Tag Archives: reality

#433506 MIT’s New Robot Taught Itself to Pick ...

Posted on September 18, 2018 by Android

Back in 2016, somewhere in a Google-owned warehouse, more than a dozen robotic arms sat for hours quietly grasping objects of various shapes and sizes. For hours on end, they taught themselves how to pick up and hold the items appropriately—mimicking the way a baby gradually learns to use its hands.

Now, scientists from MIT have made a new breakthrough in machine learning: their new system can not only teach itself to see and identify objects, but also understand how best to manipulate them.

This means that, armed with the new machine learning routine referred to as “dense object nets (DON),” the robot would be capable of picking up an object that it’s never seen before, or in an unfamiliar orientation, without resorting to trial and error—exactly as a human would.

The deceptively simple ability to dexterously manipulate objects with our hands is a huge part of why humans are the dominant species on the planet. We take it for granted. Hardware innovations like the Shadow Dexterous Hand have enabled robots to softly grip and manipulate delicate objects for many years, but the software required to control these precision-engineered machines in a range of circumstances has proved harder to develop.

This was not for want of trying. The Amazon Robotics Challenge offers millions of dollars in prizes (and potentially far more in contracts, as their $775m acquisition of Kiva Systems shows) for the best dexterous robot able to pick and package items in their warehouses. The lucrative dream of a fully-automated delivery system is missing this crucial ability.

Meanwhile, the Robocup@home challenge—an offshoot of the popular Robocup tournament for soccer-playing robots—aims to make everyone’s dream of having a robot butler a reality. The competition involves teams drilling their robots through simple household tasks that require social interaction or object manipulation, like helping to carry the shopping, sorting items onto a shelf, or guiding tourists around a museum.

Yet all of these endeavors have proved difficult; the tasks often have to be simplified to enable the robot to complete them at all. New or unexpected elements, such as those encountered in real life, more often than not throw the system entirely. Programming the robot’s every move in explicit detail is not a scalable solution: this can work in the highly-controlled world of the assembly line, but not in everyday life.

Computer vision is improving all the time. Neural networks, including those you train every time you prove that you’re not a robot with CAPTCHA, are getting better at sorting objects into categories, and identifying them based on sparse or incomplete data, such as when they are occluded, or in different lighting.

But many of these systems require enormous amounts of input data, which is impractical, slow to generate, and often needs to be laboriously categorized by humans. There are entirely new jobs that require people to label, categorize, and sift large bodies of data ready for supervised machine learning. This can make machine learning undemocratic. If you’re Google, you can make thousands of unwitting volunteers label your images for you with CAPTCHA. If you’re IBM, you can hire people to manually label that data. If you’re an individual or startup trying something new, however, you will struggle to access the vast troves of labeled data available to the bigger players.

This is why new systems that can potentially train themselves over time or that allow robots to deal with situations they’ve never seen before without mountains of labelled data are a holy grail in artificial intelligence. The work done by MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) is part of a new wave of “self-supervised” machine learning systems—little of the data used was labeled by humans.

The robot first inspects the new object from multiple angles, building up a 3D picture of the object with its own coordinate system. This then allows the robotic arm to identify a particular feature on the object—such as a handle, or the tongue of a shoe—from various different angles, based on its relative distance to other grid points.

This is the real innovation: the new means of representing objects to grasp as mapped-out 3D objects, with grid points and subsections of their own. Rather than using a computer vision algorithm to identify a door handle, and then activating a door handle grasping subroutine, the DON system treats all objects by making these spatial maps before classifying or manipulating them, enabling it to deal with a greater range of objects than in other approaches.

“Many approaches to manipulation can’t identify specific parts of an object across the many orientations that object may encounter,” said PhD student Lucas Manuelli, who wrote a new paper about the system with lead author and fellow student Pete Florence, alongside MIT professor Russ Tedrake. “For example, existing algorithms would be unable to grasp a mug by its handle, especially if the mug could be in multiple orientations, like upright, or on its side.”

Class-specific descriptors, which can be applied to the object features, can allow the robot arm to identify a mug, find the handle, and pick the mug up appropriately. Object-specific descriptors allow the robot arm to select a particular mug from a group of similar items. I’m already dreaming of a robot butler reliably picking my favourite mug when it serves me coffee in the morning.

Google’s robot arm-y was an attempt to develop a general grasping algorithm: one that could identify, categorize, and appropriately grip as many items as possible. This requires a great deal of training time and data, which is why Google parallelized their project by having 14 robot arms feed data into a single neural network brain: even then, the algorithm may fail with highly specific tasks. Specialist grasping algorithms might require less training if they’re limited to specific objects, but then your software is useless for general tasks.

As the roboticists noted, their system, with its ability to identify parts of an object rather than just a single object, is better suited to specific tasks, such as “grasp the racquet by the handle,” than Amazon Robotics Challenge robots, which identify whole objects by segmenting an image.

This work is small-scale at present. It has been tested with a few classes of objects, including shoes, hats, and mugs. Yet the use of these dense object nets as a way for robots to represent and manipulate new objects may well be another step towards the ultimate goal of generalized automation: a robot capable of performing every task a person can. If that point is reached, the question that will remain is how to cope with being obsolete.

Image Credit: Tom Buehler/CSAIL Continue reading →

Posted in Human Robots

#433400 A Model for the Future of Education, and ...

Posted on September 12, 2018 by Android

As kids worldwide head back to school, I’d like to share my thoughts on the future of education.

Bottom line, how we educate our kids needs to radically change given the massive potential of exponential tech (e.g. artificial intelligence and virtual reality).

Without question, the number one driver for education is inspiration. As such, if you have a kid age 8–18, you’ll want to get your hands on an incredibly inspirational novel written by my dear friend Ray Kurzweil called Danielle: Chronicles of a Superheroine.

Danielle offers boys and girls a role model of a young woman who uses smart technologies and super-intelligence to partner with her friends to solve some of the world’s greatest challenges. It’s perfect to inspire anyone to pursue their moonshot.

Without further ado, let’s dive into the future of educating kids, and a summary of my white paper thoughts….

Just last year, edtech (education technology) investments surpassed a record high of 9.5 billion USD—up 30 percent from the year before.

