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#433776 Why We Should Stop Conflating Human and ...

It’s common to hear phrases like ‘machine learning’ and ‘artificial intelligence’ and believe that somehow, someone has managed to replicate a human mind inside a computer. This, of course, is untrue—but part of the reason this idea is so pervasive is because the metaphor of human learning and intelligence has been quite useful in explaining machine learning and artificial intelligence.

Indeed, some AI researchers maintain a close link with the neuroscience community, and inspiration runs in both directions. But the metaphor can be a hindrance to people trying to explain machine learning to those less familiar with it. One of the biggest risks of conflating human and machine intelligence is that we start to hand over too much agency to machines. For those of us working with software, it’s essential that we remember the agency is human—it’s humans who build these systems, after all.

It’s worth unpacking the key differences between machine and human intelligence. While there are certainly similarities, it’s by looking at what makes them different that we can better grasp how artificial intelligence works, and how we can build and use it effectively.

Neural Networks
Central to the metaphor that links human and machine learning is the concept of a neural network. The biggest difference between a human brain and an artificial neural net is the sheer scale of the brain’s neural network. What’s crucial is that it’s not simply the number of neurons in the brain (which reach into the billions), but more precisely, the mind-boggling number of connections between them.

But the issue runs deeper than questions of scale. The human brain is qualitatively different from an artificial neural network for two other important reasons: the connections that power it are analogue, not digital, and the neurons themselves aren’t uniform (as they are in an artificial neural network).

This is why the brain is such a complex thing. Even the most complex artificial neural network, while often difficult to interpret and unpack, has an underlying architecture and principles guiding it (this is what we’re trying to do, so let’s construct the network like this…).

Intricate as they may be, neural networks in AIs are engineered with a specific outcome in mind. The human mind, however, doesn’t have the same degree of intentionality in its engineering. Yes, it should help us do all the things we need to do to stay alive, but it also allows us to think critically and creatively in a way that doesn’t need to be programmed.

The Beautiful Simplicity of AI
The fact that artificial intelligence systems are so much simpler than the human brain is, ironically, what enables AIs to deal with far greater computational complexity than we can.

Artificial neural networks can hold much more information and data than the human brain, largely due to the type of data that is stored and processed in a neural network. It is discrete and specific, like an entry on an excel spreadsheet.

In the human brain, data doesn’t have this same discrete quality. So while an artificial neural network can process very specific data at an incredible scale, it isn’t able to process information in the rich and multidimensional manner a human brain can. This is the key difference between an engineered system and the human mind.

Despite years of research, the human mind still remains somewhat opaque. This is because the analog synaptic connections between neurons are almost impenetrable to the digital connections within an artificial neural network.

Speed and Scale
Consider what this means in practice. The relative simplicity of an AI allows it to do a very complex task very well, and very quickly. A human brain simply can’t process data at scale and speed in the way AIs need to if they’re, say, translating speech to text, or processing a huge set of oncology reports.

Essential to the way AI works in both these contexts is that it breaks data and information down into tiny constituent parts. For example, it could break sounds down into phonetic text, which could then be translated into full sentences, or break images into pieces to understand the rules of how a huge set of them is composed.

Humans often do a similar thing, and this is the point at which machine learning is most like human learning; like algorithms, humans break data or information into smaller chunks in order to process it.

But there’s a reason for this similarity. This breakdown process is engineered into every neural network by a human engineer. What’s more, the way this process is designed will be down to the problem at hand. How an artificial intelligence system breaks down a data set is its own way of ‘understanding’ it.

Even while running a highly complex algorithm unsupervised, the parameters of how an AI learns—how it breaks data down in order to process it—are always set from the start.

Human Intelligence: Defining Problems
Human intelligence doesn’t have this set of limitations, which is what makes us so much more effective at problem-solving. It’s the human ability to ‘create’ problems that makes us so good at solving them. There’s an element of contextual understanding and decision-making in the way humans approach problems.

AIs might be able to unpack problems or find new ways into them, but they can’t define the problem they’re trying to solve.

Algorithmic insensitivity has come into focus in recent years, with an increasing number of scandals around bias in AI systems. Of course, this is caused by the biases of those making the algorithms, but underlines the point that algorithmic biases can only be identified by human intelligence.

Human and Artificial Intelligence Should Complement Each Other
We must remember that artificial intelligence and machine learning aren’t simply things that ‘exist’ that we can no longer control. They are built, engineered, and designed by us. This mindset puts us in control of the future, and makes algorithms even more elegant and remarkable.

Image Credit: Liu zishan/Shutterstock Continue reading

Posted in Human Robots

#433754 This Robotic Warehouse Fills Orders in ...

