Every year TIME magazine publishes a list of what it considers to be the year’s 25 great inventions. This year’s selection covers a broad range of ideas, from a levitating light bulb to shoes that tie themselves.
And some of them are truly life-changing.
1. Uneven football fields
Who says a football field has to be rectangular? Thai property development company AP Thailand has turned unused and unevenly shaped spaces in Bangkok into football fields.
The Unusual Football Field was first developed in the Khlong Toei community, a highly populated area in Bangkok. If found unused but awkwardly sized spaces and converted them into spots where schoolchildren could go to play the nation’s favourite sport.
This video shows how the company converted the spaces to give children a valuable outlet, and why they did it.
2. A roof over their heads
For years, the Swedish furniture group IKEA has been designing functional and affordable furnishings. But it has also been solving problems outside the home.
The Better Shelter is designed to last for at least three years and is suitable for areas in which local materials or construction workers are in short supply. It is “a ground-breaking example of democratic design form, function, quality, sustainability and an affordable price,” says the IKEA Foundation.
Food – its quality, quantity, availability – is one of the biggest issues of our time.
The World Food Prize is a Nobel-likeinternational award that recognizes achievements of individuals who have advanced human development by improving the quality, quantity or availability of food in the world.
This year’s winners are four people who, between them, have developed a nutrient-rich vegetable and helped to extend the reach of food crops whose nutritional quality has been improved.
Three members of the team, Dr Maria Andrade of Cape Verde, Dr Robert Mwanga of Uganda, and Dr Jan Low of the United States, have created a sweet potato enriched with vitamin A. Deficiency in vitamin A contributes to high rates of blindness, diarrhoea, immune-system disorders and premature death in children and pregnant women in Africa. The potato is “the single most successful example of micronutrient and vitamin biofortification,” say the award organizers.
The fourth winner of the award, Dr Howarth Bouis, has been recognized for pioneering the concept of “biofortification” – improving the nutritional content of a food – and turning the idea into a global movement.
“Through the combined efforts of our four Laureates, over 10 million persons are now positively impacted by biofortified crops, with a potential of several hundred million more having their nutrition and health enhanced in the coming decades,” say the World Food Prize panel.
Diabetes occurs when the pancreas cannot produce enough insulin, or the body cannot effectively use the insulin that it does produce.
Without insulin, sugar can build to harmful levels in the blood. This can cause damage to all our major organs, from our hearts to our kidneys. It can also cause blindness, impotence and infections that can result in amputation.
It’s a chronic disease that impacts the daily lives of its sufferers, who have to regularly check their blood sugar levels.
In September of this year, a new device was approved for use by the Federal Drug Administration in the US.
“The MiniMed® 670G system is an insulin pump that automatically adjusts the delivery of insulin based on its sensor, which measures blood-sugar levels every five minutes. The company says it aims to help people living with Type 1 diabetes “spend less time managing their disease and more time enjoying life”.
5. Artificial limbs
Colombia’s 50-year civil war turned it into one of the most heavily landmined countries in the world. Many children have borne the brunt, losing their lives or surviving with lost limbs.
Colombian-born designer Carlos Arturo Torres took the typical prosthesis design and viewed it from the eyes of a child. ‘What if kids could make their own prosthetics?’ he asked himself.
The result is the IKO Creative Prosthetic System, an adaptable prosthesis designed on the back of extensive research into how children feel about wearing a prosthesis. It is both functional – it has a functioning hand attachment – and convertible into a toy. It has a LEGO space ship with a laser and a remote-controlled digger arm that can be attached instead.
“Missing a limb shouldn’t be a disability for a kid when you have the opportunity to explore and augment their potential by creating, playing and learning,” says Torres, “It’s all about kids being kids.”
6. The fundraising Power Band
Wearable fitness devices havebeen one of the hottest products of recent years. Now there is one that aims to solve two problems at once: reducing obesity among children in wealthier nations while also helping children at risk of starvation in poorer ones.
