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Making artificial intelligence practical, productive, and 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, etc). The focus is on productive implementations and real-world scenarios that are accessible to everyone. If you want to keep up with the latest advances in AI, while k ...
 
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 ...
 
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 ...
 
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 ...
 
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.
 
This is the audio podcast for the ML Street Talk YouTube channel at https://www.youtube.com/c/MachineLearningStreetTalk Thanks for checking us out! We think that scientists and engineers are the heroes of our generation. Each week we have a hard-hitting discussion with the leading thinkers in the AI space. Street Talk is unabashedly technical and non-commercial, so you will hear no annoying pitches. Corporate- and MBA-speak is banned on street talk, "data product", "digital transformation" a ...
 
En esta serie de Podcast titulado Machine Learning en Español se discutirán temas relacionado a Machine Learning (aprendizaje maquina), Data Science (ciencia de datos), Big Data, Artificial Intelligence (inteligencia artificial), Business Intelligence (inteligencia de negocios) y Deep learning entre otros. Su anfitrión Gustavo Lujan, quien es un Data Scientist trabajando para Intel, compartirá su experiencia y tendencias en este fascinante mundo de Machine Learning.
 
Machine Learning with Coffee is a podcast where we are going to be sharing ideas about Machine Learning and related areas such as: artificial intelligence, business intelligence, business analytics, data mining and Big data. The objective is to promote a healthy discussion on the current state of this fascinating world of Machine Learning. We will be sharing our experience, sharing tricks, talking about latest developments and interviewing experts, all these on a very laid back, friendly man ...
 
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.
 
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.
 
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 ...
 
Smart machines based upon the principles of artificial intelligence and machine learning are now prevalent in our everyday life. For example, artificially intelligent systems recognize our voices, sort our pictures, make purchasing suggestions, and can automatically fly planes and drive cars. In this podcast series, we examine such questions such as: How do these devices work? Where do they come from? And how can we make them even smarter and more human-like? These are the questions that wil ...
 
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Organisations work hard on creating and building data science capabilities and models that will provide a real positive impact for their users. However, the feedback we’ve had from the Data Futurology audience shows there is a struggle to get ROI from their AI. So what do you do when your models and AI efforts are not living up to their promises? T…
 
AI is being used to transform the most personal instrument we have, our voice, into something that can be “played.” This is fascinating in and of itself, but Yotam Mann from Never Before Heard Sounds is doing so much more! In this episode, he describes how he is using neural nets to process audio in real time for musicians and how AI is poised to c…
 
Today we close out our 2021 ICML series joined by Lina Montoya, a postdoctoral researcher at UNC Chapel Hill. In our conversation with Lina, who was an invited speaker at the Neglected Assumptions in Causal Inference Workshop, we explored her work applying Optimal Dynamic Treatment (ODT) to understand which kinds of individuals respond best to spec…
 
Xavier shares his experience deploying healthcare models, augmenting primary care with AI, the challenges of "ground truth" in medicine, and robustness in ML.---Xavier Amatriain is co-founder and CTO of Curai, an ML-based primary care chat system. Previously, he was VP of Engineering at Quora, and Research/Engineering Director at Neflix, where he s…
 
В гостях Максим Купрашевич - руководитель команды машинного обучения в Layer, SberDevices. Максим рассказал о некоторых очень интересных применениях компьютерного зрения в решении как бизнес-задач, так и в общественно-значимых некоммерческих инициативах. Поговорили о том, как искусственный интеллект помогает распознавать интересные пользователю объ…
 
Since its beginning in the 1950s, the field of artificial intelligence has vacillated between periods of optimistic predictions and massive investment and periods of disappointment, loss of confidence, and reduced funding. Even with today’s seemingly fast pace of AI breakthroughs, the development of long-promised technologies such as self-driving c…
 
Governments, consumers, and companies across the world are becoming more aware and attentive to the risks and causes of climate change. From recycling to using solar power, people are looking for ways to reduce their carbon footprint. Markets like the financial sector, governments, and consulting are looking for ways to understand climate data to m…
 
