Intel IT Center

Latin America is one of the fastest-evolving AI security markets in the world — and it plays by its own rules.

What does it really take to turn a legacy camera system into a fully intelligent AI security platform — without blowing your budget or sending a single byte of data to the cloud?

What happens when a former Meta infrastructure exec — someone who spent billions on Nvidia GPUs for Instagram and WhatsApp — decides the future of AI security isn't in the cloud at all?

Deploying AI in a robot, a surgical system, or an autonomous vehicle isn't just an engineering challenge, it's a safety and security challenge.

Most companies approaching edge AI make the same two mistakes: they either under-spec the hardware and run out of resources within a year, or they throw expensive, oversized accelerators at the

Most companies investing in edge AI are either over-spending on hardware they don't need, or under-building platforms that can't keep up.

The question used to be whether your system could run AI. That question is no longer being asked. Customers have moved on — and the bar they've set next will catch a lot of vendors off guard.

For years, the answer to "we need more AI performance" was simple: add a discrete GPU.

Thousands of companies have run successful edge AI pilots. A fraction of them have successfully scaled.

What if you could transform your existing security cameras into an intelligent AI system — without replacing a single device or going over budget?

Most edge AI conversations happen in the context of factories and retail stores. This one is different.

Every company wants intelligent, autonomous machines at the edge. Most of them are trying to get there in one leap and that's exactly where things go wrong.

In regulated industries like healthcare, a platform decision made today will still be running in hospitals, clinics, and medical devices a decade from now.

There's a fundamental architectural decision sitting at the center of every AI deployment right now and most companies aren't asking it early enough.

The edge is no longer just a data collection point, it's becoming the most strategically important layer of the modern enterprise.

Most organizations deploying AI at the edge are flying blind.

Most factory defects don't announce themselves. Some can't even be described ,only shown.

Everyone wants to talk about AI in industrial environments.

In the most advanced factories on the planet, supplying names you'd recognize instantly, there is no room for a discrete GPU, no tolerance for downtime, and no second chances.

In a world where edge AI deployments are expected to run for a decade or more, security and longevity aren't optional features, they're the foundation everything else is built on.

Somewhere between a robotic arm that flips burgers and a humanoid that can navigate a hospital, there's a raging debate about how robots should think — and which approach will actually win in the

When one of the world's largest technology consultancies ran a head-to-head benchmark between two of the leading AI platforms for robotics and edge computing, the results weren't what most people

For years, industrial AI deployments have been held back by a frustrating trade-off: raw compute power versus practical constraints like cost, power consumption, and long product life cycles.

Everyone is talking about humanoid robots. Very few are actually deploying them at scale, in real factories, alongside real people. This conversation is with one of the companies that is.

What if you could search through thousands of hours of video across thousands of cameras for anything, without knowing in advance what you'd be looking for?

Most edge AI demos work perfectly in a lab.

Everyone assumes that building a smarter robot means adding more GPU power. The CEO of a company building AI brains for humanoid robots ran the benchmarks and discovered the assumption is wrong.

Intel CRO Greg Ernst joins Dell Technologies World Live to discuss the next phase of AI innovation and the enduring Intel–Dell partnership.

An update on Nokia and Intel mobile core collaboration—driving lower TCO, higher performance, and stronger security with the latest platform capabilities – Intel Xeon 6, Infrastructure Power

What happens when AI lives directly inside the 5G core?