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Making artificial intelligence practical, productive & accessible to everyone. Practical AI is a show in which technology professionals, business people, students, enthusiasts, and expert guests engage in lively discussions about Artificial Intelligence and related topics (Machine Learning, Deep Learning, Neural Networks, GANs, MLOps, AIOps, LLMs & more). The focus is on productive implementations and real-world scenarios that are accessible to everyone. If you want to keep up with the lates ...
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Machine Learning is growing in leaps and bounds both in capability and adoption. Listen to our experts discuss the ideas and fundamentals needed to succeed as a Machine Learning Engineer. Become a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.
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Machine Learning Street Talk (MLST)

Machine Learning Street Talk (MLST)

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Welcome! We engage in fascinating discussions with pre-eminent figures in the AI field. Our flagship show covers current affairs in AI, cognitive science, neuroscience and philosophy of mind with in-depth analysis. Our approach is unrivalled in terms of scope and rigour – we believe in intellectual diversity in AI, and we touch on all of the main ideas in the field with the hype surgically removed. MLST is run by Tim Scarfe, Ph.D (https://www.linkedin.com/in/ecsquizor/) and features regular ...
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Machine learning and artificial intelligence are dramatically changing the way businesses operate and people live. The TWIML AI Podcast brings the top minds and ideas from the world of ML and AI to a broad and influential community of ML/AI researchers, data scientists, engineers and tech-savvy business and IT leaders. Hosted by Sam Charrington, a sought after industry analyst, speaker, commentator and thought leader. Technologies covered include machine learning, artificial intelligence, de ...
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Это подкаст о машинном обучении от неспециалиста для неспециалистов. Буду рассказывать о развитии индустрии, проводить ликбез, объяснять терминологию и профессиональные жаргонизмы, общаться с профессионалами из индустрии Искусственного Интеллекта. Я сам не так давно начал погружаться в эту тему и по мере своего развития буду делиться своим пониманием этой интересной и перспективной области знаний. Почта для обратной связи: kms101@yandex.ru Сообщество подкаста в ВК: https://vk.com/mlpodcast Т ...
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AI Chat is the podcast where we dive into the world of ChatGPT, cutting-edge AI news and its impact on our daily lives. With in-depth discussions and interviews with leading experts in the field, we'll explore the latest advancements in language models, machine learning, and more. From its practical applications to its ethical considerations, AI Chat will keep you informed and entertained on the exciting developments in the world of AI. Tune in to stay ahead of the curve on the latest techno ...
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Machine learning audio course, teaching the fundamentals of machine learning and artificial intelligence. It covers intuition, models (shallow and deep), math, languages, frameworks, etc. Where your other ML resources provide the trees, I provide the forest. Consider MLG your syllabus, with highly-curated resources for each episode's details at ocdevel.com. Audio is a great supplement during exercise, commute, chores, etc.
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The Machine Learning podcast by edureka! will talk about what is Machine Learning, types of Machine learning and Machine Learning Algorithms. You will also get to know enough reasons for learning Machine Learning. Website: https://www.edureka.co/masters-program/machine-learning-engineer-training Please write back to us at sales@edureka.co or call us at IND: 9606058406 / US: 18338555775 (toll-free) for more information.
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ClusterOne is the best AI platform for distributed machine learning. It is developed with an aim to help machine learning teams in development of complex AI applications. For more detail, visit: https://clusterone.com/distributed-machine-learning/
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Short, simple summaries of machine learning topics, to help you prepare for exams, interviews, reading the latest papers, or just a quick brush up. In less than two minutes, we'll cover the most obscure jargon and complex topics in machine learning. For more details, including small animated presentations, please visit erikpartridge.com. Please do join the conversation on Twitter for corrections, conversations, support and more at #mlbytes
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This is a machine-learning-focused Podcast, where we interview people in the field of Artificial Intelligence and discuss interesting technical topics of Machine Learning. In the episodes, we focus on business-related use-cases (especially with Deep Learning ) and we also try to bring some technical white papers to the ground, not forgetting on the way that there are always some people behind the technology, so we try to understand their motivation and drive.
