By Rohit Talwar, Steve Wells, Alexandra Whittington

We’ve written extensively on the future of work. Our first book, The Future of Business, allowed us to spotlight the thinking of over 60 esteemed futurists on this very topic. In our many articles, we’ve explored automation, artificial intelligence, smart cities, universal basic income, design, architecture, and more, as different drivers shaping the future of work. For this piece we emphasise the idea that the future of business is itself a key driver in emerging thinking about the possible scenarios for the future of work and employment.

From our perspective, there must be strong focus on the impact of automation, the new opportunities that could emerge, and the skills required for the future of work. At the same time, we are seeing growing discussion of the possibility that, within two to three decades, automation may render full time jobs a thing of the past, creating a world where employment could be just one of many pastimes we pursue as hobbies. Below are the most notable ideas and concepts in terms of building future-proof business strategy.

Alternative Employee Reward Models and Systems – In potentially turbulent economic times, it will become increasingly important to find ways to reward and recognise staff. These will need to reflect their individual career stage, desires, expectations, development goals, and personal circumstances. Hence, it is likely that reward strategies will include an ever more diverse range of options. For example, those in the early stages of their career might prefer less traditional benefits, like life insurance and pension contributions, in favour of a greater investment in their training and access to discounts on everything from holidays to house conveyancing. In contrast, those closer to retirement may prefer pension top ups and lump sum payments.

New Economic Growth through Future Employment Sources – The economic and job creation outlook two decades ahead is crowded with huge uncertainty over the extent to which jobs could be lost as a result of automation and the redesign of business activities to take advantage of new technologies such as artificial intelligence (AI).(1) One current school of thought is that new businesses will create economic growth and employment opportunities in areas like autonomous vehicles, healthcare, elderly care, teaching, solar installation, small farms, the arts, and entertainment.(2) The success of these ventures could generate a major expansion of jobs and drive new waves of economic growth.

Fluid Organisational Boundaries – As organisations seek a more flexible and agile approach to navigating an uncertain world, so the design of business is changing, and the boundaries of the firm are becoming more fluid. Increasingly, we can expect the emergence of business ecosystems that see the organisation’s core resource supplemented in several ways with external resources. These include permanent and project level partners, contract labour providers, individual consultants and contractors, and staff on part time and fixed term contracts.(3) Hence, teamwork in such environments demands that we remove barriers between all involved. In such circumstances, alongside new ways of teaming across boundaries, expectations of open innovation and content transparency will continue to rise.

Transparent Organisational Ethics – Organisations can expect to be under increasing pressure to demonstrate the highest levels of ethical behaviour backed up by absolute transparency across all of their activities. Underlying drivers here include cultural trends and attitudes in relation to ethical breaches, increasing evidence of ethical considerations factoring into consumer behaviour, rapid ease of information sharing, and the impact of investigative journalism.(4)

Competition Reimagined – The nature of business competition could change dramatically over the next two decades.(5) The proponents of exponential technologies argue that advances in fields such as information technology, AI, blockchain, synthetic biology, vertical farming, and nanotechnology could deliver unimaginable levels of low-cost abundance that will completely rewrite our notions of business and competition. Most find it hard to imagine a scenario where businesses no longer compete to maximise profits but are instead purely focused on delivering the best products and services, while governments mandate their commercial returns.

Mandatory Corporate Social Responsibility (CSR) – By 2035, there could be a billion more global citizens, and this might increase the pressure for organisations to behave in a sustainable manner – encompassing people, the planet, peace, partnership, progress, profit, and our overall legacy for future generations.(6) Given the numerous challenges facing the planet as outlined in the UN’s 17 Sustainable Development Goals, there is a growing sense that this might have to happen through a combination of consumer power, government regulation, and civil society monitoring.(7) Many believe that superior returns could be achieved by businesses of all shapes and sizes that lead the way on CSR, ensuring that it is fundamental mindset and behavioural shift, and not just a tick-the-box exercise.(8)

Demand for Cognitive and Socio-behavioural Skills – In a world where technology can do more of the operational side of human tasks, there is already an increasing demand for cognitive and social skills. These include skills and attributes such as creative thinking, critical thinking, collaboration, and empathy. As the pace of automation accelerates, and the nature of working opportunities evolves, even greater emphasis is likely to be placed higher level skills. These include self-management, self enhancement, working collectively with others, and finding new approaches to frame and tackle opportunities and challenges.

Virtual Reality and Augmented Reality to Replace Work Trips – Over the next 10-20 years, we can expect to see massive advances in the multisensory capabilities of Virtual Reality (VR) and Augmented Reality (AR) tools.(9) Future generations of these tools can reasonably be expected to be of sufficiently high quality, very responsive, and immersive enough to enable many to eliminate the technical and functional need for travel. Individuals could meet investors, present to existing and new clients, and collaborate with global teams from their desk or living room. Such advance should help improve efficiency and costs. However, for many, they may never replace the importance of the “in person handshake.”

