Natural Computing’s Post

I finished writing an exhaustive piece on a novel neural network architecture that beats MLPs, KANs, conventional Transformers, and Mamba in different real-world tasks. It's a deep dive where we learn to build this architecture from the ground up, both mathematically and then in code, all in easy-to-follow language. This is a ton of value! Publishing soon. Follow along: https://intoai.pub #artificialintelligence #ai #datascience #tech

Just finished the course “Generative AI: Introduction to Large Language Models” by Frederick Nwanganga! Covered fundamentals and topics such as: Self-Attention Transformer Architecture CNN RNN Check it out: https://lnkd.in/de4Kim4P #generativeai #largelanguagemodels.#genai #llmops #langchain

🚀 Exciting Update! 🚀 I'm thrilled to share that our latest research paper, "Navigating Challenges and Technical Debt in Large Language Models Deployment," has been accepted for publication in the prestigious EuroMLSys conference and ACM Journal! The paper is a collaboration between the Mastercard AI Engineering Team led by Bijay Kumar and Dr. Pasquale Minervini, PhD from the School of Informatics at the University of Edinburgh. It delves into the complexities surrounding the deployment of Large Language Models (LLMs), shedding light on unique challenges including memory management, parallelism strategies, model compression, and attention optimization. These challenges underscore the necessity for tailored solutions and sophisticated engineering approaches to ensure the seamless integration of LLMs into production environments.   👇The paper is available to read in ACM Journal Library 📚 https://lnkd.in/epGWvzEt   📢 This marks my second post on this topic! I'm excited to share that you can now explore more about our research through the following resources:   Conference Page: https://lnkd.in/d7p5-Nu9 Video Presentation: https://lnkd.in/dZaDGV2F Here's to surmounting challenges, forging new pathways, and shaping the future of LLM deployment! #mastercard #llms #LargeLanguageModels #EuroMLSys #llmops #generativeai #ai #technology

We've sent the next issue of the Warsaw.AI News newsletter with information found for you in the week of 11-17.11.2024: https://lnkd.in/dQFJic8x Check it out to read about the Watermark Anything Model, Counterfactual Generation from Language Models, AlphaFold 3, AI for quantum computing, and more!

We've sent the next issue of the Warsaw.AI News newsletter with information found for you in the week of 11-17.11.2024: https://lnkd.in/dqkdE9rA Check it out to read about the Watermark Anything Model, Counterfactual Generation from Language Models, AlphaFold 3, AI for quantum computing, and more!

One of my recent research works, "Onboard Class Incremental Learning for Resource-Constrained scenarios using Genetic Algorithm and TinyML", was accepted for the Genetic and Evolutionary Computation Conference (GECCO 2024). While I have had my share of experience engaging with 'Large' language models and Generative AI, this research endeavour was an altogether different beast, active in a parallel space of the AI multiverse! This exercise posed both theoretical and practical challenges as enumerated: 1. Working with noisy human gestures captured as time series sensor data 2. Allowing real-time, class incremental learning (unseen data) while avoiding catastrophic forgetting 3. Creating a synergic interaction between connectionist and evolutionary AI techniques (Neural Networks + Genetic Algorithms) 4. Grappling with TinyML to fit the above AI strategies on an actual resource-constrained device (~4 MB of flash memory, battery operated and the size of a thumb) TinyML is an exciting area and an equally relevant problem to focus on, considering the increasing number of low-form factor computing devices around us, AI needs not only to be 'Larger' but 'tinier' as well! Thank you #GECCO for the opportunity, see you at Melbourne! #TinyML #GECCO2024 #ArtificialIntelligence #DeepLearning #EvolutionaryAlgorithms 

Understanding the generative AI development process https://lnkd.in/gfTxqiYr Back in the ancient days of machine learning, before you could use large language models (LLMs) as foundations for tuned models, you essentially had to train every possible machine learning model on all of your data to find the best (or least bad) fit. By ancient, I mean prior to the seminal paper on the transformer neural network architecture, “Attention is all you need,” in 2017. To read this article in full, please click here

Here's me gushing to Mind the Product about the work we have been doing at Clarivate using generative AI to solve challenges for researchers and students 🚀 https://lnkd.in/eF32sSUr

🚀 Unlocking the Future of Time-Series Prediction 🌌 I’m thrilled to share my latest exploration into the world of AI: Integrating Hyperdimensional Computing and Neuro-Symbolic AI with LSTM Networks. 🧠✨ This deep dive combines the robustness of hyperdimensional representations, the predictive power of LSTMs and the explainability of neuro-symbolic reasoning to tackle complex time-series problems. In the blog, I cover: ➡️The mathematics behind hyperdimensional computing and LSTM networks 📊 ➡️A step-by-step code walkthrough with actionable insights 💻 ➡️Real-world use cases in forecasting, anomaly detection, and NLP 🔍 ➡️How this hybrid approach offers key advantages over traditional methods 🔑 ➡️A detailed analysis of results with lessons learned 📈 If you’re curious about how to make time-series prediction not just smarter but also more interpretable, this is for you. Check out the full article on Medium Let’s discuss how these innovations could transform predictive modeling across industries! 🚀 👉 Read here #AI #TimeSeriesAnalysis #HyperdimensionalComputing #NeuroSymbolicAI #LSTM #MachineLearning #Innovation #TechInsights