>> And determining their median he showed that if the crowd
were one its estimate is keen.
>> He showed that if the crowd were one, its estimate is keen.
>> Keenius.
>> That's because while no individual get the actual rates,
the average of all the guesses is exactly right.
>> The average will generally be better than a randomly selected individual guess.
>> The average of the masses assures us of.
Success. >> I think he was talking about the media.
>> And the larger the number of guesses we toss in.
>> The more likely we are to get the right answer about the oxen.
>> His premature prognostication.
They cannot help but scoff.
>> Gelman should've gathered more data before he went shooting his mouth off.
>> Sir Francis' hypothesis was rocked by ignoramuses.
He lost the proof he had avowed.
He found the wisdom of the crowds.
>> If you have a group of people and they each have tiny bits of information,
then you can learn a lot if you can just gather all of these bits together.
>> It's just like Wikipedia.
>> Well, this isn't exactly like Wikipedia,
[LAUGH] it's a little bit different.
>> It could maybe be like.
[SOUND] And you don't even need to be an expert, but if you know something then
you're able to contribute, and that entry is able to be more informed.
>> Another sample of this fair?
>> Who wants to be a millionaire?
>> Yeah the audience life line.
>> If a person feels like they can't answer the question by themselves,
as the audience.
The audience is right over 90% of the time.
>> There you go.
>> How about that, Gelman?
>> The wrong Gelman.
>> Sorry. >> One by one we're not too smart, but
every guess it plays its part and
when you add them up you'll find the wisdom of the crowds.
[NOISE] So what you saw from the video is that when you put
the minds of many people together you can potentially
achieve more than what one person can achieve.
So the rest of this lecture would be discussing information that
was published in a review article By Benjamin Good and Andrew Su.
And this is a really great review that covers many of the types of crowdsourcing
projects in the field of systems biology and bioex schematics.
So the first one is Crowdsourcing.
So the term Crowdsourcing was coined in an article in Wired magazine in 2006.
And a definition stated that Crowdsourcing
is the act of taking a traditional job Performed by an employee and
outsourcing it to an undefined, generally large group in an open call.
So the review article in Bioinformatics by divides
crowdsourcing projects into two types, microtasks And mega-tasks.
Micro-tasks are projects where you don't need to know too much to participate in.
You are presented with a relatively easy task.
For example, a task that we saw in the video of guessing the weight of the ox.
And the combination of the input of many people results in
a final great product that would be very difficult to achieve using,
for example, complicated computer programs.
The other types of tasks, the megatasks are typically very hard.
Problems and can potentially be solved by individuals.
These megatasks are typically set as challenges or competitions,
and only the top few solvers of the task provide a solution to the problem.
Now we are going to over several examples
of microtasks with megatasks that are out there.
But before we go into some examples, let's think about.
What could be motivating people to participate in any Crowdsourcing project?
So the first would be people just like to volunteer and
be a part of something bigger and great.
Crowdsourcing projects are also delivered sometimes as games.
And the reason people want to play games is mostly for fun.
There are also now on the web microtask markets.
One example is the famous Amazon Mechanical term
that we will discuss a little bit later on in the next few slides.
Sometimes you guys are participating in microtasks without even knowing it.
For example,
when you search Google, you're helping Google to improve their search engine.
Because they process your search terms to improve their algorithms.
And the last one is through education.
So by performing those micro or mega tasks, you are actually learning,
and especially now when you have those MOOCs, an instructor
can give the students projects that they can all work on together.
Let's look at some Examples of microtasks.
So the first example is called Cell Slider.
So this microtasks ask you to label various kinds and
quantities of different cells from cancer patient biopsies.
So let's go to the website and look at this example.