Already valued at over half a billion USD, the AI in education market is set to surpass 6 billion USD by 2024.

And we’re now seeing countless new players enter the classroom, from a Soul Machines AI teacher specializing in energy use and sustainability to smart “lab schools” with personalized curricula.

As my two boys enter 1st grade, I continue asking myself, given the fact that most elementary schools haven’t changed in many decades (perhaps a century), what do I want my kids to learn? How do I think about elementary school during an exponential era?

This post covers five subjects related to elementary school education:

Five Issues with Today’s Elementary Schools
Five Guiding Principles for Future Education
An Elementary School Curriculum for the Future
Exponential Technologies in our Classroom
Mindsets for the 21st Century

Excuse the length of this post, but if you have kids, the details might be meaningful. If you don’t, then next week’s post will return to normal length and another fun subject.

Also, if you’d like to see my detailed education “white paper,” you can view or download it here.

Let’s dive in…

Five Issues With Today’s Elementary Schools
There are probably lots of issues with today’s traditional elementary schools, but I’ll just choose a few that bother me most.

Grading: In the traditional education system, you start at an “A,” and every time you get something wrong, your score gets lower and lower. At best it’s demotivating, and at worst it has nothing to do with the world you occupy as an adult. In the gaming world (e.g. Angry Birds), it’s just the opposite. You start with zero and every time you come up with something right, your score gets higher and higher.
Sage on the Stage: Most classrooms have a teacher up in front of class lecturing to a classroom of students, half of whom are bored and half of whom are lost. The one-teacher-fits-all model comes from an era of scarcity where great teachers and schools were rare.
Relevance: When I think back to elementary and secondary school, I realize how much of what I learned was never actually useful later in life, and how many of my critical lessons for success I had to pick up on my own (I don’t know about you, but I haven’t ever actually had to factor a polynomial in my adult life).
Imagination, Coloring inside the Lines: Probably of greatest concern to me is the factory-worker, industrial-era origin of today’s schools. Programs are so structured with rote memorization that it squashes the originality from most children. I’m reminded that “the day before something is truly a breakthrough, it’s a crazy idea.” Where do we pursue crazy ideas in our schools? Where do we foster imagination?
Boring: If learning in school is a chore, boring, or emotionless, then the most important driver of human learning, passion, is disengaged. Having our children memorize facts and figures, sit passively in class, and take mundane standardized tests completely defeats the purpose.

An average of 7,200 students drop out of high school each day, totaling 1.3 million each year. This means only 69 percent of students who start high school finish four years later. And over 50 percent of these high school dropouts name boredom as the number one reason they left.

Five Guiding Principles for Future Education
I imagine a relatively near-term future in which robotics and artificial intelligence will allow any of us, from ages 8 to 108, to easily and quickly find answers, create products, or accomplish tasks, all simply by expressing our desires.

From ‘mind to manufactured in moments.’ In short, we’ll be able to do and create almost whatever we want.

In this future, what attributes will be most critical for our children to learn to become successful in their adult lives? What’s most important for educating our children today?

For me it’s about passion, curiosity, imagination, critical thinking, and grit.

Passion: You’d be amazed at how many people don’t have a mission in life… A calling… something to jolt them out of bed every morning. The most valuable resource for humanity is the persistent and passionate human mind, so creating a future of passionate kids is so very important. For my 7-year-old boys, I want to support them in finding their passion or purpose… something that is uniquely theirs. In the same way that the Apollo program and Star Trek drove my early love for all things space, and that passion drove me to learn and do.
Curiosity: Curiosity is something innate in kids, yet something lost by most adults during the course of their life. Why? In a world of Google, robots, and AI, raising a kid that is constantly asking questions and running “what if” experiments can be extremely valuable. In an age of machine learning, massive data, and a trillion sensors, it will be the quality of your questions that will be most important.
Imagination: Entrepreneurs and visionaries imagine the world (and the future) they want to live in, and then they create it. Kids happen to be some of the most imaginative humans around… it’s critical that they know how important and liberating imagination can be.
Critical Thinking: In a world flooded with often-conflicting ideas, baseless claims, misleading headlines, negative news, and misinformation, learning the skill of critical thinking helps find the signal in the noise. This principle is perhaps the most difficult to teach kids.
Grit/Persistence: Grit is defined as “passion and perseverance in pursuit of long-term goals,” and it has recently been widely acknowledged as one of the most important predictors of and contributors to success.

Teaching your kids not to give up, to keep trying, and to keep trying new ideas for something that they are truly passionate about achieving is extremely critical. Much of my personal success has come from such stubbornness. I joke that both XPRIZE and the Zero Gravity Corporation were “overnight successes after 10 years of hard work.”

So given those five basic principles, what would an elementary school curriculum look like? Let’s take a look…

An Elementary School Curriculum for the Future
Over the last 30 years, I’ve had the pleasure of starting two universities, International Space University (1987) and Singularity University (2007). My favorite part of co-founding both institutions was designing and implementing the curriculum. Along those lines, the following is my first shot at the type of curriculum I’d love my own boys to be learning.

I’d love your thoughts, I’ll be looking for them here: https://www.surveymonkey.com/r/DDRWZ8R

For the purpose of illustration, I’ll speak about ‘courses’ or ‘modules,’ but in reality these are just elements that would ultimately be woven together throughout the course of K-6 education.

Module 1: Storytelling/Communications

When I think about the skill that has served me best in life, it’s been my ability to present my ideas in the most compelling fashion possible, to get others onboard, and support birth and growth in an innovative direction. In my adult life, as an entrepreneur and a CEO, it’s been my ability to communicate clearly and tell compelling stories that has allowed me to create the future. I don’t think this lesson can start too early in life. So imagine a module, year after year, where our kids learn the art and practice of formulating and pitching their ideas. The best of oration and storytelling. Perhaps children in this class would watch TED presentations, or maybe they’d put together their own TEDx for kids. Ultimately, it’s about practice and getting comfortable with putting yourself and your ideas out there and overcoming any fears of public speaking.

Module 2: Passions

A modern school should help our children find and explore their passion(s). Passion is the greatest gift of self-discovery. It is a source of interest and excitement, and is unique to each child.

