Adaption Labs will focus on novel approaches to efficient AI.<\/p>\n
<\/p>\n
Former leaders from Toronto-based Cohere and its research division are launching a startup aimed at building efficient artificial intelligence (AI) that can \u201cadapt and continuously learn.\u201d<\/p>\n
\u201cThis will change how everyday users and companies around the world are able to shape and own AI.\u201d<\/p>\n
Sara Hooker<\/p>\n
Former Cohere Labs head Sara Hooker will lead the new venture alongside her former Cohere colleague, Sudip Roy, who was the company\u2019s senior director of inference.<\/p>\n
Adaption Labs will work on \u201cadaptable intelligence,\u201d according to a post on X<\/a> by Hooker. In a separate LinkedIn post<\/a>, she said the company will lead in \u201cextremely efficient adaptation\u201d that will enable AI to evolve in real time. \u201cThis will change how everyday users and companies around the world are able to shape and own AI,\u201d she wrote.<\/p>\n In his own post, Roy wrote that he is taking on a new challenge \u201cat the intersection of efficiency and adaptability.\u201d He said the next AI breakthrough \u201cwon\u2019t just be about larger models\u2014it will be about systems that can efficiently adapt without the computational overhead of traditional gradient-based methods.\u201d<\/p>\n Gradient methods, such as gradient descent<\/a>, are machine learning methods widely used to train large-language models (LLMs). They involve optimizing the parameters of a mathematical model to obtain the fewest possible errors. Other approaches to training AI do not involve gradients. One example is evolutionary algorithms<\/a>, a computational method inspired by natural selection where solutions to a math problem are represented as individuals in a population.\u00a0<\/p>\n Industry-standard approaches to training AI models, such as those used by OpenAI and Google, have involved massive amounts of data and energy<\/a>. Adaption Labs appears to be pursuing efficiency in its AI development to cut both energy consumption and costs. Some of the job postings mention \u201csevere compute budgets,\u201d which the company says will force it to innovate and collaborate across the tech stack.\u00a0<\/p>\n RELATED: Marzieh Fadaee promoted to head of Cohere Labs<\/a><\/p>\n Despite its California headquarters, Adaption Labs is hiring for some hybrid positions in Canada and the United Kingdom, including a brand designer, a design engineer, and an efficiency research engineer.\u00a0<\/p>\n After spending five years as a research scientist with Google DeepMind, Hooker joined Cohere in 2022 to lead Cohere Labs. Earlier this summer, she announced<\/a> she was leaving but stayed on through September to oversee applications for Cohere\u2019s Research Scholars program. After Hooker\u2019s departure, Cohere Labs promoted research scientist Marzieh Fadaee to lead the division.<\/p>\n Hooker led research at Cohere Labs across a number of fronts, including multilingual capabilities, inference efficiency, and LLM alignment and safety. Cohere\u2019s Aya series of models brought LLM support to 55 new languages, she said.\u00a0<\/p>\n The researcher also co-authored a paper this spring<\/a> pointing out alleged unreliability in the rankings of popular chatbot leaderboard LM Arena, which drew largely applause but some backlash from the AI research community.\u00a0<\/p>\n For his part, Roy joined Cohere in 2021 after seven years at Google as a research scientist. Before becoming the Canadian startup\u2019s senior director of inference, he was the director of engineering. According to his LinkedIn, Roy will be Adaption Labs\u2019s CTO.<\/p>\n Founded in 2019 by former Google researchers, Cohere builds LLMs that power chatbots and applications for enterprises and governments. Valued at $7 billion USD, it is one of Canada\u2019s best-funded and most valuable private technology companies.<\/p>\n Feature image courtesy Sudip Roy via LinkedIn<\/a> and Sara Hooker via LinkedIn<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"Adaption Labs will focus on novel approaches to efficient AI.
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