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#434772 Traditional Higher Education Is Losing ...

Should you go to graduate school? If so, why? If not, what are your alternatives? Millions of young adults across the globe—and their parents and mentors—find themselves asking these questions every year.

Earlier this month, I explored how exponential technologies are rising to meet the needs of the rapidly changing workforce.

In this blog, I’ll dive into a highly effective way to build the business acumen and skills needed to make the most significant impact in these exponential times.

To start, let’s dive into the value of graduate school versus apprenticeship—especially during this time of extraordinarily rapid growth, and the micro-diversification of careers.

The True Value of an MBA
All graduate schools are not created equal.

For complex technical trades like medicine, engineering, and law, formal graduate-level training provides a critical foundation for safe, ethical practice (until these trades are fully augmented by artificial intelligence and automation…).

For the purposes of today’s blog, let’s focus on the value of a Master in Business Administration (MBA) degree, compared to acquiring your business acumen through various forms of apprenticeship.

The Waning of Business Degrees
Ironically, business schools are facing a tough business problem. The rapid rate of technological change, a booming job market, and the digitization of education are chipping away at the traditional graduate-level business program.

The data speaks for itself.

The Decline of Graduate School Admissions
Enrollment in two-year, full-time MBA programs in the US fell by more than one-third from 2010 to 2016.

While in previous years, top business schools (e.g. Stanford, Harvard, and Wharton) were safe from the decrease in applications, this year, they also felt the waning interest in MBA programs.

Harvard Business School: 4.5 percent decrease in applications, the school’s biggest drop since 2005.
Wharton: 6.7 percent decrease in applications.
Stanford Graduate School: 4.6 percent decrease in applications.

Another signal of change began unfolding over the past week. You may have read news headlines about an emerging college admissions scam, which implicates highly selective US universities, sports coaches, parents, and students in a conspiracy to game the undergraduate admissions process.

Already, students are filing multibillion-dollar civil lawsuits arguing that the scheme has devalued their degrees or denied them a fair admissions opportunity.

MBA Graduates in the Workforce
To meet today’s business needs, startups and massive companies alike are increasingly hiring technologists, developers, and engineers in place of the MBA graduates they may have preferentially hired in the past.

While 85 percent of US employers expect to hire MBA graduates this year (a decrease from 91 percent in 2017), 52 percent of employers worldwide expect to hire graduates with a master’s in data analytics (an increase from 35 percent last year).

We’re also seeing the waning of MBA degree holders at the CEO level.

For decades, an MBA was the hallmark of upward mobility towards the C-suite of top companies.

But as exponential technologies permeate not only products but every part of the supply chain—from manufacturing and shipping to sales, marketing and customer service—that trend is changing by necessity.

Looking at the Harvard Business Review’s Top 100 CEOs in 2018 list, more CEOs on the list held engineering degrees than MBAs (34 held engineering degrees, while 32 held MBAs).

There’s much more to leading innovative companies than an advanced business degree.

How Are Schools Responding?
With disruption to the advanced business education system already here, some business schools are applying notes from their own innovation classes to brace for change.

Over the past half-decade, we’ve seen schools with smaller MBA programs shut their doors in favor of advanced degrees with more specialization. This directly responds to market demand for skills in data science, supply chain, and manufacturing.

Some degrees resemble the precise skills training of technical trades. Others are very much in line with the apprenticeship models we’ll explore next.

Regardless, this new specialization strategy is working and attracting more new students. Over the past decade (2006 to 2016), enrollment in specialized graduate business programs doubled.

Higher education is also seeing a preference shift toward for-profit trade schools, like coding boot camps. This shift is one of several forces pushing universities to adopt skill-specific advanced degrees.

But some schools are slow to adapt, raising the question: how and when will these legacy programs be disrupted? A survey of over 170 business school deans around the world showed that many programs are operating at a loss.

But if these schools are world-class business institutions, as advertised, why do they keep the doors open even while they lose money? The surveyed deans revealed an important insight: they keep the degree program open because of the program’s prestige.

Why Go to Business School?
Shorthand Credibility, Cognitive Biases, and Prestige
Regardless of what knowledge a person takes away from graduate school, attending one of the world’s most rigorous and elite programs gives grads external validation.

With over 55 percent of MBA applicants applying to just 6 percent of graduate business schools, we have a clear cognitive bias toward the perceived elite status of certain universities.

To the outside world, thanks to the power of cognitive biases, an advanced degree is credibility shorthand for your capabilities.

Simply passing through a top school’s filtration system means that you had some level of abilities and merits.

And startup success statistics tend to back up that perceived enhanced capability. Let’s take, for example, universities with the most startup unicorn founders (see the figure below).

When you consider the 320+ unicorn startups around the world today, these numbers become even more impressive. Stanford’s 18 unicorn companies account for over 5 percent of global unicorns, and Harvard is responsible for producing just under 5 percent.

