This blog post is co-authored with Devesh Raj, Senior Vice President and Head of Strategy and Planning at Comcast-NBCU. Devesh and I are friends and both Young Global Leader at the World Economic Forum. In this blog post we share some of our observations and thoughts after attending the World Economic Forum's annual meeting in Davos.
This year's World Economic Forum Annual Meeting in Davos focused on the Fourth Industrial Revolution, a term coined by Klaus Schwab to describe the new generation of technological advances – sensors, robotics, artificial intelligence, 3D printing, precision medicine – coming together to define the next wave of progress.
These new technologies have the potential to transform our lives. Beyond sci-fi like scenarios – such as each of us having our own personal R2-D2, summoning our Batmobile, or colonizing Mars – these advances also have the potential to solve many real-world problems. With more intelligent, automated technology, we could generate renewable energy, address climate change, connect billions of people to the internet, develop affordable housing solutions and cure chronic diseases.
These advances are not far into the future. A recent report on Technology Tipping Points and Societal Impact anticipates many such moments of inflection within our lifetimes – in fact, we may see major advances in transportation, artificial intelligence, and new payment technology as soon as the next decade. Yet, somewhat surprisingly, much of the discussion in Davos last month focused on the negative impacts of these technologies, rather than their positive potential.
|- Storage for all||- Robots and services||- The Internet of Things
- Wearable internet
- 3D printing and manufacturing
|- Implantable technologies
- Big Data for decisions
- Vision as the new interface
- Our digital presence
- Governments and the block chain
- A supercomputer in your pocket
|- Ubiquitous computing
- 3D printing and human health
- The connected home
|- 3D printing and consumer products
- AI and white-collar jobs
- The sharing economy
|- Driverless cars
- AI and decision-making
- Smart cities
One consistent, fearful theme was the potential for job losses. As automation continues to replace manufacturing or blue collar jobs, artificial intelligence will subsequently do the same for skilled, white collar jobs in banking, law or medicine. Estimates as to the impact this will have on jobs vary, but many prognostications in Davos suggested a depressive impact on the global economy. While it's true that technological leaps have often eliminated older, human-powered methods of doing things, many in Davos also recognized that advances in technology create new jobs, most of which we can't even dream of today. For example, the invention of the airplane created hundreds of thousands of jobs, from pilots, to stewards, to airport personnel, to international agents and more prognostications not to mention the transformative economic impact of billions of people traveling vast distances in a short span of time.
A second concern at Davos was growing inequality in the world between "digital haves" and "have-nots". This was reflected both as a challenge among nations – developed vs. developing – but also an issue for specific socio-economic groups within individual nations, some of which arguably are still not past the second or third industrial revolution. What does 3D printing or precision medicine do, for example, for rural parts of India and Africa that still don't have reliable electricity, while urban centers in those same countries race towards an era of smart, automated living?
A third common concern (particularly driven by robotics and artificial intelligence) was the "dehumanization" of our lives. There was a case for a renewed emphasis on qualities that make us uniquely human – empathy, sensitivity, creativity and inspiration.
Another issue centered on the ethical and moral challenges of many advances. Some conversations at Davos discussed the dangerous potential of eugenics-like scenarios in medicine, enabled by advances such as CRISPR/Cas9. On the flip side, could machines make positive decisions regarding human lives, such as a self-driving car making a choice between hitting a pedestrian or sacrificing its passenger?
One could argue some of these concerns are overblown Luddism. But in some ways, it doesn't matter – the march of technological progress is inevitable, as it has always been. Certainly, no one at Davos suggested slowing down the pace of technological advancement. The gist of the discussions was that we should figure out how to avoid, or address, the negative, unintended consequences of these changes.
We believe there is a major challenge with the Fourth Industrial Revolution that didn't get adequate attention in Davos – the issue of prioritization.
To date, the technological innovation that has driven the Fourth Industrial Revolution is shaped by the commercial prospects of small or large firms in the market. After all, one definition of "innovation" is the commercial application of invention. As an example, investment in alternative energy R&D fluctuates depending on oil prices, just as demand for hybrid or electric vehicles become more or less attractive depending on gasoline prices.
What if, instead of being driven solely by commercial returns, we could focus the Fourth Industrial Revolution more directly on the big problems our world faces? What if we could prioritize technological advances that have the most beneficial impact to society?
The world has recently defined its problems very clearly in a set of 17 Sustainable Development Goals (Wikipedia), also known as the Global Goals, that were adopted by all countries last year to "end poverty, protect the planet and ensure prosperity for all". The goals cover poverty, hunger and food security, health, education, energy, and water and sanitation – to name a few. A successor list to the earlier Millennium Development Goals, the Sustainable Development Goals get quite specific.
