Bhaskara Reddy Sannapureddy的动态

Quantization and LLMs: Condensing Models to Manageable Sizes: High costs can make it challenging for small business deployments to train and power an advanced AI. Here is where quantization comes in handy.

High costs can make it challenging for small business deployments to train and power an advanced AI. Here is where quantization comes in handy. #ai #llms #quantization

??#LiquidFoundationModels (#LFMs) are a new generation of #AIsystems developed by #LiquidAI. They are designed to be both highly efficient and powerful, using minimal system memory while delivering exceptional computing performance. ??Key Features of LFMs: Efficiency and Performance: LFMs are built to handle various types of sequential data, including text, audio, images, video, and signals, with minimal memory usage. ??Innovative Architecture: They are based on Liquid Neural Networks (LNNs), a newer architecture developed at MIT’s Computer Science and Artificial Intelligence Laboratory. LNNs use fewer neurons than traditional deep learning models by combining them with advanced mathematical formulations. ??Real-Time Adjustments: LFMs can perform real-time adjustments during inference without the heavy computational overheads typical of traditional large language models (LLMs). This allows them to handle up to 1 million tokens efficiently. Models in the #LFM Family: ??LFM-1B: A dense model with 1.3 billion parameters, designed for resource-constrained environments. ??LFM-3B: A model with 3.1 billion parameters, optimized for edge deployments such as mobile applications, robots, and drones. ??LFM-40B: A powerful “mixture of experts” model with 40.3 billion parameters, intended for complex tasks on cloud servers. ??Liquid AI’s LFMs are grounded in the principles of dynamical systems, numerical linear algebra, and signal processing, making them versatile for a wide range of applications. ??Liquid AI Inc., an MIT spinoff, has launched its first set of generative AI models called Liquid Foundation Models (LFMs). These models are built on a new architecture known as Liquid Neural Networks (LNNs), which differ significantly from traditional Generative Pre-trained Transformer (GPT) models like OpenAI’s GPT series and Google’s Gemini models. ??Key Points: ??Founders: The startup was founded by MIT researchers Ramin Hasani, Mathias Lechner, Alexander Amini, and Daniela Rus. ??Architecture: LFMs are based on LNNs, which use fewer neurons and advanced mathematical formulations to achieve high performance with greater efficiency. ??Performance: LFMs are designed to deliver performance on par with or superior to some of the best large language models available today. ??Mission: Liquid AI aims to create highly capable and efficient general-purpose models suitable for organizations of all sizes, from network edge deployments to enterprise-grade applications. ??Liquid AI’s LFMs are designed to be adaptable and efficient, capable of real-time adjustments during inference without significant computational overheads. This makes them suitable for a wide range of applications, including text, audio, images, video, and signals. ?? #liquid.ai #LiquidFoundationModels #Ai

How smart are you compared with AI? Could a machine really replace you at work? ? So, what is intelligence anyway? Historically we’ve focused on mathematical, memory and reasoning/logic intelligence when measuring a person’s IQ. Then we decided that emotional intelligence was important – Emotional Quotient (EQ). In my latest book we coined the term Technical Quotient (TQ) and claimed that it’s essential in the age of machine intelligence. ? IQ + EQ + TQ = Effective Intelligence ? AI is already mopping the floor with inferior human capability for memory recall and computing. It is easy to believe that AI or machine Intelligence will never match our EQ or ability to contextualise, but that would be a mistake. AI is evolving fast yet what you see in this Google Gemini demo recording took tens of thousands of AI engineers and mind-boggling computing infrastructure to deliver. https://lnkd.in/gB-aEYcg Despite this... all technology becomes cheaper, faster and ubiqutous as time goes by. For the first time in human history we have invented something that can create, replicate and improve itself; and we’ve done it without guard rails. GPT 4 is teaching GPT 5 which is improving itself as it adds trillions of connections within the large language model neural network. The architecture is based on the human brain. Of major concern is the fact that we’ve trained these large language models with data from the toxic sewer of human interactions in social media. How's that for a way to impart values into an emerging super-intelligence? Transformers with Large Language Models replicating human-style neural networks (ChatGPT and Google Gemini) may very well be mankind’s last great invention. ? Utopian vs dystopian views abound. The promise of augmented humanity vs?Terminator scenarios are both potential realities. It has never been more important to understand the opportunites and threats from the most disruptive invention in Human history. #AI #sales #leadership

Computer Vision: Shape Classification via Explainable AI

As AI and machine learning technologies continue to evolve, the future of embedded systems is bright. ?? Our latest blog explores the integration of AI and ML capabilities in MCUs, paving the way for smarter and more efficient embedded systems. Read the full blog to learn more about these advancements and their impact on the industry: #EmbeddedSystems #AI #MachineLearning https://lnkd.in/g9MdBHC6

Computer Vision: Shape Classification via Explainable AI

Meet ModelSmith! Optimize your ML models for real-world use with Cisco's open-source tool. Boost efficiency and performance across devices. Learn more on Cisco Outshift Blog: cs.co/9001eySST #AI #MachineLearning #ModelOptimization #Cisco #ModelSmith #OpenSource

Computer Vision: Shape Classification via Explainable AI