Category Archives: Technology

The Tech in My Life #1 – Telstar!

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This week’s blog is the first in a series on key technology that has helped to shape my life. It will be a very personal perspective, and obviously it can’t capture every tech development, interface or environment that I have engaged with. But as I hope to establish, my selections do reveal three recurring themes: information, communication, and music. These threads should become apparent in the coming weeks.

First up is a record, “Telstar”, released by The Tornados in 1962.

I have blogged about this record before, mainly from a musical perspective. But as a tech artefact it also provides other context.

It is probably the first 7″ vinyl record that I can recall hearing – and certainly one of the only pieces of music from my early childhood that remains both familiar and intriguing to this day. Maybe it was also the first record I got to play on the family Dansette myself, so there is a reinforced tactile connection with the record as a “data object” and the gramophone player as a “playback device”. And while the tune itself is relatively simple, the arrangement and recording techniques reveal a lot more depth than a normal pop record. For a start, the choice of electronic keyboards, and the treatment of the electric guitar give it a suitably unworldly atmosphere.

More specifically, and without my realising it at the time, it is probably my earliest experience of musique concrète. I distinctly remember putting my ear as close to the speaker as possible to absorb the opening passage of Joe Meek’s sound effects, tape manipulation and general studio wizardry. What were these strange noises? Why did they fascinate me? Was it really music, or was I hearing something else entirely, like signals from outer space?

Which is exactly the point, as “Telstar” is named after the communications satellite of the same name. Leaving aside Joe Meek’s fascination/obsession with spiritualism and the occult, he was something of an autodidact in the studio – if he needed a particular effect (“what would a satellite signal sound like?”), he would create or improvise from whatever technology was to hand (primitive or otherwise).

In coming weeks, the significance of “Telstar” (the satellite) on my childhood will emerge. But if I was ever invited to appear on “Desert Island Discs”, I’m certain that “Telstar” would be included in my playlist of 8 records.

Next week #2: Goonhilly

 

 

The cost of AI

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A variant on Moore’s law is the observation that the financial capital required to launch a new business decreases exponentially as technology gets cheaper.

Pre-internet, and using a notional geometric scale for the purposes of illustration, you might have needed $5m to found and build a new venture. The World Wide Web probably reduced that to $500k, while cloud computing brought it down to $50k. With the expansion of SaaS and API solutions, that cost might have been $5k to get going. Now, vibe coding and $500 of AI prompts can probably launch a new website, build a back end database, implement an e-commerce solution and deploy agentic AI bots to go and find your first customers.

This is a great outcome if measured by a lower barrier to market entry. It also enables founders to “fail fast, fail cheap”, and incentivises innovation by financially de-risking the process.

But even though the cost of AI tools is extraordinarily cheap in terms of the computing and processing power they deliver, there is a huge cost to our rapid adoption of AI that needs to be accounted for.

First, we are seeing corporate lay-offs among tech firms and parts of the service industry that no longer need as many human bodies and minds to operate at scale. So there is a human, economic and societal cost of increased un(der)employment.

Second, traditional skills and expertise are being hugely reduced in perceived value – why pay a graphic artist to design an image when I can use dall-e for free?

Third, as more and more creative tasks are being outsourced or delegated to AI (“create a short story about an F1 race in the style of Ernest Hemingway”) we risk losing our own innate creativity (that comes with experimentation, curiosity, play and reflection). This in turn devalues the creative process itself (thanks to cheaper, AI-enabled production).

Fourth, AI (and the Large Language Models on which it is trained) has no great respect for intellectual property. It doesn’t recognise boundaries between copyright material, content that is subject to creative commons, content that is in the public domain, and content which is publicly available. Again, if copyright owners and original content creators are not recognised or compensated for their work, why would anyone aspire to creating anything original?

Finally, there is the cost of resources (energy, water, rare earth metals) needed to maintain huge AI processing plants and data centres. (But at least this demand is accelerating the development of renewable energy.)

A few years ago, I posted a blog about the importance of the human factor, in the face of technological progress brought by automation and AI. I still remain cautiously optimistic that AI will bring huge benefits, despite the rampant growth of AI in the three years since I wrote that piece. But we are currently in an awkward and comfortable transition phase. If more jobs are lost to AI, and if human-led output is increasingly devalued, perhaps we will need to revisit the debate about Universal Basic Income and other policies to facilitate this transition.

Next week: Music, music everywhere…. and none of it very memorable 

Whose side is AI on?

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At the risk of coming across as some sort of Luddite, recent commentary on Artificial Intelligence suggests that it is only natural to have concerns and misgivings about its rapid development and widespread deployment. Of course, at its heart, it’s just another technology at our disposal – but by its very definition, generative AI is not passive, and is likely to impact all areas of our life, whether we invite it in or not.

