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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.

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Posted in Human Robots

#433696 3 Big Ways Tech Is Disrupting Global ...

Disruptive business models are often powered by alternative financing. In Part 1 of this series, I discussed how mobile is redefining money and banking and shared some of the dramatic transformations in the global remittance infrastructure.

In this article, we’ll discuss:

Peer-to-peer lending
AI financial advisors and robo traders
Seamless Transactions

Let’s dive right back in…

Decentralized Lending = Democratized Access to Finances
Peer-to-peer (P2P) lending is an age-old practice, traditionally with high risk and extreme locality. Now, the P2P funding model is being digitized and delocalized, bringing lending online and across borders.

Zopa, the first official crowdlending platform, arrived in the United Kingdom in 2004. Since then, the consumer crowdlending platform has facilitated lending of over 3 billion euros ($3.5 billion USD) of loans.

Person-to-business crowdlending took off, again in the U.K., in 2005 with Funding Circle, now with over 5 billion euros (~5.8 billion USD) of capital loaned to small businesses around the world.

Crowdlending next took off in the US in 2006, with platforms like Prosper and Lending Club. The US crowdlending industry has boomed to $21 billion in loans, across 515,000 loans.

Let’s take a step back… to a time before banks, when lending took place between trusted neighbors in small villages across the globe. Lending started as peer-to-peer transactions.

As villages turned into towns, towns turned into cities, and cities turned into sprawling metropolises, neighborly trust and the ability to communicate across urban landscapes broke down. That’s where banks and other financial institutions came into play—to add trust back into the lending equation.

With crowdlending, we are evidently returning to this pre-centralized-banking model of loans, and moving away from cumbersome intermediaries (e.g. high fees, regulations, and extra complexity).

Fueled by the permeation of the internet, P2P lending took on a new form as ‘crowdlending’ in the early 2000s. Now, as blockchain and artificial intelligence arrive on the digital scene, P2P lending platforms are being overhauled with transparency, accountability, reliability, and immutability.

Artificial Intelligence Micro Lending & Credit Scores
We are beginning to augment our quantitative decision-making with neural networks processing borrowers’ financial data to determine their financial ‘fate’ (or, as some call it, your credit score). Companies like Smart Finance Group (backed by Kai Fu Lee and Sinovation Ventures) are using artificial intelligence to minimize default rates for tens of millions of microloans.

Smart Finance is fueled by users’ personal data, particularly smartphone data and usage behavior. Users are required to give Smart Finance access to their smartphone data, so that Smart Finance’s artificial intelligence engine can generate a credit score from the personal information.

The benefits of this AI-powered lending platform do not stop at increased loan payback rates; there’s a massive speed increase as well. Smart Finance loans are frequently approved in under eight seconds. As we’ve seen with other artificial intelligence disruptions, data is the new gold.

Digitizing access to P2P loans paves the way for billions of people currently without access to banking to leapfrog the centralized banking system, just as Africa bypassed landline phones and went straight to mobile. Leapfrogging centralized banking and the credit system is exactly what Smart Finance has done for hundreds of millions of people in China.

Blockchain-Backed Crowdlending
As artificial intelligence accesses even the most mundane mobile browsing data to assign credit scores, blockchain technologies, particularly immutable ledgers and smart contracts, are massive disruptors to the archaic banking system, building additional trust and transparency on top of current P2P lending models.

Immutable ledgers provide the necessary transparency for accurate credit and loan defaulting history. Smart contracts executed on these immutable ledgers bring the critical ability to digitally replace cumbersome, expensive third parties (like banks), allowing individual borrowers or businesses to directly connect with willing lenders.

Two of the leading blockchain platforms for P2P lending are ETHLend and SALT Lending.

ETHLend is an Ethereum-based decentralized application aiming to bring transparency and trust to P2P lending through Ethereum network smart contracts.

Secure Automated Lending Technology (SALT) allows cryptocurrency asset holders to use their digital assets as collateral for cash loans, without the need to liquidate their holdings, giving rise to a digital-asset-backed lending market.

