Web Science: How is it different? Daniel Tunkelang Head of Query Understanding

Понравилась презентация – покажи это...

Слайд 0

Daniel Web Science: How is it different? Daniel Tunkelang Head of Query Understanding

Слайд 1

Слайд 2

tl;dr: The scientific method is alive and well. Big data has just changed the economics.

Слайд 3

How have the web and big data changed science? Let’s ask some of the experts.

Слайд 4

“You have to kiss a lot of frogs to find one prince. So how can you find your prince faster? By finding more frogs and kissing them faster and faster.” Mike Moran Do It Wrong Quickly: How the Web Changes the Old Marketing Rules, 2007 Cited by Kohavi in Online Controlled Experiments at Large Scale, 2013

Слайд 5

Web Science = faster, cheaper experiments.

Слайд 6

“The cost of experimentation is now the same or less than the cost of analysis. You can get more value…by doing a quick experiment than from doing a sophisticated analysis.” Michael Schrage Value-Creation, Experiments, and Why IT Does Matter, 2010

Слайд 7

Web Science = more experiments, less analysis?

Слайд 8

“with massive data, this approach to science — hypothesize, model, test — is becoming obsolete… Petabytes allow us to say: "Correlation is enough." We can stop looking for models…analyze the data without hypotheses…throw the numbers into the biggest computing clusters the world…and let…algorithms find patterns where science cannot.” Chris Anderson The End of Theory, 2008

Слайд 9


Слайд 10


Слайд 11

Let’s rewind.

Слайд 12

What makes it science?

Слайд 13


Слайд 14


Слайд 15


Слайд 16

The scientific method still works today. What’s changed is the economics.

Слайд 17

Scientific Method 1747

Слайд 18

Scientific Method Today

Слайд 19

It’s the economy, science. Yesterday Experiments are expensive, choose hypotheses wisely. Today Experiments are cheap, do as many as you can!

Слайд 20

What about Web Science?

Слайд 21

A/B testing: everybody’s doing it.

Слайд 22

Google: 20k search experiments per year

Слайд 23


Слайд 24

The Myth of Insight

Слайд 25

Scientists gain insight by staring at data.

Слайд 26

Big data tools improve data exploration.

Слайд 27

In hypothesis generation, quantity trumps quality.

Слайд 28

Except when it doesn’t.

Слайд 29

Слайд 30

Easier to analyze data than research humans.

Слайд 31

But we pay the price. Example: search engine improvements in batch evaluations don’t always predict real user benefits. [Hersh et al, 2000] Do Batch and User Evaluations Give the Same Results? [Turpin & Hersh, 2001] Why Batch and User Evaluations do not Give the Same Results [Turpin, Scholer, 2006] User Performance versus Precision Measures for Simple Search Tasks But also see… [Smucker & Jethani, 2010] Human Performance and Retrieval Precision Revisited

Слайд 32

When local optimization is cheap, you neglect the rest.

Слайд 33

To summarize: how is web science different? Online testing is cheaper and scalable. Data exploration tools make hypothesis generation cheaper and easier. But the experiments that are easy and cheap aren’t always the most valuable. Easy to forget our biases as scientists.

Слайд 34

Take-Aways The scientific method is alive and well. Big data has just changes the economics. Cheaper hypothesis testing and generation has already been transformative. That’s why big data matters. But we neglect the human side of scientific experimentation at our peril.

Слайд 35

Daniel Tunkelang dtunkelang@linkedin.com https://linkedin.com/in/dtunkelang