视频:人工智能平台能做什么和不能做什么

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小君海德: 在我们进行的过程中, 看看这些不同服务的标签, 我们想到了一些有趣的想法. We found out that Google really excelled at human and animal anatomy. So it could say, “Hey that's an elbow on an arm, or it's a beak on a bird.”

这很有趣. Interesting things that you'll find out as you start getting your feet wet and playing.

AWS, apparently, is very good with plant genus and cloud formations. My favorite part is the machine learning algorithm that detected a cumulus cloud taught my developer what a cumulus cloud is. 这很酷.

所以机器在教人类. Valossa does really well with actions--smiling, laughing etc. And then Microsoft, they can also detect actions and there are different actions there.

但微软尤其擅长打斗场面. 这很有趣. 因为其他一些服务, 当我们浏览内容时, 当战争爆发时, 他们说电子游戏. But Microsoft says, no that's fighting, that's not a video game.

So my recommendation to you is utilize these trial accounts and pick the content profile that is a subset of the type of content that you have. 如果你是一家新闻机构, get a bunch of content that's people on a news team talking about things. If you're a nature company, get that nature video library and create these models.

但在此之前，先试试这些服务. Play with the Video Indexer, play with Valossa, play with Veritone etc.

And, I definitely, AI isn't here to replace us, it's here to help us. So it's going to be humans and AI, it's not just AI and it's not just humans. 一个很好的例子就是模型训练, I submitted this black and white cartoon to one of the services, 我不记得是哪一个了, and maybe I'm saying that because I don't want to get one in trouble, 但那是1930年的一部黑白动画片. And this guy is hopping across like this with this black cape. 你觉得它探测到了什么? 不是一个穿黑斗篷的人. 它发现了一只蝙蝠. And it's like, “okay, I can see why that's a bat, I can see.”

就像一个五岁的孩子. It's never quite seen something so it's using what it understands through its own trained model to make it's best guess. 这就是自信水平的由来. 而且我认为它不太确定那是一只蝙蝠, but I thought it was funny that it thought it was a bat because I could see how it thought that it was a bat. Just like the mirror example earlier with the computer monitors.

所以人工智能和人类, if you want to go down this unified path of leveraging both, Clarifai has a really interesting model where you can just do machine object detection, or you can combine it with their team of manual taggers or you can combine it with your own team.

所以这是两全其美的. You're getting some basic stuff in and then you're also leveraging human metadata taggers to help out. Hive有两个不同的服务.

One is called Predict, which is machine learning, and the other one is humans, it's Hive Data. 确保这些检查和平衡. 也许你有足够的理由说, hey I'm going to use Video Indexer and any of the labels that come back, 我要确保它有90%的信心. And then you just go through some of those initial index videos and just make sure it's helping you out in the way you need it to.