For a long time we had a fun bit of technology in the house that would make a cheering sound whenever Arsenal scored, running off a Raspberry Pi, using the BBC live football “videprinter” and our streaming music sound system. I was always incredulous to find it still worked at the start of each new football season: an untouched, unloved little piece of code, never upgraded, continuing to quietly do its thing. Until eventually it stopped. Not because, as my amusing friends would have it, Arsenal stopped scoring goals, but because the BBC service it relied on finally got moved.

Is it really possible to build technology and create services that last for 10 years or more? I don’t mean by burying it in a mountain like the 10,000 year clock, or sending it into space like the Voyager space probes or the Solar Orbiter. I am talking about practical small web projects rather than industrial or embedded systems. In my case: as I refresh various 7-year-old projects like the Arsenal cheer creator and our fridge dashboard, how long will they last this time? …

This article represents my personal views and not those of Digital Catapult, NHS Digital or any other organisation.

In a few years time there’ll be lots of AI apps to help with radiology. I don’t mean research studies or clinical trials. I mean apps deployed and approved and working [1]. This is great news: the demand for radiology is growing and there aren’t enough radiologists [2]. Any tool that can prioritise the urgent cases, quickly eliminate the normal scans, highlight abnormal areas in images, help with a second opinion or assist a more junior clinician will be of great value. However it is also worrying news: how on earth will hospitals do separate contracts and integrations with hundreds of AI providers to cover all the different conditions? …

It’s been called the AR Cloud by many, the Magicverse by Magic Leap, the Mirrorworld by Wired, the Cyberverse by Huawei, Planet-scale AR by Niantic and Spatial Computing by academics.

It’s a set of ideas straight out of science fiction, literally. “Cyberspace” was coined by SF author William Gibson. In his 1984 classic Neuromancer, characters entered a virtual reality world called “the matrix” (inspiration for the 1999 film of the same name by the Wachowskis). In Neal Stephenson’s Snow Crash of 1992, the internet has been superseded by the Metaverse, a multi-person shared virtual reality with both human-controlled avatars and system “daemons”. Augmented vision is commonplace in futuristic films, from Minority Report to Iron Man. When you’re “wearing” in Vernor Vinge’s 2006 book Rainbow’s End, contact lenses and computers woven into clothing serve up an assortment of competing realities and overlays. …

This post also published by Business User magazine in Germany: Sind KI-Systeme Team-Player?

We’re overrun with predictions about AI systems taking people’s jobs, expressed in numbers and percentages and trillions of dollars. But all the jobs I’ve ever had have always been in organisations, in teams, with colleagues and structures, social dynamics and office politics. The question we should be asking is: what role will AI systems take in organisations? Will they be managers? Or co-workers? Or just better tools we can use?

AI as a manager

If you think you’ve never met someone whose manager is an AI system, think again. Every Uber driver’s work is allocated by an algorithm; they have just 15 seconds to accept or reject a ride request, without knowing the destination or fare. Their performance is reviewed automatically, their pay is determined by the system, and if their ratings drop too low that same system will deny them work. Morale boosting motivation comes from an app, and their recourse to help is a customer service agent in a faraway country rather than a human manager. AI recruitment systems are screening candidates. In fact, in organisations around the world, AI systems are performing every traditional management function already¹. But today’s AI managers are creating dehumanised systems where workers are treated without respect or dignity. 90% of Amazon’s Madrid logistics staff walked off the job during Black Friday in 2018, there were protests at 5 sites across the UK, and in Staten Island workers are trying to be the first to unionise among US Amazon staff. AI-managed staff protest their working conditions with banners that read “We are not robots”. …

Applications of AI are blossoming across industries, especially for repetitive individual tasks: looking for lesions in a mammogram, translating an article to French, or predicting when factory machinery will break down. But as AI systems are deployed into environments where they need to interact with us, a need for back and forth communication, understanding of intentions, and sharing of knowledge will be needed. Without some of the psychological constructs we learn as small children, even sophisticated AI systems will struggle in real world settings.

Here is an example. I was cycling to work last week, down a hill, and saw a pedestrian crossing the road. …

Every morning at 6:45 my alarm wakes me. Not because I need to get up at 6:45, but so that I in turn can start the long process of getting sleepy teenagers out the door and off the school. Occasionally they make it out on their own, managing their shifting adolescent sleep patterns (their cruel early start is apparently equivalent to 4:45am for a 50-year-old), and their screen-induced melatonin suppression. On good days, a couple of times a year, I reflect that our time as parents will be over all too quickly, and these little moments are precious. Mostly I complain bitterly. But no more, as I have now entirely automated myself. …

As part of my role at the Digital Catapult I recently participated in the The Royal Society’s investigation of machine learning, and this is a blog post contributed there. I give a little background to where machine learning is today, but then claim that we have a bigger set of challenges around systems that make decisions, regardless of whether they use machine learning algorithms or more conventional software.

Machine learning grew from the broader field of Artificial Intelligence, and gradually became a significant discipline in its own right. Machine Learning (ML) algorithms “learn” from examples and improve from experience, rather than being explicitly programmed. But why the sudden excitement? …

In among what’s undoubtedly the world’s densest concentration of pixels, clamouring for attention across the impossibly vast halls of the annual Mobile World Congress in Barcelona, it is hard to remember the quiet promises of pervasive, ambient, ubiquitous computing. The dream of what’s now called the Internet of Things was of specific devices for specific purposes, discreetly tucking themselves into pockets of your life.

“The most profound technologies are those that disappear. They weave themselves into the fabric of everyday life until they are indistinguishable from it” — Mark Weiser, The Computer for the 21st Century, Scientific American, Sept 1991

Around 10 years after Weiser’s seminal article, Roy Want el al noted that the hardware was beginning to “disappear”. Since the development of Bluetooth, Wifi and improvements in processing and storage, our tasks are less likely to be interrupted by connection issues or capacity constraints. …