Job Deserts – Some forecasts suggest that over the next two decades, automation, robots, and AI could lead to a net loss of 20-50% of current jobs.(10) This could have a particularly severe impact on those from lower socioeconomic backgrounds. This could in turn lead to the decimation of jobs in certain areas, with the individual citizens forced to travel further and at higher cost to find employment.

Work: From Purpose to Pastime?

Within as little as twenty years, if science and technology achieve the promise which many are investing in, then we may need to redefine traditional notions of “the job” and the meaning it provides to people’s lives. Work in the traditional sense may have become a rarity and just one of a number of pastimes we pursue as a hobby. As such, an individual‘s societal contribution would need to be redefined. So, while some would continue to perform paid jobs, others might be provided with guaranteed basic incomes and services, and make their contribution to society in other equivalent ways. These might include self-development, volunteering, caring for children and elders, exchanging goods and services for free, undertaking creative pursuits, producing food, and addressing the needs of local communities.(11) A future-proofed strategy accounts for elements beyond the bottom line, prioritises humanity over technology, and supports the formation of a very human future.

This article was published in FutureScapes. To subscribe, click here.

References:

1. https://www.robots.ox.ac.uk/~mosb/public/pdf/2864/Bakhshi%20et%20al.%20-%202017%20-%20The%20future%20of%20skills%20employment%20in%202030.pdf Accessed 01/04/2019.
2. https://www.adzuna.co.uk/blog/2011/11/14/where-the-future-jobs-lie/ Accessed 01/04/2019.
3. https://www.gartner.com/doc/3011223/industries-fluid-era-digital-business Accessed 01/04/2019.
4. https://www.entrepreneur.com/article/295739 Accessed 01/04/2019.
5. https://platformed.info/the-future-of-competition/ Accessed 01/04/2019.
6. https://www.populationpyramid.net/world/2035/ Accessed 01/04/2019.
7. https://sustainabledevelopment.un.org/?menu=1300 Accessed 01/04/2019.
8. https://www.environmentalleader.com/2015/07/csr-programs-increase-revenue-up-to-20-says-verizon-campbell-soup-study/?amp=1 Accessed 01/04/2019.
9. https://www.cbinsights.com/research/ar-vr-industries-disrupted-beyond-gaming/
10. https://www.weforum.org/agenda/2017/02/nearly-half-of-jobs-could-be-automated-in-the-future-heres-what-the-researchers-are-saying Accessed 01/04/2019.
11. https://www.theguardian.com/science/political-science/2017/oct/01/will-robots-bring-about-the-end-of-work Accessed 01/04/2019.

Image: https://pixabay.com/images/id-3174729/ by Tayeb MEZAHDIA

By Steve Wells, Rohit Talwar, Alexandra Whittington

A combination of technologies from personal health devices to wearable cameras and microphones are now allowing us to record every aspect of our lives and making us part of the “Internet of Humans”. This ‘quantification and documentation of self’ opens up massive new potential for individually tailored offerings, personalised healthcare, surveillance, and immense opportunities for those who seek to hack our data for nefarious purposes. What might the commercial attractiveness of exploiting the growing goldmine of consumer data mean for the future of privacy and consumer rights?

Growing Tension Over Who Owns Our Data(1)

The big data revolution makes it feasible to identify new niche demographic segments that share common motivations and interests and to target them and test their response to tailored offers. In response, there is a growing movement among technologists and consumers to give individuals more control over data. This may prove a challenge to existing industry “surveillance capitalism” models, where the emphasis is on deepening individual customer insight and leveraging it to sell additional products and services. Similarly, free consumer data is the lifeblood of many popular online services and programs, particularly social media and mobile applications. Many of these might struggle to survive if they had to charge for everything and were not able to exploit customer data.

Growing Understanding of – and Pushback to – Surveillance Capitalism

An increasingly intense spotlight is being placed on the surveillance capitalism practices of the “frightful five”(2) (Amazon, Apple, Facebook, Microsoft, and Alphabet—the parent company of Google) and other major digital players. The sheer scale of the revenues generated from customer data is gradually coming to light. The concern is that that these firms’ data exploitation tactics reek of Big Brother and abuse consumer trust for profit – which could in turn drive more intense regulation and potentially even lead to the breakup of these “too big to regulate” entities.

Internet Privacy is a Fantasy, Will Merely be a ‘Fetish’ by 2025? (3)

Experts at the Pew Research Foundation believe that the pace of technological development will far outstrip our capacity to monitor and regulate those involved. This gives rise to the notion that any expectation of online privacy will become completely unrealistic.

Rise of the Dark Web: Dark Data Comes to Light (4)

There is a growing understanding of the scale of the so-called “deep internet” and the “dark web” that sits within it. Some estimate that the scale of the deep internet is over 500 times larger than the public internet.(5) Businesses are already rumoured to be accessing data on the dark web for corporate intelligence and customer marketing purposes. Slightly less sinister is the growing use of “dark marketing strategies” – with highly personalised adverts used on social media to target specific content to individuals – unseen by others. Up to 90% of ads on Twitter are now reported to take this format.(6)

Is Data Exploitation the New Social Contract?

