Category Archives: Artificial Intelligence

AI vs IP

Posted on by
Reply

Can Artificial Intelligence software claim copyright in any work that was created using their algorithms?

The short answer is “no”, since only humans can establish copyright in original creative works. Copyright can be assigned to a company or trust, or it can be created under various forms of creative commons, but there still needs to be a human author behind the copyright material. While copyright may lapse over time, it then becomes part of the public domain.

However, the extent to which a human author can claim copyright in a work that has been created with the help of AI is now being challenged. A recent case in the USA has determined that the author of a graphic novel, which included images created using Midjouney, cannot claim copyright in those images. While it was accepted that the author devised the text and other prompts that the software used as the generative inputs, the output images themselves could not be the subject of copyright protection – meaning they are either in the public domain, or they fall under some category of creative commons? This case also indicates that, in the USA at least, failing to declare the use of AI tools in a work when applying for copyright registration may result in a rejected application.

Does this decision mean that the people who write AI programmes could claim copyright in works created using their software? Probably not – as this would imply that Microsoft could establish copyright in every novel written using Word, especially its grammar and spelling tools.

On the other hand, programmers and software developers who use copyright material to train their models may need to obtain relevant permission from the copyright holders (as would anyone using the AI tools and who uses copyright content as prompts), unless they could claim exemptions under “fair dealing” or “fair use” provisions.

We’re still early in the lengthy process whereby copyright and other intellectual property laws are tested and re-calibrated in the wake of AI. Maybe the outcomes of future copyright cases will depend on whether you are Ed Sheeran or Robin Thicke….

Next week: Customer Experience vs Process Design

 

An AI Origin Story

Posted on by
Reply

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?

 

Smart Contracts… or Dumb Software

Posted on by
Reply

The role of smart contracts in blockchain technology is creating an emerging area of jurisprudence which largely overlaps with computer programming. However, one of the first comments I heard about smart contracts when I started working in the blockchain and crypto industry was that they are “neither smart, nor legal”. What does this paradox mean in practice?

First, smart contracts are not “smart”, because they still largely rely on human coders. While self-replicating and self-executing software programs exist, a smart contact contains human-defined parameters or conditions that will trigger the performance of the contract terms once those conditions have been met. The simplest example might be coded as a type of  “if this, then that” function. For example, I could create a smart contract so that every time the temperature drops below 15 degrees, the heating comes on in my house, provided that there is sufficient credit in the digital wallet connected to my utilities billing account.

Second, smart contracts are not “legal”, unless they comprise the necessary elements that form a legally binding agreement: intent, offer, acceptance, consideration, capacity, certainty and legality. They must be capable of being enforceable in the event that one party defaults, but they must not be contrary to public policy, and parties must not have been placed under any form of duress to enter into a contract. Furthermore, there must be an agreed governing law, especially if the parties are in different jurisdictions, and the parties must agree to be subject to a legal venue capable of enforcing or adjudicating the contract in the event of a breach or dispute.

Some legal contacts still need to be in a prescribed form, or in hard copy with a wet signature. A few may need to be under seal or attract stamp duty. Most consumer contracts (and many commercial contracts) are governed by rules relating to unfair contract terms and unconscionable conduct. But assuming a smart contract is capable of being created, notarised and executed entirely on the blockchain, what other legal principles may need to be considered when it comes to capacity and enforcement?

We are all familiar with the process of clicking “Agree” buttons every time we sign up for a social media account, download software or subscribe to digital content. Let’s assume that even with a “free” social media account, there is consideration (i.e., there’s something in it for the consumer in return for providing some personal details), and both parties have the capacity (e.g., they are old enough) and the intent to enter into a contract, the agreement is usually no more than a non-transferable and non-exclusive license granted to the consumer. The license may be revoked at any time, and may even attract penalties in the event of a breach by the end user. There is rarely a transfer of title or ownership to the consumer (if anything, social media platforms effectively acquire the rights to the users’ content), and there is nothing to say that the license will continue into perpetuity. But think how many of these on-line agreements we enter into each day, every time we log into a service or run a piece of software. Soon, those “Agree” buttons could represent individual smart contracts.

When we interact with on-line content, we are generally dealing with a recognised brand or service provider, which represents a known legal entity (a company or corporation). In turn, that entity is capable of entering into a contract, and is also capable of suing/being sued. Legal entities still need to be directed by natural persons (humans) in the form of owners, directors, officers, employees, authorised agents and appointed representatives, who act and perform tasks on behalf of the entity. Where a service provider comprises a highly centralised entity, identifying the responsible party is relatively easy, even if it may require a detailed company search in the case of complex ownership structures and subsidiaries. So what would be the outcome if you entered into a contract with what you thought was an actual person or real company, but it turned out to be an autonmous bot or an instance of disembodied AI – who or what is the counter-party to be held liable in the event something goes awry?

