So data and analytics are dramatically changing our everyday lives.

資料和分析不斷為我們的 日常生活帶來重大改變。

Not just online,

不只是在線上或遙遠的未來，

not just in some distant future,

而是在現實世界中， 真實具體地呈現。

but in the physical world,

過去 11 年，我都在 麻省理工學院當極客，

and in very real and tangible ways.

在大數據實驗室裡工作，

I spent the past 11 years of my life as a geek at MIT,

企圖用資料科學來研究實體世界，

working in big data labs

並嘗試解決社會上的重大問題。

that seek to use data science to study the physical world

大數據領域企圖運用 計算工具分析大量資料，

and try to solve society's great problems.

以找出模式和趨勢。

The field of big data seeks to analyze massive pools of data

資料能化身為說故事高手，

using computational tools to find patterns and trends.

揭開日常事物背後所藏的故事，

Data can be a really extraordinary storyteller,

或許我們原本無從知道的。

unveiling the hidden narratives of things in our everyday lives

為無生命的事物說出生動的故事，

that we never would have seen.

我覺得非常有吸引力。

I find the personal stories of inanimate things brought to life

想先分享我在麻省理工學院 所做的兩個亮點專案，

to be extraordinarily compelling.

我認為它們非常適合說明這個現象。

I want to highlight, first, two projects from my time at MIT

第一個是「垃圾軌跡」，

that I think highlight this phenomenon really well.

這個計畫的目標是 更了解廢棄物管理系統，

The first is called Trash Track,

以回答這個問題：

and in this project, we sought to better understand the waste-management system,

「當你把垃圾丟掉之後， 它會到哪裡去？」

to answer the question

你的舊咖啡杯，

"Where does your trash go when you throw it away?"

或者在 2000 年代初期 你帶在身上的掀蓋式手機，

Your old coffee cup or that flip phone

或貝果，或今天早上的報紙——

that you carried around in the early 2000s,

這些東西到哪裡去了？

or a bagel or this morning's paper --

這些資料並不存在， 所以得要由我們來創造。

where do these things go?

我們以視覺化的方式解答這個問題，

This data didn't exist, so we had to create it.

透過在一些垃圾當中 裝上小型感測器，

We answered and then visualized this question

再將它們丟到廢棄物系統中。

by installing small sensors into pieces of trash

各位看到的就是這些資料。

and then throwing them into the waste system.

各位看到的每一個點、每一條線，

And what you're seeing here is the data.

就代表一個垃圾在西雅圖中移動，

Every line, every node that you see

接著到州內的其他地方，

is a single piece of trash moving through the city of Seattle,

接著到國內的其他地方，

and then across the state,

隨著每週、每月過去的移動。

and then across the country,

視覺化呈現這些資料十分重要， 因為應該沒有人會揣想：

as weeks and months go by.

「是的，看起來沒錯。」

And it's important to visualize this data,

（笑聲）

because none of you are, probably, sitting here thinking,

「就應該是這樣，對吧？」

"Yeah, that looks right."

因為不是。

(Laughter)

（笑聲）

"That's working like it should, right?"

資料所呈現的是個效率極低的系統，

Because, no --

我們或許無法看見系統本身的殘缺，

(Laughter)

若不是這些感測器幫我們收集資料。

What the data shows us is a highly inefficient system

我要分享的第二個亮點專案，

whose inherent brokenness I don't think we really would have seen

是在打造能夠潛入下水道

had the sensors not done the journalism for us.

做廢水採樣的機器人。

A second project that I'd have to highlight

我知道汙水的形象不佳，

has to do with creating robots that dive into sewers

但它其實還蠻棒的，

and sample wastewater.

因為它能告訴我們很多 關於社區健康的資訊。

I know that sewage kind of gets a bad rap,

開發出這項技術的是叫做 Biobot Analytics 的集團，

but it's actually kind of awesome,

他們創造出先進的技術，

because it can tell us an incredible amount

將我們的下水道轉變成 現代的健康觀測台。

about the health of our communities.

