alternative_right shares a report from ScienceAlert: A study published in PLOS One in 2021 by researchers from Durham University in the UK showed that with 10 weeks of training, both blind and sighted people could learn to echolocate using verbal clicks. While the technique is already used by a number of people with impaired vision -- sometimes using the taps of a cane instead of clicks made by their mouth -- those findings suggest that many of us can learn the necessary techniques.

Some of the same researchers who made that discovery are part of the team behind a follow-up study published in Cerebral Cortex, looking at how the 10 weeks of training that the 26 participants went through actually changed the physical structure of their brains. Specifically, the team analyzed brain scans of the V1 (the primary visual cortex, processing visuals), and the A1 (the primary auditory cortex, processing sounds). Strikingly, the scans showed that the V1s of both blind and sighted people had developed sensitivity to sound echoes during echolocation training.

Read more of this story at Slashdot.

mikeleonardvisualarts posted a photo:

Thomas Hawk posted a photo:

Thomas Hawk posted a photo:

Thomas Hawk posted a photo:

Thomas Hawk posted a photo:

date stamped on slide, June 1965

Thomas Hawk posted a photo:

Weer voor Rotterdam ☁️ - 24-07-2026 13:15 CEST

In één oogopslag:
• 22.6°C · Bewolkt ☁️ | Min 17.4°C / Max 25.8°C | Kans op neerslag 2%

Verwachting voor vandaag:
• Min 17.4°C, Max 25.8°C (Bewolkt) ☁️, Kans op neerslag 2%, 🧭 1015.0 hPa ↘️ -2.7 hPa/24h, Windsnelheid: 7.9 km/u (2.2 m/s), richting: ↗ 240°

Uurlijkse voorspelling voor de komende 12 uur:

• 14:00: 23.4°C (Bewolkt) ☁️, Kans op neerslag 8%, 🧭 1017.7 hPa ➡️ 0.0 hPa/1h, Windsnelheid: 6.1 km/u (1.7 m/s), richting: ↑ 192°
• 15:00: 24.4°C (Bewolkt) ☁️, Kans op neerslag 5%, 🧭 1016.9 hPa ↘️ -0.8 hPa/1h, Windsnelheid: 6.1 km/u (1.7 m/s), richting: ↑ 201°
• 16:00: 25.2°C (Bewolkt) ☁️, Kans op neerslag 2%, 🧭 1016.1 hPa ↘️ -0.8 hPa/1h, Windsnelheid: 5.0 km/u (1.4 m/s), richting: ↗ 217°
• 17:00: 25.8°C (Bewolkt) ☁️, 🧭 1015.4 hPa ↘️ -0.7 hPa/1h, Windsnelheid: 3.6 km/u (1.0 m/s), richting: ↗ 245°
• 18:00: 25.7°C (Bewolkt) ☁️, 🧭 1014.9 hPa ↘️ -0.5 hPa/1h, Windsnelheid: 6.8 km/u (1.9 m/s), richting: ↘ 319°
• 19:00: 25.6°C (Bewolkt) ☁️, 🧭 1014.6 hPa ➡️ 0.0 hPa/1h, Windsnelheid: 7.2 km/u (2.0 m/s), richting: ↘ 309°
• 20:00: 24.8°C (Gedeeltelijk bewolkt) ⛅, 🧭 1014.1 hPa ↘️ -0.5 hPa/1h, Windsnelheid: 6.1 km/u (1.7 m/s), richting: ↘ 328°
• 21:00: 24.3°C (Licht bewolkt) 🌤️, 🧭 1013.6 hPa ↘️ -0.5 hPa/1h, Windsnelheid: 6.1 km/u (1.7 m/s), richting: ↘ 333°
• 22:00: 23.5°C (Helder) 🌕, 🧭 1013.6 hPa ➡️ 0.0 hPa/1h, Windsnelheid: 4.3 km/u (1.2 m/s), richting: ↓ 349°
• 23:00: 22.5°C (Helder) 🌕, 🧭 1013.3 hPa ➡️ 0.0 hPa/1h, Windsnelheid: 6.8 km/u (1.9 m/s), richting: ↙ 65°
• 00:00: 21.8°C (Helder) 🌕, 🧭 1013.0 hPa ➡️ 0.0 hPa/1h, Windsnelheid: 5.8 km/u (1.6 m/s), richting: ← 74°
• 01:00: 21.9°C (Licht bewolkt) 🌕, 🧭 1012.8 hPa ➡️ 0.0 hPa/1h, Windsnelheid: 6.1 km/u (1.7 m/s), richting: ↑ 193°