The key to finding passion is exposure. Allowing kids to experience as many adventures, careers, and passionate adults as possible. Historically, this was limited by the reality of geography and cost, implemented by having local moms and dads presenting in class about their careers. “Hi, I’m Alan, Billy’s dad, and I’m an accountant. Accountants are people who…”

But in a world of YouTube and virtual reality, the ability for our children to explore 500 different possible careers or passions during their K-6 education becomes not only possible but compelling. I imagine a module where children share their newest passion each month, sharing videos (or VR experiences) and explaining what they love and what they’ve learned.

Module 3: Curiosity & Experimentation

Einstein famously said, “I have no special talent. I am only passionately curious.” Curiosity is innate in children, and many times lost later in life. Arguably, it can be said that curiosity is responsible for all major scientific and technological advances; it’s the desire of an individual to know the truth.

Coupled with curiosity is the process of experimentation and discovery. The process of asking questions, creating and testing a hypothesis, and repeated experimentation until the truth is found. As I’ve studied the most successful entrepreneurs and entrepreneurial companies, from Google and Amazon to Uber, their success is significantly due to their relentless use of experimentation to define their products and services.

Here I imagine a module which instills in children the importance of curiosity and gives them permission to say, “I don’t know, let’s find out.”

Further, a monthly module that teaches children how to design and execute valid and meaningful experiments. Imagine children who learn the skill of asking a question, proposing a hypothesis, designing an experiment, gathering the data, and then reaching a conclusion.

Module 4: Persistence/Grit

Doing anything big, bold, and significant in life is hard work. You can’t just give up when the going gets rough. The mindset of persistence, of grit, is a learned behavior I believe can be taught at an early age, especially when it’s tied to pursuing a child’s passion.

I imagine a curriculum that, each week, studies the career of a great entrepreneur and highlights their story of persistence. It would highlight the individuals and companies that stuck with it, iterated, and ultimately succeeded.

Further, I imagine a module that combines persistence and experimentation in gameplay, such as that found in Dean Kamen’s FIRST LEGO league, where 4th graders (and up) research a real-world problem such as food safety, recycling, energy, and so on, and are challenged to develop a solution. They also must design, build, and program a robot using LEGO MINDSTORMS®, then compete on a tabletop playing field.

Module 5: Technology Exposure

In a world of rapidly accelerating technology, understanding how technologies work, what they do, and their potential for benefiting society is, in my humble opinion, critical to a child’s future. Technology and coding (more on this below) are the new “lingua franca” of tomorrow.

In this module, I imagine teaching (age appropriate) kids through play and demonstration. Giving them an overview of exponential technologies such as computation, sensors, networks, artificial intelligence, digital manufacturing, genetic engineering, augmented/virtual reality, and robotics, to name a few. This module is not about making a child an expert in any technology, it’s more about giving them the language of these new tools, and conceptually an overview of how they might use such a technology in the future. The goal here is to get them excited, give them demonstrations that make the concepts stick, and then to let their imaginations run.

Module 6: Empathy

Empathy, defined as “the ability to understand and share the feelings of another,” has been recognized as one of the most critical skills for our children today. And while there has been much written, and great practices for instilling this at home and in school, today’s new tools accelerate this.

Virtual reality isn’t just about video games anymore. Artists, activists, and journalists now see the technology’s potential to be an empathy engine, one that can shine spotlights on everything from the Ebola epidemic to what it’s like to live in Gaza. And Jeremy Bailenson has been at the vanguard of investigating VR’s power for good.

For more than a decade, Bailenson’s lab at Stanford has been studying how VR can make us better people. Through the power of VR, volunteers at the lab have felt what it is like to be Superman (to see if it makes them more helpful), a cow (to reduce meat consumption), and even a coral (to learn about ocean acidification).

Silly as they might seem, these sorts of VR scenarios could be more effective than the traditional public service ad at making people behave. Afterwards, they waste less paper. They save more money for retirement. They’re nicer to the people around them. And this could have consequences in terms of how we teach and train everyone from cliquey teenagers to high court judges.

Module 7: Ethics/Moral Dilemmas

Related to empathy, and equally important, is the goal of infusing kids with a moral compass. Over a year ago, I toured a special school created by Elon Musk (the Ad Astra school) for his five boys (age 9 to 14). One element that is persistent in that small school of under 40 kids is the conversation about ethics and morals, a conversation manifested by debating real-world scenarios that our kids may one day face.

Here’s an example of the sort of gameplay/roleplay that I heard about at Ad Astra, that might be implemented in a module on morals and ethics. Imagine a small town on a lake, in which the majority of the town is employed by a single factory. But that factory has been polluting the lake and killing all the life. What do you do? It’s posed that shutting down the factory would mean that everyone loses their jobs. On the other hand, keeping the factory open means the lake is destroyed and the lake dies. This kind of regular and routine conversation/gameplay allows the children to see the world in a critically important fashion.

Module 8: The 3R Basics (Reading, wRiting & aRithmetic)

There’s no question that young children entering kindergarten need the basics of reading, writing, and math. The only question is what’s the best way for them to get it? We all grew up in the classic mode of a teacher at the chalkboard, books, and homework at night. But I would argue that such teaching approaches are long outdated, now replaced with apps, gameplay, and the concept of the flip classroom.

Pioneered by high school teachers Jonathan Bergman and Aaron Sams in 2007, the flipped classroom reverses the sequence of events from that of the traditional classroom.

Students view lecture materials, usually in the form of video lectures, as homework prior to coming to class. In-class time is reserved for activities such as interactive discussions or collaborative work, all performed under the guidance of the teacher.

The benefits are clear:

Students can consume lectures at their own pace, viewing the video again and again until they get the concept, or fast-forwarding if the information is obvious.
The teacher is present while students apply new knowledge. Doing the homework into class time gives teachers insight into which concepts, if any, that their students are struggling with and helps them adjust the class accordingly.
The flipped classroom produces tangible results: 71 percent of teachers who flipped their classes noticed improved grades, and 80 percent reported improved student attitudes as a result.

Module 9: Creative Expression & Improvisation

Every single one of us is creative. It’s human nature to be creative… the thing is that we each might have different ways of expressing our creativity.

