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Amazon Leo má zatím na nízkých oběžných drahách v provozu pouhých 238 družic – naprostý zlomek oproti konkurenčnímu Starlinku –, cloudový gigant ale přesto začíná oznamovat první služby pro podnikové klienty. A jde tvrdě po SpaceX. Starlink už několik let prodává palubní internetovou konektivitu ...

Společnost Blackmagic Design oznámila vydání verze 21 svého proprietárního softwaru pro editování videí a korekci barev DaVinci Resolve běžícího také na Linuxu. Z novinek je nutno vypíchnout možnost editování fotografií. Základní verze DaVinci Resolve je k dispozici zdarma. Plnou verzi DaVinci Resolve Studio lze koupit za 295 dolarů.

Magnetická klávesnice Rapture Foxtrot HE zlevnila o 40 % na 1499 Kč. • Alza si účtuje dvakrát až třikrát méně než Logitech, Corsair a další výrobci. • Klávesnice je přesná, na poměry mechanik příjemně tichá a kvalitně vyrobená.

Cybersecurity researchers have discovered a new campaign in which a cluster of 108 Google Chrome extensions has been found to communicate with the same command-and-control (C2) infrastructure with the goal of collecting user data and enabling browser-level abuse by injecting ads and arbitrary JavaScript code into every web page visited. According to Socket, the extensions (complete list Ravie Lakshmananhttp://www.blogger.com/profile/[email protected]

Analytická společnost KB Securities připravila výhled, který obsahuje žebříček 11 firem podle nejvyššího provozního zisku očekávaného na příští rok. V čele stojí Samsung, který překonal i Nvidii…

Nvidia CEO Jensen Huang often shares the story of hand-delivering an AI supercomputer to OpenAI in 2016, back before it was the hotshot company it’s become in recent years.

A key ingredient in the box was Nvidia’s CUDA toolkit, which helped turn OpenAI’s experiments into a foundation for modern AI applications. Huang credits the software platform as being the foundation for Nvidia’s success in AI and high-performance computing.

At 20 years old, CUDA is still going. It’s driving Nvidia’s re-imagination of hardware in new areas that range from quantum computing to robotics, modern machinery and even autonomous vehicles.

Put simply, the CUDA toolkit includes programming tools, a compiler stack and libraries and effectively unlocks the computing capacity of GPUs. For nearly two decades, CUDA architect Stephen Jones has had a front-row seat to the toolkit’s evolution and he’s continuing the work of engineers such as the late John Nickolls, who championed CUDA’s development.

Computerworld sat down recently with Jones to talk about CUDA, AI, and the future.

CUDA architect Stephen Jones has been at Nvidia for nearly two decades overseeing and shaping the CUDA toolkit’s evolution.

Nvidia

It’s been 20 years of CUDA. How’s it been? ”A wild ride. When you build something and people do stuff with it that you had never thought of, that’s the true reward of engineering.

“We started out building a tool good at parallel programming…[it] and turns out everybody needs parallel programming. What’s ChatGPT at now? Like one-third of the world population. I wasn’t one of the founders of CUDA. I joined after CUDA 1.0…. I was in the first dozen and it’s amazing.

“And now it’s used widely around the world every day on ChatGPT by people that have accounts and they’ve touched it. The best engineering is invisible, right?”

Did you know AI was coming before the hardware was developed? And what was the thought process at the programming level to get ready for it? “The watershed moment for AI was AlexNet in 2012. A niche mathematical subject suddenly beat humans and became interesting and powerful. 

“At that point we already had people inside Nvidia working on what was going to become CuDNN, because it was looking interesting. That watershed moment really just shifted the gears and opened this whole new avenue of things we could support.

I worked very hard in CUDA to build as general a base as possible, because I don’t know what’s coming. Even just in AI, it changes every six months. But AI is just one piece. There’s supercomputing, there’s robotics and machinery control.

“A huge goal for us is to not build single-purpose tools [and] make sure that what we build can be applied generally. And when we spot someone doing something really interesting, we expand in that direction.

“Especially now, the explosion of the things people are doing — there’s always new stuff around the corner.”

How did CUDA evolve? “We announced it in 2006; 1.0 officially was early 2007. When we first were building it, barely anything worked. The hard part was what can we enable that makes it useful at least a bit. You have to get feedback and people trying as you build things.

“My first week at Nvidia, they said, ‘Go write a program in CUDA.’ And I said, ‘How do I debug it? Where’s my printf?’ And they said there is no printf. So I wrote printf. The most useful thing I’ve ever done at Nvidia was writing printf in my first week. Because you need to debug your stuff.