Until DAOs (Decentralised Autonomous Organisations) are given formal legal recognition (including the ability to be sued), it is a grey area as to who may or may not be responsible for the actions of a DAO-based project, and which may be the counter-party to a smart contract. More importantly, who will be responsible for the consequences of the DAO’s actions, once the project is in the community and functioning according to its decentralised rules of self-governance? Some jurisdictions are already drafting laws that will recognise certain DAOs as formal legal entities, which could take the form of a limited liability partnership model or perhaps a particular type of special purpose vehicle. Establishing authority, responsibility and liability will focus on the DAO governance structure: who controls the consensus mechanism, and how do they exercise that control? Is voting to amend the DAO constitution based on proof of stake?

Despite these emerging uncertainties, and the limitations inherent in smart contracts, it’s clear that these programs, where code is increasingly the law, will govern more and more areas of our lives. I see huge potential for smart contracts to be deployed in long-dated agreements such as life insurance policies, home mortgages, pension plans, trusts, wills and estates. These types of legal documents should be capable of evolving dynamically (and programmatically) as our personal circumstances, financial needs and living arrangements also change over time. Hopefully, these smart contracts will also bring greater certainty, clarity and efficiency in the drafting, performance, execution and modification of their terms and conditions.

Next week: Free speech up for sale

 

Antler Virtual Demo Day

Posted on by
Reply

As with other virtual demo days I have attended this year, it was remarkable to hear how far the teams in Antler’s Sydney Cohort #3 had progressed in light of the current pandemic and associated lock-down restrictions.

Each participating team was categorised into an industry sector:

Consumer Tech

Remote Social is designed to connect remote and hybrid teams. The ethos is that with the shift in working patterns (heightened by the current pandemic) corporate culture and organisational engagement are “at risk”. The solution aims to foster socialisation and build culture through curated games and activities. Claiming to have generated over 200 organic signups, including team members at big tech brands, the founders are adopting a “bottom, land and expand” customer acquisition strategy. In addition to a seat-based subscription model, the platform will also offer a revenue share for marketplace providers.

Coder One claims to be “the home for AI sports”. The team describe their project as an API platform for AI games, with unique combination of AI and e-sports. The goal is to make AI and programming more accessible via an AI Sports League, where bots compete, programmed by developers. With more than 250 registrations for an upcoming competition, the team are also looking to secure sponsorship deals. The commercial model has three components: free access to programs for developers, individual subscription fees to access games/tournaments, and corporate fees to access talent for potential recruitment.

Feather is an online platform which enables instructors and creators to deliver and monetise their digital services. According to the founders, existing tools are not fit for purpose, complex or clunky. Initially targeting yoga teachers, the solution will sell tiered subscriptions, plus take a small revenue share. The team also see themselves as part of the “creator economy”, but I was confused by the name – is it a deliberate attempt to suggest a link to Dumbo Feather magazine? Plus, there was some feedback that the platform may be vulnerable once tools like Zoom start putting up more pay walls.

Tactiq is a tool to “capture valuable insights from remote meetings”. The founders claim it can be used with any conferencing software, and is platform agnostic (although currently limited to a Google Meet via a Chrome Extension). The product, essentially “speech to text plus”, also generates AI assisted summaries, and the team has attracted over 100,000 users from around 4,500 organisations. Pricing is $9 per user per month, plus $20 per month to access team functionality. While the team appears to know and understand their target users, they were questioned about privacy and security issues. Although the transcription content is not stored on the platform, my experience of other similar tools is that once they are integrated, they have a tendency to “take over” and insert themselves, unprompted, into e-mail and calendar applications – “you seem to have a meeting now – would you like me to record it?”

SaaS

Upflowy wants to help B2B companies improve their customer conversion rates. Intended to be a “no-code” sign-up engine, the team explained that from their experience, in-house developers tend to focus on product features, rather than improving the sign-up experience. Typically, in-house sign-up optimisation is slow, expensive or totally non-existent – the key issues being scaleability and reliability. Essentially a form builder, the solution enables A/B testing, and claims to deliver a 40% improvement in conversion rates (compared to 17% improvement achieved with other optimization tools).

