Placeholder Image

字幕列表 影片播放

  • Hello, and welcome to Tech Decoded.

  • I'm Henry Gabb, editor of The Parallel Universe,

  • Intel's magazine devoted to software innovation.

  • And today, I'm here with Charlotte Dryden,

  • who leads a team developing software for visual computing,

  • from Edge to Cloud.

  • Charlotte has 20 years of experience in semiconductors

  • in international business.

  • She holds a degree in electrical and biomedical engineering

  • from Vanderbilt University, and an MBA from the University

  • of San Diego.

  • Welcome, Charlotte.

  • Thanks for coming.

  • Thank you, Henry.

  • I've done some work in computational linguistics--

  • essentially, teaching a computer how to read.

  • Your teaching computers how to see more like humans.

  • What do you see as the future of computer vision?

  • So, I work in the computer vision software technology

  • space.

  • And our whole goal is to enable machines

  • to see exactly as we do.

  • And what that will allow is many,

  • many things, such as autonomous vehicles, valet copters,

  • robots to move as humans move, and see as humans see.

  • So I see a huge set of opportunities there.

  • Now, computer vision is obviously

  • a hot topic right now.

  • But you and I may define it differently.

  • How do you define computer vision, as the expert?

  • All the science and technology that's needed--

  • both hardware and software--

  • to enable what we are discussing,

  • which is allowing machines to see as humans see.

  • Do you still need a PhD to do it?

  • No.

  • I think that this technology is becoming

  • rapidly more accessible to developers of all types.

  • The ninja developers who are the PhD experts,

  • maybe they're ninjas without PhDs,

  • and the maker community and students, and even just

  • people who like technology and want to try it out.

  • I think that there's enough abstraction

  • with the technologies that we've built,

  • that many of the technologies are accessible to many.

  • The barrier to entry seems to have come down quite a bit.

  • And I agree, part of that has to do with the software

  • abstraction.

  • But what else is lowering the barrier to entry

  • to people who want to get in to computer vision?

  • Yeah.

  • Yeah, the one thing that's helped

  • is the reduction in hardware--

  • hardware cost.

  • So, it used to require a big set of servers and a lot of storage

  • to develop any computer vision technology.

  • If you look at deep learning, it used

  • to require very expensive hardware and large amounts

  • of time in large pools of data to train a model

  • to do any sort of object recognition.

  • But now, the processor speeds are faster.

  • The price of the hardware is reduced.

  • And the hardware is more available

  • to the average person.

  • So, with that, we see more innovation

  • from many different types of developers.

  • So Charlotte, Intel is heavily invested

  • in the area of computer vision.

  • Can you tell us more about what we're doing in this segment?

  • Yes.

  • So, Intel has a broad portfolio of hardware and software

  • for computer vision.

  • And in addition to the CPU and the GPU for visual computing,

  • we now have a suite of hardware accelerator options

  • that can allow the right performance

  • and power for the right visual computing workload.

  • So we have the Movidius IP that we recently acquired,

  • the Myriad product.

  • We also have the FPGA that we acquired from Altera

  • a few years ago.

  • And now, we've recently-- last year--

  • acquired Mobileye, so that we have computer vision hardware

  • for autonomous driving.

  • With that, we're in the developer products group.

  • So we've designed software tools to make

  • that hardware accessible to computer vision algorithm

  • developers.

  • For example, we've developed the computer vision SDK,

  • which includes a large number of OpenCV library functions

  • that have been finely tuned for all of the various hardware

  • accelerators.

  • And then, we have deep learning tools to help with optimizing

  • trained models for object detection--

  • or facial recognition, for example--

  • so that they run best on Intel hardware.

  • And then we have a host of tools for custom coding.

  • When you bring up OpenCV--

  • which has an interesting history,

  • because it originated in Intel labs almost 20 years ago.

  • And as a matter of fact, a few months

  • after I joined Intel back in 2000,

  • Gary Bradski, the creator, had just

  • published an interesting article in Dr. Dobb's Journal

  • describing the OpenCV library, and the things

  • it could do to teach your computer how to see.

  • And at the time, as a new Intel employee, I thought,

  • I didn't know Intel was doing computer vision.

  • But now, almost 20 years later, it's

  • almost the de facto industry standard for computer vision.

