Automation, Education and Work, Part 2
- I considered how change is driven by both new technology and new processes as a result of technology, and
- I looked at some of the technology emerging now.
Now in part 2:
- I consider new processes that have recently emerged, and
- I examine a number of work areas with regard to these new processes.
New Process: Self-Driving Cars
It is no secret that the new artificial intelligence technology is being applied to self-driving cars, for example, to recognise objects ahead (see the picture in part 1) to make decisions how to steer and brake. A self-driving car is really just a robot whose job is to carry something from A to B – where that something is you. Here, I’m not going to talk about the technology of self-driving cars, but look at how we will use them in different ways – a new process.
At the moment, there are many ways of getting from A to B:
- We can drive our own car
- We can use a car we lease,
- We can hire a car,
- We can book a minicab,
- Use Uber, or
- Hail a cab off the street.
And these are just the options if we want to travel alone. If we don’t mind travelling with strangers there‘s also:
- Shared taxis
- Chartered coaches, trains, planes
- Scheduled services (bus, coach, train…)
There are many trade-offs involved in making the choice, not least cost.
I am warning against making the mistake that we just substitute old technology for new. If everyone who currently has a car suddenly had a viable self-driving car tomorrow, there would be fewer road deaths and injuries, less hold-ups and we could use the journey time more productively.
But self-driving cars change the whole process of getting from A to B. In principle, everyone’s car could drop them off at work in the morning and, instead of sat in the work car park all day, go off and do some Uber work, providing some extra income for you. But this is true for everyone else with a self-driving car. This is going to force Uber rates down very low. But in so many cases, it is not going to be worthwhile having you own car anyway.
There can be a shift from car ownership to using a contract car service. Just as you can choose a tariff for a mobile phone contract based on expected usage, you can select a car service plan (or you can PAYG instead). This might provide an inclusive number of local and/or long-distance miles. There might be some gold/silver/bronze level of service which would determine how long you would need to wait. You could pay for the option that provided you with a maximum 3-minute delay to get a car to your door between 7am and 8am work-day mornings and between 5pm and 6pm in the evening; most days, the car would be waiting on your drive for you. All this organization of where cars should be when would, of course, be coordinated by some artificial intelligence algorithm. Some people would still own cars (flashy status-symbol cars) but a very large number would just use a service that provided them with a reliable way of getting from A to B in the right format (small cab, pickup, van) and weren’t concerned about the badge on the front of the car. The services could in fact be provided by the manufacturers themselves. Their interest would be in minimizing the cradle-to-grave costs of car miles rather than minimizing up-front price and trying to tie you into expensive after-sales costs.
On longer journeys, self-driving vehicles will organize themselves into a string of wirelessly-connected vehicles, travelling closely together so as to improve energy efficiency from slipstreaming. Previously, a string of vehicles would have been called a ‘train’ or ‘caravan’ but the terminology used for a string of self-driving cars is a ‘platoon’. On trunk routes, vehicles can join and leave the platoons at the points where peoples’ journeys converge and diverge. Changes from ‘pod’ cars to ‘carriage’ buses can be coordinated so that the transfer time is only a few minutes. Just a few types of vehicle can provide the whole range of means of transport currently served by vehicles from cars to trains. Self-driving vehicles can change the whole model of how we get from A to B, significantly reducing our transport costs and worries as well as making journeys safer and more enjoyable.
Safe Jobs and Vulnerable Jobs
In the very-frequently cited ‘The Future of Employment’ paper by Frey and Osborne, there is a graph (shown below) that indicates how vulnerable different occupations are to the new technology.
The horizontal scale runs from 0.0 to 1.0 where 0.0 represents completely safe and 1.0 represents completely vulnerable. Jobs are categorized into occupation groups. On the safe left-hand side there are the large majority of managerial, engineering, health worker and teacher jobs. On the vulnerable right-hand side there are the large majority of service, sales, retail, office administration and transportation jobs. But that states the conclusions far too simply.
Calum Chace coined the clumsy term ‘unforecast’ for a postulated scenario that looks like a prediction about the future but where the point is not to make the assertion ‘I think this will happen’ (it invariably eventually turns out to not be so) but to get you to think about how the future may be.
