If training data was the new oil, are AI labs and downstream solution providers part of the new hydrocarbon industry? 🤔
| Blog | https://citizensensing.cc |
JP de Vooght
@jdevoo
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Looks like I am not the only one feeling annoyed by these not-so-helpful bots...
I'm confused: what's with the project descriptions at https://pkgx.dev/pkgs/? · Issue #5358 · pkgxdev/pantry
Sorry if this has been reported elsewhere already, or if this is explained in docs somewhere, but I don't understand the contents you have in https://pkgx.dev/pkgs/. Lets take a few popular project...
Blast from my past... this INIT would let me customize my Mac IIsi as I would be plowing through Scott Knaster books and Inside Macintosh volumes
Attended my first #TinyML demo event today at Logitech - here a nice use of models for audio cancellation on a RISC V Gap 9 application processor from Green Waves
Systematic review of thought vs language capabilities in brain vs LLMs #appliedmldays https://arxiv.org/abs/2301.06627 @ev_fedorenko
Dissociating language and thought in large language models
Large Language Models (LLMs) have come closest among all models to date to mastering human language, yet opinions about their linguistic and cognitive capabilities remain split. Here, we evaluate LLMs using a distinction between formal linguistic competence -- knowledge of linguistic rules and patterns -- and functional linguistic competence -- understanding and using language in the world. We ground this distinction in human neuroscience, which has shown that formal and functional competence rely on different neural mechanisms. Although LLMs are surprisingly good at formal competence, their performance on functional competence tasks remains spotty and often requires specialized fine-tuning and/or coupling with external modules. We posit that models that use language in human-like ways would need to master both of these competence types, which, in turn, could require the emergence of mechanisms specialized for formal linguistic competence, distinct from functional competence.
Competing scenarios for #sustainability - "Digitalization and the Anthropocene" Creutzig et al. https://www.annualreviews.org/doi/10.1146/annurev-environ-120920-100056
While there's Hava and Cloudcraft, AWS also provides assistance in automated drawing of architecture diagrams with Workload Discovery as well as PlantUML - below a comparison of a random view with symbols straddling lines (top) vs an AWS PlantUML version (below)
Having fun with pre-annotations in Label-Studio from custom Restrserve/MLflow backend #MLOps