Created by the Ammunition Group, the UNICEF Kid Power Band encourages children to get active and earn points. The points are converted into funding used by UNICEF to deliver lifesaving packets of therapeutic food to severely malnourished children around the world.
The World Health Organization says the number of deaths attributed to air pollution is 6.5 million a year. That’s more than the number of people killed by HIV/AIDS, tuberculosis and road injuries combined.
Wynd is a portable air cleaner that monitors the air around you and purifies where needed. It removes dust, allergens, smoke, and pollution from your personal space, in a device that is the size of a water bottle.
It has been created by Wynd Technologies, Inc., a start-up based in California. Founder Ray Wu often travelled to Asia, where poor air quality is a well-known problem. “We created Wynd out of a need to create clean environments for ourselves and our families wherever we go,” says the company.
Artificial Intelligence (AI) may be the single most disruptive technology the world has seen since the Industrial Revolution. Granted, there is a lot of hype out there on AI, along with doomsday headlines and scary movies. But the reality is that it will positively and materially change how we engage with the world around us. It’s going to improve not only how business is done, but the kind of work we do – and unleash new levels of creativity and ingenuity.
In fact, research from Accenture estimates that artificial intelligence could double annual economic growth rates of many developed countries by 2035, transforming work and fostering a new relationship between humans and machines. The report projects that AI technologies in business will boost labor productivity by up to 40 percent. Rather than undermining people, we believe AI will reinforce their role in driving business growth. As AI matures, it will potentially serve as a powerful antidote for the stagnant productivity and shortages in skilled labor of recent decades.
While it is early days, we are already seeing AI’s impact. Combined with cloud, sophisticated analytics and other technologies, it is starting to change how work is done by both people and computers. It’s also changing how organizations interact with consumers, sometimes in startling ways.
AI is flourishing now because of the rise of ubiquitous computing, low-cost cloud services, near unlimited inexpensive storage, new algorithms, and other related technology innovations. Cloud computing along with advances in Graphical Processing Units (GPU’s) has provided necessary computational power. AI algorithms and architectures have progressed rapidly, often enabled by open source software.
Artificial Intelligence is not just one technology, but rather a variety of different sorts of software that can be applied in numerous ways for different applications.
But equally important is a vast increase in the availability of data. AI does not think for itself. Its insights are possible because the software gets fed information, and the more information it gets, the more insight it can produce. Over the last decade, crowdsourced data in particular has proliferated on internet and social media. People in their daily lives upload massive quantities of images, videos, social media comments, and chat dialogues. All that creates labelled data that is available for machines to use in what’s called machine learning.
While many believe that AI will supplant humans, we think it will instead mostly enable people to do more exceptional work. Certainly, AI will cause displacement of jobs, but it may also significantly boost the productivity of labor. Innovative AI technologies will enable people to make more efficient use of their time and do what humans do best – create, imagine and innovate new things.
With technology overall and AI in particular, the key ingredient for success and creating value is taking a “people first” approach. But to make this transition means both companies and governments must acknowledge the challenges and change how they behave. They must be thoroughly prepared—intellectually, technologically, politically, ethically and socially.
Governments and businesses will need to take several steps, many of which are not easy:
-Prepare the next generation. Re-evaluate the type of knowledge and skills required for the future, and address the need for education and training. AI presents the opportunity to prepare an entirely new sort of skilled and trained workers that do not exist today. This training should be targeted to help those who are disproportionately affected by the coming changes in employment and incomes.
-Advocate for and develop a code of ethics for AI. Ethical debates, challenging as they will be, should be supplemented by tangible standards and best practices in the development and use of intelligent machines.
-Encourage AI-powered regulation. Update old laws and use AI itself to create adaptive, self-improving new ones to help close the gap between the pace of technological change and the pace of regulatory response. This will require government to think and act in new ways appropriate to the new landscape, and means more technologically-trained people must play an active role in government.
-Work to integrate human intelligence with machine intelligence.Businesses must begin reimagining business processes, and reconstructing work to take advantage of the respective strengths of people and machines.
