自治车是主流的什么时候?

丹尼尔Faggella.
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Daniel Faggella是Emerj研究的首脑。丹尼尔呼吁由联合国,世界银行,国际刑警组织和龙头企业,丹尼尔是AI对商业和政府领导者竞争战略影响的全球追捧。

医学成像中的深度学习应用7

摘要摘要:本周我们与首席执行官和创始人交谈Nexar Inc.Eran Shir,其公司创建了一个仪表板应用程序,允许驱动程序安装智能手机,然后收集可视信息和其他数据,例如从加速度计的速度,以帮助检测和防止事故。

该应用程序还可作为重建碰撞中发生的事情的一种方式 - 一个大型和未开发的市场中的独特解决方案。In this episode, Shir gives his vision of a world where the roads are filled with cyborgs, rather than autonomous robots, i.e. people augmented with new sensory information that trigger notifications, warnings or prompts for safer driving behavior, amongst a network of cloud-connected cars. He also touches on what the transition might look like in response to the question – when will autonomous cars be mainstream?

iTunes-podcast.
soundloud-podcast.
Google-Podcast.
Stitcher-Podcast.

专业知识:事物,机器学习和大数据,复杂网络

简要认识:Eran Shir是一个连续的企业家和Nexar Inc.的创始人,此前,他在Aleph居住的企业家在Tel Aviv中的一个VC中,她专注于像事互联网,大数据,加密货币,市场和市场等领域网络,以及这些技术如何扰乱重要的市场。在Aleph之前,Shir ran为雅虎的全球创意创新中心运行,在那里他负责雅虎的广告创意技术战略和投资组合,以及其下一代广告创意个性化平台的发展。

Shir加入Yahoo!in October 2010 when the search giant acquired Dapper, a dynamic ads and semantic web startup Shir co-founded in 2007. Shir holds a Master’s in Physics from Technion – Israel Institute of Technology and a PhD in Electrical Engineering and Computer Science from Tel Aviv University; he is also a graduate of Singularity University.

目前的附属机构:Nexar Inc的首席执行官和创始人;董事会成员以色列通过教育卓越中心

面试亮点:

以下是完整音频访谈的浓缩版本,可在Emerj SoundCloud和iTunes站上的上面的链接中使用。

1:50 - 我想第一次发言Ai正在玩在Nexar的角色...... AI在哪里发挥其在所有这些传感器和所有技术中的作用?

Eran Shir:“我们以各种方式使用AI进行各种用例。我们作为一个应用程序的主要焦点是警告您对潜在的碰撞,因为我们正在实时跟踪您周围的所有车辆并检测并试图防止潜在的事故通过警告您遇到危险情况;we use the vision sensor and tracking vehicles and basically deploying virtual sensors on other vehicles around you, and we leverage also a vehicle to vehicle network to share that information across longer distances so we can warn you about a dangerous situation that’ s happening five cars ahead of you immediately…”

5:20 – Is it possible that, say, there’s a particularly dangerous interaction during rush hour…is there a red light that will come on as we’re approaching it, or will we only (be alerted) if a car in front of us just had an accident 10 minutes ago and they’re still in the middle of the road?

es:“A lot of accidents, when you’re driving in rush hour, is just someone ahead of you is pressing the brakes and you don’t notice because you’re distracted and I’m trying to warn you…that use case is called a forward-collision warning and that’s really the first use case we have implemented, but there are many others…one of the most dangerous things you can do is drive much faster or much slower than the swarm around you…that has a great correlation to risk and I want to detect the car’s swarm velocity, this is an example of another use case…”

8:00 - 与用户的互动是什么,提示他们'嘿,你最好开始移动'或'嘿,你更好地抽出休息吗?

es:“Obviously there’s an audio prompt…but there’s also a visual UI, and the visual UI is very simple, it tells you seconds to impact, in the case of the vehicle-to-vehicle use case where you don’t actually see the car that is now getting into an accidents, it tells you the distance and it actually tracks the distance as you close it, and it tells you in a very colorful way the level of dangers, so something that is very basic and very primary from our perspective, you can see it from the side of your eye, and that will be enough, or you can listen to the audio prompt and react to that.”

11:47 - 对于传感器融合,通过对您的意思谈论观众...

es:“We have a smart phone set up on your dashboard basically looking ahead, so if someone hits you from behind, obviously the camera won’t see it, but even if in that situation.we are able to reconstruct exactly what happened, whether someone hit you at an angle of 175 degrees or 192 degrees at a force of 3.5G or 5.3G and whether there’s a chance of 10 percent or 30 percent, you got whiplash, so all of these things can be deduced from just a single phone sitting on a single place in the car, and it really doesn’t matter in that sense where you put it, and just doing a lot of sensor fusion, a lot of physics, a lot of calibration and machine learning, to reconstruct the scene.”

13:31 - 它听起来像是开始时,你可能需要很多崩溃的测试数据......所有被拉从哪里?

es:“Thankfully that part is relatively solved, there is about 50 years of medical research on the impact of collision in various different shapes and forms across thousands and tens of thousands and hundreds of thousands of collisions…from that perspective, we didn’t develop new data sets but what’s really cool is we have data to enter into those models (now)…”

17:43 - 当越来越多的车辆拥有所有这些传感器并连接时,世界开始看起来像什么?

es:“它应该启用 - 最终 - 一个无事故世界;I’ll be a bit of contrarian here and say we really don’t need the deployment of autonomous vehicles at the massive scale in rode to prevent car accidents, there are a lot of great reasons why I can’t wait for autonomous vehicles…but safety is actually not one of them; if we deploy enough sensors, if we make sense of sensors in real-time and warn drivers in a smart way and connect those vehicles in a network, we can eventually reduce car collisions by at least tens of percent…

......关于社会影响......当你感觉到所有内容时,一切都是负责任的,你可以开始在刻度转向个性化的保险,你可以开始警告人们关于他们旁边的坏司机,你可以开始做一堆有一堆的东西impact of changing peoples’ behavior, not just reacting in real-time but actually coaching people how to become better drivers and giving them the incentives to become better drivers, and I think we’ll see a lot of that in the next decade…”

22:34 - 我们之前写的自动驾驶汽车时间表,您认为自动车辆市场在几十年内看起来像什么?

es:“在接下来的20年或30年里,我们将居住在一个杂交世界;initially there will be a few autonomous vehicles in specific places and lots of human-driven cars, and then over time it will gradually change, that’s the story, this is not going to be a big bang, if anything because you don’t have enough factories to replace all the vehicles on the road…during that time, we’re going to be in a very peculiar state, in which you’ll have human drivers and autonomous vehicles share the road…

......今天我们习惯于紧急管理,道路正在管理,因为每个人在各种车辆中与其他车辆互动,你可以相信人们在某些方面照顾局势;where you have lots of autonomous vehicles, you’re actually going to need to add a management layer…that understands what every car does in the atomic sense, one that can provide guidelines, hints and sometimes orders to the vehicles on what to do…”

大想法:

1 -Nexar的车辆应用解决方案中的驾驶问题不是责任,而且碰撞严重程度,保险人和保险公司的主要问题;使用收集的数据重建场景是有助于解决几十年的旧问题,其中一个机器人趋势该社会将在未来十年中见证。

2 -AI和机器学习技术有一个潜在的巨大在改变人们的行为方面发挥作用在路上,撇开自动车辆。随着时间的推移,这种类型的指导还可作为总体管理播放器,允许自动车辆以规模扩展。

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