Analytics Vidhya

OCTOBER 26, 2024

Imagine if you could automate the tedious task of analyzing earnings reports, extracting key insights, and making informed recommendations—all without lifting a finger. In this article, we’ll walk you through how to create a multi-agent system using OpenAI’s Swarm framework, designed to handle these exact tasks. You’ll learn how to set up and orchestrate three […] The post Building an Earnings Report Agent with Swarm Framework appeared first on Analytics Vidhya.

Automation 180

Automation Generative AI AI AI 180

Unite.AI

OCTOBER 26, 2024

Have you ever wished you had an extra set of hands to help write, edit, or debug code ? Imagine if, instead of staring at that blank page or troubleshooting errors, you had a tool that could draft, refine, and even improve your work alongside you! This is the promise of ChatGPT-4o Canvas : a collaborative workspace for writing and coding that feels like you're working with a personal, knowledgeable assistant.

ChatGPT 189

ChatGPT Natural Language Processing AI Tools AI 189

Eugene Yan

OCTOBER 26, 2024

Look at and label your data, build and evaluate your LLM-evaluator, and optimize it against your labels.

Automation 168

Automation LLM 168

Marktechpost

OCTOBER 26, 2024

In the rapidly evolving world of finance, the demand for models that provide robust insights has never been greater. Traditional financial analysis requires an understanding of complex relationships, macroeconomic indicators, and financial nuances. Despite progress in AI, most language models struggle with the intricate aspects of financial data. They often lack the ability to fully comprehend sector-specific terminology, advanced mathematical constructs, and the depth of domain-specific knowled

Inference Engine 128

Inference Engine NLP ML AI Modeling 128

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

Flipboard

OCTOBER 26, 2024

Artificial intelligence (AI) can be trained to see details in images that escape the human eye.

Neural Network 123

Neural Network Artificial Intelligence Artificial Intelligence AI 123

Marktechpost

OCTOBER 26, 2024

Artificial intelligence (AI) is making significant strides in natural language processing (NLP), focusing on enhancing models that can accurately interpret and generate human language. Researchers are working to develop models that grasp complex linguistic structures and generate coherent, contextually relevant responses over extended dialogues. Advancements in this area are vital for applications such as automated customer service, content creation, and machine translation, where language preci

NLP 112

NLP Inference Engine Natural Language Processing BERT 112

SAS Software

OCTOBER 26, 2024

They say nothing in life is certain other than death and taxes, but there something else I’ve found I can count on from experience: sending out invites for a party on social media only to receive a few affirmative responses and a whole slew of “maybe”. I know my friends [.] The post How to handle RSVP ‘maybe’s’ appeared first on SAS Blogs.

Data Science 52

Data Science 52

Marktechpost

OCTOBER 26, 2024

Large Language Models (LLMs) have shown remarkable potential in solving complex real-world problems, from function calls to embodied planning and code generation. A critical capability for LLM agents is decomposing complex problems into executable subtasks through workflows, which serve as intermediate states to improve debugging and interpretability.

Large Language Models 111

Large Language Models LLM Inference Engine Algorithm 111

Machine Learning Research at Apple

OCTOBER 26, 2024

Manufacturing quality audits are pivotal for ensuring high product standards in mass production environments. Traditional auditing processes, however, are labor-intensive and heavily reliant on human expertise, posing challenges in maintaining transparency, accountability, and continuous improvement across complex global supply chains. To address these challenges, we propose a smart audit system empowered by large language models (LLMs).

LLM 52

LLM Large Language Models 52

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

OCTOBER 26, 2024

Games can be thought of as either finite or infinite. Finite games are structured around achieving a specific outcome, with set rules, boundaries, and a clear endpoint. In contrast, infinite games focus on continuing play indefinitely, adapting regulations and boundaries. Most traditional video games are finite because programming and graphic design constraints limit them to a fixed set of mechanics and visual assets, making them closed systems with limited actions and specific win conditions.

Generative AI 105

Generative AI AI Modeling Large Language Models Inference Engine 105

Marktechpost

OCTOBER 26, 2024

Vision Language Models (VLMs) have demonstrated remarkable capabilities in generating human-like text in response to images, with notable examples including GPT-4, Gemini, PaLiGemma, LLaVA, and Llama 3 Vision models. However, these models frequently generate hallucinated content that lacks proper grounding in the reference images, highlighting a critical flaw in their output reliability.

AI Researcher 101

AI Researcher AI Research Data Scarcity Inference Engine 101

Marktechpost

OCTOBER 26, 2024

Machine learning has considerably improved in evaluating large language models (LLMs) for their mathematical reasoning abilities, especially in handling complex arithmetic and deductive reasoning tasks. The field focuses on testing LLMs’ capacity to generalize and solve new types of problems, especially as arithmetic problems increase in complexity.

Inference Engine 98

Inference Engine LLM Large Language Models Machine Learning 98

Marktechpost

OCTOBER 26, 2024

Large language models (LLMs) sometimes learn the things that we don’t want them to learn and understand knowledge. It’s important to find ways to remove or adjust this knowledge to keep AI accurate, precise, and in control. However, editing or “unlearning” specific knowledge in these models is very tough. The usual methods to do this often end up affecting other information or general information in the model, which can affect its overall abilities.

Inference Engine 87

Inference Engine Large Language Models AI AI 87

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

OCTOBER 26, 2024

Large Language Models (LLMs) have emerged as crucial tools for handling intricate information-seeking queries due to techniques that improve both retrieval and response generation. Retrieval-augmented generation (RAG) is a well-known framework in this area that has drawn a lot of interest since it can produce responses that are more accurate and pertinent to the context.

Inference Engine 62

Inference Engine LLM Large Language Models Machine Learning 62

Marktechpost

OCTOBER 26, 2024

Despite rapid advancements in language technology, significant gaps in representation persist for many languages. Most progress in natural language processing (NLP) has focused on well-resourced languages like English, leaving many others underrepresented. This imbalance means that only a small portion of the world’s population can fully benefit from AI tools.

Inference Engine 104

Inference Engine Natural Language Processing NLP AI 104