Artificial intelligence

Artificial intelligence

The result came as a surprise to researchers at the Icaro Lab in Italy. They set out to examine whether different language styles in this case prompts in the form of poems influence AI models' ability to recognize banned or harmful content. And the answer was a resounding yes. Using poetry, researchers were able to get around safety guardrails and it's not entirely clear why.

Senior partners at the global management consulting firm, which has been steadily cutting its worldwide workforce over the past few years, are understood to have held initial talks with the heads of non-client-facing departments about shrinking their teams by as much as 10 per cent. A McKinsey spokesman would not confirm how many roles were at risk, but Bloomberg, which first reported the plans, estimated that there could be "a few thousand" layoffs staggered over the next 18 to 24 months.

SAN and NAS are distinct ways of deploying storage. They differ at fundamental levels of storage architecture, according to their relation to file system and block and physical addressing, but also the network or fabric by which input/output (I/O) travels. Here, we look at the key differences in performance and applicability between block and file storage, focusing on key contemporary workloads in artificial intelligence (AI), virtual machines and containerised environments.

Last week at AWS re:Invent, amid many product announcements and cloud messages, AWS introduced AWS AI Factories. The press release emphasizes accelerating artificial intelligence development with Trainium, Nvidia GPUs, and reliable, secure infrastructure, all delivered with the ease, security, and sophistication you've come to expect from Amazon's cloud. If you're an enterprise leader with a budget and a mandate to "do more with AI," the announcement is likely to prompt C-suite inquiries about deploying your own factory.

Others foresee a revolution that might add between US$17 trillion and $26 trillion to annual global economic output and automate up to half of today's jobs by 2045. But even before the full impacts materialize, beliefs about our AI future affect the economy today - steering young people's career choices, guiding government policy and driving vast investment flows into semiconductors and other components of data centres.

Former Palantir CIO Jim Siders has departed the company to join Shield Technology Partners as CEO, in a bid he says is meant to bring AI to bear in the sprawling managed services landscape.

Lyft has rearchitected its machine learning platform LyftLearn into a hybrid system, moving offline workloads to AWS SageMaker while retaining Kubernetes for online model serving. Its decision to choose managed services where operational complexity was highest, while maintaining custom infrastructure where control mattered most, offers a pragmatic alternative to unified platform strategies. Lyft's engineers migrated LyftLearn Compute, which manages training and batch processing, to AWS SageMaker, eliminating background watcher services, cluster autoscaling challenges, and eventually-consistent state management, which had consumed significant engineering effort.

"As efforts shift from hype to execution, businesses are under pressure to show ROI from rising AI spend," the company wrote. "Large-cap CEOs are seeing solid returns on current programs, particularly across administration, internal efficiency, and customer-facing applications. However, 84% of these CEOs predict that positive returns from new AI initiatives will take longer than six months to achieve. In contrast, investors are pushing for faster impact: 53% expect positive ROI in six months or less."

Slurm is used to schedule computing tasks and allocate resources within large server clusters in research, industry, and government. SchedMD was founded in 2010 by the original developers of Slurm. The company not only focuses on the further development of the software, but also provides commercial support and advice to organizations that use Slurm in production. According to SiliconANGLE, SchedMD serves several hundred customers, including government agencies, banks, and organizations in the healthcare sector.

Matt Garman, the CEO of Amazon Web Services, is looking to change that. At the recent AWS re:Invent conference, Garman announced a bunch of frontier AI models, as well as a tool designed to let AWS customers build models of their own. That tool, Nova Forge, allows companies to engage in what's known as custom pretraining-adding their data in the process of building a base model-which should allow for vastly more customized models that suit a given company's needs.