The market demand and opportunity for AI is expanding rapidly, with analyst firm IDC predicting that the worldwide content analytics, discovery and cognitive systems software market will grow from $4.5 billion in 2014 to $9.2 billion in 2019. In fact, Accenture’s Technology Vision 2016—research that gathers input from more than 3,100 global business and IT executives—found that 70% of them are making significantly more investments in AI-related technologies than two years ago, with 55% planning to use machine learning and embedded artificial intelligence. Equity financings for AI companies have risen from $282 million in 2011 to $2.4 billion in 2015, or 746%, according to researchers at CB Insights. AI patents are being granted at a rate five times greater than 10 years ago. AI start-ups in the US alone have increased 20-fold in just 4 years.
A major Italian government agency offers a good example of how AI can dovetail with the work people do and enable them to be more effective. Employees there were spending the majority of their time attending to routine customer queries. The agency worked with Accenture to automate the process with AI. An intelligent Virtual Agent application now handles real-time voice calls and webchat interactions, using a combination of cognitive-semantic analysis and machine-learning algorithms. After just three months, the Virtual Agent application has already successfully served more than 70,000 users. Employees can now take on more demanding and rewarding tasks, which can positively impact their engagement.
AI is also positively impacting how governments operate. The Singapore government’s Safe City program uses the latest in video analytics and image recognition to assist in public safety. It increases security, delivers services more effectively and makes more efficient use of city resources.
The Accenture Institute for High Performance and Accenture Technology, in collaboration with Frontier Economics, modeled the impact of artificial intelligence on 12 developed economies that together generate more than 50 percent of the world’s economic output. The research compared the size of each country’s economy in 2035 under a baseline scenario, in which economic growth continues under current conditions, with an AI scenario, in which the impact of AI has been absorbed into the economy.
AI was found to yield the highest economic benefits for the United States, increasing annual growth from 2.6 percent to 4.6 percent by 2035, translating to an additional $8.3 trillion in gross value added (GVA). In the United Kingdom, AI could add an additional $814 billion to the economy in the same period. Japan has the potential to more than triple its annual rate of GVA growth by 2035, and Finland, Sweden, the Netherlands, Germany and Austria could see their growth rates double.
AI can empower people to create, imagine and innovate at entirely new levels to drive growth and productivity. Far from simply eliminating repetitive tasks, AI should put people at the center, augmenting the workforce by applying the capabilities of machines so people can focus on higher-value analysis, decision-making and innovation.
Almost a year ago, we published our now-annual landscape of machine intelligence companies, and goodness have we seen a lot of activity since then. This year’s landscape has a third more companiesthan our first one did two years ago, and it feels even more futile to try to be comprehensive, since this just scratches the surface of all of the activity out there.
As has been the case for the last couple of years, our fund still obsesses over “problem first” machine intelligence—we’ve invested in 35 machine intelligence companies solving 35 meaningful problems in areas from security to recruiting to software development. (Our fund focuses on the future of work, so there are some machine intelligence domains where we invest more than others.)
At the same time, the hype around machine intelligence methods continues to grow: the words “deep learning” now equally represent a series of meaningful breakthroughs (wonderful) but also a hyped phrase like “big data” (not so good!). We care about whether a founder uses the right method to solve a problem, not the fanciest one. We favor those who apply technology thoughtfully.
What’s the biggest change in the last year? We are getting inbound inquiries from a different mix of people. For v1.0, we heard almost exclusively from founders and academics. Then came a healthy mix of investors, both private and public. Now overwhelmingly we have heard from existing companies trying to figure out how to transform their businesses using machine intelligence.
For the first time, a “one stop shop” of the machine intelligence stack is coming into view—even if it’s a year or two off from being neatly formalized. The maturing of that stack might explain why more established companies are more focused on building legitimate machine intelligence capabilities. Anyone who has their wits about them is still going to be making initial build-and-buy decisions, so we figured an early attempt at laying out these technologies is better than no attempt.
