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Nvidia Makes AI computing possible in Cameras
Why You Should Work There

Nvidia makes AI computing possible within cameras - Why You Should Work There

Nvidia is a com­pa­ny most don’t asso­ciate with cut­ting edge tech­nol­o­gy.  Most just think of them as a “graph­ics board” com­pa­ny.

Into “edge com­put­ing” and AI, Nvidia is impress­ing the world with its con­tin­u­ous tech­nol­o­gy inno­va­tions.

If you’re in the tech­nol­o­gy field in any sig­nif­i­cant way, then Nvidia should be a top con­sid­er­a­tion for you. Unless you’ve kept up with them recent­ly you might still auto­mat­i­cal­ly think gam­ing when you hear that name, but their newest inno­va­tions are putting them at the fore­front of two very for­ward-think­ing tech­nolo­gies — AI and so-called “edge com­put­ing”.

At Media Jobs we are here to present the great com­pa­nies first

Nvidia is on the Cutting Edge of the “Edge”

If you’re unfa­mil­iar with what is called “the edge”, it’s not dif­fi­cult to under­stand (and no, it’s not the gui­tarist for U2). The data from our devices is pret­ty broad­ly sent into the cloud today for stor­age and pro­cess­ing. That’s unsus­tain­able going for­ward, accord­ing to many peo­ple, for sev­er­al rea­sons. The pri­ma­ry two are prob­a­bly band­width and laten­cy, with pri­va­cy being a close third place. Pri­va­cy is a com­mon sense rea­son — the less data is moved around and trans­mit­ted, the less chance there is for intru­sion or being hacked.

Band­width lim­i­ta­tions come from the increas­ing amount of video being uploaded and the increas­ing­ly high def­i­n­i­tion nature of that video. As more 4K video is used and uploaded, the less band­width will be avail­able. Laten­cy is also a big con­cern for the future as we get clos­er to tech­nolo­gies in the realm of self-dri­ving cars, which need to process infor­ma­tion and make deci­sion in a split sec­ond. If it takes your car a few sec­onds to decide that the wreck in front of you on the high­way needs to be avoid­ed, well… that’s not real­ly ide­al, is it?

The answer is to have more data pro­cess­ing done on the edge — not in the cloud but at the point of col­lec­tion, such as right inside the cam­era or drone doing the col­lect­ing. That’s where Nvidia is focus­ing much of their efforts now, and the results are impres­sive.

Meet George Jetson, v2.0

Nvidi­a’s Jet­son TX1 card for embed­ded com­put­ing was impres­sive. Their new Jet­son TX2 card is even more amaz­ing, allow­ing for twice the pro­cess­ing speed of the TX1 or, if that’s unnec­es­sary for the appli­ca­tion in ques­tion, half of the pow­er required with the TX1 for the same pro­cess­ing pow­er. Lots of com­pa­nies have already begun using the Jet­son TX2 in upcom­ing projects, and the appli­ca­tions are pret­ty cool to say the least.

For instance, Live Plan­et intro­duced a 4K, 360-degree 3D cam­era that uses AI to encode 3D video in real time using Jet­son. The cam­era pro­duces a stream of 65 GB, which is just too much to upload to a cloud serv­er. Teal Drones will start ship­ping a smart drone in a few months, using Jet­son TX2 cards, that will be able to under­stand and react to what it’s cam­eras are see­ing in real time. The impli­ca­tions are both excit­ing and a lit­tle scary.

Invest­ment banks are telling peo­ple to buy Nvidia stock and pre­dict­ing that they’ll hurt the com­pe­ti­tion, like AMD. We just think that this is a great oppor­tu­ni­ty to get on board with a tech com­pa­ny that isn’t rest­ing on their lau­rels and may become one of the major play­ers in forg­ing our high tech future. What do you think?

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