While blockchain poses a threat to many of the large, centralized banking institutions, some are taking advantage of the new technology to optimize their internal lending, credit scoring, and collateral operations.

In March 2018, ING and Credit Suisse successfully exchanged 25 million euros using HQLA-X, a blockchain-based collateral lending platform.

HQLA-X runs on the R3 Corda blockchain, a platform designed specifically to help heritage financial and commerce institutions migrate away from their inefficient legacy financial infrastructure.

Blockchain and tokenization are going through their own fintech and regulation shakeup right now. In a future blog, I’ll discuss the various efforts to more readily assure smart contracts, and the disruptive business model of security tokens and the US Securities and Exchange Commission.

Parallels to the Global Abundance of Capital
The abundance of capital being created by the advent of P2P loans closely relates to the unprecedented global abundance of capital.

Initial coin offerings (ICOs) and crowdfunding are taking a strong stand in disrupting the $164 billion venture capital market. The total amount invested in ICOs has risen from $6.6 billion in 2017 to $7.15 billion USD in the first half of 2018. Crowdfunding helped projects raise more than $34 billion in 2017, with experts projecting that global crowdfunding investments will reach $300 billion by 2025.

In the last year alone, using ICOs, over a dozen projects have raised hundreds of millions of dollars in mere hours. Take Filecoin, for example, which raised $257 million  in only 30 days; its first $135 million was raised in the first hour. Similarly, the Dragon Coin project (which itself is revolutionizing remittance in high-stakes casinos around the world) raised $320 million in its 30-day public ICO.

Some Important Takeaways…

Technology-backed fundraising and financial services are disrupting the world’s largest financial institutions. Anyone, anywhere, at anytime will be able to access the capital they need to pursue their idea.

The speed at which we can go from “I’ve got an idea” to “I run a billion-dollar company” is moving faster than ever.

Following Ray Kurzweil’s Law of Accelerating Returns, the rapid decrease in time to access capital is intimately linked (and greatly dependent on) a financial infrastructure (technology, institutions, platforms, and policies) that can adapt and evolve just as rapidly.

This new abundance of capital requires financial decision-making with ever-higher market prediction precision. That’s exactly where artificial intelligence is already playing a massive role.

Artificial Intelligence, Robo Traders, and Financial Advisors
On May 6, 2010, the Dow Jones Industrial Average suddenly collapsed by 998.5 points (equal to 8 percent, or $1 trillion). The crash lasted over 35 minutes and is now known as the ‘Flash Crash’. While no one knows the specific reason for this 2010 stock market anomaly, experts widely agree that the Flash Crash had to do with algorithmic trading.

With the ability to have instant, trillion-dollar market impacts, algorithmic trading and artificial intelligence are undoubtedly ingrained in how financial markets operate.

In 2017, CNBC.com estimated that 90 percent of daily trading volume in stock trading is done by machine algorithms, and only 10 percent is carried out directly by humans.

Artificial intelligence and financial management algorithms are not only available to top Wall Street players.

Robo-advisor financial management apps, like Wealthfront and Betterment, are rapidly permeating the global market. Wealthfront currently has $9.5 billion in assets under management, and Betterment has $10 billion.

Artificial intelligent financial agents are already helping financial institutions protect your money and fight fraud. A prime application for machine learning is in detecting anomalies in your spending and transaction habits, and flagging potentially fraudulent transactions.

As artificial intelligence continues to exponentially increase in power and capabilities, increasingly powerful trading and financial management bots will come online, finding massive new and previously lost streams of wealth.

How else are artificial intelligence and automation transforming finance?

Disruptive Remittance and Seamless Transactions
When was the last time you paid in cash at a toll booth? How about for a taxi ride?

EZ-Pass, the electronic tolling company implemented extensively on the East Coast, has done wonders to reduce traffic congestion and increase traffic flow.

Driving down I-95 on the East Coast of the United States, drivers rarely notice their financial transaction with the state’s tolling agencies. The transactions are seamless.