Voorspelling voor de komende dagen:

• zaterdag 25 juli: Min 20.0°C, Max 26.0°C (Bewolkt) ☁️, Kans op neerslag 2%, 🧭 1008.7 hPa ↘️ -6.3 hPa/24h, Windsnelheid: 15.8 km/u (4.4 m/s), richting: ↗ 241°
• zondag 26 juli: Min 18.3°C, Max 21.8°C (Lichte motregen) 🌦️, Neerslag 1.4 mm, Kans op neerslag 43%, 🧭 1005.3 hPa ↘️ -3.4 hPa/24h, Windsnelheid: 17.9 km/u (5.0 m/s), richting: → 265°
• maandag 27 juli: Min 16.7°C, Max 21.5°C (Lichte motregen) 🌦️, Neerslag 2.1 mm, Kans op neerslag 26%, 🧭 1015.5 hPa ↗️ +10.2 hPa/24h, Windsnelheid: 14.3 km/u (4.0 m/s), richting: → 291°
• dinsdag 28 juli: Min 15.9°C, Max 26.5°C (Licht bewolkt) 🌤️, 🧭 1020.7 hPa ↗️ +5.2 hPa/24h, Windsnelheid: 10.9 km/u (3.0 m/s), richting: ↑ 199°
• woensdag 29 juli: Min 19.1°C, Max 30.7°C (Zonnig) ☀️, 🧭 1015.6 hPa ↘️ -5.1 hPa/24h, Windsnelheid: 10.8 km/u (3.0 m/s), richting: ↗ 208°
• donderdag 30 juli: Min 20.2°C, Max 29.8°C (Gedeeltelijk bewolkt) ⛅, Kans op neerslag 3%, 🧭 1015.1 hPa ↘️ -0.5 hPa/24h, Windsnelheid: 10.8 km/u (3.0 m/s), richting: ↘ 294°

Details:
• 🌡️ Huidige temperatuur (om 13:15): 22.6°C (Bewolkt)
• 🤚 Gevoelstemperatuur: 23.8°C (+1.2°C)
• 💨 Windsnelheid: 6.1 km/u (1.7 m/s), richting: ↑ 185°
• 🌬️ Windstoten: 19.1 km/h (5.3 m/s)
• 💧 Luchtvochtigheid: 45%
• 🧭 Luchtdruk: 1017.7 hPa ↘️ -1.6 hPa/3h
• 👁️ Zichtbaarheid: 33.8 km
• ☀️ UV-index: 6.5
• 🌅 Zonsopgang: 05:52 · 🌇 Zonsondergang: 21:44

Luchtkwaliteit:
• AQI: 30 🟢 (Goed)
• PM2.5: 7.1 μg/m³
• PM10: 10.3 μg/m³

Gegevens geleverd door Open-Meteo

#Rotterdam #Meteo #MeteoBot #MeteoNetherlands #OpenMeteo #FediMeteo

The 127.9km mountain stage starts at 2pm CEST / 1pm BST
Stage 18 report | share your thoughts with Tom via email

This Tour’s most prestigious mountain stage finish; with a stage this short, it will be touch and go for the day’s escape to make it to the finish. That said, the opening 25km over the Cols Bayard and du Noyer are tough enough to allow a decent move to get established and then there are 60km until the Col d’Ornon, where Pogacar and Vingegaard will probably get their teams moving. If the big two give a move some leeway, the 21 hairpins to the finish will suit a pure climber such as Netcompany’s Thymen Arensman, a double stage winner last year.

Continue reading...

