Miguel-Angel Monjas (@mmonjasEngineer) header
Miguel-Angel Monjas (@mmonjasEngineer) profile picture

Python. MEng Telecom @La_UPM. MSc DataScience @URJC. Ericsson. Patent holder. DataSci&Engineering. RT≠endorsement. 'What I cannot create, I do not understand'

Madrid, Comunidad de Madrid
Joined February 2010
Photo by Miguel-Angel Monjas (@mmonjasEngineer)Photo by Miguel-Angel Monjas (@mmonjasEngineer)Photo by Miguel-Angel Monjas (@mmonjasEngineer)Photo by Miguel-Angel Monjas (@mmonjasEngineer)Photo by Miguel-Angel Monjas (@mmonjasEngineer)Photo by Miguel-Angel Monjas (@mmonjasEngineer)Photo by Miguel-Angel Monjas (@mmonjasEngineer)
Miguel-Angel Monjas retweeted
Not Everyone Has an Inner Voice Streaming through Their Head The extent to which people experience “inner speech” varies greatly, and the differences matter for performing certain cognitive tasks. scientificamerican.com/artic…
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Miguel-Angel Monjas retweeted
🚨BREAKING: The @Stanford Institute for Human-Centered AI publishes its Artificial Intelligence Index Report 2024, one of the most authoritative sources for data and insights on AI. Below are its top 10 takeaways: 1. AI beats humans on some tasks, but not on all; 2. Industry continues to dominate frontier AI research; 3. Frontier models get way more expensive; 4. The United States leads China, the EU, and the U.K. as the leading source of top AI models; 5. Robust and standardized evaluations for LLM responsibility are seriously lacking; 6. Generative AI investment skyrockets; 7. The data is in: AI makes workers more productive and leads to higher quality work; 8. Scientific progress accelerates even further, thanks to AI; 9. The number of AI regulations in the United States sharply increases; 10. People across the globe are more cognizant of AI’s potential impact—and more nervous. ➡️Read the @StanfordHAI report below. ➡️For more information on AI policy & regulation, subscribe to my newsletter (link in bio).
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Miguel-Angel Monjas retweeted
This is a mind-blowing announcement. Mistral AI, the French company that has been fighting tooth and nail to water down the #AIAct's foundation model rules, is partnering up with Microsoft. So much for 'give us a fighting chance against Big Tech'. A 🧵1/8 azure.microsoft.com/en-us/bl…
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Miguel-Angel Monjas retweeted
Advanced Retrieval-Augmented Generation (RAG) techniques address the limitations of naive RAG pipelines. A recent survey on RAG classifies advanced RAG techniques into pre-retrieval, retrieval, and post-retrieval optimizations. 🔗 Paper: arxiv.org/pdf/2312.10997.pdf My latest article gives an overview of advanced RAG techniques: 🦙 Pre-retrieval includes techniques like sliding windows, enhancing data granularity, adding metadata, or optimizing index structures, such as sentence window retrieval. 🦙 Retrieval includes optimizing the embedding models (e.g., fine-tuning) or advanced retrieval techniques like hybrid search 🦙 Post-retrieval includes reranking or prompt compression. We also implement a naive RAG pipeline using @llama_index and then enhance it to an advanced RAG pipeline using the following: • Sentence window retrieval (as a pre-retrieval optimization) • Hybrid search (as a retrieval optimization) • Re-ranking (as a post-retrieval optimization) 💻 Jupyter Notebooks: github.com/weaviate/recipes/… Read more on @TDataScience: towardsdatascience.com/advan…
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Miguel-Angel Monjas retweeted
Scientists Are Putting ChatGPT Brains Inside Robot Bodies. What Could Possibly Go Wrong? bit.ly/49nZKT0 The effort to give robots AI brains is revealing big practical challenges—and bigger ethical concerns
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Miguel-Angel Monjas retweeted
¿Conoces #LangChain? Es un framework que nos ayuda a desarrollar nuestras aplicaciones basadas en #IAGenerativa de una manera profesional. En el webinar de hoy @oscarfesa nos contará cómo funciona. 🕓 Arrancamos a las 16h 👇

