and British bike and offered a thumbs-up or light curiosity. If anyone was concerned about the oddity of this quirky ride, it was me, mostly because I obsessed over whether I could and should lock it up outside or not.

But for the most part, the Brompton fits in, and it works as a bike. It sat next to me at bars and coffee shops and outdoor eateries, it rode the DC Metro, it went on a memorial group ride, and it went to the grocery store. I repeatedly hauled it to a third-floor walkup apartment and brought it on a week’s vacation, fitting it on the floor behind the car driver’s seat. And with an electric battery pack, it was even easier to forget that it was any different from a stereotypical bike—so long as you didn’t look down.

Still, should you pay a good deal more than $3,000 (and probably more like $4,000 after accessories) for a bike with 16-inch tires—especially one you might never want to leave locked up outside?

Let’s get into that.

  • The Brompton C Line, pre-fold (mid-beer).

    Kevin Purdy

  • Step 1: Release a clasp and pull the bike frame up, allowing the rear wheel to swing forward underneath.

    Kevin Purdy

  • Step 2: Loosen the clamp and fold the front half back to align with the rear wheel, lining up a little hook on the wheel with the frame.

    Kevin Purdy

  • Step 3: Remove the battery (technically unnecessary, but wise), loosen a clamp holding up the handlebar, then fold it down onto the frame, letting a nub tuck into a locking notch.

    Kevin Purdy

  • Step 4: Drop down the seat (which also locks the frame into position), rotate one pedal onto the tire, and flip the other pedal up.

    Kevin Purdy

Learning The Fold

Whether you buy it at a store or have it shipped to you, a Brompton C Line is possibly the easiest e-bike to unpack, set up, and get rolling. You take out the folded-up bike, screw in the crucial hinge clamps that hold it together, put on the saddle, and learn how to unfold it for the first time. Throw some air in the tires, and you could be on your way about 20 minutes after getting the bike.

But you shouldn’t head out without getting some reps in on The Fold. The Fold is the reason the Brompton exists. It hasn’t actually changed that much since Andrew Ritchie designed it in 1975. Release a rear frame clip and yank the frame up, and the rear wheel and its frame triangle roll underneath the top tube. Unscrew a hinged clamp, then “stir” the front wheel backward, allowing a subtle hook to catch on the rear frame. Drop the seat and you’ll feel something lock inside the frame. You can then unhinge and fold the handlebar down, or you can keep it up to push the bike around on its tiny frame wheels in “shopping cart mode.”

If you forget the sequence of the fold, there are little reminders in a few spots on the bike.

If you forget the sequence of the fold, there are little reminders in a few spots on the bike.

Kevin Purdy

After maybe five attempts, I began to get The Fold done in less than a minute. After around a dozen tries, I started to appreciate its design and motions. The way a Brompton folds up is great for certain applications, like fitting into a car instead of using a rack, bringing on public transit or train rides, tucking underneath a counter or table, or fitting into the corner of the most space-challenged home. It can also be handy if you’re heading somewhere you’re wary of locking it up outside (more on that in a moment).

Brompton C Line Electric review: Fun and foldable, fits better than you’d think Read More »

can-a-technology-called-rag-keep-ai-models-from-making-stuff-up?

Can a technology called RAG keep AI models from making stuff up?

Can a technology called RAG keep AI models from making stuff up?

Aurich Lawson | Getty Images

We’ve been living through the generative AI boom for nearly a year and a half now, following the late 2022 release of OpenAI’s ChatGPT. But despite transformative effects on companies’ share prices, generative AI tools powered by large language models (LLMs) still have major drawbacks that have kept them from being as useful as many would like them to be. Retrieval augmented generation, or RAG, aims to fix some of those drawbacks.

Perhaps the most prominent drawback of LLMs is their tendency toward confabulation (also called “hallucination”), which is a statistical gap-filling phenomenon AI language models produce when they are tasked with reproducing knowledge that wasn’t present in the training data. They generate plausible-sounding text that can veer toward accuracy when the training data is solid but otherwise may just be completely made up.

Relying on confabulating AI models gets people and companies in trouble, as we’ve covered in the past. In 2023, we saw two instances of lawyers citing legal cases, confabulated by AI, that didn’t exist. We’ve covered claims against OpenAI in which ChatGPT confabulated and accused innocent people of doing terrible things. In February, we wrote about Air Canada’s customer service chatbot inventing a refund policy, and in March, a New York City chatbot was caught confabulating city regulations.