Take Goal 3 as an example: "Ensure healthy lives and promote well-being for all at all ages". This goal is linked to 12 targets, including these top three:
- By 2030: reduce the global maternal mortality ratio to less than 70 per 100,000 live births.
- By 2030: end preventable deaths of newborns and children under 5 years of age, with all countries aiming to reduce neonatal mortality to at least as low as 12 per 1,000 live births and under-5 mortality to at least as low as 25 per 1,000 live births.
- By 2030: end the epidemics of AIDS, tuberculosis, malaria and neglected tropical diseases and combat hepatitis, water-borne diseases and other communicable diseases.
Of course, technological advancement is not the only solution to all Sustainable Development Goals – there is much more to do – but it is likely one of the major contributors.
As the world thinks through how to harness the Fourth Industrial Revolution, we think it is worth questioning which technologies we should be prioritizing to meet these Sustainable Development Goals. How do we draft policies and create economic incentives to encourage the right types of technology advances? What should governments and the private sector do differently to focus technology on addressing these goals? How do we direct the energy and creativity of millions of entrepreneurs towards improving the state of the world?
The world's innovation system is powerful and has generally worked well. However, it could use a guiding hand to nudge it in a direction that will benefit the planet beyond the incentives of commercial returns. Expanding our criteria for importance to solving areas of global need is not an inherently anti-capitalist idea. But it is one that would channel capitalism in the best direction for humanity as a whole. That, we hope, is the real agenda initiated by the focus in Davos on the Fourth Industrial Revolution, which the world will seek to address in the coming year.
Last week, a member of my family died in a car accident. Jasper was on his way home and was hit by a taxi. He fought for his life, but died the next day. Jasper was only 16 years old. I was at Davos and at one point I had to step out of the conference to cry. Five years ago, another family member died after she was hit by a truck when crossing the road.
It's hard to see a tragedy like this juxtaposed against a conference filled with people talking about improving the state of the world. These personal losses make me want to fast-forward to a time in the future where self-driving cars are normal, and life-saving innovations don't have as much regulatory red tape to cut through before they can have an impact. It's frustrating that we may have the right technology in sight today, but aren't making it available, especially when people's lives are at stake.
Imagine two busses full of people crashing, killing everyone on board, every single day. That is how many people die on America's roads every day. In fact, more people are killed by cars than guns, but I don't see anyone calling for a ban on automobiles. Car accidents (and traffic jams) are almost always the result of human error. It is estimated that self-driving cars could reduce deaths on the road by 90%. That is almost 30,000 lives saved each year in the US alone. The life-saving estimates for driverless cars are on par with the efficacy of modern vaccines. I hope my children, now ages 6 and 8, will never need a driver's license and can grow up in a world with driverless cars.
The self-driving car isn't as far off as you might think but is still being held back by government regulators. Delayed technology isn't limited to self-driving cars. Life-saving innovations in healthcare are often held back by regulatory requirements. The challenge of climate change could be addressed faster if the regulatory uncertainty around solar and wind power permits and policies were reduced. The self-serving interest of lobbying groups focused on maintaining the status quo for industries like Big Oil make it harder for alternative energies to gain momentum.
Regulators need to frame their jobs differently; they need to ask how they can facilitate and enable emerging disruptive innovations, rather than maintain existing systems. Their job should focus more on removing any barriers that prevent disruptions from having a faster impact. If they do this job well, some established institutions will fail. In some cases, economic sacrifices by the incumbents should be of lesser concern than advancing social health and safety for the benefit of society. I'm less concerned about technology destroying jobs, and more concerned about our children not being able to benefit from available technical advances that improve their lives. We should realize that opportunities for long-term economic growth come with short-term disruption or temporary pain.
Losing family members in fatal accidents makes one think about what could have been done. I'm often asked how one can create a "Silicon Valley" model elsewhere in the world. I may have an answer. If you want to out-"Silicon Valley" Silicon Valley, create a region with a regulatory environment that supports prompt, responsible innovation to drive the adoption and iteration of new technologies. A region where people can responsibly launch self-driving cars, fast-track healthcare and address climate change. A region where long-term advantages are valued more than short-term disadvantages. Such a region would attract capital and entrepreneurs, and would be much better for our children.
Volkswagen's recent emissions scandal highlighted the power that algorithms wield over our everyday lives. As technology advances and more everyday objects are driven almost entirely by software, it's become clear that we need a better way to catch cheating software and keep people safe.
A solution could be to model regulation of the software industry after the US Food and Drug Administration's oversight of the food and drug industry. The parallels are closer than you might think.