他們的目標是要研究 汙水中的類鴉片藥物，

This technology was spun out by a group call Biobot Analytics,

以期更了解城市中的用藥狀況。

who's creating a cutting-edge technology

這些資料正是關鍵所在，

to turn our sewers into modern-day health observatories.

因為它們能協助城市了解 人們在哪裡使用這些藥物、

Their goal is to study opioids within the sewage

如何分配資源，

to better understand consumption in cities.

以及方案的長期成效。

And this data is key,

同樣的，內建在這台機器中的 這項技術揭開了面紗，

because it really helps cities understand where people are using,

讓我們看到一些本來 無法看到的城市狀況。

how to allocate resources

所以，結果發現，如我們所見，

and the effectiveness of programming over time.

大數據真的無所不在，

Once again, the technology that's built into this machine

連你的廁所裡也有。

is pulling back the curtain

既然我們已經談過了垃圾和汙水，

and showing us something about our cities that we never would have seen without it.

咱們接著來談談……食物。

So it turns out, as we see,

（笑聲）

that big data is really everywhere --

一年前，我離開了麻省理工學院， 去追尋對食物的熱情，

even in your toilet.

2017 年，我和先生創立了「家庭晚餐」。

And so now that we've talked about trash and sewage,

我們公司的目標是為當地食物

let's move on ...

及種植這些食物的人建立社群。

to food.

為了達成這個目標，

(Laughter)

我們用了資料分析、自動化和科技，

A year ago, I left MIT to pursue a passion in food,

建造出當地農場的分散式網路，

and in 2017,

並改善食物系統。

started a company with my husband, called Family Dinner.

各位可以看到，

The goal of our company is to create community around local food

這些多重的技術 和我們試著達成的使命，

and the people who grow it.

其實和麻省理工學院 實驗室的工作相去不遠。

To make this happen, we're using data analytics,

這就帶出了一個關鍵問題：

automation and technology

為什麼會有人想要放棄在世界頂尖

to build a distributed network of local farms

都市科學實驗室工作的職涯，

and to make improvements on the food system.

開著她媽媽的 Acura 汽車 載著紅蘿蔔到處跑？

So what we see here

（笑聲）

is that the broad techniques and the mission of what we're trying to do

那是台好車。

is really not dissimilar from the work at the MIT labs.

因為我相信，當地食物的故事

Which brings us to a critical question:

需要被了解、被說出、被提升，

Why exactly would someone leave a very promising career

且在許多層面上， 我想我們這種書呆子

at one of the top urban science labs in the world

特別適合來說這樣的故事。

to drive carrots around in her mom's Acura?

所以，我們要從何開始？起點在哪？

(Laughter)

目前的國家食物系統只針對 一項指標在做最佳化：

It's a great car.

企業利潤，對吧？

Because I believe that the story of local food

想想這一點。

needs to be understood, told and elevated,

食物公司存在背後最強的驅動力，

and in many ways,

並不是要提供食物給飢餓的人，

I think that nerds like us are really uniquely poised to tell it.

也不是要做出美食。

So where are we starting?

是利潤。

What's our starting point?

在我們食物系統的每個層級， 這都是種有害的效應。

The current national food system is optimized for one thing only,

放到食物中的抗生素及殺蟲劑

and that's corporate profit, right?

對我們的健康有害。

And think about that.

價格壓力迫使小農場關門。

The most compelling reason for food companies to exist

事實上，許多你所知道的 農場特性都已不復存在。

is not to feed hungry people,

農場看起來不像農場，而像工廠。

it's not to make delicious-tasting food.

到頭來，我們所吃的食物， 品質也會受到不良影響。

It's profit.

工廠化農場產出的蕃茄 可能看起來像是一般的蕃茄：

And that has detrimental effects at all levels of our food system.

外表是亮紅色的……

The antibiotics and pesticides that are being put into our food

但當你咬下去，

are detrimental to our health.

口味和質感就是讓你覺得不對勁。

Price pressure is forcing small farms out of business.

我們知道，這當中最大的悲劇

In fact, a lot of the things that you think about farms

可能就是這些食物 有 30~40% 被浪費掉了……

no longer exist.