Today we continue our ICML series joined by Gustavo Malkomes, a research engineer at Intel via their recent acquisition of SigOpt. In our conversation with Gustavo, we explore his paper Beyond the Pareto Efficient Frontier: Constraint Active Search for Multiobjective Experimental Design, which focuses on a novel algorithmic solution for the iterati…
 
If you are trying to choose what skills you should build as part of your career, no single answer fits everyone. It depends on your strengths and ambitions. Your strengths are the things you are naturally good at. If you're not sure what your strengths are, then think about the things that people come and ask you about. When someone asks you how yo…
 
Inspired by a recent article from Erik Bernhardsson titled “Building a data team at a mid-stage startup: a short story”, Chris and Daniel discuss all things AI/data team building. They share some stories from their experiences kick starting AI efforts at various organizations and weight the pro and cons of things like centralized data management, p…
 
Today we kick off our ICML coverage joined by Virginia Smith, an assistant professor in the Machine Learning Department at Carnegie Mellon University. In our conversation with Virginia, we explore her work on cross-device federated learning applications, including where the distributed learning aspects of FL are relative to the privacy techniques. …
 
Today we’re joined by Errol Koolmeister, the head of AI foundation at H&M Group. In our conversation with Errol, we explore H&M’s AI journey, including its wide adoption across the company in 2016, and the various use cases in which it's deployed like fashion forecasting and pricing algorithms. We discuss Errol’s first steps in taking on the challe…
 
This 86th episode of Learning Machines 101 discusses the problem of assigning probabilities to a possibly infinite set of outcomes in a space-time continuum which characterizes our physical world. Such a set is called an “environmental event”. The machine learning algorithm uses information about the frequency of environmental events to support lea…
 
In part 2 of our interview with Abhi Seth, he tells us that part of his role is to really drive scale for analytics across the ten businesses within TE. We learn how the adoption of analytics is enabled throughout the organization and a big component of that is driving, understanding and building capability within the Centre of Excellence (COE). He…
 
9 out of 10 AI projects don’t end up creating value in production. Why? At least partly because these projects utilize unstable models and drifting data. In this episode, Roey from BeyondMinds gives us some insights on how to filter garbage input, detect risky output, and generally develop more robust AI systems. Discuss on Changelog News Join Chan…
 
Today we’re joined by Stefano Soatto, VP of AI applications science at AWS and a professor of computer science at UCLA. Our conversation with Stefano centers on recent research of his called Graceful AI, which focuses on how to make trained systems evolve gracefully. We discuss the broader motivation for this research and the potential dangers or n…
 
Spence shares his experience creating a product around human-in-the-loop machine translation, and explains how machine translation has evolved over the years.---Spence Green is co-founder and CEO of Lilt, an AI-powered language translation platform. Lilt combines human translators and machine translation in order to produce high-quality translation…
 
Today we’re joined by Suchi Saria, the founder and CEO of Bayesian Health, the John C. Malone associate professor of computer science, statistics, and health policy, and the director of the machine learning and healthcare lab at Johns Hopkins University. Suchi shares a bit about her journey to working in the intersection of machine learning and hea…
 
Abhi Seth started his journey as an industrial engineer which he describes as his passion, and then found his way into research. His PhD is in mechanical engineering, and human-computer interactions, which he says was kind of a double major PhD. Abhi started working with virtual reality applications for mechanical engineering and says back then it …
 
How did we get from symbolic AI to deep learning models that help you write code (i.e., GitHub and OpenAI’s new Copilot)? That’s what Chris and Daniel discuss in this episode about the history and future of deep learning (with some help from an article recently published in ACM and written by the luminaries of deep learning). Discuss on Changelog N…
 
Today we’re joined by a friend of the show Jeff Gehlhaar, VP of technology and the head of AI software platforms at Qualcomm. In our conversation with Jeff, we cover a ton of ground, starting with a bit of exploration around ML compilers, what they are, and their role in solving issues of parallelism. We also dig into the latest additions to the Sn…
 