“The hardest part of my job is that nobody ever publishes failure. There are all these papers, I accelerated my code by 100x with CUDA, but what was hard? When I see half a dozen people all doing the same thing, it says we’re missing something.

“So, we’ve transitioned from how to get it functional enough to be broadly useful to what directions do we start extending it in? That was a real mental shift for us, helped by the exponential adoption that was happening.

“If we build things so people have to write less CUDA, then we have succeeded. You can get to the GPU with less effort and fewer lines of code.”

Do you build CUDA first and then decide the GPU design, or is there cooperation between the two? ”As one of the CUDA architects, literally half my job is working with the hardware team. The thing that Nvidia has really done magnificently well is the co-design between the hardware and the software. 

“When the hardware [team] says, ‘Here are some things we’re thinking of building,’ we from CUDA are in the room saying, ‘Make these small changes so that we can project it outwards in software.’

“Or we’ll say, our users are lacking this thing… can we build something in hardware to make this faster? It is literally half my hours in any week talking to hardware people and half to software people.

“A chip takes about four years to build, and we are there for all those four years. We’ve got Rubin sort of around the corner, but there are ones beyond that that haven’t even been named, and we’re already working on those. 

“There is no ‘build it and then adapt the software’ to it. We really build it holistically, trying to think of the whole thing.”

Clearly, you needed to know AI was coming to make sure GPUs were ready for it. And AI is a whole new style of computing. How did you change the CUDA stack? “The secret of CUDA is that it’s not one thing, right? It’s like this massive stack of hundreds and hundreds of things…. You can pick and choose which one you want to use. 

“It’s also all the things that everybody else builds on top of it. We can support, but we couldn’t do anything without all the people who are also building on top of it.

“What’s fascinating to me about AI and HPC is that they’re really built on the same heavy computational basis. Two different angles of viewing the same thing. All the tools we built for supercomputing applied to the AI world. And the lessons in AI are now being applied back to supercomputing. It’s going both ways.

“Classical von Neumann computing is that logical, repeatable, systematic computing that’s been around for 50 years. AI is probabilistic. It’s not repeatable. It’s approximate. 

“Your LLM isn’t necessarily producing the same tokens two or three times in a row, whereas I definitely want my bank statement to produce the same numbers.

“The software and hardware teams work closely together, so we can see this coming. The emphasis on matrix workloads in AI is pushing up against the laws of physics, Moore’s law, information theory. It drives reductions in precision.

“I wouldn’t say it was predictable because we didn’t know what the models would look like, but the things the models need to function, that’s governed by the laws of physics. A lot of it is reacting to the constraints the world puts on us to take advantage of this AI universe that just opened up.”

Are you seeing the gap narrow between when CUDA features come out and when enterprises actually adopt them? “With any engineering tool, especially a platform as big as CUDA, different people pick it up at different rates. Academic researchers are on something the day we put it out. Established companies who are more risk-averse take more time.

“But you can easily imagine people who have established businesses and established software stacks don’t want to pull something in right away. And it’s more expensive for them. If you’ve got a million lines of code, adopting something new is much harder than if you’ve got 10,000 lines of code.

“So any feature we put out, we see early adopters, mid-adopters and late adopters. We have a chance to evolve it. It’s not static, we are definitely reacting and responding.

“In some ways, the time for the big players to adopt things is reducing. It’s part of that incredibly rapid change that AI is undergoing. We make heavy use of AI tools internally ourselves, so that helps us keep up. Everybody is using these tools that get invented to make more tools. The way that software is developed is even shifting in this day and age.

“It’s not like we build a thing and throw it over a wall. We’re building something in response to what people need, and as we build the tools, people can do more and there’s a feedback loop — the same feedback loop that drove how we developed CUDA in the early days. We’re seeing it show up in AI, we’re seeing it show up in quantum.

“People are developing quantum algorithms on GPUs today before quantum computers are ready, so that when the quantum computer technology catches up, they’re not starting from scratch.”

Keeping in mind that you track the future of computing for CUDA, what excites you the most? “We talked about how AI is opening new doors that have never been opened before. I was at Stanford a few months ago talking to some brilliant undergrads and graduate students about their startup ideas. The energy they have for what they want to do —  realized I was envious of where they are.

“I came of age in the ‘90s, when the internet was just showing up. That was fascinating, all these things you could do with the .com. But this is an even bigger step. Everybody coming out of college now has a million choices. There’s all these things to explore that nobody’s even thought of before.

“What they do is going to feed back and change what we build as the underlying platform. I wish I could live to see all the things that they’ll do, but they’re younger than me. 

