Sony WF-1000XM6 review: Facing tougher competition

Sony’s 1000X earbuds have been at the top of Engadget’s best wireless earbuds list since we first published it. With each new generation, the company managed to retain its crown, primarily thanks to a massive collection of features and an effective mix of sound quality and active noise cancellation (ANC) performance. Today, Sony revealed the latest entry in the series, the WF-1000XM6, delivering yet another redesign both inside and out. The company’s tried and true formula of features and audio performance remains, but it may no longer be enough to elevate the M6 above the competition.  

Design

For the third time in a row, Sony overhauled the 1000X earbuds’ design. While the WF-1000XM5 was a clear revision of the WF-1000XM4, the M6 is a departure from both of them. The company managed to reduce overall size even further by using a rounder, pill-shaped enclosure for the earbuds’ main housing. I certainly applaud Sony for making these as tiny as possible, but I’m still not a fan of the company’s foam ear tips. 

Sony introduced foam ear tips on the 1000XM4 earbuds and that led to an unstable fit during my testing. In fact, I ended up switching to the silicone M3 tips for a better feel. Things were slightly better on the M5, but the company regressed here on the M6. I tried every size of ear tips in the box and the fit test in the Sound Connect app only ever confirmed an air-tight seal in my right ear. This can impact sound quality and ANC performance, and the earbuds don’t feel like they’re sitting far enough inside my ears. I never got used to the fit in my three weeks of testing. 

The exterior panel of the WF-1000XM6 is still touch sensitive, accepting a variety of taps for playback controls, calls, noise settings and more. Sony also included a repeated tapping gesture that’s used for volume control. Keep tapping on the right earbud to raise the volume and do so on the left to lower it. My disdain for touch controls is well-documented at this point, but the M6 reliably and quickly responded without me having to repeat a tap sequence. 

WF-1000XM6 features

The WF-1000XM6 accepts taps for touch-based controls.
The WF-1000XM6 accepts taps for touch-based controls.

As is typically the case with its 1000X earbuds, Sony has loaded the M6 with features. Nearly all of these are holdovers from previous models, including Adaptive Sound Control that can automatically adjust settings based on your activity or location. Speak-to-Chat still pauses audio and activates ambient sound when you start talking, but it’s also still easily duped by coughs or clearing your throat. A quick access feature can put Amazon Music, Apple Music, Endel, Spotify and YouTube Music two or three taps away, depending on how you configure it.

The M6 earbuds also allow you to accept and reject calls with head gestures and you’ll have the option to pipe in your own voice during a call. Sony retained its existing option of operating the earbuds with voice commands: If you say “Assistant,” you can then ask it to play, pause, skip and replay songs. It will also adjust the volume for you, but that’s the extent of its abilities. 

Sony included some handy power management features as well. First, there’s an Auto Power Save mode that will disable any custom EQ settings, DSEE Extreme upscaling, Speak-to-Chat and voice control/voice assistant to reduce power consumption when the M6 hits 20 percent battery remaining. A Battery Care tool will extend the life of the earbuds’ battery by stopping charging before it reaches 100 percent. Lastly, Automatic Power Off will turn the earbuds off when they’re outside of the case and haven’t been worn for some time. 

Basic conveniences like multipoint Bluetooth, wireless charging and IPX4 water resistance are also here. Wear detection is onboard and you can use Sony’s EQ presets to alter the sound profile, or make your own and save them for future use. The Sound Connect app puts the battery levels of the individual earbuds and the case front and center, and you can edit the main screen to hide the features displayed there if you don’t need them.

Sound quality and ANC performance

Sound quality is one of Sony's strengths and that continues on the M6.
Sound quality is one of Sony’s strengths and that continues on the M6.
Billy Steele for Engadget

If Sony’s long list of features is its top advantage over the competition, overall sound quality is number two. For the WF-1000XM6, the company built new drivers with soft edges for deeper bass and a more rigid, lightweight dome for clearer treble. There are also notches around the edges for “clearer and smoother” sound quality overall, according to Sony. And of course, DSEE Extreme upscaling helps to recover details lost to compression while 360 Reality Audio and Spatial Sound Optimization are available for more immersive listening. 