So if generative AI aims to be the technology that propels humanity into the future, someone needs to iron out the confabulation kinks along the way. That’s where RAG comes in. Its proponents hope the technique will help turn generative AI technology into reliable assistants that can supercharge productivity without requiring a human to double-check or second-guess the answers.

“RAG is a way of improving LLM performance, in essence by blending the LLM process with a web search or other document look-up process” to help LLMs stick to the facts, according to Noah Giansiracusa, associate professor of mathematics at Bentley University.

Let’s take a closer look at how it works and what its limitations are.

A framework for enhancing AI accuracy

Although RAG is now seen as a technique to help fix issues with generative AI, it actually predates ChatGPT. Researchers coined the term in a 2020 academic paper by researchers at Facebook AI Research (FAIR, now Meta AI Research), University College London, and New York University.

As we’ve mentioned, LLMs struggle with facts. Google’s entry into the generative AI race, Bard, made an embarrassing error on its first public demonstration back in February 2023 about the James Webb Space Telescope. The error wiped around $100 billion off the value of parent company Alphabet. LLMs produce the most statistically likely response based on their training data and don’t understand anything they output, meaning they can present false information that seems accurate if you don’t have expert knowledge on a subject.

LLMs also lack up-to-date knowledge and the ability to identify gaps in their knowledge. “When a human tries to answer a question, they can rely on their memory and come up with a response on the fly, or they could do something like Google it or peruse Wikipedia and then try to piece an answer together from what they find there—still filtering that info through their internal knowledge of the matter,” said Giansiracusa.

But LLMs aren’t humans, of course. Their training data can age quickly, particularly in more time-sensitive queries. In addition, the LLM often can’t distinguish specific sources of its knowledge, as all its training data is blended together into a kind of soup.

In theory, RAG should make keeping AI models up to date far cheaper and easier. “The beauty of RAG is that when new information becomes available, rather than having to retrain the model, all that’s needed is to augment the model’s external knowledge base with the updated information,” said Peterson. “This reduces LLM development time and cost while enhancing the model’s scalability.”

Can a technology called RAG keep AI models from making stuff up? Read More »

windows-recall-demands-an-extraordinary-level-of-trust-that-microsoft-hasn’t-earned

Windows Recall demands an extraordinary level of trust that Microsoft hasn’t earned

The Recall feature as it currently exists in Windows 11 24H2 preview builds.

Enlarge / The Recall feature as it currently exists in Windows 11 24H2 preview builds.

Andrew Cunningham

Microsoft’s Windows 11 Copilot+ PCs come with quite a few new AI and machine learning-driven features, but the tentpole is Recall. Described by Microsoft as a comprehensive record of everything you do on your PC, the feature is pitched as a way to help users remember where they’ve been and to provide Windows extra contextual information that can help it better understand requests from and meet the needs of individual users.

This, as many users in infosec communities on social media immediately pointed out, sounds like a potential security nightmare. That’s doubly true because Microsoft says that by default, Recall’s screenshots take no pains to redact sensitive information, from usernames and passwords to health care information to NSFW site visits. By default, on a PC with 256GB of storage, Recall can store a couple dozen gigabytes of data across three months of PC usage, a huge amount of personal data.

The line between “potential security nightmare” and “actual security nightmare” is at least partly about the implementation, and Microsoft has been saying things that are at least superficially reassuring. Copilot+ PCs are required to have a fast neural processing unit (NPU) so that processing can be performed locally rather than sending data to the cloud; local snapshots are protected at rest by Windows’ disk encryption technologies, which are generally on by default if you’ve signed into a Microsoft account; neither Microsoft nor other users on the PC are supposed to be able to access any particular user’s Recall snapshots; and users can choose to exclude apps or (in most browsers) individual websites to exclude from Recall’s snapshots.

This all sounds good in theory, but some users are beginning to use Recall now that the Windows 11 24H2 update is available in preview form, and the actual implementation has serious problems.

“Fundamentally breaks the promise of security in Windows”

This is Recall, as seen on a PC running a preview build of Windows 11 24H2. It takes and saves periodic screenshots, which can then be searched for and viewed in various ways.

