WITHIN a decade, virtual-reality (VR) technology is expected to transform the way businesses interact with customers. Immersive, 360-degree experiences, complete with touch and temperature sensations, should become the norm. As early as 2020, spending is forecast to reach $7.9 billion ≈ net worth of Rupert Murdoch, media mogul, 2011
≈ net worth of Steve Jobs, founder of Apple, 2011
≈ Domestic box office gross, 2011
“>[≈ cost of 2011 Hurricane Irene] on VR headsets and $3.3 billion ≈ net worth of George Lucas, creator of Star Wars, 2011
≈ total US football salaries for all teams, 2011
≈ Beijing Airport Terminal 3
“>[≈ box office sales of Gone with the Wind, 1939] on VR entertainment. In the short run, however, VR primarily remains the preserve of gamers. The companies releasing the latest wave of console and headset devices are not only bringing joy to aficionados of “The Lab” and “Gunjack”, but also jockeying for position to compete in a much larger market once the technology goes mainstream.
So far, the VR-gaming industry has roughly been divided into a casual sector, dominated by Samsung and Google, and the high end led by Facebook’s Oculus Rift and HTC’s Vive (both unveiled this spring). Sony, which released its own headset on October 13th with much fanfare, came relatively late to the game. But with a product more powerful than the mass-market devices, and more affordable than the top-tier ones, it may have a sweet spot all to itself. Moreover, it can rely on a captive global customer base of over40m Playstation 4 users, forecast to surpass 50m by Christmas. As a result, the Sony headset is expected to make an immediate impact. IHS Markit, an analytics provider, projects the firm will make $134m ≈ Finance industry 2011 political donations”>[≈ net worth of Dr. Dre, rapper, 2011] from VR sales in the next few months.
Digital technologies and advanced analytics have the potential to create the Invisible Bank of the future. Powered by artificial intelligence (AI) and activated by voice, virtual banking assistants can become an integral part of our daily lives.
Banking today is becoming less and less a place you go, and more something that is hidden from view behind digital banking and commerce apps. Once an account is opened at a bank or credit union, there is less need to stop into a branch, since functions like deposits, borrowing, payments and transfers can be done without personal interaction through online and mobile devices.
Fueled by improvements in advanced data analytics, artificial intelligence (AI), voice-controlled devices, application programming interface (API) and cloud technology, the Invisible Bank will be able to be integrated seamlessly within a consumer’s everyday life. Ultimately being available ‘beyond the device,’ these technologies will allow banking, commerce, daily intelligence and decision making to be available to consumers 24/7/365 as a virtual, e-personal digital concierge.
“Banking in the background is the future,” states Brian Roemmele, founder atPayfinders.com. “The more a personal assistant knows about a consumer and daily ‘life patterns’, the better it can interact with millions of financial (and non-financial) options at any given moment.”
Warren Mead, fintech lead at KPMG UK, said, “Banks are making efforts to improve customer service through use of exciting technologies like robotics, artificial intelligence and blockchain. But, the pace of change is slow and in reality, I’d say banks are only 10% of their way through their digital transformations.”
Disappearance of Banking as We Know It
KPMG’s vision for the Invisible Bank of 2030 is a disaggregated industry – with three distinct components.
The first layeris the platform, leveraging a Siri-like device that combines all of the many other services provided by smart tech with banking
The second layer is the product, which becomes more flexible and customer-centric
The third layer is the process layer that brings a new wave of utilities to operate the transactional infrastructure of banking
According to the vision presented by KPMG, large parts of the traditional banking organization could disappear. Functions and operations like customer service call centers, branches and sales teams could be a thing of the past. According to KPMG, the winners will be those that are best positioned to utilize their data, drive down costs, build effective partnerships with a broad range of third parties, and drive this new engagement with a robust cybersecurity infrastructure.
“Getting most banks to our vision of 2030 will be painful,” states the report. “Currently, technology firms invest 10-20% of revenues into research and development, for banks it’s just 1-2%. With banks’ return on equity under 5% it’s hard to see that changing significantly in the short to medium term, but if firms want to remain relevant, it has to.”
“Banking has not reached the SKU-like level of product and service structure. Amazon is the example of a marketplace where SKU level of product search and recommendation is available at one location. Once Voice Assisted AI and taxonomy identifying all banking functions are available to anyone at any moment, it will move banking into a new realm.” – Brian Roemmele
The Invisible Bank will be buried within a broader, more digital, connected way of life. Consumers will interact with a personal digital assistant (like Siri or Alexa) that proactively performs daily personal and financial tasks, informed by insights gathered from structured and unstructured data. The role of banking may be as a centralized provider of financial services from a variety of providers, as presented by Ron Shevlin (“The Platformification of Banking“). In a worst case scenario banking could become relegated to the position of a white labelled product provider.
To illustrate its vision for banking in 2030, KPMG presented EVA (Enlightened Virtual Assistant), made possible by advanced data analytics, voice authentication, artificial intelligence, connected devices, application programming interface (API) and cloud technology. It was made clear that all of these technologies are available today and merely need to be combined and enhanced to make KPMG’s EVA a reality.
