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Tyto filmy a seriály jsou na Apple TV mezi českými předplatiteli nejoblíbenější. Nerozlišujeme žánr, stáří ani hodnocení na filmových webech. Jde o souhrnnou oblíbenost, kterou zjišťuje web FlixPatrol.

The US-Iran war has delivered a critical lesson for IT leaders. 

Enterprises have always had to deal with bad data in their environments, whether from someone cutting corners, an ancient database that everyone is scared to delete, or conflicting systems digested during one of the dozens of acquisitions over the last decade. But AI is going to supercharge those data problems, and ignoring them is no longer a viable choice.

Consider the US military’s bombing of an Iranian girls’ school on Feb. 28, which killed at least 165 people, most of them children. According to The New York Times, military investigators say that the cause of the incorrect bombing was bad data. Apparently, that building had been — years earlier — used by the Iranian military. The building was separated from the military compound and converted to a school a decade ago, but no one updated the US intelligence records. That’s why the AI-powered targeting system selected it for a bombing strike.

“Officers at U.S. Central Command created the target coordinates for the strike using outdated data provided by the Defense Intelligence Agency,” The Times reported. “Military targeting is very complex and involves multiple agencies. Many officers would have been responsible for verifying that the data is correct, and officers at Central Command are responsible for checking the information they receive from the Defense Intelligence Agency or another intelligence agency. But in a fast-moving situation, like the opening days of a war, information is sometimes not verified.”

To be clear, the investigation shows that AI is not to blame for the school bombing; the error came from faulty data and people who didn’t verify it. As The Guardian pointed out, “The school appeared in Iranian business listings. It was visible on Google Maps. A search engine could have found it. Nobody searched.”

This gets us into the practical logistical realities, both for a massive military complex delivering a large number of simultaneous attacks and for an enterprise leveraging billions of bits of data being crunched by genAI or autonomous agents. It’s all but impossible for anyone to verify every single data point.

Remember that the key advantage of AI deployments is that they can deal with petabytes of data in ways that human teams cannot.

The school bombing tragedy is an extreme example, but it reinforces the concern that AI is going to use whatever data it can access. That’s especially dangerous with autonomous systems, which will assume the data is accurate and leverage that data to make decisions and take action. This is every bit as true when a hospital is analyzing test results, a retailer is trying to project product assortment needs, or a manufacturer is trying to predict how much raw material it needs for upcoming projects.

IT professionals know better. For dozens of reasons, outdated or flawed data is in the system. They understand how it got there initially. What doesn’t make sense, and yet is still understandable, is why no one has tried verifying all the data and removing bad data.

IT leaders are worried about the existing 67 projects they are trying to juggle. It is never going to rise to the top of the IT director’s triage list to assign someone to do a deep dive into petabytes of data, across all divisions, business units, and subsidiaries globally, somehow trying to sniff out flawed data.

The task above sounds like a perfect assignment for generative AI. But what if it hallucinates while it is trying to verify data?

This gets potentially worse. A lot of this data was put into the system when acquisition databases were absorbed. During the first months of the assimilation of a company being acquired, there are a ton of things that have to happen to keep revenue flowing. Verifying the legitimacy of databases typically doesn’t make the cut. 

But today, years after that data was absorbed from a team that may no longer be around, what procedure could meaningfully evaluate that old data for accuracy? And the longer such an evaluation is delayed, the larger the number of errors that will permeate the environment.

An IT working group could use a variety of guidelines to weed out such data, not by determining the accuracy of the old data, but by identifying large chunks of data that can simply be wiped. An example might be: “Any prospect list that is more than 10 years old should be automatically wiped, given the strong chance that little to none of that data would be viable.”

David Neuman, the COO at consulting firm Acceligence, pointed out that enterprises should also identify databases that should be retained for as long as possible, “such as scientific data, especially meteorological data.”

This brings us back to agentic systems. When these autonomous agents are rummaging through your environment trying to perform complex tasks and find obscure answers, they are going to run into that bad data and act on it. 

