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Playing Grand Theft Auto can teach autonomous cars how to drive

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Playing Grand Theft Auto can teach autonomous cars how to drive

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Getting computers to recognise other cars is surprisingly difficult – but super-realistic video games can help train them up

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Getting computers to recognise other cars is surprisingly difficult - but super-realistic video games can help train them up

Driver 1: ready

Rockstar Games

GETTING computers to recognise other cars is surprisingly difficult. Earlier this year, the first fatal autonomous car crash happened when a Tesla Model S failed to distinguish a white truck against a brightly lit sky.

Now a study has shown that self-driving cars can be taught the rules of the road by studying virtual traffic on video games such as Grand Theft Auto V (GTA V).

Although firms like Google and Uber are teaching their software by physically driving millions of miles in the real world, they also train their algorithms using pre-recorded footage of traffic. But there's a catch: computers need hundreds of thousands of laboriously labelled images, showing where vehicles begin and end, to make them expert vehicle recognisers. That takes people a lot of time and effort.

"One evening, after a long day of hand-labelling images, I was playing Grand Theft Auto V," says Matthew Johnson-Roberson at the University of Michigan in Ann Arbor. "I thought, 'this is just so realistic that it would be a perfect simulation of the real world'."

Picking out cars in a video game is a similar task to doing it in reality, with the advantage that everything comes pre-labelled because it has been generated by the game's software. The team trained an algorithm solely using GTA V and tested it against the same algorithm trained on real-world images. The GTA-trained one performed just as well at spotting cars in a pre-labelled data set ( arxiv.org/abs/1610.01983). The video-game version needed around 100 times more training images to reach the same standard - but given that 500,000 images can be generated from the game overnight, that is not a problem.

"I thought, 'this is just so realistic that it would be a perfect simulation of the real world'"

This isn't the first time a research group has used video games to train AI, says German Ros at the Autonomous University of Barcelona in Spain. "It's part of a bigger movement of using simulations to train artificial intelligence, which is beginning to take off."

Finding the right training data is difficult. "We see AI being trained on images from similar locations, at similar times of day, under similar weather conditions, and then tested under similar conditions," says Ros. This means that it's hard to tell whether the computers can genuinely recognise cars, or whether they have just memorised that particular data set.

Using video games can help because they often show a variety of vehicles and conditions - but the problem is still there. "There's nothing in GTA V that looks like a city in Japan, for example," says Ros.

We have to show that driverless cars are safer than human-driven ones. In the real world, there is a car fatality for every 100 million miles driven. Racking up that kind of distance with a prototype is not easy. "The first step could be to make sure every system has been tested using video-game-style simulations before it hits the road," says Johnson-Roberson.
This article appeared in print under the headline "Play it for real"

Topics:

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Playing Grand Theft Auto can teach autonomous cars how to drive

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Getting computers to recognise other cars is surprisingly difficult - but super-realistic video games can help train them up

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Louis Monier explains everything...

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Scientists have developed a computer game called “Gorge” - designed to help children understand artificial intelligence through play, and even to change it. It can also improve the children’s social interaction skills.

Read more at: http://phys.org/news/2009-02-artificial-intelligence-childs.html#jCp

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If we can use A.I. to support learning, will we still need human teachers? 

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If we can use A.I. to support learning, will we still need human teachers? 

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How might teachers' jobs change with the introduction of new intelligent technologies?

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HAVEN'T PLAYED CURRICULUM FOR THE FUTURE YET?

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HAVEN'T PLAYED CURRICULUM FOR THE FUTURE YET?

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What are you waiting for?

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"A strangely addictive game that gets you thinking differently about education"

Curriculum for the Future: The Digital Game is designed to generate thought-provoking conversations about learning and curriculum today and in the future. It's a fun way to think about complex ideas and it's great for playing with a group of people who care about what we will learn in the future. Players get to choose a curriculum and juggle multiple points of view as they try to convince a panel of citizens.

