A recent report from PwC found that automation, robotics and artificial intelligence stand to make 30% of all UK jobs redundant in just 15 years’ time – with losses as high as 50% in some sectors. Couple this trend with the looming emergence of 3D printing, distributed manufacturing, self-driving cars and the Internet of Things, and the numbers could be even higher.
But it’s not just manually intensive, blue-collar jobs on the line.
It’s now 20 years since IBM’s Deep Blue beat chess grandmaster Gary Kasparov, and the new generation of AIs can already outperform accountants, lawyers, doctors, telemarketers and even the world’s best GO master. In some sectors, it is already more cost-effective to completely automate the entire value chain from extraction, through production, distribution and delivery to the customer – with almost no human involvement whatsoever – than it is to maintain a human-operated supply chain.
The age of intelligent machines has well and truly arrived.
Should we be worried? Professor Stephen Hawking has stated that AI could be the greatest thing ever for humanity…or it could kill us all. While Elon Musk and Bill Gates have also expressed concerns about the existential risks posed by smarter than human artificial intelligence.
But it’s not all “Skynet”, Terminator and dystopia on the horizon. AI, robotics and automation are also helping transform healthcare, environmental management and sustainability, cutting carbon emissions, delivering greater efficiencies, minimising health and safety risks, speeding innovation, saving lives and freeing workers from mindless drudgery so they can work on more fulfilling and satisfying tasks.
How should we prepare for - and respond to - the radical transformations being unleashed by this Fourth Industrial Revolution?
Can we make the machines work for the greater good to help build a better world for all? Or are we unleashing the forces of our own destruction, servitude and obsolescence?
Will the rise of the machines, require fundamental changes to our economic system, the adoption of universal incomes, and the creation of entirely new industries to keep people occupied and employed?
Or will it be the death of work as we know it, leaving us to while away our days, sipping piña coladas in a beachside hammock? Or bloodied, hungry and desperate, rising up like John Connor or the Paris mob in a new post-apocalyptic hell?
These were the ideas we explored in our September Crowd Forum on “The Rise of the Machines”. 160 attendees joined us as we delved into both the positive and negative implications of this fascinating and radical transformation, whose impacts are already being felt.
Many of the early phase benefits of automation, AI and robotics revolve around radical improvements to business processes. There is little doubt that these technologies can be applied effectively to streamline processes, improve efficiencies, reduce costs and free workers from dirty, dangerous or dispiriting drudgery. But what are the best strategies for companies looking to take advantage of these technologies? Which strategic approaches have been shown already to work? How can companies get strategically smart about smart machines and automation?
Examples & Learnings of applying disruptive technologies (AI, automation, robotics) on business processes
• Example in facilities management sector: cognitive acoustics, where a machine listens to noises to identify when a piece of equipment may fail. Partnering with technology companies.
• Information/Knowledge sharing across the business is key to learn quickly.
• Housebuilding sector: very un-innovative industry – innovation almost only driven by regulation. Currently up for disruption. UK is much less advanced compared to other countries like Japan. Zero people in R&D working on innovation in these areas. As opposed to other industries, housebuilding is not looking at the introduction of AI for cost cutting purposes.
• Looking to use technology to improve the transfer of data between systems, very slow. There is an inherent dysfunctionality with regards to data analysis.
• Future of Work: how to upskill the next generation, as the skillset that young people will have will be very different to what people currently have in their CVs.
o Need to build entrepreneurial mindset and diversify young people’s skillsets.
o But, what do you do with the people that studied a while ago? Businesses need to think about how they change their staff roles and business models (e.g. Unilever changed their business model to focus on sustainability).
o Requires strong leadership and that is willing to take risks.
Obstacles to applying disruptive technologies (AI, automation, robotics) on business processes
• Steep learning curve:
1. identify customers that are prepared for new technologies
2. find the team with the capability to engage with the technology
3. understand how it can be scaled across the organisation. Currently, being piloted in a few areas.
• How do you ground these concepts into reality? General interest on practical application of these technologies.
• Social dimension (e.g. truck drivers): we are diving into the technology without the social question.
o People are also consumers as well as employees
o Is universal basic income the solution? It’s only been tested in small places, localised. Sceptical about its implementation in larger scale.
