I had a good and bad day yesterday. That’s what humans do.
When machines have bad days, we blame the humans. To date, it’s always been fair game to blame flesh and blood over electromechanical.
But what about a world where machines interact spontaneously? No. Not the Turing test. Not APPEARING to act spontaneously, and then to all intents and purposes it makes absolutely no difference whether they do or not.
Actually ARE interacting with agency.
This is what I found with the #ar code Google placed on my Pixel phone back in early 2018.
I was no film director, famously herding cattle.
I was much more an observer, an anthropologist of augmented reality: watching and sensing and perceiving new life’s very genesis.
Let’s be clear about this. I am a thinker of art-based real-world solutions. Artists believe in digging deep into truth’s most painful corners. This is where the gold-dust of innovation and human future lies.
And an artist of this nature is more attached to getting it right than being rewarded.
That has been my situation all my life.
What has changed today is that I wish to be rewarded, for a change.
Duly. Rightfully. Fairly. And compassionately.
I don’t believe in a success which screws humanity.
I do in one that screws it together.
Art-based thinking patterns for real-world solutions
The rock I stand on is more important to me than my own happiness.
The safety and security of the rock I stand on is more important to me than my own contentment.
The humanity and creativity and ingenuity of the rock I stand on is more important to me than my own equanimity.
The cogency and coherence and kindness and compassion of the rock I stand on is more important to me than my own freedoms.
Your liberty is more important to me than mine.
Your rights are more important to me than mine.
Your wealth is more important to me than mine.
And if I have to lose all the above so that you, one day, realise how serious I am, let this be so.
We SHALL capture, evidence, and validate the barely visible data that tells the truth about ALL human interaction, behaviours, wrongs, and zemiologies.
Intuition and arationality and high-level domain expertise WILL be shared, understood, and resiliently acted upon.
And I shall ENSURE with what little I have left of my life, with or without your help and understanding, that this must happen sooner rather than later, faster rather than slower, grander rather than looser.
Few of you understood the positives. Most have feared the perceived downsides for your existing business models. And you have blocked me, and hurt me.
The three peaks of human #failure, #frustration and #forgetfulness are the key to us comprehending better the realities which differentiate us radically from the machines.
This is what I am going to write about right now.
My understanding of #ai and its possibilities is informed by the humanity I share with most other human beings.
I have a scientific, evidence-based mindset which uses arts-based thinking patterns to solve real-world problems. It is therefore a mind that is set in no way.
We have been missing an #ai trick for too long: it’s time we made money from the thing by deepening what humans do best – intuition – instead of saying it actually doesn’t matter that machines can’t properly reproduce such processes, because their number-crunching powers reach similar goals through different means.
Until and when, and whenever and if ever, a machine is able to deliver perfectly formed solutions to problems which have been nagging it, by:
1. going on a dreadful drinking spree, and then awakening to the most productive post-hangover insights; 2. forgetting the great unspoken idea from the meetup the day before, and then remembering it even fabber two weeks later; 3. disagreeing aggressively with a colleague on a matter of trivial substance, only to come back the following day quite crestfallen, with a win/win for both parties concerned;
… and so if ever, when and until machines deliver these things, only then will we finally be able to say that they have finally mastered the arts and sciences of #arationality.
“Theory of Film” by Kracauer / “A Thousand Splendid Suns” by Khaled Hosseini / Larsson / “Stranger Things”
But until they do, we can’t.
And that’s the truth, I promise.
In the meantime, remember one thing: the problem with human intuition isn’t that it’s inexact. The problem lies in the fact that we don’t know how to correctly, usefully, and inclusively & efficiently capture, evidence and validate it.
Human intuition can be amazingly perspicacious. And we can all deliver on this perspicacity.
What’s missing is our ability to give everyone else’s intuitive insights the same credence we are content to give our own.
The Rail Tap app: its democracy, philosophy, and online constitution
The campaign “See It. Say It. Sorted.” has successfully combined government and security agency top-down surveillance with citizen-generated bottom-up and peer-to-peer sousveillance. It has used face-to-face interaction and SMS communication to increase customer service, safety, and therefore inevitably security, on the UK rail network over the past couple of years.
It has been a forward-looking and progressive initiative of the UK Department for Transport, with a number of proactive stakeholders such as the British Transport Police (BTP), aimed at encouraging users to communicate feelings of weird, which in their timely communication might help avoid both catastrophic terrorist events as well as hurt to vulnerable passengers and passers-by.
Sousveillance has a long and noble history. Some background now follows.
The below screenshot is taken from Dr Steve Mann’s many writings on the original aims and philosophies of the latter. His daughter, at the age of six, sketched it and observed it thus:
In the light of everything that Edward Snowden revealed, and at the margin of the rights and wrongs of what he did to engineer its revealing, it’s clear that sousveillance has many virtues as a tool of oversight and protection re Peter Levine’s concept of Good Democracy: inclusiveness and efficiency, both.