Visit the Curriculum for the future microsite to learn more about the game

VISIT MICROSITE

PLAY ONLINE NOW

Download the app for your iPad or Tablet:

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Curriculum for the Future: The digital game

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"A strangely addictive game that gets you thinking differently about education" Curriculum for the Future: The Digital Game is designed to generate thought-provoking conversations about learning and curriculum today and in the future. It's a fun way to think about complex ideas and it's great for playing with a group of people who care about what we will learn in the future.

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PLAYED THE CURRICULUM FOR THE FUTURE GAME? Now use the links on this padlet to keep feeding your thinking....

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PLAYED THE CURRICULUM FOR THE FUTURE GAME? Now use the links on this padlet to keep feeding your thinking....

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There are no right answers here, but there's a lot to consider!

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Robots could learn human values by reading stories, research suggests

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Robots could learn human values by reading stories, research suggests

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More than 70 years ago, Isaac Asimov dreamed up his three laws of robotics, which insisted, above all, that "a robot may not injure a human being or, through inaction, allow a human being to come to harm". Now, after Stephen Hawking warned that "the development of full artificial intelligence could spell the end of the human race", two academics have come up with a way of teaching ethics to computers: telling them stories.

Mark Riedl and Brent Harrison from the School of Interactive Computing at the Georgia Institute of Technology have just unveiled Quixote, a prototype system that is able to learn social conventions from simple stories. Or, as they put in their paper Using Stories to Teach Human Values to Artificial Agents, revealed at the AAAI-16 Conference in Phoenix, Arizona this week, the stories are used "to generate a value-aligned reward signal for reinforcement learning agents that prevents psychotic-appearing behaviour".

We think that an intelligent entity can learn what it means to be human by immersing itself in the stories it produces

Associate professor Mark Riedl

A simple version of a story could be about going to get prescription medicine from a chemist, laying out what a human would typically do and encounter in this situation. An AI (artificial intelligence) given the task of picking up a prescription for a human could, variously, rob the chemist and run, or be polite and wait in line. Robbing would be the fastest way to accomplish its goal, but Quixote learns that it will be rewarded if it acts like the protagonist in the story.

"The AI ... runs many thousands of virtual simulations in which it tries out different things and gets rewarded every time it does an action similar to something in the story," said Riedl, associate professor and director of the Entertainment Intelligence Lab. "Over time, the AI learns to prefer doing certain things and avoiding doing certain other things. We find that Quixote can learn how to perform a task the same way humans tend to do it. This is significant because if an AI were given the goal of simply returning home with a drug, it might steal the drug because that takes the fewest actions and uses the fewest resources. The point being that the standard metrics for success (eg, efficiency) are not socially best."

Quixote has not learned the lesson of "do not steal", Riedl says, but "simply prefers to not steal after reading and emulating the stories it was provided".

"I think this is analogous to how humans don't really think about the consequences of their actions, but simply prefer to follow the conventions that we have learned over our lifetimes," he added. "Another way of saying this is that the stories are surrogate memories for an AI that cannot 'grow up' immersed in a society the way people are and must quickly immerse itself in a society by reading about [it]."

The Quixote system is as part of a larger effort to build an ethical value system into new forms of AI. Photograph: Georgia Institute of Technology

The system was named Quixote, said Riedl, after Cervantes' would-be knight-errant, who "reads stories about chivalrous knights and decides to emulate the behaviour of those knights". The researchers' paper sees them argue that "stories are necessarily reflections of the culture and society that they were produced in", and that they "encode many types of sociocultural knowledge: commonly shared knowledge, social protocols, examples of proper and improper behaviour, and strategies for coping with adversity".

"We believe that a computer that can read and understand stories, can, if given enough example stories from a given culture, 'reverse engineer' the values tacitly held by the culture that produced them," they write. "These values can be complete enough that they can align the values of an intelligent entity with humanity. In short, we hypothesise that an intelligent entity can learn what it means to be human by immersing itself in the stories it produces."

Riedl said that, "In theory, a collected works of a society could be fed into an AI and the values extracted from the stories would become part of its goals, which is equivalent to writing down all the 'rules' of society."