It has been over three decades since the statistical revolution overtook AI by a storm and over two decades since deep learning (DL) helped usher the latest resurgence of artificial intelligence (AI). However, the disappointing progress in conversational agents, NLU, and self-driving cars, has made it clear that progress has not lived up to the pro…
 
Today we continue our AI in Innovation series joined by Dan Bohus, senior principal researcher at Microsoft Research, and Siddhartha Sen, a principal researcher at Microsoft Research. In this conversation, we use a pair of research projects, Maia Chess and Situated Interaction, to springboard us into a conversation about the evolution of human-AI i…
 
Roger and DJ share some of the history behind data science as we know it today, and reflect on their experiences working on California's COVID-19 response.---Roger Magoulas is Senior Director of Data Strategy at Astronomer, where he works on data infrastructure, analytics, and community development. Previously, he was VP of Research at O'Reilly and…
 
Всегда полезно иметь доступ к сообществу профессионалов и любителей темы, которая входит в круг ваших интересов. Профессионалы и любители данных, машинного обучения, искусственного интеллекта и так далее в русскоязычном сегменте имеют крутейшее сообщество, известное как Open Data Science или ODS. В этом выпуске поговорили с Петром Ермаковым - основ…
 
In part two of the Ultimate Uplevel for Business-focused Data Professionals, with Lillian Pierson, we learn about her data action strategy plan. She created a data evaluation use case workbox with 31 use cases broken down by industry and function and tells us that if you are innovative then it's actually everything you could possibly need. You don'…
 
Today we’re joined by Julieta Martinez, a senior research scientist at recently announced startup Waabi. Julieta was a keynote speaker at the recent LatinX in AI workshop at CVPR, and our conversation focuses on her talk “What do Large-Scale Visual Search and Neural Network Compression have in Common,” which shows that multiple ideas from large-sca…
 
Today we continue our CVPR 2021 coverage joined by Claire Monteleoni, an associate professor at the University of Colorado Boulder. We cover quite a bit of ground in our conversation with Claire, including her journey down the path from environmental activist to one of the leading climate informatics researchers in the world. We explore her current…
 
Amelia and Filip give insights into the recommender systems powering Pandora, from developing models to balancing effectiveness and efficiency in production.---Amelia Nybakke is a Software Engineer at Pandora. Her team is responsible for the production system that serves models to listeners.Filip Korzeniowski is a Senior Scientist at Pandora workin…
 
We are joined by Lillian Pierson, a very well-known figure in the data space. She started her own business as a data consultant around 2013 and eventually started helping others become better data leaders and entrepreneurs. Currently, she has helped train and educate over a million data professionals! Lillian is not only a great data business mento…
 
Today we kick off our CVPR coverage joined by Amir Habibian, a senior staff engineer manager at Qualcomm Technologies. In our conversation with Amir, whose research primarily focuses on video perception, we discuss a few papers they presented at the event. We explore the paper Skip-Convolutions for Efficient Video Processing, which looks at trainin…
 
Today we’re joined by Noam Slonim, the principal investigator of Project Debater at IBM Research. In our conversation with Noam, we explore the history of Project Debater, the first AI system that can “debate” humans on complex topics. We also dig into the evolution of the project, which is the culmination of 7 years and over 50 research papers, an…
 
From Apache TVM to OctoML, Luis gives direct insight into the world of ML hardware optimization, and where systems optimization is heading.---Luis Ceze is co-founder and CEO of OctoML, co-author of the Apache TVM Project, and Professor of Computer Science and Engineering at the University of Washington. His research focuses on the intersection of c…
 
In part 2 of this episode, Nitish shares his thoughts on how to ensure that the data scientists expertise are well utilized. He says their key metric last year for the entire team was to hit exactly one number - 100 active users for their data platform with only 50 data scientists! He also says organizations should focus on enabling self service ac…
 
Pinecone is the first vector database for machine learning. Edo Liberty explains to Chris how vector similarity search works, and its advantages over traditional database approaches for machine learning. It enables one to search through billions of vector embeddings for similar matches, in milliseconds, and Pinecone is a managed service that puts t…
 