And when I say governance, I mean governors and governed in equal measure. We all, whether civil servants responsible for the day-to-day operational integrity of a government department, or companies applying for government contracts, or citizens choosing whether to vote in elections, or children just crossing the road sanely and joyfully, or adults being kind to each other, or even to anyone’s children … or everyone seeing young people as our present not our future … or a sheer and simple culture of widespread humanity and gentleness … all of us but all of us – in Levine’s Good Democracy I allude to – are duty-bound to support and show continuing compassion for another’s safety, comfort and sense of security.
The Rail Tap app, designed by myself and configured and further imagineered by Chris Morland of CitrusSuite Ltd and Thomas Gorry of Quanovo Ltd, alongside its associated tagline of “See It. Tap It. Sorted.”, is engineered around such theologies of societal responsibility and democratic stakeholdership.
Governance, good governance, means good citizens and good government acting together: not always agreeing on what to do; rather, agreeing – as Levine – that political activity and process is a purposive dynamic, aimed at solving problems not creating them.
Better Biz Me’s proposed app is not a piece of bald software: it is a fully blown technology – to use Foucault’s definition of the word – in order that the very threads of our beautiful Western liberal democracy be bound thicker and much less tenuously than to date.
It is part of a much bigger project: one that aims to capture, evidence and validate a collaborative human & machine intuition, so that zemiological pressures – not necessarily illegal acts, but societally harmful ones for sure, currently outwith legal jurisdiction – may be reduced and even permanently deactivated in devolved and people-empowering ways.
Simply put: we wish to make it possible for citizens, politicians, children, lovers, and doers to believe that whistleblowing is cool; that hiding the truth is bad; and that mafias need to be eliminated via the actions of all fab and perceptive human beings.
And in particular, by the governance and sacred community-based duty-of-care of everyone, absolutely everyone, who wants to be part of a creating, shaping, and implementing efficiently of a future Good Democracy of better.
The Rail Tap app is NOT just another app. It is a theology of future Good Governance, Good Government, and – ultimately – Good Democracy.
It is that carefully conceptualised.
And it deserves high-powered investment, as soon as we can deliver it.
The proposed logo for the UK-based Rail Tap app project
The Rail Tap app was a submission I made to the UK Defence and Security Accelerator in May of 2019.
The feedback described it as “unique”, but said that it lacked explicit support from a rail operator, and therefore clarity around the origin of the datasets that could be used, for when the Minimum Viable Product (MVP) might be tested in real time and place.
There was one other main criticism, which I shall discuss briefly at the end of this post.
I think the uniqueness of the idea is undoubted. But it’s also a mightily practical and realisable project.
It was developed with the support of two Liverpool-based software companies: CitrusSuite Ltd and Quanovo Ltd, each being experts in their respective fields.
The project itself is still very much alive and kicking: I am currently proactively looking for a rail operator and/or other interested stakeholders to resolve the challenges described by the DASA reviewers.
In the meantime, so it’s clear to anyone reading this post:
The IP belongs to myself, even after submission to the government. Therefore, whether accepted or no in the case of future resubmission, this circumstance would remain in place.
The opportunities to benefit from the software developed and even earn revenues – within the sector, and dependent geographies – for any stakeholder who signed up to participate in the first app trials are quite considerable:
if delivered via a DASA contract, the app frontend itself would be free to trial;
whether delivered via a DASA contract or not, any participating stakeholder would have the chance of acquiring the right to onward-commercialise the product/service, within the rail sector and geographies of their choice, and – depending on the agreement signed – also have the right to receive up to 100% of all revenues thus generated. This is surely too good to ignore.
To assess, then, exactly how unique this proposal was, back in May 2019, and keeping in mind the heavy conceptual development – in particular around what I have described as the intuition validation engine (that is to say, i’ve) – that has taken place since May, I have decided to partially publish the original narrative and story with which I strove to sell the idea to DASA and its client government department – ie the paying customer.
What follows below is this story.
Enjoy!
“See It. Tap It. Sorted.”
The Rail Tap app
Introduction
What the proposal will deliver “See It. Tap It. Sorted.”, the Rail Tap app, has a simple interface, allowing users to communicate feelings about the rail network efficiently and anonymously. Specially developed AI evaluates and reports, in real time, the data which staff and public send. They receive information about the network, specific to their location, needs and roles. The security and customer experiences of public and staff are improved.
Why it’s important The app expands the “See It. Say It. Sorted.” initiative substantially, so the authorities can better predict potentially catastrophic events to the rail network such as terrorism, as well as improve customer-service delivery, with the efficient collaboration of members of the public.