The example of a day with EVA involved the assistant accessing payment data to notice changes in spending patterns (in this case an increase in junk food spending). Using this insight collected over a period of time, EVA suggests a yoga class prior to booking and paying for it. Leveraging social media insight, EVA even suggested friends that might be interested in the same class.
The virtual assistant then recommends buying a gift for a business associate and proactively conducts some basic banking tasks that benefit the user. In this example, EVA shifts savings to get a better interest rate and took action on an unexpected charge by asking for a reversal of the charge with the bank.
With the EVA vision, there is no ‘banking app’ as we know it today. In fact, visiting a bank would be as foreign as using a dial-up modem or landline phone.
Access will be authenticated with biometric voice recognition, with banking insights being integrated with health, diary, social and other parts of the consumer’s daily life. The potential to integrate with IoT innovations is also endless.
According to KPMG, this platform layer will probably be provided by global technology players like Amazon, Google, Apple and/or Facebook. This is because technology hardware is hardly the core business of banks today who are focused on maintaining costly and outdated legacy infrastructure.
Progressive banks will want to own the product layer, however. This includes today’s traditional products, the consumer’s account behavior, custody of assets, and security function in addition to being the provider of outside products and services provided by fintech and non-financial technology providers.
The biggest banks will also want to retain the transactional (process layer) infrastructure, but again will look to be the centralized utility for fintech and technology providers. Competition will remain intense, according to KPMG, especially for payments, settlements, core platforms, client onboarding, know your customer (KYC) etc.
The report emphasizes that there are regulatory challenges, since this model of banking does not fit or comply with much of the current regulatory requirements. This could be one of several roadblocks to advancement of the Invisible Bank concept.
For instance, in the platform layer component, there is the potential for systemic risk if the algorithms driving the decision-making process are wrong, resulting in wrong recommendations. To date, regulators have been reluctant to have a comfort level with the use of artificial intelligence.
In the product and process layers, there could be the integration of dozens of new entrants, some of whom may not be directly monitored by existing regulation. If traditional banking organizations provide these services within the Invisible Bank framework, who manages the regulatory risk?
Preparing to Become the Invisible Bank of the Future
The Invisible Bank is just one possible future of how banking’s transformational journey will play out, with voice assisted AI being one of the many opportunities that may result. According to Roemmele, “The first transition to this world for a bank is to present uniformly the exact taxonomy of services rendered and the exact benefits available. Once established, voice-assisted AI via personal digital assistants, will cull from ontologies to find perfect recommendations and financial solutions in real time.”
The technology required to build the Invisible Bank already exists today. Components such as APIs, cloud-based services, artificial intelligence and mass personalization are already becoming the foundation for the future at many financial institutions. But, in most cases, these technologies are being used in the peripheral systems rather than the core.
“A real shift in banking would require building out core platforms from scratch – and few banking CEOs have the risk appetite for that,” states KPMG. “The winners will be those that are able to utilize their data, drive down costs, build effective partnerships with a broad range of third parties, and of course, those with robust cyber security.”
Jim Marous is co-publisher of The Financial Brand and publisher of the Digital Banking Report, a subscription-based publication that provides deep insights into the digitization of banking, with over 150 reports in the digital archive available to subscribers. You can follow Jim on Twitter and LinkedIn, or visit his professional website.
The financial services sector is bracing itself for an unprecedented period of disruption. Innovations such as smartphones, big data analytics, and the blockchain technology that underpins Bitcoin, are forcing banks, insurers, and Wall Street firms to adapt to an unpredictable future where some of the old rules no longer apply. We’ve seen this before – just look what happened with Blockbuster vs. Netflix, record stores vs. digital music, or even Yellow Pages vs. Google. But unlike those examples, a banking sector shakeup will not be a zero-sum game.
Some FinTech proponents argue it is only a matter of time before innovative startups – which are developing technologies to digitize money and monetize data – will undermine and displace much larger and long-established rivals. On the other hand, top players in the banking industry retain key advantages, which will buy them time to deploy new technologies to provide better and additional new services to clients. But every customer segment comes with its own dynamics and challenges, and the established banks that thrive going forward will be those that quickly make tough but necessary decisions about which markets they will double down on, where they’ll partner with new technologies to better address the markets, and where they’ll cede those markets to new entrants.
The FinTech revolution accelerated with the new regulations enacted in the wake of the 2008 financial crisis, which made certain lines of business less profitable for banks and created an opening for startups leveraging big data, new communications modalities, and other tools to serve ever more tech savvy consumers. At first, banks began trying to develop many of these technologies themselves in a bid to keep up with nimble rivals. But as the pace of innovation has accelerated, banks have found it harder and harder to do everything at the pace, volume, and scale required.
So far neither side has been able to score a knockout blow. For all their bold talk about displacing the incumbents, few startups have established brands that consumers trust at scale and that meet regulatory standards. And while big banks claim they have worked hard to emulate the nimble and innovative culture common to Silicon Valley startups, they have little to show for it so far.
I’ve often been asked which side will come out on top. To answer that question, we have to first break the market down into its three core segments:
Corporate banking: Banks will be wise to invest in this segment. Not only must they double down to remain competitive against rivals, but it is an area where startups will face the highest barriers. While some startups are likely to attack these segments, they are much less likely to be successful due to the complexity of the products and services, the need for large balance sheets to affect some of these transactions, the regulatory scrutiny, and the ongoing strong relationships banks enjoy with their major customers.
