But the greatest potential lies in linking the two datasets.

但是最潜力是连接两数据组。

" The goal is to build this huge dataset and make it available."

“我们目标是建立数据集并使其可被利用。”

So that's how the engineers get these giant datasets.

这就是工程师们获得这些巨数据集方式。

There are a ton of great hand datasets that can really understand hands like this one with 11,000 hands.

有很多很棒手部数据集可以真正理解手，比如这个有 11000 只手数据集。

Just as an example, available datasets like Flickr HQ has 70,000 faces. 70,000.

举个例子，像 Flickr HQ 这样可用数据集有 7 万脸。7 万欸。

" Our dataset represents a massive collecting effort of thousands of scientists, " Schultheiss said.

“我们数据集展现了数千名科学家规模收集工作，”舒尔特斯说。

They then checked their findings against larger datasets that included thousands of adults.

然后，他们将他们发现与包括成千上万成年人在内型数据集进行对比。

MegaFace's creators say it's the largest publicly available facial-recognition dataset out there.

MegaFace 创建者说，这是目前最公开面部识别数据集。

Those observations were supported in larger datasets of hundreds of newborns and thousands of 9-year-olds.

这些观察结果得到了数百名新生儿和数千名9岁儿童型数据集支持。

The children’s math skills are improving and the datasets collected by the robots are huge.

孩子们数学能力正在提高，机器人收集数据集也非常。

We don't know exactly what's in the datasets used by OpenAI or Midjourney.

我们不知道 OpenAI 或 Midjourney 使用数据集中到底有什么。

All the data collected will be stored by the Open Science Foundation as a public dataset.

所有收集到数据都将由开放科学基金会作为公共数据集存储。

Amazon and Microsoft, two tech giants, now offer tools to help companies improve management of their unstructured datasets, as does Google.

亚马逊和微软这两家科技巨头现在提供工具，帮助公司改善对非结构化数据集管理，谷歌也有类似行动。

Just don't forget the datasets and metrics.

只是不要忘记数据集和指标。

D) They have trouble converting face datasets into the right format.

D) 他们在将人脸数据集转换为正确格式时遇到了麻烦。

And when I put the two datasets together, it was just perfect.

当我将两个数据集放在一起时，结果非常完美。

A) No computer is yet able to handle huge datasets of human faces.

A) 目前还没有计算机能够处理人脸数据集。

It is not only the size of datasets that counts. The better the data, the better the model.

重要不仅仅是数据集小。数据越好，模型就越好。

This means that it can synthesize new samples from the learned dataset.

也就是说它可以从已学习数据集中合成新样本。

When new solutions pop up in the future, this dataset can be used as a yardstick to compare results with.

将来有新算法时，这个数据集可以作为比较标杆。