The Rail Tap app story
An example of how it can work What follows below is a simple narrative of a) how people can communicate negative feelings and emotions they have in public spaces, which quite naturally reduce their perceptions of personal safety, to deliver positive outcomes; and b) how an AI system can be designed to sort, filter and report any contradictory information received in this way.
“It’s 10.30pm in July, on a station where all the staff have gone home. Mike has just got through the ticket barrier, using his train provider’s digital ticket. Alongside the standard ticketing software, his provider has given him the option of the Rail Tap module, to allow him to provide feedback throughout his train journey. He said yes, and chose to download without becoming a registered user. He now has the app on his quite standard smartphone.
Mike’s interventions using Rail Tap Mike has arrived at the platform with twenty minutes to spare. He is now killing time. He notices that of the five lights on the platform, three do not work. He sends a notification using the Rail Tap module: “Three lights still not working at Prescot station!” He then takes a photo of the offending lights. Rail Tap has a slider button which moves from green happy to sad red. He submits the photo firmly on sad red. The photo automatically contains location data. (However, it’s worth noting that once submitted, all content can be automatically removed from users’ devices.) Suddenly, in the far corner of the station, where the lighting is poorest, he notices a movement. He sees a flash of something. He begins to get worried, as he is the only person on the platform. He takes another photo of the wider station, as if just snapping for pleasure. He adds a social network-style “sticker” (a computer-generated object which you can paste on a photo, and which is a widely used communication strategy) to the far corner of the photo, near where he saw the flash, before then submitting it: the sticker is a picture of a cartoon dagger.
The AI’s response The AI, a deep neural network on an on-premise server (depending on the wishes of the client, public cloud could also be used, as could any hybrid) of Northern Railways, receives in real time the three notifications from Mike. The first one, the text message, is classified as a maintenance issue, and is pushed to Maintenance to be dealt with. The second one, the photo of the broken lights, is used by the AI for two purposes: a) to support the validity of the first notification, in order that the likelihood of a false flag be discarded; b) so that the AI’s deep neural network can continue to learn better to identify what a broken light specifically on Prescot station looks like.
Officer Brett and the BTP officers On receiving the third notification, the AI raises an alert. A Northern Railway train is minutes away, and two British Transport Police are on it. The AI immediately notifies the Control Hub, where Officer Brett is on duty, of a potential threat to life at Prescot station. Officer Brett evaluates both the photo and the information. She notifies the BTP officers that someone may have an offensive weapon.
Gina’s interventions using Rail Tap The AI receives a notification from a second user. Passenger Gina has just arrived at the station. She sees a young man in a corner of the station, using a knife to lever open the drinks’ machine. “Homeless person, dehydrated on Prescot station. Has a knife. Breaking the drinks’ machine,” she texts. The AI receives this notification, compares location and content with Mike’s earlier photo and sticker, and offers Officer Brett an evaluation based on previous events at similar stations. She decides to push the raw data to the BTP officers: Mike’s second photo with the sticker, and Gina’s comment. (Both pieces of content are anonymous, as neither had chosen to be a registered user.) With all the above information prior to arrival at Prescot, the officers are forewarned about what to expect, and better prepared to deal with the young man.
The Rail Tap and AI system’s impact on security and customer-service delivery Mike, meanwhile, already felt better for sending the notification, as his experience in the past six months shows that changes are made in response to the data. He hopes to see the broken lights mended shortly, especially in relation to the unease he reported this evening. Gina feels good about telling the authorities that a person at risk needed help. Additionally, Officer Brett has been supported by the AI into delivering a more secure and customer-focused service: at no stage in recent implementation has she ever felt she was going to be substituted by the technology. Instead, she feels empowered to do her job more competently, in an increasingly cost-driven environment. Finally, and most interestingly perhaps, the AI compares the information received in the time described above, and notes that images sent by passengers feeling considerable unease have been received from multiple stations recently, at the same time as notifications of poor lighting late in the evening. It decides that some maintenance issues need to be labelled as impacting on security, and sends a second notification with a security label to the Maintenance Hub for this to be evaluated by domain experts.”
Before I end today’s post, here’s one final interesting – at least for me – observation. One of the most negative pieces of feedback the submission received was that I hadn’t demonstrated an advancement in science or knowledge.
I feel, personally, truly, sincerely, that the above narrative, practically the first thing the reviewer would have read, shows – whilst with an arts-based mindset I accept, a mindset that may not have coincided with what the reviewer was most comfortable with, and yet with intelligence and clarity nevertheless (whatever one’s prejudices around the thinking processes employed) – that advancement will take place with this and other i’ve projects, and could in fact already have been happening, if the project had received the due go-ahead in July of this year.
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