Important LLMs Papers for the Week from 08/07 to 14/07
Author(s): Youssef Hosni Originally published on Towards AI. Stay Updated with Recent Large Language Models Research Large language models (LLMs) have advanced rapidly in recent years. As new generations of models are developed, researchers and engineers need to stay informed on the …
Revolutionizing Named Entity Recognition with Efficient Bidirectional Transformer Models
Author(s): Chien Vu Originally published on Towards AI. The light NER model outperforms both ChatGPT and fine-tuned LLMs in zero-shot evaluations on various NER benchmarks.Image by author Named Entity Recognition (NER) is an important task in Natural Language Processing (NLP) that involves …
Fine-Tuning LLMs with Synthetic Data for High-Quality Content Generation
Author(s): Vin Busquet Originally published on Towards AI. Evaluation data analysis featured in this article. (Photo of the author’s monitor) Table of Contents · Table of Contents· The POC Trek Begins· Fine-Tuning VS RAG ∘ What is fine-tuning? ∘ So, what is …
Quantization: Post Training Quantization, Quantization Error, and Quantization Aware Training
Author(s): JAIGANESAN Originally published on Towards AI. Photo by Jason Leung on Unsplash Most of us used open-source Large Language Models, VLMs, and Multi-Modal Models in our system, colab, or Kaggle notebook. You might have noticed that most of the time we …
GraphRAG Is the Logical Step From RAG — So Why the Sudden Hype?
Author(s): Daniel Voyce Originally published on Towards AI. Photo by Steve Johnson on Unsplash It seems like everyone is currently talking about GraphRAG as the successor to RAG (Retrieval-Augmented Generation) in the Generative AI / LLM world right now. But is it …
Top Important Computer Vision Papers for the Week from 08/07 to 14/07
Author(s): Youssef Hosni Originally published on Towards AI. Stay Updated with Recent Computer Vision Research Every week, researchers from top research labs, companies, and universities publish exciting breakthroughs in various topics such as diffusion models, vision language models, image editing and generation, …
An Introduction to Using NVIDIA’s NIM API
Author(s): Harpreet Sahota Originally published on Towards AI. An Introduction to Using NVIDIA’s NIM API This is THE hands-on coding tutorial using the NIM API you’ve been looking for! Photo by Resul Kaya on Unsplash I recently got a chance to hack …
TAI #108:Conflicting Developments in the AI Regulation Debate
Author(s): Towards AI Editorial Team Originally published on Towards AI. What happened this week in AI by Louie The ongoing debate over AI regulation gained focus again this week, with both positive and negative newsflow for the future of open-source AI. There …
Towards AI Tested Launchpad by Latitude.sh: A Container-based GPU Cloud for Inference and Fine-Tuning
Author(s): Towards AI Editorial Team Originally published on Towards AI. Latitude.sh’s Launchpad At Towards, we’re particularly interested in how Latitude.sh’s platform supports machine learning models, especially for inference and fine-tuning large language models (LLMs). Our team recently explored their latest offering, Launchpad …
Significance of Image Labeling in AI
Author(s): Rayan Potter Originally published on Towards AI. The capability of AI to see and perceive its surroundings has myriad advantages. In this blog, we will explore further the invaluable role image labeling plays in training AI to see like humans. Image …
How to Call Machine Learning Algorithms on R for Spatial Analysis.
Author(s): Stephen Chege-Tierra Insights Originally published on Towards AI. R has become ideal for GIS, especially for GIS machine learning as it has topnotch libraries that can perform geospatial computation. R has simplified the most complex task of geospatial machine learning and …
In-Depth Understanding of Vector Search for RAG and Generative AI Applications
Author(s): Talib Originally published on Towards AI. I will start by describing why we need a vector search for RAG, and how vectors and vector databases work, and then focus on the Azure AI search. You might have used large language models …
Can You Actually Beat the Dealer in Blackjack? — Simulation of Most Popular Strategies
Author(s): Eram Khan Originally published on Towards AI. In this article I explore if it is actually possible to beat the blackjack dealer using strategic thought. Of course, the underlying idea here is to show the use of simulation and how a …
TimesFM — Google’s Foundational Model for Time Series Forecasting
Author(s): Satyajit Chaudhuri Originally published on Towards AI. Introduction Imagine if you could forecast future trends with the same ease that language models understand text. Whether you’re predicting stock prices, healthcare demands, or optimizing logistics, accurate time-series forecasting is crucial. Traditional methods …
Preventing Prompt Injection in OpenAI : A Case Study with Priceline’s OpenAI Tool “Penny”
Author(s): Jonathan Bennion Originally published on Towards AI. Image created by the author Another of the dirty little secrets of AI systems (and the hype surrounding their future) are ongoing prompt injection issues. Not a new security issue, yet we will be …
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