The Uber app enables me to travel without my wallet. I can forget about payment on my trip, free up my mental bandwidth and time for higher-priority tasks. The entire process is digitized and, by extension, automated and integrated into Uber’s platform (Note: This incredible convenience many times causes me to accidentally walk out of taxi cabs without paying!).

In January 2018, we saw the success of the first cutting-edge, AI-powered Amazon Go store open in Seattle, Washington. The store marked a new era in remittance and transactions. Gone are the days of carrying credit cards and cash, and gone are the cash registers. And now, on the heals of these early ‘beta-tests’, Amazon is considering opening as many as 3,000 of these cashierless stores by 2023.

Amazon Go stores use AI algorithms that watch various video feeds (from advanced cameras) throughout the store to identify who picks up groceries, exactly what products they select, and how much to charge that person when they walk out of the store. It’s a grab and go experience.

Let’s extrapolate the notion of seamless, integrated payment systems from Amazon Go and Uber’s removal of post-ride payment to the rest of our day-to-day experience.

Imagine this near future:

As you near the front door of your home, your AI assistant summons a self-driving Uber that takes you to the Hyperloop station (after all, you work in L.A. but live in San Francisco).

At the station, you board your pod, without noticing that your ticket purchase was settled via a wireless payment checkpoint.

After work, you stop at the Amazon Go and pick up dinner. Your virtual AI assistant passes your Amazon account information to the store’s payment checkpoint, as the store’s cameras and sensors track you, your cart and charge you auto-magically.

At home, unbeknownst to you, your AI has already restocked your fridge and pantry with whatever items you failed to pick up at the Amazon Go.

Once we remove the actively transacting aspect of finance, what else becomes possible?

Top Conclusions
Extraordinary transformations are happening in the finance world. We’ve only scratched the surface of the fintech revolution. All of these transformative financial technologies require high-fidelity assurance, robust insurance, and a mechanism for storing value.

I’ll dive into each of these other facets of financial services in future articles.

For now, thanks to coming global communication networks being deployed on 5G, Alphabet’s LUNE, SpaceX’s Starlink and OneWeb, by 2024, nearly all 8 billion people on Earth will be online.

Once connected, these new minds, entrepreneurs, and customers need access to money and financial services to meaningfully participate in the world economy.

By connecting lenders and borrowers around the globe, decentralized lending drives down global interest rates, increases global financial market participation, and enables economic opportunity to the billions of people who are about to come online.

We’re living in the most abundant time in human history, and fintech is just getting started.

Join Me
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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.

Join Me & Get Involved!
Abundance Digital Online Community: I have created a Digital/Online community of bold, abundance-minded entrepreneurs called Abundance Digital. This is my ‘onramp’ for exponential entrepreneurs – those who want to get involved and play at a higher level. Click here to learn more.

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#432880 Google’s Duplex Raises the Question: ...

By now, you’ve probably seen Google’s new Duplex software, which promises to call people on your behalf to book appointments for haircuts and the like. As yet, it only exists in demo form, but already it seems like Google has made a big stride towards capturing a market that plenty of companies have had their eye on for quite some time. This software is impressive, but it raises questions.

Many of you will be familiar with the stilted, robotic conversations you can have with early chatbots that are, essentially, glorified menus. Instead of pressing 1 to confirm or 2 to re-enter, some of these bots would allow for simple commands like “Yes” or “No,” replacing the buttons with limited ability to recognize a few words. Using them was often a far more frustrating experience than attempting to use a menu—there are few things more irritating than a robot saying, “Sorry, your response was not recognized.”

Google Duplex scheduling a hair salon appointment:

Google Duplex calling a restaurant:

Even getting the response recognized is hard enough. After all, there are countless different nuances and accents to baffle voice recognition software, and endless turns of phrase that amount to saying the same thing that can confound natural language processing (NLP), especially if you like your phrasing quirky.

You may think that standard customer-service type conversations all travel the same route, using similar words and phrasing. But when there are over 80,000 ways to order coffee, and making a mistake is frowned upon, even simple tasks require high accuracy over a huge dataset.