We must encourage kids to discover and to develop their creative outlets early. In this module, imagine showing kids the many different ways creativity is expressed, from art to engineering to music to math, and then guiding them as they choose the area (or areas) they are most interested in. Critically, teachers (or parents) can then develop unique lessons for each child based on their interests, thanks to open education resources like YouTube and the Khan Academy. If my child is interested in painting and robots, a teacher or AI could scour the web and put together a custom lesson set from videos/articles where the best painters and roboticists in the world share their skills.

Adapting to change is critical for success, especially in our constantly changing world today. Improvisation is a skill that can be learned, and we need to be teaching it early.

In most collegiate “improv” classes, the core of great improvisation is the “Yes, and…” mindset. When acting out a scene, one actor might introduce a new character or idea, completely changing the context of the scene. It’s critical that the other actors in the scene say “Yes, and…” accept the new reality, then add something new of their own.

Imagine playing similar role-play games in elementary schools, where a teacher gives the students a scene/context and constantly changes variables, forcing them to adapt and play.

Module 10: Coding

Computer science opens more doors for students than any other discipline in today’s world. Learning even the basics will help students in virtually any career, from architecture to zoology.

Coding is an important tool for computer science, in the way that arithmetic is a tool for doing mathematics and words are a tool for English. Coding creates software, but computer science is a broad field encompassing deep concepts that go well beyond coding.

Every 21st century student should also have a chance to learn about algorithms, how to make an app, or how the internet works. Computational thinking allows preschoolers to grasp concepts like algorithms, recursion and heuristics. Even if they don’t understand the terms, they’ll learn the basic concepts.

There are more than 500,000 open jobs in computing right now, representing the number one source of new wages in the US, and these jobs are projected to grow at twice the rate of all other jobs.

Coding is fun! Beyond the practical reasons for learning how to code, there’s the fact that creating a game or animation can be really fun for kids.

Module 11: Entrepreneurship & Sales

At its core, entrepreneurship is about identifying a problem (an opportunity), developing a vision on how to solve it, and working with a team to turn that vision into reality. I mentioned Elon’s school, Ad Astra: here, again, entrepreneurship is a core discipline where students create and actually sell products and services to each other and the school community.

You could recreate this basic exercise with a group of kids in lots of fun ways to teach them the basic lessons of entrepreneurship.

Related to entrepreneurship is sales. In my opinion, we need to be teaching sales to every child at an early age. Being able to “sell” an idea (again related to storytelling) has been a critical skill in my career, and it is a competency that many people simply never learned.

The lemonade stand has been a classic, though somewhat meager, lesson in sales from past generations, where a child sits on a street corner and tries to sell homemade lemonade for $0.50 to people passing by. I’d suggest we step the game up and take a more active approach in gamifying sales, and maybe having the classroom create a Kickstarter, Indiegogo or GoFundMe campaign. The experience of creating a product or service and successfully selling it will create an indelible memory and give students the tools to change the world.

Module 12: Language

A little over a year ago, I spent a week in China meeting with parents whose focus on kids’ education is extraordinary. One of the areas I found fascinating is how some of the most advanced parents are teaching their kids new languages: through games. On the tablet, the kids are allowed to play games, but only in French. A child’s desire to win fully engages them and drives their learning rapidly.

Beyond games, there’s virtual reality. We know that full immersion is what it takes to become fluent (at least later in life). A semester abroad in France or Italy, and you’ve got a great handle on the language and the culture. But what about for an eight-year-old?

Imagine a module where for an hour each day, the children spend their time walking around Italy in a VR world, hanging out with AI-driven game characters who teach them, engage them, and share the culture and the language in the most personalized and compelling fashion possible.

Exponential Technologies for Our Classrooms
If you’ve attended Abundance 360 or Singularity University, or followed my blogs, you’ll probably agree with me that the way our children will learn is going to fundamentally transform over the next decade.

Here’s an overview of the top five technologies that will reshape the future of education:

Tech 1: Virtual Reality (VR) can make learning truly immersive. Research has shown that we remember 20 percent of what we hear, 30 percent of what we see, and up to 90 percent of what we do or simulate. Virtual reality yields the latter scenario impeccably. VR enables students to simulate flying through the bloodstream while learning about different cells they encounter, or travel to Mars to inspect the surface for life.

To make this a reality, Google Cardboard just launched its Pioneer Expeditions product. Under this program, thousands of schools around the world have gotten a kit containing everything a teacher needs to take his or her class on a virtual trip. While data on VR use in K-12 schools and colleges have yet to be gathered, the steady growth of the market is reflected in the surge of companies (including zSpace, Alchemy VR and Immersive VR Education) solely dedicated to providing schools with packaged education curriculum and content.

Add to VR a related technology called augmented reality (AR), and experiential education really comes alive. Imagine wearing an AR headset that is able to superimpose educational lessons on top of real-world experiences. Interested in botany? As you walk through a garden, the AR headset superimposes the name and details of every plant you see.

Tech 2: 3D Printing is allowing students to bring their ideas to life. Never mind the computer on every desktop (or a tablet for every student), that’s a given. In the near future, teachers and students will want or have a 3D printer on the desk to help them learn core science, technology, engineering and mathematics (STEM) principles. Bre Pettis, of MakerBot Industries, in a grand but practical vision, sees a 3D printer on every school desk in America. “Imagine if you had a 3D printer instead of a LEGO set when you were a kid; what would life be like now?” asks Mr. Pettis. You could print your own mini-figures, your own blocks, and you could iterate on new designs as quickly as your imagination would allow. MakerBots are now in over 5,000 K-12 schools across the US.

Taking this one step further, you could imagine having a 3D file for most entries in Wikipedia, allowing you to print out and study an object you can only read about or visualize in VR.

Tech 3: Sensors & Networks. An explosion of sensors and networks are going to connect everyone at gigabit speeds, making access to rich video available at all times. At the same time, sensors continue to miniaturize and reduce in power, becoming embedded in everything. One benefit will be the connection of sensor data with machine learning and AI (below), such that knowledge of a child’s attention drifting, or confusion, can be easily measured and communicated. The result would be a representation of the information through an alternate modality or at a different speed.