Combined, just these two universities (out of over 5,000 in the US, and thousands more around the world) account for 1 in 10 of the billion-dollar private companies in the world.

By the numbers, the prestigious reputation of these elite business programs has a firm basis in current innovation success.

While prestige may be inherent to the degree earned by graduates from these business programs, the credibility boost from holding one of these degrees is not a guaranteed path to success in the business world.

For example, you might expect that the Harvard School of Business or Stanford Graduate School of Business would come out on top when tallying up the alma maters of Fortune 500 CEOs.

It turns out that the University of Wisconsin-Madison leads the business school pack with 14 CEOs to Harvard’s 12. Beyond prestige, the success these elite business programs see translates directly into cultivating unmatched networks and relationships.

Relationships
Graduate schools—particularly at the upper echelon—are excellent at attracting sharp students.

At an elite business school, if you meet just five to ten people with extraordinary skill sets, personalities, ideas, or networks, then you have returned your $200,000 education investment.

It’s no coincidence that some 40 percent of Silicon Valley venture capitalists are alumni of either Harvard or Stanford.

From future investors to advisors, friends, and potential business partners, relationships are critical to an entrepreneur’s success.

Apprenticeships
As we saw above, graduate business degree programs are melting away in the current wave of exponential change.

With an increasing $1.5 trillion in student debt, there must be a more impactful alternative to attending graduate school for those starting their careers.

When I think about the most important skills I use today as an entrepreneur, writer, and strategic thinker, they didn’t come from my decade of graduate school at Harvard or MIT… they came from my experiences building real technologies and companies, and working with mentors.

Apprenticeship comes in a variety of forms; here, I’ll cover three top-of-mind approaches:

Real-world business acumen via startup accelerators
A direct apprenticeship model
The 6 D’s of mentorship

Startup Accelerators and Business Practicum
Let’s contrast the shrinking interest in MBA programs with applications to a relatively new model of business education: startup accelerators.

Startup accelerators are short-term (typically three to six months), cohort-based programs focusing on providing startup founders with the resources (capital, mentorship, relationships, and education) needed to refine their entrepreneurial acumen.

While graduate business programs have been condensing, startup accelerators are alive, well, and expanding rapidly.

In the 10 years from 2005 (when Paul Graham founded Y Combinator) through 2015, the number of startup accelerators in the US increased by more than tenfold.

The increase in startup accelerator activity hints at a larger trend: our best and brightest business minds are opting to invest their time and efforts in obtaining hands-on experience, creating tangible value for themselves and others, rather than diving into the theory often taught in business school classrooms.

The “Strike Force” Model
The Strike Force is my elite team of young entrepreneurs who work directly with me across all of my companies, travel by my side, sit in on every meeting with me, and help build businesses that change the world.

Previous Strike Force members have gone on to launch successful companies, including Bold Capital Partners, my $250 million venture capital firm.

Strike Force is an apprenticeship for the next generation of exponential entrepreneurs.

To paraphrase my good friend Tony Robbins: If you want to short-circuit the video game, find someone who’s been there and done that and is now doing something you want to one day do.

Every year, over 500,000 apprentices in the US follow this precise template. These apprentices are learning a craft they wish to master, under the mentorship of experts (skilled metal workers, bricklayers, medical technicians, electricians, and more) who have already achieved the desired result.

What if we more readily applied this model to young adults with aspirations of creating massive value through the vehicles of entrepreneurship and innovation?

For the established entrepreneur: How can you bring young entrepreneurs into your organization to create more value for your company, while also passing on your ethos and lessons learned to the next generation?

For the young, driven millennial: How can you find your mentor and convince him or her to take you on as an apprentice? What value can you create for this person in exchange for their guidance and investment in your professional development?

The 6 D’s of Mentorship
In my last blog on education, I shared how mobile device and internet penetration will transform adult literacy and basic education. Mobile phones and connectivity already create extraordinary value for entrepreneurs and young professionals looking to take their business acumen and skill set to the next level.

For all of human history up until the last decade or so, if you wanted to learn from the best and brightest in business, leadership, or strategy, you either needed to search for a dated book that they wrote at the local library or bookstore, or you had to be lucky enough to meet that person for a live conversation.

Now you can access the mentorship of just about any thought leader on the planet, at any time, for free.

Thanks to the power of the internet, mentorship has digitized, demonetized, dematerialized, and democratized.

What do you want to learn about?

Investing? Leadership? Technology? Marketing? Project management?

You can access a near-infinite stream of cutting-edge tools, tactics, and lessons from thousands of top performers from nearly every field—instantaneously, and for free.

For example, every one of Warren Buffett’s letters to his Berkshire Hathaway investors over the past 40 years is available for free on a device that fits in your pocket.