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This podcast helps Machine Learning Engineers become the best at what they do. Join host Charlie You every week as he talks to the brightest minds in data science, artificial intelligence, and software engineering to discover how they bring cutting edge research out of the lab and into products that people love. You'll learn the skills, tools, and best practices you can use to build better ML systems and accelerate your career in this flourishing new field.
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VnMetric là nền tảng review đầu tiên tại Việt Nam ứng dụng công nghệ Blockchain và Machine Learning, giúp người dùng chọn được sản phẩm tốt nhất. Website VnMetric là nền tảng review: https://vnmetric.com/ CEO: Lionel Nghia Tên doanh nghiệp: VnMetric Địa chỉ: Tầng 7, 229 Tây Sơn, phường Ngã Tư Sở, quận Đống Đa, Hà Nội, Việt Nam Mã bưu điện: 100000 Số điện thoại: 024 6713 0592 Gmail: vnmetric.contact@gmail.com
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A4N — the Artificial Neural Network News Network — is a lighthearted podcast covering the latest developments in artificial intelligence, machine learning, and data science, in which we both introduce technical aspects of these advances, as well as their social implications. The intended audience is anyone interested in automation, A.I., or the future, with brief sections catering especially to professionals working in the fields of data science or software engineering.
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Welcome to the Machine Commons podcast, where we explore the various and far reaching impacts of machine learning - the software approach better known as artificial intelligence (or, AI). The world has teetered over an invisible edge, where more of our lives are governed by software that is decreasingly coded and increasingly trained - a world where our governing software 'learns'. It is the technology at the cross section of multiple advanced fields. Come hang out with Alex and Lucie, as th ...
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In this course we will explore the challenges presented when designing AI-powered services. In particular, we will take a look at Machine Learning (such as deep learning and generative adversarial networks), and how that can be used in human-centered design of digital services. This course is created for User Experience (UX) professionals, Service Designers, and Product Managers as a way to help take a human-centered approach to AI in their work. The course is also useful for developers and ...
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This podcast series was put together by data science intern Leo Elworth to spread knowledge on these hot topics to the broader community. As the buzz around data science and machine learning continues to grow, more and more people are developing a curiosity for these topics, as well as their applications to the specific field of oil and gas. Interviews with expert data scientists and geologists serve to highlight innovative problems and share entertaining anecdotes. Podcast editing assistanc ...
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Artificial intelligence is a tremendously beneficial technology that's advancing at an incredibly rapid pace. As more and more organisations adopt and implement AI we find that the main challenges are not in the technology itself but in the human side, ie: the approaches, chosen problems and what's called 'the last mile', etc. That's why Data Futurology focuses on the leadership side of AI and how to get the most value from it. Join me, Felipe Flores, a Data Science executive with almost 20 ...
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This is Learning Machine. A podcast about digital marketing, content, strategy, startups, and business. If you like Mixergy, Entrepreneur on Fire, Tropical MBA, Rocketship.fm, SaaStr, Tim Ferriss show, or Startups for the Rest of Us, you will love Content and Coffee.
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Elham Tabassi, the Chief AI Advisor at the U.S. National Institute of Standards & Technology (NIST), joins Chris for an enlightening discussion about the path towards trustworthy AI. Together they explore NIST’s ‘AI Risk Management Framework’ (AI RMF) within the context of the White House’s ‘Executive Order on the Safe, Secure, and Trustworthy Deve…
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We discuss how to build Agentic Retrieval Augmented Generation (RAG) systems, which use AI agents to retrieve information from various sources to answer user queries. The author details the challenges he faced when building an Agentic RAG system to answer customer support questions, and provides insights into techniques like prompt engineering and …
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Today, we're joined by Scott Stephenson, co-founder and CEO of Deepgram to discuss voice AI agents. We explore the importance of perception, understanding, and interaction and how these key components work together in building intelligent AI voice agents. We discuss the role of multimodal LLMs as well as speech-to-text and text-to-speech models in …
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Professor Michael Levin explores the revolutionary concept of diverse intelligence, demonstrating how cognitive capabilities extend far beyond traditional brain-based intelligence. Drawing from his groundbreaking research, he explains how even simple biological systems like gene regulatory networks exhibit learning, memory, and problem-solving abil…