Ready player world
Many of the most impressive looking feats we’ve seen have been in the gaming world, from DeepMind beating Atari classics and the world’s best at Go, to the OpenAI gym, which allows anyone to train intelligent agents across an array of gaming environments.
The gaming world offers a perfect place to start machine intelligence work (e.g., constrained environments, explicit rewards, easy-to-compare results, looks impressive)—especially for reinforcement learning. And it is much easier to have a self-driving car agent go a trillion miles in a simulated environment than on actual roads. Now we’re seeing the techniques used to conquer the gaming world moving to the real world. A newsworthy example of game-tested technology entering the real world was when DeepMind used neural networks to make Google’s data centers more efficient. This begs questions: What else in the world looks like a game? Or what else in the world can we reconfigure to make it look more like a game?
Ah, the great chatbot explosion of 2016, for better or worse—we liken it to the mobile app explosion we saw with the launch of iOS and Android. The dominant platforms (in the machine intelligence case, Facebook, Slack, Kik) race to get developers to build on their platforms. That means we’ll get some excellent bots but also many terrible ones—the joys of public experimentation.
The danger here, unlike the mobile app explosion (where we lacked expectations for what these widgets could actually do), is that we assume anything with a conversation interface will converse with us at near-human level. Most do not. This is going to lead to disillusionment over the course of the next year but it will clean itself up fairly quickly thereafter.
When our fund looks at this emerging field, we divide each technology into two components: the conversational interface itself and the “agent” behind the scenes that’s learning from data and transacting on a user’s behalf. While you certainly can’t drop the ball on the interface, we spend almost all our time thinking about that behind-the-scenes agent and whether it is actually solving a meaningful problem.
We get a lot of questions about whether there will be “one bot to rule them all.” To be honest, as with many areas at our fund, we disagree on this. We certainly believe there will not be one agent to rule them all, even if there is one interface to rule them all. For the time being, bots will be idiot savants: stellar for very specific applications.
We’ve written a bit about this, and the framework we use to think about how agents will evolve is a CEO and her support staff. Many Fortune 500 CEOs employ a scheduler, handler, a research team, a copy editor, a speechwriter, a personal shopper, a driver, and a professional coach. Each of these people performs a dramatically different function and has access to very different data to do their job. The bot / agent ecosystem will have a similar separation of responsibilities with very clear winners, and they will divide fairly cleanly along these lines. (Note that some CEO’s have a chief of staff who coordinates among all these functions, so perhaps we will see examples of “one interface to rule them all.”)
You can also see, in our landscape, some of the corporate functions machine intelligence will re-invent (most often in interfaces other than conversational bots).
On to 11111000001
Successful use of machine intelligence at a large organization is surprisingly binary, like flipping a stubborn light switch. It’s hard to do, but once machine intelligence is enabled, an organization sees everything through the lens of its potential. Organizations like Google, Facebook, Apple, Microsoft, Amazon, Uber, and Bloomberg (our sole investor) bet heavily on machine intelligence and have its capabilities pervasive throughout all of their products.
Other companies are struggling to figure out what to do, as many boardrooms did on “what to do about the Internet” in 1997. Why is this so difficult for companies to wrap their heads around? Machine intelligence is different from traditional software. Unlike with big data, where you could buy a new capability, machine intelligence depends on deeper organizational and process changes. Companies need to decide whether they will trust machine intelligence analysis for one-off decisions or if they will embed often-inscrutable machine intelligence models in core processes. Teams need to figure out how to test newfound capabilities, and applications need to change so they offer more than a system of record; they also need to coach employees and learn from the data they enter.
Unlike traditional hard-coded software, machine intelligence gives only probabilistic outputs. We want to ask machine intelligence to make subjective decisions based on imperfect information (eerily like what we trust our colleagues to do?). As a result, this new machine intelligence software will make mistakes, just like we do, and we’ll need to be thoughtful about when to trust it and when not to.