The case for tighter regulation
When Volkswagen was exposed for programming its emissions-control software to fool environmental regulators, many people called for more transparency and oversight over the technology.
One option discussed by the software community was to open-source the code behind these testing algorithms. This would be a welcome step forward, as it would let people audit the source code and see how the code is changed over time. But this step alone would not solve the problem of cheating software. After all, there is no guarantee that Volkswagen would actually use the unmodified open-sourced code.
Open-sourcing code would also fail to address other potential dangers. Politico reported earlier this year that Google's algorithms could influence the outcomes of presidential elections, since some candidates could be featured more prominently in its search results.
Research by the American Institute for Behavioral Research and Technology has also shown that Google search results could shift voting preferences by 20% or more (up to 80% in certain demographic groups). This could potentially flip the margins of voting elections worldwide. But since Google's private algorithm is a core part of its competitive advantage, open-sourcing it is not likely to be an option.
The same problem applies to the algorithms used in DNA testing, breathalyzer tests and facial recognition software. Many defense attorneys have requested access to the source code for these tools to verify the algorithms' accuracy. But in many cases, these requests are denied, since the companies that produce the proprietary criminal justice algorithms fear a threat to their businesses' bottom line. Yet clearly we need some way to ensure the accuracy of software that could put people behind bars.
What we can learn from the FDA
So how exactly could software take a regulatory page from the FDA in the United States? Before the 20th century, the government made several attempts to regulate food and medicine, but abuse within the system was still rampant. Food contamination caused widespread illness and death, particularly within the meatpacking industry.
Meanwhile, the rise of new medicines and vaccines promised to eradicate diseases, including smallpox. But for every innovation, there seemed to be an equal amount of extortion by companies making false medical claims or failing to disclose ingredients. The reporting of journalists like Upton Sinclair made it abundantly clear by the early 1900s that the government needed to intervene to protect people and establish quality standards.
In 1906, President Theodore Roosevelt signed the Food and Drug Act into law, which prevented false advertising claims, set sanitation standards, and served as a watchdog for companies that could cause harm to consumers' welfare. These first rules and regulations served as a foundation for our modern-day FDA, which is critical to ensuring that products are safe for consumers.
The FDA could be a good baseline model for software regulation in the US and countries around the world, which have parallel FDA organizations including the European Medicines Agency, Health Canada, and the China Food and Drug Administration.
Just as the FDA ensures that major pharmaceutical companies aren't lying about the claims they make for drugs, there should be a similar regulator for software to ensure that car companies are not cheating customers and destroying the environment in the process. And just as companies need to disclose food ingredients to prevent people from ingesting poison, companies like Google should be required to provide some level of guarantee that they won't intentionally manipulate search results that could shape public opinion.
It's still relatively early days when it comes to discovering the true impact of algorithms in consumers' lives. But we should establish standards to prevent abuse sooner rather than later. With technology already affecting society on a large scale, we need to address emerging ethical issues head-on.
(I originally wrote this blog post as a guest article for Quartz.)
The Industrial Revolution, started in the middle of the 18th century, transformed the world. It marks the start of a major turning point in history that would influence almost every aspect of daily life. The Industrial Revolution meant the shift from handmade to machine-made products and increased productivity and capacity. Technological change also enabled the growth of capitalism. Factory owners and others who controlled the means of production rapidly became very rich and working conditions in the factories were often less than satisfactory. It wasn't until the 20th century, 150 years after its beginning, that the Industrial Revolution ended creating a much higher standard of living than had ever been known in the pre-industrial world. Consumers benefited from falling prices for clothing and household goods. The impact on natural resources, public health, energy, medicine, housing and sanitation meant that chronic hunger, famines and malnutrition started to disappear and the life expectancy started to increase dramatically.
An undesired side-effect of the Industrial Revolution is that instead of utilizing artisans to produce hand-made items, machines started to take the place of the artisans. Before the industrial revolution, custom-made goods and services were the norm. The one-on-one relationships that guilds had with their customers sadly got lost in an era of mass-production. But what is exciting me about the world today is that we're on the verge of being able to bring back one-on-one relationships with our customers, while maintaining increased productivity and capacity.
As the Big Reverse of the Web plays out and information and services are starting to come to us, we'll see the rise of a new trend I call "B2One". We're starting to hear a lot of buzz around personalization, as evidenced by companies like The New York Times making delivery of personalized content a core part of their business strategy. Another recent example is Facebook testing shopping concepts, letting users browse a personal feed of clothing and other items based on their "likes". I'd imagine these types of feeds could get smarter and smarter, refining themselves over time as a user browses or buys. Or just yesterday, Facebook launched Notify, an iOS app that pushes you personalized notifications from up to 70 sites.