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In this episode, Michael and Ben dive deep into the intersection of education and technology with their insightful guest, Daniel Hiterer. Michael, a data engineering and machine learning expert, and Ben, an integrator of Gen AI tools, navigate through Danny's unique perspective on the impact of nurturing educational environments. Currently working …
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В гостях этого выпуска Екатерина Кондратьева — специалист по анализу медицинских изображений с более чем 7-летним опытом. Екатерина закончила аспирантуру в Сколтехе и работала в Институте AIRI. Последние два года она возглавляла команду по машинному зрению в израильском healthech стартапе LiteBC. В этом выпуске Екатерина расскажет о текущем состоян…
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Let's get RE(a)L, U! This research paper explores the impact of different activation functions, specifically ReLU and L-ReLU, on the performance of deep learning models. The authors investigate how the choice of activation function, along with factors like the number of parameters and the shape of the model architecture, influence model accuracy ac…
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This lecture from Stanford University's CS229 course, "Machine Learning," focuses on the theory and practice of linear regression and gradient descent, two fundamental machine learning algorithms. The lecture begins by motivating linear regression as a simple supervised learning algorithm for regression problems where the goal is to predict a conti…
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This video discusses the vanishing gradient problem, a significant challenge in training deep neural networks. The speaker explains how, as a neural network becomes deeper, gradients—measures of how changes in network parameters affect the loss function—can decrease exponentially, leading to a situation where early layers of the network are effecti…
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A scientific paper exploring the development and evaluation of language agents for automating data-driven scientific discovery. The authors introduce a new benchmark called ScienceAgentBench, which consists of 102 diverse tasks extracted from peer-reviewed publications across four disciplines: Bioinformatics, Computational Chemistry, Geographical I…
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We are on the other side of “big data” hype, but what is the future of analytics and how does AI fit in? Till and Adithya from MotherDuck join us to discuss why DuckDB is taking the analytics and AI world by storm. We dive into what makes DuckDB, a free, in-process SQL OLAP database management system, unique including its ability to execute lightin…
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In this episode, we discuss Runway's new AI tool, "Act One," which simplifies face rigging for creators. We explore how this technology is set to streamline the animation process in video production. Get on the AI Box Waitlist: ⁠⁠https://AIBox.ai/⁠⁠ Join my AI Hustle Community: https://www.skool.com/aihustle/about…
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Will Williams is CTO of Speechmatics in Cambridge. In this sponsored episode - he shares deep technical insights into modern speech recognition technology and system architecture. The episode covers several key technical areas: * Speechmatics' hybrid approach to ASR, which focusses on unsupervised learning methods, achieving comparable results with…
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We discuss how to utilize the processing power of Graphics Processing Units (GPUs) to speed up deep learning calculations, particularly in the context of training neural networks. It outlines how to assign data to different GPUs to minimize data transfer times, a crucial aspect of performance optimization. The text highlights the importance of unde…
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This paper provides a comprehensive overview of deep generative models (DGMs) and their applications within transportation research. It begins by outlining the fundamental principles and concepts of DGMs, focusing on various model types such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Normalizing Flows, and Diffusion…
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This research paper presents the development and evaluation of an AI-driven Smart Video Solution (SVS) designed to enhance community safety. The SVS utilizes existing CCTV infrastructure and leverages recent advancements in AI for anomaly detection, leveraging pose-based data to ensure privacy. The system provides real-time alerts to stakeholders t…
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Dr. Sanjeev Namjoshi, a machine learning engineer who recently submitted a book on Active Inference to MIT Press, discusses the theoretical foundations and practical applications of Active Inference, the Free Energy Principle (FEP), and Bayesian mechanics. He explains how these frameworks describe how biological and artificial systems maintain stab…
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In this episode, we explore Claude AI's new ability to control your computer to perform tasks on your behalf. We discuss the implications and potential use cases of this powerful feature. Get on the AI Box Waitlist: ⁠⁠https://AIBox.ai/⁠⁠ Join my AI Hustle Community: https://www.skool.com/aihustle/about…
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The book titled "Mathematics for Machine Learning" explains various mathematical concepts that are essential for understanding machine learning algorithms, including linear algebra, analytic geometry, vector calculus, and probability. It also discusses topics such as model selection, parameter estimation, dimensionality reduction, and classificatio…