  • It's open source.

  • It has come a long way.

  • What are some of the opportunities

  • now for developers who are using OpenCV for computer vision

  • apps?

  • OpenCV is the de facto standard.

  • A lot of expert technologists over the years

  • have made Open CV a great starting point for computer

  • vision algorithm developers who want

  • to add any sort of vision function

  • to their application or their algorithm.

  • So, Open CV will continue to evolve, especially

  • as Intel leverages various hardware

  • accelerators, especially for low-powered situations--

  • even high performance compute situations in the cloud.

  • So OpenCV will continue to evolve,

  • and will continue to have more and more functions so

  • that machines can behave like a human eye.

  • For the future for developers, I see them

  • continuing to leverage OpenCV.

  • I see us continuing to educate developers of all types

  • on the use of OpenCV, so that they know that it's accessible

  • and it's not as hard as it used to be.

  • See, one of the things I love about OpenCV,

  • is that it makes me feel like a computer vision expert,

  • when I'm not.

  • I love that.

  • Most people don't admit that.

  • They use OpenCV, and then they act

  • as if they built the whole thing ground-up.

  • It's raised the level of abstraction

  • to allow that to happen.

  • And I get access to highly-tuned functions

  • that do computer vision.

  • Exactly.

  • What do you see coming with OpenCV.js?

  • So, I see a lot of future.

  • OpenCV.js is a big one, because it

  • makes computer vision functions accessible to web developers.

  • So, with the Edge to the Cloud and this whole internet

  • of things, we're going to have a lot of web apps.

  • And having strong vision functions

  • available to web app developers, those

  • are worlds that didn't used to come together.

  • When you combine those worlds with some of the deep learning

  • technologies that we have, and other advancements with custom

  • coding-- again--

  • I see a bright future for the internet

  • of things with very sophisticated vision functions.

  • And now that virtual reality and augmented reality

  • are meeting computer vision, what kind

  • of compelling applications do you see coming in the future?

  • So we have good applications for AR today that are useful.

  • We can take our smartphones and overlay our smartphone

  • on an image and get extra information

  • about that particular image.

  • Or, see video streams or other images on top of the image

  • that we've selected.

  • That's augmented reality.

  • And for virtual reality, I think we're just getting started.

  • We see virtual reality a lot at trade shows.

  • But the price of the hardware isn't accessible to many yet.

  • So I see opportunities for that area to evolve.

  • When you're taking multiple video streams

  • and combining that with motion detection

  • to create a surreal environment, that's

  • very heavy and complicated technology.

  • But I can see where that would help medicine quite a bit--

  • or education, or industrial use cases.

  • And if we change gears a little bit

  • and think about the socio-technical issues

  • of living in an era of ubiquitous cameras,

  • that we're under constant surveillance,

  • cameras are everywhere, there's certainly good and bad.

  • But what's your take on what it means

  • to live in the era of constant surveillance?

  • And what do you try to convey to your team

  • as they develop these software solutions?

  • Some people love that we live in an era of ubiquitous computers.

  • Some people just love their selfies, and their videos,

  • and their cameras.

  • I, personally, am a little stressed

  • out by all of the cameras everywhere.

  • I value my privacy and I value the safety of my data.

  • I'm guessing I'm not alone.

  • So, to me, the ubiquity of cameras

  • brings up the concerns around ethics.

  • And for my team, who develops developer products,

  • we need to think about how to help developers

  • be more responsible when they're developing their vision

  • applications.

  • How do we give them the right controls

  • so that they can give the end user more

  • choices around privacy and security,

  • while still having the convenience that these vision

  • applications allow?

  • Well, thank you, Charlotte, for sharing your insights

  • on computer vision and its broader impact.

  • I'm Henry Gabb with Tech Decoded.

  • Please visit our website for more detailed webinars

  • on computer vision in Intel's hardware and software

  • portfolio for visual computing.

  • [INTEL THEME JINGLE]

Hello, and welcome to Tech Decoded.

字幕與單字

單字即點即查 點擊單字可以查詢單字解釋

B1 中級 美國腔

計算機視覺。為什麼是現在和未來?英特爾軟件 (Computer Vision: Why Now and What's Ahead ? Intel Software)

  • 23 1
    alex 發佈於 2021 年 01 月 14 日
影片單字