Flow of Work Co is positioning itself as the “Future of Work SaaS”. With a mission to help companies to retain the best people, it is HR tech using AI in the form of a smart matching engine to identify in-house talent, based on proprietary ontology. It also helps employees to find development resources, as well as to match projects with in-house talent. According to the founders, talented staff leave because they are bored or lack career development. With an initial focus on software companies, the team then plans to tackle the financial services sector. The team was asked about integration with existing HR tech stacks, and how they ensure objective assessment of competing project candidates – but it wasn’t so clear how they achieve either.

Portant is an end-to-end project reporting tool, designed to be a “consolidated single source of truth”. Asked why existing project management tools don’t work, the founders identified a number of factors: teams are using different tools, the process is often repetitive and/or highly manual, or project tracking typically relies on data from different sources. The team have launched an MVP on Google Workspace Marketplace, and will soon launch on Microsoft AppSource (and appears to use AWS Comprehend as the analytical tool?). There is an SaaS pricing model, and content privacy is ensured via end-to-end encryption plus the use of private keys.

StackGo helps clients achieve stronger B2B sales via SaaS marketplaces, rather than relying on direct sales. However, the initial setup costs and effort required to connect to existing SaaS marketplaces can be daunting. With an approach based on “build once, deploy many”, StackGo enables users to connect to multiple SaaS marketplaces via a single solution. However, the team, did not explain what the setup costs are for StackGo nor were they very specific about the price range or typical sales value their clients achieve – “free to hundreds of dollars per month”.

EdTech

GradVantage wants to reduce the cost of getting graduates job ready, and reckons it can save employers $30k per new hire. Offering a personalised learning experience for each user, the founders have adopted a “Slack” model – team first, then enterprise sale. Acknowledging that the EdTech sector is crowded, the team think their point of differentiation is the fact that they are a career-entry solution, and not in the K-12 market. The focus is on tech talent and SaaS vendors. Employers pay per learner, and the platform saves time and reduces on-boarding costs. Typically, 30% of program content is about tech applications, and 70% on how to use the tech. A more fundamental issue is the huge gap between university courses and actual job requirements.

HealthTech

eQALY is an integrated tech platform that enables the elderly to achieve a higher quality of life in their own home, by predicting their individual needs in advance, and identifying the right Home Care Package funding (which can be worth up to $26k). Using 360 degree data inputs, a risk model, and a proactive care plan, the product takes into account client needs as well as family concerns, plus financial considerations. Although the aged care industry is regarded as being slow to adopt new technology, the founders plan to focus on aged care organisations, who will then distribute predictive data and analytics to care providers and managers. The platform is tech agnostic but IoT devices, AI tools and virtual assistants can be integrated, plus new voice analysis technology is emerging that can monitor client well-being, and
all of the activity monitoring tech is passive. meaning the end user does not have to worry about learning new applications.

Retail and E-Commerce

The One Two is a very specific, and very targeted, D2C solution offering a hyperpersonalised service for fitting and buying bras. According to the founders, current customer experience suffers from ill-fitting products, poor product design, bad materials, and inadequate size configurations. As a result, customers feel overwhelmed and give up. The basic product IP has been tested, along with an on-line measuring and fitting tool, combining to provide better customer diagnosis and product tips. The team have already secured a startup partnership with a global lingerie manufacturer and distributor.

m8buy did not make much sense to me. Maybe I’m the wring demographic, but why would anyone want to “shop online with [their] friends”? Describing itself as “a social layer on any e-commerce store”, it feels like this is aimed at the “buy now, pay later” audience.
According to the founders, merchants will only pay a commission (“low single digit %”?) on successful sales. But it’s not clear whether this is a group buying service, a discount marketplace, or a loyalty programme, nor how it will be differentiated within the Shopify marketplace.

PropTech

Sync Technologies is a digital solution for construction industry – with the tag line of “turning data into insights”. The problem being addressed can be summarised as follows: 1) Building
sites are fragmented and complex 2) Progress reporting and bottleneck identification is poorly done 3) 12% of a typical job has to be reworked 4) 80% of projects are late and/or run over budget. Using a digital twin concept, the solution aims to provide a “single source of truth”, and the team are already working with some key firms, and have 2021 forecast revenue of $2m.
Key obstacles to overcome are entrenched on-site behaviours, and slow the tech adoption across so many stakeholders in the construction industry. The founders claim to have identified the solution via their Construction Assistance System which offers better project and status visualisation via the digital twin.

Next week: Version / Aversion