Unite.AI

OCTOBER 2, 2024

For more than three decades, mobile network operators (MNOs) have been channeling their research and development efforts into five key areas: messaging, roaming, policy, signaling, and clearing. Given the vast quantities of data processed through these systems, it's only natural that MNOs are increasingly focusing on leveraging artificial intelligence (AI) to enhance features, maximize resource efficiency, and safeguard customer data, all while fulfilling their service commitments.

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Analytics Vidhya

OCTOBER 2, 2024

Introduction Never would have the inventor of email –Ray Tomlinson– thought of how far this piece of tech would reach in the future. Today, email is the prime pillar of corporate and professional communications and is used in innumerable facets of the working world. And this has propelled the creation of a whole set of […] The post Automating Email Sorting and Labelling with CrewAI appeared first on Analytics Vidhya.

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Unite.AI

OCTOBER 2, 2024

In today’s data-driven world, geospatial information is essential for gaining insights into climate change, urban growth, disaster management, and global security. Despite its vast potential, working with geospatial data presents significant challenges due to its size, complexity, and lack of standardization. Machine learning can analyze these datasets yet preparing them for analysis can be time-consuming and cumbersome.

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IBM Journey to AI blog

OCTOBER 2, 2024

Business intelligence (BI) users often struggle to access the high-quality, relevant data necessary to inform strategic decision making. These professionals encounter a range of issues when attempting to source the data they need, including: Data accessibility issues: The inability to locate and access specific data due to its location in siloed systems or the need for multiple permissions, resulting in bottlenecks and delays.

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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

OCTOBER 2, 2024

CopilotKit has emerged as a leading open-source framework designed to streamline the integration of AI into modern applications. Widely appreciated within the open-source community, CopilotKit has garnered significant recognition, boasting over 10.5k+ GitHub stars. The platform enables developers to create custom AI copilots, in-app agents, and interactive assistants capable of dynamically engaging with their application’s environment.

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Extreme Tech

OCTOBER 2, 2024

It'll work with Lunar Lake, Meteor Lake, or with an Arc GPU.

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AWS Machine Learning Blog

OCTOBER 2, 2024

Large enterprises are building strategies to harness the power of generative AI across their organizations. However, scaling up generative AI and making adoption easier for different lines of businesses (LOBs) comes with challenges around making sure data privacy and security, legal, compliance, and operational complexities are governed on an organizational level.

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Extreme Tech

OCTOBER 2, 2024

Retro Game Corps, a channel with over half a million subscribers, has received copyright strikes for at least two emulation-related videos.

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AWS Machine Learning Blog

OCTOBER 2, 2024

Personalization can improve the user experience of shopping, entertainment, and news sites by using our past behavior to recommend the products and content that best match our interests. You can also apply personalization to conversational interactions with an AI-powered assistant. For example, an AI assistant for employee onboarding could use what it knows about an employee’s work location, department, or job title to provide information that is more relevant to the employee.

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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

Extreme Tech

OCTOBER 2, 2024

The world orbits Barnard's Star, the closest single-star system to the Sun.

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Marktechpost

OCTOBER 2, 2024

Large Language Models (LLMs) have made significant strides in various Natural Language Processing tasks, yet they still struggle with mathematics and complex logical reasoning. Chain-of-Thought (CoT) prompting has emerged as a promising approach to enhance reasoning capabilities by incorporating intermediate steps. However, LLMs often exhibit unfaithful reasoning, where conclusions don’t align with the generated reasoning chain.

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Extreme Tech

OCTOBER 2, 2024

Though a bit unwieldy, the headset is the first-ever device to predict strokes non-invasively.

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Marktechpost

OCTOBER 2, 2024

ChatGPT is a versatile tool with immense potential for businesses across diverse industries. Its capability to comprehend and generate human-like text enables its use in numerous applications, making it valuable for companies aiming to optimize operations, boost customer engagement, and foster innovation. Let’s look at the top 10 ChatGPT use cases for businesses, showcasing how it can be effectively leveraged to meet various needs.

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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

Extreme Tech

OCTOBER 2, 2024

The technology is no longer in preview and can be used by anyone with a late-model Radeon GPU.

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Marktechpost

OCTOBER 2, 2024

Recent advances in autoregressive language models have brought about an amazing transformation in the field of Natural Language Processing (NLP). These models, such as GPT and others, have exhibited excellent performance in text creation tasks, including question-answering and summarization. However, their high inference latency poses a significant barrier to their general application, particularly in highly deep models with hundreds of billions of parameters.