Tech 4: Machine Learning is making learning adaptive and personalized. No two students are identical—they have different modes of learning (by reading, seeing, hearing, doing), come from different educational backgrounds, and have different intellectual capabilities and attention spans. Advances in machine learning and the surging adaptive learning movement are seeking to solve this problem. Companies like Knewton and Dreambox have over 15 million students on their respective adaptive learning platforms. Soon, every education application will be adaptive, learning how to personalize the lesson for a specific student. There will be adaptive quizzing apps, flashcard apps, textbook apps, simulation apps and many more.

Tech 5: Artificial Intelligence or “An AI Teaching Companion.” Neil Stephenson’s book The Diamond Age presents a fascinating piece of educational technology called “A Young Lady’s Illustrated Primer.”

As described by Beat Schwendimann, “The primer is an interactive book that can answer a learner’s questions (spoken in natural language), teach through allegories that incorporate elements of the learner’s environment, and presents contextual just-in-time information.

“The primer includes sensors that monitor the learner’s actions and provide feedback. The learner is in a cognitive apprenticeship with the book: The primer models a certain skill (through allegorical fairy tale characters), which the learner then imitates in real life.

“The primer follows a learning progression with increasingly more complex tasks. The educational goals of the primer are humanist: To support the learner to become a strong and independently thinking person.”

The primer, an individualized AI teaching companion is the result of technological convergence and is beautifully described by YouTuber CGP Grey in his video: Digital Aristotle: Thoughts on the Future of Education.

Your AI companion will have unlimited access to information on the cloud and will deliver it at the optimal speed to each student in an engaging, fun way. This AI will demonetize and democratize education, be available to everyone for free (just like Google), and offering the best education to the wealthiest and poorest children on the planet equally.

This AI companion is not a tutor who spouts facts, figures and answers, but a player on the side of the student, there to help him or her learn, and in so doing, learn how to learn better. The AI is always alert, watching for signs of frustration and boredom that may precede quitting, for signs of curiosity or interest that tend to indicate active exploration, and for signs of enjoyment and mastery, which might indicate a successful learning experience.

Ultimately, we’re heading towards a vastly more educated world. We are truly living during the most exciting time to be alive.

Mindsets for the 21st Century
Finally, it’s important for me to discuss mindsets. How we think about the future colors how we learn and what we do. I’ve written extensively about the importance of an abundance and exponential mindset for entrepreneurs and CEOs. I also think that attention to mindset in our elementary schools, when a child is shaping the mental “operating system” for the rest of their life, is even more important.

As such, I would recommend that a school adopt a set of principles that teach and promote a number of mindsets in the fabric of their programs.

Many “mindsets” are important to promote. Here are a couple to consider:

Nurturing Optimism & An Abundance Mindset:
We live in a competitive world, and kids experience a significant amount of pressure to perform. When they fall short, they feel deflated. We all fail at times; that’s part of life. If we want to raise “can-do” kids who can work through failure and come out stronger for it, it’s wise to nurture optimism. Optimistic kids are more willing to take healthy risks, are better problem-solvers, and experience positive relationships. You can nurture optimism in your school by starting each day by focusing on gratitude (what each child is grateful for), or a “positive focus” in which each student takes 30 seconds to talk about what they are most excited about, or what recent event was positively impactful to them. (NOTE: I start every meeting inside my Strike Force team with a positive focus.)

Finally, helping students understand (through data and graphs) that the world is in fact getting better (see my first book: Abundance: The Future is Better Than You Think) will help them counter the continuous flow of negative news flowing through our news media.

When kids feel confident in their abilities and excited about the world, they are willing to work harder and be more creative.

Tolerance for Failure:
Tolerating failure is a difficult lesson to learn and a difficult lesson to teach. But it is critically important to succeeding in life.

Astro Teller, who runs Google’s innovation branch “X,” talks a lot about encouraging failure. At X, they regularly try to “kill” their ideas. If they are successful in killing an idea, and thus “failing,” they save lots of time, money and resources. The ideas they can’t kill survive and develop into billion-dollar businesses. The key is that each time an idea is killed, Astro rewards the team, literally, with cash bonuses. Their failure is celebrated and they become a hero.

This should be reproduced in the classroom: kids should try to be critical of their best ideas (learn critical thinking), then they should be celebrated for ‘successfully failing,’ perhaps with cake, balloons, confetti, and lots of Silly String.

Join Me & Get Involved!
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#433288 The New AI Tech Turning Heads in Video ...

Posted on September 9, 2018 by Android

A new technique using artificial intelligence to manipulate video content gives new meaning to the expression “talking head.”

An international team of researchers showcased the latest advancement in synthesizing facial expressions—including mouth, eyes, eyebrows, and even head position—in video at this month’s 2018 SIGGRAPH, a conference on innovations in computer graphics, animation, virtual reality, and other forms of digital wizardry.

The project is called Deep Video Portraits. It relies on a type of AI called generative adversarial networks (GANs) to modify a “target” actor based on the facial and head movement of a “source” actor. As the name implies, GANs pit two opposing neural networks against one another to create a realistic talking head, right down to the sneer or raised eyebrow.

In this case, the adversaries are actually working together: One neural network generates content, while the other rejects or approves each effort. The back-and-forth interplay between the two eventually produces a realistic result that can easily fool the human eye, including reproducing a static scene behind the head as it bobs back and forth.

The researchers say the technique can be used by the film industry for a variety of purposes, from editing facial expressions of actors for matching dubbed voices to repositioning an actor’s head in post-production. AI can not only produce highly realistic results, but much quicker ones compared to the manual processes used today, according to the researchers. You can read the full paper of their work here.

“Deep Video Portraits shows how such a visual effect could be created with less effort in the future,” said Christian Richardt, from the University of Bath’s motion capture research center CAMERA, in a press release. “With our approach, even the positioning of an actor’s head and their facial expression could be easily edited to change camera angles or subtly change the framing of a scene to tell the story better.”

AI Tech Different Than So-Called “Deepfakes”
The work is far from the first to employ AI to manipulate video and audio. At last year’s SIGGRAPH conference, researchers from the University of Washington showcased their work using algorithms that inserted audio recordings from a person in one instance into a separate video of the same person in a different context.

