An AI Origin Story

Nowadays, no TV or movie franchise worth its salt is deemed complete unless it has some sort of origin story – from “Buzz Lightyear” to “Alien”, from “Mystery Road” to “Inspector Morse”. And as for “Star Wars”, I’ve lost count as to which prequel/sequel/chapter/postscript/spin-off we are up to. Origin stories can be helpful in explaining “what came before”, providing background and context, and describing how we got to where we are in a particular narrative. Reading Jeanette Winterson’s recent collection of essays, “12 Bytes”, it soon becomes apparent that what she has achieved is a tangible origin story for Artificial Intelligence.

Still from “Frankenstein” (1931) – Image sourced from IMDb

By Winterson’s own admission, this is not a science text book, nor a reference work on AI. It’s a lot more human than that, and all the more readable and enjoyable as a result. In any case, technology is moving so quickly these days, that some of her references (even those from barely a year ago) are either out of date, or have been superceded by subsequent events. For example, she makes a contemporaneous reference to a Financial Times article from May 2021, on Decentralized Finance (DeFi) and Non-Fungible Tokens (NFTs). She mentions a digital race horse that sold for $125,000. Fast-forward 12 months, and we have seen parts of the nascent DeFi industry blow-up, and an NFT of Jack Dorsey’s first Tweet (Twitter’s own origin story?) failing to achieve even $290 when it went up for auction, having initially been sold for $2.9m. Then there is the Google engineer who claimed that the Lamda AI program is sentient, and the chess robot which broke its opponent’s finger.

Across these stand-alone but interlinked essays, Winterson builds a consistent narrative arc across the historical development, current status and future implications of AI. In particular, she looks ahead to a time when we achieve Artificial General Intelligence, the Singularity, and the complete embodiment of AI, and not necessarily in a biological form that we would recognise today. Despite the dystopian tones, the author appears to be generally positive and optimistic about these developments, and welcomes the prospect of transhumanism, in large part because it is inevitable, and we should embrace it, and ultimately because it might the only way to save our planet and civilisation, just not in the form we expect.

The book’s themes range from: the first human origin stories (sky-gods and sacred texts) to ancient philosophy; from the Industrial Revolution to Frankenstein’s monster; from Lovelace and Babbage to Dracula; from Turing and transistors to the tech giants of today. There are sections on quantum physics, the nature of “binary” (in computing and in transgenderism), biases in algorithms and search engines, the erosion of privacy via data mining, the emergence of surveillance capitalism, and the pros and cons of cryogenics and sexbots.

We can observe that traditional attempts to imagine or create human-made intelligence were based on biology, religion, spirituality and the supernatural – and many of these concepts were designed to explain our own origins, to enforce societal norms, to exert control, and to sustain existing and inequitable power structures. Some of these efforts might have been designed to explain our purpose as humans, but in reality they simply raised more questions than they resolved. Why are we here? Why this planet? What is our destiny? Is death and extinction (the final “End-Time”) the only outcome for the human race? Winterson rigorously rejects this finality as either desirable or inevitable.

Her conclusion is that the human race is worth saving (from itself?), but we have to face up to the need to adapt and continue evolving (homo sapiens was never the end game). Consequently, embracing AI/AGI is going to be key to our survival. Of course, like any (flawed) technology, AI is just another tool, and it is what we do with it that matters. Winterson is rightly suspicious of the male-dominated tech industry, some of whose leaders see themselves as guardians of civil liberties and the saviours of humankind, yet fail to acknowledge that “hate speech is not free speech”. She acknowledges the benefits of an interconnected world, advanced prosthetics, open access to information, medical breakthroughs, industrial automation, and knowledge that can help anticipate danger and avert disaster. But AI and transhumanism won’t solve all our existential problems, and if we don’t have the capacity for empathy, compassion, love, humour, self-reflection, art, satire, creativity, imagination, music or critical thinking, then we will definitely cease to be “human” at all.

The Bibliography to this book is an invaluable resource in itself – and provides for a wealth of additional reading. One book that is not listed, but which might be of interest to her readers, is “Chimera”, a novel by Simon Gallagher, published in 1981 and subsequently adapted for radio and TV. Although this story is about genetic engineering (rather than AI), nevertheless it echoes some of Winterson’s themes and concerns around the morals and ethics of technology (e.g., eugenics, organ harvesting, private investment vs public control, playing god, and the over-emphasis on the preservation and prolongation of human lifeforms as they are currently constituted). Happy reading!

Next week: Digital Perfectionism?

 

StartupVic’s Machine Learning / AI pitch night

Machine Learning and AI are such hot topics, that I was really intrigued by the prospect of this particular StartupVic pitch night. First, this was a chance to visit inspire9‘s recently established Dream Factory – a tech co-working facility, maker space, and VR lab in Melbourne’s western suburb of Footscray. Second, the Dream Factory, housed in a landmark building owned by Impact Investment Group, was a major beneficiary of LaunchVic funding, and this event could be seen as a showcase for Melbourne’s tech startup sector. Third, with so many buzzwords circling AI, it offered a great opportunity to help demystify some of the jargon and provide some practical insights.