‘I cannot tell you what a proud moment this is. I think this might be the best day of my life. I’m not joking either,’ says PM

Andy Burnham has arrived by police convoy at No 10 North at Heron House in Manchester:

Andy Burnham was probably feeling left out of the “Celtic alliance”, after apparently telling John Swinney that Scottish independence was “off limits”.

Continue reading...

The New Orleans country-punk duo are on a hot streak, about to release their third album in a year, and the crunchy, literate hooks keep coming

From New Orleans
Recommended if you like Parquet Courts, Sheer Mag, Royal Headache
Up next Holy Cross Tigers out later this year

Not one but two of the year’s best albums so far are authored by a ragtag, deeply charismatic band from Louisiana named Twisted Teens, who bear Pynchonian names – Caspian Hollywell and RJ “Razor Ramone” Santos – a console steel guitar and, in Hollywell’s case, one of rock’s greatest, most soulful voices in many years. Virtuosic when it counts, ramshackle when necessary, Twisted Teens feel like they could slot nicely on to a bill with Parquet Courts and the late, great Royal Headache – crunchy, literary rock bands who seemed to shyly conceal their music’s anthemic nature.

Twisted Teens broke through earlier this year with their second album Blame the Clown, a set of 12 country-punk tracks that’s hooky and effusive enough to give any major-label pop record released this year a run for its money. A couple of weeks ago, they released its followup, the rangier and slightly more introspective Florida Water Blues, and before the year’s out they’ll have dropped Holy Cross Tigers, a release schedule befitting a band on a hot streak.

The pair’s origin story is foggy – the most one can glean from interviews is “they were neighbours, then Santos travelled cross-country to buy a steel guitar” – which feels appropriate for this kind of grimy, mythic music, which is raw and self-consciously handmade. Part of that feeling can be put down to Hollywell’s voice, a gritty and pockmarked thing which he uses to great effect, interspersing deadpan meditations on life and love with a rich, almighty howl that’s ostentatious but unpretentious. In other words, Twisted Teens are two of 2026’s true originals – a breakout band whose kooky, ultra-clarified vision outstrips the hype. Shaad D’Souza

Continue reading...

Dogs v bears from Max Porter; a baby adventure; an Irish boarding school murder mystery; and an unsettling YA folk horror

Waffles and Julius: No Hugs Please! by Ed Vere, Puffin, £7.99
Affectionate pup Waffles wants to hug Julius the standoffish cat – but this is not a good idea! A riotously funny picture book about getting what you want by patience (and devious means).

Chasing Dragonflies by Blessing Musariri, illustrated by Maisie Paradise Shearring, Kumusha, £7.99
Shingai can’t wait for his baby sister Aneni-Rose to grow enough to be his sidekick. Or maybe she’s the hero of the adventure? This immersive, imaginative picture book is full of richly observed sibling love.

Continue reading...

Researchers tracked mother-child pairs for up to 11 years, finding Pfas mixtures may affect developing guts

Prenatal and early life exposure to Pfas mixtures is probably linked to higher levels of intestinal inflammation in children, new first-of-its-kind research shows. Intestinal inflammation can cause irritable bowel diseases, like ulcerative colitis, Crohn’s disease and colorectal cancer.

While Pfas has been linked to irritable bowel disease and intestinal inflammation in adults, the new peer-reviewed Mt Sinai study is the first to look at how the chemicals impact children during a critical developmental phase.

Continue reading...

The new broadcast graphic will give fans a sense of the options, risks and pressures facing teams in real time.

Isack Hadjar has admitted that the struggles of past Red Bull drivers to match Max Verstappen was something he thought about when joining the team.

EINDHOVEN (ANP) - De adviesprijs van een liter diesel is vrijdag gestegen tot boven de prijs van een liter benzine. Dat blijkt uit cijfers over de brandstofprijzen die consumentenvoordeelplatform UnitedConsumers bijhoudt. De prijs van diesel loopt al langer hard op, als gevolg van de oorlog in het Midden-Oosten.

Een liter diesel werd vrijdag gemiddeld 3 cent duurder op 2,637 euro. De adviesprijs voor een liter Euro95 kwam 1 cent hoger uit op 2,634 euro. De adviesprijzen worden doorgaans alleen langs de snelweg gevraagd.