Advances in audio processing, neural networks, and NLP, as well as raw computing power, have meant that basic recognition of what someone is trying to say is less of an issue. Soundhound’s virtual assistant prides itself on being able to process complicated requests (perhaps needlessly complicated).

The deeper issue, as with all attempts to develop conversational machines, is one of understanding context. There are so many ways a conversation can go that attempting to construct a conversation two or three layers deep quickly runs into problems. Multiply the thousands of things people might say by the thousands they might say next, and the combinatorics of the challenge runs away from most chatbots, leaving them as either glorified menus, gimmicks, or rather bizarre to talk to.

Yet Google, who surely remembers from Glass the risk of premature debuts for technology, especially the kind that ask you to rethink how you interact with or trust in software, must have faith in Duplex to show it on the world stage. We know that startups like Semantic Machines and x.ai have received serious funding to perform very similar functions, using natural-language conversations to perform computing tasks, schedule meetings, book hotels, or purchase items.

It’s no great leap to imagine Google will soon do the same, bringing us closer to a world of onboard computing, where Lens labels the world around us and their assistant arranges it for us (all the while gathering more and more data it can convert into personalized ads). The early demos showed some clever tricks for keeping the conversation within a fairly narrow realm where the AI should be comfortable and competent, and the blog post that accompanied the release shows just how much effort has gone into the technology.

Yet given the privacy and ethics funk the tech industry finds itself in, and people’s general unease about AI, the main reaction to Duplex’s impressive demo was concern. The voice sounded too natural, bringing to mind Lyrebird and their warnings of deepfakes. You might trust “Do the Right Thing” Google with this technology, but it could usher in an era when automated robo-callers are far more convincing.

A more human-like voice may sound like a perfectly innocuous improvement, but the fact that the assistant interjects naturalistic “umm” and “mm-hm” responses to more perfectly mimic a human rubbed a lot of people the wrong way. This wasn’t just a voice assistant trying to sound less grinding and robotic; it was actively trying to deceive people into thinking they were talking to a human.

Google is running the risk of trying to get to conversational AI by going straight through the uncanny valley.

“Google’s experiments do appear to have been designed to deceive,” said Dr. Thomas King of the Oxford Internet Institute’s Digital Ethics Lab, according to Techcrunch. “Their main hypothesis was ‘can you distinguish this from a real person?’ In this case it’s unclear why their hypothesis was about deception and not the user experience… there should be some kind of mechanism there to let people know what it is they are speaking to.”

From Google’s perspective, being able to say “90 percent of callers can’t tell the difference between this and a human personal assistant” is an excellent marketing ploy, even though statistics about how many interactions are successful might be more relevant.

In fact, Duplex runs contrary to pretty much every major recommendation about ethics for the use of robotics or artificial intelligence, not to mention certain eavesdropping laws. Transparency is key to holding machines (and the people who design them) accountable, especially when it comes to decision-making.

Then there are the more subtle social issues. One prominent effect social media has had is to allow people to silo themselves; in echo chambers of like-minded individuals, it’s hard to see how other opinions exist. Technology exacerbates this by removing the evolutionary cues that go along with face-to-face interaction. Confronted with a pair of human eyes, people are more generous. Confronted with a Twitter avatar or a Facebook interface, people hurl abuse and criticism they’d never dream of using in a public setting.

Now that we can use technology to interact with ever fewer people, will it change us? Is it fair to offload the burden of dealing with a robot onto the poor human at the other end of the line, who might have to deal with dozens of such calls a day? Google has said that if the AI is in trouble, it will put you through to a human, which might help save receptionists from the hell of trying to explain a concept to dozens of dumbfounded AI assistants all day. But there’s always the risk that failures will be blamed on the person and not the machine.

As AI advances, could we end up treating the dwindling number of people in these “customer-facing” roles as the buggiest part of a fully automatic service? Will people start accusing each other of being robots on the phone, as well as on Twitter?