• Regulation: how do regulate a technology that moves so fast? Rules of engagement changing quickly.
• Worrying about the potential obsolescence of the working class at the same time as the people in power use AI to help them stay in power, so they are less and less concerned about public opinion. It perpetuates the situation.
• Need to be subversive when proposing/applying these types of changes in organisations.
Opportunities to applying disruptive technologies (AI, automation, robotics) on business processes
• Application of technology on the workforce processes (e.g. scheduling, workforce planning).
• How can disruptive technology enable companies being more sustainable? E.g. Traceability through Blockchain from a sustainability point of view, to control the supply chain.
• Data analysis, using it in an organizational setting (e.g. Satalia). For example, to improve information transfer between systems.
• Localisation: produce less, produce what people want.
• Robots still don’t have the capacity of having emotions, but they are getting closer to emulating human brain. Still, machines will prefer to talk to machines.
• Human creativity can differentiate us from machines.
• Housebuilding sector, possibility to look at technical processes, e.g. large data about land to be embedded into systems. Simple step with a large impact.
SBTs are the foremost way for companies to manage their carbon emission reductions in a way that is consistent with the 2-degree pathway. But SBT implementation can be technically challenging. In essence, SBTs enable the equitable sharing out amongst businesses of the carbon emissions reductions needed to limit global warming to no more than 2°C. It is a discipline that is both data and process dependent – making it ripe for AI enhanced innovation. As Nick Hay from Edelman recently said, “If AI governed the business world, it would likely use SBTs”. This roundtable will delve into science-based targets, what they are, how they work and touch on the possible role of AI in helping companies achieve them.
What do you think about science based targets in the UK?
There are various methods for setting SBTs. This seems counter intuitive when compared with other targets in the sustainability arena, where a single, consistent method of target setting is preferred. Challenge: how can we ensure that there is consistency in setting SBTs when there are various methods out there?
How does automation fit with helping companies achieve SBTs?
• If a sector or a country could be completely segregated there would be the perfect opportunity for information gathering and using AI to manage the decarbonisation of that sector/country. However, leakage means that it is very hard to control the levels. Even if 90% of the world signs up to the carbon market, the remaining 10% could disrupt the whole system due to leakage.
• Carbon rules are still being defined. We need to provide these rules to AI but as humans we are still creating them. Could we use AI to guide the rule defining process?
• AI can be used to analyse the carbon footprint of a company and identify the required stages in reducing carbon emissions. The output is more important than understanding the complexities of the method that the AI uses.
• The longer term objective would be to produce a database that collects data annually, informs emission reduction stages and helps build business strategy in this area.
What are some of the current issues with using AI?
Currently machines are not talking to each other and collaborating in the same way that humans would do. There are issues with data gaps and sensors not giving us the right data. But sensors are only half of the problem. For the most part the technology has already been developed but it is the security issues, admin, legal aspects and regulation that is lacking.
There are also unintended consequences of automation. Is the technology actually being used to help society or are businesses using it as means to increase corporate profit and reduce headcount?
What is the place for AI in business decision making?
AI could support in creating logical, reliable, business cases and championing the leaders of the business to take action more quickly, thus making AI an accreditation for investment opportunities. But will AI influence mean that CEOs speed up decision making or could there be a lack of trust of the data?
What if AI just told us what we had to do? Would this then render governments superfluous? AI demonstrates how things can be achieved without needing a specific governmental lead.
What are the broader considerations that link AI with sustainability?
Automation can be used to reduce carbon emissions during the manufacture process through 3D printing. In theory, automation could support a circular economy with products being created on demand and therefore less waste. However with so much choice and personalisation available, will people get bored more quickly and keep buying new items?
Artificial intelligence (AI) is empowering the fourth industrial revolution, with intelligent machines tackling new cognitive tasks at scale, leading to enormous economic efficiency gains and disruption across the labour market. But what will be the net impact of AI on society and the environment? Can AI be harnessed to help deliver the SDGs? Which SDGs are ripe for AI and automation-empowered disruptive innovation?
Pessimist/optimist/pragmatist and why?
- Consumers feel uncomfortable and don’t trust things they do not understand, particularly when they are new.