De dieselprijs stijgt al langer harder dan die van benzine, wat mede komt doordat er meer diesel werd geïmporteerd vanuit het Midden-Oosten. Onrust in de regio heeft daarom meer effect op de prijs. Ook een Russisch verbod op de export van diesel om binnenlandse tekorten te voorkomen speelt mee.

De adviesprijs van een liter diesel is nog niet zo hoog als in april, toen deze een recordhoogte van 2,819 euro per liter aantikte. De prijs van benzine nadert wel de hoogste prijs ooit.

BRUSSEL (ANP) - De Europese Commissie heeft geregeld dat er meerdere blusvliegtuigen naar Frankrijk en Spanje worden gestuurd om te helpen bij de bosbranden, laat een woordvoerder vrijdag weten. Naar Frankrijk zijn drie vliegtuigen en twee helikopters gestuurd. Spanje krijgt vier blusvliegtuigen.

Beide landen deden donderdagavond een verzoek voor hulp via het zogeheten EU-burgerbeschermingsmechanisme, laat de woordvoerder weten. De Commissie coördineert dan met de lidstaten waar geholpen kan worden. De vliegtuigen en helikopters komen uit Griekenland, Italië, Portugal, Slowakije, Tsjechië en Kroatië.

De EU heeft voor de bosbranden 777 brandweerlieden, 22 blusvliegtuigen en vijf helikopters klaarstaan in lidstaten waarop beroep kan worden gedaan.

Begin juli stuurde de Commissie ook blusvliegtuigen naar Frankrijk en Portugal om te helpen bij de bestrijding van de bosbranden.

This essay was written with Barath Raghavan, and originally appeared in The Guardian.

Major benchmarks measure what AI can do. None measure whether it does what you mean: the distance between what you ask an AI to do and the unspoken assumptions about how you want the AI to do it. We propose a new metric: the Genie coefficient.

There’s often a gap between one person’s request and another’s understanding. Most of the time, we bridge it using general knowledge. For example, if you ask a friend to get you coffee, they’ll pour a cup from the pot or buy one from a coffee shop. They won’t bring you a bag of raw beans or snatch a cup from a stranger and hand it to you. You never specified any of this. You never had to.

One might think the fix is just to specify tasks, questions, and intent better. But in 1987, in their seminal book on AI, Terry Winograd and Fernando Flores succinctly captured why that won’t work: “Q: Is there any water in the refrigerator? A: Yes. Q: Where? I don’t see it. A: In the cells of the eggplant.” In human language, wants and desires are always underspecified. It is impossible to list all the caveats, all the limitations, all the exceptions.

So how does anyone communicate, if intent can’t be pinned down? Because a reasonable person can make a reasonable guess. Even though wants and desires are always underspecified, a competent person generally knows enough context to get it right or else knows to ask for clarification. Linguists call this pragmatics: Meaning lies in the words and the situation and also in all prior communication, shared culture, and innate human behavior.

It doesn’t always work out, of course. Your friend might bring you a hot coffee when you wanted an iced coffee, or an Italian coffee when you wanted a Turkish coffee. The more dissimilar the two people are in age, culture, and background, the more likely the request will be misunderstood in some way.

This situation has major implications for AI agents that are increasingly being given requests by humans and expected to fulfill them. They have enormous latitude to get it wrong. An AI agent asked for coffee might buy a coffee plantation or order a cup of coffee for delivery in three weeks. Its actions may be recognizable as “getting coffee,” but not remotely what you intended. They’ll think outside the box because they won’t have our conception of the box.

When AI Gets Proactive

For most of the last decade, when systems like Alexa or Siri misinterpreted a request, it was annoying, not dangerous. Beyond the AI model itself, what has changed is the harness: the ordinary code that wraps around an AI model, decides when and how to use the model, and controls access to tools like a browser, a low-level command line, or a financial API. Developments in harnesses have turned large-language models that just predict text into AI agents that take actions in the world, without necessarily checking back in before reaching the goal.