Shopping is becoming less and less of a consumer experience—or, for many, less of a chore—as the list of things that can be bought online and delivered to our homes grows to include, well, almost anything you can think of. An Israeli startup is working to make shopping and deliveries even faster and cheaper—and they’re succeeding.

Last week, CommonSense Robotics announced the launch of its first autonomous micro-fulfillment center in Tel Aviv. The company claims the facility is the smallest of its type in the world at 6,000 square feet. For comparison’s sake—most fulfillment hubs that incorporate robotics are at least 120,000 square feet. Amazon’s upcoming facility in Bessemer, Alabama will be a massive 855,000 square feet.

The thing about a building whose square footage is in the hundred-thousands is, you can fit a lot of stuff inside it, but there aren’t many places you can fit the building itself, especially not in major urban areas. So most fulfillment centers are outside cities, which means more time and more money to get your Moroccan oil shampoo, or your vegetable garden starter kit, or your 100-pack of organic protein bars from that fulfillment center to your front door.

CommonSense Robotics built the Tel Aviv center in an area that was previously thought too small for warehouse infrastructure. “In order to fit our site into small, tight urban spaces, we’ve designed every single element of it to optimize for space efficiency,” said Avital Sterngold, VP of operations. Using a robotic sorting system that includes hundreds of robots, plus AI software that assigns them specific tasks, the facility can prepare orders in less than five minutes end-to-end.

It’s not all automated, though—there’s still some human labor in the mix. The robots fetch goods and bring them to a team of people, who then pack the individual orders.

CommonSense raised $20 million this year in a funding round led by Palo Alto-based Playground Global. The company hopes to expand its operations to the US and UK in 2019. Its business model is to charge retailers a fee for each order fulfilled, while maintaining ownership and operation of the fulfillment centers. The first retailers to jump on the bandwagon were Super-Pharm, a drugstore chain, and Rami Levy, a retail supermarket chain.

“Staying competitive in today’s market is anchored by delivering orders quickly and determining how to fulfill and deliver orders efficiently, which are always the most complex aspects of any ecommerce operation. With robotics, we will be able to fulfill and deliver orders in under one hour, all while saving costs on said fulfillment and delivery,” said Super-Pharm VP Yossi Cohen. “Before CommonSense Robotics, we offered our customers next-day home delivery. With this partnership, we are now able to offer our customers same-day delivery and will very soon be offering them one-hour delivery.”

Long live the instant gratification economy—and the increasingly sophisticated technology that’s enabling it.

Image Credit: SasinTipchai / Shutterstock.com Continue reading

Posted in Human Robots

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

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.

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#433288 The New AI Tech Turning Heads in Video ...

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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#432303 What If the AI Revolution Is Neither ...

Why does everyone assume that the AI revolution will either lead to a fiery apocalypse or a glorious utopia, and not something in between? Of course, part of this is down to the fact that you get more attention by saying “The end is nigh!” or “Utopia is coming!”

But part of it is down to how humans think about change, especially unprecedented change. Millenarianism doesn’t have anything to do with being a “millennial,” being born in the 90s and remembering Buffy the Vampire Slayer. It is a way of thinking about the future that involves a deeply ingrained sense of destiny. A definition might be: “Millenarianism is the expectation that the world as it is will be destroyed and replaced with a perfect world, that a redeemer will come to cast down the evil and raise up the righteous.”

Millenarian beliefs, then, intimately link together the ideas of destruction and creation. They involve the idea of a huge, apocalyptic, seismic shift that will destroy the fabric of the old world and create something entirely new. Similar belief systems exist in many of the world’s major religions, and also the unspoken religion of some atheists and agnostics, which is a belief in technology.

Look at some futurist beliefs around the technological Singularity. In Ray Kurzweil’s vision, the Singularity is the establishment of paradise. Everyone is rendered immortal by biotechnology that can cure our ills; our brains can be uploaded to the cloud; inequality and suffering wash away under the wave of these technologies. The “destruction of the world” is replaced by a Silicon Valley buzzword favorite: disruption. And, as with many millenarian beliefs, your mileage varies on whether this destruction paves the way for a new utopia—or simply ends the world.

There are good reasons to be skeptical and interrogative towards this way of thinking. The most compelling reason is probably that millenarian beliefs seem to be a default mode of how humans think about change; just look at how many variants of this belief have cropped up all over the world.

These beliefs are present in aspects of Christian theology, although they only really became mainstream in their modern form in the 19th and 20th centuries. Ideas like the Tribulations—many years of hardship and suffering—before the Rapture, when the righteous will be raised up and the evil punished. After this destruction, the world will be made anew, or humans will ascend to paradise.

