Analytics Vidhya

NOVEMBER 2, 2024

Jamba 1.5 is an instruction-tuned large language model that comes in two versions: Jamba 1.5 Large with 94 billion active parameters and Jamba 1.5 Mini with 12 billion active parameters. It combines the Mamba Structured State Space Model (SSM) with the traditional Transformer architecture. This model, developed by AI21 Labs, can process a 256K effective […] The post Jamba 1.5: Hybrid Mamba-Transformer Model for Advanced NLP appeared first on Analytics Vidhya.

NLP 244

NLP Large Language Models Python 244

Unite.AI

NOVEMBER 2, 2024

In a significant advancement for document processing, Anthropic has unveiled new PDF support capabilities for its Claude 3.5 Sonnet model. This development marks a crucial step forward in bridging the gap between traditional document formats and AI analysis, enabling organizations to leverage advanced AI capabilities across their existing document infrastructure.

AI 189

AI AI Automation Artificial Intelligence 189

Eugene Yan

NOVEMBER 2, 2024

ML systems, production & scaling, execution & collaboration, building for users, conference etiquette.

ML 192

ML 192

Towards AI

NOVEMBER 2, 2024

Last Updated on November 2, 2024 by Editorial Team Author(s): Get The Gist Originally published on Towards AI. Plus: Claude AI Gets Desktop App This member-only story is on us. Upgrade to access all of Medium. Welcome to Get The Gist, where every weekday we share an easy-to-read summary of the latest and greatest developments in AI — news, innovations, and trends — all delivered in under 5 minutes!

OpenAI 98

OpenAI ChatGPT AI AI 98

Speaker: Kevin Burke

AI is reshaping marketing and sales, empowering professionals to work smarter, faster, and more effectively. This webinar will provide a practical introduction to AI, focusing on its current applications, transformative potential, and strategies for successful implementation in your organization. Using real-world examples and actionable insights, we’ll examine how businesses are leveraging AI to increase efficiency, enhance personalization, and drive measurable results.

AI

Marktechpost

NOVEMBER 2, 2024

Tactile sensing plays a crucial role in robotics, helping machines understand and interact with their environment effectively. However, the current state of vision-based tactile sensors poses significant challenges. The diversity of sensors—ranging in shape, lighting, and surface markings—makes it difficult to build a universal solution. Traditional models are often developed and designed specifically for certain tasks or sensors, which makes scaling these solutions across different applications

Robotics 89

Robotics AI AI Automation 89

Marktechpost

NOVEMBER 2, 2024

In recent years, multimodal large language models (MLLMs) have revolutionized vision-language tasks, enhancing capabilities such as image captioning and object detection. However, when dealing with multiple text-rich images, even state-of-the-art models face significant challenges. The real-world need to understand and reason over text-rich images is crucial for applications like processing presentation slides, scanned documents, and webpage snapshots.

Large Language Models 96

Large Language Models ML AI AI 96

Towards AI

NOVEMBER 2, 2024

Last Updated on November 3, 2024 by Editorial Team Author(s): Fernando Guzman Originally published on Towards AI. Support Vector Machines, or SVM, is a machine learning algorithm that, in its original form, is utilized for binary classification. The SVM model seeks to determine the optimal separation line between two classes, understood as the best margin between these classes, as demonstrated in the following example: SVM Example by OSCAR CONTRERAS CARRASCO As shown in the image, we have a sepa

Machine Learning 111

Machine Learning Algorithm AI AI 111

Marktechpost

NOVEMBER 2, 2024

In recent times, large language models (LLMs) built on the Transformer architecture have shown remarkable abilities across a wide range of tasks. However, these impressive capabilities usually come with a significant increase in model size, resulting in substantial GPU memory costs during inference. The KV cache is a popular method used in LLM inference.

Machine Learning 91

Machine Learning LLM Large Language Models ML 91

Flipboard

NOVEMBER 2, 2024

25 Simple Concepts We’re Tired of Explaining Again and Again

Explainability 65

Explainability Machine Learning 65

Speaker: Joe Stephens, J.D., Attorney and Law Professor

Ready to cut through the AI hype and learn exactly how to use these tools in your legal work? Join this webinar to get practical guidance from attorney and AI legal expert, Joe Stephens, who understands what really matters for legal professionals! What You'll Learn: Evaluate AI Tools Like a Pro 🔍 Learn which tools are worth your time and how to spot potential security risks before they become problems.