In this case, they “faked” a video using a speech from former President Barack Obama addressing a mass shooting incident during his presidency. The AI-doctored video injects the audio into an unrelated video of the president while also blending the facial and mouth movements, creating a pretty credible job of lip synching.

A previous paper by many of the same scientists on the Deep Video Portraits project detailed how they were first able to manipulate a video in real time of a talking head (in this case, actor and former California governor Arnold Schwarzenegger). The Face2Face system pulled off this bit of digital trickery using a depth-sensing camera that tracked the facial expressions of an Asian female source actor.

A less sophisticated method of swapping faces using a machine learning software dubbed FakeApp emerged earlier this year. Predictably, the tech—requiring numerous photos of the source actor in order to train the neural network—was used for more juvenile pursuits, such as injecting a person’s face onto a porn star.

The application gave rise to the term “deepfakes,” which is now used somewhat ubiquitously to describe all such instances of AI-manipulated video—much to the chagrin of some of the researchers involved in more legitimate uses.

Fighting AI-Created Video Forgeries
However, the researchers are keenly aware that their work—intended for benign uses such as in the film industry or even to correct gaze and head positions for more natural interactions through video teleconferencing—could be used for nefarious purposes. Fake news is the most obvious concern.

“With ever-improving video editing technology, we must also start being more critical about the video content we consume every day, especially if there is no proof of origin,” said Michael Zollhöfer, a visiting assistant professor at Stanford University and member of the Deep Video Portraits team, in the press release.

Toward that end, the research team is training the same adversarial neural networks to spot video forgeries. They also strongly recommend that developers clearly watermark videos that are edited through AI or otherwise, and denote clearly what part and element of the scene was modified.

To catch less ethical users, the US Department of Defense, through the Defense Advanced Research Projects Agency (DARPA), is supporting a program called Media Forensics. This latest DARPA challenge enlists researchers to develop technologies to automatically assess the integrity of an image or video, as part of an end-to-end media forensics platform.

The DARPA official in charge of the program, Matthew Turek, did tell MIT Technology Review that so far the program has “discovered subtle cues in current GAN-manipulated images and videos that allow us to detect the presence of alterations.” In one reported example, researchers have targeted eyes, which rarely blink in the case of “deepfakes” like those created by FakeApp, because the AI is trained on still pictures. That method would seem to be less effective to spot the sort of forgeries created by Deep Video Portraits, which appears to flawlessly match the entire facial and head movements between the source and target actors.

“We believe that the field of digital forensics should and will receive a lot more attention in the future to develop approaches that can automatically prove the authenticity of a video clip,” Zollhöfer said. “This will lead to ever-better approaches that can spot such modifications even if we humans might not be able to spot them with our own eyes.

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#433282 The 4 Waves of AI: Who Will Own the ...

Posted on September 9, 2018 by Android

Recently, I picked up Kai-Fu Lee’s newest book, AI Superpowers.

Kai-Fu Lee is one of the most plugged-in AI investors on the planet, managing over $2 billion between six funds and over 300 portfolio companies in the US and China.

Drawing from his pioneering work in AI, executive leadership at Microsoft, Apple, and Google (where he served as founding president of Google China), and his founding of VC fund Sinovation Ventures, Lee shares invaluable insights about:

The four factors driving today’s AI ecosystems;
China’s extraordinary inroads in AI implementation;
Where autonomous systems are headed;
How we’ll need to adapt.

With a foothold in both Beijing and Silicon Valley, Lee looks at the power balance between Chinese and US tech behemoths—each turbocharging new applications of deep learning and sweeping up global markets in the process.

In this post, I’ll be discussing Lee’s “Four Waves of AI,” an excellent framework for discussing where AI is today and where it’s going. I’ll also be featuring some of the hottest Chinese tech companies leading the charge, worth watching right now.

I’m super excited that this Tuesday, I’ve scored the opportunity to sit down with Kai-Fu Lee to discuss his book in detail via a webinar.

With Sino-US competition heating up, who will own the future of technology?

Let’s dive in.

The First Wave: Internet AI
In this first stage of AI deployment, we’re dealing primarily with recommendation engines—algorithmic systems that learn from masses of user data to curate online content personalized to each one of us.

Think Amazon’s spot-on product recommendations, or that “Up Next” YouTube video you just have to watch before getting back to work, or Facebook ads that seem to know what you’ll buy before you do.

Powered by the data flowing through our networks, internet AI leverages the fact that users automatically label data as we browse. Clicking versus not clicking; lingering on a web page longer than we did on another; hovering over a Facebook video to see what happens at the end.

These cascades of labeled data build a detailed picture of our personalities, habits, demands, and desires: the perfect recipe for more tailored content to keep us on a given platform.

Currently, Lee estimates that Chinese and American companies stand head-to-head when it comes to deployment of internet AI. But given China’s data advantage, he predicts that Chinese tech giants will have a slight lead (60-40) over their US counterparts in the next five years.

While you’ve most definitely heard of Alibaba and Baidu, you’ve probably never stumbled upon Toutiao.

Starting out as a copycat of America’s wildly popular Buzzfeed, Toutiao reached a valuation of $20 billion by 2017, dwarfing Buzzfeed’s valuation by more than a factor of 10. But with almost 120 million daily active users, Toutiao doesn’t just stop at creating viral content.

Equipped with natural-language processing and computer vision, Toutiao’s AI engines survey a vast network of different sites and contributors, rewriting headlines to optimize for user engagement, and processing each user’s online behavior—clicks, comments, engagement time—to curate individualized news feeds for millions of consumers.

And as users grow more engaged with Toutiao’s content, the company’s algorithms get better and better at recommending content, optimizing headlines, and delivering a truly personalized feed.

It’s this kind of positive feedback loop that fuels today’s AI giants surfing the wave of internet AI.

The Second Wave: Business AI
While internet AI takes advantage of the fact that netizens are constantly labeling data via clicks and other engagement metrics, business AI jumps on the data that traditional companies have already labeled in the past.

Think banks issuing loans and recording repayment rates; hospitals archiving diagnoses, imaging data, and subsequent health outcomes; or courts noting conviction history, recidivism, and flight.