“The next 10 years is going to be wild. I’ve had a great journey, don’t get me wrong, but my God, I am envious of a 25-year-old. It’s unlimited what you can do. I wish I was 25 again.”

Francouzská vláda za deset miliard korun ovládla firmu Bull, aby de facto čelila americké hegemonii • . • Tuzemští odborníci z DataSentics profilují evropskou ofenzivu v oblasti vývoje umělé inteligence. • Legenda IT infrastruktury uspěla v Ostravě a dodá klíčové prvky pro národní superpočítačové ...

A critical security vulnerability impacting ShowDoc, a document management and collaboration service popular in China, has come under active exploitation in the wild. The vulnerability in question is CVE-2025-0520 (aka CNVD-2020-26585), which carries a CVSS score of 9.4 out of 10.0. It relates to a case of unrestricted file upload that stems from improper validation of Ravie Lakshmananhttp://www.blogger.com/profile/[email protected]

Kvadratická kvantová gravitace nahrazuje teoretickou singularitu stabilním stavem • Nový model navíc přirozeně vysvětluje extrémně rychlé rozpínání vesmíru • Hypotézu lze reálně ověřit pomocí detekce primordiálních gravitačních vln

Zvěst o zdražování procesorů AMD byla vyvrácena poklesem cen většiny modelů řady Ryzen 9000. Zatímco nové Core Ultra 200 Plus není k dostání za oficiální cenu, cena Ryzen 7 9800X3D a dalších klesá…

The U.S. Cybersecurity and Infrastructure Security Agency (CISA) on Monday added half a dozen security flaws to its Known Exploited Vulnerabilities (KEV) catalog, citing evidence of active exploitation. The list of vulnerabilities is as follows - CVE-2026-21643 (CVSS score: 9.1) -  An SQL injection vulnerability in  Fortinet FortiClient EMS that could allow an Ravie Lakshmananhttp://www.blogger.com/profile/[email protected]

Vědci z University of Southern California představili paměťový čip, který funguje i při 700 °C. Studie publikovaná 26. března 2026 v časopise Science ukazuje technologii, která může změnit vesmírné mise, průmysl i umělou inteligenci.

Multipatformní renderovací jádro webového prohlížeče Servo je na crates.io. S vydáním verze 0.1.0 (LTS).

The technology harnesses the brain’s own blood chemistry to assemble soft, light-controlled electrodes around neurons.

A single shot transforms the mice’s brains into biomanufacturing machines. Blood proteins churn the injected chemicals into a soft, flexible electrode mesh that seamlessly wraps around delicate neurons. Pulses of light aimed at the mesh quiet hyperactive cells. All the while, the mice go about their merry ways, with no inkling they’ve been turned into cyborgs.

This science fiction-like invention is the brainchild of Purdue University scientists seeking to reimagine brain implants.

These devices, often composed of rigid microelectrode chips, have already changed lives. They can collect electrical signals from the brain or spinal cord and translate these signals into speech or movement—returning lost abilities to people with paralysis or diseases of the brain. Implants can also jolt brain activity and pull people out of severe depression.

Yet most implants require extensive surgery and risk damaging the brain’s delicate tissue. The new technology would avoid these downsides by building electrodes directly at the target.

“Our work points to a future where doctors could ‘grow’ soft, wire-free electronic interfaces inside the brain using the patient’s own blood, then gently dial brain activity up or down from outside the head using harmless near-infrared light,” study author Krishna Jayant said in a press release.

Probes Galore

The brain produces every one of our sensations, movements, emotions, and decisions. Scientists have long sought to decode and manipulate its activity with a range of hardware.

Some devices use electrodes to monitor single neurons in a lab dish. Others are physically inserted into brain regions that encode cognition and emotion. Some designs sit atop the brain, without puncturing its delicate tissue, and capture dynamic brain waves like a wide-lens camera.

But brain tissue is soft and squishy; microelectrodes are not. The mismatch often leads to scarring, signal loss, and shortened device lifetimes. Replacing broken or infected implants is surgically complex and can further damage the brain. Some experts have even raised ethical concerns about long-term care.

A recent explosion of soft, biocompatible materials suggests alternatives are possible, and we’ve seen a wave of creative new probes. In one example, a silk-like mesh drapes over the brain’s surface, and a related version maps electrical activity in brain organoids. Another device is smaller than a cell and, after injection, hitches a ride on immune cells into the brain. These systems can record and alter brain activity. But prebuilt implants often require surgery and struggle to integrate with their hosts without damaging surrounding tissue.

So, why not grow an electrode directly inside the brain?

“The ability to synthesize [conductive] materials on demand at a target site could overcome the limitations of conventional synthetic implants,” wrote M.R. Antognazza and G. Lanzani at the Italian Institute of Technology, who were not involved in the study.