The idea of this new machine trust is daunting and makes machine intelligence harder to adopt than traditional software. We’ve had a few people tell us that the biggest predictor of whether a company will successfully adopt machine intelligence is whether they have a C-Suite executive with an advanced math degree. These executives understand it isn’t magic—it is just (hard) math.
Machine intelligence business models are going to be different from licensed and subscription software, but we don’t know how. Unlike traditional software, we still lack frameworks for management to decide where to deploy machine intelligence. Economists like Ajay Agrawal, Joshua Gans, and Avi Goldfarb have taken the first steps toward helping managers understand the economics of machine intelligence and predict where it will be most effective. But there is still a lot of work to be done.
In the next few years, the danger here isn’t what we see in dystopian sci-fi movies. The real danger of machine intelligence is that executives will make bad decisions about what machine intelligence capabilities to build.
Peter Pan’s never-never land
We’ve been wondering about the path to grow into a large machine intelligence company. Unsurprisingly, there have been many machine intelligence acquisitions (Nervana by Intel, Magic Pony by Twitter, Turi by Apple, Metamind by Salesforce, Otto by Uber, Cruise by GM, SalesPredict by Ebay, Viv by Samsung). Many of these happened fairly early in a company’s life and at quite a high price. Why is that?
Established companies struggle to understand machine intelligence technology, so it’s painful to sell to them, and the market for buyers who can use this technology in a self-service way is small. Then, if you do understand how this technology can supercharge your organization, you realize it’s so valuable that you want to hoard it. Businesses are saying to machine intelligence companies, “forget you selling this technology to others, I’m going to buy the whole thing.”
This absence of a market today makes it difficult for a machine intelligence startup, especially horizontal technology providers, to “grow up”—hence the Peter Pans. Companies we see successfully entering a long-term trajectory can package their technology as a new problem-specific application for enterprise or simply transform an industry themselves as a new entrant (love this). We flagged a few of the industry categories where we believe startups might “go the distance” in this year’s landscape.
Inspirational machine intelligence
Once we do figure it out, machine intelligence can solve much more interesting problems than traditional software. We’re thrilled to see so many smart people applying machine intelligence for good.
Established players like Conservation Metrics and Vulcan Conservation have been using deep learning to protect endangered animal species; the ever-inspiring team at Thorn is constantly coming up with creative algorithmic techniques to protect our children from online exploitation. The philanthropic arms of the tech titans joined in, enabling nonprofits with free storage, compute, and even developer time. Google partnered with nonprofits to found Global Fishing Watch to detect illegal fishing activity using satellite data in near real time, satellite intelligence startup Orbital Insight (in which we are investors) partnered with Global Forest Watch to detect illegal logging and other causes of global forest degradation. Startups are getting into the action, too. The Creative Destruction Lab machine intelligence accelerator (with whom we work closely) has companies working on problems like earlier diseasedetectionand injuryprevention. One area where we have seen some activity but would love to see more is machine intelligence to assist the elderly.
In talking to many people using machine intelligence for good, they all cite the critical role of open source technologies. In the last year, we’ve seen the launch of OpenAI, which offers everyone access to world class research and environments, and better and better releases of TensorFlow and Keras. Non-profits are always trying to do more with less, and machine intelligence has allowed them to extend the scope of their missions without extending budget. Algorithms allow non-profits to inexpensively scale what would not be affordable to do with people.
On a lighter note, we’ve also heard whispers of more artisanal versions of machine intelligence. Folks are doing things like using computer vision algorithms to help them choose the best cocoa beans for high-grade chocolate, write poetry, cook steaks, and generate musicals.
Curious minds want to know. If you’re working on a unique or important application of machine intelligence we’d love to hear from you.
We see all this activity only continuing to accelerate. The world will give us more open sourced and commercially available machine intelligence building blocks, there will be more data, there will be more people interested in learning these methods, and there will always be problems worth solving. We still need ways of explaining the difference between machine intelligence and traditional software, and we’re working on that. The value of code is different from data, but what about the value of the model that code improves based on that data?