I recently implemented a feature here on my own blog that uses OpenAI's GPT to help me correct spelling and punctuation in posted blog comments. Because I was curious, and because the scale is so small, I take the same prompt and fire it off three times. The pseudo code looks like this: for model in ("gpt-5", "gpt-5-mini", "gpt-5-nano"): response = completion( model=model, api_key=settings.OPENAI_API_KEY, messages=messages, ) record_response(response)

The conversation about AI in the workplace has been dominated by the simplistic narrative that machines will inevitably replace humans. But the organizations achieving real results with AI have moved past this framing entirely. They understand that the most valuable AI implementations are not about replacement but collaboration. The relationship between workers and AI systems is evolving through distinct stages, each with its own characteristics, opportunities, and risks. Understanding where your organization sits on this spectrum-and where it's headed-is essential for capturing AI's potential while avoiding its pitfalls.

It is becoming increasingly difficult to separate the signal from the noise in the world of artificial intelligence. Every day brings a new benchmark, a new "state-of-the-art" model, or a new claim that yesterday's architecture is obsolete. For developers tasked with building their first AI application, particularly within a larger enterprise, the sheer volume of announcements creates a paralysis of choice.

Remember the Hans Christian Andersen story The Emperor's New Clothes? It's about an emperor who is convinced by some vendors' BS to buy a set of what's described as a beautiful set of clothes. There's only one problem; the clothes are imaginary. When the emperor wears (or actually doesn't wear) the clothes in a big parade, his constituents are afraid to say that he's wearing no clothes. Until a young child blurts out the truth: "The emperor is wearing no clothes!"

Test-time scaling for AI agents is increasingly shifting from longer thinking to controlling tool calls. In many practical applications, such as web search and document analysis, the number of external actions determines how deep an agent can dig. Each tool call increases the context window, increases token consumption, and incurs additional API costs. For companies, this can quickly add up.

The world is converging onto two AI stacks. One is championed by the People's Republic of China (PRC), state-directed, closed, and surveillance-heavy; ours is democratic, market-driven, and safety-aligned. Every country will end up on one of these two stacks, whether they choose to or not. The strategic imperative for the US is to ensure that the democratic stack prevails.

AI may be blamed for this year's layoffs, but a new global survey says the technology could fuel a rebound in some entry-level hiring next year. Public-company CEOs say AI is creating more jobs in 2026, according to an annual outlook survey conducted by advisory firm Teneo released this month. Sixty-seven percent of theCEOs surveyed said they expect AI to increase entry-level hiring in 2026, and 58% said they plan to add senior-leadership roles as well.

Zoom released its AI assistant to the web today as part of its AI Companion 3.0 release. The company is also allowing free users to access the assistant's features, such as summarizing the meetings, listing action items, or getting insights from meetings with limits. The company said that basic plan users get to use the AI companion within three meetings every month, which will each include a meeting summary, in-meeting questions, and AI note-taking capabilities.

Those tools are also expanding as they gather and model more prompt data, so that companies can see their AI visibility in Google's AI Overviews and AI Mode. That's a big deal. Previously, publishers and brands have been largely flying blind when it came to AI-driven discovery but now they are getting rare visibility into a part of AI search that has largely operated as a black box.

For the last year and a half, two hacked white Tesla Model 3 sedans each loaded with five extra cameras and one palm-sized supercomputer have quietly cruised around San Francisco. In a city and era swarming with questions about the capabilities and limits of artificial intelligence, the startup behind the modified Teslas is trying to answer what amounts to a simple question: How quickly can a company build autonomous vehicle software today?

Technology stocks are buzzing this morning with a wave of developments. Among them, Tesla ( Nasdaq: TSLA) has captured the spotlight. Wedbush tech analyst Dan Ives is calling 2026 a "monster year" for Tesla and Elon Musk as the EV maker leans harder into autonomous driving and robotics. He sees Tesla's valuation climbing to around $2 trillion next year, with a bull-case scenario of $3 trillion by year-end 2026 amid a successful AI strategy. Wedbush has reemphasized its "outperform" rating on TSLA stock.

Inside some of the Air Force's oldest refueling aircraft, technicians are crawling through tight, dirty spaces, painstakingly cleaning sealant on fuel tanks and tightening loose rivets. They climb into the dark, cramped tanks with little more than a flashlight, some tools, and shaky comms. It can be hard to breathe, the air smells like jet fuel, the fixes aren't always clear, and the punishing work can be dangerous if done wrong.