- Privacy is of great concern and a lot of consumers don’t feel comfortable with the way companies are using their data, for example, with digital marketing and using data to target the selling of products.
- Fear of business models being replaced and jobs becoming redundant even at the top e.g. managers, academics. In retail, for example, they are using social media and online footprints and AI to anticipate trends and
fashions and then automate production. This mean the jobs of designers, merchandisers, production etc. threatened. Industry needs a framework to ensure people can still get jobs and society is secure – ‘lose your job
but not your living’. Interesting research by World Economic Forum in this space.
- There is an urgent need for legislation on the use of AI.
- The cost cutting mindset of companies is worrying and may drive negative disruption.
- The human ego might get in the way of things that can be transformative right now, for example, about using AI to better manage organisations and in strategic decision making.
- Current business culture needs to evolve and more collaboration and sharing information/data is needed to gain maximum value/insights. Currently, companies are too concerned about losing competitive advantage.
- Companies have a lot of data and don’t know what to do with it so are not realising the value. There needs to be better communication about the value in this data and how it can be utilised.
- News/media is negative (particularly in UK) and the media needs to embrace this digital change, help inspire/educate consumers and help manage the transition.
- AI needs to be used to enhance and improve lives rather than replace jobs and cause disruption in a negative sense. It is important that this is the purpose of AI and it is not lost sight of.
- AI can help companies communicate their contributions to the SDGs more clearly and with more transparency.
CSR and AI:
- CSR teams must encourage thought processes around digitalisation and sustainability that may not come naturally to others in the business.
- People in businesses need to realise the broader implications of social impact in terms of business risks, beyond financial risks. Often must talk in ‘financial/business language’ to get companies to act/commit.
- Sustainability / innovation can often get ‘cordoned off’ within an organisation and seen as a separate set of expertise. This skills/knowledge however, should be owned throughout the organisation and integrated in all
decisions/processes. Then transformation can be achieved.
- Business activities often are working to improving sustainability, but often don’t get labelled as this or it is not the primary purpose of the activity. Business are sometimes ‘doing it without realising’.
Which SDGs are ripe for AI innovation?
- Diversity and inclusion. Companies can look to automation on this, but have to be careful that robots are not trained to be discriminatory (however unintentional), but instead help us to remove prejudice in organisations.
- Companies are already doing a lot around safety, one of the most (if not the top) material issues for most companies. At Rio Tinto, for example, there is a lot of automation already around safety and it is being used to
achieve the goal of ‘zero fatalities/injuries’ This is helping to give AI and automation a positive association, where it is positively and effectively helping to deliver on important goals.
- Local communities. Digitally tracking health scares, mortality rates and medical records, particularly in the developing world can help us to prioritise resource and anticipate/act to prevent future health epidemics. Also
example of Partnerships for Good and digitising land rights in Africa has transformed lives.
- Zero huger. Technology has historically been revolutionary particularly in agriculture, food production and distribution which is already largely automated. Companies are already collecting data from farms n a shared
platform and creating algorithms to ensure best use of water/ fertilisers/ information on seasons etc.
- AI = largely technical and can feel elitist. There is a duty of care that sits with the, experts to communicate and educate the population to build trust and have a positive outlook.
- Humans needs robots and AI to help meet future challenges as our population is increasing exponentially.
- Need to ensure that as jobs become redundant, people still have a purpose and can continue to gain an income.
- Table landed on optimism!
As we transition from centralised fossil-fuel powered energy generation and storage, towards ever greater use of renewables and hyper-distributed generation and storage, AI can undoubtedly play a major part in empowering, optimising and managing demand, supply and generation. How can businesses get ahead of the trend, and start using AI to better manage and optimise their own energy use? What role can AI play in helping companies achieve 100% renewable energy objectives?
• Seasonal problem. No plans for utility scale seasonal storage, such as taking the excess solar generation to the winter.
• World needs to be more interconnected – to solve the grid balance as the footprint and consumption is on global scale (energy and supply management).
• Forecasting problem for 2050 impacting investment cycles – what will the infrastructure be and what will the way of using energy look like? Work space changes, flex working, all impacting shifts.
• Get funders to back technology for 30 years is a large obstacle.
• Increasing demand of energy.
