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.

With a tag line of “powering the future of mental health” the team at 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.


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.


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.)


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?

Law and Technology – when AI meets Smart Contracts…

Among the various ‘X’-Tech start-up themes (e.g., FinTech, EdTech, MedTech, InsurTech) one of the really interesting areas is LegTech (aka LawTech), and its close cousin, RegTech. While it’s probably some time before we see a fully automated justice system, where cases are decided by AI and judgments are delivered by robots, there are signs that legal technology is finally coming into its own. Here’s a very personal perspective on law and technology:

Photo by Lonpicman via Wikimedia Commons

1. Why are lawyers often seen as technophobes or laggards, yet in the 1980s and 1990s, they were at the vanguard of new technology adoption?

In the 1970s, law firms invested in Telex and document exchange (remember DX?) to communicate and to share information peer-to-peer. Then came the first online legal research databases (Lexis and Westlaw) which later gave rise to “public access” platforms such as AustLII and its international counterparts.

Lawyers were also among the first professional service firms to invest in Word Processing (for managing and drafting precedents) and e-mail (for productivity). Digitization meant that huge print libraries of reference materials (statutes and case-law) could be reduced to a single CD-ROM. Law firms were early adopters of case, practise, document and knowledge management tools – e.g., virtual document discovery rooms, precedent banks, drafting tools.

2. But, conversely, why did the legal profession seem to adopt less-optimal technology?

The trouble with being early adopters can mean you don’t make the right choices. For example, law firms in the 80s and 90s seemed to demonstrate a preference for Lotus Notes (not Outlook), Wang Computers and WordStar (not IBM machines or MS Office Word), and DOS-based interfaces (rather than GUIs).

Some of the first CD-ROM publications for lawyers were hampered by the need to render bound volumes as exact facsimiles of the printed texts (partly so lawyers and judges could refer to the same page/paragraph in open court). There was a missed opportunity to use the technology to its full potential.

3. On the plus side, legal technology is having a significant a role to play…

…in law creation (e.g., parliamentary drafting and statute consolidation), the administration of law (delivery of justice, court room evidence platforms, live transcripts, etc.), legal practice (practice management tools) and legal education (research, teaching, assessment, accreditation). Plus, decision support systems combining rules-based logic, precedent and machine learning, especially in the application of alternative dispute resolution.

4. Where next?

In recent years, we have seen a growing number of “virtual” law firms, that use low-cost operating models to deliver custom legal advice through a mix of freelance, part-time and remote lawyers who mainly engage with their clients online.

Blockchain solutions are being designed to register and track assets for the purposes of wills and trusts, linked to crypto-currency tokens and ID management for streamlining the transfer of title. Governments and local authorities are exploring the use of distributed ledger technology to manage land title registration, vehicle and driver registration, fishing permits and the notion of “digital citizenship”.

We are seeing the use of smart contracts powered by oracles on the Ethereum blockchain to run a range of decision-making, transactional, financial, and micro-payment applications. (Although as one of my colleagues likes to quip, “smart contracts are neither smart nor legal”.)

Artificial Intelligence (AI) is being explored to “test” legal cases before they come to trial, and more knowledge management and collaboration tools will continue to lower the cost of legal advice (although I doubt we will see lawyers being totally disintermediated by robots, but their role will certainly change).

There is further opportunity to take some of the friction and costs out of the legal system to improve access to justice.

Finally, and this feels both exciting and scary, is the notion of “crowd-sourcing policy“; some governments are already experimenting with hackathons to develop policy-making models, and even the policies themselves. But this does sound like we would be moving closer and closer to government by mini-plebiscites, rather than by parliamentary democracy.

Next week: Digital currencies are the new portals


When robots say “Humans do not compute…”

There’s a memorable scene in John Carpenter‘s 1970’s sci-fi classic, “Dark Star” where an astronaut tries to use Cartesian Logic to defuse a nuclear bomb. The bomb is equipped with artificial intelligence and is programmed to detonate via a timer once its circuits have been activated. Due to a circuit malfunction, the bomb believes it has been triggered, even though it is still attached to the spaceship, and cannot be mechanically released. Refuting the arguments against its existence, the bomb responds in kind, and simply states: “I think, therefore I am.”

Dark Star’s Bomb 20: “I think, therefore I am…”

Dark Star’s Bomb 20: “I think, therefore I am…”

The notion of artificial intelligence both thrills us, and fills us with dread: on the one hand, AI can help us (by doing a lot of routine thinking and mundane processing); on the other, it can make us the subjects of its own ill-will (think of HAL 9000 in “2001: A Space Odyssey”, or “Robocop”, or “Terminator” or any similar dystopian sci-fi story).

The current trend for smarter data processing, fuelled by AI tools such as machine learning, semantic search, sentiment analysis and social graph models, is making a world of driverless cars, robo-advice, the Internet of Things and behaviour prediction a reality. But there are concerns that we will abnegate our decision-making (and ultimately, our individual moral responsibility) to computers; that more and more jobs will be lost to robots; and we will end up being dehumanized if we no longer have to think for ourselves. Worse still, if our human behaviours cease making sense to those very same computers that we have programmed to learn how to think for us, then our demise is pre-determined.

The irony is, that if AI becomes as smart as we might imagine, then we will impart to the robots a very human fallibility: namely, the tendency to over-analyse the data (rather than examine the actual evidence before us). As Brian Aldiss wrote in his 1960 short story, “The Sterile Millennia”, when robots get together:

“…they suffer from a trouble which sometimes afflicts human gatherings: a tendency to show off their logic at the expense of the object of the meeting.”

Long live logic, but longer still live common sense!

Next week: 101 #Startup Pitches – What have we learned?