The rise of audio—particularly podcasts and audiobooks—is another underestimated driving force away from traditional graduate business programs and toward apprenticeships.

Over 28 million podcast episodes are available for free. Once you identify the strong signals in the noise, you’re still left with thousands of hours of long-form podcast conversation from which to learn valuable lessons.

Whenever and wherever you want, you can learn from the world’s best. In the future, mentorship and apprenticeship will only become more personalized. Imagine accessing a high-fidelity, AI-powered avatar of Bill Gates, Richard Branson, or Arthur C. Clarke (one of my early mentors) to help guide you through your career.

Virtual mentorship and coaching are powerful education forces that are here to stay.

Bringing It All Together
The education system is rapidly changing. Traditional master’s programs for business are ebbing away in the tides of exponential technologies. Apprenticeship models are reemerging as an effective way to train tomorrow’s leaders.

In a future blog, I’ll revisit the concept of apprenticeships and other effective business school alternatives.

If you are a young, ambitious entrepreneur (or the parent of one), remember that you live in the most abundant time ever in human history to refine your craft.

Right now, you have access to world-class mentorship and cutting-edge best-practices—literally in the palm of your hand. What will you do with this extraordinary power?

Join Me
Abundance-Digital Online Community: I’ve created a Digital/Online community of bold, abundance-minded entrepreneurs called Abundance-Digital. Abundance-Digital 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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Posted in Human Robots

#434767 7 Non-Obvious Trends Shaping the Future

When you think of trends that might be shaping the future, the first things that come to mind probably have something to do with technology: Robots taking over jobs. Artificial intelligence advancing and proliferating. 5G making everything faster, connected cities making everything easier, data making everything more targeted.

Technology is undoubtedly changing the way we live, and will continue to do so—probably at an accelerating rate—in the near and far future. But there are other trends impacting the course of our lives and societies, too. They’re less obvious, and some have nothing to do with technology.

For the past nine years, entrepreneur and author Rohit Bhargava has read hundreds of articles across all types of publications, tagged and categorized them by topic, funneled frequent topics into broader trends, analyzed those trends, narrowed them down to the most significant ones, and published a book about them as part of his ‘Non-Obvious’ series. He defines a trend as “a unique curated observation of the accelerating present.”

In an encore session at South by Southwest last week (his initial talk couldn’t fit hundreds of people who wanted to attend, so a re-do was scheduled), Bhargava shared details of his creative process, why it’s hard to think non-obviously, the most important trends of this year, and how to make sure they don’t get the best of you.

Thinking Differently
“Non-obvious thinking is seeing the world in a way other people don’t see it,” Bhargava said. “The secret is curating your ideas.” Curation collects ideas and presents them in a meaningful way; museum curators, for example, decide which works of art to include in an exhibit and how to present them.

For his own curation process, Bhargava uses what he calls the haystack method. Rather than searching for a needle in a haystack, he gathers ‘hay’ (ideas and stories) then uses them to locate and define a ‘needle’ (a trend). “If you spend enough time gathering information, you can put the needle into the middle of the haystack,” he said.

A big part of gathering information is looking for it in places you wouldn’t normally think to look. In his case, that means that on top of reading what everyone else reads—the New York Times, the Washington Post, the Economist—he also buys publications like Modern Farmer, Teen Vogue, and Ink magazine. “It’s like stepping into someone else’s world who’s not like me,” he said. “That’s impossible to do online because everything is personalized.”

Three common barriers make non-obvious thinking hard.

The first is unquestioned assumptions, which are facts or habits we think will never change. When James Dyson first invented the bagless vacuum, he wanted to sell the license to it, but no one believed people would want to spend more money up front on a vacuum then not have to buy bags. The success of Dyson’s business today shows how mistaken that assumption—that people wouldn’t adapt to a product that, at the end of the day, was far more sensible—turned out to be. “Making the wrong basic assumptions can doom you,” Bhargava said.

The second barrier to thinking differently is constant disruption. “Everything is changing as industries blend together,” Bhargava said. “The speed of change makes everyone want everything, all the time, and people expect the impossible.” We’ve come to expect every alternative to be presented to us in every moment, but in many cases this doesn’t serve us well; we’re surrounded by noise and have trouble discerning what’s valuable and authentic.

This ties into the third barrier, which Bhargava calls the believability crisis. “Constant sensationalism makes people skeptical about everything,” he said. With the advent of fake news and technology like deepfakes, we’re in a post-truth, post-fact era, and are in a constant battle to discern what’s real from what’s not.

2019 Trends
Bhargava’s efforts to see past these barriers and curate information yielded 15 trends he believes are currently shaping the future. He shared seven of them, along with thoughts on how to stay ahead of the curve.