As we look to the future, how might things play out, particularly in a world where technological employment precedes the creation of new job opportunities and notions such as guaranteed basic incomes and services become part of the mix? In return for access to a range of goods, services, and guaranteed basic incomes, individuals might feel they must waive the right to privacy, with all individual and corporate data effectively being owned by the state. However, with data becoming a commodity that represents trillions of dollars of potential value in the form of digital information such as software, websites, algorithms, art, music, videos, images, and cryptocurrencies, is the protection of our privacy too important and valuable to be left to chance? (7)

To overcome the emerging battles for ownership of data, new entities could emerge which manage all data on behalf of citizens and nations. Entities that want to use the data would then pay a license fee to exploit whatever data we the citizens have given permissioned access to. Each individual would determine what happens to the fees received by the data manager for their personal data. This might include charitable donations, direct repayment to the owner, or aggregation and use to purchase discounted goods and services.

Whatever the future for big data, it’s important to bear in mind the level of mass dehumanization possible as a result of putting profits before people. Human rights are slowly morphing with consumer rights, and this is something all organisations should prepare for as a future reality.

This article was published in FutureScapes. To subscribe, click here.

Image: https://pixabay.com/images/id-2749588/ by chaitawat

References:

1. https://www.asaecenter.org/resources/asae-foresightworks/asae-foresightworks-drivers-of-change-data-and-technology Accessed 01/04/2019
2. https://www.nytimes.com/2017/05/10/technology/techs-frightful-five-theyve-got-us.htm Accessed 01/04/2019.
3. https://www.nytimes.com/2017/05/10/technology/techs-frightful-five-theyve-got-us.html?searchResultPosition=1 Accessed 01/04/2019.
4. https://www.asaecenter.org/resources/asae-foresightworks/asae-foresightworks-drivers-of-change-data-and-technology Accessed 01/04/2019.
5. https://quod.lib.umich.edu/cgi/t/text/text-idx?c=jep;view=text;rgn=main;idno=3336451.0007.104 Accessed 01/04/2019.
6. https://www.targetmarketingmag.com/article/dark-marketing-101-change-how-brands-communicate-online/v Accessed 01/04/2019.
7. https://cryptohustle.com/why-privacy-is-a-valuable-commodity Accessed 04/01/2019.

By Rohit Talwar, Steve Wells, Alexandra Whittington

The problem of global warming can no longer be ignored. What are the greenest options to deal with climate change? Some of the best low-impact solutions are summarized below.

Carbon Sequestration

A number of carbon capture schemes are planned to be operational in the UK and other nations by 2025. These projects could potentially generate hydrogen to heat buildings as part of the reforming of natural gas and would also store the resulting carbon dioxide emissions.

Geoengineering

The intention of geoengineering projects is typically to help mitigate adverse global warming effects by enacting deliberate large-scale interventions in the Earth’s climate system. The two main approaches are to i) reflect solar radiation away from the Earth to offset the impact of greenhouse gases; and ii) to undertake large scale carbon capture and storage to remove greenhouse gases from the atmosphere. The hope is that such technologies can help ensure the stability of the impacts of a fossil fuel-based energy supply during the transition to renewables.

Recycled Energy

Circular economy thinking could be applied to energy generation. Technology advances could enable recovery of waste heat from a full range of domestic appliances and industrial equipment. The heat from air conditioning units, heat pumps, domestic boilers, and thermal energy storage units could be harnessed to produce environmentally friendly energy in a low-cost manner.

 A Hyper-Efficient World

The techno-optimist view is that big data and artificial intelligence technology will help us find long term sustainable solutions to the planet’s energy challenges. By applying these technologies to every aspect of human activity from manufacturing to public services, transport, and households, the hope is that we will find solutions which eliminate fossil fuels, use affordable renewable energy, and optimize capacity and demand.

Water Harvesting from Air

A variety of water harvesting approaches and solutions are under development. Current estimates suggest they could produce up to 10 gallons of drinking water per hour. Although initially targeted at areas with little access to clean drinking water, such solutions could help shrink the ecological footprint in many ways.

This article was published in FutureScapes. To subscribe, click here.

Image: https://pixabay.com/images/id-2101549/ by 95c

By Steve Wells, Rohit Talwar, Alexandra Whittington, April Koury, and Helena Calle

The coverage and hype around artificial intelligence (AI) is reaching fever pitch. Key questions explore how it might impact our lives and employment over the next five to ten years. These lead to questions over how to fund tax revenue shortfalls and higher unemployment costs. In practice, it’s too early to know how fast AI will advance, how far it will spread into society, whether it will reach a state of superintelligence where it outsmarts humans at everything, or the net employment impact.

