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  • Hey everyone, I'm Jabril and welcome to the final episode of Crash Course AI.

  • We've covered a lot of ground together, from the basics of neural networks to game

  • playing, language modeling, and algorithmic bias.

  • We've even experimented with code in labs!

  • And as we've been learning about different parts of artificial intelligence as a field,

  • there have been a couple themes that keep coming up..

  • First, AI is in more places than ever before.

  • The machine learning professor Andrew Ng says thatArtificial Intelligence is the New

  • Electricity.”

  • This is a pretty bold claim, but lots of governments are taking it seriously and planning to grow

  • education, research, and development in AI.

  • China's plan alone calls for over 100 billion U.S. dollars in funding over the next 10 years.

  • Second, AI is awesome.

  • It can help make our lives easier and sort of gives us superpowers.

  • Who knows what we can accomplish with the help of machine learning and AI?

  • And third, AI doesn't work that well yet.

  • I still can't ask my phone or anysmartdevice to do much, and we're far away from

  • personal robot butlers.

  • So what's next?

  • What's the future of AI?

  • INTRO

  • One way to think about the future of AI is to consider milestones AI hasn't reached

  • yet.

  • Current soccer robots aren't quite ready to take on human professionals, and Siri still

  • has a lot of trouble understanding exactly what I'm saying.

  • For every AI system, we can try and list what abilities would take the current technology

  • to the next level.

  • In 2014, for example, the Society of Automotive Engineers attempted to do just that for self-driving

  • cars.

  • They defined five levels of automation.

  • For each additional level, they expected that the AI controlling the car can do more without

  • human help.

  • At level 1, cruise control automatically accelerates and decelerates to keep the car at a constant

  • speed, but everything else is on the human driver.

  • At level 3, the car is basically on its own.

  • It's driving, monitoring its surroundings, navigating, and so on... but a human driver

  • will need to take over if something goes wrong, like really bad weather or a downed powerline.

  • And at level 5, the human driver can just sit back, have a smoothie, and watch Crash

  • Course AI while the car takes them to work through rush-hour traffic.

  • And obviously, we don't have cars with the technology to do all this yet.

  • But these levels are a way to evaluate how far we've come, and how far our research

  • still has to go.

  • We can even think about other AIs usinglevels of automation.”

  • Like, for example, maybe we have level 1 AI assistants right now that can set alarms for

  • us, but we still need to double-check their work.

  • But what are levels 2 through 5?

  • What milestones would need to be achieved for AI to be as good as a human assistant?

  • What would be milestones for computer vision or recommender systems or any of the other

  • topics in this course?

  • We'd love to read your ideas in the comments!

  • Sometimes it's useful to think about different kinds of AI on their own as we make progress

  • on each very difficult problem.

  • But sometimes people try and imagine an ultimate AI for all applications: an Artificial General

  • Intelligence or AGI.

  • To understand why there's such an emphasis on beinggeneral,” it can be helpful

  • to remember where all this AI stuff first started.

  • For that, let's go to the Thought Bubble.

  • Alan Turing was a British mathematician who helped break the German enigma codes during

  • World War II, and helped define the mathematical theory behind computers.

  • In his paperComputing Machinery and Intelligencefrom 1950, he introduced the now-famousTuring

  • Test”, orThe Imitation Game.”

  • Turing proposed an adaptation of a guessing game.

  • In his version, there's aninterrogatorin one room, and a human and a machine in

  • the other.

  • The interrogator talks to the hidden players and tries to figure out which is a human and

  • which is a machine.

  • Turing even gave a series of talking points, like:

  • Please write me a sonnet on the subject of the Forth Bridge.

  • Add 34,957 and 70,764.

  • Do you play chess?

  • I have K at K1 and no other pieces.

  • You have only K at K6 and R at R1.

  • It's your move.

  • What do you play?

  • The goal of The Imitation Game was to test a machine's intelligence about any human

  • thing, from math to poetry.

  • We wouldn't just judge howreal” a robot's fake human skin looks.

  • As Turing put it: “We do not wish to penalize the machine for its inability to shine in

  • beauty competitions, nor to penalise a man for losing in a race against an aeroplane.”

  • This idea suggests a unified goal for AI, an artificial general intelligence.

  • But over the last 70 years, AI researchers focused on subfields like computer vision,

  • knowledge representation, economic markets, planning, and so on.

  • Thanks, Thought Bubble!