Retro Trust
We tend to trust things we have a history with. “People like nostalgic experiences,” Bhargava said. With tech moving as fast as it is, old things are quickly getting replaced by shinier, newer, often more complex things. But not everyone’s jumping on board—and some who’ve been on board are choosing to jump off in favor of what worked for them in the past.

“We’re turning back to vinyl records and film cameras, deliberately downgrading to phones that only text and call,” Bhargava said. In a period of too much change too fast, people are craving familiarity and dependability. To capitalize on that sentiment, entrepreneurs should seek out opportunities for collaboration—how can you build a product that’s new, but feels reliable and familiar?

Muddled Masculinity
Women have increasingly taken on more leadership roles, advanced in the workplace, now own more homes than men, and have higher college graduation rates. That’s all great for us ladies—but not so great for men or, perhaps more generally, for the concept of masculinity.

“Female empowerment is causing confusion about what it means to be a man today,” Bhargava said. “Men don’t know what to do—should they say something? Would that make them an asshole? Should they keep quiet? Would that make them an asshole?”

By encouraging the non-conforming, we can help take some weight off the traditional gender roles, and their corresponding divisions and pressures.

Innovation Envy
Innovation has become an over-used word, to the point that it’s thrown onto ideas and actions that aren’t really innovative at all. “We innovate by looking at someone else and doing the same,” Bhargava said. If an employee brings a radical idea to someone in a leadership role, in many companies the leadership will say they need a case study before implementing the radical idea—but if it’s already been done, it’s not innovative. “With most innovation what ends up happening is not spectacular failure, but irrelevance,” Bhargava said.

He suggests that rather than being on the defensive, companies should play offense with innovation, and when it doesn’t work “fail as if no one’s watching” (often, no one will be).

Artificial Influence
Thanks to social media and other technologies, there are a growing number of fabricated things that, despite not being real, influence how we think. “15 percent of all Twitter accounts may be fake, and there are 60 million fake Facebook accounts,” Bhargava said. There are virtual influencers and even virtual performers.

“Don’t hide the artificial ingredients,” Bhargava advised. “Some people are going to pretend it’s all real. We have to be ethical.” The creators of fabrications meant to influence the way people think, or the products they buy, or the decisions they make, should make it crystal-clear that there aren’t living, breathing people behind the avatars.

Enterprise Empathy
Another reaction to the fast pace of change these days—and the fast pace of life, for that matter—is that empathy is regaining value and even becoming a driver of innovation. Companies are searching for ways to give people a sense of reassurance. The Tesco grocery brand in the UK has a “relaxed lane” for those who don’t want to feel rushed as they check out. Starbucks opened a “signing store” in Washington DC, and most of its regular customers have learned some sign language.

“Use empathy as a principle to help yourself stand out,” Bhargava said. Besides being a good business strategy, “made with empathy” will ideally promote, well, more empathy, a quality there’s often a shortage of.

Robot Renaissance
From automating factory jobs to flipping burgers to cleaning our floors, robots have firmly taken their place in our day-to-day lives—and they’re not going away anytime soon. “There are more situations with robots than ever before,” Bhargava said. “They’re exploring underwater. They’re concierges at hotels.”

The robot revolution feels intimidating. But Bhargava suggests embracing robots with more curiosity than concern. While they may replace some tasks we don’t want replaced, they’ll also be hugely helpful in multiple contexts, from elderly care to dangerous manual tasks.

Back-storytelling
Similar to retro trust and enterprise empathy, organizations have started to tell their brand’s story to gain customer loyalty. “Stories give us meaning, and meaning is what we need in order to be able to put the pieces together,” Bhargava said. “Stories give us a way of understanding the world.”

Finding the story behind your business, brand, or even yourself, and sharing it openly, can help you connect with people, be they customers, coworkers, or friends.

Tech’s Ripple Effects
While it may not overtly sound like it, most of the trends Bhargava identified for 2019 are tied to technology, and are in fact a sort of backlash against it. Tech has made us question who to trust, how to innovate, what’s real and what’s fake, how to make the best decisions, and even what it is that makes us human.

By being aware of these trends, sharing them, and having conversations about them, we’ll help shape the way tech continues to be built, and thus the way it impacts us down the road.

Image Credit: Rohit Bhargava by Brian Smale Continue reading

Posted in Human Robots

#434759 To Be Ethical, AI Must Become ...

As over-hyped as artificial intelligence is—everyone’s talking about it, few fully understand it, it might leave us all unemployed but also solve all the world’s problems—its list of accomplishments is growing. AI can now write realistic-sounding text, give a debating champ a run for his money, diagnose illnesses, and generate fake human faces—among much more.

After training these systems on massive datasets, their creators essentially just let them do their thing to arrive at certain conclusions or outcomes. The problem is that more often than not, even the creators don’t know exactly why they’ve arrived at those conclusions or outcomes. There’s no easy way to trace a machine learning system’s rationale, so to speak. The further we let AI go down this opaque path, the more likely we are to end up somewhere we don’t want to be—and may not be able to come back from.