AI

Marktechpost

NOVEMBER 2, 2024

Promptfoo is a command-line interface (CLI) and library designed to enhance the evaluation and security of large language model (LLM) applications. It enables users to create robust prompts, model configurations, and retrieval-augmented generation (RAG) systems through use-case-specific benchmarks. This tool supports automated red teaming and penetration testing to ensure application security.

LLM 91

LLM AI Tools Large Language Models Automation 91

Marktechpost

NOVEMBER 2, 2024

Quantization is an essential technique in machine learning for compressing model data, which enables the efficient operation of large language models (LLMs). As the size and complexity of these models expand, they increasingly demand vast storage and memory resources, making their deployment a challenge on limited hardware. Quantization directly addresses these challenges by reducing the memory footprint of models, making them accessible for more diverse applications, from complex natural langua

Algorithm 94

Algorithm Large Language Models Machine Learning Natural Language Processing 94

Marktechpost

NOVEMBER 2, 2024

A key question about LLMs is whether they solve reasoning tasks by learning transferable algorithms or simply memorizing training data. This distinction matters: while memorization might handle familiar tasks, true algorithmic understanding allows for broader generalization. Arithmetic reasoning tasks could reveal if LLMs apply learned algorithms, like vertical addition in human learning, or if they rely on memorized patterns from training data.

Algorithm 81

Algorithm ML Large Language Models Artificial Intelligence 81

Marktechpost

NOVEMBER 2, 2024

Multimodal Retrieval Augmented Generation (RAG) technology has opened new possibilities for artificial intelligence (AI) applications in manufacturing, engineering, and maintenance industries. These fields rely heavily on documents that combine complex text and images, including manuals, technical diagrams, and schematics. AI systems capable of interpreting both text and visuals have the potential to support intricate, industry-specific tasks, but such tasks present unique challenges.

Large Language Models 92

Large Language Models AI AI Artificial Intelligence 92

Speaker: Carolyn Clark and Miriam Connaughton

Forget predictions, let’s focus on priorities for the year and explore how to supercharge your employee experience. Join Miriam Connaughton and Carolyn Clark as they discuss key HR trends for 2025—and how to turn them into actionable strategies for your organization. In this dynamic webinar, our esteemed speakers will share expert insights and practical tips to help your employee experience adapt and thrive.

AI

Marktechpost

NOVEMBER 2, 2024

Large Language Models (LLMs) have emerged as powerful tools in natural language processing, yet understanding their internal representations remains a significant challenge. Recent breakthroughs using sparse autoencoders have revealed interpretable “features” or concepts within the models’ activation space. While these discovered feature point clouds are now publicly accessible, comprehending their complex structural organization across different scales presents a crucial resea

Natural Language Processing 85

Natural Language Processing Large Language Models LLM ML 85

Marktechpost

NOVEMBER 2, 2024

Escalation in AI implies an increased infrastructure expenditure. The massive and multidisciplinary research exerts economic pressure on institutions as high-performance computing (HPC) costs an arm and a leg. HPC is financially draining and critically impacts energy consumption and the environment. By 2030, AI is projected to account for 2% of global electricity consumption.

Neural Network 81

Neural Network ML AI AI 81

Marktechpost

NOVEMBER 2, 2024

The Evidence Lower Bound (ELBO) is a key objective for training generative models like Variational Autoencoders (VAEs). It parallels neuroscience, aligning with the Free Energy Principle (FEP) for brain function. This shared objective hints at a potential unified machine learning and neuroscience theory. However, both ELBO and FEP lack prescriptive specificity, partly due to limitations in standard Gaussian assumptions in models, which don’t align with neural circuit behaviors.

Neural Network 45

Neural Network Machine Learning Algorithm ML 45

Marktechpost

NOVEMBER 2, 2024

The ability to generate accurate conclusions based on data inputs is essential for strong reasoning and dependable performance in Artificial Intelligence (AI) systems. The softmax function is a crucial element that supports this functionality in modern AI models. A major component of differentiable query-key lookups is the softmax function, which enables the model to concentrate on pertinent portions of the input data in a way that can be improved or learned over time.

Artificial Intelligence 85

Artificial Intelligence Artificial Intelligence Neural Network Computer Vision 85

Speaker: Joe Stephens, J.D., Attorney and Law Professor

Get ready to uncover what attorneys really need from you when it comes to trial prep in this new webinar! Attorney and law professor, Joe Stephens, J.D., will share proven techniques for anticipating attorney needs, organizing critical documents, and transforming complex information into compelling case presentations. Key Learning Objectives: Organization That Makes Sense 🎯 Learn how to structure and organize case materials in ways that align with how attorneys actually work and think.