Google has provided plenty of reassurances about how the system will be used. They have said they will ensure that the system is identified, and it’s hardly difficult to resolve this problem; a slight change in the script from their demo would do it. For now, consumers will likely appreciate moves that make it clear whether the “intelligent agents” that make major decisions for us, that we interact with daily, and that hide behind social media avatars or phone numbers are real or artificial.

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#432431 Why Slowing Down Can Actually Help Us ...

Leah Weiss believes that when we pay attention to how we do our work—our thoughts and feelings about what we do and why we do it—we can tap into a much deeper reservoir of courage, creativity, meaning, and resilience.

As a researcher, educator, and author, Weiss teaches a course called “Leading with Compassion and Mindfulness” at the Stanford Graduate School of Business, one of the most competitive MBA programs in the world, and runs programs at HopeLab.

Weiss is the author of the new book How We Work: Live Your Purpose, Reclaim your Sanity and Embrace the Daily Grind, endorsed by the Dalai Lama, among others. I caught up with Leah to learn more about how the practice of mindfulness can deepen our individual and collective purpose and passion.

Lisa Kay Solomon: We’re hearing a lot about mindfulness these days. What is mindfulness and why is it so important to bring into our work? Can you share some of the basic tenets of the practice?

Leah Weiss, PhD: Mindfulness is, in its most literal sense, “the attention to inattention.” It’s as simple as noticing when you’re not paying attention and then re-focusing. It is prioritizing what is happening right now over internal and external noise.

The ability to work well with difficult coworkers, handle constructive feedback and criticism, regulate emotions at work—all of these things can come from regular mindfulness practice.

Some additional benefits of mindfulness are a greater sense of compassion (both self-compassion and compassion for others) and a way to seek and find purpose in even mundane things (and especially at work). From the business standpoint, mindfulness at work leads to increased productivity and creativity, mostly because when we are focused on one task at a time (as opposed to multitasking), we produce better results.

We spend more time with our co-workers than we do with our families; if our work relationships are negative, we suffer both mentally and physically. Even worse, we take all of those negative feelings home with us at the end of the work day. The antidote to this prescription for unhappiness is to have clear, strong purpose (one third of people do not have purpose at work and this is a major problem in the modern workplace!). We can use mental training to grow as people and as employees.

LKS: What are some recommendations you would make to busy leaders who are working around the clock to change the world?

LW: I think the most important thing is to remember to tend to our relationship with ourselves while trying to change the world. If we’re beating up on ourselves all the time we’ll be depleted.

People passionate about improving the world can get into habits of believing self-care isn’t important. We demand a lot of ourselves. It’s okay to fail, to mess up, to make mistakes—what’s important is how we learn from those mistakes and what we tell ourselves about those instances. What is the “internal script” playing in your own head? Is it positive, supporting, and understanding? It should be. If it isn’t, you can work on it. And the changes you make won’t just improve your quality of life, they’ll make you more resilient to weather life’s inevitable setbacks.

A close second recommendation is to always consider where everyone in an organization fits and help everyone (including yourself) find purpose. When you know what your own purpose is and show others their purpose, you can motivate a team and help everyone on a team gain pride in and at work. To get at this, make sure to ask people on your team what really lights them up. What sucks their energy and depletes them? If we know our own answers to these questions and relate them to the people we work with, we can create more engaged organizations.

LKS: Can you envision a future where technology and mindfulness can work together?

LW: Technology and mindfulness are already starting to work together. Some artificial intelligence companies are considering things like mindfulness and compassion when building robots, and there are numerous apps that target spreading mindfulness meditations in a widely-accessible way.

LKS: Looking ahead at our future generations who seem more attached to their devices than ever, what advice do you have for them?

LW: It’s unrealistic to say “stop using your device so much,” so instead, my suggestion is to make time for doing things like scrolling social media and make the same amount of time for putting your phone down and watching a movie or talking to a friend. No matter what it is that you are doing, make sure you have meta-awareness or clarity about what you’re paying attention to. Be clear about where your attention is and recognize that you can be a steward of attention. Technology can support us in this or pull us away from this; it depends on how we use it.

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