Enlarge / This is Recall, as seen on a PC running a preview build of Windows 11 24H2. It takes and saves periodic screenshots, which can then be searched for and viewed in various ways.

Andrew Cunningham

Security researcher Kevin Beaumont, first in a thread on Mastodon and later in a more detailed blog post, has written about some of the potential implementation issues after enabling Recall on an unsupported system (which is currently the only way to try Recall since Copilot+ PCs that officially support the feature won’t ship until later this month). We’ve also given this early version of Recall a try on a Windows Dev Kit 2023, which we’ve used for all our recent Windows-on-Arm testing, and we’ve independently verified Beaumont’s claims about how easy it is to find and view raw Recall data once you have access to a user’s PC.

To test Recall yourself, developer and Windows enthusiast Albacore has published a tool called AmperageKit that will enable it on Arm-based Windows PCs running Windows 11 24H2 build 26100.712 (the build currently available in the Windows Insider Release Preview channel). Other Windows 11 24H2 versions are missing the underlying code necessary to enable Recall.

  • Windows uses OCR on all the text in all the screenshots it takes. That text is also saved to an SQLite database to facilitate faster searches.

    Andrew Cunningham

  • Searching for “iCloud,” for example, brings up every single screenshot with the word “iCloud” in it, including the app itself and its entry in the Microsoft Store. If I had visited websites that mentioned it, they would show up here, too.

    Andrew Cunningham

The short version is this: In its current form, Recall takes screenshots and uses OCR to grab the information on your screen; it then writes the contents of windows plus records of different user interactions in a locally stored SQLite database to track your activity. Data is stored on a per-app basis, presumably to make it easier for Microsoft’s app-exclusion feature to work. Beaumont says “several days” of data amounted to a database around 90KB in size. In our usage, screenshots taken by Recall on a PC with a 2560×1440 screen come in at 500KB or 600KB apiece (Recall saves screenshots at your PC’s native resolution, minus the taskbar area).

Recall works locally thanks to Azure AI code that runs on your device, and it works without Internet connectivity and without a Microsoft account. Data is encrypted at rest, sort of, at least insofar as your entire drive is generally encrypted when your PC is either signed into a Microsoft account or has Bitlocker turned on. But in its current form, Beaumont says Recall has “gaps you can drive a plane through” that make it trivially easy to grab and scan through a user’s Recall database if you either (1) have local access to the machine and can log into any account (not just the account of the user whose database you’re trying to see), or (2) are using a PC infected with some kind of info-stealer virus that can quickly transfer the SQLite database to another system.

Windows Recall demands an extraordinary level of trust that Microsoft hasn’t earned Read More »

no-physics?-no-problem-ai-weather-forecasting-is-already-making-huge-strides.

No physics? No problem. AI weather forecasting is already making huge strides.

AI weather models are arriving just in time for the 2024 Atlantic hurricane season.

Enlarge / AI weather models are arriving just in time for the 2024 Atlantic hurricane season.

Aurich Lawson | Getty Images

Much like the invigorating passage of a strong cold front, major changes are afoot in the weather forecasting community. And the end game is nothing short of revolutionary: an entirely new way to forecast weather based on artificial intelligence that can run on a desktop computer.

Today’s artificial intelligence systems require one resource more than any other to operate—data. For example, large language models such as ChatGPT voraciously consume data to improve answers to queries. The more and higher quality data, the better their training, and the sharper the results.

However, there is a finite limit to quality data, even on the Internet. These large language models have hoovered up so much data that they’re being sued widely for copyright infringement. And as they’re running out of data, the operators of these AI models are turning to ideas such as synthetic data to keep feeding the beast and produce ever more capable results for users.

If data is king, what about other applications for AI technology similar to large language models? Are there untapped pools of data? One of the most promising that has emerged in the last 18 months is weather forecasting, and recent advances have sent shockwaves through the field of meteorology.

That’s because there’s a secret weapon: an extremely rich data set. The European Centre for Medium-Range Weather Forecasts, the premiere organization in the world for numerical weather prediction, maintains a set of data about atmospheric, land, and oceanic weather data for every day, at points around the world, every few hours, going back to 1940. The last 50 years of data, after the advent of global satellite coverage, is especially rich. This dataset is known as ERA5, and it is publicly available.