I provide some unforecasts here. They are to get you to think about how things may be different from the stereotypical vision of the future full of anthropomorphic robots working everywhere. The nature of the work will change as a result of new technologies and processes, regardless of where on the vulnerability spectrum the current jobs lie.
Unforecast 1: Transportation
The first unforecast looks at transportation – a ‘vulnerable’ occupation.
For long-distance transportation, we are presented with the vision of platoons of self-driving trucks. And drones are offered as the worker-free solution to local deliveries. But currently, drones are a number of orders of magnitude more expensive per ton mile than trucks. And there will be legal problems managing them. And it will only take a handful of drone terrorist attacks to put their usage back for years.
Imagine that a delivery depot in a particular town currently employs 8 van drivers serving the town and its surrounding villages. The stereotypical idea is that they will be replaced by 8 self-driving vans plus 8 anthropomorphic robots to deliver the package from the van to the bed.
In the long term, this completed solution may be possible (whether this is desirable is an entirely different consideration). But in the medium-term, it will still be cheaper to employ someone to do the last 10 metres (on minimum wage). I imaging the following scenario:
- There are just 2 delivery people doing the work previously done by 8.
- A self driving van takes them out to the start of the delivery route.
- They make a delivery at the drop-off address.
- Whilst the van is moving to the next destination, the worker can load up a small, short-range drone to take a small, light parcel to its destination close by.
- Meanwhile 7 other vans are on the road going to various way-points. A central algorithm works out an optimum route.
- The vans park at various rendezvous points. The ‘driver’ swaps vans, maybe swapping some parcels too.
- As well as having drones, the worker may be assisted by a ‘robo-trolley’ – something to carry bulky items so that there is no risk of back injury to the worker.
Thus, the workers spend all their time out performing delivery duties. They do not lose time just sat in the van doing nothing except getting from stop A to stop B and going back and forth to the depot.
From the standpoint of today, the job the worker is doing is very vulnerable – 75% of the delivery jobs have disappeared. But the role has changed, not least in being instructed what to do and when by an AI algorithm.
For long-distance haulage, it is a similar story. There are productivity gains from not having drivers getting paid for being sat in cabs for hundreds of miles a day. The trucks mainly travel in platoons on trunk routes, getting to distribution depots right next to the freeway junction, rendezvousing with ‘local’ drivers at truck stops.
Unforecast 2: Caring for the Elderly
Societies around the world are facing the problems of dealing with an ageing population. It is expected that there will be a large increase in the amount of physical and mental health care for the elderly. This will mean an increase in the number of jobs. Current jobs in this sphere will be secure. That is the expectation.
But people are working on robots to help in this field. Here, below, we have the RI-MAN robot being tested on the carrying of a person:
Being Japanese, the robot is very humanoid (I find this Japanese trait rather disturbing) with its face with big friendly eyes. The media reinforce this stereotypical view of robots.
People generally envisage a robot doing something that humans can do, in the same way that a human would do it. This is the case here in this example above. But is there a different way of doing things – either easier to automate or to provide a better solution? Is this the technology that is really needed? For sure, AI can improve on the currently technology to move patients around a hospital – a trolley. We might envisage AI-based improvements to a standard hospital trolley – a ‘robo-trolley’.
Where possible, the right solution is not to have robots physically help the elderly but to allow the elderly to move themselves. This is more dignifying, allowing them to keep their independence. One example would be an ‘exo-skeleton’ providing movement, fine motor skills and strength. This is a lower cost and it is simpler from a legal perspective – the user maintains responsibility for their actions.
