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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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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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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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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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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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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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In this episode, we explore how Numeric has raised $28 million to develop AI technology aimed at transforming the accounting industry. We discuss what this means for the future of accounting and how AI could streamline financial processes. My Podcast Course: https://podcaststudio.com/courses/ Get on the AI Box Waitlist: ⁠⁠https://AIBox.ai/⁠⁠ Join m…
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Ivan Zhang, co-founder of Cohere, discusses the company's enterprise-focused AI solutions. He explains Cohere's early emphasis on embedding technology and training models for secure environments. Zhang highlights their implementation of Retrieval-Augmented Generation in healthcare, significantly reducing doctor preparation time. He explores the shi…
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As Argilla puts it: “Data quality is what makes or breaks AI.” However, what exactly does this mean and how can AI team probably collaborate with domain experts towards improved data quality? David Berenstein & Ben Burtenshaw, who are building Argilla & Distilabel at Hugging Face, join us to dig into these topics along with synthetic data generatio…
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Today, we're joined by Arvind Narayanan, professor of Computer Science at Princeton University to discuss his recent works, AI Agents That Matter and AI Snake Oil. In “AI Agents That Matter”, we explore the range of agentic behaviors, the challenges in benchmarking agents, and the ‘capability and reliability gap’, which creates risks when deploying…
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В гостях выпуска Виталий Кулиев - разработчик ИИ-проектов и автор YouTube-канала, который так и называется "Виталий Кулиев". С Виталием сначала я познакомился заочно через просмотр его роликов по ML и компьютерному железу, которое требуется для локального запуска опенсорсных моделей машинного обучения, а теперь и лично. Разговариваем о том, какие е…
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In today's episode, Michael is joined by Hikari Senju the Founder and CEO at Omneky. He starts by discussing how he built Omneky, an AI-Driven Marketing Platform. They dive into Hikari's approach to working with customers on brand strategy and content. They also talk about the increasing importance of brands in a digital, AI-driven world. Additiona…
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This paper presents a new architecture for large language models called DIFF Transformer. The paper argues that conventional Transformers over-allocate attention to irrelevant parts of the input, drowning out the signal needed for accurate output. DIFF Transformer tackles this issue by using a differential attention mechanism that subtracts two sof…
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The source is a blog post that describes the author's journey in exploring the potential of data pruning to improve the performance of AI models. They start by discussing the Minipile method, a technique for creating high-quality datasets by clustering and manually discarding low-quality content. The author then explores the concept of "foundationa…
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This paper details the authors' research journey to replicate OpenAI's "O1" language model, which is designed to solve complex reasoning tasks. The researchers document their process with detailed insights, hypotheses, and challenges encountered. They present a novel paradigm called "Journey Learning" that enables models to learn the complete explo…
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Let's get into the core processes of forward propagation and backpropagation in neural networks, which form the foundation of training these models. Forward propagation involves calculating the outputs of a neural network, starting with the input layer and moving towards the output layer. Backpropagation then calculates the gradients of the network…
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This research introduces MLE-bench, a benchmark for evaluating how well AI agents perform machine learning engineering tasks. The benchmark is comprised of 75 Kaggle competitions, chosen for their difficulty and representativeness of real-world ML engineering skills. Researchers evaluated several state-of-the-art language models on MLE-bench, findi…
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This systematic literature review investigates the use of convolutional neural networks (CNNs) for segmenting and classifying dental images. The review analyzes 45 studies that employed CNNs for various tasks, including tooth detection, periapical lesion detection, caries identification, and age and sex determination. The authors explore the differ…
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This research paper proposes an AI-driven diagnostic system for Temporomandibular Joint Disorders (TMD) using MRI images. The system employs a segmentation method to identify key anatomical structures like the temporal bone, temporomandibular joint (TMJ) disc, and condyle. Using these identified structures, the system utilizes a decision tree based…
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This research explores the potential for integrating ChatGPT and large language models (LLMs) into dental diagnostics and treatment. The authors investigate the use of these AI tools in various areas of dentistry, including diagnosis, treatment planning, patient education, and dental research. The study examines the benefits and limitations of LLMs…