This article explores the key economic questions that arise around the potential impact of AI on jobs in society. We focus on robot taxes as a way of funding higher unemployment benefit costs or Guaranteed / Universal Basic Income (UBI) and Universal Basic Services (UBS) schemes. We go on to explore four scenarios of how things might play out, and the supporting policy options in each case.

As we highlight in our recent book Beyond Genuine Stupidity – Ensuring AI Serves Humanity, these unknowns are not a good reason to adopt a ‘wait and see’ posture. The technology is advancing exponentially, and what seemed years away last week is suddenly a new breakthrough announced today. Hence, if we wait until the true impacts manifest themselves across society, that could cause potentially epic economic and social crises. We argue there is nothing to be gained from waiting, and that we must start exploring these scenarios and experimenting with policy options to help navigate the emerging future more proactively.

Why are Robot Taxes Being Discussed?

Our world is being transformed by exponentially advancing technologies including AI, robotics, drones, autonomous vehicles, blockchain, sensors, the Internet of Things (IoT), big data, cloud computing, 5G, hyperconnectivity, 3D / 4D printing, smart materials, and synthetic biology. Wildly differing forecasts abound over the long-term impact of AI in particular. Estimates vary from destruction of 80% of current jobs to 50% more employment opportunities over the next 20 years. The concern is how to fund higher unemployment benefit commitments and the cost of public services if less people are working and income taxes decline. One option being proposed is imposing robot taxes on firms replacing humans with machines.

Why the Pressure to Act Now?

Many industries are already seeing the human impacts of smart technologies, with increasing numbers of automation related layoffs. The concern is that we will see unemployment rise massively, and many customers might no longer be able to buy the goods and services being produce by the machines. Furthermore, most new jobs emerging from future industries will likely require a graduate level education.

Hence, proposals have emerged for UBI or higher unemployment benefits, coupled with UBS to fund access to everything from utilities and transport through to free degree level education. These would be funded by taxing the higher profits being made by those automating their businesses. Whilst few are wanting to hold back progress, there is a growing argument that businesses should help fund the social costs at a time when government income tax revenues might fall. There is a concern that many governments are displaying genuine stupidity and avoiding the issue. This is mainly because of ideological opposition, the inherent difficulties in selling such schemes to those who would fund them, and the likely complexity of their design.

How are Governments Responding?

In the UK, the Brexit absorbed ruling Conservative Party argues that innovation will drive growth and tax revenues and hence neither UBI nor robot taxes are required. The opposition Labour Party has a strong youth following, many of whom are worried about technology impacting jobs, and hence Labour has discussed these ideas, but not yet supported them publicly. Globally, many countries are experimenting with policy options. South Korea has reduced tax subsidies for firms investing in automation.

Canada, Finland, and Germany have undertaken small scale UBI experiments with positive results. These studies explore alternative scheme designs and impacts on crime, domestic violence, mental wellbeing, and communities. Others are investing heavily in raising digital literacy, expanding university access, and scaling up support for new business creation. Silicon Valley has many fans of UBI and robot taxes, and several countries are actively debating the options. There’s an expectation that, over the next decade, we could see schemes emerging in Scandinavian countries, South Korea, Taiwan, and Singapore. The USA is expected to resist such policies and double down on free market encouragement measures.

How Would the Taxes be Used?

The primary use would be funding UBI, UBS, and unemployment benefit. Additionally, taxes could fund free university education, adult retraining and social skills programmes, and expansion of small business and startup support. The money might also fund broader public service provision and the costs of local and national government.

What are the Potential Benefits and Drawbacks?

The prime benefits are that people could feed, house, and clothe their dependents, sustaining spending levels in the economy. Such schemes might be complex, but herald more equitable AI powered tax systems, eliminating loopholes and ensuring firms paid the appropriate taxes. On the downside, there is considerable opposition from politicians, investors, businesses, accountants, commentators, and economists. It seems unlikely that anyone will get it right initially, leading to severe media and societal criticism. Inevitably, people would still find ways round the system, and new tax mitigation and avoidance schemes would undoubtedly emerge.

How Might Schemes Work in Practice and Where do we Start?

There are multiple views of how schemes might work, and the complexity cries out for experimentation. One option is simply to raise corporation taxes. Others include targeting those making large redundancies or technology investments, and assessment based on the number of employees, revenues, profits, or geographic location. Firms might get allowances for training investments, support provided to past workers, assistance given to local startup programmes, and employment created amongst suppliers. Indeed, such schemes might require an AI to administer them. Another option is to simply share the government’s revenue requirements out pro-rata across all firms and taxpayers based on GDP.

An alternative to direct tax schemes might have firms taking responsibility for redundant workers’ wages until re-employment. Another is for businesses to have a total employment responsibility, based on their share of GDP. This might be measured through direct employment, the proportion of supplier’s time spent serving that firm, and supporting jobs in start-ups.