In a panel at the South by Southwest interactive festival last week titled “Ethics and AI: How to plan for the unpredictable,” experts in the field shared their thoughts on building more transparent, explainable, and accountable AI systems.

Not New, but Different
Ryan Welsh, founder and director of explainable AI startup Kyndi, pointed out that having knowledge-based systems perform advanced tasks isn’t new; he cited logistical, scheduling, and tax software as examples. What’s new is the learning component, our inability to trace how that learning occurs, and the ethical implications that could result.

“Now we have these systems that are learning from data, and we’re trying to understand why they’re arriving at certain outcomes,” Welsh said. “We’ve never actually had this broad society discussion about ethics in those scenarios.”

Rather than continuing to build AIs with opaque inner workings, engineers must start focusing on explainability, which Welsh broke down into three subcategories. Transparency and interpretability come first, and refer to being able to find the units of high influence in a machine learning network, as well as the weights of those units and how they map to specific data and outputs.

Then there’s provenance: knowing where something comes from. In an ideal scenario, for example, Open AI’s new text generator would be able to generate citations in its text that reference academic (and human-created) papers or studies.

Explainability itself is the highest and final bar and refers to a system’s ability to explain itself in natural language to the average user by being able to say, “I generated this output because x, y, z.”

“Humans are unique in our ability and our desire to ask why,” said Josh Marcuse, executive director of the Defense Innovation Board, which advises Department of Defense senior leaders on innovation. “The reason we want explanations from people is so we can understand their belief system and see if we agree with it and want to continue to work with them.”

Similarly, we need to have the ability to interrogate AIs.

Two Types of Thinking
Welsh explained that one big barrier standing in the way of explainability is the tension between the deep learning community and the symbolic AI community, which see themselves as two different paradigms and historically haven’t collaborated much.

Symbolic or classical AI focuses on concepts and rules, while deep learning is centered around perceptions. In human thought this is the difference between, for example, deciding to pass a soccer ball to a teammate who is open (you make the decision because conceptually you know that only open players can receive passes), and registering that the ball is at your feet when someone else passes it to you (you’re taking in information without making a decision about it).

“Symbolic AI has abstractions and representation based on logic that’s more humanly comprehensible,” Welsh said. To truly mimic human thinking, AI needs to be able to both perceive information and conceptualize it. An example of perception (deep learning) in an AI is recognizing numbers within an image, while conceptualization (symbolic learning) would give those numbers a hierarchical order and extract rules from the hierachy (4 is greater than 3, and 5 is greater than 4, therefore 5 is also greater than 3).

Explainability comes in when the system can say, “I saw a, b, and c, and based on that decided x, y, or z.” DeepMind and others have recently published papers emphasizing the need to fuse the two paradigms together.

Implications Across Industries
One of the most prominent fields where AI ethics will come into play, and where the transparency and accountability of AI systems will be crucial, is defense. Marcuse said, “We’re accountable beings, and we’re responsible for the choices we make. Bringing in tech or AI to a battlefield doesn’t strip away that meaning and accountability.”

In fact, he added, rather than worrying about how AI might degrade human values, people should be asking how the tech could be used to help us make better moral choices.

It’s also important not to conflate AI with autonomy—a worst-case scenario that springs to mind is an intelligent destructive machine on a rampage. But in fact, Marcuse said, in the defense space, “We have autonomous systems today that don’t rely on AI, and most of the AI systems we’re contemplating won’t be autonomous.”

The US Department of Defense released its 2018 artificial intelligence strategy last month. It includes developing a robust and transparent set of principles for defense AI, investing in research and development for AI that’s reliable and secure, continuing to fund research in explainability, advocating for a global set of military AI guidelines, and finding ways to use AI to reduce the risk of civilian casualties and other collateral damage.

Though these were designed with defense-specific aims in mind, Marcuse said, their implications extend across industries. “The defense community thinks of their problems as being unique, that no one deals with the stakes and complexity we deal with. That’s just wrong,” he said. Making high-stakes decisions with technology is widespread; safety-critical systems are key to aviation, medicine, and self-driving cars, to name a few.

Marcuse believes the Department of Defense can invest in AI safety in a way that has far-reaching benefits. “We all depend on technology to keep us alive and safe, and no one wants machines to harm us,” he said.

A Creation Superior to Its Creator
That said, we’ve come to expect technology to meet our needs in just the way we want, all the time—servers must never be down, GPS had better not take us on a longer route, Google must always produce the answer we’re looking for.

With AI, though, our expectations of perfection may be less reasonable.