It was not created to fuel AI applications, but ERA5 has turned out to be incredibly useful for this purpose. Computer scientists only really got serious about using this data to train AI models to forecast the weather in 2022. Since then, the technology has made rapid strides. In some cases, the output of these models is already superior to global weather models that scientists have labored decades to design and build, and they require some of the most powerful supercomputers in the world to run.

“It is clear that machine learning is a significant part of the future of weather forecasting,” said Matthew Chantry, who leads AI forecasting efforts at the European weather center known as ECMWF, in an interview with Ars.

It’s moving fast

John Dean and Kai Marshland met as undergraduates at Stanford University in the late 2010s. Dean, an electrical engineer, interned at SpaceX during the summer of 2017. Marshland, a computer scientist, interned at the launch company the next summer. Both graduated in 2019 and were trying to figure out what to do with their lives.

“We decided we wanted to solve the problem of weather uncertainty,” Marshland said, so they co-founded a company called WindBorne Systems.

The premise of the company was simple: For about 85 percent of the Earth and its atmosphere, we have no good data about weather conditions there. A lack of quality data, which establishes initial conditions, represents a major handicap for global weather forecast models. The company’s proposed solution was in its name—wind borne.

Dean and Marshland set about designing small weather balloons they could release into the atmosphere and which would fly around the world for up to 40 days, relaying useful atmospheric data that could be packaged and sold to large, government-funded weather models.

Weather balloons provide invaluable data about atmospheric conditions—readings such as temperature, dewpoints, and pressures—that cannot be captured by surface observations or satellites. Such atmospheric “profiles” are helpful in setting the initial conditions models start with. The problem is that traditional weather balloons are cumbersome and only operate for a few hours. Because of this, the National Weather Service only launches them twice daily from about 100 locations in the United States.

No physics? No problem. AI weather forecasting is already making huge strides. Read More »

driverless-racing-is-real,-terrible,-and-strangely-exciting

Driverless racing is real, terrible, and strangely exciting

people showed up to watch —

The Abu Dhabi Autonomous Racing League proves it’s possible, just very hard.

Several brightly colored race cars are parked at a race course

Enlarge / No one’s entirely sure if driverless racing will be any good to watch, but before we find that out, people have to actually develop driverless race cars. A2RL in Abu Dhabi is the latest step down that path.

A2RL

ABU DHABI—We live in a weird time for autonomous vehicles. Ambitions come and go, but genuinely autonomous cars are further off than solid-state vehicle batteries. Part of the problem with developing autonomous cars is that teaching road cars to take risks is unacceptable.

A race track, though, is a decent place to potentially crash a car. You can take risks there, with every brutal crunch becoming a learning exercise. (You’d be hard-pressed to find a top racing driver without a few wrecks smoldering in their junior career records.)

That’s why 10,000 people descended on the Yas Marina race track in Abu Dhabi to watch the first four-car driverless race.

Test lab

The organizers of the Abu Dhabi Autonomous Racing League (A2RL) event didn’t brief me on what to expect, so I wasn’t sure if we would see much car movement. Not because the project was likely to fail—it certainly had a lot of hardware and software engineering behind it, not to mention plenty of money. But creating a high-speed, high-maneuverability vehicle that makes its own choices is an immense challenge.

Just running a Super Formula car—the chassis modified for the series—is a big task for any race team, even with an expert driver in the cockpit. I was ready to be impressed if teams got out of the pit lane without the engine stalling.

But the cars did run. Lap times weren’t close to those of a human driver or competitive across the field, but the cars did repeatedly negotiate the track. Not every car was able to do quick laps, but the ones that did looked like actual race cars being driven on a race track. Even the size of the crashes showed that the teams were finding the confidence to begin pushing limits.

Each of these Dallara Super Formula cars has been modified by its team to operate without a human driver onboard or in control.

Enlarge / Each of these Dallara Super Formula cars has been modified by its team to operate without a human driver onboard or in control.

A2RL

Is it the future of motorsport? Probably not. But it was an interesting test lab. After a year of development, six weeks of code-jam crunch, 14 days of practice, and one event, teams are going home with suitcases full of data and lessons they can use next year.

The track and the cars

A2RL is one of three competitions being run by Aspire, the “technology transition pillar” of Abu Dhabi’s Advanced Technology Research Council.