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This research paper explores the link between temporomandibular disorder (TMD) and obstructive sleep apnea (OSA). The authors created a machine learning algorithm to predict the presence of OSA in TMD patients using multimodal data, including clinical characteristics, portable polysomnography, X-ray, and MRI. Their model achieved high accuracy, wit…
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This article describes a clinical validation study that investigates the effectiveness of a deep learning algorithm for detecting dental anomalies in intraoral radiographs. The algorithm is trained to detect six common anomaly types and is compared to the performance of dentists who evaluate the images without algorithmic assistance. The study util…
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This paper introduces a new variational autoencoder called VF-Net, specifically designed for dental point clouds. The paper highlights the limitations of existing point cloud models and how VF-Net overcomes them through a novel approach, ensuring a one-to-one correspondence between points in the input and output clouds. The paper also introduces a …
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This research paper focuses on the development of a deep learning model, Hierarchical Fully Convolutional Branch Transformer (H-FCBFormer), designed to automatically detect occlusal contacts in dental images. The model utilizes a combination of Vision Transformer and Fully Convolutional Network architectures and incorporates a Hierarchical Loss Fun…
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This research paper explores the use of deep learning to improve the accuracy of detecting and segmenting the mental foramen in dental orthopantomogram images. The authors compared the performance of various deep learning models, including U-Net, U-Net++, ResUNet, and LinkNet, using a dataset of 1000 panoramic radiographs. The study found that the …
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This article from AI Magazine explores the rise of knowledge graphs (KGs) as a powerful tool for organizing and integrating information. It delves into the history of KGs, highlighting their evolution from early semantic networks to the large-scale, complex systems we see today. The article contrasts key approaches to building and using KGs, includ…
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This research paper examines the relationship between the size of language models (LMs) and their propensity to hallucinate, which occurs when an LM generates information that is not present in its training data. The authors specifically focus on factual hallucinations, where a correct answer appears verbatim in the training set. To control for the…
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The paper proposes a new research area called Automated Design of Agentic Systems (ADAS), which aims to automatically create powerful AI systems, including inventing new components and combining them in novel ways. The authors introduce Meta Agent Search, an algorithm that uses a meta agent to iteratively program increasingly sophisticated agents b…
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This article from The Generalist examines Avra Capital, a new kind of venture fund founded by Anu Hariharan, a former Y Combinator executive. Avra’s unique approach combines a selective program for growth-stage entrepreneurs with a venture fund. The program provides founders with tactical masterclasses, taught by experienced CEOs, covering crucial …
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The provided sources describe a novel approach, Dynamic Diffusion Transformer (DyDiT), designed to improve the computational efficiency of Diffusion Transformer (DiT) models for image generation. DyDiT dynamically adapts its computational resources based on the varying complexities associated with different timesteps and spatial regions during imag…
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This research paper from Meta AI describes "Movie Gen," a series of foundational models capable of generating high-quality videos and synchronized audio. The paper discusses the models' capabilities, including text-to-video synthesis, video personalization, video editing, and audio generation. It outlines the architecture, training process, and eva…
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This article, written by the Head of Developer Community at SignalFire, a venture capital firm, provides a guide for startup founders on how to develop a successful developer relations strategy. The author emphasizes the importance of focusing on the "aha" moment, or the point at which developers experience the core value of a product. The article …
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The text explores the ability of Large Language Models (LLMs) to understand and reason about the knowledge states of different individuals. It does this by testing nine LLMs on the "Cheryl's Birthday Problem," a logic puzzle that requires the solver to deduce the correct birthday based on statements made by two people with varying levels of knowled…
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This briefing document analyzes the logic puzzle "Cheryl's Birthday," its sequel, and a related variant. The document explores the origins of the puzzle, presents the puzzle statement and solution, examines a common incorrect solution, and discusses subsequent iterations of the puzzle. Origins "Cheryl's Birthday" is a knowledge puzzle that gained w…
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This research paper proposes two methods for improving the performance of neural retrieval models by incorporating contextual information. The first method involves a training procedure that clusters documents into batches based on similarity, creating more challenging training examples. The second method introduces a new architecture that augments…
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This study investigates whether the reasoning abilities of large language models (LLMs) are still influenced by their origins in next-word prediction. The authors examine the performance of a new LLM from OpenAI called o1, which is specifically optimized for reasoning, on tasks that highlight the limitations of LLMs based on their autoregressive na…