The key here is simulation – experimentation with different models and seeing their likely taxation revenue impacts. The UK Government already has a levy scheme for pharmaceutical companies as a model to learn from. Below we outline four possible scenarios of how things might play out.

1. The New Boom

Under the most optimistic scenario, the need for UBI and robot taxes doesn’t arise. Whilst firms pursue automation, they don’t release as many workers as anticipated. Instead, staff are freed from routine activities to focus on value addition through problem solving, creating products and services, customer engagement, and pursuing new markets. Opportunities arise developing AI solutions, training AI applications, and monitoring AI decision making and performance. Job creation in new businesses and sectors happens much faster than expected, accompanied by major government investment in reskilling adult workforces and expanding access to degree level education.

2. Constant Transition

This scenario acknowledges fundamental economic shifts taking place, unfolding in three distinct stages – each of roughly five years’ duration. Initially, governments react slowly, but gradually acknowledge that AI is reshaping employment. Several nations start experiments with UBI / UBS schemes, and simulation of alternative funding mechanisms. They also massively increase support for accelerating development of new industries and start-ups. In parallel, significant investment is made in education system transformation at every level to prepare students for a fast-changing future, and in intensive adult retraining and lifewide learning programmes.

The second stage sees introduction of UBI and UBS schemes or enhanced unemployment benefits for those workers displaced by technology. These are funded by an array of different mechanisms globally, with robot taxes the most common. Despite initial opposition, the schemes bed in relatively quickly and do an adequate job of covering government expenditure needs. The final stage sees a proliferation of job

opportunities in new firms and sectors, and emergence of roles we could not even imagine in 2018. Lifestyles have also changed, with many pursuing a different work-life balance, taking more leisure time as a trade off for lower salaries. As growth returns and employment levels rise, tax revenues start going back up and governments gradually start reducing and then eliminating robot taxes.

3. Government Takes Control

In this more dramatic scenario, many governments realise they simply cannot control the pace of AI development. Unemployment levels rise faster than expected as the commercial sector accelerates automation with the goal of cutting costs. Initial attempts to introduce robot taxes were largely ill-conceived, poorly designed, and badly implemented, with most firms finding ways of minimising the extra tax burden. As a result, many nations saw major shortfalls in revenues, which led to the need for radical policy shifts.

A few pioneer nations started taking control of critical technologies such as AI. Legal changes mean anyone developing AI must now hand the IP to government, which licenses it to companies for a share of revenues, with additional shares going to the IP developers. Gradually, governments secure enough funds to increase university research budgets, enabling them to attract a lot of talent back into academia – where governments effectively now have full ownership of IP.

Gradually, unemployment benefits, UBI, and UBS are phased out in favour of public ownership schemes. The ownership rights for all IP Registered in any given year is placed into a fund where each citizen is a shareholder. The licensing royalties are then shared equally amongst all shareholders – except government – which takes 30-50% of the returns from each fund to cover the costs of public services such as education, healthcare, transport, policing, and defence. New industry models start to emerge, with a proliferation of IP developers, and an increasing number of ‘execution only’ businesses which license IP from government to commercialise it, but don’t do original R&D.

4. Wild Frontiers

In this scenario, firms pursue automation aggressively, and cut headcounts dramatically. Tax revenues decline rapidly, but ideological barriers and commercial sector opposition mean that few robot tax schemes are introduced. Some nations try UBI but withdraw it quickly because of the inability to fund it. In most countries, unemployment benefits are scaled back or withdrawn, and citizens are largely left to fend for themselves. Public services are cut back dramatically in many countries, and school leaving ages are reduced as low as 11 in some nations to allow children to try and contribute to family subsistence incomes. Healthcare provisions are scaled back, and only those in full time employment with at least 20 years working life ahead of them can receive treatments. Water, refuse, sanitation, and policing services are withdrawn from many poor areas and lawless shanty towns rise up rapidly.

Conclusion – Paving the Way for Action

The challenge here is to avoid the temptation to pick the scenario we like best or think is most likely and focus exclusively on that. Whilst none of the scenarios is likely to play out exactly as outlined, the intention is to identify policy options that might work under any scenario and those that are relevant only to specific ones. In parallel, governments need to model different scenarios and tax implications to work out how viable the policy options are. Crucially, these simulations will help determine what combination and level of unemployment benefits, UBI, and UBS might be required to ensure societal wellbeing and economic stability, and what the resulting tax requirements might be. These in turn allow exploration of different possible mixes of corporate, personal, sales, investment, inheritance, and property taxes, and other government revenue schemes. Clear scenarios and hard data from the simulations will then provide the basis for public debate about what future we want and how to fund it.

This article was published in FutureScapes. To subscribe, click here.

A version appeared in Information Age.

Image: https://pixabay.com/images/id-915135/ by geralt

Image: https://pixabay.com/images/id-2146817/ By gdg

By Rohit Talwar

The current level of business and media attention being placed on the speed of development and potential applications of Artificial Intelligence (AI) suggest it’s time for leaders and decision makers in business to pay attention. Below I explore 10 key questions business leaders should be asking as they assess the opportunities and invest in AI’s transformative potential.

