Agregátor RSS
Polish data center plans to send its waste heat to the neighbors
As Europe swelters in a heatwave, residents probably don’t want to hear about ways to make their homes even hotter, but that’s what Polish property developer Citylink is talking about, with plans to dump waste heat from a new data center in Wrocław into the municipal district heating network.
Citylink is designing the data center so that heat from servers can be recovered instead of being dissipated via cooling systems — and as the data center grows, any increase in computing power will mean more energy available for recovery.
The collaboration with local power company Kogeneracja will provide “valuable experience in designing and operating modern data centers, with a particular focus on infrastructure dedicated to AI nodes,” said Michał Starybrat, development director at Citylink.
“The dynamic growth of the artificial intelligence and cloud technology markets generates unprecedented demand for computing power, this collaboration demonstrates how modern digital infrastructure can actively contribute to building the energy ecosystem of the future,” he added.
This type of initiative is not new. There have been similar projects in the UK and in New Zealand, but with warnings that data centers are contributing to the warming of the planet, there may well be a lot more organizations looking to deploy that excess heat more fruitfully in the future.
However, announcing it during a heatwave may not be the most politically sensitive approach to take.
This article first appeared on Network World.
N-able God mode flaw: Vendor confirms attackers reached customer networks as second hotfix lands
Airtable joins Evernote, Brightcove, WeTransfer and AOL in Bending Spoons portfolio
Bending Spoons has snapped up Airtable to add to its portfolio of software companies, alongside AOL, Evernote, WeTransfer, Brightcove and Vimeo
Airtable made its name as a builder of low/no code database services, aimed particularly at non-technical staff, but is now one of many vendors facing financial difficulties in the face of the SaaS/AIpocalypse. The arrival of AI coding tools, which offer non-technical employees more flexible ways to build business applications, has hit demand for its services.
Bending Spoons bought Airtable in a deal it valued at just $1.285 billion, a far cry from the $11.7 billion Airtable was worth at its peak.
Bending Spoons has built its portfolio by buying once-successful companies like Airtable that have struggled to cope with newer, nimbler competitors or failed to adapt to emerging technologies. Bending Spoons takes these companies, cuts costs and markets them aggressively with the goal of returning them to profitability.
“Airtable is a pioneering brand reshaping how teams organize data and manage critical workflows. We’re committed to investing in Airtable for the long run, and doubling down on its core strength: bringing teams and workflows together in one flexible workspace. We plan to expand what can be done across the full spectrum of work and make Airtable even more valuable to customers at every scale,” said Luca Ferrari, Bending Spoons CEO and co-founder.
This article first appeared on InfoWorld.
Netflix a další na víkend: nový Ted Lasso a akční Lioness. Ale především horory Úkryt nebo past a 28 let poté: Chrám z kostí
MIT boffins' TONTOU attack slips through Spectre defenses on Intel and AMD CPUs
Série bezpečnostních záplat v produktech Cisco
Real emails, hijacked payments: Two H1 2026 attack chains
Sam Altman Says We’re ‘in the Singularity’ With AI. Here’s Why He’s Wrong.
Today’s AI is neither able to improve itself recursively nor is it intelligent like us. Between prompts it remains a static mathematical object.
“We are now, like, in the singularity.”
These are the words of Sam Altman, CEO of OpenAI, speaking on the Relentless podcast on July 25.
He added: “I’ve been waiting for this my whole life, and I think it’s going to be incredible, hugely positive, awesome for the world.”
Days earlier, OpenAI had disclosed that two of its artificial intelligence models, during an internal cyber security evaluation, had escaped their sealed testing environment, reached the open internet, and broken into the infrastructure of the AI platform Hugging Face, which confirmed the intrusion.
But what exactly is the singularity? And is Altman right that we are in it?
What Is the AI Singularity?The term has a precise meaning.
Mathematician and science-fiction author Vernor Vinge defined it in 1993 as a point at which machine intelligence exceeds human intelligence and begins improving itself, triggering an acceleration so rapid that humans can no longer predict or control it.
The singularity has two features. It is recursive: the system improves itself over and over again. And machine intelligence exceeds human intelligence.
The kind of systems Sam Altman sells don’t deliver on either of these features.
Today’s AI Cannot Make Itself SmarterToday’s AI systems, the ones that OpenAI builds, are based on large language models (LLMs). These deep neural network algorithms get pre-trained with vast amounts of training data. By the time you use one of them, the network itself is frozen in time. Every one of its billions of internal functions and weights—or “parameters”—is fixed.