Over the next few weeks, I will be discussing some non-technical themes relating to AI – creativity and AI, legal implications of AI, and form over substance when it comes to AI itself.

To start with, these are a few of the questions that I have been mulling over:

– Is AI working for us, as a tool that we control and manage?  Or is AI working with us, in a partnership of equals? Or, more likely, is AI working against us, in the sense that it is happening to us, whether we like it or not, let alone whether we are actually aware of it?

– Is AI being wielded by a bunch of tech bros, who feed it with all their own prejudices, unconscious bias and cognitive limitations?

– Who decides what the Large Language Models (LLMs) that power AI are trained on?

– How does AI get permission to create derived content from our own Intellectual Property? Even if our content is on the web, being “publicly available” is not the same as “in the public domain”

– Who is responsible for what AI publishes, and are AI agents accountable for their actions? In the event of false, incorrect, misleading or inappropriate content created by AI, how do we get to clarify the record, or seek a right of reply?

– Why are AI tools adding increased caveats? (“This is not financial advice, this is not to be relied on in a court of law, this is only based on information available as at a certain point in time, this is not a recommendation, etc.”) And is this only going to increase, as in the recent example of changes to Google’s AI-generated search results? (But really, do we need to be told that eating rocks or adding glue to pizza are bad ideas?)

– From my own experience, tools like Chat GPT return “deliberate” factual errors. Why? Is it to keep us on our toes (“Gotcha!”)? Is it to use our responses (or lack thereof) to train the model to be more accurate? Is it to underline the caveat emptor principle (“What, you relied on Otter to write your college essay? What were you thinking?”). Or is it to counter plagiarism (“You could only have got that false information from our AI engine”). If you think the latter is far-fetched, I refer you to the notion of “trap streets” in maps and directories.

– Should AI tools contain better attribution (sources and acknowledgments) in their results? Should they disclose the list of “ingredients” used (like food labelling?) Should they provide verifiable citations for their references? (It’s an idea that is gaining some attention.)

– Finally, the increased use of cloud-based services and crowd-sourced content (not just in AI tools) means that there is the potential for overreach when it comes to end user licensing agreements by ChatGPT, Otter, Adobe Firefly, Gemini, Midjourney etc. Only recently, Adobe had to clarify latest changes to their service agreement, in response to some social media criticism.

Next week: AI and the Human Factor

“The Digital Director”

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Last year, the Australian Institute of Company Directors (AICD) ran a series of 10 webinars under the umbrella of “The Digital Director”. Despite the title, there was very little exploration of “digital” technology itself, but a great deal of discussion on how to manage IT within the traditional corporate structure – as between the board of directors, the management, and the workforce.

There was a great deal of debate on things like “digital mindset”, “digital adaption and adoption”, and “digital innovation and evolution”. During one webinar, the audience were encouraged to avoid using the term “digital transformation” (instead, think “digital economy”) – yet 2 of the 10 sessions had “digital transformation” in the title.

Specific technical topics were mainly confined to AI, data privacy, data governance and cyber security. It was acknowledged that while corporate Australia has widely adopted SaaS solutions, it lacks depth in digital skills; and the percentage of the ASX market capitalisation attributable to IP assets shows we are “30 years behind the USA”. There was specific mention of blockchain technology, but the two examples given are already obsolete (the ASX’s abandoned project to replace the CHESS system, and CBA’s indefinitely deferred roll-out of crypto assets on their mobile banking app).

Often, the discussion was more about change management, and dealing with the demands of “modern work” from a workforce whose expectations have changed greatly in recent years, thanks to the pandemic, remote working, and access to new technology. Yet, these are themes that have been with us ever since the first office productivity tools, the arrival of the internet, and the proliferation of mobile devices that blur the boundary between “work” and “personal”.

The series missed an opportunity to explore the impact of new technology on boards themselves, especially their decision-making processes. We have seen how the ICO (initial coin offering) phase of cryptocurrency markets in 2017-19 presented a wholly new dimension to the funding of start-up ventures; and how blockchain technology and smart contracts heralded a new form of corporate entity, the DAO (decentralised autonomous organisation).

Together, these innovations mean the formation and governance of companies will no longer rely on the traditional structure of shareholders, directors and executives – and as a consequence, board decision-making will also take a different format. Imagine being able to use AI tools to support strategic planning, or proof-of-stake to vote on board resolutions, and consensus mechanisms to determine AGMs.

As of now, “Digital Directors” need to understand how these emerging technologies will disrupt the boardroom itself, as well as the very corporate structures and governance frameworks that have been in place for over 400 years.

Next week: Back in the USA