Once we understand machine intelligence deeply, we might look back on the era of traditional software and think it was just a prologue to what’s happening now. We look forward to seeing what the next year brings.
That’s a big deal, because it offers researchers a system for dealing with addiction as a single neurological problem, and fitting the puzzle pieces individual kinds of habitual substance abuse into a larger whole.
It makes it easier to ask and answer questions like Why are certain people more likely to develop addictions? Why do so many addicted people have this gene? and, most importantly How can we prevent and treat addictions once they form?
Addiction, the paper’s authors argue, based on a review of existing research, has three keys:
Executive function: The human brain is really good at zooming out to think about big-picture challenges and how to deal with them. It faces complex questions and offers complex answers.
People with addictions tend to have problems with this kind of thinking though — especially when it comes to long-term planning. They struggle with attention, inhibition, long-term planning, and judgments about the past and future.
These kinds of deficits show up in people with addictions to substances ranging from nicotine to cocaine to cannabis, and seem to play a significant role in addiction as a mental disease.
Incentive salience: Why did you eat that gross, sugary cinnamon bun this morning? Because your mind doesn’t make all of its decisions at the level of executive function. A lot of the choices we make come down to more primal reward seeking.
When your brain is trained to want something, whether a sweet treat or a shot of alcohol, a rewards system kicks in, and you develop a craving. And when it gets it, it releases a surge of reward chemicals, including dopamine — the most well known hormone in the brain. This is the underlying system behind any habit.
In people with addictions, that reward system is altered. The addictive substance gets outsized salience. That is, the addicted brain weighs it as more important and kicks in larger rewards when it arrives.
Negative emotionality: This is the simplest of the three keys. People with addictions display more negativity. Present them with some stimulus, and their reaction is more likely to be sad or angry.
Negative feelings (which researchers term “hypohedonia”) make addicted people more susceptible to their cravings. And the substances that drive their addictions become temporary salves for that internal hurt.
Those three keys drive addictions as common as nicotine habits and as ravaging as opioids and amphetamines. And they track with genetic factors like mental health and family history, as well as environmental factors like class and education.
This paper aims to become a kind of frame on top of which researchers can build future developments in addiction science. Expect to see a lot of studies citing it down the road.
Os livros digitais se popularizaram bastante na última década. Hoje existem vários tipos de aplicativos e equipamentos para que os leitores possam apreciá-los da forma mais confortável possível. Porém, ainda há quem prefira a versão impressa, a qual pode ser folheada e colocada em algum lugar da estante.
Pensando nesse novo cenário atual, essa livraria parisiense chamadaLes Puf resolveu unir tecnologia com o velho hábito de levar seu livro pra casa. Depois de fechar o estabelecimento por um tempo, já que as vendas das publicações não cobriam os gastos com aluguel, os donos voltaram a reabri-la com uma nova proposta, usando muita criatividade: ao invés de armazenar no local várias publicações, o cliente pode imprimir na hora o título que desejar.
A Les Puf oferece um catálogo de cerca de 5 mil títulos e mais 3 milhões de outras opções, fornecidos pela On Demand Books, empresa que criou a impressora portátil de livros. O processo é bem interessante: o cliente escolhe a publicação e a máquina Espresso Book Machine faz o resto.
Ela imprime direto do banco de dados (PDF original), cola e encapa o título. O nome da impressora faz menção ao café Espresso italiano, já que o tempo de impressão de um livro é o mesmo do preparo e consumo de um café. Incrível, não?
A novidade não está somente disponível na França. Muitas empresas ao redor do globo já estão aderindo a ideia, como a tradicional Barnes and Noble, nos EUA. Com esse método, a livraria parisiense consegue vender cerca de 40 livros diários e como não tem publicações físicas em sua loja (apenas o mostruário virtual), os proprietários conseguem tocar o negócio em um lugar menor, ao cobrar o mesmo valor de um exemplar tradicional.
Além disso, um dos grandes diferenciais é que o modelo permite que os leitores tenham acesso a livros que já estão esgotados. A Les Puf vai trazer em breve cerca de 2 mil títulos que já não são mais comercializados.