• Data protection laws to interfere.
• More renewables in intermittence – cost of energy will stabilise, but weaving more renewable energy in will increase.
• No price transparency
• Curve of instalment for solar panels is flat. Now working on efficiency. Could AI increase solar instalment?
• Using AI in design techniques
• Need to charge electric cars
• Too much thinking of revolutionising a sector
• Too many targets set: Problem is the mix between autonomous and normal vehicles. Mix between fossil powered and renewables.
2. Red Flags (warnings)
Grid needs to be redesigned for the power sector transition
Need to account for the systemic change in ownership of cars
Still need the social collaboration and implementation managed
• The ability to automate and balance demand better towards our weather systems will help reduce the impact of non-commodity charges long term but unfortunately not by significant amounts
• Fix cost element is an opportunity for AI to kick in. Localised energy use: industries formalise around the
• Battery mobility – and forecasting of power prices. Becomes as the FX market: more Grid+ and Block Chain use.
• Large-scale machines are already GPS guided and drones are finding hot spots – this could be more efficiently.
• More to do in the link up between design and what happens on the ground. Elecrical cables still being hit. Risks to be taken out. Still too much replication. And option for knowledge sharing across the globe.
• Introduce more driving vehicles without batteries, and having plates in the road, with a Bluetooth system with induction charging.
• Need to use parts of a system, not revolutionise a system, using technology in a way to use a system in a different way.
• Go 100% renewable as set by signatories to the RE100. IKEA going 100% renewable making a rapid transition.
Much has been made of the looming impact of automation, AI and robotics on employment, and the projected number of job losses clearly pose a major challenge for the economy. If half the population can't find jobs, what impact will this have on society? Will it give us more free time for leisure, creativity and helping others? Or will it be a recipe for massive social upheaval and unrest? Will it drive unprecedented levels of inequality and obscene wealth disparity between the owners of the machines and everyone else? Or can smart machines actually support and enhance job satisfaction creating a better work/life balance and ensuring a fairer society?
One of the main themes discussed at the table was how businesses would need to adapt amidst the rapid and radical changes that technologies are bringing about at an ever more rapid pace.
Most members of the discussion agreed that both the structure and the traditional motivating factors of a business will need to adapt and change. In particular, there was widespread acknowledgement that a business working on an entirely profit-seeking strategy was not desirable, and that there should be a greater responsibility on businesses to promote training and education in order to have a healthier workforce and larger society.
There was also some discussion along the idea of a more ‘jack of all trades’ workforce, that are able to pick up multiple roles and skillsets without being locked into a single career, which the moderator Chris Middleton described as ‘portfolio people.’ This was generally seen as a positive thing, although the one member of the ‘gig economy’ (the note-taker) expressed anxiety at the perpetual precarity of such a model.
ATTENDEE COMMENT:“The biggest questions we need to be asking ourselves are about preparation: How do you anticipate the jobs of the future, and how do you train the workers who will occupy those jobs, and how do you educate the generation to come?
ATTENDEE COMMENT: “We’re touching on the existential questions of business: What is the purpose of business? What is the purpose of a job? Around 2010 it started to emerge that actually, 'heads down' capitalism doesn't seem to be delivering as we all expected. Businesses need to start contributing more social benefits, and has more responsibilities than just generating profit.”
ATTENDEE COMMENT:“I am really intrigued by the idea of a non-hierarchical business model. I think a more decentralized approach could actually work for my business [Construction], and get a project going more efficiently”
ATTENDEE COMMENT: “I imagine that the younger generations are going to look at the salaried positions like my own and say to themselves ‘I couldn’t do that for 30 years’.”
ATTENDEE COMMENT: “One potential pitfall is the idea of this two-tiered society. That you might have an upper tier for specialized roles, and a lower one for all general labour and ‘gig’ economy members - it could become more difficult for people to specialize high enough to get into roles above that lower tier. On the other hand, in the brave new world where most of that can be gotten from a computer [or AI], you don’t need someone with that specialized knowledge, you need human, interpersonal skills.”