These recent examples are early signs of how we're evolving from B2C to B2One (or from B2B2C to B2B2One), a world where all companies have a one-on-one relationship with their customers and personalized experiences will become the norm. Advances in technology allow us to get back what we lost hundreds of years ago in the Industrial Revolution, which in turn enables the world to innovate on business models. The B2One paradigm will be a very dramatic shift that disrupts existing business models (advertising, search engines, online and offline retailers) and every single industry.
For example, an athletic apparel company such as Nike could work sensor technology into its shoes, telling you once you've run a certain number of miles and worn them out. Nike would have enough of a one-on-one relationship with you to push an alert to your smartphone or smartwatch with a "buy" button for new shoes, before you even knew you needed them. This interaction is a win-win for both you and Nike; you don't need to re-enter your sizing and information into a website, and Nike gets a sale directly from you disrupting both the traditional and online retail supply chain (basically, this is bad news for intermediaries like Amazon, Zappos, clothing malls, Google, etc).
I believe strongly in the need for data-driven personalization to create smarter, pro-active digital experiences that bring back one-on-one relationships between producers and consumers. We have to dramatically improve delivering these personal one-on-one interactions. It means we have to get better at understanding the user's journey, the user's context, matching the right information/service to the user and making technology disappear in the background.
Algorithms are shaping what we see and think -- even what our futures hold. The order of Google's search results, the people Twitter recommends us to follow, or the way Facebook filters our newsfeed can impact our perception of the world and drive our actions. But think about it: we have very little insight into how these algorithms work or what data is used. Given that algorithms guide much of our lives, how do we know that they don't have a bias, withhold information, or have bugs with negative consequences on individuals or society? This is a problem that we aren't talking about enough, and that we have to address in the next decade.
Open Sourcing software quality
In the past several weeks, Volkswagen's emissions crisis has raised new concerns around "cheating algorithms" and the overall need to validate the trustworthiness of companies. One of the many suggestions to solve this problem was to open-source the software around emissions and automobile safety testing (Dave Bollier's post about the dangers of proprietary software is particularly good). While open-sourcing alone will not fix software's accountability problems, it's certainly a good start.
As self-driving cars emerge, checks and balances on software quality will become even more important. Companies like Google and Tesla are the benchmarks of this next wave of automotive innovation, but all it will take is one safety incident to intensify the pressure on software versus human-driven cars. The idea of "autonomous things" has ignited a huge discussion around regulating artificially intelligent algorithms. Elon Musk went as far as stating that artificial intelligence is our biggest existential threat and donated millions to make artificial intelligence safer.
While making important algorithms available as Open Source does not guarantee security, it can only make the software more secure, not less. As Eric S. Raymond famously stated "given enough eyeballs, all bugs are shallow". When more people look at code, mistakes are corrected faster, and software gets stronger and more secure.
Less "Secret Sauce" please
Automobiles aside, there is possibly a larger scale, hidden controversy brewing on the web. Proprietary algorithms and data are big revenue generators for companies like Facebook and Google, whose services are used by billions of internet users around the world. With that type of reach, there is big potential for manipulation -- whether intentional or not.
There are many examples as to why. Recently Politico reported on Google's ability to influence presidential elections. Google can build bias into the results returned by its search engine, simply by tweaking its algorithm. As a result, certain candidates can display more prominently than others in search results. Research has shown that Google can shift voting preferences by 20 percent or more (up to 80 percent in certain groups), and potentially flip the margins of voting elections worldwide. The scary part is that none of these voters know what is happening.
And, when Facebook's 2014 "emotional contagion" mood manipulation study was exposed, people were outraged at the thought of being tested at the mercy of a secret algorithm. Researchers manipulated the news feeds of 689,003 users to see if more negative-appearing news led to an increase in negative posts (it did). Although the experiment was found to comply with the terms of service of Facebook's user agreement, there was a tremendous outcry around the ethics of manipulating people's moods with an algorithm.
In theory, providing greater transparency into algorithms using an Open Source approach could avoid a crisis. However, in practice, it's not very likely this shift will happen, since these companies profit from the use of these algorithms. A middle ground might be allowing regulatory organizations to periodically check the effects of these algorithms to determine whether they're causing society harm. It's not crazy to imagine that governments will require organizations to give others access to key parts of their data and algorithms.
Ethical early days
The explosion of software and data can either have horribly negative effects, or transformative positive effects. The key to the ethical use of algorithms is providing consumers, academics, governments and other organizations access to data and source code so they can study how and why their data is used, and why it matters. This could mean that despite the huge success and impact of Open Source and Open Data, we're still in the early days. There are few things about which I'm more convinced.