Yas is an artificial island built as a leisure attraction, housing theme parks and hotels alongside the circuit, with an influencer photo opportunity around every corner. The island was the focus of the Emirate restyling itself for tourism, and its facilities now play secondary host to another image makeover as a technology hub. An F1 track is now finding a second use as a testing lab, and it’s probably the only track in the region that could afford the kind of excess that two weeks of round-the-clock, floodlit, robotic testing represents.

Although the early ambition was to use Formula 1 cars to reflect Yas Marina’s purpose as a circuit, the cost compared to a Super Formula car was absurd. Plus, it would have required eight identical F1 chassis. Even in the days of unrestricted F1 budgets, few teams could afford that many chassis in a season.

So Aspire’s Technology Innovation Institute (TII) went to the manufacturer Dallara, which supplies almost every high-level single-seater chassis, including parts of some F1 cars, but also every IndyCar, Super Formula, Formula E, Formula 2, and Formula 3 car, plus a whole array of endurance prototypes. Dallara was also involved in the 2021 Indy Autonomous Challenge via the IndyNXT chassis.

TII in Abu Dhabi was also involved in the Indy Autonomous Challenge as part of a university’s team, so it got to see how the cars had been rapidly adapted to accommodate a robotic “driver.”

  • The computer that controls the driving and interprets the sensor stack, situated in the cockpit—almost like a human driver.

    Hazel Southwell

  • The Meccanica42 actuators that operate throttle, brake, and steering onboard the adjusted SF23 chassis.

    Hazel Southwell

  • L-R: The robotic array that sits lower in the car’s cockpit for the actuators to operate the car, and the computer that sits above it for maximum ventilation.

    Hazel Southwell

  • A look at one of the car’s sensor pods.

    A2RL

Driverless racing is real, terrible, and strangely exciting Read More »

small,-cheap,-and-weird:-a-history-of-the-microcar

Small, cheap, and weird: A history of the microcar

Small, cheap, and weird: A history of the microcar

Aurich Lawson

European car manufacturers are currently tripping over themselves to figure out how personal transport and “last mile” solutions will look in the years to come. The solutions are always electric, and they’re also tiny. What most companies (bar Citroen, Renault, and Fiat) seem to have forgotten is that we’ve had an answer to this problem all along: the microcar.

The microcar is a singular little thing—its job is to frugally take one person (or maybe two people) where they need to go while taking up as little space as possible. A few have broken their way into the public consciousness—Top Gear made a global megastar of Peel’s cars, BMW’s Isetta remains a design icon, and the Messerschmitt KR200 is just plain cool—but where did these tiny wonders come from? And do they have a future?

Well, without the microcar’s predecessors, we may not have the modern motorcar as we know it. Sort of.

Let’s roll back to the genesis of the car: the Mercedes-Benz Patent Motorwagen. While not a microcar by any means (though it seats only two people and has a tiny engine and only three wheels), it got plenty of people thinking.

While Karl Benz was inventing the car and his wife was road-tripping it in 1885, a French inventor named Léon Bollée put his thinking cap on. He was 15 at the time, but it gave him time to be with his thoughts. At that age, he had a keen brain—one that invented a pedal boat of sorts. Bollée was smart, to say the least—he built calculators to help his father’s business, one of which won an award at the 1889 Paris Exposition and went on to be patented all over the world.

  • Most people agree that the 1885 Mercedes-Benz Patent Motorwagen was the first automobile.

    Newspress

  • By 1898, Louis Renault had created the Renault Voiturette.

    Newspress

  • The 1905 Laurent and Klement Voiturette.

    Skoda

In 1895, Bollée and his father created “La Novelle,” a steam-powered trike, and in the same year, Bollée created a gasoline-powered… thing as well. A year later, Bollée founded Léon Bollée Automobiles to mass-produce his tiny cars, dubbing them “Voiturette”—a mashup of the French for automobile (voiture) and the suffix you throw on a word to make it small (ette). Small car, basically.

A few years later, Renault (maker of tiny hatchbacks and the gloriously silly Avantime and popularizer of the people carrier in Europe) became a car manufacturer with the release of its descriptively named Voiturette. Louis Renault’s small mechanical wonder was built in 1898, and the first was sold on Christmas Eve of the same year to a friend of Louis’ father—he liked the fuel economy from its one-cylinder De Dion-Bouton 273 cc 1.75 hp (1.3 kW) engine and the fact that it could get around town with ease.

