Sun.Feb 04, 2024

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10 Python String Interpolation Approaches

Analytics Vidhya

Introduction As Python enthusiasts, we understand that crafting clean, readable, and concise code is not just a skill – it’s an art. So, what’s stopping you from becoming an expert? String interpolation becomes your brush, allowing you to seamlessly weave expressions, variables, and functions into the very fabric of your strings. It is a powerful […] The post 10 Python String Interpolation Approaches appeared first on Analytics Vidhya.

Python 264
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Michelangelo’s David Meets Modern 3D Imaging Technology

Unite.AI

The evolution of facial recognition technology has taken a big leap forward with the development of a new, more compact 3D surface imaging system. Spearheaded by researchers, this innovative technology significantly streamlines the facial recognition process, commonly used in unlocking smartphones and securing online bank accounts. Unlike traditional systems that rely on bulky projectors and lenses, this new approach utilizes flatter, simplified optics, making it a game-changer in the realm of p

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Google DeepMind Researchers Unveil a Groundbreaking Approach to Meta-Learning: Leveraging Universal Turing Machine Data for Advanced Neural Network Training

Marktechpost

Meta-learning, a burgeoning field in AI research, has made significant strides in training neural networks to adapt swiftly to new tasks with minimal data. This technique centers on exposing neural networks to diverse tasks, thereby cultivating versatile representations crucial for general problem-solving. Such varied exposure aims to develop universal capabilities in AI systems, an essential step toward the grand vision of artificial general intelligence (AGI).

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Creating Movie Trailers With AI

Towards AI

Last Updated on February 5, 2024 by Editorial Team Author(s): Dimitre Oliveira Originally published on Towards AI. Photo by Jeremy Yap on Unsplash AI has been used in the movie industry in many different use cases, from visual effects to the recent usage of generative AI to create scripts or actual movies; some use cases are less obvious, like movie recommendations or creating content to promote the movie, this project targets this last use case, with it, a user will be able to automatically gen

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From Developer Experience to Product Experience: How a Shared Focus Fuels Product Success

Speaker: Anne Steiner and David Laribee

As a concept, Developer Experience (DX) has gained significant attention in the tech industry. It emphasizes engineers’ efficiency and satisfaction during the product development process. As product managers, we need to understand how a good DX can contribute not only to the well-being of our development teams but also to the broader objectives of product success and customer satisfaction.

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AIWaves Introduces Weaver: A Family of LLMs Specialized for Writing Endeavors

Marktechpost

Large language models (LLMs) have become a prominent force in the rapidly evolving landscape of artificial intelligence. These models, built primarily on Transformer architectures, have expanded AI’s capabilities in understanding and generating human language, leading to diverse applications. Yet, a notable challenge in this realm is enhancing LLMs for creative writing.

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Alibaba Researchers Introduce Mobile-Agent: An Autonomous Multi-Modal Mobile Device Agent

Marktechpost

Mobile device agents utilizing Multimodal Large Language Models (MLLM) have gained popularity due to the rapid advancements in MLLMs, showcasing notable visual comprehension capabilities. This progress has made MLLM-based agents viable for diverse applications. The emergence of mobile device agents represents a novel application, requiring these agents to operate devices based on screen content and user instructions.

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Celebrating Community at MLearning.ai

Mlearning.ai

Medium’s Boost nominee Continue reading on MLearning.

ML 98
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This AI Paper from CMU and Apple Unveils WRAP: A Game-Changer for Pre-training Language Models with Synthetic Data

Marktechpost

Large Language Models (LLMs) have gathered a massive amount of attention and popularity among the Artificial Intelligence (AI) community in recent months. These models have demonstrated great capabilities in tasks including text summarization, question answering, code completion, content generation, etc. LLMs are frequently trained on inadequate web-scraped data.

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How Nesta uses NLP to process 7m job ads and shed light on the UK’s labor market

Explosion

A case study on Nesta’s workflow for extracting 7 million job ads to better understand UK skill demand, using a custom mapping step to match skills to any government taxonomy.

NLP 63
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Peak Performance: Continuous Testing & Evaluation of LLM-Based Applications

Speaker: Aarushi Kansal, AI Leader & Author and Tony Karrer, Founder & CTO at Aggregage

Software leaders who are building applications based on Large Language Models (LLMs) often find it a challenge to achieve reliability. It’s no surprise given the non-deterministic nature of LLMs. To effectively create reliable LLM-based (often with RAG) applications, extensive testing and evaluation processes are crucial. This often ends up involving meticulous adjustments to prompts.

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This Paper Reveals The Surprising Influence of Irrelevant Data on Retrieval-Augmented Generation RAG Systems’ Accuracy and Future Directions in AI Information Retrieval

Marktechpost

In advanced machine learning, Retrieval-Augmented Generation (RAG) systems have revolutionized how we approach large language models (LLMs). These systems extend the capabilities of LLMs by integrating an Information Retrieval (IR) phase, which allows them to access external data. This integration is crucial, as it enables the RAG systems to overcome the limitations faced by standard LLMs, which are typically constrained to their pre-trained knowledge and limited context window.

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How I Built a Chatbot that Crushed ChatGPT with Zero Cost AI Tools

Mlearning.ai

Challenge Accepted! How I created a chatbot that surpassed the performance of the famous ChatGPT model using free and open source AI tools… Continue reading on MLearning.

