The Scientific Method

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Evaluating Problems

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Macquarie University

Evaluating Problems

12 classificações

Curso 2 de 4 em Specialization Solving Complex Problems

The second course of the specialization EVALUATING PROBLEMS will show you how humans think and how to utilize different disciplinary approaches to tackle problems more effectively. It advances your knowledge of your own field by teaching you to look at it in new ways. EVALUATING PROBLEMS is constructed in the following way: Week I. “Thinking about Thinking” – How problem solving evolved in nature, how the mechanics of our brains work, and the psychological biases that can emerge when we think. Week II. “Philosophy, Science, and Problem Solving” – How humans have historically approached problem solving, from ancient times to the present. Week III. “Approaching Problems in the Natural Sciences” – How people in the natural sciences deconstruct problems. Week IV. “Statistics and Problem Solving” – How statistics can be used to evaluate problems and think critically. Week V. “Approaching Problems in the Humanities” – How people in the social sciences and humanities deconstruct problems. Week VI. “Evaluating the Anthropocene” – How to evaluate the problems of the Anthropocene.

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Approaching Problems in the Natural Sciences

This module will look at how we evaluate problems in the sciences in its various branches, and contemplate how these methods may lend themselves to our own approach to complex problems.

The Scientific Method5:25

Conheça os instrutores

David Baker
Associate Lecturer
Big History Institute
David Christian
Professor
Big History Institute
Shawn Ross
Associate Professor
Big History Institute

The big question for this segment is what

methods do scientists use as they study the world?

[MUSIC]

Module three looks at some of the methods used in different areas of modern science.

It's a reminder that different problems require different approaches,

different tools, and different methods.

But natural sciences usually study systems who's components are relatively well

behaved, the sort of systems described in the course on complexity as complex

physical systems.

Here logic, rigor and very careful observation can often take you a long way.

That's why scientist do expect to solve many of the problems they tackled.

Still, the puzzles they take up are very diverse and so,

too, are the methods they use.

Cosmologists and astronomers are really,

really good at analyzing electromagnetic radiation from stars or galaxies.

Or even from the Big Bang itself.

They can tease an astonishing amount of information

from light emitted by objects many billions of light years away.

Was it emitted by an object traveling away from us?

If so, how fast was it traveling?

Did that object contain hydrogen or iron?

Using these techniques, they can even answer questions about the nature of

the universe itself and how it first appeared.

Scientists can also go in the opposite direction and study very small objects.

How do you study atoms?

Even microscopes won't work here because they used visible light.

And atoms are smaller than the wavelength of visible light.

So, we cannot resolve them.

But we now have instruments that can show us molecules and

atoms, sometimes in exquisite detail.

And they have revealed that the world of the very, very small, the size of

atoms and smaller, works very differently from the world as we humans experience it.

For example, notions of time and space get a bit blurry.

We can't say with certainty exactly where an electron is or how fast it's traveling.

And that has the odd consequence that we can never be absolutely sure whether space

itself is empty or not.

Perhaps it's sort of both empty and not empty.

This is the weird world of quantum physics.

Paradoxically, the machines we use to study the very small,

are often very large.

Machines such as the large hadron collider outside of Geneva, a machine as large

as an airport, and the most expensive piece of scientific equipment ever built.

It smashes protons together at very high speeds and high temperatures and

that can tell us something about the universe just after the big bang.

Engineers focus on the solving aspect of problem solving,

rather than the knowledge aspect.

They try to use science to make things or build machines that can do things.

They're on the edge of understanding because often what they build shows up

unexpected limits to what we understand.

Particularly if they obey all the rules of science but

their machines still don't quite work.

That sends both engineers and scientists scuttling back to the drawing board

to tweak both our understanding and our methods of problem solving.

The engineers who built the first modern steam engines, forced scientists to think

much more carefully and deeply about what energy is and how it does work.

And that research led to some of the most fundamental ideas of modern science,

the laws of thermodynamics.

So there's a constant interplay between understanding a problem and

using that understanding to solve a problem.

One of the most complex engineering problems today is that they're building

artificial brains that can help us all really difficult problems.

AI or artificial intelligence as the challenge is known,

draws on psychology, computer science, mathematics,

network theory and many other domains to solve a deep problem.

Can we build machines that can help us solve big complex problems

that involve huge amounts of information?

The history of computers and artificial intelligence is really very recent but

its seen astonishing progress in the spookiest of engineering tasks

building mechanical brains that are cleverer than us.

Clearly our methods of problem solving have improved fast in recent

centuries Is there a danger that will get too good at problem solving?

If we solve the mega problem of artificial intelligence

can we be sure the machines we build will obey us?

Some machines can already defeat human chess grand masters.

Should we be scared of our rapidly increasing problem solving skills?

[MUSIC]

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