Kiwi co-founds collaborative way of AI training which raises $32m from big-name backers
A lot of AI start-ups are socalled “ChatGPT wrappers” that add a dash of chatbot smarts to existing software. But a start-up co-founded by a New Zealander addresses one of the most fundamental elements of the super-hot new technology. Chris Keall reports.
In March last year, just as most of us were becoming aware of ChatGPT for the first time, Nic Lane co-founded a start-up, Flower Labs, that is aiming for a radical shift in the way we “train” artificial intelligence software, a huge and controversial field.
“We believe that artificial intelligence, and the world in general, will be much better off if AI becomes a collaborative activity between many organisations and people,” Lane told the Herald from the UK — where the former pupil of Western Heights High School in Rotorua and Waikato University graduate is now a full professor at Cambridge, leading the university’s Machine Learning Systems Lab. He also serves as Flower Labs’ chief scientific officer.
“Right now, it is heading in the opposite direction. AI is dominated by a rapidly decreasing number of companies,” he explained.
“This is because AI training is done in large, centralised data centres that require the data to be all in one place, copied in, and processed by many colocated GPUs.”
GPUs, or graphical processing units, are the super-powerful, superexpensive and super-power-hungry graphics chips — mostly made by Nvidia — used in AI training, or using a collection of data (today, typically the likes of books, or Wikipedia or Reddit content) to school-up an AI and teach it how to speak or write like a human.
Lane said a centralised approach “requires a huge amount of money. This is shutting out important stakeholders like NGOs [nongovernment organisations], government departments and small companies”.
“People think this is the only way to do it. But there are decentralised AI alternatives, like federated learning, that could be used — [though] they have been unstudied, under-utilised and not sufficiently invested in; but the methods do exist to support a future AI landscape where everyone can participate.”
It’s that kind of decentralised approach that Lane and co-founders (and Cambridge colleagues) Daniel Beutel and Taner Topal champion.
Flower is a bid to decentralise the AI training process through a platform that allows developers to train models on data spread across thousands of devices and locations.
Its “federated learning” approach provides only indirect access to the data to allay privacy and security concerns.
Instead of one pool of data being used to train an AI at a colossal data centre, Flower Labs’ “training at the
edge” decentralised, federated approach could utilise, for example, “A handful of GPUs in a hospital IT dept, a cluster of CPUs in an office environment, or even the increasing amount of computation available in a smartphone.”
Flower pushes an open-source approach — or software that anyone can access, freely, and contribute to. Lane calls it community-driven.
Big-name backers come on board
That sounds like a quixotic or even dare we say it an ivory tower, idealised approach.
But just a couple of months after its launch, Flower landed US$3.6 million ($5.8m) in seed funding in a round led by Spark Ventures, which numbers Eric Schmidt as a partner (Schmidt is famed for being Google’s CEO from 2001 to 2011 — the designated-adult who kept the firm’s wunderkind founders Larry Page and Sergey Brin in line).
Earlier this month, Flower raised US$20m ($32m) at a US$100m ($161m) valuation. The round was led by Felicis Ventures, a Silicon Valley venture capital firm that was an early backer of Canva, Shopify and Twitch.
While the concept of federated learning has been around for some time, it’s historically been a lot thicker than centralised systems. For three years before Flower’s launch, its founders pursued an academic research project to make it fundamentally easier. Community collaboration was essential to the project.
“To make the barrier to entry to these methods radically lower, we created an open-source ecosystem of tools that were as easy to use as existing ML [machine-learning] tooling — and where there was a global community building prototypes, deploying real systems, experimenting and inventing when that what was needed, and doing this all by working together transparently and in the open.”
There are already more than 3000 developers using Flower’s technology for more than 1100 AI projects.
They come from organisations that span major universities (including MIT, Oxford and Harvard) to the UK’s National Health Service (where a trial saw anonymised data from 130,000 patients used to create better Covid screening) to corporates like Nokia, Porsche, Bosch, Siemens and Samsung ( Lane was formerly the director of Cambridge’s Samsung AI Centre at Cambridge). A major bank is using Flower to help detect money laundering. There are also smaller outfits on board like Brave, the creator of a privacy-focused web browser.
“In the end, we think all AI models will need to be trained in this way, because distributed data, scattered in various computing systems in offices, companies, homes etc, is much larger and representative of the real world than the web-scrapped data used to train our AI models today.
“Models trained in this decentralised manner will be less biased, better able to generalise and overall superior to centralised data centre built AI.”