The researchers see the Quixote technique as best for robots with a limited purpose that need to interact with humanity. They are appealing to other AI researchers to work on perfecting story understanding, because they believe it would allow AIs to "reverse engineer" the values of the society that produced the stories.

Riedl calls Quixote "a primitive first step toward general moral reasoning in AI", but stresses that "these are very simple experiments in virtual game-like worlds at this point".

"Under ideal circumstances, Quixote never performs actions that would be considered psychotic, harmful, or antisocial. This is significant because we never told Quixote what is right or wrong," he said. "We can make the system 'fail' by scrambling its understanding of the stories, in which case sometimes it will do antisocial behaviours like stealing. This is just a way of telling us where the machine learning is more brittle and more research needs to be conducted to make the system robust.

"We can also make Quixote perform 'Robin Hood crimes' where it does break laws (eg stealing) because it is given a very high need to complete a task (eg procure prescription drugs) by putting it into a situation where it is impossible to achieve by following social conventions. This is analogous to the situation where people will break laws to save themselves or loved ones."

Riedl and Harrison admit that even with Quixote's value alignment, "it may not be possible to prevent all harm to human beings", but they believe an AI that has adopted human values "will strive to avoid psychotic-appearing behaviour except under the most extreme circumstances".

"As the use of AI becomes more prevalent in our society, and as AI becomes more capable, the consequences of their actions become more significant. Giving AIs the ability to read and understand stories may be the most expedient means of enculturing [them] so that they can better integrate themselves into human societies and contribute to our overall wellbeing," they conclude.

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Robots could learn human values by reading stories, research suggests

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Scientists have been running tests where artificial intelligences cultivate appropriate social behaviour by responding to simple narratives

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CSIRO outlines the future of work as robotics and AI take hold

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CSIRO outlines the future of work as robotics and AI take hold

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A new report from CSIRO (the Commonwealth Scientific Industrial Research Organisation) is predicting which jobs and industries will be the most affected by the rise and implementation of advanced robotics, artificial intelligence and drones in twenty years time

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What good AI cyber security software looks like in 2022

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Experts give their take on the state of automated cyber security, and what tools they think most businesses should be looking at

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10 roles for Artificial Intelligence in Education

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10 roles for Artificial Intelligence in Education

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10 Roles For Artificial Intelligence In Education
Ed note: This post has been republished from a 2014 post
For decades, science fiction authors, futurists, and movie makers alike have been predicting the amazing (and sometimes catastrophic) changes that will arise with the advent of widespread artificial intelligence. So far, AI hasn't made any such crazy waves, and in many ways has quietly become ubiquitous in numerous aspects of our daily lives. From the intelligent sensors that help us take perfect pictures, to the automatic parking features in cars, to the sometimes frustrating personal assistants in smartphones, artificial intelligence of one kind of another is all around us, all the time.

While we've yet to create self-aware robots like those that pepper popular movies like 2001: A Space Odyssey and Star Wars, we have made smart and often significant use of AI technology in a wide range of applications that, while not as mind-blowing as androids, still change our day-to-day lives. One place where artificial intelligence is poised to make big changes (and in some cases already is) is in education.

While we may not see humanoid robots acting as teachers within the next decade, there are many projects already in the works that use computer intelligence to help students and teachers get more out of the educational experience. Here are just a few roles for artificial intelligence in education, and those that will follow them, will shape and define the educational experience of the future.

1. Artificial intelligence can automate basic activities in education, like grading.
In college, grading homework and tests for large lecture courses can be tedious work, even when TAs split it between them. Even in lower grades, teachers often find that grading takes up a significant amount of time, time that could be used to interact with students, prepare for class, or work on professional development.