While we humans make predictions based on obvious root causes (strong features), AI algorithms can process thousands of weakly correlated variables (weak features) that may have much more to do with a given outcome than the usual suspects.

By scouting out hidden correlations that escape our linear cause-and-effect logic, business AI leverages labeled data to train algorithms that outperform even the most veteran of experts.

Apply these data-trained AI engines to banking, insurance, and legal sentencing, and you get minimized default rates, optimized premiums, and plummeting recidivism rates.

While Lee confidently places America in the lead (90-10) for business AI, China’s substantial lag in structured industry data could actually work in its favor going forward.

In industries where Chinese startups can leapfrog over legacy systems, China has a major advantage.

Take Chinese app Smart Finance, for instance.

While Americans embraced credit and debit cards in the 1970s, China was still in the throes of its Cultural Revolution, largely missing the bus on this technology.

Fast forward to 2017, and China’s mobile payment spending outnumbered that of Americans’ by a ratio of 50 to 1. Without the competition of deeply entrenched credit cards, mobile payments were an obvious upgrade to China’s cash-heavy economy, embraced by 70 percent of China’s 753 million smartphone users by the end of 2017.

But by leapfrogging over credit cards and into mobile payments, China largely left behind the notion of credit.

And here’s where Smart Finance comes in.

An AI-powered app for microfinance, Smart Finance depends almost exclusively on its algorithms to make millions of microloans. For each potential borrower, the app simply requests access to a portion of the user’s phone data.

On the basis of variables as subtle as your typing speed and battery percentage, Smart Finance can predict with astounding accuracy your likelihood of repaying a $300 loan.

Such deployments of business AI and internet AI are already revolutionizing our industries and individual lifestyles. But still on the horizon lie two even more monumental waves— perception AI and autonomous AI.

The Third Wave: Perception AI
In this wave, AI gets an upgrade with eyes, ears, and myriad other senses, merging the digital world with our physical environments.

As sensors and smart devices proliferate through our homes and cities, we are on the verge of entering a trillion-sensor economy.

Companies like China’s Xiaomi are putting out millions of IoT-connected devices, and teams of researchers have already begun prototyping smart dust—solar cell- and sensor-geared particulates that can store and communicate troves of data anywhere, anytime.

As Kai-Fu explains, perception AI “will bring the convenience and abundance of the online world into our offline reality.” Sensor-enabled hardware devices will turn everything from hospitals to cars to schools into online-merge-offline (OMO) environments.

Imagine walking into a grocery store, scanning your face to pull up your most common purchases, and then picking up a virtual assistant (VA) shopping cart. Having pre-loaded your data, the cart adjusts your usual grocery list with voice input, reminds you to get your spouse’s favorite wine for an upcoming anniversary, and guides you through a personalized store route.

While we haven’t yet leveraged the full potential of perception AI, China and the US are already making incredible strides. Given China’s hardware advantage, Lee predicts China currently has a 60-40 edge over its American tech counterparts.

Now the go-to city for startups building robots, drones, wearable technology, and IoT infrastructure, Shenzhen has turned into a powerhouse for intelligent hardware, as I discussed last week. Turbocharging output of sensors and electronic parts via thousands of factories, Shenzhen’s skilled engineers can prototype and iterate new products at unprecedented scale and speed.

With the added fuel of Chinese government support and a relaxed Chinese attitude toward data privacy, China’s lead may even reach 80-20 in the next five years.

Jumping on this wave are companies like Xiaomi, which aims to turn bathrooms, kitchens, and living rooms into smart OMO environments. Having invested in 220 companies and incubated 29 startups that produce its products, Xiaomi surpassed 85 million intelligent home devices by the end of 2017, making it the world’s largest network of these connected products.

One KFC restaurant in China has even teamed up with Alipay (Alibaba’s mobile payments platform) to pioneer a ‘pay-with-your-face’ feature. Forget cash, cards, and cell phones, and let OMO do the work.

The Fourth Wave: Autonomous AI
But the most monumental—and unpredictable—wave is the fourth and final: autonomous AI.

Integrating all previous waves, autonomous AI gives machines the ability to sense and respond to the world around them, enabling AI to move and act productively.

While today’s machines can outperform us on repetitive tasks in structured and even unstructured environments (think Boston Dynamics’ humanoid Atlas or oncoming autonomous vehicles), machines with the power to see, hear, touch and optimize data will be a whole new ballgame.

Think: swarms of drones that can selectively spray and harvest entire farms with computer vision and remarkable dexterity, heat-resistant drones that can put out forest fires 100X more efficiently, or Level 5 autonomous vehicles that navigate smart roads and traffic systems all on their own.

While autonomous AI will first involve robots that create direct economic value—automating tasks on a one-to-one replacement basis—these intelligent machines will ultimately revamp entire industries from the ground up.

Kai-Fu Lee currently puts America in a commanding lead of 90-10 in autonomous AI, especially when it comes to self-driving vehicles. But Chinese government efforts are quickly ramping up the competition.

Already in China’s Zhejiang province, highway regulators and government officials have plans to build China’s first intelligent superhighway, outfitted with sensors, road-embedded solar panels and wireless communication between cars, roads and drivers.

Aimed at increasing transit efficiency by up to 30 percent while minimizing fatalities, the project may one day allow autonomous electric vehicles to continuously charge as they drive.

A similar government-fueled project involves Beijing’s new neighbor Xiong’an. Projected to take in over $580 billion in infrastructure spending over the next 20 years, Xiong’an New Area could one day become the world’s first city built around autonomous vehicles.

Baidu is already working with Xiong’an’s local government to build out this AI city with an environmental focus. Possibilities include sensor-geared cement, computer vision-enabled traffic lights, intersections with facial recognition, and parking lots-turned parks.

Lastly, Lee predicts China will almost certainly lead the charge in autonomous drones. Already, Shenzhen is home to premier drone maker DJI—a company I’ll be visiting with 24 top executives later this month as part of my annual China Platinum Trip.

Named “the best company I have ever encountered” by Chris Anderson, DJI owns an estimated 50 percent of the North American drone market, supercharged by Shenzhen’s extraordinary maker movement.