That is why data cleaning is now urgent. Five years ago, bad data would have likely slowed things down, but it would have been unlikely for many workers to access it and to rely on it. Not so with AI agents. Unless they are told otherwise, they treat all data as valid. 

Do you want the benefits of accelerated AI systems and especially autonomous ones? You better yank a bunch of people off of LOB projects and figure out a way to sniff out and remove that bad data before an agent finds it.

Je 31. března a tedy Světový den zálohování (World Backup Day). Co by se stalo, kdyby Vám právě teď odešel počítač, tablet nebo telefon, který používáte?

Digitální a informační agentura (DIA) přistupuje ke změně formátu důvěryhodného seznamu České republiky z verze TLv5 na verzi TLv6, která nastane 29. dubna 2026 v 00:00 (CET). Ke změně formátu důvěryhodných seznamů členských států (tzv. Trusted Lists) dochází na základě změn příslušné unijní legislativy. Důvěryhodné seznamy se používají v rámci informačních systémů a aplikací zejména pro účely ověřování platnosti elektronických podpisů, pečetí a časových razítek. Pokud občané využívají informační systémy a aplikace, kde se ověřuje platnost elektronických podpisů, pečetí a časových razítek, DIA doporučuje si včas u vývojářů těchto systémů a aplikací ověřit, zda jsou tyto systémy a aplikace připraveny na změnu formátu důvěryhodných seznamů členských států.

PlayStation 6 by měl stát 700 dolarů, jde o nejvyšší nominální cenu v historii. • Před 20 lety však PS3 se započtením inflace stál víc. • Paměťová krize, celní války a nulová konkurence nedonutí Sony zlevňovat.

Hijacked maintainer account let attackers slip cross-platform trojan into 100M-downloads-a-week Axios

Updated  One of npm's most widely used HTTP client libraries briefly became a malware delivery vehicle after attackers hijacked a maintainer's account and slipped a remote-access trojan (RAT) into two seemingly legitimate axios releases, in what's being described as "one of the most impactful npm supply chain attacks on record."…

Eurotel se v roce 1998 propagoval spotem, který si lidé pamatují. • Nástupnické O2 slaví 20 let na trhu a rozhodlo se spot oživit. • Nová verze obsahuje živé herce, ale Česko vygenerovala AI.

Plzeňský Prazdroj spustil novou českou reality show. Kamery tentokrát namísto znuděných celebrit kdesi na pláži nepřetržitě natáčejí proces výroby piva. Roztomilá propagační kampaň během 280 dnů a formou živých přenosů zmapuje všechny fáze molekulární transformace ječmene a chmele v tekutinu. ...

Microsoft is expanding its Microsoft 365 Copilot “Researcher” agent with new multi-model capabilities designed to improve the accuracy and depth of AI-generated research outputs.

The update introduces a “Critique” system that assigns separate roles for generation and evaluation, alongside a “Council” feature that compares outputs from multiple models and highlights agreement, divergence, and unique insights.

Internal testing using the DRACO benchmark showed that Researcher with Critique outperformed previously reported systems by 13.8% (7.0 points) in aggregate score.

“We see the largest improvement in Breadth and Depth of Analysis (+3.33), followed by Presentation Quality (+3.04) and Factual Accuracy (+2.58),” Microsoft said in a blog post. “All dimensions show statistically significant improvements (paired t-test, p < 0.0001).”

The Council feature runs multiple models in parallel to generate independent reports, with a judge system synthesizing key differences and insights to help IT teams compare interpretations.

“In simple terms, it’s like having a smart professional plus a strict reviewer,” said Pareekh Jain, CEO of Pareekh Consulting. “But it’s still incremental, not magic. It reduces errors but does not eliminate them.”

Others point out that model orchestration alone may not be enough to drive meaningful enterprise outcomes.

“Multi-model systems reach their full potential when integrated with internal enterprise data such as CRM and HRM systems,” said Neil Shah, VP for research at Counterpoint Research. “This ensures that AI-driven insights are contextually nuanced, reflecting the company’s unique market position, customer heuristics, and the specific requirements of the decision-maker.”