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In this episode, we discuss the significant investments in generative AI startups, which reached $3.9 billion in the third quarter of 2024. We explore the factors driving this surge in funding and what it means for the future of AI innovation. My Podcast Course: https://podcaststudio.com/courses/ Get on the AI Box Waitlist: ⁠⁠https://AIBox.ai/⁠⁠ Jo…
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Here we discuss three different papers (see links below) on using D-CNNs to detect breast cancer. The first source details the development and evaluation of HIPPO, a novel explainable AI method that enhances the interpretability and trustworthiness of ABMIL models in computational pathology. HIPPO aims to address the challenges of opaque decision-m…
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Today, we're joined by Tim Rocktäschel, senior staff research scientist at Google DeepMind, professor of Artificial Intelligence at University College London, and author of the recently published popular science book, “Artificial Intelligence: 10 Things You Should Know.” We dig into the attainability of artificial superintelligence and the path to …
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This LessWrong post explores various methods to enhance human intelligence, aiming to create individuals with significantly higher cognitive abilities than the current population. The author, TsviBT, proposes numerous approaches ranging from gene editing to brain-computer interfaces and brain emulation, discussing their potential benefits and drawb…
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The first source is a blog post by Max Mynter, a machine learning engineer, outlining a five-to-seven step roadmap for becoming a machine learning engineer. The post emphasizes the importance of both software engineering and data science skills alongside mathematics and domain knowledge. It then offers concrete resources, including courses and book…
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Dr. Joscha Bach discusses advanced AI, consciousness, and cognitive modeling. He presents consciousness as a virtual property emerging from self-organizing software patterns, challenging panpsychism and materialism. Bach introduces "Cyberanima," reinterpreting animism through information processing, viewing spirits as self-organizing software agent…
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Alessandro Palmarini is a post-baccalaureate researcher at the Santa Fe Institute working under the supervision of Melanie Mitchell. He completed his undergraduate degree in Artificial Intelligence and Computer Science at the University of Edinburgh. Palmarini's current research focuses on developing AI systems that can efficiently acquire new skil…
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In this episode, we discuss the latest legal issues AI Box is facing, along with the progress being made on their platform. We also cover their updated launch timeline and what users can expect going forward. AI Box Update YouTube Video: https://youtu.be/kB6c7VMeR5Q Get on the AI Box Waitlist: ⁠⁠https://AIBox.ai/⁠⁠…
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We discusses the importance of generalization in classification, where the goal is to train a model that can accurately predict labels for previously unseen data. The text first explores the role of test sets in evaluating model performance, emphasizing the need to use them sparingly and cautiously to avoid overfitting. It then introduces the conce…
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In this episode, we discuss Google’s recent update to NotebookLM, enhancing its audio summarization feature with the ability to guide conversations and focus on specific topics. We also explore how this feature has driven a significant increase in user traffic and engagement. Get on the AI Box Waitlist: ⁠⁠https://AIBox.ai/⁠⁠ Join my AI Hustle Commu…
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Recognizing laughter in audio is actually a very difficult ML problem, filled with failure. Much like most comedians' jokes. Let's hope some good stuff survives. This is a review of a student's final year project for a University of Edinburgh computer science course. The project focused on creating a machine learning model to detect laughter in vid…
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Today, they dive deep into the fascinating intersection of open-source development and machine learning. Michael and Ben are joined by distinguished guest, Görkem Erkan, CTO and seasoned engineer at Jozu. Görkem shares his illustrious career journey from Nokia to Red Hat, his contributions to the Eclipse Foundation, and his current focus on MLOps. …
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AI startup Mistral has launched its newest AI models, "Les Ministraux," designed to run on edge devices like laptops and phones. The two available versions, Ministral 3B and Ministral 8B, have a 128,000-token context window, capable of processing the equivalent of a 50-page book. Get on the AI Box Waitlist: ⁠⁠https://AIBox.ai/⁠⁠ Join my AI Hustle C…
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Solving an impossible mystery... forget what you thought was possible! This is a discussion of a video from Stanford's CS224W course which focuses on the many applications of graph machine learning, a field that utilizes graph data structures to solve complex problems. The speaker highlights different tasks and their associated applications, classi…
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A research team from EyeLevel.ai has found that vector databases, which are commonly used in RAG (Retrieval-Augmented Generation) systems, have a scaling problem. Their research shows that the accuracy of vector similarity search degrades significantly as the number of pages in the database increases, leading to a substantial performance hit. This …
