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Introduction Recently, with the rise of large language models and AI, we have seen innumerable advancements in natural language processing. Models in domains like text, code, and image/video generation have archived human-like reasoning and performance. These models perform exceptionally well in general knowledge-based questions. Models like GPT-4o, Llama 2, Claude, and Gemini are trained on publicly […] The post A Comprehensive Guide to Fine-Tune Open-Source LLMs Using Lamini appeared fir
A group of researchers at the University of Limerick have unveiled an innovative approach to designing molecules for computational purposes. This method, which draws inspiration from the human brain's functioning, has the potential to dramatically enhance the speed and energy efficiency of artificial intelligence systems. The research team, led by Professor Damien Thompson at the Bernal Institute, has discovered novel techniques for manipulating materials at the most fundamental molecular level.
Introduction The OpenAI o1 model family significantly advances reasoning power and economic performance, especially in science, coding, and problem-solving. OpenAI’s goal is to create ever-more-advanced AI, and o1 models are an advancement over GPT-4 in terms of performance and safety. This article will explain how to build games with OpenAI o1, such as Brick Breaker […] The post How to Build Games with OpenAI o1?
By Scott Stevenson, CEO, Spellbook. A couple of weeks ago we bet big on AI agents with the launch of Spellbook Associate. We believe that agentic approaches will.
Start building the AI workforce of the future with our comprehensive guide to creating an AI-first contact center. Learn how Conversational and Generative AI can transform traditional operations into scalable, efficient, and customer-centric experiences. What is AI-First? Transition from outdated, human-first strategies to an AI-driven approach that enhances customer engagement and operational efficiency.
Large language models (LLMs) have emerged as powerful general-purpose task solvers, capable of assisting people in various aspects of daily life through conversational interactions. However, the predominant reliance on text-based interactions has significantly limited their application in scenarios where text input and output are not optimal. While recent advancements, such as GPT4o, have introduced speech interaction capabilities with extremely low latency, enhancing user experience, the open-s
Artificial intelligence (AI) has been advancing in developing agents capable of executing complex tasks across digital platforms. These agents, often powered by large language models (LLMs), have the potential to dramatically enhance human productivity by automating tasks within operating systems. AI agents that can perceive, plan, and act within environments like the Windows operating system (OS) offer immense value as personal and professional tasks increasingly move into the digital realm.
Summary: DALL-E 2 is an AI-powered model by OpenAI that creates high-resolution images from text prompts. Its advanced features, like inpainting and flexible image generation, revolutionise visual content creation. Learn to use DALL-E 2 effectively and explore its diverse marketing, design, and media applications. Introduction DALL-E 2, an advanced image generation model by OpenAI, transforms textual descriptions into high-quality images with remarkable creativity.
Recent advancements in diffusion models have significantly improved tasks like image, video, and 3D generation, with pre-trained models like Stable Diffusion being pivotal. However, adapting these models to new tasks efficiently remains a challenge. Existing fine-tuning approaches—Additive, Reparameterized, and Selective-based—have limitations, such as added latency, overfitting, or complex parameter selection.
Summary: Siamese Neural Networks use twin subnetworks to compare pairs of inputs and measure their similarity. They are effective in face recognition, image similarity, and one-shot learning but face challenges like high computational costs and data imbalance. Introduction Neural networks form the backbone of Deep Learning , allowing machines to learn from data by mimicking the human brain’s structure.
Today’s buyers expect more than generic outreach–they want relevant, personalized interactions that address their specific needs. For sales teams managing hundreds or thousands of prospects, however, delivering this level of personalization without automation is nearly impossible. The key is integrating AI in a way that enhances customer engagement rather than making it feel robotic.
XVERSE Technology made a significant leap forward by releasing the XVERSE-MoE-A36B , a large multilingual language model based on the Mixture-of-Experts (MoE) architecture. This model stands out due to its remarkable scale, innovative structure, advanced training data approach, and diverse language support. The release represents a pivotal moment in AI language modeling, positioning XVERSE Technology at the forefront of AI innovation.
Upgraded ChatGPT Thinks at the PhD Level OpenAI is out with a new upgrade to ChatGPT that features extremely advanced, in-depth thinking — and outperforms PhD students in physics, chemistry and biology. The software undergirding the new upgrade — dubbed OpenAI o1 — also offers head-turning new performance highs in math and computer coding.
Web navigation agents revolve around creating autonomous systems capable of performing tasks like searching, shopping, and retrieving information from the internet. These agents utilize advanced language models to interpret instructions and navigate through digital environments, making decisions to execute tasks that typically require human intervention.
Articles Google wrote an article on DataGemma where they focus on how important the data is in developing LLM; specifically the LLM family of Gemma. Gemma is a family of lightweight, state-of-the-art, open models built from the same research and technology used to create our Gemini models. DataGemma expands the capabilities of the Gemma family by harnessing the knowledge of Data Commons to enhance LLM factuality and reasoning.