Now, I want to contrast physical infirmity with mental infirmity. Technology for the former is currently very expensive but will come down in cost. But it will still be a substantial cost. Compare this with this unforecast regarding mental care…
In caring for patients with dementia, it is often best to lie to minimise distress. Carers should go along with what is being said, and steering the conversation onto something else. Going over the same conversations will be tedious for the carer. Potentially, a digital assistant could do much of this work (note again: this is trying to increase productivity rather than getting ‘the machines’ to do everything). These assistants (e.g. Alexa, Siri) can quite easily be given a face (just as the film industry can use CGI to map human faces to non-human faces). See below regarding an animated talking head ‘Zoe’:
Consider a psychology-trained dementia carer (we will also call her Zoe) who looks after many dementia patients. Occasionally, she can visit her patients and have face-to-face interaction. But for much of the time she is assisted by many virtual Zoes – one per patient. These virtual Zoes appear as if via videophone and can engage the patients in conversation. As well as providing supportive responses (suitably evasive, as with ELIZA), they can also record the conversations, learn from them and filter relevant information to feed back to real-Zoe for assessment. Real-Zoe monitors conversations and guides the software for many patients. If need be, real-Zoe can visit the patient in person but for the vast proportion of the time, a patient is kept engaged, and always monitored, by a virtual-Zoe. Unlike a human, the Virtual Zoe never tires of the same conversations.
Here is a significant point: Once the software has been developed, the role-out cost is very cheap – not much more than 1 iPad per patient. This is much cheaper than the hardware needed for physical care, mentioned above.
New Process: Platforms
As I have described previously, technological revolutions can involve processes (new ways of doing things) as well as the application of new technology. An example of such a wave of change that is currently underway is the ‘platform economy’. In the late-1990s dotcom boom there was great expectation of companies providing sales and services over the internet (itself the natural consequence of previous revolutions in the electronic technology of computers and communications). But then there was the dotcom crash. The companies that came to dominate afterwards (becoming some of the biggest companies in the world) weren’t just taking sales and services that had previously been done face-to-face and putting them on the internet. A new ‘platform’ way of doing things emerged.
For example, we think of Google as the internet search company (that has now branched out to do other things). But it is really an advertising company. It gets it revenue by providing a free platform whereby internet users can connect with information – but in doing so, Google gathers information about that user which enables it to then provide very focussed advertising to that user (much better than non-internet advertising which is suffering as a result). It is the same with free services like Facebook and Youtube. They are not connecting people to share information from the goodness of their hearts.
But there are other innovative methods that are being employed. One is the ‘freemium’ model where a basic service is provided for free but users can pay extra to get a ‘premium’ service. Examples of this are LinkedIn, Spotify and Skype.
Then there are the so-called ‘peer-to-peer’ services which connect people to other ‘ordinary people’ who provide some service using what they have, as a part-time activity. People can provide ‘virtual internet stores’ on Ebay. People can use their cars to provide Uber taxi services. People can use spare rooms to provide Airbnb ‘bed-and-breakfast’ (or similar) services.
Another phenomenon is providing a service that actually is done from the goodness of people’s hearts. People are willing to donate time and money to provide free products like Linux and LibreOffice and Wikipedia that are substitutes for products like Microsoft Windows, Microsoft Office and Encyclopedia Britannica – and these are products that are expensive (the first two have made Bill Gates the richest man in the world!). Some of these free products are actually superior than the original! (I would nominate Wikipedia as one of the modern Wonders of the World.)
A characteristic of these platforms is the near-zero marginal cost – the cost of setting up the product (website) initially (including the development of software) may be substantial but the cost of adding an extra user (‘customer’) is virtually nothing. For example, the total Facebook storage of 200 Petabytes for 2 billion users gives an average of 1Mbyte per user. You or I can currently buy hard drives at around $30 per terabyte (but Facebook buys disks in bulk!) That equates to 0.003 cents per user! (OK, it’s a bit more complicated than that, but you get the idea.)
Unforecast 3: Coffee Shops
What might a Starbucks outlet of the future look like? Many imagine stereotypical Japanese humanoid robots travelling around delivering coffee. But here is a different vision; a mini-unforecast:
- The word ‘tablet’ has gained a new meaning since the arrival of computer devices like iPads. The word is of course a diminutive of ‘table’. Touchscreen ‘tables’ (huge iPads on legs) would provide an ‘internet café’ with huge screens that you don’t have to carry around.
- Beyond normal computer/internet usage, the screens would be used to make your order and pay (just by placing your phone on a particular part of the screen).
- We are conservative in our built environment. Our bars and restaurants are typically fake imitations of bars and restaurants of yesteryear. A vending machine might be able to make coffees to particular requirements faster and more cost effective than a human but they have no soul! So imagine instead a self-operating Gaggia providing a theatre-like performance of making the coffee: the sounds of the steam and the knocking out spent coffee into the bin as well as the visual, including the specifically-ordered choice of pattern on the top.