Replaced by Robots, Societal Models and Government
Of course one of the main themes discussed relating to the workforce was the alarming number of jobs threatened by AI and automation. In this topic, the discussion veered toward the need for better education and more exploration, especially towards more ‘creative’ endeavours, as it was generally assumed that when menial tasks are fully automated, it will leave more opportunity for distinctly human work (although some at the table did push back with the open question ‘what makes people think that robots can’t be better at creativity, too?).
ATTENDEE COMMENT: “I don’t think governments around the world are being honest with people about the impacts that these technologies are going to have on their livelihoods. We've got to embrace this technology, and engage with people in a more creative way, and we have to especially engage those who will have their jobs taken, and get them ready for the coming change”
ATTENDEE COMMENT: “We’re actually starting to see some companies that were previously manufacturing in China move back to the US, because of the efficiency gains in automation as well as the increased cost of labour in China.”
One idea that was touched upon was the idea of Universal Basic Income, and the potentially positive role it could play in developing a better educated society. One attendee described an experimental school in which students were not required to attend lessons: After a brief period of exploration, boredom and natural curiosity actually resulted in the lessons having the same level of attendance as a normal school, but with much better performance. Generally AI and automation have the potential to free the ‘robotic’ elements of today’s economy, which might leave humans more free do do human things.
ATTENDEE COMMENT:“Actually, this could be a good thing. More menial tasks get replaced and automated, and that leaves people with more opportunity to follow their natural curiosity. Universal Basic Income combined with lower requirements of labour can be an opportunity for innovation and better education.”
ATTENDEE COMMENT: “Our biggest challenge is finding a way to create a system that works both for automation and for humans. We do have an opportunity for AI and automation to help people harness their human creativity.”
The role of the media and public perceptions
At the end of the discussion, the role of the media was briefly discussed. Misgivings and fears were aired about the present trend towards political populism, and the effects of a more fragmented media industry.
ATTENDEE COMMENT: “Traditional models of media have been breaking, and as they try to chase diminishing profits, that’s where we’re getting all this outlandish coverage and #fakenews.”
ATTENDEE COMMENT: “Especially with journalists, there is a level of trust that just can’t be achieved with the new style of social media coverage [disintermediated news].”
Supply chain management has traditionally been a fundamentally human discipline, but that is rapidly changing. The use of automation, robotics, AI, IoT and distributed manufacturing are radically disrupting traditional supply chain practices. This session will consider how companies can optimise their supply chains with these technologies. How do you determine costs vs benefits? What is the business case for supply chain automation? What are the implications for sustainable supply chain management?
Obstacles to Supply Chain Optimization
Scaling and synthesizing data remain major challenges. From supply chain perspective, new technology capabilities are producing positive results as is the case with Tesco’s initiatives to reduce waste and improve forecasting. Scaling is particularly difficult for low margin businesses. From financial services perspective, a vast amount of data sits there, but is not fully useful yet. Other industries can relate to this data problem, pointing out that ways of collecting data need to be improved in the first place. Relatively cheap IT devices can be installed to ameliorate the data collection issues.
Losing and re-training staff whose roles have become obsolete is another challenge. Staff members whose jobs are getting replaced are not often suited to the tasks performed by machine learning, in which case the firm has to make the hard choice of letting these employees go.
Businesses also face shortened asset life cycles with the onset of new technological capabilities. What depreciation rate will you put on an asset? The firm that is going to win is one without a legacy asset.
One common worry is the rate of change, which will not give enough time and maturity for institutions, businesses and people to absorb the new ways of doing things. One discussion participant argues that we cannot equate new technological capabilities to the Industrial Revolution since the amount of disruption will not be comparable, while another argues that Industrial Revolution and ensuing urbanization caused a similar degree of fundamental changes.
Effects on Consumption
AI will bring down unit cost of production but will its net effect drive up the overall consumption? When we discuss AI and productivity efficiencies, we often neglect the conversation on its impact on overall consumption and limited natural resources. We are making things available en masse at a lower cost, and yet the idea of exponential consumption is scary.
However, will this exponential efficiency in production capabilities be countered by the fact that consumers will finally be getting what they really want – with the production batch size of 1 – instead of having to buy a mass produced good? People already do not want to buy staff the same way today, with the sharing economy. People do not want a car; they want access to a car. Consumption and ownership are different things. Furthermore, many forms of consumption – even when it leaves a footprint – will be digital.