The guide for revolutionizing the customer experience and operational efficiency This eBook serves as your comprehensive guide to: AI Agents for your Business: Discover how AI Agents can handle high-volume, low-complexity tasks, reducing the workload on human agents while providing 24/7 multilingual support. Enhanced Customer Interaction: Learn how the combination of Conversational AI and Generative AI enables AI Agents to offer natural, contextually relevant interactions to improve customer exp
The cooperative operation of autonomous vehicles can greatly improve road safety and efficiency. However, securing these systems against unauthorized participants poses a significant challenge. This issue is not just about technical solutions, it also involves preventing against intentionally disrupting cooperative applications and faulty vehicles unintentionally causing disruptions due to errors.
Image Credit: OpenAI Next Week in The Sequence: Edge 431: Our series about space state models(SSMs) continues with an overview of multimodal SSMs. We discuss the Cobra SSM multimodal model and NVIDIA’s TensorRT-LLM framework. Edge 432: Dives into NVIDIA’s Minitron models distilled from Llama 3.1. You can subscribe to The Sequence below: TheSequence is a reader-supported publication.
HuggingFace has made a significant stride in AI-driven video analysis and understanding with the release of FineVideo , an expansive and versatile dataset focused on multimodal learning. FineVideo consists of over 43,000 YouTube videos, meticulously selected under Creative Commons Attribution (CC-BY) licenses. It is a critical resource for researchers, developers, and AI enthusiasts aiming to advance video comprehension, mood analysis, and multimedia storytelling models.
Generative models have advanced significantly, enabling the creation of diverse data types, including crystal structures. In materials science, these models can combine existing knowledge to propose new crystals, leveraging their ability to generalize from large datasets. However, current models often require detailed input or large numbers of samples to generate new materials.
Speaker: Ben Epstein, Stealth Founder & CTO | Tony Karrer, Founder & CTO, Aggregage
When tasked with building a fundamentally new product line with deeper insights than previously achievable for a high-value client, Ben Epstein and his team faced a significant challenge: how to harness LLMs to produce consistent, high-accuracy outputs at scale. In this new session, Ben will share how he and his team engineered a system (based on proven software engineering approaches) that employs reproducible test variations (via temperature 0 and fixed seeds), and enables non-LLM evaluation m
Infrastructure systems must be managed effectively to preserve sustainability, protect public safety, and uphold economic stability. Transportation, communication, energy distribution, and other functions are made possible by these networks, which are the cornerstone of any functioning society. However, there is a great deal of difficulty in maintaining these enormous and intricate networks.
Data science is a rapidly evolving field that leverages large datasets to generate insights, identify trends, and support decision-making across various industries. It integrates machine learning, statistical methods, and data visualization techniques to tackle complex data-centric problems. As the volume of data grows, there is an increasing demand for sophisticated tools capable of handling large datasets and intricate and diverse types of information.
A significant challenge in information retrieval today is determining the most efficient method for nearest-neighbor vector search, especially with the growing complexity of dense and sparse retrieval models. Practitioners must navigate a wide range of options for indexing and retrieval methods, including HNSW (Hierarchical Navigable Small-World) graphs, flat indexes, and inverted indexes.
With the rapid expansion and application of large language models (LLMs), ensuring these AI systems generate safe, relevant, and high-quality content has become critical. As LLMs are increasingly integrated into enterprise solutions, chatbots, and other platforms, there is an urgent need to set up guardrails to prevent these models from generating harmful, inaccurate, or inappropriate outputs.
The DHS compliance audit clock is ticking on Zero Trust. Government agencies can no longer ignore or delay their Zero Trust initiatives. During this virtual panel discussion—featuring Kelly Fuller Gordon, Founder and CEO of RisX, Chris Wild, Zero Trust subject matter expert at Zermount, Inc., and Principal of Cybersecurity Practice at Eliassen Group, Trey Gannon—you’ll gain a detailed understanding of the Federal Zero Trust mandate, its requirements, milestones, and deadlines.
Large Language Models (LLMs) have revolutionized natural language processing in recent years. The pre-train and fine-tune paradigm, exemplified by models like ELMo and BERT, has evolved into prompt-based reasoning used by the GPT family. These approaches have shown exceptional performance across various tasks, including language generation, understanding, and domain-specific applications.
Traditional computing systems, primarily based on digital electronics, are facing increasing limitations in energy efficiency and computational speed. As silicon-based chips near their performance limits, there is a growing need for new hardware architectures to support complex tasks, such as artificial intelligence (AI) model training. Matrix multiplication, the fundamental operation in many AI algorithms, consumes vast amounts of energy and time on digital computers, limiting the democratizati
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