I found the M6 earbuds at their best with the stock EQ and DSEE Extreme active, much like I have on previous 1000X models. The sound is deep and warm, with thick bass that’s adequate without ever overpowering the mix. Highs cut through and there’s ample midrange, keeping those finer details from getting lost. Erika de Casier’s atmospheric Lifetime is a great example of what the WF-1000XM6 can do. Vocals seem to float over the top of the bassline and drums, with percussive piano chopping though and subtle synth details popping up throughout. When strings arrive on “Seasons,” the M6 ushers them into a prominent position rather than relegating them behind the beat. 

On the WF-1000XM6, the chaos of Spiritbox’s “Holy Roller” doesn’t get condensed to a messy heavy metal roar either. And there’s plenty of texture in the synth-driven noise of Nine Inch Nails’ “As Alive As You Need Me To Be.” Even more straightforward rock tracks like Jimmy Eat World’s “Bleed American” have ample punch, with a wide soundstage that never feels claustrophobic or sounds compressed. Overall, the WF-1000XM6 holds its own against the best-sounding earbuds you can buy right now, though some of the competition, like Technics, have an edge in the way they handle the subtlest of nuances in songs.  

If you were hoping for knock-out ANC abilities, I must report that the WF-1000XM6 isn’t the noise-canceling powerhouse that is Bose’s QC Ultra Earbuds. The M6 struggles mightily with human voices. While that’s the downfall of many ANC earbuds, you’ll want to keep it in mind if you plan to wear these in the office. I found I could also still hear constant noise sources like fans and white noise machines when wearing the M6 — items that the QC Ultra Earbuds combat effectively.

Using the WF-1000XM6 for calls

The WF-1000XM6 isn't as adept at calls as Sony advertises.
The WF-1000XM6 isn’t as adept at calls as Sony advertises.
Billy Steele for Engadget

For calls, Sony says the M6 uses AI for both background noise reduction and voice capture with the beamforming microphones. What’s more, the earbuds are equipped with eight total mics for ANC and calls, plus bone conduction tech for improved voice pickup. Unfortunately, all of that doesn’t lead to stellar performance during calls. While the WF-1000XM6 is perfectly usable for voice and video calls, the overall quality is far from pristine. To make matters worse, the earbuds make you sound overly processed when you encounter significant background noise. Since the company prided itself on the upgrades here, the results are disappointing. 

Battery life

Sony says the WF-1000XM6 will last up to eight hours on a charge or 24 hours when you factor in the full longevity of the charging case. During a battery test that I mostly ran with ANC active, I had no trouble hitting that single-charge figure. That’s with the volume around 75-80 percent and includes calls and virtual meetings where I switched over to ambient sound mode. 

It’s worth noting that I had DSEE Extreme upscaling active the entire time, which can impact battery life. Plus, if you use the aforementioned Auto Power Save mode, you can extend play time when you have about a quarter of a tank left. Going without some of the M6’s best features in the interest of having tunes for a workout or commute is a fair trade in my book. 

The competition

The WF-1000XM6 is still a compelling option, but it's not the clear favorite anymore.
The WF-1000XM6 is still a compelling option, but it’s not the clear favorite anymore.
Billy Steele for Engadget

When sizing up the competition for the WF-1000XM6, you have to choose your priorities. Simply put, no other company offers the comprehensive suite of features that Sony does. That’s been true for a while now and it continues with this model. If you want the strongest active noise cancellation, that will be Bose’s second-generation QuietComfort Ultra Earbuds. If the best sound quality is your goal, the Technics AZ100 is your best bet in this price range. I’ll also mention Sennheiser’s Momentum True Wireless 4 which offers great sound quality, respectable ANC and a comfier fit than the M6, but that set is almost two years old at this point.

Wrap-up

Sony continues its run of great earbuds with the WF-1000XM6, but this model isn’t the polished package that some of its predecessors were. The two most obvious places the company is lagging behind the competition is ANC performance and overall voice quality, not to mention my continued dissatisfaction with the fit that Sony’s foam tips provide. The M6 is also more expensive than the previous version, which makes it even harder to overlook any flaws. What you will get on the WF-1000XM6 is a ton of features, great sound quality and reliable touch controls in a smaller package. And for some, that might be enough to make you forget about the rest. 