That same night, the story goes, Renault sold a further twelve cars. Over its mere five-year production run, the first Renault went from open-top two-seater to a four-seat covered wagon capable of over 35 mph (56 km/h). Bear in mind that less than a century earlier, Stephenson’s Rocket and its almost 30 mph (48 km/h) top speed caused great concern about whether human physiology could withstand such speeds. 35 mph was quite the achievement.

Voiturettes and their less “ette” siblings were very successful, but they were a bit too much for some people. That’s where the cyclecar came in.

First appearing around 1910, cyclecars took small engines—single cylinders, V-twins, the odd four-pot—and attached them to simple, lightweight four-wheeled bodies. To be a cyclecar, a vehicle had to have a gearbox and clutch. A huge industry popped up around them, and for good reason—regular cars were expensive to tax and run, whereas a cyclecar wasn’t.

Small, cheap, and weird: A history of the microcar Read More »

google’s-“ai-overview”-can-give-false,-misleading,-and-dangerous-answers

Google’s “AI Overview” can give false, misleading, and dangerous answers

This is fine.

Enlarge / This is fine.

Getty Images

If you use Google regularly, you may have noticed the company’s new AI Overviews providing summarized answers to some of your questions in recent days. If you use social media regularly, you may have come across many examples of those AI Overviews being hilariously or even dangerously wrong.

Factual errors can pop up in existing LLM chatbots as well, of course. But the potential damage that can be caused by AI inaccuracy gets multiplied when those errors appear atop the ultra-valuable web real estate of the Google search results page.

“The examples we’ve seen are generally very uncommon queries and aren’t representative of most people’s experiences,” a Google spokesperson told Ars. “The vast majority of AI Overviews provide high quality information, with links to dig deeper on the web.”

After looking through dozens of examples of Google AI Overview mistakes (and replicating many ourselves for the galleries below), we’ve noticed a few broad categories of errors that seemed to show up again and again. Consider this a crash course in some of the current weak points of Google’s AI Overviews and a look at areas of concern for the company to improve as the system continues to roll out.

Treating jokes as facts

  • The bit about using glue on pizza can be traced back to an 11-year-old troll post on Reddit. (via)

    Kyle Orland / Google

  • This wasn’t funny when the guys at Pep Boys said it, either. (via)

    Kyle Orland / Google

  • Weird Al recommends “running with scissors” as well! (via)

    Kyle Orland / Google

Some of the funniest example of Google’s AI Overview failing come, ironically enough, when the system doesn’t realize a source online was trying to be funny. An AI answer that suggested using “1/8 cup of non-toxic glue” to stop cheese from sliding off pizza can be traced back to someone who was obviously trying to troll an ongoing thread. A response recommending “blinker fluid” for a turn signal that doesn’t make noise can similarly be traced back to a troll on the Good Sam advice forums, which Google’s AI Overview apparently trusts as a reliable source.

In regular Google searches, these jokey posts from random Internet users probably wouldn’t be among the first answers someone saw when clicking through a list of web links. But with AI Overviews, those trolls were integrated into the authoritative-sounding data summary presented right at the top of the results page.

What’s more, there’s nothing in the tiny “source link” boxes below Google’s AI summary to suggest either of these forum trolls are anything other than good sources of information. Sometimes, though, glancing at the source can save you some grief, such as when you see a response calling running with scissors “cardio exercise that some say is effective” (that came from a 2022 post from Little Old Lady Comedy).

Bad sourcing

  • Washington University in St. Louis says this ratio is accurate, but others disagree. (via)

    Kyle Orland / Google

  • Man, we wish this fantasy remake was real. (via)

    Kyle Orland / Google

Sometimes Google’s AI Overview offers an accurate summary of a non-joke source that happens to be wrong. When asking about how many Declaration of Independence signers owned slaves, for instance, Google’s AI Overview accurately summarizes a Washington University of St. Louis library page saying that one-third “were personally enslavers.” But the response ignores contradictory sources like a Chicago Sun-Times article saying the real answer is closer to three-quarters. I’m not enough of a history expert to judge which authoritative-seeming source is right, but at least one historian online took issue with the Google AI’s answer sourcing.