被丟棄。

Farms don't look like farms, they look like factories.

換算出來是 16 億公噸。

And at the end of the day,

我實在無法想像這樣的數字。

the quality of the food that we're eating really suffers, too.

16 億公噸。

A factory-farm tomato may kind of look like a regular tomato:

一年丟棄的食物就價值 1.2 兆美金。

bright red exterior ...

這就是隨選隨吃、方便性，

But when you bite into it,

及出問題的食物系統 所產生的代價。

the taste and texture just leave you wanting.

這些浪費發生在何處？

And we know that perhaps the greatest tragedy in all of this

到底哪裏浪費了？

is that between 30 and 40 percent of this food is just wasted ...

我們知道在農場裡會發生，

thrown away.

看起來不夠漂亮的 馬鈴薯不會被選中。

That is 1.6 billion tons.

我們知道在運輸過程會發生，

I can't even wrap my head around that number.

在倉庫裡會發生，在雜貨店會發生。

1.6 billion tons.

最後，在自家廚房 流理台上也會發生，

That's 1.2 trillion dollars a year

當我們認定長了斑點的 褐色香蕉，看起來不好吃了。

in wasted food.

那麼多浪費，那麼多心力。

That is the cost of on-demand eating

食物被種植下去、

and convenience

栽培、收穫、運送，

and the broken food system.

然後就只是被丟掉。

Now, where's this waste happening?

我們認為一定有更好的方式。

Where's all this waste coming from?

我們要如何改善？

Well, we know that it happens in the field

我們要如何做出更好的系統？

when you don't pick the sexiest-looking potatoes.

為了辦到，

We know that it happens in transit,

我們知道必須去除 食物供應鏈中的浪費。

at the warehouses,

我們得要把資料送到農民手中，

in the grocery stores.

讓他們做更好的預測。

And finally, on our own kitchen counters,

這樣他們才能和大企業競爭。

when we determine that that spotty, brown banana no longer looks so yummy.

接著，最後，

All that waste, all that effort.

身為公司，我們得要重視

Food is planted,

品質和口味勝過其他一切，

grown, harvested, shipped,

如此，大家才會珍視 他們盤中的美味食物。

and then just thrown away.

我們相信這才是比較好的系統。

We think that there has to be a better way.

這才是比較好的方式。

And so how to we improve upon this?

而通往更好方式的路， 是由資料鋪墊而成的。

How do we make a better system?

讓我用一個關於 兩顆蕃茄的故事來說明。

In order to do this,

我們依序來談。

we understand that we need to eliminate waste

蕃茄本身就含有美麗的縮影，

in the food supply chain.

內含關於它生命週期的一切資訊：

We need to get data in the hands of farmers,

它生長在哪裡、被如何對待、

so that they can make better predictions.

營養價值、

So they can, you know, kind of compete with the big guy.

走了多遠才到你的盤子裡、

And then finally,

碳足跡。

we need to prize, as a company,

所有這些資訊，

quality and taste above everything,

所有這些小章節， 都含在一顆小小的水果中。

so that people really value the delicious food on their plates.

這挺讓人興奮。

This, we believe, is the better system.

這是一號蕃茄。

This is the better way.

你可以在全世界的三明治店、

And the path to that better way is paved with data.

超級市場、速食店分店買到它。

To highlight all of this, I want to tell the tale of two tomatoes.

它的背景故事又長又複雜。

We'll talk about them one by one.

它喝過宛如雞尾酒般的 多種混和殺蟲劑，

A tomato in itself contains a beautiful snapshot

運送了至少 1,600 英里才到達你家。

of everything you might want to know about the life cycle of that fruit:

這裡的圖是綠色的，

where it was grown, what it was treated with,

因為這些蕃茄在還是綠色 且像石頭一樣硬時被選中，

nutritional value,

運輸途中使用氣調保鮮，

miles traveled to get to your plate,

好讓它們抵達目的地時，

CO2 emissions along the way.

外表看來鮮豔欲滴。

All of that information,

所有那些心力，