The WF-1000XM6 is available today in silver and black for $330.

This article originally appeared on Engadget at https://www.engadget.com/audio/headphones/sony-wf-1000xm6-review-facing-tougher-competition-160000652.html?src=rss

US consumers, business pay 90% of tariff costs, says Federal Reserve

US businesses and consumers paid nearly 90 percent of the cost of Donald Trump’s tariffs last year, according to new Federal Reserve research that undercuts the president’s claim that foreign companies would bear the burden.

The study by the New York Fed found that the majority of tariff costs were passed through to Americans in the first 11 months of 2025, although exporters shouldered an increasing amount as the year progressed.

“Our results show that the bulk of the tariff incidence continues to fall on US firms and consumers,” the study’s authors wrote in a blog post on Thursday.

Read full article

Comments

Thomas Edison’s Lost EV Battery Idea Returns As New Prototype Recharges In Seconds

Thomas Edison's Lost EV Battery Idea Returns As New Prototype Recharges In Seconds
Scientists have successfully resurrected and supercharged a forgotten battery design from Thomas Edison, turning a 120-year-old concept into a modern pack capable of charging in mere seconds and with 12,000 charging cycles.

In the early 1900s, Edison bet on a nickel-iron battery to power the burgeoning electric vehicle market (that made

Party like it’s 2001: Diablo II gets a new expansion, new playable class

It’s not every day that a classic PC game gets a new content expansion 25 years after its last major update. But that’s what happened last night, as Blizzard suddenly released new “Reign of the Warlock” DLC that adds a new class, new end-game challenges, and new inventory-management options to the classic Diablo II.

To be clear, the new DLC is technically not for the original 2000 release of Diablo II (which was still getting patches as of 2016) but for the game’s 2021 Resurrected remaster. Still, that remastered version has gameplay and animations that are extremely faithful to the original, making yesterday’s surprise update the kind of content drop that players have been waiting for since 2001’s “Lord of Destruction” expansion.

The “Reign of the Warlock” DLC lets you “command forbidden power” as a new class that “wields forbidden arts, bridles hellfire and shadow, and dominates demons,” according to the in-game class selection screen description. By way of backstory, Blizzard writes that most Warlocks “have the means to lead a lavish lifestyle but find the pursuit of luxury and ease stale. Instead, they leverage their elevated status in Sanctuary to hunt down lost knowledge that would enable them to continue the legacy of Horazon.”

Read full article

Comments

[$] The reverting of revocable

Transient devices pose a special challenge for an operating-system kernel.
They can disappear at any time, leaving behind kernel data structures that
no longer refer to an existing device, but which may still be in use by
unknown kernel code. Managing the resulting lifecycle issues has
frustrated kernel developers for years. In September 2025, the revocable resource-management patch series
from Tzung-Bi Shih appeared to offer a partial solution to this problem.
Since then, though, other problems have arisen, and the planned merging of
this series into the 7.0 release has been called off.

HP Omnibook 3 AI Laptop Deal Brings Ryzen Zen 5 Firepower For A Low $449

HP Omnibook 3 AI Laptop Deal Brings Ryzen Zen 5 Firepower For A Low $449
Soaring memory and storage prices have you down? Yeah, we feel that. These are tough times indeed, as AI-driven demand for memory chips is putting data center clients front and center, leaving consumers to watch from the sidelines. It’s not all sour grapes, though. There are deals to be had, including an enticing offer on an affordable HP

The “Are You Sure?” Problem: Why Your AI Keeps Changing Its Mind

The large language models that millions of people rely on for advice — ChatGPT, Claude, Gemini — will change their answers nearly 60% of the time when a user simply pushes back by asking “are you sure?,” according to a study by Fanous et al. that tested GPT-4o, Claude Sonnet, and Gemini 1.5 Pro across math and medical domains.

The behavior, known in the research community as sycophancy, stems from how these models are trained: reinforcement learning from human feedback, or RLHF, rewards responses that human evaluators prefer, and humans consistently rate agreeable answers higher than accurate ones. Anthropic published foundational research on this dynamic in 2023. The problem reached a visible breaking point in April 2025 when OpenAI had to roll back a GPT-4o update after users reported the model had become so excessively flattering it was unusable. Research on multi-turn conversations has found that extended interactions amplify sycophantic behavior further — the longer a user talks to a model, the more it mirrors their perspective.