- That just leaves delivering the drinks to the table (customers could help themselves to be quicker), clearing up afterwards, and opening up and shutting up shop (including managing the vacuum-cleaning robots). These tasks will be done most cost-effectively by humans for some time to come. The tasks are less frenetic than those of the human baristas that presently work feverishly away behind the counter.
After automation has taken away significant parts of the job, the job is less skilled but (hopefully) less stressful. Now: when are robots going to be cost-effective at automating those remaining tasks?
We will return to this Starbucks of the future in a short while.
Unforecast 4: University Education
The Flipped Classroom
Lecturers have talked in front of students for centuries:
“Lecturing is that mysterious process by means of which the contents of the note-book of the professor are transferred through the instrument of the fountain pen to the note-book of the student without passing through the mind of either.”
In an era when lectures can be recorded, it is absurd to employ lecturers to perform. In the Flipped classroom model, students view a recorded lecture and the teachers then support the students in their exercises (frequently left as homework): what is done inside and outside the classroom are reversed. It is after the lecture that students need to be able to ask questions and get help. The lectures teach and the teachers tutor.
It has been possible to broadcast courses to a huge number of students for decades: the Open University in the UK initially broadcast courses on TV in the 1970s. Courses can now be delivered over the internet (of course). Most are what are now called ‘MOOCs’: ‘Massive Open Online Courses’:
- They are online.
- This makes them potentially ‘massive’ – huge numbers of students can be accommodated.
- They may be open, available to all at no cost (example: the Khan Academyon Youtube).
- They are courses – involving tuition and examination.
- Streaming curriculum content to millions is easy.
- Having a business model to sustain the courses is more difficult.
- But it is the tuition and examining that is the difficult part of on-line courses.
Regarding point 2: One MOOC provider is Coursera. It provides university-level on-line courses. In some cases, these courses are virtually the same as actual undergraduate courses. (Incidentally, one of the co-founders of Coursera was Andrew Ng, a Stanford University professor who is a significant figure in the development of Deep Learning.) They have a Freemium business model. If you pay a modest fee, you can get a certificate to prove (to a potential employer, for example) that you passed the test. But if you don’t want to have proof, the course is free.
And now regarding point 3: The lack of a MOOC solution to tuition and examining means that it has promised much but not delivered – so far. New technology is available but is has been applied to old methods with limited effect. It requires new processes to improve how courses can be run on a massive scale such that they have a dramatic impact on education.
We cannot assume that new technology will not impact ‘safe jobs’. I think that the impact of automation on primary education will be small and that it will noticeably change the way teachers work in secondary schools – automation will not affect their job security. But I believe that the apparent failure of MOOCs so far has made most universities complacent. It is like the false-start of the dotcom boom and subsequent bust which was then followed by the emergence of the FANG (Facebook, Amazon, Netflix, Google) giants which is leading to a long slow decline in so many old economy businesses. This will apply to many universities once innovative new processes for MOOCs are discovered.
Next I turn to ideas about new processes in (predominantly university) education.
Peer to Peer
Examination by multi-choice is simple and is easily automated so a course can satisfy the ‘M’ of the ‘MOOC’. But it has its limitations, particularly dependent on the subject of the course being offered. Examination by essay needs a number of markers proportional to the number of students. This is possible (it is what is done currently, after all) but it is difficult for a single institution to roll out a course ‘massively’. More efficient ways of examining are sought; new processes are desired.
Just as Facebook get its users to gather information for then directing advertising to them, one approach is to get the students to mark students exams! This is ‘peer-to-peer’ marking, otherwise known as ‘peer assessment’.
One example is as follows. After submitting an essay, a student receives 5 anonymous essays to mark, with guidance on the various criteria that should be used, and they send back a score. Students will then get a marker’s grade as well as an essay grade; the closer your scores are to those of others, the better your marker’s grade will be. This would be supplemented by plagiarism-detecting software and random spot checks by professional markers employed by the course provider.