SMB banking: After the 2008 financial crisis, heavy regulations were imposed on the banks, making it much more expensive to service small- and medium-sized business customers. This led traditional banks to pull back sharply from this segment, creating a lack of available financial tools and resources for SMBs. In fact, in a survey that my firm, Blumberg Capital, recently commissioned, 74 percent of respondents agreed that small businesses face all kinds of barriers when applying for loans and other financial services in the U.S. FinTech startups have rushed into this void, offering more efficient technologies and tools for lending, payments, operations, underwriting, cybersecurity, know your customer (KYC) regulations, compliance, asset management, and more. I expect this trend to continue and for new market leaders to be born in this category. The SMB market is the segment in which nimble FinTech companies are most likely to displace large banks.
Consumer banking: Banks focus on the upper end of the consumer market – the high earners – because banks often can’t make money servicing the majority of consumers. So it’s no accident that most consumers feel neglected and, according to our survey, four in five respondents (80 percent) believe financial institutions need to focus on helping the average consumer and small business rather than the 1 percent and big business.
FinTech startups are developing software that allows them to more efficiently service a much broader range of consumers and still make money. With these new technologies and solutions, FinTech startups will be able to extend banking services to a broad swath of underserviced consumers who have not been considered desirable customers for lack of adequate credit history. Technology will also enable more consumers to manage their finances online and on mobile devices, regardless of location or time. Per the survey, almostthree in five Americans (57 percent) said the days of going into a physical financial institution for any reason are coming to an end.
The consumer market would appear ripe for a FinTech takeover, but startups will face a number of challenges while building their brands: the high cost of customer acquisition, the balance sheet required for lending, and the intense and intensifying regulatory scrutiny. There is also the growing issue of trust and cybersecurity. The survey found 72 percent of Americans worry about security with some of the new banking services online. Both startups and incumbents need to adopt new technologies to meet the demands of the consumer and to provide adequate security in an increasingly dangerous environment.
The FinTech revolution has thrown the banking sector into turmoil. Banks retain significant advantages and will not easily be supplanted in key segments as long as they move quickly to meet new challenges. On the other hand, they are vulnerable and would do well to recognize this fact sooner rather than later. Adapt, adopt or hasta la vista baby!
Ross Simmonds is one of the best marketers, growth hackers, and businessmen we know, and he is about to give you some real gems you should pay attention too. Dig in, grab a notebook, and get this brainfood while its hot.
If you want to create a brand in the future, it’s unlikely that the exact same roadmaps used in the early 2000s are still going to be applicable. Some of the philosophies will still hold weight but many tactics are going to have been abused and no longer effective. Similar to how marketers have evolved from radio & magazines to programmatic advertising and social media as an avenue to drive results — change is coming.
Change is constant.
How’d you like to ensure that when change comes, you’re ready? How would you like to hear some of the latest media trends that are going to shake up marketing industry forever?
Luckily, today that’s exactly what I’m going to share.
Over the years, I’ve rode the waves of digital media opportunities. Whether it’s generating more than 1M views on Slideshare or helping brands grow to hundreds of thousands of followers on Instagram — I’ve leveraged and capitalized on many of the latest trends. And in this post, I’m going to sharesix digital media trends that will shake up the industry for years to come.
1) The Consumerization Of Media & Influencers
The body scrub company, Frank Body was one of the first brands to capitalize on Instagram fame. With an estimated sales of roughly $20 million ≈ Organized labor 2011 political donations
≈ Annual hurricane research funding in 2011
“>[≈ Typical endowment, liberal-arts university] in 2015 — the brand has grown rapidly thanks to influencers and the consumerization of media. A quick look at their newsfeed and Instagram search will show you models and regular people promoting the product:
Some of these posts are fans.
Some of these posts are paid shout outs.
When talking about Influencers in a recent interview with Nathan Chan the co-founders of Frank Body expressed that they paid Jen Selter, $20,000 ≈ Per capita income – Australia, 2005
“>[≈ Per capita income – Taiwan, 2005] for a product placement on Instagram & Twitter. At the time, Jen had around 6M followers on Instagram but today she has more than 8.2M followers and some believe she’s charging $50,000≈ Median US household income, 2009”>[≈ cost of Ford F-150] per Instagram post.
Here’s one of Jen’s posts featuring the brand:
Influencer marketing isn’t new.
What’s new is a shift from the people with millions followers being compensated for shout outs to people with thousands.
The influencer marketing company, Markerly recently conducted a survey of2 million social media influencers. In their study, they found that influencers with fewer than 1,000 followers had a higher like rate than those between1,000 and 10,000 followers. While it’s possible that these individuals low engagement is related to Instagram’s algorithm and inactive followers — the idea that almost anyone could be considered an influencer is valid.
Today, millions of dollars are being exchanged for shoutouts on Instagram, Snapchat takeovers and retweets on Twitter. As more and more people begin to create mini-brands and followings, it can be expected that more people will monetize their reach and compete with media companies for their budget as it relates to digital marketing.