Other times, a source that Google trusts as authoritative is really just fan fiction. That’s the case for a response that imagined a 2022 remake of 2001: A Space Odyssey, directed by Steven Spielberg and produced by George Lucas. A savvy web user would probably do a double-take before citing citing Fandom’s “Idea Wiki” as a reliable source, but a careless AI Overview user might not notice where the AI got its information.

Google’s “AI Overview” can give false, misleading, and dangerous answers Read More »

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On self-driving, Waymo is playing chess while Tesla plays checkers

A Waymo autonomous taxi in San Francisco.

Enlarge / A Waymo autonomous taxi in San Francisco.

David Paul Morris/Bloomberg via Getty Images

Tesla fans—and CEO Elon Musk himself—are excited about the prospects for Tesla’s Full Self Driving (FSD) software. Tesla released a major upgrade—version 12.3—of the software in March. Then, last month, Musk announced that Tesla would unveil a purpose-built robotaxi on August 8. Last week, Musk announced that a new version of FSD—12.4—will come out in the coming days and will have a “5X to 10X improvement in miles per intervention.”

But I think fans expecting Tesla to launch a driverless taxi service in the near future will be disappointed.

During a late March trip to San Francisco, I had a chance to try the latest self-driving technology from both Tesla and Google’s Waymo.

During a 45-minute test drive in a Tesla Model X, I had to intervene twice to correct mistakes by the FSD software. In contrast, I rode in driverless Waymo vehicles for more than two hours and didn’t notice a single mistake.

So while Tesla’s FSD version 12.3 seems like a significant improvement over previous versions of FSD, it still lags behind Waymo’s technology.

However, Waymo’s impressive performance comes with an asterisk. While no one was behind the wheel during my rides, Waymo has remote operators that sometimes provide guidance to its vehicles (Waymo declined to tell me whether—or how often—remote operators intervened during my rides). And while Tesla’s FSD works on all road types, Waymo’s taxis avoid freeways.

Many Tesla fans see these limitations as signs that Waymo is headed for a technological dead end. They see Tesla’s FSD, with its capacity to operate in all cities and on all road types, as a more general technology that will soon surpass Waymo.

But this fundamentally misunderstands the situation.

Safely operating driverless vehicles on public roads is hard. With no one in the driver’s seat, a single mistake can be deadly—especially at freeway speeds. So Waymo launched its driverless service in 2020 in the easiest environment it could find—residential streets in the Phoenix suburbs—and has been gradually ratcheting up the difficulty level as it gains confidence in its technology.

In contrast, Tesla hasn’t started driverless testing because its software isn’t ready. For now, geographic restrictions and remote assistance aren’t needed because there’s always a human being behind the wheel. But I predict that when Tesla begins its driverless transition, it will realize that safety requires a Waymo-style incremental rollout.

So Tesla hasn’t found a different, better way to bring driverless technology to market. Waymo is just so far ahead that it’s dealing with challenges Tesla hasn’t even started thinking about. Waymo is playing chess while Tesla is still playing checkers.

The current excitement around Tesla’s FSD reminds me of the hype that surrounded Waymo in 2018. Early that year, Waymo announced deals to purchase 20,000 I-Pace sedans from Jaguar and 62,000 Pacifica minivans from Fiat Chrysler.

But the service Waymo launched in December 2018 was a disappointment. There were still safety drivers behind the wheel on most rides, and access was limited to a handpicked group of passengers.

It wasn’t until October 2020 that Waymo finally launched a fully driverless taxi service in the Phoenix area that was open to the general public. And even after that, Waymo expanded slowly.

Waymo began offering commercial service in San Francisco in 2023 and is now expanding to Los Angeles and Austin. Today, the company has only a few hundred vehicles in its commercial fleet—far fewer than the 82,000 vehicles it was planning to purchase six years ago.

What went wrong? In an August 2018 article, journalist Amir Efrati reported on the limitations of Waymo’s technology. Efrati wrote that “Waymo vans have trouble with many unprotected left turns and with merging into heavy traffic in the Phoenix area.” In addition, “the cars have trouble separating people, or cyclists, who are in groups, especially people near shopping centers or in parking lots.”

On self-driving, Waymo is playing chess while Tesla plays checkers Read More »