Making a TEDEd Lesson Visualizing big ideas

Do you ever struggle to find the perfect description

when trying to convey an idea?

Like a foggy picture,

adjectives and modifiers fail to depict

what’s in your mind.

Illustrators often face a similar challenge,

especially when attempting to explain

complex and difficult concepts.

Sometimes the imagery is intangible

or way too complicated to explain with a picture.

Although complex information could be relayed

using charts and stats,

this could get pretty boring.

Instead, just like when writing an essay

to describe, for example, emotions,

illustrators can use visual metaphors

to bring to life difficult concepts.

Just as a written metaphor is a description

that relates one object to another,

a visual metaphor uses imagery to suggest

a particular association or point of similarity.

Our lesson “Big Data” is a great example

of a situation where visual metaphors

played a huge role in explaining the concept.

What is Big Data in the first place?

Good question!

Big Data is a huge amount of digital information

produced worldwide on a daily basis,

challenging us to find solutions

for storing,

analyzing,

and also imagining it visually.

Quite an elusive concept!

How should we depict this?

Let’s take a look at our “Big Data” script.

We start with smaller computer servers

that branch out into bigger networks

to produce data,

then even bigger networks

and production of even more data.

You see where we’re going with this –

an object growing and branching out in many directions

and producing something as a result?

Does that remind you of something?

Just like those computer networks,

a tree grows and branches out

to produce more leaves each year.

And every year, just as the data accumulates

and faces us with a challenge

to find storage solutions,

it gets harder to collect those piles of leaves

when they fall off the tree.

Aha! There’s our visual metaphor!

Okay, so we have the script,

audio,

and a visual metaphor.

The next step in visual development

is to design the characters

and environments of the animation.

To do so, we think

of an appropriate and appealing style

to illustrate the ideas

and help the viewer better understand

what they’re hearing.

Let’s go back to the script

and see if we can find any clues there.

Our story starts in the 1960s

when the first computer networks were built.

This decade will serve as a good point

to make the stylistic choice for our animation

as it will allow us to refer to artwork

from that era.

You may want to start

by looking at some art books

(design, illustrations, cartoons, etc.)

from that era

and find a style that may our own purpose.

Look closely,

study the material,

and try to understand the choices

artists of that time made and why.

For example, the 1960s minimalist animation style

was a significant departure

from the cinematic realism

that was popular in animated films at the time.

The choice to use limited animation techniques

was originally made for budgetary reasons,

but it became a signature style

that influenced many future generations of animators.

In this stylistic approach,

the simplified characters,

flat backgrounds,

and angular shapes come together

to create new interpretations of reality,

which also sounds like a good place

to begin visualizing our own Big Data.

Well, let’s try an experiment.

“In the 1980s islands of similar networks

speaking different dialects

sprung up all over Europe and the States,

making remote access possible but tortuous.”

Is this better?

“In the 1980s islands of similar networks

speaking different dialects

sprung up all over Europe and the States,

making remote access possible but tortuous.

To make it easy for our physicists across the world

to access the ever-expanding Big Data

stored at CERN without traveling,

the networks needed to be talking

with the same language.”

As you probably observed,

graphic representations are a great way

to capture the interest of your audience.

By depicting what you want to present and explain

with strong, memorable visuals,

you can communicate your idea more effectively.

So, now, challenge yourself.

Think of an abstract concept

that cannot be explained with simple words.

Go ahead and try your hand

at visually developing that idea.

在尝试传达一个想法时,您是否曾经努力寻找完美的描述

就像一幅模糊的图画,

形容词和修饰语无法描述

你的想法。

插画家经常面临类似的挑战,

尤其是在试图解释

复杂和困难的概念时。

有时图像是无形的,

或者太复杂而无法用图片来解释。

尽管可以

使用图表和统计数据传递复杂的信息,

但这可能会变得非常无聊。

相反,就像在写一篇文章

来描述情绪一样,

插画家可以使用视觉隐喻

来实现困难的概念。

正如书面隐喻

是将一个对象与另一个对象联系起来的描述一样

,视觉隐喻使用图像来暗示

特定的关联或相似点。

我们的“大数据”课是

视觉隐喻

在解释概念方面发挥巨大作用的一个很好的例子。

首先什么是大数据?

好问题!

大数据

是每天在全球范围内产生的大量数字信息,

挑战我们

寻找存储、

分析

和可视化想象的解决方案。

真是一个难以捉摸的概念!

我们应该如何描述这一点?

让我们看一下我们的“大数据”脚本。

我们从较小的计算机服务器开始,这些服务器

扩展到更大的网络

以产生数据,

然后是更大的网络

并产生更多的数据。

你明白我们的目标是什么——

一个物体在多个方向生长和分支,

并产生一些结果?

这会让你想起什么吗?

就像那些计算机网络一样,

一棵树每年都会生长并分叉

以产生更多的叶子。

每年,随着数据的积累

,我们面临

着寻找存储解决方案的挑战,当

树叶从树上掉下来时,收集这些树叶变得越来越困难

啊哈! 这是我们的视觉隐喻!

好的,所以我们有了脚本、

音频

和视觉隐喻。

视觉开发的下一步

是设计动画的角色

和环境。

为此,我们会

想出一种合适且吸引人的风格

来说明这些想法

并帮助观众更好地

理解他们所听到的内容。

让我们回到剧本

,看看我们是否能从中找到任何线索。

我们的故事始于 1960 年代,

当时建立了第一批计算机网络。

这十年将成为

我们动画风格选择的一个好时机,

因为它可以让我们参考

那个时代的艺术作品。

您可能想

从查看那个时代的一些艺术书籍

(设计、插图、卡通等)

开始,

并找到一种可能符合我们自己目的的风格。

仔细观察,

研究材料,

并尝试了解

当时艺术家所做的选择以及原因。

例如,1960 年代的极简主义动画风格

与当时流行的动画电影中的电影现实主义有很大的不同。

选择使用有限的动画技术

最初是出于预算原因,

但它成为

影响许多后代动画师的标志性风格。

在这种风格的方法中

,简化的字符、

平坦的背景

和棱角分明的形状结合在一起

,创造了对现实的新解释,

这听起来也是

开始可视化我们自己的大数据的好地方。

好吧,让我们尝试一个实验。

“在 1980 年代,使用不同方言的类似网络岛屿

在欧洲和美国如雨后春笋般涌现,

使得远程访问成为可能,但也很曲折。”

这是否更好?

“在 1980 年代,使用不同方言的类似网络岛屿

在欧洲和美国如雨后春笋般涌现,

使远程访问成为可能,但

也很曲折。让我们世界各地的物理学家无需旅行

即可轻松访问存储在 CERN 的不断扩展的大数据

,网络需要

使用相同的语言进行交流。”

正如您可能观察到的那样,

图形表示是

吸引观众兴趣的好方法。

通过使用强烈、令人难忘的视觉效果来描述您想要呈现和解释的内容

您可以更有效地传达您的想法。

所以,现在,挑战自己。

一个不能用简单的词来解释的抽象概念。

继续尝试

在视觉上发展这个想法。