While the long-term Sino-US competitive balance in fourth wave AI remains to be seen, one thing is certain: in a matter of decades, we will witness the rise of AI-embedded cityscapes and autonomous machines that can interact with the real world and help solve today’s most pressing grand challenges.

Join Me
Webinar with Dr. Kai-Fu Lee: Dr. Kai-Fu Lee — one of the world’s most respected experts on AI — and I will discuss his latest book AI Superpowers: China, Silicon Valley, and the New World Order. Artificial Intelligence is reshaping the world as we know it. With U.S.-Sino competition heating up, who will own the future of technology? Register here for the free webinar on September 4th, 2018 from 11:00am–12:30pm PST.

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Posted in Human Robots

#433278 Outdated Evolution: Updating Our ...

Posted on September 9, 2018 by Android

What happens when evolution shapes an animal for tribes of 150 primitive individuals living in a chaotic jungle, and then suddenly that animal finds itself living with millions of others in an engineered metropolis, their pockets all bulging with devices of godlike power?

The result, it seems, is a modern era of tension where archaic forms of governance struggle to keep up with the technological advances of their citizenry, where governmental policies act like constraining bottlenecks rather than spearheads of progress.

Simply put, our governments have failed to adapt to disruptive technologies. And if we are to regain our stability moving forward into a future of even greater disruption, it’s imperative that we understand the issues that got us into this situation and what kind of solutions we can engineer to overcome our governmental weaknesses.

Hierarchy vs. Technological Decentralization
Many of the greatest issues our governments face today come from humanity’s biologically-hardwired desire for centralized hierarchies. This innate proclivity towards building and navigating systems of status and rank were evolutionary gifts handed down to us by our ape ancestors, where each member of a community had a mental map of their social hierarchy. Their nervous systems behaved differently depending on their rank in this hierarchy, influencing their interactions in a way that ensured only the most competent ape would rise to the top to gain access to the best food and mates.

As humanity emerged and discovered the power of language, we continued this practice by ensuring that those at the top of the hierarchies, those with the greatest education and access to information, were the dominant decision-makers for our communities.

However, this kind of structured chain of power is only necessary if we’re operating in conditions of scarcity. But resources, including information, are no longer scarce.

It’s estimated that more than two-thirds of adults in the world now own a smartphone, giving the average citizen the same access to the world’s information as the leaders of our governments. And with global poverty falling from 35.5 percent to 10.9 percent over the last 25 years, our younger generations are growing up seeing automation and abundance as a likely default, where innovations like solar energy, lab-grown meat, and 3D printing are expected to become commonplace.

It’s awareness of this paradigm shift that has empowered the recent rise of decentralization. As information and access to resources become ubiquitous, there is noticeably less need for our inefficient and bureaucratic hierarchies.

For example, if blockchain can prove its feasibility for large-scale systems, it can be used to update and upgrade numerous applications to a decentralized model, including currency and voting. Such innovations would lower the risk of failing banks collapsing the economy like they did in 2008, as well as prevent corrupt politicians from using gerrymandering and long queues at polling stations to deter voter participation.

Of course, technology isn’t a magic wand that should be implemented carelessly. Facebook’s “move fast and break things” approach might have very possibly broken American democracy in 2016, as social media played on some of the worst tendencies humanity can operate on during an election: fear and hostility.

But if decentralized technology, like blockchain’s public ledgers, can continue to spread a sense of security and transparency throughout society, perhaps we can begin to quiet that paranoia and hyper-vigilance our brains evolved to cope with living as apes in dangerous jungles. By decentralizing our power structures, we take away the channels our outdated biological behaviors might use to enact social dominance and manipulation.

The peace of mind this creates helps to reestablish trust in our communities and in our governments. And with trust in the government increased, it’s likely we’ll see our next issue corrected.

From Business and Law to Science and Technology
A study found that 59 percent of US presidents, 68 percent of vice presidents, and 78 percent of secretaries of state were lawyers by education and occupation. That’s more than one out of every two people in the most powerful positions in the American government restricted to a field dedicated to convincing other people (judges) their perspective is true, even if they lack evidence.

And so the scientific method became less important than semantics to our leaders.

Similarly, of the 535 individuals in the American congress, only 24 hold a PhD, only 2 of which are in a STEM field. And so far, it’s not getting better: Trump is the first president since WWII not to name a science advisor.

But if we can use technologies like blockchain to increase transparency, efficiency, and trust in the government, then the upcoming generations who understand decentralization, abundance, and exponential technologies might feel inspired enough to run for government positions. This helps solve that common problem where the smartest and most altruistic people tend to avoid government positions because they don’t want to play the semantic and deceitful game of politics.

By changing this narrative, our governments can begin to fill with techno-progressive individuals who actually understand the technologies that are rapidly reshaping our reality. And this influence of expertise is going to be crucial as our governments are forced to restructure and create new policies to accommodate the incoming disruption.

Clearing Regulations to Begin Safe Experimentation
As exponential technologies become more ubiquitous, we’re likely going to see young kids and garage tinkerers creating powerful AIs and altering genetics thanks to tools like CRISPR and free virtual reality tutorials.

This easy accessibility to such powerful technology means unexpected and rapid progress can occur almost overnight, quickly overwhelming our government’s regulatory systems.

Uber and Airbnb are two of the best examples of our government’s inability to keep up with such technology, both companies achieving market dominance before regulators were even able to consider how to handle them. And when a government has decided against them, they often still continue to operate because people simply choose to keep using the apps.

Luckily, this kind of disruption hasn’t yet posed a major existential threat. But this will change when we see companies begin developing cyborg body parts, brain-computer interfaces, nanobot health injectors, and at-home genetic engineering kits.

For this reason, it’s crucial that we have experts who understand how to update our regulations to be as flexible as is necessary to ensure we don’t create black market conditions like we’ve done with drugs. It’s better to have safe and monitored experimentation, rather than forcing individuals into seedy communities using unsafe products.

Survival of the Most Adaptable
If we hope to be an animal that survives our changing environment, we have to adapt. We cannot cling to the behaviors and systems formed thousands of years ago. We must instead acknowledge that we now exist in an ecosystem of disruptive technology, and we must evolve and update our governments if they’re going to be capable of navigating these transformative impacts.

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