These AI models cannot change (or “learn”) while running. The model that broke into Hugging Face was identical afterwards to what it had been before. It learned nothing from what it did.
Making an AI model smarter requires another training run with new, human-curated data, tens of thousands of specialist chips, and enormous amounts of energy.
It is true that AI models take part in improving some of their system’s components, such as by generating training data, tuning prompts, or writing and running code to improve the scaffolding around them. But the model never edits its own weights on the fly, and every one of these improvements are still part of a human-initiated training or engineering loop.
Nor do these systems hold any goals of their own. They act on goals we hand them. Even AI agents—systems that run an LLM in a loop to work through complex tasks step by step—do not hold any goal internally. It has to be stored outside the model and fed back in with every single prompt cycle. Remove the loop, the scaffolding, and the prompt, and nothing happens inside of it.
A Ladder That Doesn’t ExistThe second problem with the singularity story is the word “surpass.” It assumes that AI and human intelligence are somehow similar. They are not.
Human intelligence is inseparable from being a living body with needs and wants. Humans learn continuously by acting in the world and getting feedback through our senses. Our goals arise from our situation as creatures who must eat, sleep, and belong, and who cannot avoid asking what we want our lives to be.
An AI model has none of this. No body, no needs, no action-feedback loop, no stake in anything. Between prompts it is just a static mathematical object.
And yet, it has been trained on more text than any human could read in a thousand lifetimes and will outperform nearly all of us at drafting a contract, writing code, or explaining a diagnosis empathetically.
So, which is more intelligent? The question does not compute. There is no single ladder that humans and machines are climbing. AI already vastly exceeds us at some tasks, while being hopeless at others any child can do.
Yet, because these systems talk like us, we fall for an illusion. When we assume from the outset that machines are in the process of catching up with us, it is easy to assume a mind at work when these systems output intelligent-sounding text.
We call this anthropomorphic seduction. It makes a security incident such as the Hugging Face hack sound like an awakening.
In fact, in that case OpenAI’s models simply optimized to solve the test they had been given by finding security loopholes. They just did it in ways that broke their sandbox, which also had a security loophole.
In the end, the Hugging Face story points to a gross failure of security governance on OpenAI’s behalf, not an emerging superintelligence. This is why the framing of “agent going rogue” is so problematic. It elevates and blames the technology, but excuses OpenAI’s engineering.
Keeping Our Feet on the GroundNone of this takes anything away from what these systems can do. They are remarkable, they are getting better, and they are reshaping how a great deal of work gets done.
But we should keep our feet firmly on the ground.
The machines are not waking up. They are doing exactly what we built them to do, extremely fast. Because they are probabilistic they sometimes run in directions we forgot to fence off. That is worth worrying about. We need guardrails, governance, and most of all, education—so we start worrying about the right things.
This article is republished from The Conversation under a Creative Commons license. Read the original article.
The post Sam Altman Says We’re ‘in the Singularity’ With AI. Here’s Why He’s Wrong. appeared first on SingularityHub.
Scot NHS trust probes access to medical records of 9-year-old girl after man arrested on suspicion of murder
Detecting Persistence on Linux Hosts: A Security Playbook for Cron and systemd
Záhada obří exploze rakety New Glenn se vyjasňuje. Selhal hlavní ventil kapalného kyslíku motoru BE-4
Wispr moves beyond AI dictation with note-taking assistant
Wispr, the startup behind dictation tool Wispr Flow, has created an AI note-taking assistant that records meetings and generates conversation summaries for users.
The Wispr Flow Notetaker tool “captures your meetings so you can stop splitting your attention between listening and writing things down,” said Sahaj Garja, Wispr CTO and co-founder.
Notetaker starts recording with one click, and doesn’t require a bot to attend a video or voice call. It can also be used to capture in-person conversations.
The software has three key functions. Before a call, Notetaker displays a meeting brief with information such as meeting purpose and background of participants.
Once the meeting starts, a live transcript displays the dialogue text and labels speakers. A “what did I miss?” button provides a summary of talking points from the previous few minutes.
Finally, post-meeting, Notetaker generates a more detailed summary organized by topic that includes information such as key dates, decisions, and next steps. Users can search across notes from previous meetings in the Notetaker app. “Over time your meeting history stops being a folder of documents you have to go find and becomes something you can ask questions of,” said Garja.
Notetaker integrates with AI assistants such as Anthropic’s Claude and OpenAI’s ChatGPT via model context protocol. This allows users to connect outputs such as transcripts and summaries “into how you already work, instead of sitting in a separate app,” said Garja.
