AI topics’ Post

Introduction to Algorithms (Fourth Edition) https://lnkd.in/dcc2ypQG

Day 58: Exploring Sorting Algorithms Today, I delved into the world of sorting algorithms, which are essential for organizing data in a specific order. I learned about various sorting techniques, including bubble sort, insertion sort, and selection sort. Through hands-on implementation, I gained insights into their underlying mechanisms, time complexities, and suitable use cases. This knowledge is crucial for optimizing data processing tasks and laying the foundation for more advanced algorithms. #DoHardThings #ALXSE

Approximate Nearest Neighbor (ANN) algorithms allow vector databases to quickly find similar items in large datasets! Unlike exact search methods (kNN), ANN algorithms trade off some accuracy for significant speed improvements, powering features like recommendation systems, image recognition, and semantic search engines. Read more about Weaviate’s custom-built ANN algorithm: https://lnkd.in/g39efvbh

15 Sorting Algorithms in 6 Minutes 💡 Ever wondered how different sorting algorithms work? This video provides a quick and visual explanation of 15 sorting algorithms, all in just 6 minutes. It's a great resource for anyone looking to understand the basics of sorting techniques in computer science. 📺 Check it out and enhance your algorithm knowledge! 🔗 https://lnkd.in/eVJ4ydS8

⚡ Seeking faster inference speeds without sacrificing accuracy? To answer this question, I recently tried out the latest 💡QoQ (quattuor-octo-quattuor), a W4A8KV4 quantization algorithm with 4-bit weight, 8-bit activation, and 4-bit KV cache on 🔍Llama-3-8B-Instruct-262k. First step was to generate QoQ quantized checkpoints using LMQuant and dump the fake-quantized models. Afterwards, Qserve provides a checkpoint converter to real-quantize and pack the model into QServe format I ran the throughput benchmark on 1x A100 in order to compare the findings with the Qserve documented values for Llama-3-8B on A100. 📈 Impressive results achieved! With an average throughput of 2925 tok/s over 3 rounds and a batch size of 256, QoQ showcases its efficiency and scalability. 🤗 Huggingface: https://lnkd.in/dsmd5qxq ⚙️Qserve: https://lnkd.in/duHyQx7U ⚙️lmquant: https://lnkd.in/dq4XhDMM

🌟 New Research Announcement! 🌟 Exciting news on the convergence properties of convex message passing algorithms in graphical models. This latest blog post presents a novel proof technique to prove convergence to a fixed point, with the added achievement of precision $\varepsilon\>0$ in $\mathcal\{O\}\(1/\varepsilon\)$ iterations. Read the full article here: https://bit.ly/3ICai5k \#SocialMediaMarketing \#ResearchUpdate \#ConvexAlgorithms \#GraphicalModels \#NewResearch \#ConvergenceProperties

🎅🔬 Nerd alert! Every year, I share the geeky results from the “Santa Claus Challenge 2020,” exploring TSP algorithms for optimizing massive routes. With over a million locations crunched in just an hour, innovative techniques like k-opt local search and neighborhood graphs led the way. Despite some trade-offs, parallel processing brought actionable results in mere minutes. Perfect for anyone curious about real-world optimization challenges! 🎁🔍 Also, I'm no scientist, but I think this proves Santa is real... https://lnkd.in/gXKCywzP #TSP #DataScience #Optimization

The ScaNN vector search library was open-sourced in 2020 to highlight innovations in vector search algorithms, critical for many #ML applications. Today, learn how SOAR introduces redundancy to ScaNN’s vector index to improve vector search efficiency → https://goo.gle/3vUKkao

I don't usually talk about early work, but this is exciting! Yash Akhauri's new preprint, **Attamba**, compresses multiple tokens into a single state using SSMs, while using conventional attention for long-range dependencies between compressed token states. Fundamentally, we do not need to perform computation at the same granularity of tokenization, therefore, introducing compressors (SSMs in our case) to merge information from multiple tokens into a single state can be effective. However, we still need to _attend_ to long-range dependencies, so we maintain full attention between the _compressed_ token states. The end result is Px more efficient attention, where P is our token compression rate. Early results show a favourable accuracy-efficiency tradeoff but we need much more work to fully assess this new architecture -- get in touch if you are interested in collaborating, especially if you have a lot of GPUs :). Read more (super early preprint): https://lnkd.in/e88kjKBb And stay tuned for more on this soon!

SAT, the first problem proven to be NP-complete, poses significant computational challenges. All problems in the NP complexity class are no more difficult than SAT. While no efficient algorithm exists for solving each SAT problem, heuristic SAT-algorithms from 2007 can handle large instances, making them practical for various fields like artificial intelligence and circuit design. Discover more about SAT here: https://lnkd.in/dmfJ6wa3.