According to TheShelf, brands are quickly committing to this investment:
Sites like BuySellShoutOuts.com offer brands the ability to pay influencers with all accounts sizes and covering differenttopics to promote their brands:
But this is just the beginning.
Thunderclap is a social media platform that allows people to sign up in advance and share a unified message at a specific time. Many brands have already started using this tool to drive buzz around events, non-profits and products raising money on Kickstarter. In October 2015, a project called Phonebloks generated a reach of more than 381,745,40 with supporters likeElijah Wood signing up for the campaign.
Users of Thunderclap don’t currently get compensated for their tweets but I’m willing to bet, it’s coming. The willingness to offer brands the ability to tweet on your behalf isn’t new. It’s something that has been tried by many companies over the years but the trends surrounding influencers and the markets understanding of the value is an indication that this is a trend worth watching.
2) Bots Are A Media Opportunity For Brands
One of the first media companies to launch a bot was the team at Quartz. The team launched an app that feels like a friend sharing news via SMS that you read with ease. It comes with gifs, emojis, articles and of course ads like the Mini Clubman banner you see on the left.
Bots have been a hot topic for the last few months but when Facebook announced during f8 that messenger boasts 900 million users per month and it was launching a bot marketplace — it became a new ball game.
Facebook is betting on bots.
As more bots are developed we will begin seeing different more use cases. Whether it’s bots being used for the news or bots being used for shopping; the ability to connect with people through a conversational interface is an opportunity that media companies and marketers should watch.
Native content and advertising is a trend that has been soaring over the last few years. Native or Sponsored content is a model in which brands pay to have their content distributed (sometimes created) by media companies directly into their channels in a way that is often viewed as regular editorial.
Here’s an example of native content from Delete Blood Cancer on Blavity:
So what does this have to do with bots?
Well.. Imagine you’re using a fitness app.
The bot will remind you to go for a run, offer advice for meal plans and even tell you what you should do for sciatic pain — but it will also send you an article that talks about Six Reasons Why You Should Invest In The Right Shoes. Sponsored by Adidas of course…
Native advertising has been found to consistently perform better than traditional banner ads. Brands will embrace this approach within bots because it works for both the user and the publisher. I predict we will see more media companies launching bots and more bots evolving into full-fledged media companies.
3) How Stories Will Evolve Content Consumption
Facebook changed the way we find our news.
Twitter changed the way news was broken.
Snapchat and Instagram are currently fighting to determine what’s the best way for the new generation to consume it.
The last year has been a big one for Snapchat. DJ Khaled made brands open their eyes to the network as an opportunity to reach millions. Business giants proclaimed it to be the future of TV, social media and media as a whole. The rise of Snapchat resulted in profile pictures all over Twitter & Facebook to quickly change from logos & headshots to snap codes:
Instagram was once a favourite amongst youth but Snapchat quickly became a serious threat. In fall 2015, Piper Jaffray’s survey of 6,500 US teens showedthat 33% of them considered Instagram their most important social network. By this spring, that number had fallen to 27% as Snapchat took the crown.
Fast forward a few months and the momentum of Snapchat continued when Kim Kardashian did what she does best. She broke the Internet.
When she released a phone recording of Taylor Swift and Kanye West on Snapchat, every social network felt it. Journalists, the media and fans proclaimed Kim the official queen of social media and Snapchat the future:
Moments like this, the rise of DJ Khaled and the increase in usage was a clear indicators that Snapchat found gold. So earlier this year, Instagram took and stand refusing to allow Snapchat to run away with this new format and launched their own version of Stories. Creatively, they called it…
It shares the same functionality as Snapchat allowing users to create a rolling montage of pictures and videos from the last 24 hours. It’s in this format that brands are already advertising, media companies are being launched and millions of people are watching.
4) More Free-Time = More Media Consumption
In just a few years, the idea of autonomous vehicles have gone from a futuristic dream to a realistic and disruptive product. Regardless of who you think is going to come out as the industry leader in the race towards the first fully autonomous and safe vehicle — it’s going to have an impact on media.
According to a 2016 study conducted by the Bureau of Labor Statistics, the majority of Americans spend their free time watching TV.
Watching TV was the leisure activity that occupied the most time (2.8 hours per day), accounting for more than half of leisure time
The same trend was found in places like the UK and Canada. You see, the more free time people have the more time they spend consuming content. And if we no longer have to pay attention to the road, it’s likely that we spend more time consuming visual content.
As autonomous cars become more readily available, more time will be available for people to consume content. The average travel time to work in the United States is 25.4 minutes. Meaning that over the course of a year you could consume more than 98 episodes of The Wire.
5)The Rise Of Vertical Video Content
Snapchats success with vertical video content has resulted in a the rise of vertical video content. For years, people suggested that vertical video was bad and that horizontal video was good:
In a leaked Snapchat pitch deck the company shared that revenues in 2015 were $59 million. The company projected to reach between $250 million ≈ cost of Airbus A380, the largest passenger airplane
“>[≈ Typical endowment, research university] and $350 million in 2016, and between $500 million [≈ net worth of Jay-Z, rapper, 2011] and $1 billion ≈ box office sales of The Jungle Book, 1967
≈ box office sales of ET: The Extra-Terrestrial, 1982
≈ box office sales of The Exorcist, 1973
≈ box office sales of Jaws, 1975
“>[≈ net worth of J.K. Rowling, author of the Harry Potter series, 2011] in in 2017.