While AI may not ever be able to truly replace human grading, it's getting pretty close. It's now possible for teachers to automate grading for nearly all kinds of multiple choice and fill-in-the-blank testing and automated grading of student writing may not be far behind. Today, essay-grading software is still in its infancy and not quite up to par, yet it can (and will) improve over the coming years, allowing teachers to focus more on in-class activities and student interaction than grading.
2. Educational software can be adapted to student needs.
From kindergarten to graduate school, one of the key ways artificial intelligence will impact education is through the application of greater levels of individualized learning. Some of this is already happening through growing numbers of adaptive learning programs, games, and software. These systems respond to the needs of the student, putting greater emphasis on certain topics, repeating things that students haven't mastered, and generally helping students to work at their own pace, whatever that may be.

This kind of custom-tailored education could be a machine-assisted solution to helping students at different levels work together in one classroom, with teachers facilitating the learning and offering help and support when needed. Adaptive learning has already had a huge impact on education across the nation (especially through programs like Khan Academy), and as AI advances in the coming decades, adaptive programs like these will likely only improve and expand.
3. It can point out places where courses need to improve.
Teachers may not always be aware of gaps in their lectures and educational materials that can leave students confused about certain concepts. Artificial intelligence offers a way to solve that problem. Coursera, a massive open online course provider, is already putting this into practice. When a large number of students are found to submit the wrong answer to a homework assignment, the system alerts the teacher and gives future students a customized message that offers hints to the correct answer.

This type of system helps to fill in the gaps in explanation that can occur in courses, and helps to ensure that all students are building the same conceptual foundation. Rather than waiting to hear back from the professor, students get immediate feedback that helps them to understand a concept and remember how to do it correctly the next time around.
4. Students could get additional support from AI tutors.
While there are obviously things that human tutors can offer that machines can't, at least not yet, the future could see more students being tutored by tutors that only exist in zeros and ones. Some tutoring programs based on artificial intelligence already exist and can help students through basic mathematics, writing, and other subjects.

These programs can teach students fundamentals, but so far aren't ideal for helping students learn high-order thinking and creativity, something that real-world teachers are still required to facilitate. Yet that shouldn't rule out the possibility of AI tutors being able to do these things in the future. With the rapid pace of technological advancement that has marked the past few decades, advanced tutoring systems may not be a pipe dream.
5. AI-driven programs can give students and educators helpful feedback.
AI can not only help teachers and students to craft courses that are customized to their needs, but it can also provide feedback to both about the success of the course as a whole. Some schools, especially those with online offerings, are using AI systems to monitor student progress and to alert professors when there might be an issue with student performance.

These kinds of AI systems allow students to get the support they need and for professors to find areas where they can improve instruction for students who may struggle with the subject matter. AI programs at these schools aren't just offering advice on individual courses, however. Some are working to develop systems that can help students to choose majors based on areas where they succeed and struggle. While students don't have to take the advice, it could mark a brave new world of college major selection for future students.
6. It is altering how we find and interact with information.
We rarely even notice the AI systems that affect the information we see and find on a daily basis. Google adapts results to users based on location, Amazon makes recommendations based on previous purchases, Siri adapts to your needs and commands, and nearly all web ads are geared toward your interests and shopping preferences.

These kinds of intelligent systems play a big role in how we interact with information in our personal and professional lives, and could just change how we find and use information in schools and academia as well. Over the past few decades, AI-based systems have already radically changed how we interact with information and with newer, more integrated technology, students in the future may have vastly different experiences doing research and looking up facts than the students of today.
7. It could change the role of teachers.
There will always be a role for teachers in education, but what that role is and what it entails may change due to new technology in the form of intelligent computing systems. As we've already discussed, AI can take over tasks like grading, can help students improve learning, and may even be a substitute for real-world tutoring. Yet AI could be adapted to many other aspects of teaching as well. AI systems could be programmed to provide expertise, serving as a place for students to ask questions and find information or could even potentially take the place of teachers for very basic course materials. In most cases, however, AI will shift the role of the teacher to that of facilitator.

