LLMs

We generally lean towards picking more advanced commercial LLMs to quickly validate our ideas and obtain early feedback from users. Although they may be expensive, the general idea is that if problems can't be adequately solved with state-of-the-art foundational models like GPT-4, then more often than not, those problems may not be addressable... See more

Overview

MaxText is a high performance , highly scalable , open-source LLM written in pure Python/Jax and targeting Google Cloud TPUs and GPUs for training and inference . MaxText achieves high MFUs and scales from single host to very large clusters while staying simple and "optimization-free" thanks to the power of Jax and the XLA compiler.

MaxText... See more

We identified 30 types of tasks that UX professionals used generative AI tools for in their work. We grouped these tasks under four roles: content editor, research assistant, ideation partner, or design assistant.

• Content editor : Generating and editing text, from microcopy to social media posts, based on specifications or copy given by UX

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Here's my read on the situation:

* The TAM is massive, still so many businesses trying to figure out AI

* If you do deployments you’ll need to spend a of time hand holding clients through scoping projects (not unlike other dev works) since the material is so new

* Lot’s of opportunity in education

* The hard part isn’t the expertise, it’s distribution... See more

a couple of the top of my head:

• LLM in the loop with preference optimization


• synthetic data generation


• cross modality "distillation" / dictionary remapping


• constrained decoding

One interesting thing about LLMs is that they can actually recover (and without error loops). You can have a step that doesn't work right, and a later step can use its common-sense knowledge to ignore some of the missing results, conflicting information, etc. One of the problems with developing with LLMs is that the machine will often cover up... See more