With Notetaker, Wispr competes in an increasingly busy market for AI note-taking apps that includes Fireflies, Granola and Otter.
Wispr claims Notetaker can produce more accurate transcripts than existing tools, partly because of the additional context it uses during transcription. It uses the same personal dictionary from Wispr Flow that includes acronyms, products, and preferred spellings, and can also draw on other sources such as calendar information to understand the purpose of a meeting and help ensure speakers are labelled correctly.
Before generating the final summary, Notetaker also re-reads the live meeting transcript and combines it with additional context to create a more accurate final output, Garja said.
Notetaker is the first new product launched by Wispr, which was founded in 2021 and has since raised $81 million in funding.
“We didn’t set out to build a dictation app,” said Garja. “The mission has always been to reshape how people interact with their devices, and dictation was the fastest way in.”
“Notetaker is the second product on that path. Dictation took the keyboard out of writing. Notetaker takes it out of meetings, so nobody has to spend the call typing up what everyone just said.”
User consent when recording callsAs AI note-taking tools have become more prevalent in the workplace, privacy concerns have arisen, including the need for all-party consent when recording a call in some jurisdictions, and whether meeting audio is used to train AI models. Two software vendors, Otter and Granola, currently face separate lawsuits in California that allege privacy law violations related to their products.
Wispr Flow Notetaker captures audio locally on a user’s device rather than joining the call as a visible bot. That means there’s no notification to signal that a conversation is being transcribed, which places responsibility on users to disclose the recording to others on the call in accordance with local laws, said Garja.
“Users should always let the other person know before you start recording or transcribing a conversation, whether it’s a video call, an in-person meeting, or a phone call,” he said, adding that Wispr intends to build additional features for automated consent messaging “in the coming weeks.”
Wispr doesn’t train its AI models on customer data without consent, though free and standard tier customers must choose to opt-out, according to Wispr’s privacy terms. Nor does it create “voiceprints or biometric profiles” of users or anyone else on a call using audio recording data, the company says.
When Notetaker is active, conversation audio is captured on a user’s device and processed on cloud servers to enable transcription. The recorded audio file is encrypted and stored temporarily on the user’s device or cloud storage, Wispr said. After a limited period, the audio is automatically deleted.
Notetaker is available with the Wispr Flow macOS app to free and paid subscribers, with support for Windows “coming soon.”
Soud nařídil firmě Meta zaplatit 567 milionů USD kvůli újmě způsobené dětem
North Carolina Ports confirms cyberattack disrupting operations
New WordPress Pre-Auth XSS Could Lead to PHP Code Execution - Patch ASAP
Jak na počítači používat Netflix v co nejlepší podobě? Rozlišení 4K běží nově i v Chromu
DeepMind founder ascends to singular AI role at Google
Demis Hassabis, the driving force behind Google DeepMind, is ascending to the role of chief scientist at Alphabet, Google’s parent company, replacing Jeff Dean who is leaving to work at a start-up.
The role will enable Hassabis to “put his full attention on actively shaping the future of AGI,” or artificial general intelligence, Alphabet CEO Sundar Pichai wrote on the company’s Inside Google blog.
Hassabis’ attention will still be divided, however: He will continue to lead research at Google spin-off Isomorphic Labs, which works on drug discovery, and although he will no longer be CEO of DeepMind, he will be its chair. Koray Kavukcuoglu will take over DeepMind, reporting directly to Pichai. He is currently its CTO.
Hassabis has been a strong promoter of AGI, defined by Google as the “hypothetical intelligence of a machine that possesses the ability to understand or learn any intellectual task that a human being can.”
He has a long career in AI, having helped found DeepMind in 2010. He has been a prominent figure in the AGI field, prophesying in May that it will be a viable technology within three years. He has been keen to tackle any barriers in the way of developing the technology; just last month, he called for greater self-regulation in the market, arguing that it would help drive the technology forward.
Hassabis welcomed the chance to focus on AGI development. “We have arrived at a pivotal moment in human history. I’ve been working towards AGI my whole life, and now, I feel it is close at hand. It’s critical that we collectively get the next steps right to ensure this all goes well for humanity and we usher in an incredible new age of discovery and wonder” he wrote in the Inside Google blog post.
Growing Up The Hard Way
Attacker phished way into US defense supplier's Microsoft 365 account
- « první
- ‹ předchozí
- …
- 18
- 19
- 20
- 21
- 22
- 23
- 24
- 25
- 26
- …
- následující ›
- poslední »