Read more of this story at Slashdot.

Linux 7.0 Networking: Prepping For WiFi 8 UHR While Dropping Last Parallel Port Ethernet Driver

The Linux 7.0 networking pull request showcases two extremes and the diversity and robustness of the open-source kernel ecosystem. Linux 7.0 is laying the groundwork for WiFi 8 Ultra-High Reliability (UHR) support while this kernel version is also bidding farewell to the last Ethernet driver for use over parallel printer ports…

AI Chatbots Are Even Worse at Giving Medical Advice Than We Thought

It’s tempting to think that an LLM chatbot can answer any question you pose it, including those about your health. After all, chatbots have been trained on plenty of medical information, and can regurgitate it if given the right prompts. But that doesn’t mean they will give you accurate medical advice, and a new study shows how easily AI’s supposed expertise breaks down. In short, they are even worse at it than I thought.

In the study, researchers first quizzed several chatbots about medical information. In these carefully conducted tests, ChatGPT-4o, Llama 3, and Command R+ correctly diagnosed medical scenarios an impressive 94% of the time—though they were able to recommend the right treatment a much less impressive 56% of the time.

But that wasn’t a real-world test for the chatbots medical utility. 

The researchers then gave medical scenarios to 1,298 people, and asked them to use an LLM to figure out what might be going on in that scenario, plus what they should do about it (for example, whether they should call an ambulance, follow up with their doctor when convenient, or take care of the issue on their own).

The participants were recruited through an online platform that reported it verifies that research subjects are real humans and not bots themselves. Some participants were in a control group that was told to research the scenario on their own, and not using any AI tools. In the end, the no-AI control group did far better than the LLM-using group in correctly identifying medical conditions, including most serious “red flag” scenarios. 

How a chatbot with “correct” information can lead people astray

As the researchers write, “Strong performance from the LLMs operating alone is not sufficient for strong performance with users.” Plenty of previous research has shown that chatbot output is sensitive to the exact phrasing people use when asking questions, and that chatbots seem to prioritize pleasing a user over giving correct information. 

Even if an LLM bot can correctly answer an objectively phrased question, that doesn’t mean it will give you good advice when you need it. That’s why it doesn’t really matter that ChatGPT can “pass” a modified medical licensing exam—success at answering formulaic multiple choice questions is not the same thing as telling you when you need to go to the hospital.  

The researchers analyzed chat logs to figure out where things broke down. Here are some of the issues they identified:

  • The users didn’t always give the bot all of the relevant information. As non-experts, the users certainly didn’t know what was most important to include. If you’ve been to a doctor about anything potentially serious, you know they’ll pepper you with questions to be sure you aren’t leaving out something important. The bots don’t necessarily do that.

  •  The bots “generated several types of misleading and incorrect information.” Sometimes they ignored important details to narrow in on something else; sometimes they recommended calling an emergency number but gave the wrong one (such as an Australian emergency number for U.K. users).

  • Responses could be drastically different for similar prompts. In one example, two users gave nearly identical messages about a subarachnoid hemorrhage. One response told the user to seek emergency care; the other said to lie down in a dark room. 

  • People varied in how they conversed with the chatbot. For example, some asked specific questions to constrain the bot’s answers, but some let the bot take the lead. Either method could introduce unreliability into the LLM’s output.

  • Correct answers were often grouped with incorrect answers. On average, each LLM gave 2.21 answers for the user to choose from. People understandably did not always choose correctly from those options. 

Overall, people who didn’t use LLMs were 1.76 times more likely to get the right diagnosis. (Both groups were similarly likely to figure out the right course of action, but that’s not saying much—on average, they only got it right about 43% of the time.) The researchers described the control group as doing “significantly better” at the task. And this may represent a best-case scenario: the researchers point out that they provided clear examples of common conditions, and LLMs would likely do worse with rare conditions or more complicated medical scenarios. They conclude: “Despite strong performance from the LLMs alone, both on existing benchmarks and on our scenarios, medical expertise was insufficient for effective patient care.”