1. What’s the fuss about? – From security to blockchain technology and 3D printing – every new technology development or resulting issue comes with exhortations for business leaders to place it top of their agenda. However, in the case of AI it may well be the most important change we’ll see in the philosophy, practice and management of business in our lifetimes. Artificial intelligence draws on – and is combining with – exponential performance improvements in technologies such as computer hardware, big data management, the internet of things and the fields of machine learning, neural networks and robotics. As a result, AI is beginning to fulfill its true potential of transforming businesses and replacing even senior management and leadership roles. Business leaders and decision makers have to make sure they are investing the time and attention to understand what AI is, why so much is being invested and where the opportunities are.
2. What’s its potential? – The place to start is to educate management about its potential and undertake internal analysis of where it could be deployed and what competitors are doing. The key is to use non-trivial applications to learn from but to deliver them quickly so the organization can get a true sense of AIs potential. Medium to large enterprises in particular are bringing in AI experts, university researchers and creative future thinkers to take a broader perspective of the potential roles AI could play from product design and smarter production management, through to customer targeting and broad-based decision making.
3. How fast is it moving? -The pace of AI development has caught most unaware. We are beginning to understand the scale of the investment being made by companies as diverse as technology-centric firms like Google, IBM, Microsoft, Uber and Baidu, auto-makers, airlines, and banks. In some cases, the firm is literally “betting the ranch” on AI. Many of the biggest developments and research projects are kept under the radar until launch and we can only speculate on the spectrum of what we might see next – from robot lawyers to ultra-intelligent mobile personal assistants and total home monitoring and management systems embedded in our appliances.
4. How deep should we take it? – Many firms are looking at relatively narrow deployments to automate rule-based decision making and apply AI to accumulated data to predict future demand and customer behaviour. Others are looking at much broader deployments such as intelligent HR, finance and legal advisors and real-time data analytics of live transactions. Deployment of AI could lead to deep insights into the potential behaviour of employees, customers and partners – how deep should we take this? For example, how would we use information from an HR system that suggested an employee has the potential to commit fraud?
5. Could it take the CEO’s Job? – Perhaps some of the most extreme applications include the creation of “human free” automated businesses where everything from strategy to operational processes and all business rules are embedded “in the system”. These so-called “distributed autonomous organisations” (DAOs) could become increasingly common in a wide range of sectors. They are already being used to manage millions of transactions – e.g. the automated dispute resolution systems in online auction platforms.
6. Who should lead? – The temptation is to see this as just another Information Technology (IT) project and hand responsibility to the CIO or IT director. However, some see it having a much broader role and are making it the responsibility of the CEO, COO or business transformation head to drive the identification, piloting and application of AI solutions across all aspects of the business.
7. What would success look like? – With AI, there are likely to be a number of unsuccessful experiments – indeed up to 90% might fail as promising ideas fail to yield viable solutions. The mantra should be to “fail fast and cheap” – bringing in suppliers, customers and other value chain partners early on to see if there is commercial merit in an idea. There can be as much learning from a failed project as a successful one.
8. How do we preserve the feminine? – Organisations and individuals display a mix of feminine and masculine characteristics that manifest themselves in how we interact, organisational culture, service philosophy and the assumptions and beliefs that underpin decision making. The challenge is to maintain the feminine in the human face of the organisation, and avoid the tendency for AI systems to display a more masculine and “robotic” persona.
9. How will staff respond? – AI is already being deployed to automate clerical, manual and semi-skilled labour and is now supporting and replacing professionals in domains such as engineering, medicine, legal, and accountancy. What’s our strategy for overcoming resistance to change and helping those who will be displaced by AI? How will we ensure those making it to senior positions have the necessary experience and expertise if we are reducing the number of staff further down the pyramid?
10. How do we address social impact? – There is a growing concern that if every firm replaces a large part of its human workforce with smart software and robots, unemployment levels could rise on a permanent basis. Estimates vary from 30-80% for the proportion of current jobs that could be automated out of existence in the next 5-20 years as a result of AI. As a business leader, how should we address this and the potential criticism that we are creating a two-tier society? Should we support the ideas of a guaranteed basic income for all – as is being explored by Finland, Canada and the Netherlands?

The pace of development and potential of AI mean that it is not something we can afford to put off – it is perhaps the single most important area of decision making business leaders will face over the next few years. The depth of a CEOs understanding of AI could be the crucial differentiator between success and failure of firms in a fast-changing world.

This article was published in FutureScapes. To subscribe, click here.