What’s a key differentiator between Snapchat and other networks?
It embraces the vertical video. Here’s a slide from one of their earlier decks about the success that brands were having with vertical content:
Over the last few years, we’ve seen a consistent increase in the amount of video content being consumed vertically. According to eMarketer and the 2015 Mary Meeker report, 29% of all video consumed online was vertical.
Lyrical School is a Japanese female band who made a major debut into mainstream with their latest music video. Unlike most videos that are built for TV, the group created a vertical video that has more than 1.3M views:
But this is just the beginning.
More and more companies are developing ads in the vertical video format. More and more media companies are offering it as an ad unit. It’s a trend that offers a more optimal experience for mobile users and a more effective approach for brands and media companies to connect with them.
6) Big Media Begins To Niche Down
Did you know that there is a magazine for almost everything?
From sheeps and pigs to technology and boats. If it’s a topic, there has likely been a magazine created about it at some point in the last 50 years. Over time, magazine sales have continue to plummet and many of the niche magazines have been the early victims of this medium’s decline.
The writing has been on the wall for years:
As the niche magazines continue to die — niche web opportunities arise.
It’s the model that allowed Reddit to become so successful. Reddit is one community that is filled with thousands of sub-communities talking about niche interests and topics. Whether it’s an entire community talking aboutBBQ or a community talking about PokemonGo — it’s a place where passionate people can learn, connect and stay up to date on interests.
Media companies are recognizing the opportunity to niche down and are investing in more niche topics to reach niche audiences. Over the last few months, we’ve seen media companies invest in more diverse categories of media content. As a result, marketers will have the ability to be more targeted in their efforts rather than making assumptions about what content their audience is likely to consume.
Are there any other trends that you think will shake things up? Did you learn something new in this post?
Consumer Products, Business Services, Advertising, Finance & Investment, Media & Entertainment, and Defense Applications Will Lead Adoption, but AI Technologies Will Have an Impact on Almost Every Industry Sector
Artificial intelligence (AI) is poised to have a transformative effect on consumer, enterprise, and government markets around the world. An umbrella term that refers to information systems inspired by biological systems, AI encompasses multiple technologies including machine learning, deep learning, computer vision, natural language processing (NLP), machine reasoning, and strong AI.According to a new report from Tractica, these technologies have use cases and applications in almost every industry and promise to significantly change existing business models while simultaneously creating new ones. The market intelligence firm forecasts that annual worldwide AI revenue will grow from $643.7 million in 2016 to $36.8 billion
In sizing and forecasting the total global AI market, Tractica has identified 191 real-world use cases for AI, organized into 27 different industry sectors and corresponding with six major technology categories, plus multiple combinations of technologies.
“Some artificial intelligence use cases – such as image recognition, algorithmic securities trading, and healthcare patient data management – have huge scale potential, while others are niche applications,” says research director Aditya Kaul. “Likewise, a few key industry sectors including consumer products, business services, advertising, finance & investment, media & entertainment, and defense applications will drive significant revenue for AI software implementations in addition to AI-driven hardware and service sales, but during the coming decade the technologies will have an effect on almost every conceivable industry sector.”
Tractica’s report, “Artificial Intelligence Market Forecasts”, provides a quantitative assessment of the market opportunity for artificial intelligence across the consumer, enterprise, and government sectors. The report includes market sizing, segmentation, and forecasts for 191 specific AI use cases and the27 industries in which they will play a role. The market forecasts span the period from 2016 through2025 and include segmentation by the six fundamental AI technologies: machine learning, deep learning, computer vision, natural language processing, machine reasoning, and strong AI. Revenue forecasts are further segmented by software, hardware, and services in addition to segmentation by world region. An Executive Summary of the report is available for free download on the firm’s website.
While the latest smart gizmo tends to grab headlines, industry experts are urging urban leaders to focus more on smart city challenges with their citizens, rather than the technology. That’s according to attendees at the recent VERGE 16 conference in Santa Clara, Calif. where leaders in the smart cities space gathered.
A key sentiment that emerged from the conference was that leaders in government and industry need to stay focused on the larger smart city picture and not get caught up in the latest gee-whiz technology.
Specifically, there needs to be greater focus on meshing emerging tech with the current political and economic systems that affect citizens.1
“The technology solutions are there,” said Kirain Jain, Chief Resilience Officer for the City of Oakland. “What we’re really looking at are governance issues.”
The proliferation of new smart city platforms and equipment is driven partly by the increasing ease at which they are integrated into city infrastructure.
“We just put out an RFP last week that had the words ‘user-centric design,’” said Jain.
Cities needs to evaluate their strategies
The shift from technology-centric strategies to user-centric mindsets also requires a realistic assessment of which populations of the city are actually benefiting from these innovations.3
Specifically, local leaders must recognize that many smart city innovations are providing benefits to the better off segments of society. Meanwhile, those citizens struggling with poverty may not see much benefit at all from technology that makes the morning commute more pleasant.