Image sourced from StartupVic

Instead, the pitches felt underdone – probably not helped by the building’s acoustics, the poor PA system, and the inability of many of the audience to be able to read the presenters’ slides. I wasn’t expecting the founders to reveal the “secret sauce” of their algorithms, or to explain in detail how they program or train their “smart” applications. But I had hoped to hear some concrete evidence of how these emerging platforms actually work and how the resulting data is specifically analyzed and applied to client solutions.

Amelie.ai

With a tag line of “powering the future of mental health” the team at Amelie.ai are hoping to have a positive impact in helping to reduce suicide rates. Unfortunately, judging by the way some key statistics are presented on their home page, the data (and the methodology) are not as clear as the core message.

Using technology to help scale the provision of mental health and well-being services, combined with mixed delivery methods, the solution aims to offer continuity of care. Picking up on user dialogue and providing some semi-automated and curated intervention, the presentation was big on phrases like “triage packages”, “customer journey”, “technical architecture”, “chatbots” and of course, “AI” itself, but I would have like a bit more explanation on how it worked.

I understand that the platform is designed to integrate with third-party providers, but how does this happen in practice?

Only when asked by the judges about their competitive advantage (as there are similar tools out there – see Limbr from a previous pitch night) did the presenters refer to their proprietary language models, developed with and based on user trials. This provides  a structured taxonomy, which is currently English-only, but it can be translated.

There were also questions about data privacy (not fully explained?) and sales channels – which may include workplace EAPs and health insurers.

Businest

According to the founder, “dashboards and KPIs only diagnose pain, Businest fixes it“. In short, this is intelligence business analysis for SMEs.

With a focus on tracking working capital and cashflow, as far as I can tell, Businest applies some AI on top of existing third-party accounting software. It identifies key metrics for a specific business, then provides coaching and videos to change business behaviour and improve financial performance. There is a patent pending in the US for the underlying algorithm, which prioritizes the KPIs.

Again, I was not totally clear how the desired results are achieved. For example, are SMEs benchmarked against their peers (e.g., by size/industry/geography/maturity/risk profile)? Do clients know what incremental benefits they should be able to generate over a given time period? How does the financial spreadsheet analysis assist with improving structural or operational efficiencies that are outside the realm of financial accounting?

Available under a freemium SaaS model, Businest is sold direct and via accountants and bookkeepers. A key to success will be how fast the product can scale – via partnering and its integration with Xero, MYOB and QuickBooks.

AiHello

I must admit, I was initially curious, and then totally bemused, by this pitch. It started by asking some major philosophical and existentialist questions:

Q: How do we define “intelligence”?
Q: Are we alone? Or not alone?

No, this is not IBM’s Watson trained on the works of John-Paul Sartre (cf. Dark Star and the struggle with Cartesian Logic). Instead, it is an analytical and predictive app for Amazon sellers. It claims to know what products will sell, where and when. And with trading volumes worth $2.5m of goods per month, it must be doing something right. Serving Amazon sellers in the US and India (and Australia, once Amazon goes live here), AiHello charges fees based on fixed licences and transaction values. The apparent benefits to retailers are speed and savings.

Asked where the trading data is coming from, the presenter referred to existing trading platform APIs, and “big data and deep learning”. It also uses Amazon product IDs to make specific predictions – currently delivering 60% accuracy, but aiming for 90%. According to the founder, “Amazon focuses on buyers, we focus on sellers”. (Compare this, perhaps, to the approach by Etsy.)

C-SIGHT

A new service from the team at Pax Republic, this latest iteration is designed to avoid some of the policy and reputation issues involved with managing, supporting and protecting whistleblowers. Understanding that whistleblowers can pose an internal threat to brand value, and present a significant human risk, C-SIGHT provides a psychologically safe environment for the Board, C-suite and workforce alike, and can act as an early warning system before problems get out of hand.

Sold under a SaaS model, C-SIGHT analyses text-based and anonymous dialogue, with “real-time data sent to different AI apps”. I understood that C-SIGHT combines human and robot facilitation, while preserving anonymity, and also deploys natural language processing – but I didn’t fully understand how.

In one client use case, with the College of Surgeons, there were 1,000 “contributions” – again, it was not clear to me how this input was generated, captured, processed or analysed. Client pricing is based on the number of invitations sent and the number of these “contributions” – what the presenter referred to as an “instance” model (presumably he meant instance-based learning?).

Asked about privacy, C-SIGHT de-identifies contributions (to what degree was not clear), and operates outside the firewall. There was also a question from the judges about the use and analysis of idiom and the vernacular – I don’t believe this addressed in much detail, although the presenter did suggest that the platform could be used as a way to drive “citizen engagement”.

Overall, I was rather underwhelmed by these presentations, although each of them revealed a kernel of a good idea – while in the case of AiHello (which was the winner on the night), sales traction is very promising; and in the case of Businest, industry recognition, especially in the US, has opened up some key opportunities.

Next week: Bitcoin – to fork or not to fork?