Despite being dogmatically atheist, Marxism has many of the same beliefs. It is all about a deterministic view of history that builds to a crescendo. In the same way as Rapture-believers look for signs that prophecies are beginning to be fulfilled, so Marxists look for evidence that we’re in the late stages of capitalism. They believe that, inevitably, society will degrade and degenerate to a breaking point—just as some millenarian Christians do.

In Marxism, this is when the exploitation of the working class by the rich becomes unsustainable, and the working class bands together and overthrows the oppressors. The “tribulation” is replaced by a “revolution.” Sometimes revolutionary figures, like Lenin, or Marx himself, are heralded as messiahs who accelerate the onset of the Millennium; and their rhetoric involves utterly smashing the old system such that a new world can be built. Of course, there is judgment, when the righteous workers take what’s theirs and the evil bourgeoisie are destroyed.

Even Norse mythology has an element of this, as James Hughes points out in his essay in Nick Bostrom’s book Global Catastrophic Risks. Ragnarok involves men and gods being defeated in a final, apocalyptic battle—but because that was a little bleak, they add in the idea that a new earth will arise where the survivors will live in harmony.

Judgement day is a cultural trope, too. Take the ancient Egyptians and their beliefs around the afterlife; the Lord of the underworld, Osiris, weighs the mortal’s heart against a feather. “Should the heart of the deceased prove to be heavy with wrongdoing, it would be eaten by a demon, and the hope of an afterlife vanished.”

Perhaps in the Singularity, something similar goes on. As our technology and hence our power improve, a final reckoning approaches: our hearts, as humans, will be weighed against a feather. If they prove too heavy with wrongdoing—with misguided stupidity, with arrogance and hubris, with evil—then we will fail the test, and we will destroy ourselves. But if we pass, and emerge from the Singularity and all of its threats and promises unscathed, then we will have paradise. And, like the other belief systems, there’s no room for non-believers; all of society is going to be radically altered, whether you want it to be or not, whether it benefits you or leaves you behind. A technological rapture.

It almost seems like every major development provokes this response. Nuclear weapons did, too. Either this would prove the final straw and we’d destroy ourselves, or the nuclear energy could be harnessed to build a better world. People talked at the dawn of the nuclear age about electricity that was “too cheap to meter.” The scientists who worked on the bomb often thought that with such destructive power in human hands, we’d be forced to cooperate and work together as a species.

When we see the same response over and over again to different circumstances, cropping up in different areas, whether it’s science, religion, or politics, we need to consider human biases. We like millenarian beliefs; and so when the idea of artificial intelligence outstripping human intelligence emerges, these beliefs spring up around it.

We don’t love facts. We don’t love information. We aren’t as rational as we’d like to think. We are creatures of narrative. Physicists observe the world and we weave our observations into narrative theories, stories about little billiard balls whizzing around and hitting each other, or space and time that bend and curve and expand. Historians try to make sense of an endless stream of events. We rely on stories: stories that make sense of the past, justify the present, and prepare us for the future.

And as stories go, the millenarian narrative is a brilliant and compelling one. It can lead you towards social change, as in the case of the Communists, or the Buddhist uprisings in China. It can justify your present-day suffering, if you’re in the tribulation. It gives you hope that your life is important and has meaning. It gives you a sense that things are evolving in a specific direction, according to rules—not just randomly sprawling outwards in a chaotic way. It promises that the righteous will be saved and the wrongdoers will be punished, even if there is suffering along the way. And, ultimately, a lot of the time, the millenarian narrative promises paradise.

We need to be wary of the millenarian narrative when we’re considering technological developments and the Singularity and existential risks in general. Maybe this time is different, but we’ve cried wolf many times before. There is a more likely, less appealing story. Something along the lines of: there are many possibilities, none of them are inevitable, and lots of the outcomes are less extreme than you might think—or they might take far longer than you think to arrive. On the surface, it’s not satisfying. It’s so much easier to think of things as either signaling the end of the world or the dawn of a utopia—or possibly both at once. It’s a narrative we can get behind, a good story, and maybe, a nice dream.

But dig a little below the surface, and you’ll find that the millenarian beliefs aren’t always the most promising ones, because they remove human agency from the equation. If you think that, say, the malicious use of algorithms, or the control of superintelligent AI, are serious and urgent problems that are worth solving, you can’t be wedded to a belief system that insists utopia or dystopia are inevitable. You have to believe in the shades of grey—and in your own ability to influence where we might end up. As we move into an uncertain technological future, we need to be aware of the power—and the limitations—of dreams.

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