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Algorithm Natural Language Processing AI AI 109

databricks

OCTOBER 2, 2024

Introduction Applying Large Language Models (LLMs) for code generation is becoming increasingly prevalent, as it helps you code faster and smarter. A primary.

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Large Language Models 107

Extreme Tech

OCTOBER 2, 2024

AMD is looking to match Intel's memory speeds with its new X870 platform.

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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.

databricks

OCTOBER 2, 2024

We are thrilled to announce that embedding for AI/BI Dashboards is now available. Embedding enables you to seamlessly integrate Databricks AI/BI Dashboards into.

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AI AI 106

Marktechpost

OCTOBER 2, 2024

Climate and weather prediction has experienced rapid advancements through machine learning and deep learning models. Researchers have started to rely on artificial intelligence (AI) to enhance predictions’ accuracy and computational efficiency. Traditional numerical weather prediction (NWP) models have been effective but require substantial computational resources, making them less accessible and harder to apply at larger scales.

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Extreme Tech

OCTOBER 2, 2024

The long-awaited update includes changes to the Start menu, command prompt, and AI features.

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Marktechpost

OCTOBER 2, 2024

Instruction-tuned LMs have shown remarkable zero-shot generalization but often fail on tasks outside their training data. These LMs, built on large datasets and billions of parameters, excel in In-Context Learning (ICL), generating responses based on a few examples without re-training. However, the training dataset’s scope limits its effectiveness on unfamiliar tasks.

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Speaker: David Warren and Kevin O'Neill Stoll

Transitioning to a usage-based business model offers powerful growth opportunities but comes with unique challenges. How do you validate strategies, reduce risks, and ensure alignment with customer value? Join us for a deep dive into designing effective pilots that test the waters and drive success in usage-based revenue. Discover how to develop a pilot that captures real customer feedback, aligns internal teams with usage metrics, and rethinks sales incentives to prioritize lasting customer eng

NVIDIA

OCTOBER 2, 2024

Editor’s note: This post is part of the AI Decoded series , which demystifies AI by making the technology more accessible, and showcases new hardware, software, tools and accelerations for GeForce RTX PC and NVIDIA RTX workstation users. From games and content creation apps to software development and productivity tools, AI is increasingly being integrated into applications to enhance user experiences and boost efficiency.

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Marktechpost

OCTOBER 2, 2024

LLMs, characterized by their massive parameter sizes, often lead to inefficiencies in deployment due to high memory and computational demands. One practical solution is semi-structured pruning, particularly the N: M sparsity pattern, which enhances efficiency by maintaining N non-zero values among M parameters. While hardware-friendly, such as for GPUs, this approach faces challenges due to the vast parameter space in LLMs.

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AWS Machine Learning Blog

OCTOBER 2, 2024

This post was co-written with Anthony Medeiros, Manager of Solutions Engineering and Architecture for North America Artificial Intelligence, and Adrian Boeh, Senior Data Scientist – NAM AI, from Schneider Electric. Schneider Electric is a global leader in the digital transformation of energy management and automation. The company specializes in providing integrated solutions that make energy safe, reliable, efficient, and sustainable.

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Marktechpost

OCTOBER 2, 2024

Large language models (LLMs) have garnered significant attention for their ability to understand and generate human-like text. These models possess the unique capability to encode factual knowledge effectively, thanks to the vast amount of data they are trained on. This ability is crucial in various applications, ranging from natural language processing (NLP) tasks to more advanced forms of artificial intelligence.

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AWS Machine Learning Blog

OCTOBER 2, 2024

Building intelligent agents that can accurately understand and respond to user queries is a complex undertaking that requires careful planning and execution across multiple stages. Whether you are developing a customer service chatbot or a virtual assistant, there are numerous considerations to keep in mind, from defining the agent’s scope and capabilities to architecting a robust and scalable infrastructure.

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Artificial Corner

OCTOBER 2, 2024

Image via Shutterstock Python is known for being a slow programming language. Although it’s a fact that Python is slower than other languages, there are some ways to speed up our Python code. How? Simple, optimize your code. If we write code that consumes little memory and storage, we’ll not only get the job done but also make our Python code run faster.

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Marktechpost

OCTOBER 2, 2024

Large language models (LLMs) have advanced significantly in recent years. However, its real-world applications are restricted due to substantial processing power and memory requirements. The need to make LLMs more accessible on smaller and resource-limited devices drives the development of more efficient frameworks for model inference and deployment.

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