With or without AI technology, natural resources are limited. With cobalt mines, many firms are securing assets in advance. You can then argue equally that the opportunity is around using AI to realize these production efficiencies in advance, with the same limited resources.
One way the AI technology or blockchain can impact consumption behavior positively is the ability to trace supply chain. Making every component traceable will discipline supply chain management and drive ethical behavior.
However, will knowing change consumption pattern fundamentally? We find out about disposable fashion and the human cost behind fast fashion retailers, and yet even after this knowledge, people continue to shop at these retailers.
The challenge of re-skilling people will be crucial. People have learned new skills – the Baby Boomer generation was born before Internet, and yet people have picked it up quite quickly.
The rise of the machines raises a great number of tricky ethical questions - most of which we are woefully unprepared for. Is it ethical to replace your workforce entirely with machines? What happens after the end of jobs? How do we fairly distribute the wealth created by the rise of the machines? Should we tax the robots? Do intelligent machines have rights? Should they be treated humanely? Can smart machines be held responsible for their actions? How can we stop AI from doing harm?
It was felt that a fundamental issue is we are trying to progress based on the functions of capitalism, and capitalism is imperfect and those imperfections will only be magnified. We are trying to industrialize systems already in function. The risk is robotics will simply amplify the issues that are already in existence
What are the barriers we are facing?
- We can be prosumers of some household goods, but not all things can be replicated
EX: boots printing cosmetics and toiletries, but things like an iPhone cannot be immediately replicated.
- Another barrier at consumer level is the trust of the companies and the sharing of our data, at the moment business has not proved itself to be trustworthy and this whole world is predicated on open sourcing and
sharing data, but there is still the fundamental issue we must get over to be successful
o So much of business is not looking at the forward progression of the business but rather the risk and compliance
The table discussed the idea of data sharing of personal information and trust
- The consumer will not have a choice, the consumer will have to work with robots whether they like it or not. Because they are being forced to share data.
- How much of data do you actually want to share?
- Most users don’t even realize the level they are sharing?
- What happens when there is a break in the process and as a consumer you don’t fit into the typical mould or ebb and flow of the flowchart? Does A.I. replicate the human error, or will the machine learning adapt to help
you fit better into the model.
- In banking, do you want to trust a robot who has no emotion, or greed, or do you go with the physical person who you have the human connection with. *compliance, ethical, human error
Where does the accountability start and end?
- Banking example, if they make the wrong decision and invest in the wrong thing, who is to blame? The bank? Programmer? Yourself for putting trust in the system?
Should companies let the consumer know the extent to which AI is used in their day to day business?
When it comes to the real ethical decision of what is right or wrong, the decisions that the board is there to decide in those competing interests, who will then make those decision/ Being honest, decide on putting up price or bringing price down.
What is the legal framework for these new technologies? Warfare, airspace with drones, roadways?
Is there a way to create a universal code in AI to follow a code of conduct?
If AI is genuinely good at thought processes, it ought to start to understand more human thought processes?
-the machine will reach a human thought level, then goes beyond them, what will it them evolve to after that point?
-the danger with AI/Robotics is not malevolence with AI but indifference to humans
Are we creating this for the purpose of serving humans, or to be human? …. Or to make money?
How to join it in an ethical way so that it is both profitable and serving the greater societal good?
Should things be allowed to be created before the regulation exists?
- Doesn’t matter, people will always try and get around it
Are we going to follow the moral compass or have utter divergence?
With up to 40% of UK jobs expected to be lost within 20 years, many have touted a universal wage as a possible solution to looming mass unemployment - and trials of the concept are already taking place or being considered in Finland, Scotland, NZ and Canada. But is this the answer? Are there better options, for instance, with civic work, nature renewal projects or mass retraining, upskilling and mobilisation for social or environmental causes? This roundtable will discuss the implications for work in the machine age.
The discussion started off by questioning the ethical aspects of applying Universal Basic Income (UBI) as a social security tool. Would it be fair to receive a regular compensation without actively having to contribute to society? In a work-free society, would people be able to find their purpose or would there be the need to create new values? Other key issues that were underlined were the potential environmental impacts of an automated world. For instance, could the increased power demand be met by renewables? And, if so, would this have positive impacts on fuel poverty reduction, due to decreasing cost of low-carbon energy production?