Image: https://pixabay.com/images/id-2730034/ by O12

Image: https://pixabay.com/images/id-3520178/ by geralt

By Steve Wells, Rohit Talwar, Alexandra Whittington, Helena Calle

Facial recognition is entering everyday life at an accelerating pace and driving a global debate about whether the benefits warrant the potential intrusion of personal privacy and anonymity in public places. The Wall Street Journal reports that China alone has more than 170 million surveillance cameras with plans to install a further 450 million new cameras by 2020. The imagery from these systems is already being used to allow people to “pay with a face” for fast food, gain access to public transport, and help the police identify persons of interest.

The thought of such broad use of facial recognition technology in public spaces triggers fears of the rise of the surveillance society and the spectre of Big Brother watching our every move. However, as with so many modern day technologies, there could also be significant benefits to health, safety, and wellness if the appropriate privacy protocols can be put in place. A critical question here is how can we ensure the technology is used in service of humanity rather than to control it?

Our faces, our micro-expressions, subtle muscle movements, skin complexion, and perspiration can all be used as health pointers and behavioural indicators. They are also increasingly thought of as windows to one’s soul, revealing, for example, what makes our pulse race or a possible tendency to antisocial or deviant behaviour. Clearly, there are vast current and potential benefits to face recognition, such as processing passengers rapidly through airport security and boarding. Indeed, facial recognition technology is one of the few biometric methods that possess the merits of both high accuracy and low intrusiveness. The challenge for society is deciding where to draw the line on the use of facial recognition systems and how to ensure compliance with any legal or ethical expectations around the use of such data.

Below we outline four different applications of facial recognition technology and how they may evolve in the future to benefit or constrain society.

1. Health monitoring – Scientists have designed a computer model that accurately predicts some health issues based on the shape of a person’s face. Facial shape analysis has also been shown to detect markers of physiological health in individuals of different ethnicities. Machine learning is giving scientists a new way to interpret the subtle micro-facial movements. A multi-modal algorithm analyses facial expression based on 68 separate points on the face, including the eyebrows, eye corners, mouth, and nose. Another system can also track a person’s head position, the direction of that person’s gaze, and the person’s body orientation in real time. This level of detail can be surprisingly revealing; differentiating between a happy smile and an angry smile, for example, or a smile that’s triggered by a social situation rather than an actual emotion, which could potentially be important in identifying depression.

In other studies, machine learning has been deployed to train a computer to recognize people’s body fat, BMI, and blood pressure from the shape of their faces. Potentially, these developments could provide the opportunity for public facial recognition systems to email or send text alerts to the patient, suggesting they seek medical advice. They could one day be used to mobilize emergency health professionals to administer on-the-spot advice and care which could possibly prevent hospitalization.

2. Personality profiling – A controversial use of facial recognition technology seeks to characterize a person literally at first glance. Using artificial intelligence (AI) to analyse facial biometrics, researchers now claim to be able to detect everything about a person—from intelligence to sexual orientation—simply by scanning the face. A major down-side of facial recognition technology is that it could help the powers that be to unjustly target individuals with relative ease. Aside from the invasion of privacy, could such a technology erode the exercise of free will in society?

One can easily see how these types of innovations could gain mainstream approval in many countries and cities, the question is what level of evidential proof of their infallibility will be required before they are put into service. For example, it would not be acceptable to use biometric detection to assess someone’s intelligence in order to determine their future job—or would it? Within a few decades, if widespread automation created a scarcity of jobs, might that justify dire measures to ensure the right candidates were chosen?

Such examples highlight one of the biggest issue around biometrics, namely who controls the technology? A recent art exhibit displayed portraits that were painted with features that defy AI facial recognition. Would the deliberate alteration of our facial features to defy biometric recognition be considered a subversive act in the future?

3. Mental wellbeing – With the rise of mental health issues across society, facial recognition could be used in bathroom mirrors to read human emotions. This development could become a new tool for mental health tracking and intervention. Findings from the field of cognitive psychology indicate that the ability to detect an emotion and name it accurately results in higher emotional intelligence. Emotion recognition also impacts the ability to process experiences and events, hence allows for better coping mechanisms in stressful or traumatic situations.

Tracking emotions over time is essential to detect mental health issues such as depression, anxiety, or even bipolar disorder. Hence, bathroom mirrors that read facial expressions could help in monitoring a person’s changing emotions and state of mind.

The Superflux design studio developed an interactive software application that can read your emotions from a bathroom mirror in real time. Created for “The Future Starts Here” exhibit at the Victoria and Albert Museum in London, the device tracks the person´s face, gives you information about your emotions and presents sponsored content based on the information captured. Using facial recognition for retail purposes is one way of giving the general public access to this technology.

4. Comprehension assessment – The technology could rapidly reach the stage where it could make continuous assessments of how well someone understands what is being said or presented to them. In an individual conversation, our AI might notice that the other person isn’t taking in what we are saying. Using a micro-earpiece, the AI might whisper to us to slow down or change how we are presenting the information. Equally, it might detect boredom and encourage us to speed up or liven up what we are saying.