Today we’re joined by Madhur Behl, an Assistant Professor in the department of computer science at the University of Virginia. In our conversation with Madhur, we explore the super interesting work he’s doing at the intersection of autonomous driving, ML/AI, and Motorsports, where he’s teaching self-driving cars how to drive in an agile manner. We …
 
Dr. Ishan Misra is a Research Scientist at Facebook AI Research where he works on Computer Vision and Machine Learning. His main research interest is reducing the need for human supervision, and indeed, human knowledge in visual learning systems. He finished his PhD at the Robotics Institute at Carnegie Mellon. He has done stints at Microsoft Resea…
 
Today we continue our AI Innovation series joined by Microsoft’s Chief Scientific Officer, Eric Horvitz. In our conversation with Eric, we explore his tenure as AAAI president and his focus on the future of AI and its ethical implications, the scope of the study on the topic, and how drastically the AI and machine learning landscape has changed sin…
 
Matthew explains how combining machine learning and computational biology can provide mainstream medicine with better diagnostics and insights.---Matthew Davis is Head of AI at Invitae, the largest and fastest growing genetic testing company in the world. His research includes bioinformatics, computational biology, NLP, reinforcement learning, and …
 
When Global Head of Engineering, Nitish Mathew looks back at his journey over the last four years with Afterpay, he describes it as a phenomenal ride and a tremendous learning journey. They have had to reinvent themselves three times in a very real way, getting rid of their first and second platform and building with the view of not assuming that t…
 
William Falcon wants AI practitioners to spend more time on model development, and less time on engineering. PyTorch Lightning is a lightweight PyTorch wrapper for high-performance AI research that lets you train on multiple-GPUs, TPUs, CPUs and even in 16-bit precision without changing your code! In this episode, we dig deep into Lightning, how it…
 
Today we’re joined by Parvez Ahammad, head of data science applied research at LinkedIn. In our conversation, Parvez shares his interesting take on organizing principles for his organization, starting with how data science teams are broadly organized at LinkedIn. We explore how they ensure time investments on long-term projects are managed, how to …
 
Давно уже хотелось сделать выпуск про рекомендательные системы и, вот, наконец-то выпала такая возможность. В гостях Виталий Моисеев - руководитель группы качества рекомендаций видео в Яндексе, с которым получился очень интересный диалог о том, как вообще работают автоматизированные рекомендации, скрываются ли за ними нейросети, что такое коллабора…
 
Today we’re joined Katherine J. Kuchenbecker, director at the Max Planck Institute for Intelligent Systems and of the haptic intelligence department. In our conversation, we explore Katherine’s research interests, which lie at the intersection of haptics (physical interaction with the world) and machine learning, introducing us to the concept of “h…
 
Clem explains the virtuous cycles behind the creation and success of Hugging Face, and shares his thoughts on where NLP is heading.---Clément Delangue is co-founder and CEO of Hugging Face, the AI community building the future. Hugging Face started as an open source NLP library and has quickly grown into a commercial product used by over 5,000 comp…
 
In Part two of this episode, Nonna talks about the role culture plays in a data-driven organization and the importance of role accountability and the support of leadership. One of the things that Nonna strongly subscribes to is the RMIT vision to support their students in every possible way including a new initiative called the Data Innovation Hub …
 
Chris and Daniel sit down to chat about some exciting new AI developments including wav2vec-u (an unsupervised speech recognition model) and meta-learning (a new book about “How To Learn Deep Learning And Thrive In The Digital World”). Along the way they discuss engineering skills for AI developers and strategies for launching AI initiatives in est…
 
Today we continue our coverage of the AWS ML Summit joined by Chris Fregly, a principal developer advocate at AWS, and Antje Barth, a senior developer advocate at AWS. In our conversation with Chris and Antje, we explore their roles as community builders prior to, and since, joining AWS, as well as their recently released book Data Science on AWS. …
 
Mark Saroufim is the author of an article entitled “Machine Learning: The Great Stagnation”. Mark is a PyTorch Partner Engineer with Facebook AI. He has spent his entire career developing machine learning and artificial intelligence products. Before joining Facebook to do PyTorch engineering with external partners, Mark was a Machine Learning Engin…
 
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