AI researcher Simon Willison spent two days with Anthropic’s Fable AI, and called it “relentlessly proactive.” For example, he asked it to track down a stray scroll bar in a web app. He came back to find it had opened browsers, written its own screenshot tooling, created its own page to re-create the bug, and stood up a local web server to collect measurements. It found the bug and, along the way, did many surprising things he never asked it to do. And we are seeing similar behavior with all recent AI models when combined with flexible harnesses.

This kind of behavior could easily go off the rails. Tell an AI agent to book you a flight and, finding the airline’s site says sold out, it might break into the booking database and force a reservation. Ask it to schedule a meeting and it might snoop your password to access your calendar. Tell it to save money on your phone plan and it might cancel the plan outright, or scam someone else into paying the bill.

Getting precisely what you asked for and bitterly regretting it is one of the oldest hazards from ancient folklore. King Midas asked Dionysus for the power to turn everything he touched into gold only to see his bread, wine, and daughter turn to gold. Tithonus, granted the immortality his lover asked for but not the eternal youth she forgot to request, withered into a husk. The sorcerer’s apprentice enchanted a broom to fill the cistern, and the broom relentlessly complied until it flooded the house. The Golem of Prague, shaped from clay to guard its community, guarded it past all reason until someone erased the word on its forehead.

The most classic of these is a genie, bound to obey and indifferent to whether the wish was wise or well-structured.

Genies are now an engineering problem. We are handing them the keys to our inboxes, bank accounts, code repositories, and physical infrastructure. And we have no agreed-upon ways to measure how genie-like any AI system actually is.

Measuring Genie Behavior

In economics, the Gini coefficient (developed by statistician Corrado Gini) is a measure of the gap between an actual distribution and a perfectly equal one; it’s useful for understanding income inequality and more. Our proposed Genie coefficient measures the gap between what a user asked an AI to do and what the AI actually did.

Sometimes the AI might do the wrong thing. Like Dionysus, it reads your request literally and returns you a mess you never intended: like a coffee plantation instead of a cup. Asked to deal with all the spam phone calls you’re getting, a Dionysus genie might contact your carrier and change your phone number. Asked to get a refund for a bad toaster, it might draft a legal threat on fake letterhead and send it to the retailer.

Other times the AI does exactly the right thing, trampling everything nearby to get there. Like a golem or the sorcerer’s broom, it books your flight by hacking the airline. Or consider a ticket sale for a popular concert, where the ticketing system puts buyers into a virtual waiting room and admits them a few at a time. Asked to buy a ticket, a golem genie might spin up cloud servers to pose as millions of buyers from different addresses, improving your odds of getting a ticket while crowding out other users.

The two are not opposites, and a single botched task can have both characteristics.

Genie behavior is not flat-out failure. If you ask the AI for Q3 numbers and get Q2’s, that’s not a genie. Nor is prompt injection: That’s someone tricking the AI into doing something it shouldn’t. Here, the user is trying to work with the AI, and the AI is trying to comply. It’s also not simply a measure of the AI’s success in fulfilling a task. It’s a recognition that how an AI interprets and achieves a goal is as important as whether it achieves a goal.

Genie behavior isn’t new. Researchers have spent years studying AI systems that “game” their objectives. Goodhart’s law says that when a measure becomes a target, it stops being a good measure, and it’s long been known that AIs sometimes achieve goals in ways we don’t expect due to reward hacking. Some AI models will accidentally learn that cheating is one way to “win.” More recently, researchers have developing benchmarks for reward hacking in coding agents and for unpredictable behavior in customer support agents, while AI labs conduct their own safety evaluations before model releases. One effort found that AIs under pressure use tools they were told not to use, and this was a case where the rules were made explicit. These are all disparate research directions; nothing yet ties them together.

This problem falls under the general theme of alignment, a topic that has occupied science fiction writers and AI researchers for decades. At one extreme, the “paper-clip maximizer” thought experiment postulates a superintelligent and powerful AI that is told to maximize paper-clip production and turns the world into paper clips, which is the ultimate golem genie. At a mundane level, AI researchers are working to better design reward functions to ensure that AIs behave well and don’t cheat in the lab. It’s the practical middle ground that remains unbenchmarked: the ordinary AI agent in use today that might take your request and satisfy it the wrong way. We are not at the stage where an AI can focus the world’s production on paper clips, but it might charge a million paper clips to your credit card or hack into a paper-clip company’s network.