Such an application would prove valuable in a classroom, large auditorium or conference hall where the speaker couldn’t possibly gauge every single face in the room all the time. However, cameras could scan the audience on a continuous basis and provide continuous feedback to a presenter’s monitor that gave an overall sense of the level of comprehension in the room and also highlight hotspots where clusters of people were either struggling or wanting the paced to be increased.

This article was published in FutureScapes. To subscribe, click here.

Image: https://pixabay.com/images/id-4512304/ by geralt

By Steve Wells, Rohit Talwar, Alexandra Whittington, April Koury, and Helena Calle

Is there a future for higher education? There is probably no other industry or social institution quite as invested in the future than education, yet its struggles with self-reinvention manifest as a ticking time bomb, putting the future of both the institutions and wider society at risk. Almost every commentary on a number of social ills has a subtext that highlights the uncertainties around the future of education. Poor civic engagement? Blame education. Job preparation? Fix education. STEM skills? Reinvent education.

In this context, a constructive futurist approach would be to ask what aspects of higher education today are worth preserving, which ones could or should be relegated to history, and which have the most potential to create desirable futures?

The world is changing fast and requiring solutions to ever-more complex problems. Society is looking to education to provide the foundations from which individuals can address these global challenges. As a thought exercise in identifying the various pressure points in the future of education, here are 10 ways higher education could be transformed to support the needs of a changing world.

1. Blended Learning. Students should be able to draw on inputs from multiple organizations, combining online courses, live participation, in work, and in-community activities to create customized qualifications
2. A centre of community learning. Higher education institutions could become genuine 24/7/365 community centres where adults can come to socialise with peers, take part in any lecture, deliver their own lectures, and participate in the research of the university. At night, classroom facilities could be used to educate those working elsewhere during the day, and even provide facilities from which they can launch and run new businesses.
3. Dropout University. Higher education should encourage dropouts, to allow students to learn what they want to learn, when they want to learn it. There should be no expectations for students to commit to a certain amount of time in the institution or take a certain sequential set of courses. Higher education should also set aside its largely exclusive association with the young adult age group and embrace all ages and life stages as pools of potential students.
4. Self-Grading and Peer Grading. The use of technology to allow for students to conduct highly transparent and constructive forms of self-grading and peer grading could bring about more meaningful learning experiences. Rather than depend on instructors’ subjective evaluations, more courses should require students to assess their own growth and learning, and that of their peers. Technologies such as artificial intelligence, the internet of things, and blockchain could provide the records and support a student would need to evaluate their own work objectively and give them the ability to provide anonymous input on the work of their peers. In this scenario, instructors are facilitators and mentors, and the classroom is less hierarchical than the past and today.
5. Uber for Tutoring. Online platforms could provide a complementary service to match learners with local tutors certified by the institution. Seniors in retirement communities, aspiring actresses, and international students could all become tutors on history, theatre, and foreign languages, for example. The reciprocal rating system would guarantee a mutually beneficial experience, and ultimately a high-quality performance on both parts. In those cases where local tutors are not available, online options could also be provided, but the student would ideally attend a special location where he or she would have a fully immersive experience to connect with the online tutor.
6. From Credits to Blockchain Badges. Following the Scout method of badges or patches to signify achievements, each person would have an un-hackable blockchain identity to keep record of the lessons learned in and outside of school. Other members of society would serve as witnesses or seconders of the accomplishments, and their blockchain identities would share that specific interaction. The decentralization of education is coming with a peer-to-peer, lifelong, and lifewide learning.
7. Pay-It-Forward System to Fund your Degree. A flexible payment system for courses. Students would not need money to pay for tuition. They can pay the fees of enrollment by contributing with their expertise on a subject they master. Students will be able to develop new courses or enhance existing ones in exchange for their own enrollment fees. This approach motivates lifelong learners to share their knowledge and skills with other students, but also ensures up-to-date content in each subject.
8. Biometrics for Personalized Learning. Online courses might incorporate biometric observation, such as eye-tracking software that measures pupil dilatation, which identifies the level of motivation and understanding that a student has on a specific topic. This technology would allow courses to automatically adapt towards students’ needs and ensure deep understanding of each subject.
9. Virtual Reality (VR), Augmented Reality (AR) and Holographic Technology to Create Contexts to Practice Skills. Classroom facilities equipped with technology that allows students to practice specific skills such as team work, creative thinking, problem solving, and other work skills of the future. Students would be exposed virtually to different situations typically encountered in the work place, and would learn how to respond accurately in each situation. This learning experience could provide students the opportunity to train themselves and actively learn essential employability skills.
10. A Strong Connection Between the School System, University Programs, and Employers. There could be a close connection between each of the learning stages and the industries and sectors which will ultimately employ the majority of students. They could work collaboratively to understand the student needs and employer expectations. This collaboration process is likely to require constantly adapting curricula to meet with changing industry demands. Technology and connectivity could help facilitate this process.

This article was published in FutureScapes. To subscribe, click here.

Image: https://pixabay.com/images/id-4195953/ by the digital artist