“Right now we’re holding machines to superhuman standards,” Marcuse said. “We expect them to be perfect and infallible.” Take self-driving cars. They’re conceived of, built by, and programmed by people, and people as a whole generally aren’t great drivers—just look at traffic accident death rates to confirm that. But the few times self-driving cars have had fatal accidents, there’s been an ensuing uproar and backlash against the industry, as well as talk of implementing more restrictive regulations.

This can be extrapolated to ethics more generally. We as humans have the ability to explain our decisions, but many of us aren’t very good at doing so. As Marcuse put it, “People are emotional, they confabulate, they lie, they’re full of unconscious motivations. They don’t pass the explainability test.”

Why, then, should explainability be the standard for AI?

Even if humans aren’t good at explaining our choices, at least we can try, and we can answer questions that probe at our decision-making process. A deep learning system can’t do this yet, so working towards being able to identify which input data the systems are triggering on to make decisions—even if the decisions and the process aren’t perfect—is the direction we need to head.

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Posted in Human Robots

#434701 3 Practical Solutions to Offset ...

In recent years, the media has sounded the alarm about mass job loss to automation and robotics—some studies predict that up to 50 percent of current jobs or tasks could be automated in coming decades. While this topic has received significant attention, much of the press focuses on potential problems without proposing realistic solutions or considering new opportunities.

The economic impacts of AI, robotics, and automation are complex topics that require a more comprehensive perspective to understand. Is universal basic income, for example, the answer? Many believe so, and there are a number of experiments in progress. But it’s only one strategy, and without a sustainable funding source, universal basic income may not be practical.

As automation continues to accelerate, we’ll need a multi-pronged approach to ease the transition. In short, we need to update broad socioeconomic strategies for a new century of rapid progress. How, then, do we plan practical solutions to support these new strategies?

Take history as a rough guide to the future. Looking back, technology revolutions have three themes in common.

First, past revolutions each produced profound benefits to productivity, increasing human welfare. Second, technological innovation and technology diffusion have accelerated over time, each iteration placing more strain on the human ability to adapt. And third, machines have gradually replaced more elements of human work, with human societies adapting by moving into new forms of work—from agriculture to manufacturing to service, for example.

Public and private solutions, therefore, need to be developed to address each of these three components of change. Let’s explore some practical solutions for each in turn.

Figure 1. Technology’s structural impacts in the 21st century. Refer to Appendix I for quantitative charts and technological examples corresponding to the numbers (1-22) in each slice.
Solution 1: Capture New Opportunities Through Aggressive Investment
The rapid emergence of new technology promises a bounty of opportunity for the twenty-first century’s economic winners. This technological arms race is shaping up to be a global affair, and the winners will be determined in part by who is able to build the future economy fastest and most effectively. Both the private and public sectors have a role to play in stimulating growth.

At the country level, several nations have created competitive strategies to promote research and development investments as automation technologies become more mature.

Germany and China have two of the most notable growth strategies. Germany’s Industrie 4.0 plan targets a 50 percent increase in manufacturing productivity via digital initiatives, while halving the resources required. China’s Made in China 2025 national strategy sets ambitious targets and provides subsidies for domestic innovation and production. It also includes building new concept cities, investing in robotics capabilities, and subsidizing high-tech acquisitions abroad to become the leader in certain high-tech industries. For China, specifically, tech innovation is driven partially by a fear that technology will disrupt social structures and government control.

Such opportunities are not limited to existing economic powers. Estonia’s progress after the breakup of the Soviet Union is a good case study in transitioning to a digital economy. The nation rapidly implemented capitalistic reforms and transformed itself into a technology-centric economy in preparation for a massive tech disruption. Internet access was declared a right in 2000, and the country’s classrooms were outfitted for a digital economy, with coding as a core educational requirement starting at kindergarten. Internet broadband speeds in Estonia are among the fastest in the world. Accordingly, the World Bank now ranks Estonia as a high-income country.

Solution 2: Address Increased Rate of Change With More Nimble Education Systems
Education and training are currently not set for the speed of change in the modern economy. Schools are still based on a one-time education model, with school providing the foundation for a single lifelong career. With content becoming obsolete faster and rapidly escalating costs, this system may be unsustainable in the future. To help workers more smoothly transition from one job into another, for example, we need to make education a more nimble, lifelong endeavor.

Primary and university education may still have a role in training foundational thinking and general education, but it will be necessary to curtail rising price of tuition and increase accessibility. Massive open online courses (MooCs) and open-enrollment platforms are early demonstrations of what the future of general education may look like: cheap, effective, and flexible.

Georgia Tech’s online Engineering Master’s program (a fraction of the cost of residential tuition) is an early example in making university education more broadly available. Similarly, nanodegrees or microcredentials provided by online education platforms such as Udacity and Coursera can be used for mid-career adjustments at low cost. AI itself may be deployed to supplement the learning process, with applications such as AI-enhanced tutorials or personalized content recommendations backed by machine learning. Recent developments in neuroscience research could optimize this experience by perfectly tailoring content and delivery to the learner’s brain to maximize retention.