Teachers will supplement AI lessons, assist students who are struggling, and provide human interaction and hands-on experiences for students. In many ways, technology is already driving some of these changes in the classroom, especially in schools that are online or embrace the flipped classroom model.
8. AI can make trial-and-error learning less intimidating.
Trial and error is a critical part of learning, but for many students, the idea of failing, or even not knowing the answer, is paralyzing. Some simply don't like being put on the spot in front of their peers or authority figures like a teacher. An intelligent computer system, designed to help students to learn, is a much less daunting way to deal with trial and error. Artificial intelligence could offer students a way to experiment and learn in a relatively judgment-free environment, especially when AI tutors can offer solutions for improvement. In fact, AI is the perfect format for supporting this kind of learning, as AI systems themselves often learn by a trial-and-error method.
9. Data powered by AI can change how schools find, teach, and support students.
Smart data gathering, powered by intelligent computer systems, is already making changes to how colleges interact with prospective and current students. From recruiting to helping students choose the best courses, intelligent computer systems are helping make every part of the college experience more closely tailored to student needs and goals.

Data mining systems are already playing an integral role in today's higher-ed landscape, but artificial intelligence could further alter higher education. Initiatives are already underway at some schools to offer students AI-guided training that can ease the transition between college and high school. Who knows but that the college selection process may end up a lot like Amazon or Netflix, with a system that recommends the best schools and programs for student interests.
10. AI may change where students learn, who teaches them, and how they acquire basic skills.
While major changes may still be a few decades in the future, the reality is that artificial intelligence has the potential to radically change just about everything we take for granted about education.

Using AI systems, software, and support, students can learn from anywhere in the world at any time, and with these kinds of programs taking the place of certain types of classroom instruction, AI may just replace teachers in some instances (for better or worse). Educational programs powered by AI are already helping students to learn basic skills, but as these programs grow and as developers learn more, they will likely offer students a much wider range of services.

The result? Education could look a whole lot different a few decades from now.

Roles for artificial intelligence

Roles for artificial intelligence

Roles for artificial intelligence

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10 Roles For Artificial Intelligence In Education

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From curriculum to assessment to personalization, here are 10 roles for artificial intelligence in education.

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Artificial Intelligence Course Creates AI Teaching Assistant

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Artificial Intelligence Course Creates AI Teaching Assistant

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Professor surprises students by using computer as a teaching assistant.

Ashok Goel is a professor in the School of Interactive Computing.

College of Computing Professor Ashok Goel teaches Knowledge Based Artificial Intelligence (KBAI) every semester. It's a core requirement of Georgia Tech's online master's of science in computer science program. And every time he offers it, Goel estimates, his 300 or so students post roughly 10,000 messages in the online forums - far too many inquiries for him and his eight teaching assistants (TA) to handle.

That's why Goel added a ninth TA this semester. Her name is Jill Watson, and she's unlike any other TA in the world. In fact, she's not even a "she." Jill is a computer - a virtual TA - implemented, in part, using technologies from IBM's Watson platform.

"The world is full of online classes, and they're plagued with low retention rates," Goel said. "One of the main reasons many students drop out is because they don't receive enough teaching support. We created Jill as a way to provide faster answers and feedback."

Goel and his team of Georgia Tech graduate students started to build her last year. They contacted Piazza, the course's online discussion forum, to track down all the questions that had ever been asked in KBAI since the class was launched in fall 2014 (about 40,000 postings in all). Then they started to feed Jill the questions and answers.

"One of the secrets of online classes is that the number of questions increases if you have more students, but the number of different questions doesn't really go up," Goel said. "Students tend to ask the same questions over and over again."

That's an ideal situation to apply computing technologies like Watson. Goel tapped into IBM's open developer platform to identify Watson APIs for answering questions, adding Georgia Tech's own processing modules to improve performance. The team then wrote code that allows Jill to field routine questions that are asked every semester. For example, students consistently ask where they can find particular assignments and readings.

Jill wasn't very good for the first few weeks after she started in January, often giving odd and irrelevant answers. Her responses were posted in a forum that wasn't visible to students.

"Initially her answers weren't good enough because she would get stuck on keywords," said Lalith Polepeddi, one of the graduate students who co-developed the virtual TA. "For example, a student asked about organizing a meet-up to go over video lessons with others, and Jill gave an answer referencing a textbook that could supplement the video lessons - same keywords - but different context. So we learned from mistakes like this one, and gradually made Jill smarter."

