"i got a lot of intellectual enrichment and influence from early 2010s feminist tumblr," fisher-quann tells me. "lower case? it started with single-case tty model 33. "the more senior you get, the less formal your emails are," michelle liang, a tech growth investor, tells me. "reading my posts should be like scrubbing your eyeballs with sandpaper. I let an AI agent hack all my gadgets --and I'd do it again Tiffany Ng is a tech and culture writer based in Hong Kong. Her work has appeared in publications including Vox, MIT Tech Review, Vice, Insider, and Vogue.

Following an instruction involves distinct sub-processes, such as reading content, reading the instruction, executing it, and producing an answer. We ask where, along the layer stack, instruction following begins, the point where reading gives way to doing. We introduce three simple datasets (Key-Value, Quote Attribution, Letter Selection) and two hop compositions of these tasks. Using activation patching on minimal-contrast prompt pairs, we measure a layer-wise flip rate that indicates when substituting selected residual activations changes the predicted answer. Across models in the Llama family, we observe an inflection point, which we term onset, where interventions that change predictions before this point become largely ineffective afterward. Multi-hop compositions show a similar onset location. These results provide a simple, replicable way to locate where instruction following begins and to compare this location across tasks and model sizes.

Sailor2 is a family of cutting-edge multilingual language models for South-East Asian (SEA) languages, available in 1B, 8B, and 20B sizes to suit diverse applications. Building on Qwen2.5, Sailor2 undergoes continuous pre-training on 500B tokens (400B SEA-specific and 100B replay tokens) to support 13 SEA languages while retaining proficiency in Chinese and English. Sailor2-20B model achieves a 50-50 win rate against GPT-4o across SEA languages. We also deliver a comprehensive cookbook on how to develop the multilingual model in an efficient manner, including five key aspects: data curation, pre-training, post-training, model customization and evaluation. We hope that Sailor2 model (Apache 2.0 license) will drive language development in the SEA region, and Sailor2 cookbook will inspire researchers to build more inclusive LLMs for other under-served languages.

Are you struggling to keep your design system organized and consistent? Chat GPT can help you create design tokens quickly and easily. This guide will teach you how to use Chat GPT to create organized, consistent tokens for your design system.

We hear about machine learning a lot these days, and in fact it's all around us. It can sound kind of mysterious, or even scary, but it turns out that machine learning is just math. And to prove that it's just math, I will write this article the old-school way, with hand-written equations instead of code. If you prefer to learn by listening and watching, I posted a video that covers the content of this article here. To explain what machine learning is and how math makes it work, we will do a full walk-through of logistic regression, a fairly simple but fundamental model that is in some sense the building block of more complex models like neural networks.

Natural Language Processing, also known as NLP, is a subfield of computer science, specifically artificial intelligence, that focuses on understanding written and spoken text. It covers various tasks some of which are speech recognition, sentiment analysis and language generation; And, it has been applied in several use cases such as machine translation, spam detection, virtual assistants and chatbots. The project covered in this article is a sentiment analysis project called Natural Language Processing with Disaster Tweets. Sentiment analysis is the process to extract subjective qualities from text such as emotion or attitude. The objective of the project is to identify if a specific tweet is a real disaster or not. The project is ideal for beginners in NLP.

In this paper, we introduce a new approach to dealing with the problem of case-sensitiveness in Language Modelling (LM). We propose simple architecture modification to the RoBERTa language model, accompanied by a new tokenization strategy, which we named Unified Case LM (UniCase). We tested our solution on the GLUE benchmark, which led to increased performance by 0.42 points. Moreover, we prove that the UniCase model works much better when we have to deal with text data, where all tokens are uppercased (+5.88 point).

Rule-based matching is one of the steps in extracting information from unstructured text. It's used to identify and extract tokens and phrases according to patterns (such as lowercase) and grammatical features (such as part of speech). Rule-based matching can use regular expressions to extract entities (such as phone numbers) from an unstructured text. It's different from extracting text using regular expressions only in the sense that regular expressions don't consider the lexical and grammatical attributes of the text. In this example, pattern is a list of objects that defines the combination of tokens to be matched. Both POS tags in it are PROPN (proper noun).

This book contains proven steps and strategies on learning Python Programming quickly and easily. Python is a powerful and flexible programming language. It uses concise and easy-to-learn syntax which enables programmers to write more codes and develop more complex programs in a much shorter time. Python: The Ultimate Beginner's Guide provides all essential programming concepts and information you need to start developing your own Python program. The book provides a comprehensive walk-through of Python programming in a clear, straightforward manner that beginners will appreciate. Important concepts are introduced through a step-by-step discussion and reinforced by relevant examples and illustrations. You can use this book as a guide to help you explore, harness, and gain appreciation of the capabilities and features of Python. Thanks again for purchasing this book, I hope you enjoy it!

I'll start with my favourite word in the English language. My colleagues, friends, and students are at the point of rolling their eyes when I say it. But it is so important and the main reason your Twitter analysis sucks. What do I mean by context? If you are going to datafy something – turn a tweet, a representation of a thought, emotion, idea into data –, then you need to think about the context of a) the user and why they tweeted, b) the dataset you are looking at, c) the problem you are trying to solve by datafying that tweet in the first place, and d) the tools you are using.