Finally, companies looking for more customized skills may take a larger role in education, providing on-the-job training for specific capabilities. One potential model involves partnering with community colleges to create apprenticeship-style learning, where students work part-time in parallel with their education. Siemens has pioneered such a model in four states and is developing a playbook for other companies to do the same.

Solution 3: Enhance Social Safety Nets to Smooth Automation Impacts
If predicted job losses to automation come to fruition, modernizing existing social safety nets will increasingly become a priority. While the issue of safety nets can become quickly politicized, it is worth noting that each prior technological revolution has come with corresponding changes to the social contract (see below).

The evolving social contract (U.S. examples)
– 1842 | Right to strike
– 1924 | Abolish child labor
– 1935 | Right to unionize
– 1938 | 40-hour work week
– 1962, 1974 | Trade adjustment assistance
– 1964 | Pay discrimination prohibited
– 1970 | Health and safety laws
– 21st century | AI and automation adjustment assistance?

Figure 2. Labor laws have historically adjusted as technology and society progressed

Solutions like universal basic income (no-strings-attached monthly payout to all citizens) are appealing in concept, but somewhat difficult to implement as a first measure in countries such as the US or Japan that already have high debt. Additionally, universal basic income may create dis-incentives to stay in the labor force. A similar cautionary tale in program design was the Trade Adjustment Assistance (TAA), which was designed to protect industries and workers from import competition shocks from globalization, but is viewed as a missed opportunity due to insufficient coverage.

A near-term solution could come in the form of graduated wage insurance (compensation for those forced to take a lower-paying job), including health insurance subsidies to individuals directly impacted by automation, with incentives to return to the workforce quickly. Another topic to tackle is geographic mismatch between workers and jobs, which can be addressed by mobility assistance. Lastly, a training stipend can be issued to individuals as means to upskill.

Policymakers can intervene to reverse recent historical trends that have shifted incomes from labor to capital owners. The balance could be shifted back to labor by placing higher taxes on capital—an example is the recently proposed “robot tax” where the taxation would be on the work rather than the individual executing it. That is, if a self-driving car performs the task that formerly was done by a human, the rideshare company will still pay the tax as if a human was driving.

Other solutions may involve distribution of work. Some countries, such as France and Sweden, have experimented with redistributing working hours. The idea is to cap weekly hours, with the goal of having more people employed and work more evenly spread. So far these programs have had mixed results, with lower unemployment but high costs to taxpayers, but are potential models that can continue to be tested.

We cannot stop growth, nor should we. With the roles in response to this evolution shifting, so should the social contract between the stakeholders. Government will continue to play a critical role as a stabilizing “thumb” in the invisible hand of capitalism, regulating and cushioning against extreme volatility, particularly in labor markets.

However, we already see business leaders taking on some of the role traditionally played by government—thinking about measures to remedy risks of climate change or economic proposals to combat unemployment—in part because of greater agility in adapting to change. Cross-disciplinary collaboration and creative solutions from all parties will be critical in crafting the future economy.

Note: The full paper this article is based on is available here.

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#434673 The World’s Most Valuable AI ...

It recognizes our faces. It knows the videos we might like. And it can even, perhaps, recommend the best course of action to take to maximize our personal health.

Artificial intelligence and its subset of disciplines—such as machine learning, natural language processing, and computer vision—are seemingly becoming integrated into our daily lives whether we like it or not. What was once sci-fi is now ubiquitous research and development in company and university labs around the world.

Similarly, the startups working on many of these AI technologies have seen their proverbial stock rise. More than 30 of these companies are now valued at over a billion dollars, according to data research firm CB Insights, which itself employs algorithms to provide insights into the tech business world.

Private companies with a billion-dollar valuation were so uncommon not that long ago that they were dubbed unicorns. Now there are 325 of these once-rare creatures, with a combined valuation north of a trillion dollars, as CB Insights maintains a running count of this exclusive Unicorn Club.

The subset of AI startups accounts for about 10 percent of the total membership, growing rapidly in just 4 years from 0 to 32. Last year, an unprecedented 17 AI startups broke the billion-dollar barrier, with 2018 also a record year for venture capital into private US AI companies at $9.3 billion, CB Insights reported.

What exactly is all this money funding?

AI Keeps an Eye Out for You
Let’s start with the bad news first.

Facial recognition is probably one of the most ubiquitous applications of AI today. It’s actually a decades-old technology often credited to a man named Woodrow Bledsoe, who used an instrument called a RAND tablet that could semi-autonomously match faces from a database. That was in the 1960s.

Today, most of us are familiar with facial recognition as a way to unlock our smartphones. But the technology has gained notoriety as a surveillance tool of law enforcement, particularly in China.