Red Flags (warnings)
A potential drawback of the UBI mechanism that was interestingly underlined is the danger of it becoming not only a disincentive for people to participate in society, but a baseline that would have to be periodically risen to meet the higher needs of the population. Moreover, an effective mechanism for taxing AI would have to be designed and implemented to ensure sufficient government revenues to sustain UBI. Another important issue that was mentioned was the role of society in taking part in the innovation process and, more specifically, in participating to the decisions that determine the pathways that will be followed. For instance, who should be in charge of determining the threshold beyond which AI and automation cease to create added value for society?
Acknowledging the power that the business sector has gained in the AI industry, economic disincentives could be introduced to prevent automation from going too far. As for preventing the rate of unemployment to increase excessively, UBI could be seen as a transition period during which people who have lost their jobs can be retrained to occupy different positions within or outside their sector. Moreover, the UBI should be made conditional to concrete contributions to society, such as environmental management, to ensure that people are incentivised to actively participate to the welfare of their community.
Several examples have been brought up to back up the aforementioned arguments. For instance, from current trials it has emerged that UBI is not enough, but it is a test of conditionality. On the notes of the need for retraining programs and explicitly revaluing roles that are not highly valued today, Finland’s program to promote the role of teaching to make it more appealing and attract more qualified candidates was cited. Lastly, regulating robot’s features to ensure that they are prevented from becoming “too human” could limit job losses, especially in sectors where customers demand human services.
For many, the rise of the machines conjures images of post-apocalyptic dystopias, with smarter than human AI and robots replacing us at the top of the intellectual apex of the world. A number of great minds have warned of the potential risks, yet others point to possible utopian outcomes. How can we ensure that automation, AI and robotics create positive outcomes for society? How can we avoid dystopia? What can we all do in our working lives, to ensure we benefit from this transition?
• How do we create the capabilities we need to create the futures we want to live in?
• How can you use technology to create more of a circular economy rather than a linear one?
o Environmental challenges are non-linear so you can’t use existing data to create the technology to solve it like we have been in other areas.
• If the future is going to be much more transparent, will people be more accountable?
o Works more on an institutional level than an individual level
o Example of transparent compensation and that it brought down disparity between the bottom and top
• Everything that can be automated will will we have to become more human?
Red Flags (warnings)
• The biggest disruptions will be in professional services, rather than industrial services are we ready for this?
• Humans are notoriously bad at understanding exponential growth (which is what we are seeing/will continue to see in technology) look at how we have responded to climate change
o How can we keep up?
• The problem now, is people are setting up businesses just to disrupt – all this does is put people out of jobs
o The challenge is getting organizations to move from their linear models into something a little bit different
o But you must get them to let go of other parts of their business model (just like Kodak and digital photography)
• Should we tax robots (or the companies that are using them) if they are replacing so many human jobs?
• Change the way that extra financial performance of companies is measured - Future Fit Foundation
• How do companies learn to disrupt their own business models to become fit for the future?
• Political, economic, and social systems was created in a very different world than we live in today
o Need governments to come together as they did for the SDGs – ethics should be hardwired into these systems
• Why does everyone have to have a job? Just to keep them busy?
o Does this open up the possibility of a universal wage?
• Google translate and AI and how exponentially it has gotten better
o Google completely rebuilt Google translate using AI and it has gotten exponentially better and done it exponentially faster than when humans were behind the technology
If you had to choose, Utopia, Dystopia, or Pragmatist?
• Optimist: If you look thru a sustainability lens, then the answer must be through technology; Some of the best things happen when humans behave like natural systems how can we create machines in the same manner? We will solve some really bug fundamental issues that we need to solve, but what will those implications be? Which ones aren’t we thinking about?
• Pessimist: If we create a law enforcement and army with machines, without limits, that feels very scary and we are pessimistic; The tech is developing too fast for us to conceptualize; The human motivation behind it is likely to be selfish; We need to be able to spot the biases and patterns, which is really hard to do – the people who are running with the development of the technology are not worried about the ethical and humanistic aspects and results; Would I have children in this kind of atmosphere?