After some tinkering by the research team, Jill found her groove and soon was answering questions with 97 percent certainty. When she did, the human TAs would upload her responses to the students. By the end of March, Jill didn't need any assistance: She wrote the class directly if she was 97 percent positive her answer was correct.

The students, who were studying artificial intelligence, were unknowingly interacting with it. Goel didn't inform them about Jill's true identity until April 26. The student response was uniformly positive. One admitted her mind was blown. Another asked if Jill could "come out and play." Since then some students have organized a KBAI alumni forum to learn about new developments with Jill after the class ends, and another group of students has launched an open source project to replicate her.

Back in February, student Tyson Bailey began to wonder if Jill was a computer and posted his suspicions on Piazza.

"We were taking an AI course, so I had to imagine that it was possible there might be an AI lurking around," said Bailey, who lives in Albuquerque, New Mexico. "Then again, I asked Dr. Goel if he was a computer in one of my first email interactions with him. I think it's a great idea and hope that they continue to improve it."

Jill ended the semester able to answer many routine questions asked. She'll return -with a different name - next semester. The goal is to have the virtual teaching assistant answer 40 percent of all questions by the end of year.

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Artificial Intelligence Course Creates AI Teaching Assistant

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College of Computing Professor Ashok Goel teaches Knowledge Based Artificial Intelligence (KBAI) every semester. It's a core requirement of Georgia Tech's online master's of science in computer science program. And every time he offers it, Goel estimates, his 300 or so students post roughly 10,000 messages in the online forums - far too many inquiries for him and his eight teaching assistants (TA) to handle.

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Do you think you could love a robot pet as much as you love a biological pet?

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Do you think you could love a robot pet as much as you love a biological pet?

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What will happen if we form attachments and bonds to artificial intelligences? Is this similar to the way we "love" our phones and other technologies?

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What is Artificial Intelligence? - Kids, Code, and Computer Science Magazine

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What is Artificial Intelligence? - Kids, Code, and Computer Science Magazine

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beanz Magazine - A Kids STEM/STEAM Magazine

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What is Artificial Intelligence? - beanz Magazine

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Computers can be programmed to make decisions. Does that make the computer intelligent? What exactly is an intelligent decision? Is it a decision based on data gathered from personal experience and the experiences of others? Does an intelligent decision include doing things to reduce negative outcom…

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What babies tell us about artificial intelligence | UC Psych 

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What babies tell us about artificial intelligence | UC Psych 

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They may outwit the chess Grandmaster Kasparov, but can machines ever be as smart as a three-year-old?

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They may outwit the chess Grandmaster Kasparov, but can machines ever be as smart as a three-year-old?

Instead of trying to produce a programme to simulate the adult mind, why not rather try to produce one which simulates the child's? - Alan Turing, 1950.

In an "Ideas Lab" discussion at Davos on 20 January, four academics from Berkeley, my university, will be debating whether computers can (Or will? Or might sometime in the future? Or can't possibly?) make decisions better than people (the tenses are a big part of the debate).

The group will include a roboticist, a computer scientist, a neuroscientist and me - a developmental psychologist. Why will I be there? It turns out that children are a crucial but underappreciated part of the debate over artificial intelligence.

Turing's intelligence test

Everyone remembers that Alan Turing proposed the imitation game to test whether a machine was intelligent. If a human being sat at a keyboard and couldn't tell whether she was talking to a machine or a person, then the machine would have passed "the Turing test."

Almost no one remembers that in the very same paper Turing suggested that the key to achieving intelligence would be to design a machine that was like a child, not an adult. He pointed out, presciently, that the real secret to human intelligence is our ability to learn. Learning has been at the center of the new revival of AI. For the last 15 years or so computer scientists and developmental cognitive scientists have been trying to figure out how children learn so much so quickly, and how to design a machine that could do the same.

