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  • CC AI Take 1

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  • INTRO

  • Hey, I'm Jabril and welcome to Crash Course AI!

  • It seems like every time I look at the news, there's a new article about how AI and automation

  • is going to take everybody's jobs!

  • I'm starting to wonder if teaching John Green Bot those things was even a good idea

  • but there is a way for AI and humans to work together, besides competing for the same jobs,

  • resources, and game championships.

  • Human-AI teams can use our strengths to help each other, and collaborate to fill in each

  • other's weaknesses.

  • Together, we can make better diagnoses, brainstorm new inventions, or imagine a future where

  • humans and robots are working side-by-side.

  • AIs and humans have skills that can complement each other, AI can be good at searching through

  • lots of possibilities and making some intelligent guesses at which one to pick.

  • Plus, AI systems are consistent, and won't make mistakes because they're tired or hungry,

  • like I sometimes do.

  • On the other hand, humans can be good at insight, creativity, and understanding the nuances

  • of language and behavior.

  • We've learned from living in the world and interacting with each other, so we're better

  • than AI systems at interpreting social signals.

  • There are many ways that AI could support us, but a big one is that AI could amplify

  • our decision-making activities with the right information.

  • For an example of this kind of Human-AI collaboration, let's go to the ThoughtBubble.

  • Humans and computers can play games against each other,

  • but when they join forces, they basically become a superhero dynamic duo.

  • In chess, this particular kind of Human-AI team game is called advanced chess, cyborg

  • chess, or centaur chess!

  • In centaur chess, the computer does what it's great at: it looks several moves ahead and

  • estimates the most promising next moves.

  • And the human does what they're great at: they choose among uncertain possibilities

  • based on experience, intuition, and even what they know about the opponent.

  • Usually, the computer program is in the driver's seat for the early game, which is relatively

  • mindless because there aren't too many move choices.

  • But humans step in to guide the strategy in the middle game when things get more complicated.

  • The first centaur chess tournament was held in 2007, with the winning team led by Anson

  • Williams.

  • Anson's team, Intagrand, consists of him and the programs written by the Computer Science

  • and Math experts Yingheng Chen and Nelson Hernandez.

  • And the cool thing about this team is that it's currently considered to be the best

  • chess player in the world, among humans and AI!

  • Intagrand was able to win 2-0 against chess grandmasters, even though none of the team

  • members are grandmasters.

  • And in 2014, multiple AIs played against multiple centaur chess teams in competitions.

  • Pure AI won 42 games, while centaur teams won 53 games!

  • It seems like Human-AI collaboration is working out for chess, so that means it could be promising

  • in other parts of our lives too.

  • Thanks Thought Bubble.

  • Games provide a great constrained environment to demonstrate the possibilities of AI and,

  • in this case, human-AI collaboration.

  • Chess victories might not seem that significant, but similar Human-AI collaboration can be

  • applied to other problems.

  • AI takes on the parts that require memorization, rote response, and following rules.

  • Humans focus on aspects that require nuance, social understanding, and intuition.

  • For one, AI could help humans make decisions when we're dealing with large amounts of

  • complicated information.

  • When a doctor is trying to make a diagnosis, they try to use their medical experience and

  • intuition to make sense of their patient's symptoms and all the published clinical data.

  • AI could help wade through all that data and highlight the most probable diagnoses, so

  • the doctor can focus their experience and intuition on choosing from those which is

  • where they'll be most helpful.

  • Second, AI could help when humans are trying to come up with new inventions or designs,

  • like a new engineered structure.

  • An AI could apply predetermined physical constraints, like, for example, how much something should

  • weigh or how much force it should be able to withstand.

  • This lets the human experts think about the most practical designs, and could spark new

  • creative ideas.

  • Third, AI could also support and scale-up interaction between people.

  • It could save people from doing rote mental tasks, so that they have more time and energy

  • to help.

  • For example, in customer support, virtual assistants can help answer easy questions

  • about checking on an order or starting a returnor at least that's the goal.

  • If you've ever tried these systems, you know that they can sometimes fail in spectacular

  • ways, leaving you mashing the 0 button on your phone to try and get a representative

  • on the line.

  • And fourth, robots that are AI-enabled but guided by humans could give people more strength,

  • endurance, or precision to do certain kinds of work.

  • Some examples of this may be an exosuit worn by a construction worker, or a remotely-guided

  • search and rescue robot.

  • These devices still need some AI to apply the right amount of force or navigate effectively,

  • but all of the real decision-making is done by humans.

  • There are entire research fields, like Human-Computer Interaction and Human-Robot Interaction, dedicated

  • to investigating and building new AI, Machine Learning, and Robotics systems to complement

  • and enhance human capabilities.