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Microsoft Researchers Introduce StrokeNUWA: Tokenizing Strokes for Vector Graphic Synthesis

Marktechpost

Natural Language Processing (NLP) is one area where Large transformer-based Language Models (LLMs) have achieved remarkable progress in recent years. Also, LLMs are branching out into other fields, like robotics, audio, and medicine. Modern approaches allow LLMs to produce visual data using specialized modules like VQ-VAE and VQ-GAN, which convert continuous visual pixels into discrete grid tokens.

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What is Alteryx certification: A comprehensive guide

Pickl AI

Summary : Alteryx revolutionizes data analytics with its intuitive platform, empowering users to effortlessly clean, transform, and analyze vast datasets without coding expertise. The drag-and-drop interface of Alteryx Designer simplifies workflow creation, while automation features enhance efficiency. From predictive modeling and geospatial analysis to automated workflows, Alteryx streamlines complex tasks.

ETL 52
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The Path to Product Excellence: Avoiding Common Pitfalls and Enhancing Communication

Speaker: David Bard, Principal at VP Product Coaching

In the fast-paced world of digital innovation, success is often accompanied by a multitude of challenges - like the pitfalls lurking at every turn, threatening to derail the most promising projects. But fret not, this webinar is your key to effective product development! Join us for an enlightening session to empower you to lead your team to greater heights.

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This AI Paper from UNC-Chapel Hill Proposes ReGAL: A Gradient-Free Method for Learning a Library of Reusable Functions via Code Refactorization

Marktechpost

Optimizing code through abstraction in software development is not just a practice but a necessity. It leads to streamlined processes, where reusable components simplify tasks, increase code readability, and foster reuse. The development of generalizable abstractions, especially in automated program synthesis, stands at the forefront of current research endeavors.

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Unveiling Google Gemini: A Revolutionary Leap in Multimodal AI

Pickl AI

Unveiling Google Gemini: A Revolutionary Leap in Multimodal AI In the dynamic realm of Artificial Intelligence (AI), the relentless pursuit of innovation has brought groundbreaking advancements, and at the forefront of this technological revolution is the awe-inspiring Multimodal AI. Today, our focus turns to an exceptional manifestation of this technology — Google’s Gemini.

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TikTok Researchers Introduce ‘Depth Anything’: A Highly Practical Solution for Robust Monocular Depth Estimation

Marktechpost

Foundational models are large deep-learning neural networks that are used as a starting point to develop effective ML models. They rely on large-scale training data and exhibit exceptional zero/few-shot performance in numerous tasks, making them invaluable in the field of natural language processing and computer vision. Foundational models are also used in Monocular Depth Estimation (MDE), i.e., estimating depth from a single image, and are widely used in autonomous vehicles, robotics, and virtu

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It Pays to AI: Microsoft Profits Up 33%

Robot Writers AI

You can’t blame Microsoft for grinning ear-to-ear: Its profits are up 33% for Q4, 2023 — a clear sign that it’s getting a significant bump from its investment in AI. Observes Satya Nadella, CEO, Microsoft: “We’ve moved from talking about AI to applying AI at scale. “By infusing AI across every layer of our tech stack, we’re winning new customers and helping drive new benefits and productivity gains across every sector.

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Reimagined: Building Products with Generative AI

“Reimagined: Building Products with Generative AI” is an extensive guide for integrating generative AI into product strategy and careers featuring over 150 real-world examples, 30 case studies, and 20+ frameworks, and endorsed by over 20 leading AI and product executives, inventors, entrepreneurs, and researchers.

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Meet RAGatouille: A Machine Learning Library to Train and Use SOTA Retrieval Model, ColBERT, in Just a Few Lines of Code

Marktechpost

Creating effective pipelines, especially using RAG (Retrieval-Augmented Generation), can be quite challenging in information retrieval. These pipelines involve various components, and choosing the right models for retrieval is crucial. While dense embeddings like OpenAI’s text-ada-002 serve as a good starting point, recent research suggests that they might not always be the optimal choice for every scenario.

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Retrieval Augmented Generation (RAG) Using Azure And Langchain Tutorial

Pragnakalp

In the world of AI, large language models have changed the way computers understand human language. Their potential for business solutions relies on strong information retrieval. Retrieval Augmented Generation enhances large language models by incorporating real-time information from external knowledge bases, ensuring more accurate and up-to-date responses.

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Neural Networks 101: Forward Propagation

Mlearning.ai

A Comprehensive Training Handbook Hello, everyone. I hope you are doing well. This is the second part of my Neural Networks 101 series, in this blog we are going to discuss about the training of machine learning models. With a few examples, this section will primarily cover forward propagation. You can follow me on Twitter to learn more about this. Every day, I tweet about a variety of subjects here, such as software engineering, system design, deep learning, machine learning, and more.

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The Most Open Open Source Generative AI Release

TheSequence

Created Using DALL-E Next Week in The Sequence: Edge 367: We dive into multi-chain reasoning in LLMs including the original research paper on this topic published by Allen AI. It also explores Gradio as a very effective tool for demoing LLM apps. Edge 368: We review one of my favorite frameworks for autonomous agents: MemGPT! You can subscribe below!

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Driving Business Impact for PMs

Speaker: Jon Harmer, Product Manager for Google Cloud

Move from feature factory to customer outcomes and drive impact in your business! This session will provide you with a comprehensive set of tools to help you develop impactful products by shifting from output-based thinking to outcome-based thinking. You will deepen your understanding of your customers and their needs as well as identifying and de-risking the different kinds of hypotheses built into your roadmap.