An MVP for Simplifying AI Model Selection In today’s fast-moving AI landscape, choosing the right large language model (LLM) for your project can feel like navigating a maze. With hundreds of models, each offering different capabilities, the process can be overwhelming. That’s why Mozilla.ai is developing Lumigator,
Introduction We are witnessing explosive growth in the development of artificial intelligence (AI) applications across various industries, from virtual agents to healthcare diagnostics and autonomous driving. This growth is powered by vast datasets and advanced algorithms that enable AI systems to learn and make decisions in real-world scenarios. However, the
At the TAUS Massively Multilingual AI conference in Rome, I had the honor of moderating a panel on Multilingual AI provocatively titled “No English Please - How to move towards truly multilingual AI”. In this panel, we explored the pressing need for AI to transcend linguistic barriers and embrace global
Over the past few months at Mozilla.ai, we’ve engaged with a number of organizations to learn how they are using language models in practice. We spoke with 35 organizations across various sectors, including finance, government, startups, and large enterprises. Our interviewees ranged from ML engineers to CTOs, capturing
On our journey toward our product MVP, we are building a platform that will guide users through the process of machine learning model selection by performing evidence-based evaluation. We’re starting with text summarization as a first platform task for two main reasons: First, the product interviews we’ve conducted
In the bustling AI news cycle, where new models are unveiled at every turn, cost and evaluation don’t come up as frequently but are crucial to both developers and businesses in their use of AI systems. It is well known that LLMs are extremely costly to pre-train; but closed
Like our parent company, Mozilla.ai’s founding story is rooted in open-source principles and community collaboration. Since our start last year, our key focus has been exploring state-of-the-art methods for evaluating and fine-tuning large-language models (LLMs), including continued open-source LLM evaluation experiments and establishing our GPU cluster’s infrastructure.
In a year marked by extraordinary advancements in artificial intelligence, largely driven by the evolution of large language models (LLMs), one factor stands out as a universal accelerator: the exponential growth in computational power. Over the last few years, researchers have continuously pushed the boundaries of what a ‘large’ language
After a year of breakneck innovation and hype in the AI space, we have now moved sufficiently beyond the peak of the hype cycle to start asking a critical question: are LLMs good enough yet to solve all of the business and societal challenges we are setting them up for?
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