Viciados em livros, os que amam o cheirinho de uma publicação nova, vão adorar essa novidade, já que podem levar seu exemplar para casa logo após a impressão!
Confira no vídeo abaixo como funciona a impressora:
It’s a long-standing piece of advice: to really succeed at work, you need to be a ruthless, hard-nosed go-getter. Your employees aren’t your friends, they’re there for one thing and that’s to get the job done.
But recent research from The Empathy Business suggests it might be time to rethink that approach. In fact, those leaders and companies that embed empathy – the ability to understand and share the feelings of others – into their business models perform far better than those that don’t.
A changing world requires a new approach
The idea that empathy is the secret to business success is a far cry from the dog-eat-dog corporate mentality that has prevailed for so long. What led to this cultural shift?
For Belinda Parmar, CEO of The Empathy Business, and a Forum Young Global Leader, we have technology to thank for that. “Social media has changed everything and lifted the corporate veil. Now we can all see inside the inner workings of a company, and behaviour we don’t like or agree with is much easier to call out.”
It’s perhaps no coincidence, then, that tech companies have been the force behind this cultural change. In the latest Empathy Index – an annual ranking of businesses based on how empathetic they are – tech firms make up 60% of the top 10.
Social media giant Facebook took the top spot in this year’s index, and it’s not hard to understand why: it listens to what customers and staff are saying, tries to put itself in their shoes, and then makes changes based on that feedback.
One example of this in action is its Empathy Lab, which gives Facebook engineers the chance to experience for themselves how customers will use their products – even if those customers are visually impaired or hard of hearing.
The hope, Facebook told Wired magazine, is that designers will understand what their customers are experiencing, and build products that take their needs into account. “We wanted to build empathy into our engineering,” a representative explained.
Facebook is working to make its products accessible to all users
The shift in focus towards empathy is good news not just for customers, but for companies as well. The businesses towards the top of this year’s index increased in value twice as fast as those at the bottom, and generated 50% more earnings per employee than the worst performers.
They’ve also got a competitive edge when it comes to hiring the best and most talented staff. Increasingly, employees – particularly millennials – are “demanding empathy”, Parmar told us. “And to win the war for talent, businesses must cater to it.” That might explain why most of the leaders in this index regularly appear on lists of most desirable employers.
Empathy is not just for tech firms
If empathy is so good for business, why aren’t more companies following Silicon Valley’s lead? Parmar remains optimistic: it’s taking time, but gradually even the most old-school of companies will get on board.
“The tech industry is a lot younger and forward thinking than most industries. It will happen when companies realize the correlation to performance,” she maintains
If this year’s index is anything to go by, that change might already be taking place. Take Ryanair, the budget airline with a somewhat chequered history when it comes to customer service. Last year it finished second from bottom in the index.
Through its Always Getting Better programme, Ryanair set about listening to customer feedback and scrapping the policies people didn’t like – unallocated seating, draconian luggage rules and hidden charges.
The new approach is already paying off: this year Ryanair not only increased net profits, but it climbed 13 places in the Empathy Index. Proof that empathy isn’t just about being a nice guy: it makes good business sense.
Being a leader is difficult. That’s why most of us end up taking direction from others in our professional lives. But the ranks of the self-employed are swelling, hinting that more people are getting comfortable taking the reins in their own hands. And in fact, becoming a leader (even if it’s just of yourself) is something anyone who’s committed to the task can master. There’s no inborn quality that leaders possess. They’re ordinary people who decide at one point or another to do extraordinary things.
That doesn’t just take courage, it demands creativity—the kind you need to actively nurture and practice. I’m an artist, so I like to think about leadership as an art form. And I’ve found that in order to become a leader, you need to develop similar qualities to an artist—to tap into your creative intelligence in order to keep ahead of the crowd, stay nimble, and inspire those around you to push themselves, too.
Here are five traits the most creative driven leaders—and therefore the bestleaders, generally speaking—all possess.