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Miguel-Angel Monjas retweeted
🏃Step-back prompting A new prompting technique from Google Deepmind, can be used to improve RAG results 🦜🔗Now in LangChain! 💬"STEP-BACK PROMPTING is motivated by the observation that many tasks contain a lot of details, and are hard for LLMs to retrieve relevant facts to tackle the task" Algorithm: ❓Generate a step back question based on the users original question 🌲Do retrieval for both the original question and the step back question 📃Generate an answer based on both sets of retrieved documents Paper: arxiv.org/pdf/2310.06117.pdf LangChain Implementation: github.com/langchain-ai/lang…
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Miguel-Angel Monjas retweeted
TheGrandChallenge.eu Lo aprendido evaluando 8 sistemas ML frente al EU AI Act, mar, 21 nov 2023, 18:45 | Meetup meetup.com/es-ES/responsible… en @TheBridge_Tech Evento híbrido. Plazas presenciales limitadas. #AILaw #ResponsibleAI Cc @iaresponsable
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Miguel-Angel Monjas retweeted
We are open-sourcing SQLCoder, state of the art model for converting natural language questions to SQL queries! github.com/defog-ai/sqlcoder SQLCoder outperforms OpenAI’s gpt-3.5-turbo, and significantly outperforms all major open-source models for generic SQL schemas in Postgres. When fine-tuned on an individual DB schema, it outperforms gpt-4. The model is small enough to run on a single A100 40GB in 16 bit floats, or on a single high-end consumer GPU (like RTX 3090/4090) with 8bit quantization. We are also open-sourcing our framework for evaluating LLM generated SQL. SQL can be tricky to evaluate (see here for why: nitter.gq/rishdotblog/stat…). With rigorous, open and reproducible testing, we hope to advance the frontier of OSS text-to-SQL solutions. The model weights have a CC BY-SA 4.0 license. You can use and modify the model for any purpose – including commercial use. However, if you modify the weights (for example, by fine-tuning), you must open-source your modified weights under the same license terms. SQLCoder has been deployed inside Healthcare and Finance enterprises – where data privacy is critical – and has worked remarkably well. We will share more about the model in the coming days!
Replying to @awasthi_a_
Great question! We have a custom variant of Spider. Main issues with Spider were that it was SQLite only (which most people don't use in prod) – and a lot of the data was really messy to deal with To address this, we ended up cleaning the Spider dataset, and ported the data + queries over to Postgres Then other big thing is that two very different SQL queries can both be "correct". For the question, who are the 10 most recent users from Toronto, both of the following are correct in their own ways – so we had to build a "fuzzy matching" algorithm to evaluate query correctness `SELECT userid, username, created_at from users where city='Toronto' order by created_at DESC LIMIT 10;` `SELECT userid, firstname || ' ' || lastname, created_at from users where city='Toronto' order by created_at DESC LIMIT 10;`
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Miguel-Angel Monjas retweeted
🚀Just started using Bing search with ChatGPT and it's super exciting! I've also explored a range of plugins for web browsing, summarizing, and coding. Curious about my top pick? Discover it in my newest video and blog post: 📽️youtube.com/watch?v=RTRKcKT8… ✍️sophiamyang.medium.com/the-b…
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Miguel-Angel Monjas retweeted
Datalab: A Linter for ML Datasets buff.ly/42URFBO | "an audit for your dataset and labels that automatically detects common real-world issues like: label errors, outliers, (near) duplicates, low-quality/ambiguous examples, or non-IID sampling." #DataQuality
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Miguel-Angel Monjas retweeted
How much time and money does it take to train an LLM from scratch? MosaicGPT—7B—21d—$20k LLaMA—65B— 21d — $2.2M GPT-3—175B—34d—$1.8M PaLM—540B—50d—$7-15M GPT-4—?—6-12mo —$100M+ (?) Even with 8x of new Nvidia H100s, GPT4 could take $150M and 500+yrs to train! 1/4
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Miguel-Angel Monjas retweeted
This paper is a preview of the potential disruption from AI. The jobs that are most exposed to AI are the ones that require the most education, are the most creative & earn the most. This is a population that never faced a major automation threat before. papers.ssrn.com/sol3/papers.…
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Miguel-Angel Monjas retweeted
Prompt Engineering buff.ly/41BbHjO | Si te tomas en serio esto del 'prompt engineering', esta es una magnífica referencia: muy completa y al grano.
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📢📢 ¡ATENCIÓN! ¡ATENCIÓN! 📢📢 Información disponible en la web ACTUALIZADA!! 👇👇👇👇👇👇👇👇👇👇 2023.es.pycon.org/ 👆👆👆👆👆👆👆👆👆👆 Pero antes de entrar lee el mini🧵👇
55%La web + bonita del mundo
30%Preparando viaje...
15%Preparando ponencia...
47 votes • Final results
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Miguel-Angel Monjas retweeted
Building LLM applications for production buff.ly/3KU6C0s by @chipro "1. It’s easy to make something cool with LLMs, but very hard to make something production-ready with them. 2. LLM limitations are exacerbated by a lack of engineering rigor in prompt engineering" #MLOps
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Querida comunidad Pythonista. ¿Cómo lleváis el miércoles? Por aquí estamos de los nervios porque ya lo tenemos todo listo para daros detalles de la próxima #PyConEs23 📢📢📢📢📢📢📢📢📢📢📢📢📢📢📢
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Miguel-Angel Monjas retweeted
Knowing the underlying data distribution is an essential step for data modeling and has many applications, such as anomaly detection, synthetic data creation, and data compression, explains Erdogan Taskesen. buff.ly/3x0Py0Z
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