Performance and governance concerns

Microsoft’s DRACO benchmark results appear strong, but enterprises should approach them with measured caution.

“Think of it as a best-case test; it shows AI models can check each other and catch mistakes, but real company data is much messier with conflicting info and outdated docs,” Jain said. “There’s also a risk of judge bias; if both AIs are similar, the reviewer might miss the same errors. And while benchmarks measure logic, they don’t capture real business value.”

The shift to multi-model systems introduces new layers of operational complexity for enterprise IT teams. Systems are more powerful but also harder to manage.

Instead of a single input-output flow, organizations must now track a chain of interactions that includes the initial draft, critique, and final output.

“This creates a bigger audit trail that security and compliance teams must review to understand how decisions were made,” Jain added. “It also increases cost and latency, since one question can trigger many model calls. Another challenge is accountability. If something goes wrong, it’s harder to know which part failed, like the generator, the reviewer, or the system managing them.”

Analysts say this will require enterprises to rethink governance frameworks around AI deployment.

“Enterprises must prioritize governance of the model to the output selection process, and the refinement of how multiple responses are blended or selected,” Shah said. “This continuous monitoring and calibration will become a fundamental part of Process Quality Management.” Enterprises will also need structured mechanisms to evaluate outputs and their real-world impact, ensuring traceability across the decision-making process and improving how multi-model systems are managed over time, Shah added.

U.S. prosecutors have charged a Maryland man with stealing more than $53 million after hacking the Uranium Finance crypto exchange twice and laundering the proceeds through a cryptocurrency mixer. [...]

Extra odolný povrch je především příjemný na dotek a odolává otiskům • Nová generace procesorů Intel konečně zlepšuje výkon • Extra jasný Tandem OLED zvládá dotyky na matném povrchu

Notebooky s mobilní variantou GB10, tedy ARM SoC spojující křemík MediaTeku a GPU Nvidie, nejspíš CEO Nvidia uvede na letošním Computexu, tedy před pololetím 2026…

The Dutch Ministry of Finance took some of its systems offline, including the digital portal for treasury banking, while investigating a cyberattack detected two weeks ago. [...]

V super akcích teď koupíte OLED televizory. • Samsung s úhlopříčkou 42" nebo LG se 48" zlevnily pod 17 000 Kč. • Za 28 tisíc pak koupíte i obří 77" LG, běžně stojí o třetinu více.

The U.S. Cybersecurity and Infrastructure Security Agency (CISA) ordered government agencies to patch their Citrix NetScaler appliances against an actively exploited vulnerability by Thursday. [...]

Tchajwanská společnost Jmem Tek v dubnu otevře pobočku v Praze, přičemž se zaměří na vývoj bezpečných čipů pro drony a obranu • . • Nové brněnské centrum ACDRC propojuje české vědce s tchajwanskými firmami a pomáhá s komerčním využitím pokročilého výzkumu. • Spolupráce láká další investory jako ...

The popular HTTP client known as Axios has suffered a supply chain attack after two newly published versions of the npm package introduced a malicious dependency that delivers a trojan capable of targeting Windows, macOS, and Linux systems. Versions 1.14.1 and 0.30.4 of Axios have been found to inject "plain-crypto-js" version 4.2.1 as a fake dependency. According to StepSecurity, the two

The popular HTTP client known as Axios has suffered a supply chain attack after two newly published versions of the npm package introduced a malicious dependency that delivers a trojan capable of targeting Windows, macOS, and Linux systems. Versions 1.14.1 and 0.30.4 of Axios have been found to inject "plain-crypto-js" version 4.2.1 as a fake dependency. According to StepSecurity, the two Ravie Lakshmananhttp://www.blogger.com/profile/[email protected]

V návaznosti na ohlášení procesoru Ryzen 9 9950X3D2 připomenul šéf PC divize AMD letošní desetileté výročí platformy AM4 a ohlásil, že s AM5 má společnost podobné plány…

Rspamd (Wikipedie), tj. open source systému pro filtrování nevyžádané pošty, byl vydán v nové major verzi 4.0.0. Přehled novinek v Changelogu.