6/Reza Zadeh sees generative AI bringing progress to active learning, where a system picks its own examples to be labeled to improve the data. With generative AI, he sees a potential revolution in algorithms generating new data to request to be labeled.
RESEARCH
-
AI Explainability: From Engineering to Fundamental Scientific Principles
By
–
5/Been Kim discusses AI explainability. AI has taken an engineering-centric approach, where researchers devise techniques via trial and error, and she urges developing fundamental scientific principles that make explanations more trustworthy and accurate.
-
Key Directions for AI: Multimodality, Safety, Data-Centric Approaches, and Evaluation
By
–
4/Douwe Kiela points out key directions: Multimodality, grounding, and interaction so AI understands us better; alignment, attribution, and uncertainty to make models safer; data-centric AI to improve scaling; and better ways to evaluate AI models.
-
Yoshua Bengio Advocates New Architectures for Conceptual Discovery
By
–
Yoshua Bengio wants to develop new architectures that can discover and reason with high-level concepts, rather than just brute force the learning process by scaling up existing models' data and compute.
-
AI Leaders Share Hopes and Key Priorities for 2023
By
–
1/Several leaders share their hopes for AI in 2023, including finding key missing pieces that will enable algorithms to reason, building a personal data timeline, improving AI processes, discovering new principles for explainability, and using generative AI for active learning.
-
Interview on Rule of the Robots and AI Future
By
–
This post by @JimPethokoukis includes a brief interview with me about my book #RuleoftheRobots and the future of #AI. https://
fasterplease.substack.com/p/86daee5e-4bd
f-41a8-a1c2-e814a9a5a5ca
… -
How AI learns animal-environment correlations in image generation
By
–
Fijaos un detalle muy curioso que enseña la forma de operar de la IA, cómo al pedir convertir al gato en conejo, toooodo el entorno de alrededor se convierte en indoor (fondo, puerta, suelo, etc) Esto es porque la IA ha aprendido cuál es la correlación animal-entorno habitual 🙂
-
Muse AI Model: Examples and Research Paper Available
By
–
Tenéis más ejemplos y el paper sobre Muse en la web del proyecto. (WEB) https://
muse-model.github.io Gracias por leerme y seguidme para aprender más sobre Inteligencia Artificial. Si te ha gustado la info comparte el hilo completo haciendo RT -
Muse faster than Stable Diffusion but Stability pushes boundaries
By
–
¿Muse es x3 más rápido que Stable Diffusion? Ok… Pero es que Stability, impulsado por la comunidad open source, ya trabaja en una versión x30 veces más rápida! Para 2023 sólo pido a una Google más abierta. TÚ ANTES MOLABAS…
-
Release AI Lab Tools to Community for Real Evaluation
By
–
LIBERAD ALGO!!! Tenéis herramientas espectaculares: Imagen, Parti, Imagic, DreamBooth, Dreamfusion… Y no las sacáis dejando que caduquen en vuestros laboratorios sin poder tener una evaluación real por el resto de la comunidad. Un paper está bien. Pero con una demo es mejor