It’s no secret that the facial recognition algorithms developed by several of the AI unicorns from China—SenseTime, CloudWalk, and Face++ (also known as Megvii)—are used to monitor the country’s 1.3 billion citizens. Police there are even equipped with AI-powered eyeglasses for such purposes.

A fourth billion-dollar Chinese startup, Yitu Technologies, also produces a platform for facial recognition in the security realm, and develops AI systems in healthcare on top of that. For example, its CARE.AITM Intelligent 4D Imaging System for Chest CT can reputedly identify in real time a variety of lesions for the possible early detection of cancer.

The AI Doctor Is In
As Peter Diamandis recently noted, AI is rapidly augmenting healthcare and longevity. He mentioned another AI unicorn from China in this regard—iCarbonX, which plans to use machines to develop personalized health plans for every individual.

A couple of AI unicorns on the hardware side of healthcare are OrCam Technologies and Butterfly. The former, an Israeli company, has developed a wearable device for the vision impaired called MyEye that attaches to one’s eyeglasses. The device can identify people and products, as well as read text, conveying the information through discrete audio.

Butterfly Network, out of Connecticut, has completely upended the healthcare market with a handheld ultrasound machine that works with a smartphone.

“Orcam and Butterfly are amazing examples of how machine learning can be integrated into solutions that provide a step-function improvement over state of the art in ultra-competitive markets,” noted Andrew Byrnes, investment director at Comet Labs, a venture capital firm focused on AI and robotics, in an email exchange with Singularity Hub.

AI in the Driver’s Seat
Comet Labs’ portfolio includes two AI unicorns, Megvii and Pony.ai.

The latter is one of three billion-dollar startups developing the AI technology behind self-driving cars, with the other two being Momenta.ai and Zoox.

Founded in 2016 near San Francisco (with another headquarters in China), Pony.ai debuted its latest self-driving system, called PonyAlpha, last year. The platform uses multiple sensors (LiDAR, cameras, and radar) to navigate its environment, but its “sensor fusion technology” makes things simple by choosing the most reliable sensor data for any given driving scenario.

Zoox is another San Francisco area startup founded a couple of years earlier. In late 2018, it got the green light from the state of California to be the first autonomous vehicle company to transport a passenger as part of a pilot program. Meanwhile, China-based Momenta.ai is testing level four autonomy for its self-driving system. Autonomous driving levels are ranked zero to five, with level five being equal to a human behind the wheel.

The hype around autonomous driving is currently in overdrive, and Byrnes thinks regulatory roadblocks will keep most self-driving cars in idle for the foreseeable future. The exception, he said, is China, which is adopting a “systems” approach to autonomy for passenger transport.

“If [autonomous mobility] solves bigger problems like traffic that can elicit government backing, then that has the potential to go big fast,” he said. “This is why we believe Pony.ai will be a winner in the space.”

AI in the Back Office
An AI-powered technology that perhaps only fans of the cult classic Office Space might appreciate has suddenly taken the business world by storm—robotic process automation (RPA).

RPA companies take the mundane back office work, such as filling out invoices or processing insurance claims, and turn it over to bots. The intelligent part comes into play because these bots can tackle unstructured data, such as text in an email or even video and pictures, in order to accomplish an increasing variety of tasks.

Both Automation Anywhere and UiPath are older companies, founded in 2003 and 2005, respectively. However, since just 2017, they have raised nearly a combined $1 billion in disclosed capital.

Cybersecurity Embraces AI
Cybersecurity is another industry where AI is driving investment into startups. Sporting imposing names like CrowdStrike, Darktrace, and Tanium, these cybersecurity companies employ different machine-learning techniques to protect computers and other IT assets beyond the latest software update or virus scan.

Darktrace, for instance, takes its inspiration from the human immune system. Its algorithms can purportedly “learn” the unique pattern of each device and user on a network, detecting emerging problems before things spin out of control.

All three companies are used by major corporations and governments around the world. CrowdStrike itself made headlines a few years ago when it linked the hacking of the Democratic National Committee email servers to the Russian government.

Looking Forward
I could go on, and introduce you to the world’s most valuable startup, a Chinese company called Bytedance that is valued at $75 billion for news curation and an app to create 15-second viral videos. But that’s probably not where VC firms like Comet Labs are generally putting their money.

Byrnes sees real value in startups that are taking “data-driven approaches to problems specific to unique industries.” Take the example of Chicago-based unicorn Uptake Technologies, which analyzes incoming data from machines, from wind turbines to tractors, to predict problems before they occur with the machinery. A not-yet unicorn called PingThings in the Comet Labs portfolio does similar predictive analytics for the energy utilities sector.

“One question we like asking is, ‘What does the state of the art look like in your industry in three to five years?’” Byrnes said. “We ask that a lot, then we go out and find the technology-focused teams building those things.”

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