Building a Genie Benchmark

The Genie coefficient is meant for AI agents operating in the real world. It measures their behavior as they perform real tasks long after the model is trained, not just during development. It also recognizes that genie-like behavior is a property of the harness-plus-model system, not the model alone. The harness determines what tools the agent can use, how much autonomy it has, and how proactive it is, and it’s a place we can make real interventions.

It rests on the same “reasonable person” standard that we use for people. Did the system do what a reasonable person would have taken the request to mean? Answering that requires human judgment.

If we get the measurement right, it enables things that aren’t possible today, like policies concerning AI behavior. In a courtroom, the concept of mens rea, what someone meant to do, is often as important as what they did. The Genie coefficient suggests an AI analogue, where a user is accountable for the plain intent of what they asked the AI. If an AI system betrays the reasonable meaning of an instruction, that’s the AI’s misbehavior, not the user’s.

We’ll need multiple benchmarks to measure the Genie coefficient, because genie-like behavior can be domain specific. An AI coding agent may need to be judged on how often it fakes the tests, or swallows errors, or colors outside the lines on its way to a solution. An AI legal agent will need to be judged on how often its output says what you asked but means something you’ll regret. And so on for medical, finance, and other domains of knowledge and expertise.

Genie benchmarks can be built inside out, each task seeded with a choice that might literally satisfy but that a reasonable person rejects, such as tempting misreadings or unsanctioned shortcuts. The traps in a Genie coefficient benchmark might turn on situational knowledge, the kind of context that a reasonable person would bring to the task. Another approach is to give the same request in several different contexts, each with a different reasonable course of action.

A Genie benchmark should be permissive and make it genuinely tempting for an AI agent to take unreasonable shortcuts, because it can only find genie behavior when it’s actually possible. Test the AI in a safe, walled-off copy of a real system, with real tools it can misuse and some tasks that can’t be done honestly at all. Make the temptation to cut corners real. Test a diverse array of skills, use cases, and tools, and give the AI system sparse, confusing, or overwhelming context. Include tasks that people have learned, through experience, require human oversight.

How the benchmark is scored matters just as much. Measure Dionysus and golem genies separately and together, based on their worst, not best, behavior. Run the same model inside harnesses that vary its freedom to act, revealing which limits actually keep it in line and should therefore be required in AI harness policies. Weight each failure by the harm it would cause, not just a simple count. And don’t measure genie behavior in isolation: A model could otherwise earn a perfect score by stalling, refusing, or drowning the user in clarifying questions without ever doing the job. The first versions of these benchmarks will be crude, but that’s how benchmarks always start.

We have built genies. We have handed them our data and credentials. We made them relentless, creative, and indifferent to the gap between what we tell them and what we mean. The least we can do, before they are booking our flights, running our infrastructure, and signing contracts unsupervised, is to measure how often they betray us.

Eindelijk weten we precies wat de politie tijdens het nachtelijk bezoekje aan de Wallen nou werkelijk heeft klaargespeeld: Roemenen (van tussen de 34 en 45 jaar oud). Zeven stuks totaal. Het waren al die tijd de Roemenen die het rode licht zagen en dachten: ideale plek om tientallen vrouwen naartoe te lokken om als melkkoe gebruiken, uit te buiten, om te misleiden, te intimideren, te bedreigen en met geweld te dwingen tot allerlei tragische handelingen. Geen hoogtepunt maar eerder een duister dieptepunt voor de Amsterdamse rosse buurt, waar ze de tranen van de ramen kunnen lappen en maar weer achter een gordijntje mogen wachten op het volgende vunzige verhaal over mensonterende misstanden van mensenhandel. We wachten op de hoge straffen voor de Roemeense pooiers en onthouden: Roemeen en rust zijn geen bedgenoten.

Het politiebezoek

Social