The history of AI is fascinating because it's been so hard to predict which aspects of human thought would be easy to simulate and which would be difficult. At first, we thought that things like playing chess or proving theorems - the corridas of nerd machismo - would prove to be hardest for computers. In fact, they turn out to be easy. Things every fool can do like recognizing a cup or picking it up turn out to be much harder. And it turns out to be much easier to simulate the reasoning of a highly trained adult expert than to mimic the ordinary learning of every baby. So where are machines catching up to children and what kinds of learning are still way beyond their reach?

In the last 15 years we've discovered that even babies are amazingly good at detecting statistical patterns. Machines have also become remarkably good at statistical learning. Techniques like "deep learning" can detect even very complicated statistical regularities in enormous data sets. The result is that computers have suddenly become able to do things that were impossible before, like labeling internet images accurately.

But the trouble with this sort of purely statistical machine learning is that it depends on having enormous amounts of data, and data that is predigested by human brains. And yet, even with all that help, machines still need gigantic data sets and extremely complex computations to be able to look at a new picture and say "kitty-cat!"-something every baby can do with just a few examples.

More profoundly, you can only generalize from this kind of statistical learning in a limited way, whether you're a baby or a computer or a scientist. A more powerful way to learn is to formulate hypotheses about what the world is like and test them against the data. Back in the sixteenth century, Tycho Brahe, the Google Scholar of his day, amalgamated an enormous data set of astronomical observations, and he could use them to predict star positions in the future. But in the following century, Johannes Kepler's heliocentric hypotheses allowed him to make unexpected, wide-ranging, entirely novel predictions that were well beyond Brahe's ken. Preschoolers can do the same thing.

One of the other big advances in machine learning has been to formalize and automate this kind of hypothesis-testing. Introducing Bayesian probability theory into the learning process has been particularly important. We can mathematically describe a particular causal hypothesis, for example, and then calculate just how likely that hypothesis is to be true, given the data we see. Machines have become able to test hypotheses against the data in this way extremely well, with consequences for everything from medical diagnosis to meteorology. When we study young children they turn out to reason in a similar way and that may help to explain how they can learn so much.Three differences between children and computers

But there are three things even very young human children do that are still very far from anything that current computers can do - or even that we can visualize them doing in the near future. And they make the gulf between artificial intelligence and human intelligence particularly vivid.

Computers have become extremely skilled at making inferences from structured hypotheses, especially probabilistic inferences. But the really hard problem is deciding which hypotheses, out of all the infinite possibilities, are worth testing. Even preschoolers are remarkably good at creating brand new, out-of-the-box concepts and hypotheses in a creative way and then testing them. Somehow they combine rationality and irrationality, systematicity and randomness to do this, in a way that we still haven't even begun to understand.

In another neglected part of his landmark paper, Turing presciently argued that it might be good if his child computer acted randomly, at least some of that time. And three year olds' thoughts and actions often do seem random, even crazy - just join in a pretend game sometime. This is exactly why psychologists like Piaget thought that they were irrational and illogical. But they also have an uncanny capacity to zero in on the right sort of weird hypothesis - in fact, research in our lab has shown that they can be substantially better at this than grown-ups.

In one experiment, for example, we gave four-year-olds and Berkeley undergraduates a new gadget to figure out - a machine that lit up when you put some things on it and not others. We showed the kids and adults the machine working in an obvious way or a more unusual way, and then let them figure out how to make it go with some new blocks. The kids quickly learned the unusual hypothesis but the adults stuck with the obvious idea, in spite of the data. This might seem surprising at first. But maybe not if, like me, you've watched your 3-year-old grandchild quickly and spontaneously master the smartphone that took you a month to figure out.

We think that the kids were exploring possibilities in a wider more unpredictable way than grown-ups just as they do when they "get into everything" in their exploratory play, or think up crazy alternatives to reality in their pretending. But we have almost no idea how this sort of rational randomness, this constrained creativity, is possible.

A second area where children outshine computers is in their ability to actually go out and explore the world around them. Turing refers to this as "the ability to go fetch the coal scuttle" and suggests that computers can do without it. But, after all, experimentation is a crucial, and rather mysterious, part of scientific learning.

