Fuerza y resiliencia @Rockinsite . Esperemos que la tranquilidad regrese a un municipio histórico como Texcoco ya que este evento refleja tristemente lo que sucede con bastante frecuencia en las príncipales vialidades @delfinagomeza@GobCDMX@CAPUFE@Edomex@AytoTexcoco
@SamsungMexico hola samsung, me siento realmente decepcionado con el servicio de tu proveedor de galaxy canje (grupo sim) ya que argumentaron en un correo que no aceptan mi equipo anterior el cual esta en perfectas condiciones (o eso espero, ya que fue manipulado por ellos)
Nunca antes había sentido tanta fatiga física, mental y espiritual. Me rendiría ahora. ¿Pero como puedo hacerlo, después de todo el apoyo de mis seres queridos?, ¡Seguiré hasta el final!
To avoid becoming next Zillow, when hiring #datascientists for time-series related jobs data science teams should exercise caution when hiring self-declared “experts” in time series forecasting. The following TOP 10 mistakes in take home technical forecasting tasks are similar to red card in football ⚽️ should be serious red flag 🚩 for hiring panel. - Not doing EDA - Not checking if time series is stationary - Not checking if time series have gaps - Not selecting correct forecasting metric - Not reading the task properly and instead doing something else - Not using simple benchmark - Jumping into LSTM or deep learning in general when there are not many data points - Not been able to explain why selected this or that model - Not understanding the model used and what’s under the hood - Thinking that Facebook prophet is the best algorithm but not been able to explain its main drawbacks or why it is actually a very bad model #hiring #datascientists #timeseries #football
RLAIF: Scaling Reinforcement Learning from Human Feedback with AI Feedback
paper page: https://t.co/lozxQUDM31
Reinforcement learning from human feedback (RLHF) is effective at aligning large language models (LLMs) to human preferences, but gathering high quality human preference labels is a key bottleneck. We conduct a head-to-head comparison of RLHF vs. RL from AI Feedback (RLAIF) - a technique where preferences are labeled by an off-the-shelf LLM in lieu of humans, and we find that they result in similar improvements. On the task of summarization, human evaluators prefer generations from both RLAIF and RLHF over a baseline supervised fine-tuned model in ~70% of cases. Furthermore, when asked to rate RLAIF vs. RLHF summaries, humans prefer both at equal rates. These results suggest that RLAIF can yield human-level performance, offering a potential solution to the scalability limitations of RLHF.
La economía circular es la oportunidad de seguir dándole valor a los productos de los que nos beneficiamos, Sandra Herrera, consultora en medio ambiente y miembro del Consejo Consultivo The green Expo, da información relevante sobre el tema. #GanadoresyPerdedores
📺: @VictorPiz
El compost de excrementos humanos se convierte en abono ecológico y económico en Japón. Esta industria resuelve problemas de gestión y contaminación de aguas residuales, pero plantea desafíos de prejuicio.
🎥 @dw_espanol
Today, we're excited to introduce the preview of Python in Excel—making it possible to use Python's powerful data analysis with the Excel features you know and love.
Read the announcement: https://t.co/NUMRBR0cFo #Excel