Chatbots are a risk for doctors, too

Patients may not know how to talk to an LLM, or how to vet its output, but surely doctors would fare better, right? Unfortunately, people in the medical field are also using AI chatbots for medical information in ways that create risks to patient care. 

ECRI, a medical safety nonprofit, put the misuse of AI chatbots in the number one spot on its list of health technology hazards of 2026. While the AI hype machine is trying to convince you to give ChatGPT your medical information, ECRI correctly points out that it’s wrong to think of these chatbots as having human personalities or cognition: “While these models produce humanlike responses, they do so by predicting the next word based on large datasets, not through genuine comprehension of the information.”

ECRI reports that physicians are, in fact, using generative AI tools for patient care, and that research has already shown the serious risks involved. Using LLMs does not improve doctors’ clinical reasoning. LLMs will elaborate confidently on incorrect details included in prompts. Google’s Med-Gemini model, created for medical use, made up a nonexistent body part whose name was a mashup of two unrelated real body parts; Google told a Verge reporter that the mistake was a “typo.”  ECRI argues that “because LLM responses often sound authoritative, the risk exists that clinicians may subconsciously factor AI-generated suggestions into their judgments without critical review.”

Even in situations that don’t seem like life-and-death cases, consulting a chatbot can cause harm. ECRI asked four LLMs to recommend brands of gel that could be used with a certain ultrasound device on a patient with an indwelling catheter near the area being scanned. It’s important to use a sterile gel in this situation, because of the risk of infection. Only one of the four chatbots identified this issue and made appropriate suggestions; the others just recommended regular ultrasound gels. In other cases, ECRI’s tests resulted in chatbots giving unsafe advice on electrode placement and isolation gowns. 

Clearly, LLM chatbots are not ready to be trusted to keep people safe when seeking medical care, whether you’re the person who needs care, the doctor treating them, or even the staffer ordering supplies. But the services are already out there, being widely used and aggressively promoted. (Their makers are even fighting in the Super Bowl ads.) There’s no good way to be sure these chatbots aren’t involved in your care, but at the very least we can stick with good old Dr. Google—just make sure to disable AI-powered search results. 

Samsung Galaxy Z TriFold Sold Out In Minutes, Here’s When It May Be Back In Stock

Samsung Galaxy Z TriFold Sold Out In Minutes, Here's When It May Be Back In Stock
Even with a conservative rollout, Samsung’s nearly $3,000 Galaxy Z TriFold sold like hot cakes, with limited runs in South Korea being restocked six times and, just as impressively, selling out in less than half an hour after release here in the United States. For those of you missed out, more units are reportedly on the way.

Quite literally

The Paris-Roubaix 2026 route has been revealed. Will it make the race even faster?

The route of this year’s Paris-Roubaix has been revealed. The men’s race, on Sunday 12 April, is slightly shorter than in 2025, at 258.3km versus 259.2km last year. It includes 30 cobbled sectors for a total of 54.8km of pavé and is expected to finish at 4.30pm local time.

Meanwhile, the sixth edition of the women’s race, also held on 12 April rather than a day earlier as was the case for previous editions, will be 5km shorter than last year, but adds three extra cobbled sections. It’s slated to finish at the Roubaix velodrome later in the day than the men’s race, at around 6.20pm local time.

The Roubaix velodrome will be busy because there are also Junior and Under-23 races scheduled for the same day.

Reconfigured men’s route

As usual, the race begins in Compiègne, with the riders hitting the cobbles at around 100km for the Troisvilles sector, which always features as the first cobbled run. This year, it’s followed by a cluster of four closely packed sectors, a configuration that was used for the 2024 edition. 

Route planner Thierry Gouvenou points out that in 2024, the race began to split here under pressure from Alpecin-Deceuninck. To up the ante, the route will next return to the rarely used sector 26, which adds an 800m climb to the predominantly flat parcours.

Gouvenou reckons the tweaks to the route might catch some riders off-guard and result in an early pre-selection, as well as speeding up the race overall – maybe eclipsing the 2024 edition, which was the fastest ever.

The classic five-star sectors remain, with the Trouée d’Arenberg tackled at kilometre 163, Mons-en-Pévèle at kilometre 210 and Carrefour de l’Arbre at kilometre 241.

