We know that all of the objects in the picture below are chairs, right? But show this picture to a computer and see what happens. Getting computers to recognize generic objects is a hugely difficult task that’s complicated by variations of the same object (club chairs vs. office chairs vs. folding chairs) and by other objects in the computer’s field of view that can confuse the machine.

Researchers at iRobot have been working hard on object recognition. iRobot is best known as the company behind the Roomba vacuum cleaner, but they’ve been doing a lot more than just vacc’ing up the dander – these guys are doing serious research and development.

At last week’s GPU Technology Conference, the team from iRobot revealed that they’ve developed the first real-time generic object recognition algorithm. It’s based on the Deformable Part Model (DPM), which is based on the idea that objects are made of parts, and the way those parts are configured in relation to one another is what defines a person from a chair or a car from a boat.

Let’s say you’re at a gathering – maybe a cocktail party or a crowded club – and some buff athlete shows up on crutches. He immediately becomes the center of attention as he recounts the story of his injury: “Dude, it was gnarly… total sick shred. Now I’m waitin’ on the MRI results. The docs think I might never shred again, but I’m stoked to get back out there.”

Is there any way that you, a computer geek, can get the conversational focus back where it belongs –  on you? Sure you can; just say this:  “Really? You’re waiting? They can do MRIs in real time now, and not just an image, but a whole movie. Like better than YouTube, yo… here’s how it works…”

At last week’s GPU Technology Conference in San Jose, researchers from Cal Berkeley and the Max Planck Institute showed how MRI images can now be processed so quickly and accurately that they can be shown as full-motion, real-time videos instead of static, printed output. First, a little background:

Accurately modeling and rendering the ocean is highly intense from a computational perspective – but it’s considerably harder to accurately portray an artificial simulation of the human face, according to NVIDIA CEO Jen-Hsun Huang in his opening keynote address at the GTC 2013 conference in San Jose.

Human faces contain a subtlety that’s incredibly difficult to capture. Consider the wide range of skin textures, pore sizes, bumps, humps, colors, and other features that all have to interact in a completely natural manner in order for the illusion to hold. And that’s before we even think about modeling beards, goatees, and the like.

Anyone attempting to build a believable human facsimile also has to beware of the “uncanny valley.” This is a school of thought that says human copies which are almost – but not quite – perfectly human can cause big-time revulsion in us real humans.

(I would submit a picture of Carrot Top as proof positive that this is true. I’m sure you can come up with your own examples of real people who have gone over the uncanny cliff into the valley of revulsion. Spend a couple of hours on it today; it’s important, and sort of scientific too.)

NVIDIA CEO Jen-Hsun Huang’s GTC 2013 keynote was a typical whirlwind tour (with real wind, but that’s a different article) through all the various GPU-related worlds that NVIDIA is touching these days. These addresses are usually chock-full of demonstrations showing where we are in terms of state-of-the-art graphics, scientific and technical computing, entertainment, and now: finding dresses.

In this demonstration, Jen Hsun leafed through the latest edition of InStyle magazine. While the models are svelte (or starved), the magazine definitely isn’t, weighing in with 594 pages of ads. A dress from one of those ads was chosen, its picture was taken, and it was sent off for image matching. What came back was a set of likely matches that the image-matching tool found via eBay. (This can be seen in the semi-blurry picture taken from my third-row perch.)

Dell’s move to take itself private has the tech world buzzing. Our pal TPM dissects the deal here, discussing how much this move is going to cost and where the cash is coming from.

There’s a lot of talk about the motives behind the deal. Some say Dell is doing it to escape the quarterly visit to the Wall Street meat grinder, where either you meet (or exceed) their expectations or get ground into a fine slurry. Going private frees Dell of public reporting requirements and gives them more latitude to acquire, divest, or otherwise remodel the business.

Others say that Dell (the guy) and his partners from the financial industry (Silver Lake, plus others) want to buy the company so they can chop it up and part it out. Along the way, they’ll cut costs to the bone so that they can milk every bit of excess cash into their money pails.

We might well see Dell (the company) realign and reset their strategy – but I don’t see Michael Dell savagely pillaging the company that still bears his name.

I also don’t see Microsoft having much influence in Private Dell. Sure, they kicked in $2 billion, but it’s a loan; and unless they’re getting board seats, Microsoft isn’t going to be driving the Dell bus.

Value and the perception of value is the real reason behind Dell becoming Private Dell. Here’s a look at Dell’s revenue over the past five years. Note the big dip in 2010 – ouch. But also note that revenue in 2012 at $62.2 billion is marginally higher than the $61.1 billion they cash registered in 2008.