Inephany, an “AI optimisation” startup founded by former Apple Siri engineer John Torr, has raised $2.2m (£1.7m) with the promise of lowering the costs of LLM training.
As AI developers look to make grander and more powerful tools, R&D costs will likely act as a major barrier.
OpenAI’s GPT-4 cost something in the range of $60m to $100m and Anthropic CEO Dario Amodei previously claimed the highest end models could cost closer to $1bn.
Inephany claims to address this challenge by optimising the training process. The firm is currently focused on increasing the efficiency of LLM training, which it said it can do by offering an alternative to the traditional “brute force” approach.
The London-based startup also plans to expand its services down the line across the entire AI sector, from recurrent neural networks to convolutional neural networks.
“Current approaches to training LLMs and other neural networks are extremely wasteful across multiple dimensions,” said Torr.
“Our unique solution tackles this inefficiency head-on, with the potential to radically reduce both the cost and time required to train and optimise state-of-the-art models.
“As we prepare to deliver our first products later this year, we are incredibly excited to embark on the next chapter of our journey—and to help shape the ongoing AI revolution by transforming AI optimisation.”
The round was led by Oxford Nanopore investor Amadeus Capital Partners.
“[Inephany’s] innovative approach to automating and optimising neural network training has the potential to reduce costs by an order of magnitude and accelerate advancements across AI applications,” said Amadeus partner Amelia Armour.
“If rolled out at scale, the impact of this on what models can deliver will be very substantial.”
The round also included participation from Sure Valley Ventures and Prof Steve Young, a former Apple employee who – like Torr – worked on Siri.
“As the use of AI spreads ever wider, moving beyond the traditional applications of speech, language and vision into new and diverse areas such as weather prediction, healthcare, drug discovery and materials design, the need for very efficient training of accurate neural models is becoming critical,” said Young.
“The groundbreaking new approach being developed by Inephany marks a step change in neural model training technology and I am delighted to join the team as chair and investor.”
Young has also joined the company as its chair.
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