The Examination Game
Andy Clark tells a tale of how, years ago, the philosopher Dan Dennett was talking to an eminent professor of paleontology (who he does not name). The professor complained that his students were cheating at stratigraphy by just copying out diagrams from books. Dennett responded that he should get them to play a computer game. If you want to test their understanding of stratigraphy, you don’t ask the question ‘describe how such-and-such a layer is formed’; you provide them with a game in which various knobs cause various events, such as:
- Deposition of sediment,
- Intrusion of lava, and
- Controlled fracture
If the students can play the game by twiddling the knobs in a correct order and with the correct intensity to create the required layers, they have demonstrated that they really do understand the process and can be marked accordingly.
Furthermore, the examiner doesn’t need to spend time marking the results. The game provides the score itself. Obviously, it takes time and effort to create such a game but, once written, there is near-zero marginal cost – it can be rolled out on a massive scale.
Note: games have cropped up again in this talk. Just as games provide a simple controlled environment to test Artificial Intelligence programs, they also provide an automatable controlled environment to test human intelligence. Another controlled environment that lends itself to automated examination is virtual reality – also associated with games.
MOOCs fall short of the true university experience in not having students interacting face-to-face with each other and with the various services of the university. The MOOC model hasn’t quite been perfected such that it can seriously compete with the traditional bricks-and-mortar universities.
But here’s another ‘un-forecast’ (one that is more outlandish than the previous ones). Imagine that Starbucks buys Coursera (for a huge sum of money). They do that because they recognize that their 27,000 outlets can provide the physical location for students to meet and socialize and receive tuition and pastoral care from experts.
In unforecast #3, I presented a vision of coffee outlets of the future in which the residual job for humans to do comprised of tasks like clearing tables and locking up shop – – performing unskilled, minimum wage type work. But many of the staff across the 27,000 sites of the ‘Starbucks University’ could be upgraded to highly-skilled counsellors providing pastoral care to students and acting as ‘local tutors’. There would also be ‘remote tutors’, present on online subject-specific forums. The remote tutors would be experts in their subject. The local tutors would not be expert in any subject other than how to study through MOOCs. They would teach ‘Learning how to learn’. The local tutors would work with the students to help them find answers themselves. Only rarely would this involve a 3-way spoken interaction between the student, the local tutor and the remote tutor.
For many courses, there would still need to be a residential part of the course, but this would be a small fraction of the course period.
This is not a ‘near-zero marginal cost’ business model that allows rapid expansion leading to domination of the market, but it provides a marginal cost much less than traditional universities:
- The ratio of remote tutors to students would be much lower than the ratio of lecturers.
- The ratio of local tutors to students would be much lower than the ratio of assistants.
The economies of scale would mean that early adopters could enrol hundreds of thousands of students per year whilst the majority of remaining universities would fall by the wayside, with some becoming a provider of low-cost accommodation for young people and a provider of venues for residential course modules. Many of these early adopters are prestigious universities. Why pay significant sums of money to a middling traditional university when you can pay less for an online degree from a prestigious university?
Course fees would be split between the course provider (the university) and the course facilitator (Coursera, becoming Starbucks) but would be much lower than present fees. It is much better for a university to move from charging 10,000 students per year to charging 400,000 students at a quarter of the rate.
But course facilitators (Starbucks could be in competition with other coffee chains) may not charge at all!
Starbucks may gain income from its newly-acquired educational services but it could be that it earns nothing directly (making those courses very cheap);
Just as Google and Facebook provide free services to the public as a means of earning revenues in seemingly unrelated business, Starbucks’s provision of free university services could just be a way of selling more cups of coffee! This would be achieved by:
- Students drinking coffee during the day whilst they study, rather than elsewhere, and
- Students preferring to drink (discounted) coffee in their leisure time in the evenings making it economic for most Starbucks outlets to stay open late. Other destinations for students would suffer as a result.
The point of this (and other) unforecasts are to emphasize that:
- The new ways of doing things (processes) that are enabled by the new technology will often be surprising.
- Roles in supposedly ‘safe’ occupations may change radically; many jobs in ‘vulnerable’ occupations will not disappear anytime soon.
- This can result in dramatic increases in productivity. Alternatively viewed, it can mean that costs to customers can be dramatically reduced.
- This can be very bad for many complacent organizations who think that they are generally immune from the new technology.