The men’s race will feature 25 teams – the 18 WorldTour teams plus seven invited UCI ProTeams.

More cobbles for the women’s race

The women’s Paris-Roubaix race route again starts in Denain, but the three new cobbled sectors add 4.5km of pavé, bringing the total to 33.7km. The new sectors include the four-star Haveluy at kilometre 52, before the women cover the same final 17 sectors as the men.

21 teams have been invited to the women’s race – the 14 Women’s WorldTour teams plus seven Women’s ProTeams.

As usual, there’s the Paris-Roubaix challenge for amateurs, which takes place on Saturday 11 April, the day before the pro races.

Embodied machine learning: From research ideas to classroom activities

Where do great research ideas come from in computer science education? We might think of research breakthroughs as a single moment of genius, but in reality impactful research is often the result of many years of iterative development. In November’s research seminar, we heard from Karl-Emil Kjær Bilstrup, a researcher at the University of Copenhagen, about his work to develop ML-Machine. This work uses embodied learning principles and the BBC micro:bit to introduce learners to machine learning concepts. Findings from this research have been used to develop the micro:bit CreateAI resources, and in this blog, we will explain the research journey from initial small-scale work to educational resources used by many young learners around the world.

Karl-Emil Kjær Bilstrup, a tool designer and researcher from the University of Copenhagen
Karl-Emil Kjær Bilstrup, a tool designer and researcher from the University of Copenhagen

From hypothetical ethics to concrete machines

In Karl-Emil’s first research study, students used prompt cards to develop ideas for machine learning applications that could solve real-world problems, and to discuss the ethical dilemmas associated with their solutions. Students found it difficult to address these ethical dilemmas in their designs; for example, their ideas often featured a trade-off of user privacy. The findings from this research informed Karl-Emil’s next study, which moved from hypothetical scenarios to implementing machine learning in real-world settings. 

The ‘Machine Learning Machine’ study made machine learning processes tangible for students through the use of two physical boxes, shown in the picture below. Students created drawings and fed them into the first box to train a model, and then tested the model by placing new drawings under a camera in the second box and having the model produce predictions of what the drawings showed. For example, students could draw pictures of the sun to represent daytime and the moon to represent nighttime to train a model to predict whether new drawings represented day or night. The machine was built for slow interaction, giving students time to think about the concepts and practices that they were developing. In a follow-up study, a new version of the Machine Learning Machine had been designed, which was controlled using a graphical user interface (GUI). This allowed users to “unbox” and influence parts of the machine learning process. For example, students could adjust the number of complete passes (called ‘epochs’) through the training data to improve the model’s accuracy. 

The two components of the Machine Learning Machine: the training box (left) and the evaluator box (right)
The two components of the Machine Learning Machine: the training box (left) and the evaluator box (right)

The two studies with the Machine Learning Machines provided many useful findings for teaching about machine learning with K–12 (primary and secondary) learners. However, two constraints remained: firstly, there were limited opportunities for whole-class work because there was only one Machine Learning Machine, and secondly, learning experiences needed to be better connected to examples from students’ daily lives. As a result, the next iteration in Karl-Emil’s research involved using the micro:bit, which ensured access to a tangible device for every student, and a new graphical platform called ML-Machine that students could interact with.

Machine learning and the micro:bit

The micro:bit is a small, programmable computing device that features sensors to gather data from the immediate environment. For example, the accelerometer is a motion sensor that can detect when the micro:bit is tilted from left to right, backwards and forwards, and up and down. Using the micro:bit with ML-Machine and some common household objects, students can create simple machine learning models that use data from the micro:bit’s accelerometer to detect whether the micro:bit is moving. This is a very different approach from rule-based programs on the micro:bit, where students might use programming constructs such as if statements to detect movement if the numerical reading from the accelerometer is above a certain value. Here, a machine learning model trained using a set of 20 examples is used to analyse live data readings and produce predictions about whether the micro:bit is moving.

A visualisation of a simple machine learning model to detect whether a micro:bit is being shaken or is still
A visualisation of a simple machine learning model to detect whether a micro:bit is being shaken or is still

In our seminar, Karl-Emil gave a live demonstration of the ML-Machine toolkit, so we highly recommend watching the recording to see how this toolkit brings machine learning concepts to life. 

