- Meta announced its preparation for behavior analysis systems “orders of magnitude” larger than existing large language models, including ChatGPT and GPT-4. The necessity of such a scale is questioned.
Meta aims to provide transparency by explaining its AI models and algorithms, particularly in the context of content recommendation. They emphasize the use of multimodal AI to better understand and recommend appropriate content.
While the specific scale of Meta’s theoretical tens-of-trillions parameter models remains uncertain, the company aspires to train and deploy very large models efficiently at scale. The implications suggest a genuinely aspirational project underway.
Supplemental Information ℹ️
- Meta’s focus on behavior analysis of users aims to deeply understand and model people’s preferences, which raises questions about the need for such large and complex models for recommendation purposes.
- The vast amount of content, associated metadata, and complex user vectors make the problem space immense, potentially justifying the need for models larger than any existing ones.
- Meta’s ambition to build the biggest AI model, along with technical jargon, is intended to impress advertisers and reinforce the value of precise ad targeting.
Meta plans to develop behavior analysis systems larger than any existing language models, like ChatGPT and GPT-4. These systems aim to understand people’s preferences, but their necessity and practicality are debatable. The immense amount of content and metadata drives the desire for such large models. Advertisers are targeted with technical jargon to convince them of AI’s prowess in understanding user interests, despite user skepticism.