Astrologer names the NFL power couples destined to split... including the QB and his'freakier' girlfriend Shocking extent of Sam Darnold's injury revealed hours before Seahawks QB tries to make NFL playoff game Iconic '90s femme fatale Men In Black star hasn't been seen in 16 years... now the Daily Mail reveals distressing truth behind her disappearance Dark side of America's favorite vacation hotspot... where women are subjected to the most horrific sex attacks imaginable BBC news presenter Geeta Guru-Murthy is accused of'hissing' when Christian rapper DC3 tells her his biggest hero is Jesus Christ Iran's leader brands Trump a'criminal' for supporting protesters and blames US for brutal crackdown which led to death of thousands Taylor Swift and'defeated' Travis Kelce are facing'first real test' in their relationship... as insiders say'things are changing' Green Bay Packers make decision on head coach Matt LaFleur's future after playoffs humiliation The blonde, blue-eyed moms recruited in America's suburbs to bear children for global elite Mystery deepens around woman, 21, who vanished on her way home from a friend's house as cops arrest boyfriend on unrelated charges West Virginia man became'agent' for world's most inbred family then kept donations sent by their followers for himself, prosecutors say Shannen Doherty's ex-husband attempts to block divorce deal as battle over actress' estate rages on 2 years after her death Meghan shares intimate footage of her and Harry dancing in video filmed by Princess Lilibet - as Duchess hops on viral 2016 throwback trend with photo from couple's early dates The happiest-looking dogs, according to science - so, do you agree that German shepherds should top the table? Every dog owner might like to think their furry companion is the cutest. But scientists have now revealed which pets really do have the friendliest faces - and the results may surprise you. Researchers say that some scarier breeds actually have the happiest looking faces, with German Shepherds and Rottweilers topping the list. This is because these more wolf-like dogs can use more facial muscles to create their expressions, and so appear happier to humans, experts said.

Synthetically-generated data plays an increasingly larger role in training large language models. However, while synthetic data has been found to be useful, studies have also shown that without proper curation it can cause LLM performance to plateau, or even "collapse", after many training iterations. In this paper, we formalize this question and develop a theoretical framework to investigate how much curation is needed in order to ensure that LLM performance continually improves. We find that the requirements are nearly minimal. We describe a training procedure that converges to an optimal LLM even if almost all of the non-synthetic training data is of poor quality. Our analysis is inspired by boosting, a classic machine learning technique that leverages a very weak learning algorithm to produce an arbitrarily good classifier. Our training procedure subsumes many recently proposed methods for training LLMs on synthetic data, and thus our analysis sheds light on why they are successful, and also suggests opportunities for future improvement. We present experiments that validate our theory, and show that dynamically focusing labeling resources on the most challenging examples -- in much the same way that boosting focuses the efforts of the weak learner -- leads to improved performance.

We introduce Lexico, a novel KV cache compression method that leverages sparse coding with a universal dictionary. Our key finding is that key-value cache in modern LLMs can be accurately approximated using sparse linear combination from a small, input-agnostic dictionary of 4k atoms, enabling efficient compression across different input prompts, tasks and models. Using orthogonal matching pursuit for sparse approximation, Lexico achieves flexible compression ratios through direct sparsity control. Lexico maintains 90-95% of the original performance while using only 15-25% of the full KV-cache memory, outperforming both quantization and token eviction methods. Notably, Lexico remains effective in low memory regimes where 2-bit quantization fails, achieving up to 1.7 better compression on LongBench and GSM8K while maintaining high accuracy. Figure 1: Memory usage vs. performance of Lexico compared to other key-value (KV) cache compression methods on GSM8K. The figure illustrates the relationship between KV cache size and the performance of Lexico on Llama models on GSM8K 5-shot evaluation. Lexico consistently outperforms both eviction-based methods (SnapKV, PyramidKV) and quantization-based methods (per-token quantization, KIVI, ZipCache). Transformers (Vaswani et al., 2017) have become the backbone of frontier Large Language Models (LLMs), driving progress in domains beyond natural language processing. However, Transformers are typically limited by their significant memory requirements. This stems not only from the large number of model parameters, but also from the having to maintain the KV cache that grows proportional to the model size (i.e., the number of layers, heads, and also embedding dimension) and token length of the input.

A few years ago, one of the first things I did when learning about neural networks is to train a simple image classifier. Neural nets can do a marvelous job at telling what's in an image. However, one thing I have not asked myself back then is: "What are these nets actually learning?" Let me explain what I mean by that with an example: How do we humans recognize that a dog is a dog? I'd say we look for distinctive features like pointy ears, snout, tail, four legs, and similar things.

Most visual creations start with a search--for images, colors, fonts, and inspiration -- but search has always felt disconnected from the creative process. It can be tedious and time-consuming to translate brilliant, imaginative ideas into words. Search terms rarely convey the aesthetics and emotions at the heart of a creative idea – which can make image search become a mind-numbing task when it should be inspiring. However, artificial intelligence (AI) and machine learning technology can fundamentally change the nature of search and help make creative visions a reality. With deep learning, search algorithms can be trained to better understand images to recognize objects--like cars, cats, humans, or even the Eiffel Tower--as well as colors, composition, style, and mood.

After watching dotNetConf videos over the last couple of weeks, I've been really excited to try out some of the new image classification techniques in Visual Studio. The dotNetConf keynote included a section from Bri Actman, who is a Program Manager on the .NET Team (the relevant section is on YouTube from 58m16 to 1hr06m35s). This section showed how developers can integrate various ML techniques and code into their projects using the ModelBuilder tool in Visual Studio – in her example, photographs of the outdoors were classified according to what kind of weather they showed. As well as the keynote, there's another relevant dotNetConf talk by Cesar de la Torre which is also available here on what's new in ML.NET And the way to integrate this into my project looks very straightforward – right click on the project - Add Machine Learning - and choose what type of scenario you want to use, as shown in the screenshot below. I've highlighted the feature that I'm really interested in – image classification.

It's easy to get overwhelmed by the term "artificial intelligence (AI)," let alone its ramifications for the future of -- well, pretty much everything. It's a big topic for my industry, which is resourcing and deploying business applications to and from the cloud --many of which employ AI. I'll venture to say it's a big topic for yours, as well. My realm is the cloud. Though it may not be the final frontier, it certainly is my frontier for now.