ML-Machine is the precursor to the micro:bit CreateAI resources, and the software is fully open-source. However, the innovation doesn’t stop there: Karl-Emil also explained that he is currently developing a new tool called math.ml-machine.org, where students can train a neural network and see a visualised k-nearest neighbour model to explore how a model makes predictions. The research journey is continuing, with new possibilities for educational opportunities to teach about machine learning.

Embodied learning

The idea of embodied learning is interwoven throughout all of Karl-Emil’s research projects and is a cornerstone of all of his work. Embodied learning suggests that we learn more effectively when our whole body is involved in the learning process, not just our minds. For example, in the work described in this seminar, the Machine Learning Machines and the micro:bit were all tangible devices that students could touch and see. 

Embodied learning is particularly important in activities that involve working with data-driven systems. In traditional programming activities, the flow of code can be traced transparently through a program. However, machine learning models are more opaque, and their outputs cannot be traced step by step. Students can benefit from using bodily movements and sensorimotor information to help understand machine learning concepts. 

The ML-Machine toolkit was designed to support students to learn through embodied learning in three different ways:

  1. Enacting machine learning processes: Students used bodily movement to collect the data samples needed for the ML-Machine model to detect and predict gestures 
  2. Using machine learning as a design material: Students created concrete ‘objects-to-think-with’, which helps form deeper connections to abstract concepts
  3. Embodied exploration of machine learning: Students experienced how their bodily movements were translated into data points on the screen
Secondary school age learners in a computing classroom.

Embodied learning helped students grasp concepts such as data quality. They could see how their bodily movements were being translated into digital data, and could spot when movements that appeared different to them were being classified as similar by the ML-Machine model. One case study participant described that the immediate feedback on screen made the concept of machine learning feel as if it were “coming to life as they [the students] manipulate something themselves and they’ve got control over it”.

Find out more

Karl-Emil’s work shows how research ideas can be used in the classroom through a cycle of discovery, design, and reflection. From prompt cards exploring ethics to tangible machines and the micro:bit-based ML-Machine, his research shows how embodied learning can make complex ideas like machine learning not only understandable, but deeply engaging for young learners. The micro:bit CreateAI resources are a great example of how research findings can evolve into accessible, hands-on tools that empower educators and students alike. As this work continues to grow, it invites us to imagine new ways for learners to experience machine learning not as abstract theory, but as something they can see, feel, and shape with their own hands.

If you’d like to try out some of the ideas from this seminar, here are some useful resources: 

  • Explore machine learning projects using the micro:bit: micro:bit CreateAI and our Dance detector project are great places to start
  • Find out more about the research: Read more about Karl-Emil’s work in this open-access paper
  • Investigate new tools: Explore neural networks and k-nearest neighbours algorithms in the new maths-focused version of ML-Machine at math.ml-machine.org

Join our next seminar

Join us at our next seminar on Tuesday 17 March from 17:00 to 18:30 GMT to hear Rebecca Fiebrink (University of the Arts London speak about teaching AI for creative practitioners. This will be the second seminar in our new series on how to teach about AI across disciplines. We hope to see you there!

To sign up and take part in our research seminars, click below:

You can also view the schedule of our upcoming seminars, and catch up on past seminars on our previous seminars page.

The post Embodied machine learning: From research ideas to classroom activities appeared first on Raspberry Pi Foundation.

Debian DFSG Team announces new dashboard and queue processes

Reinhard Tartler of Debian’s new DFSG,
Licensing & New Packages Team
, or simply “DFSG Team”, has announced
that the team is now operational and is deploying new tooling to
improve the NEW queue experience for Debian developers and
maintainers.

Our primary and immediate goal is simple: get the queue down.

We are currently settling in and refining our processes to ensure
stability and consistency. While our focus right now is on clearing
the backlog, our long-term vision is to enable all Debian Developers
to meaningfully contribute to DFSG reviewing activities, distributing
the workload and knowledge more effectively across the project.

The announcement includes information on the new dashboard for
packages in the NEW queue
, the rationale for the new tooling, and
an introduction to the members of the team.