1. THEY RATTLE CAGES
Change is a constant. In the natural world, in politics, in business, the only thing that stays the same is the fact that nothing stays the same. Some people wait until they’re propelled into leadership positions by forces around them. But the best leaders—from Joan of Arc to Martin Luther King, Jr. to Steve Jobs—first provoke themselves into action, then the people around them. They’re constantly imagining new possibilities. They instigate change that they envision even when others don’t.
Yet perhaps the only major difference between these great leaders and the average person is that they’re willing to do something rather than let circumstances dictate life for them. That typically means rattling cages and shaking up long-standing beliefs and institutions—which is never easy or universally well-received. But that’s precisely what makes them great. To rise to your true leadership potential, chances are you’ll need to rattle a few cages as well, starting with your own.
2. THEY LISTEN TO INTUITION
There are things we know to be true and things we feel to be true. Thanks to our education, most of us tend to lean on our existing knowledge base to solve problems and make decisions. But the best leaders are those who realize that the things they sense—those possibilities that lie just beyond the realm of the known—hold a special value, too. Listening to them is how real breakthroughs happen.
Most of us have problems balancing logic with intuition. But the truth is that those faculties aren’t opposed to one another. In fact, you need to figure out how to get them working together if you’re to become a truly creative leader. Intellect without intuition makes for a smart person without impact. Intuition without intellect makes a spontaneous person without direction.
3. THEY MOVE FAST
One of the biggest stumbling blocks for anyone trying to accomplish something is perfectionism—the need to get it exactly right before taking the next step. But the best leaders realize that perfection is impossible, and pursuing perfection often stands in the way of what’s most important: progress. Leadership requires making consistent strides, no matter how big. And the quicker the stride, the greater the progress.
Don’t buy into the notion that you can take a giant leap if you spend enough time carefully mapping it out. By the time you get done planning, others will have lapped you twice and already taken that leap you spent months mulling over. Opt instead to “just go” and let the sparks fly. You will make mistakes. But in the process, you’ll learn quickly and keep moving—refining your skills and igniting new levels of creativity you didn’t know you had.
4. THEY HAVE CONVICTIONS AND STICK TO THEM
“Don’t ask what the world needs,” the great civil rights leader Howard Thurman once said. “Ask yourself what makes you come alive and then go do that. Because what the world needs is people who have come alive.” There’s something compelling about a person with conviction, whether or not you agree with everything he or she represents. But conviction is rare, because in our longing for stability and security, we often make the mistake of looking outside ourselves for direction when we should be looking inside. And over time we can lose sight of who we truly are and what’s really important to us.
Conviction can be cultivated, though—and it starts with you individually. While those who live with great conviction can always inspire you, they don’t know your passions and beliefs. Only you can ask, “What makes me come alive?” From there, the gaps between who you are and who you can still be will become clear. You might find you need something dramatic like a career change, or the exercise of answering that question might help propel you down the path you’re already on. The key is to find something that you feel you’re meant to do and give yourself to it.
5. THEY DON’T (ONLY) DO WHAT’S EXPECTED OF THEM
The ability to come up with new ideas is a defining characteristic of great leaders. They’re able to step out of the common view and imagine new possibilities that set the course for others to follow. Each of us has a tremendous capacity for originality—we’re each unique, after all—but activating it can be difficult. Why? Because our lives are full of other demands—our jobs, our families—and we spend most of our precious time and energy just trying to keep up.
In order to free your own originality, you need to be willing to stop doing only what’s required and expected of you and start doing the things that only you can do—those ideas and projects you keep shelving until you’ve got time for them. But the truth is there’s never a convenient moment to tackle them. There’s never going to come a time when you’ll be 100% certain you’ll succeed if you do. Get started on those things today and work on them every day thereafter.
Ultimately, the real difference between you and the creative leaders who inspire you is action. You have the innate capacity to develop all the qualities they possess. The key is to start. Start today. Start now. Don’t wait around until life demands something of you—it always will. That’s not what leaders do.