Perspectives Talk I - Industry: Toward Integrative AI

We have been on a quest to advance AI beyond existing techniques, by taking a more holistic, human-centric approach to learning and understanding. We view the relationship among three attributes of human cognition: monolingual text (X), audio or visual sensory signals, (Y) and multilingual (Z) to be essential to derive from the intersection: what we call XYZ-code, a joint representation to create more powerful AI that can speak, hear, see, and understand humans better. We believe XYZ-code will enable us to fulfill our long-term vision: cross-domain transfer learning, spanning modalities and languages. The goal is to have pretrained models that can jointly learn representations to support a broad range of downstream AI tasks, much in the way humans do today. The AI foundation models provided us with strong signals toward our more ambitious aspiration to produce a leap in AI capabilities, achieving multisensory and multilingual learning that is closer in line with how humans learn and understand. The journey of achieving such integrative AI also must be grounded with external knowledge sources in the downstream AI tasks.