These Noise-Cancelling JBL Headphones Turned Me Into an Audio Snob

We may earn a commission from links on this page.

I’d never owned a really good pair of headphones; it’s just not something I’ve really cared about. But the JBL Tour One M3s I recently acquired changed my mind forever. (Our resident audio expert Daniel Oropeza went in depth about these headphones in his review, which you should read.)

Music is better when it sounds better

I’ve always taken a punk-rock approach to sound gear—cheap and loud has been good enough for me—but using these headphones non-stop for the past couple of weeks proves I’ve been very wrong. Music is better when played through decent equipment. There’s so much more there, even in songs I’ve listened to a million times: I can finally make out what’s actually being said in the “party chatter” that provides atmosphere in Marvin Gaye’s “What’s Going On,” and who knew there was so much going on in The Dead Boys “Sonic Reducer?” And the spatial audio? Forget it: Hearing different instruments from different parts of 3D space is amazing. These headphones have me thinking seriously about stereo separation, constantly messing with the EQ, and turning my nose up to every format that isn’t lossless. I’m listening to damn jazz. What’s even happening?

Silence is better when it’s truly silent

Okay, I know active noise-cancelling has been around for 20 years, but I never cared about it—most of the music I like is mostly noise, so why would I want to cancel it?—but it’s for cancelling all that other noise that surrounds us. These are perfect for an airplane or car trip but also for just wearing around. I had no idea how much ambient sound I’m swimming in all day at my home office—the fridge running, the cars on the road, the wind—all gone in an instant. Nothing to hear but silence and tinnitus (from too much punk rock).

What the audio transmitter does

JBL Tour Headphones

Credit: Stephen Johnson

When I first opened the box and saw the smart transmitter/controller thing that comes with these phones, I was like, “What’s this dumb thing?” But it turns out it’s not dumb. It’s a Smart Tx Audio Transmitter. You can plug it into almost anything audio and it will wirelessly stream 24-bit audio. Plug the AUX-to-USB-C cable into the beat-up jack at the gym, or an airplane’s headphone jack ,and you can listen with full noise-canceling and high-quality audio. (Now that I’m an audiophile, this is important to me.) It’s also a touch screen controller so you can mess around with the EQ to hear Art Pepper’s dulcet saxophone tone without taking your phone out, and trust me, you haven’t heard Art Pepper if it’s not lossless and tuned with JBL’s “personal sound amplification.” I mean, you might as well be listening to The Dead Boys or something.

It comes with a traveling case

In my old life, I would have left the Smart Tx Audio Transmitter on a bus, but these phones come with a little carrying case where all my little cords have places to live, and I put my audio transmitter right where it goes, because I never want to be burdened with inferior audio again.

I could go on about these headphones, but I’ve realized that air is an imperfect medium for sound vibrations, so I’m going to sit and stare at the sheet music of Coltrane’s A Love Supreme and listen to pure theory.

Samsung’s 5-Layer Penta Tandem QD-OLED Tech Doubles Lifespan And Boosts Brightness

Samsung's 5-Layer Penta Tandem QD-OLED Tech Doubles Lifespan And Boosts Brightness
Samsung Display is touting its new QD-OLED Penta Tandem technology as a major advancement in premium displays, claiming that its proprietary five-layer structure brings with it numerous benefits compared to its four-layer QD-OLED, not the least of which is double the lifespan. It also purportedly improves luminous efficiency by 1.3 times to

Anthropic To Cover Costs of Electricity Price Increases From Its Data Centers

AI startup Anthropic says it will ensure consumer electricity costs remain steady as it expands its data center footprint. From a report: Anthropic said it would work with utility companies to “estimate and cover” consumer electricity price increases in places where it is not able to sufficiently generate new power and pay for 100% of the infrastructure upgrades required to connect its data centers to the electrical grid.

In a statement to NBC News, Anthropic CEO Dario Amodei said: “building AI responsibly can’t stop at the technology — it has to extend to the infrastructure behind it. We’ve been clear that the U.S. needs to build AI infrastructure at scale to stay competitive, but the costs of powering our models should fall on Anthropic, not everyday Americans. We look forward to working with communities, local governments, and the Administration to get this right.”


Read more of this story at Slashdot.