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Probability and statistics are fundamental components of machine learning (ML) and deep learning (DL) because they provide the mathematical framework for understanding and analyzing data, which is crucial for making predictions and decisions. This excerpt from the "Dive into Deep Learning" documentation explains the essential concepts of probabilit…
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Workflow orchestration has always been a pain for data scientists, but this is exacerbated in these AI hype days by agentic workflows executing arbitrary (not pre-defined) workflows with a variety of failure modes. Adam from Prefect joins us to talk through their open source Python library for orchestration and visibility into python-based pipeline…
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This research paper examines a new deep-learning approach to optimizing weather forecasts by adjusting initial conditions. The authors test their method on the 2021 Pacific Northwest heatwave, finding that small changes to initial conditions can significantly improve the accuracy of 10-day forecasts using both the GraphCast and Pangu-Weather deep-l…
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In this episode, we discuss how AI video clone company Beyond Presence raised $3.1M to accelerate its growth and development. We explore the potential impact of this funding on the company's future in the AI industry. My Podcast Course: https://podcaststudio.com/courses/ Get on the AI Box Waitlist: ⁠⁠https://AIBox.ai/⁠⁠ Join my AI Hustle Community:…
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In this episode, we explore the key predictions made by Anthropic's CEO about the future of AI over the next decade. We discuss how these forecasts could impact industries and the AI landscape globally. My Podcast Course: https://podcaststudio.com/courses/ Get on the AI Box Waitlist: ⁠⁠https://AIBox.ai/⁠⁠ Join my AI Hustle Community: https://www.sk…
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An introduction to the fundamental concepts of calculus, explaining how they are essential for understanding deep learning. It begins by illustrating the concept of a limit using the calculation of a circle's area, before introducing the concept of a derivative, which describes a function's rate of change. It then extends these concepts to multivar…
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Today, we're joined by Lucas García, principal product manager for deep learning at MathWorks to discuss incorporating ML models into safety-critical systems. We begin by exploring the critical role of verification and validation (V&V) in these applications. We review the popular V-model for engineering critical systems and then dig into the “W” ad…
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The source, "Generative AI's Act o1: The Reasoning Era Begins | Sequoia Capital," discusses the evolution of AI models from simply mimicking patterns to engaging in more deliberate reasoning. The authors argue that the next frontier in AI is the development of "System 2" thinking, where models can reason through complex problems and make decisions …
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Swarm is an experimental, educational framework from OpenAI that explores ergonomic interfaces for multi-agent systems. It is not intended for production use, but serves as a learning tool for developers interested in multi-agent orchestration. Swarm uses two main concepts: Agents and handoffs. Agents are entities that encapsulate instructions and …
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The provided sources detail the groundbreaking work of three scientists who were awarded the 2024 Nobel Prize in Chemistry for their contributions to protein structure prediction using artificial intelligence. David Baker, a biochemist, developed a computer program to create entirely new proteins, while Demis Hassabis and John Jumper, from Google D…
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Dario Amodei, CEO of Anthropic, argues that powerful AI could revolutionize various fields, including healthcare, neuroscience, economics, and governance, within 5-10 years. He envisions a future where AI could cure most diseases, eradicate poverty, and even promote democracy. However, this optimistic vision is met with skepticism from Reddit users…
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François Chollet discusses the limitations of Large Language Models (LLMs) and proposes a new approach to advancing artificial intelligence. He argues that current AI systems excel at pattern recognition but struggle with logical reasoning and true generalization. This was Chollet's keynote talk at AGI-24, filmed in high-quality. We will be releasi…
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This paper examines the rapidly developing field of Retrieval-Augmented Generation (RAG), which aims to improve the capabilities of Large Language Models (LLMs) by incorporating external knowledge. The paper reviews the evolution of RAG paradigms, from the early "Naive RAG" to the more sophisticated "Advanced RAG" and "Modular RAG" approaches. It e…
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This research paper investigates the challenges of detecting Out-of-Distribution (OOD) inputs in medical image segmentation tasks, particularly in the context of Multiple Sclerosis (MS) lesion segmentation. The authors propose a novel evaluation framework that uses 14 different sources of OOD, including synthetic artifacts and real-world variations…
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