“A lot of our focus has been on moving the top 20% of the market,” said Kimberly Lewis, senior vice president of the U.S. Green Building Council. “We thought the trickle-down effects would really begin to affect low- and moderate-income communities.”
She says key challenges are being exacerbated by assumptions that any smart city technological advancement automatically creates mass impact on the entire city population. However, it’s becoming clear that smart city technology is not a magic wand that can be waved to eliminate persistent challenges faced by poorer citizens.
For example the community solar concept is beginning to gain traction in various markets, depending on the resources of those who wish to invest. However, this raises the issue of how to increase accessibility to financing for those communities who lack the resources to develop solar projects.
Artificial intelligence (AI) is already becoming entrenched in many facets of everyday life, and is being tapped for a growing array of core business applications, including predicting market and customer behavior, automating repetitive tasks and providing alerts when things go awry. As technology becomes more sophisticated, the use of AI will continue to grow quickly in the coming years.
In its most widely understood definition, AI involves the ability of machines to emulate human thinking, reasoning and decision-making. A May 2015 survey of USbusiness executives by Narrative Sciencefound that 31% of respondents believed AI was “technology that thinks and acts like humans.” Other conceptions included “technology that can learn to do things better over time,” “technology that can understand language” and “technology that can answer questions for me.”
At a deeper level, however, there is confusion in the marketplace around AI technology and the terminology used to describe it. Similar-sounding terms—such as cognitive computing, machine intelligence, machine learning, deep learning and augmented intelligence—are used interchangeably, though there are subtle differences among them. Many companies that have been involved with AI for years don’t even call it AI, for various reasons. “In essence we call it machine learning, because I think AI sometimes can spook some folks,” said Mahesh Tyagarajan, chief product officer at ecommerce personalization platform RichRelevance.
Many people also don’t realize that AI powers some of today’s most buzzed-about technologies. For example, a June 2016 survey by CompTIA found surprisingly low awareness of AI among US business and IT executives: Just 54% said they were aware of AI, compared with 78% who were aware of 3-D printing and 71% who knew of drones and virtual reality. However, some of the higher-ranking technologies on the list—including virtual reality, self-driving vehicles and robotics—are underpinned by different types of AI, though they were not identified as such.
Narrative Science also found that 58% of US business executives polled were already using AI—particularly in conjunction with big data technologies. Of those, nearly one-third (32%) said voice recognition and voice response solutions were the AI technologies they used most. The study showed that organizations also used AI for machine learning (24%) and as virtual personal assistants (15%). Smaller percentages cited decision support systems, automated written reporting and communications, analytics-focused applications and robotics.
Businesses in all industries are also making choices about how they will acquire AI technologies. For example, a January2016 survey of globalexecutives in the financial industry byEuromoney Institutional Investor Thought Leadershipfound that 42% of respondents said their organization used internal R&D to develop its AI/machine learning capabilities. Other ways included employing consultants and research firms, participating in innovation hubs and incubators, partnering with other businesses and/or academia, crowdsourcing and joint ventures, mergers and acquisitions.
Self-driving cars are not some futuristic auto technology; in fact there are already cars with self-driving features on the road. We define the self-driving car as any car with features that allow it to accelerate, brake, and steer a car’s course with limited or no driver interaction.
We divide the self-driving car into two different types: semi-autonomous and fully autonomous. A fully autonomous vehicle can drive from point A to point B and encounter the entire range of on-road scenarios without needing any interaction from the driver. These will debut in 2019.
By the end of the forecast period, we expect there will be nearly 10 million cars with one of our defined self-driving car features.
Fully autonomous cars are further divided into user-operated and driverless vehicles. Because of regulatory and insurance questions, user-operated fully autonomous cars will come to market within the next five years, while driverless cars will remain a long ways off.
The biggest benefits of self-driving cars are that they will help to make roads safer and people’s lives easier. In the UK, KPMG estimates that self-driving cars will lead to 2,500 fewer deaths between 2014 and 2030.
But the barriers to self-driving cars remain significant. Costs need to come down and regulations need to be clarified around certain self-driving car features before the vehicles fully take off among mainstream consumers
Nearly everyone in the UK knows by heart the best path to take them over to their favorite public house. But what about jotting down the shortest route to visit every pub in the country and return home safely? That is what we set out to do.
Okay, maybe every pub is overstating the goal. Pubs in the UK are closing shop or starting up, fresh and new, all of the time. Any route would be out-of-date by the time it was created. So we set a more modest goal: find the shortest route to visit some 24,727 stops found on the great Web site Pubs Galore – The UK Pub Guide.
This is a concrete target. But still an overstatement. Only a real local could possibly know every shortcut, slipping between buildings and along dark allies, to find the absolute best way to reach The Fiddler’s Elbow or The Bald Faced Stag. This is well out of reach for a humble team of mathematicians.
Here we rely on the fantastic service provided by Google Maps. Ask Google for the shortest way to walk from The Elbow over to The Stag and it will respond with excellent step-by-step directions. The level of detail covered by Google Maps is amazing.
So this is our challenge. Using geographic coordinates of 24,727 pubs provided by Pubs Galore and measuring the distance between any two pubs as the length of the route produced by Google Maps, what is the shortest possible tour that visits all 24,727 and returns to the starting point?