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Let's explore multilayer perceptrons (MLPs), a type of deep neural network architecture. The text first discusses the limitations of linear models and how they struggle to capture complex non-linear relationships in data. It then introduces hidden layers as a solution, explaining how they allow MLPs to represent non-linear functions. The excerpt ex…
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This excerpt from Dive into Deep Learning explores the evolution of convolutional neural networks (CNNs) from basic multi-layered perceptrons (MLPs). It begins by showing the limitations of MLPs in processing high-dimensional data like images, particularly the large number of parameters required. The excerpt then introduces the concepts of translat…
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Let's get into the process of softmax regression, a method used in machine learning for classification problems where the goal is to predict which category a data point belongs to. It introduces the softmax function, which transforms outputs from a neural network into probabilities for each category, ensuring that they sum to 1. The cross-entropy l…
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This article from the Artificial Intelligence Review examines the opportunities and challenges of knowledge graphs, a type of graph data that accumulates and conveys knowledge of the real world. The authors discuss how knowledge graphs are used in various AI systems, such as recommender systems, question-answering systems, and information retrieval…
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This is a discussion of the original LoRA paper, which proposed a novel approach called Low-Rank Adaptation (LoRA) to make large language models (LLMs) more efficient for downstream tasks. LoRA avoids the computational and storage burden of traditional fine-tuning by freezing the pre-trained model weights and instead injects trainable low-rank matr…
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We discuss a research paper that proposes a new method called Adaptive Feature Transfer (AFT) for transferring knowledge from large foundation models to smaller, task-specific downstream models. AFT prioritizes transferring only the most relevant information from the pre-trained model to the downstream model, leading to improved performance and red…
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Let's talk about weight decay as a method of regularization to combat overfitting in machine learning models. Weight decay involves adding a penalty term to the loss function, which encourages the model to use smaller weights, thereby reducing the model's complexity and improving its ability to generalize to new data. The text introduces the mathem…
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Google Research has developed a new set of open models, known as DataGemma, that aim to ground large language models (LLMs) in real-world data using Google's Data Commons knowledge graph. DataGemma's primary goal is to improve the factuality and trustworthiness of LLMs by mitigating the risk of hallucinations, which occur when LLMs generate incorre…
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In this episode, we explore the concept of generalization in machine learning, emphasizing the challenge of training models that can accurately predict outcomes on unseen data. The text explains how overfitting occurs when models become too specialized to the training data, leading to poor performance on new data. It introduces regularization techn…
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This research paper ("ARES: An Automated Evaluation Framework for Retrieval-Augmented") introduces ARES, an Automated RAG Evaluation System, designed to assess the performance of Retrieval-Augmented Generation (RAG) systems. RAG systems are designed to use retrieved information to generate responses to user queries. ARES evaluates these systems bas…
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This episode is about linear regression, a fundamental statistical method used to predict a numerical value based on a set of features (input variables). It describes the key components of linear regression, including the model (a linear function that relates features to the target), the loss function (which quantifies the error between predictions…
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Prof. Tim Rocktäschel, AI researcher at UCL and Google DeepMind, talks about open-ended AI systems. These systems aim to keep learning and improving on their own, like evolution does in nature. Ad: Are you a hardcore ML engineer who wants to work for Daniel Cahn at SlingshotAI building AI for mental health? Give him an email! - danielc@slingshot.xy…
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In this episode, we discuss Meta's announcement of Movie Gen AI, a tool designed to generate highly realistic sound effects for film and media production. We explore how this new AI could impact the future of sound design in movies. My Podcast Course: https://podcaststudio.com/courses/ Get on the AI Box Waitlist: ⁠⁠https://AIBox.ai/⁠⁠ Join my AI Hu…
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We are constantly hearing about disillusionment as it relates to AI. Some of that is probably valid, but Mike Lewis, an AI architect from Cincinnati, has proven that he can consistently get LLM and GenAI apps to the point of real enterprise value (even with the Big Cos of the world). In this episode, Mike joins us to share some stories from the AI …
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Ben Goertzel discusses AGI development, transhumanism, and the potential societal impacts of superintelligent AI. He predicts human-level AGI by 2029 and argues that the transition to superintelligence could happen within a few years after. Goertzel explores the challenges of AI regulation, the limitations of current language models, and the need f…
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In this episode, we discuss the major new announcements OpenAI made at Dev Day 2024 regarding ChatGPT's upcoming features and capabilities. - Realtime API - Vision to the fine -tuning API- Prompt Caching - Model Distillation My Podcast Course: https://podcaststudio.com/courses/ Get on the AI Box Waitlist: ⁠⁠https://AIBox.ai/⁠⁠ Join my AI Hustle Com…
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