  • But it's not just that humans can become more effective with AI, AI also needs humans

  • to succeed!

  • This whole series we've been talking about how we can program AI to help them exist and

  • learn, but a lot of human-AI interaction is more subtle.

  • You could've supported an AI without even recognizing it.

  • First, humans can provide AI with meaning and labels, because we have so much more experience

  • with living in the real world.

  • For example, if you've ever edited Wikipedia, you've contributed to Wikipedia-based algorithms

  • such as WikiBrain.

  • Because Wikipedia puts articles into nesting structures (like how an elephant is a mammal)

  • and because articles link each other, algorithms can use this structure to understand the meaning-based

  • connection between topics.

  • In fact, when we interact with digital technology, whether it's posting content, giving something

  • a thumbs up, following driving directions on a phone, or typing a text message, we're

  • often providing training data to help make AI systems more effective.

  • Without our data, there wouldn't be recommended YouTube videos, predictive text messages,

  • or traffic data for the GPS to use in suggesting a route.

  • But sometimes providing personal data can be a double-edged sword which we'll get

  • to in the Algorithmic Bias episode.

  • Secondly, humans can also try to explain an AI system's predictions, outputs, and even

  • possible mistakes to other humans, who aren't as familiar with AI.

  • As we mentioned in the episode about artificial neural networks, the reasons for an AI producing

  • particular results can be tough to understand.

  • We can see what data go into the program and which results fit the data well, but it can

  • be hard to know what the hidden layers are doing to get those results!

  • For example, an algorithm might recommend denying a customer's loan request.

  • So a loan officer needs to be able to look at the input data and algorithm, and then

  • communicate what factors might've led to denial.

  • Many European countries are now making it a legal right to receive these kinds of explanations.

  • Third, human experts can also inspect algorithms for fairness to different kinds of people,

  • rather than producing biased results.

  • Bias is a very complicated topic, so we'll dive deeper into the nuances in an upcoming

  • episode and lab.

  • And finally, AI doesn't understand things like the potential consequences of its mistakes

  • or the moral implications of its decisions.

  • That's beyond the scope of its programming.

  • It's a common Sci-Fi trope that an AI built to minimize suffering might choose to eliminate

  • all life on Earth, because if there's no life, there's no suffering!

  • That's why humans may want to moderate and filter AI actions in the world, so we can

  • make sure they line up with societal values, morals, and thoughtful intentions.

  • The bottom line is that organic and artificial brains may be better together, and through

  • Crash Course AI, you could be on the way to becoming one of those experts that works on

  • the helpfulness and fairness of AI systems.

  • In this episode, we didn't focus on explaining one specific algorithm or AI technology.

  • Instead, it's more about where our world might be going from the AI revolution that's

  • happening nowbesides justautomation replacing jobs.”

  • We should recognize what data humans are providing to algorithms.

  • What would it mean if we could claim some credit for the ways that our data have allowed

  • algorithms to change lives for the better?

  • Or how do we claim more power in cases where data are being used in potentially harmful

  • or problematic ways?

  • Second, we should think about if and how our human jobs could be made easier by working

  • with AI -- although it's bound to be complicated.

  • For example, people had similar concerns with the spread of personal computing and tools

  • like spreadsheets.

  • And yes, spreadsheets automated many bookkeeping tasks, which put many people out of work.

  • Even though some types of jobs were destroyed, new accounting jobs were created that involved

  • human-computer collaboration.

  • Technology took over more of the rote math calculations, and humans focused on the more

  • nuanced and client-facing aspects of accounting work.

  • Even though this idea can get overblown in the mass media, AI and automation has and

  • will take people's jobs.

  • No question.

  • And we don't want to downplay the impact that has on people's lives.

  • But by understanding how AI works, what it's good at, and where it struggles, we can also

  • find opportunities to work more effectively and to create new types of jobs that involve

  • collaboration.

  • Machines can help us do things that we can't do as well (or at all) by ourselves.

  • Human-AI collaboration can help us narrow down complex decision trees and make better

  • choices.

  • Human-Robot collaboration has the potential to give us super strength or resilience.

  • Different kinds of AI will impact the world in powerful ways, but not without costs.

  • So it's up to us to decide which costs are worth it, how to minimize harm, and create

  • a future we want to live in.

  • Thanks for watching, I'll see you next week.

  • Crash Course AI is produced in association with PBS Digital Studios.

  • If you want to help keep Crash Course free for everyone, forever, you can join our community

  • on Patreon.

  • To think more about the complicated lines between AI and humans, check out this video

  • from Crash Course Philosophy.

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