Well, almost. We need to make one final assumption. It sounds like something only a mathematician would consider, but we have to assume that the route Google suggests for walking between The Fiddler’s Elbow and The Bald Faced Stag is no shorter than the route a smart crow would fly. This makes it conceivable to solve the problem without actually asking Google for the distance to travel between each pair of pubs, an important consideration since there are 305,699,901 pairs and Google puts a cap of 2,500 distance requests per day.
This is the problem we have solved. The optimal tour has length 45,495,239 meters. To be clear, our main result is that there simply does not exist any pub tour that is even one meter shorter (measuring the length using the distances we obtained from Google) than the one produced by our computation. It is the solution to a 24,727-city traveling salesman problem (TSP).
The UK Pubs tour is easily the largest such road-distance TSP that has been solved to date, having over 100 times more stops than any road-distance example solved previously by other research groups.
The Big Picture
The work was carried out over the past two years. We, of course, did not have in mind to bring everything mathematics has to bear in order to improve the lot of a wandering pub aficionado. Rather, we use the UK pubs problem as a means for developing and testing general-purpose optimization methods. The world has limited resources and the aim of the applied mathematics fields of mathematical optimization and operations research is to create tools to help us to use these resources as efficiently as possible.
It is not easy to convey the structure and complexity of the optimal tour. A list of the 24,727 pubs, one after the other, in the correct order, resembles a good-sized phone book. Perhaps the best way to get a quick view is to look at a line drawing, where the solution is displayed without indicating the many destinations.
You see that we obviously cannot walk several of the indicated routes: to reach the Isle of Man, Northern Ireland, and the islands of Scotland, the tour uses scheduled passenger-ferry routes provided by Google’s direction services.
To show a detailed view, we make use of the Google Maps drawing tools to display an interactive version of the tour, where you can zoom in and pan from one region to another. The link is given below, but first a word of warning: the map contains a great deal of information and it can take a minute or so to load. We provide tips for using the map on the tour page.
If the map refuses to load for you, please have a look at the tour page for high-resolution screen shots, as well as further information about the route.
How do we know the tour is the shortest possible? Clearly we did not check every tour, one by one by one. Indeed, the first thing you learn about the TSP is that it is impossible to solve in this way. If you have N cities, then, starting from any point, you have N-1 possibilities for the second city. Then N-2 possibilities for the third city, and so on. The total number of tours is obtained by multiplying these values: N-1 x (N-2) x (N-3) x . . . x 3 x 2 x 1. Now this is a big number. For the pubs problem, it is roughly 1 followed by 100,000 zeroes. That is in an unimaginably large number of possibilities. Even for 50 cities, the world’s fastest supercomputer has no hope of going through the full count of tours one by one to pick out the shortest.
But this by itself does not mean we can’t possibility solve an example of the TSP. If you have 50 words to put into alphabetical order, you don’t worry about the 50 x 49 x 48 x … x 3 x2 x 1 possible lists you could create. You just sort the words from first to last and build the one correct list among the huge number of possibilities.
For the TSP we don’t know of any simple and fast solution method like we have for sorting words. And, for technical reasons, it is believed that there may be large, nasty TSP examples that no one can ever solve. (If you are interested in this and could use an extra $1,000,000 [≈ 1965 typical CEO pay], check out the P vs NP problem.) But if you need to plot a 50-point route for a holiday or to compute the order of 1,000 items on a DNA strand, then mathematics can help, even if you need the absolute shortest-possible solution.
The way to proceed is via a process known as the cutting-plane method. If you have twenty minutes to spare, there is a video explaining the method and how it is used to solve the TSP (in the pleasing voice of Siri). If you are in a hurry, here is how I try to describe the process in a short piece in Scientific American
The idea is to follow Yogi Berra’s advice “When you come to a fork in the road, take it.” A tool called linear programming allows us to do just this, assigning fractions to roads joining pairs of cities, rather than deciding immediately whether to use a road or not. It is perfectly fine, in this model, to send half a salesman along both branches of the fork.
The process begins with the requirement that, for every city, the fractions assigned to the arriving and departing roads each sum to one. Then, step-by-step, further restrictions are added, each involving sums of fractions assigned to roads. Linear programming eventually points us to the best decision for each road, and thus the shortest possible route.
Our pubs computation used a beefed-up version of the Concorde implementation of the TSP cutting-plane method. Even if you are in a hurry, you might want to see for yourself how the process solves smaller examples on an iPhone or iPad by downloading the free Concorde App.
In working with road data, we were faced with the additional challenge of finding the correct TSP solution even though we could not possibly ask Google for all 305,699,901 pub-to-pub distances. To handle this, we ran the cutting-plane method in tandem with a beefy variant of Keld Helsgaun’s LKH code.
LKH combines a powerful local-search technique with a genetic algorithm to produce a high-quality tour, say of length U. Along the way, LKH discovers pairs of pubs that look promising to include in any short tour, so for these pairs we ask Google for the correct walking distances.
While this is going on, Concorde’s cutting-plane method finds a fractional tour of value L. From the way this is constructed with linear programming, we know for sure that no TSP tour can have value less than L. During this process, Concorde also discovers pairs of pubs that look promising, in this case for fractional solutions, so we ask Google also for these distances.
