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But things get worse.
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New lineages of Tumortown officials have started to forge court documents that order police to shoot at the SWAT teams.
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What the cancer cells are doing at this point is actively shutting down immune defenses by sending corrupt signals.
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The now malignant tumor is no longer a pushover and has begun creating the cancer microenvironment, a sort of borderland that's hard to cross.
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All avenues of attack have been shut down and uncontrollable growth is the consequence.
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This is a dangerous tumor.
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Cells that are strong and able to fight push your immune system back and expand further.
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If more mutations happen, then some of the cancer cells will begin to explore the world and expand into other tissues to build new towns.
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And this is exactly what makes cancer so harmful.
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It's taking up space and stealing so many nutrients that your true self has no room to function anymore.
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If this goes on for too long, organs will shut down.
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But this tactic is a dead end.
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The more successful cancer gets, the more damage it does to its world.
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When the body dies, the cancer dies too.
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It truly is a game without winners.
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Except humanity is planning to win this game.
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At this very moment, hundreds of thousands of scientists are working on new and better ways of killing cancer to destroy and burn down tumor towns for good.
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In recent years, immunotherapy has made enormous progress.
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It's a relatively new therapy in which your own immune cells are modified to kill cancer better than any medicine can do.
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It's like giving your building inspectors machine guns and flamethrowers.
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But this is a story for another time.
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For now, cancer is a battleground.
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But if human ingenuity is to be trusted, then one day, maybe in the not too distant future, we will eradicate it once and for all.
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This video was made possible in part by direct viewer support and in part through a grant by Gates Ventures.
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Please check out our source document for more background and in-depth information.
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How long do you think it will take before machines do your job better than you do?
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Automation used to mean big, stupid machines doing repetitive work in factories.
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Today, they can land aircraft, diagnose cancer, and trade stocks.
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We are entering a new age of automation unlike anything that's come before.
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According to a 2013 study, almost half of all jobs in the US could potentially be automated in the next two decades.
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But wait, hasn't automation been around for decades?
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What's different this time?
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Things used to be simple.
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Innovation made human work easier and productivity rose,
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which means that more staff or services could be produced per hour using the same amount of human workers.
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This eliminated many jobs but also created other jobs that were better, which was important because the growing population needed work.
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Overall, this worked well for a majority of people and living standards improved.
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There's a clear progression in terms of what humans did for a living.
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For the longest time, we worked in agriculture.
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With the Industrial Revolution, this shifted to production jobs.
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And as automation became more widespread, humans shifted into service jobs.
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And then, only a few moments ago in human history, the Information Age happened.
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Suddenly, the rules were different.
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Our jobs are now being taken over by machines much faster than they were in the past.
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That's worrying, of course, but innovation will clearly save us, right?
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While new Information Age industries are booming, they are creating fewer and fewer new jobs.
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In 1979, General Motors employed more than 800,000 workers and made about 11 billion US dollars.
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In 2012, Google made about 14 billion US dollars while employing 58,000 people.
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You may not like this comparison, but Google is an example of what created new jobs in the past.
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Old innovative industries are running out of steam.
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Just look at cars.
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When they became a thing 100 years ago, they created huge industries.
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Cars transformed our way of life, our infrastructure, and our cities.
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Millions of people found jobs, either directly or indirectly.
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Decades of investment kept this momentum going.
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Today, this process is largely complete.
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Innovation in the car industry does not create as many jobs as it used to.
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While electric cars are great and all, they won't create millions of new jobs.
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Some technologists argue that the Internet is an innovation on a par with the introduction of electricity.
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If we go with this comparison, we see how our modern innovation differs from the old one.
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The Internet created new industries, but they're not creating enough jobs to keep up with population growth or to compensate for the industries the Internet is killing.
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At its peak in 2004, Blockbuster had 84,000 employees and made 6 billion US dollars in revenue.
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In 2016, Netflix had 4,500 employees and made 9 billion dollars in revenue.
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Or take us for example.
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With a full-time team of just 12 people, Kurzgesagt reaches millions of people.
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A TV station with the same amount of viewers needs way more employees.
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Innovation in the information age doesn't equate to the creation of enough new jobs, which would be bad enough on its own,
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but now a new wave of automation and a new generation of machines is slowly taking over.
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To understand this, we need to understand ourselves first.
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Human progress is based on the division of labor.
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As we advanced over thousands of years, our jobs became more and more specialized.
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While even our smartest machines are bad at doing complicated jobs, they are extremely good at doing narrowly defined and predictable tasks.
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This is what destroyed factory jobs.
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But look at a complex job long and hard enough, and you'll find that it's really just many narrowly defined and predictable tasks, one after another.
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Machines are on the brink of becoming so good at breaking down complex jobs into many predictable ones, that for a lot of people, there will be no further room to specialize.
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We are on the verge of being out-competed.
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Digital machines do this via machine learning, which enables them to acquire information and skills by analyzing data.
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This makes them become better at something through the relationships they discover.
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Machines teach themselves.
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We make this possible by giving a computer a lot of data about the thing we want it to become better at.
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Show a machine all the things you bought online, and it will slowly learn what to recommend to you, so you buy more things.
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Machine learning is now meeting more of its potential, because in recent years, humans have started to gather data about everything.
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What we've created by accident is a huge library machines can use to learn how humans do things, and learn to do them better.
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These digital machines might be the biggest job killer of all, They can be replicated instantly and for free.
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When they improve, you don't need to invest in big metal things, you can just use the new code.
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And they have the ability to get better fast.
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If your work involves complex work on a computer today, you might be out of work even sooner than the people who still have jobs in factories.
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There are actual real-world examples of how this transition might be happening.
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A San Francisco company offers a project management software for big corporations, which is supposed to eliminate middle management positions.
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When it's hired for a new project, the software first decides which jobs can be automated, and precisely where it needs actual professional humans.
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It then helps assemble a team of freelancers over the internet.
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The software then distributes tasks to the humans and controls the quality of the work, tracking individual performance until the project is complete.
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Okay, this doesn't sound too bad.
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While this machine is killing one job, it creates jobs for freelancers, right?
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Well, as the freelancers complete their tasks, learning algorithms track them and gather data about their work and which tasks it consists of.
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So what's actually happening is that the freelancers are teaching a machine how to replace them.
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On average, this software reduces costs by about 50% in the first year and by another 25% in the second year.
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This is only one example of many.
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There are machines and programs getting as good or better than humans in all kinds of fields,
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All of these jobs won't disappear overnight, but fewer and fewer humans will be doing them.
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