  • And even though we're not sure if an Artificial General Intelligence is possible many communities

  • are doing interdisciplinary research, and many AI researchers are taking baby steps

  • to combine specialized subfields.

  • This involves projects like teaching a robot to understand language, or teaching an AI

  • system that models the stock market to read the news and better understand market fluctuations.

  • To be clear, most of AI is still science fictionwe're nowhere near Blade Runner, Her, or

  • any similar movies.

  • Before we get too excited about combining everything we've built to achieve AGI, we

  • should remember that we still don't know how to make specialized AIs for most problems.

  • Some subfields are making progress more quickly than others and we're seeing AI systems

  • pop up in lots of places with awesome potential.

  • To understand how AI might be able to change our lives, AI Professors Yolanda Gil and Bart

  • Selman put together the Computing Research Association's AI Roadmap for the next 20

  • years.

  • They predict AI reducing healthcare costs, personalizing education, accelerating scientific

  • discoveries, helping national defense, and more.

  • Part of the reason they expect so much progress is that more people than ever (including us!)

  • are learning how to build AI systems.

  • And all of these problems have lots of data to train new algorithms.

  • It used to be hard to collect training data, going to libraries to copy facts and transcribe

  • books.

  • But now, a lot of data is already digital.

  • If you want to know what's happening on the other side of the planet, you can download

  • newspapers or grab tweets from the Twitter API.

  • Interested in hyperlocal weather prediction?

  • You can combine free data from the weather service with personal weather stations to

  • help know when to water your plants.

  • And if you feed that data into a robot gardner, you could build a fully-automated weather-knowing

  • plant-growing food-making garden!

  • Maker communities around the globe are combining data, AI, and cheap hardware to create the

  • future and personalize AI technologies.

  • While imagining an AI/human utopia is exciting, we have to be realistic, too.

  • In many industries, automation doesn't only enhance human activities, it can replace humans

  • entirely.

  • Truck, delivery, and tractor drivers are some of the most common jobs in the US as of 2014.

  • If self-driving vehicles revolutionize transportation in the near future, will all those people

  • lose their jobs?

  • We can't know for sure, butdel Prize winning Computer Science Professor Moshe Vardi

  • points out that this is already the trend in some industries.

  • For example, U.S. manufacturing output will likely keep rising, but manufacturing jobs

  • have been decreasing a lot.

  • Plus, computers use energy, and that means we're not getting any benefits from AI for

  • free.

  • Massive amounts of machines running these algorithms can have a substantial carbon footprint.

  • On top of that, as we've discussed, you have to be pretty careful when it comes to

  • trusting AI systems because they often end up with all kinds of biases you may not want.

  • So we have to consider the benefits of massive AI deployment with the costs.

  • In a now-famous story from a few years ago, Target figured out a woman was pregnant based

  • on her shopping history,

  • and they sent her maternity coupons.

  • But she was still in high school, so her family saw the mail, even though she hadn't told

  • them.

  • Do we want our data being used like this, and potentially revealing personal details?

  • Or what about the government.

  • Should it be allowed to track people with facial recognition installed on cameras at

  • intersections?

  • When we provide companies location data from our phones we could help them build better

  • traffic models so we can get to places faster.

  • Cities could improve bus routes, but it also meanssomeoneisalwayswatching

  • you.

  • AI could also track your friends and family, where you shopped, ate, and who you hung out

  • with.

  • If statistics have shown that people who leave home late at night are more likely to commit

  • a crime... and an AI knows you left (even though it's just for some late night cookie

  • dough), should it call the police to watch you -- just in case?

  • Sooo, we can go down any number of scary thought experiments.

  • And there's a lot to consider when it comes to the future of AI.

  • AI is a really new tool and it's great that so many people have access to it, but that

  • also means there are very few laws or protections about what they can and can't do.

  • Innovations in AI have awesome potential to make positive changes, but there are also

  • plenty of risks, especially if the technology advances faster than the average person's

  • understanding of it.

  • It's probably the most accurate to say that the future is... complicated.

  • And the most important thing we can do is be educated and involved in AI as the field

  • changes.

  • Which we're doing right now!

  • In Crash Course AI labs, we used some of the same machine learning technologies that the

  • biggest companies use in their products, and that universities rely on for cutting edge

  • research.

  • So when we see a company or government rolling out a new technology, we know what questions

  • to ask:

  • Where did they get their data?

  • Is this even a situation where we want AI to help humans?

  • Is this the right tool to use?