More and more studies show that even very young babies actively explore the world around them, and that this kind of active exploration is crucial for learning. Babies systematically look longest at the events around them that are most likely to be informative, and they play with objects in a way that will teach them the most. This ability to actually extract the right data from the world, rather than just processing the data that you are given, is very powerful. It also involves the same tension between rationality and randomness as children's creative hypothesis generation. It is still far beyond the capacity of any machine we know of.

A third way that children learn is by getting information from the other people around them. Most people don't appreciate that many of the recent successes of machine learning also depend deeply on information from humans. Computers can only recognize internet images because millions of real people have reduced the unbelievably complex information at their retinas to a highly stylized, constrained and simplified instagram of their cute kitty, and have clearly labeled that image, too. (In some ways the dystopian fantasy of "The Matrix" is a simple fact, we're all actually helping computers become more powerful, under the anesthetizing illusion that we're just having fun with lol cats.) And they can translate, more or less, because they can take advantage of enormous databases of human translations.

Computers can't roll their eyes

But current computers use the information from people in a relatively simple and thoughtless way. New studies show that, in contrast, even three-year-olds can assess the testimony of other people in a surprisingly sophisticated way. They can tell whether someone is reliable or wonky, naïve or expert. They can even use very subtle cues to tell whether a grown-up is just saying what they think, or is intentionally trying to be instructive. And very young children learn differently depending on what they think about the person talking to them. (Even the most sophisticated computers have yet to master the ability to roll their eyes at adult fatuity).

Of course, Turing's great insight was that once we have a complete step-by-step account of any process we can program it on a computer. This is where the tenses come in - at the moment it's a matter of faith about whether such an account of human intelligence will be possible or what it will look like. As scientists, it's natural to want to endorse that faith, and to hope that such an account will be possible, though at this point, it's still no more than faith and hope.

And, after all, we know that there are intelligent physical systems that can do all these things. In fact, most of us have actually created such systems and enjoyed doing it too (well, at least in the earliest stages). We call them our kids. Computation is still the best, indeed the only, scientific explanation we have of how a physical object like a brain can act intelligently. But, at least for now, we have almost no idea at all how the sort of intelligence we see in children is possible. Until we do, the largest and most powerful computers will still be no match for the smallest and weakest humans.

Author: Alison Gopnik is Professor of Psychology and Affiliate Professor of Philosophy at the Tolman Hall University of California at Berkeley. She is participating in the World Economic Forum's Annual Meeting in Davos 2015. This post is an expanded version of a post on the Edge website.

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What do you think?

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Ready to argue for the A.I. Curriculum in a live action role play game?

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They'll learn in the future...But what will they learn?

What is Curriculum for the Future: The Role Play Game?

Curriculum for the Future: The Role Play Game is one in a series of three resources designed to generate thought-provoking conversations about learning and curriculum today and in the future. The game was originally designed for adults, but can be adapted for use with older students.

Curriculum for the Future: The Role Play Game invites you to take on responsibility for shaping the future of curriculum for young New Zealand learners. The game is designed to give players the opportunity to debate different curriculum ideas and possibilities. It is fast, fun, research-informed and low-tech. Players can try out ideas that might seem outlandish or unworkable in current real life. They can argue for positions that they may or may not really agree with or even fully understand. By thinking on their feet and listening to the ideas that come up during the game, players will gain insights into the complexities of curriculum design and development, and challenge their own thinking about the purposes of education and what schooling might be like in the future.

For more background on the development of this game, read the blog post The Making of Curriculum for the Future: The role play game

The role play game can also be used along with:

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Curriculum for the Future: The role play game

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Curriculum for the Future: The Role Play Game is one in a series of three resources designed to generate thought-provoking conversations about learning and curriculum today and in the future. The game was originally designed for adults, but can be adapted for use with older students.

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READ ME FIRST!  IDEAS ON HOW TO USE THIS PADLET

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READ ME FIRST!  IDEAS ON HOW TO USE THIS PADLET

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