Under Construction

Our cells are natural manufacturers, constantly assembling things like proteins, genetic messengers, and membranes. Cells rely on two essential ingredients to construct the complex structures of life: Biological building blocks and catalysts to bind them together. Synthetic materials work the same way. Monomers link like Lego blocks to form polymers with the help of a catalyst.

The discovery of electrically conductive polymers, meanwhile, has galvanized efforts to grow living bioelectronics directly inside the body. In a previous study, researchers genetically engineered cells to produce a protein catalyst that helps assemble conductive structures on the surfaces of living neurons. Another approach used hydrogen peroxide—a common first-aid staple—to compile monomers into reliable electrodes that monitor nerves in leeches.

These quirky early successes showcased the promise of brain-built electronics, but hit hard limits. The chemistry often relied on catalysts toxic to neurons. Even when successfully formed, the electrodes mostly just listened. Changing brain activity required additional physical cables.

The Purdue team rewrote the recipe. They designed a monomer, called BDF, that with the help of hemoglobin—a protein in red blood cells—becomes a soft, flexible, and electrically conductive mesh surrounding neurons at the site of injection. The willowy electrode hugs the brain’s anatomy and moves with it, minimizing physical damage. It’s responsive to near-infrared light and can translate light pulses from outside the skull into electrical signals that alter brain activity.

“Our key idea was to let the body’s own chemistry do the hard work,” said study author Sanket Samal.

The appeoach worked in several tests. Injecting BDF into store-bought beef and lamb steaks produced the electrode mesh within a day at human body temperature. In zebrafish embryos, a darling in neuroscience research, the reaction proceeded smoothly inside their yolks. Over 80 percent of the embryos survived, developed normally, and actively swam around—suggesting minimal harm.

But steak dinners and translucent fish are a far cry from our brains. Mice are closer. With the help of blood, BDF formed electrodes in mice’s motor cortexes after injection with minimal surgery. The mice’s brains maintained a normal balance of activity as they skittered around.

The team also coaxed dendrites, the tree-like input branches of a neuron, to produce the conductive mesh. Dendrites aren’t just passive cables, they’re “mini computers” that contribute to the brain’s computation and learning. Current methods struggle to precisely single out and control dendrite activity without messing with other parts of the neuron.

With near-infrared light, dendrite-built electrodes changed the way the neural branches behaved. The light temporarily lowered brain activity, and mice trained to press a lever were unable to perform the task. It didn’t wipe out their memory though: After turning off the light, the animals regained the skill. Their brains showed no signs of infection, inflammation, or over-heating throughout the study.

Inhibiting brain signals has upsides. Hyperactive brain activity in epilepsy and Parkinson’s disease, for example, is currently dampened with medication or—in severe cases—brain implants. If validated, brain-grown electrodes could be a less invasive alternative. Though to be clear, the method still requires surgery to inject the materials. Adding biocompatible magnetic ingredients, which can also control brain activity, could further boost the system’s potential.

How long the materials stay put and if they’re safe over the long term remains unclear. But in theory, the strategy could also control spinal cord nerves or heart tissue. Researchers could also adapt the strategy to use other types of materials that regulate brain activity in different ways, like ramping it up.

With further improvement, the electrode wouldn’t “just coexist with brain cells for months or years; it becomes part of them, stable across lifetimes,” said Jayant.

The post Scientists Grow Electronics Inside the Brains of Living Mice appeared first on SingularityHub.

Bývalého nájemníka nemusíte vystěhovávat měsíce, může být pryč i s posvěcením soudu už do pár týdnů. Poradíme, jak toho docílit.

Před koupí pozemku si ověřte, zda na něj máte přístup z veřejné cesty. I když je třeba zajištěná věcným břemenem práva cesty přes pozemek souseda.

Evropa chce vlastní platební systém nezávislý na Visa a Mastercard. Wero už funguje a má desítky milionů uživatelů. Digitální euro je ambicióznější, ale naráží na technické problémy a přílišnou složitost.

Na funkce pro výpočet transformací 1D signálů dnes navážeme. Ukážeme si zpracování 2D signálů, konkrétně rastrových obrázků. Budeme počítat rychlou dopřednou i zpětnou FFT, DCT i DST a odstraníme šum z obrázku.

Promyšleně upravená krajina může současně být okouzlující, pojmout spoustu uhlíku, oplývat biodiverzitou a současně fungovat jako strategická obrana před nepřátelským vpádem. Opět se potvrzuje, i když by z toho obě strany nejspíš byly v rozpacích, že vojáci a environmentalisté k sobě vlastně mají velice blízko.