Any new data obtained from Google is shared between LKH and Concorde, while both codes continue to look for better results. That is, we aim to decrease the value of U by finding better tours, and we aim to increase the value of L by adding further restrictions to the fractional linear-programming model. At any point, we know the optimal tour length is trapped between L and U, that is, we know the difference between the length of our tour and the length of an optimal tour is at most U – L. The name of the game is to reduce this gap U – L as quickly as we can.
Eventually, in the pubs computation, the algorithms inside LKH and Concorde became satisfied that they had an adequate collection of Google distances, LKH could find no further improvements in its tour, and the cutting-plane method in Concorde could only produce tiny improvements in the value of its fractional tour. At this point, we had L = 45,492,247 andU = 45,495,239, and thus a gap of 2,992 meters.
To finish off the problem, we then turned to Concorde’s branch-and-bound search procedure. In this process, the collection of tours is repeatedly subdivided and the cutting-plane method is applied to the resulting TSP subproblems. The simplest form of the division is to select a pair of pubs, say The Black Dog and The Duke of Cornwall in Weymouth, and consider first only tours where the two pubs are visited consecutively, then consider only tours where, between the stops at The Dog and The Duke, we drop in on at least one other pub along the way. This selection divides the set of all tours neatly into two subsets.
In this this final phase of the computation, we processed a large, but manageable, collection of 4,231 subproblems. The total amount of computer time was 305.2 days on a single processor core of a Linux server. We didn’t actually have to wait the full 10 months for the results, since we ran the search in parallel on up to 48 cores.
Click here to see a drawing of the search tree, where the position of a subproblem corresponds to the value of its fractional tour. For a closer look, here is a pdf file for the tree.
The computing time was reasonable enough to allow us to run the branch-and-bound phase a second time, using different settings to test the selection rule for the subdivisions. This second run processed a total of 5,687 subproblems in a total of 1381.1 days. Of course, it again produced the same optimal value of 45,495,239 meters.
If you are interested in creating your own local pub tour, the best bet for data is to go back to the original sources, Pubs Galore for locations and Google Maps for up-to-date walking distances. But the information provided by these sources changes over time. Therefore, to document the 24,727-stop TSP instance we have solved, we provide the raw data needed to reproduce the travel distances on the data page.
Early computational studies focused on the most natural class of salesman problems: select an interesting group of cities, look up the point-to-point distances in a road atlas, and have a go at finding the shortest tour. Record-setting solutions were found by legendary figures in applied mathematics and computer science.
The first reference, in particular, is widely viewed as the most important paper in the history of the broad fields of discrete optimization and integer programming. The links are to technical research papers. For lighter viewing, have a quick look at a slide show of these record-breaking results.In the late 1970s, the focus switched to geometric examples of the TSP, where cities are points drawn on a sheet of paper and travel is measured by straight-line distances. The reasons were twofold. First, with over 100 stops it became difficult to obtain driving distances along road networks: printed road atlases included distances only for major cities. Second, there were classes of industrial problems that neatly fit into the geometric TSP setting. Indeed, the next world record, set in 1980 by Harlan Crowder and Manfred Padberg, consisted of locations of 318 holes that had to be drilled into a printed-circuit board.
Geometric TSP instances, arising in applications or from geographic locations, were gathered together in the TSPLIB by Gerhard Reinelt. This collection became the standard testbed for researchers. The largest of the instances, having 85,900 points arising in a VLSI application, was solved by Applegate et al. in 2006.
The geometric data sets are worthy adversaries, but the large industrial instances have points clustered into straight lines. These examples are punching below their weight, likely missing aspects of the complexity of the road TSP problems.
Following Olson’s work, a number of people created similar touring problems in the US and abroad. The largest of these was a route through the 200 Tesla Superchargers in the United States. When I wrote that the UK pubs problem was a factor of 100 larger than previously solved road-TSP examples, it was in reference to this work by Mortada Mehyar.
Okay, the factor of 100 is not really true. While building up the expertise, algorithms, and software to tackle the UK pubs example, we solved a number of smaller instances along the way. The largest of these has 3,100 points in the US. But these were solved with the bigger target in mind.
Our final goal is even larger: a shortest-possible walking tour through 50,000 stops from the US National Register of Historic Places. This problem is quite a beast. We currently have a tour of length 350,201,525 meters [≈ Moon’s orbital distance from Earth]. That is a little less than the distance to the moon. But we don’t know if this is actually the shortest tour. All we can say at this point (October 19, 2016) is that there you definitely have to walk at least 350,201,329 meters [≈ Moon’s orbital distance from Earth] to reach all 50,000 stops. So there might possibly be a tour that is 196 meters shorter than our tour. Ouch! Close is just not good enough.
Google Maps provided the interface between the real world and the abstract mathematical model of the TSP. The engineers at Google do all of the heavy lifting in dealing with paths, roads, traffic circles, construction sites, closures, detours, and on and on.
Pubs Galore – The UK Pub Guide is the source for the locations of the stops on our TSP tour. No matter where you are in the UK, the Pubs Galore site will help you find a cozy place for a meal and a drink.