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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 ...
 
This is the audio podcast for the ML Street Talk YouTube channel at https://www.youtube.com/channel/UCMLtBahI5DMrt0NPvDSoIRQ Thanks for checking out Machine Learning Street Talk! Join in our discussion of the most exciting discussions around the latest and greatest in Machine Learning and Artificial Intelligence! This is quite a technical podcast where we interview authors of ML research papers and discuss topics such as AI Ethics and ML DevOps. This channel is managed by Yannic Kilcher, Dr. ...
 
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.
 
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.
 
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 ...
 
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.
 
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.
 
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 ...
 
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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Today we’re joined by Pieter Abbeel, a Professor at UC Berkeley, co-Director of the Berkeley AI Research Lab (BAIR), as well as Co-founder and Chief Scientist at Covariant. In our conversation with Pieter, we cover a ton of ground, starting with the specific goals and tasks of his work at Covariant, the shift in needs for industrial AI application …
 
In today's show we are joined by Francois Chollet, I have been inspired by Francois ever since I read his Deep Learning with Python book and started using the Keras library which he invented many, many years ago. Francois has a clarity of thought that I've never seen in any other human being! He has extremely interesting views on intelligence as ge…
 
A look at how Nimrod and the team at Nanit are building smart baby monitor systems, from data collection to model deployment and production monitoring.---Nimrod Shabtay is a Senior Computer Vision Algorithm Developer at Nanit, a New York-based company that's developing better baby monitoring devices. Connect with Nimrod:LinkedIn: https://www.linked…
 
В очередном выпуске беседа с Ольгой Перепелкиной Deep Learning Product Manager компании Intel. Для того, чтобы машинное обучение было эффективным - ему нужны данные и чем больше, тем лучше. Но чем быстрее развивается искусственный интеллект, тем жестче становятся законы о защите персональных данных. Проблема? Да, проблема. Но где есть проблема, там…
 
Today we have Apple’s Senior Data Scientist, Farhan Baluch as we deep dive into Data Analytics. Farhan has over 10 years of combined academic and industrial experience in machine learning. He likes to combine strategy with tactics and enjoy operating both at the micro and macro levels of data science efforts. As a neuroscientist, he worked on data …
 
Dave Lacey takes Daniel and Chris on a journey that connects the user interfaces that we already know - TensorFlow and PyTorch - with the layers that connect to the underlying hardware. Along the way, we learn about Poplar Graph Framework Software. If you are the type of practitioner who values ‘under the hood’ knowledge, then this is the episode f…
 
Ishan is a Research Scientist at Facebook AI. Much of his recent research work revolves around self-supervised learning is known for this works like including SwAV and PIRL. He completed his Ph.D. from CMU with Martial Hebert and Abhinav Gupta. and his thesis was titled “Visual Learning with Minimal Human Supervision” Ishan's homepage: http://imisr…
 
Using artificial intelligence and machine learning in a product or database is traditionally difficult because it involves a lot of manual setup, specialized training, and a clear understanding of the various ML models and algorithms. You need to develop the right ML model for your data, train the model, evaluate it, optimize it, analyze it for out…
 
Radek Osmulski is a fully self-taught machine learning engineer. After getting tired of his corporate job, he taught himself programming and started a new career as a Ruby on Rails developer. He then set out to learn machine learning. Since then, he's been a Fast AI International Fellow, become a Kaggle Master, and is now an AI Data Engineer on the…
 
Today we’re joined by Abhishek Thakur, a machine learning engineer at Hugging Face, and the world’s first Quadruple Kaggle Grandmaster! In our conversation with Abhishek, we explore his Kaggle journey, including how his approach to competitions has evolved over time, what resources he used to prepare for his transition to a full-time practitioner, …
 
Today we’re joined by Saqib Shaikh, a Software Engineer at Microsoft, and the lead for the Seeing AI Project. In our conversation with Saqib, we explore the Seeing AI app, an app “that narrates the world around you.” We discuss the various technologies and use cases for the app, and how it has evolved since the inception of the project, how the tec…
 
Today we’re joined by Jelani Nelson, a professor in the Theory Group at UC Berkeley. In our conversation with Jelani, we explore his research in computational theory, where he focuses on building streaming and sketching algorithms, random projections, and dimensionality reduction. We discuss how Jelani thinks about the balance between the innovatio…
 
Chris shares some of the incredible work and innovations behind deep space exploration at NASA JPL and reflects on the past, present, and future of machine learning.---Chris Mattman is the Chief Technology and Innovation Officer at NASA Jet Propulsion Laboratory, where he focuses on organizational innovation through technology. He's worked on space…
 
Nikola Mrkšić, CEO & Co-Founder of PolyAI, takes Daniel and Chris on a deep dive into conversational AI, describing the underlying technologies, and teaching them about the next generation of voice assistants that will be capable of handling true human-level conversations. It’s an episode you’ll be talking about for a long time! Discuss on Changelo…
 
We are joined by José Murillo, Chief Analytics Officer at Grupo Financiero Banorte, Mexico's second largest financial group and also the most profitable one pre-pandemic. José had a long tenure at Mexico’s Central Bank, where he worked at the research department. Around 7 years ago, he joined Banorte and established the Analytics Business Unit. His…
 
Today we’re joined by Stevie Chancellor, an Assistant Professor in the Department of Computer Science and Engineering at the University of Minnesota. In our conversation with Stevie, we explore her work at the intersection of human-centered computing, machine learning, and high-risk mental illness behaviors. We discuss how her background in HCC hel…
 
Dr. Christian Szegedy from Google Research is a deep learning heavyweight. He invented adversarial examples, one of the first object detection algorithms, the inceptionnet architecture, and co-invented batchnorm. He thinks that if you bet on computers and software in 1990 you would have been as right as if you bet on AI now. But he thinks that we h…
 
Linda is Product Lead at FAIRE and has in the past interned at Snap Inc., Microsft, BAIN, and graduate from Harvard University. She has a tremendous amount of experience in Product Management and openly shares her insights through newsletters here https://www.productlessons.xyz/ for people to get started with it. You can reach out to her and follow…
 
In this episode, we’re joined by Dataiku’s Director of Data Science, Conor Jensen. In our conversation, we explore the panel he lead at TWIMLcon “AI Operationalization: Where the AI Rubber Hits the Road for the Enterprise,” discussing the ML journey of each panelist’s company, and where Dataiku fits in the equation. The complete show notes for this…
 
In this episode, we’re joined by Diego Oppenheimer, Founder and CEO of Algorithmia. In our conversation, we discuss Algorithmia’s involvement with TWIMLcon, as well as an exploration of the results of their recently conducted survey on the state of the AI market. The complete show notes for this episode can be found at twimlai.com/go/470.…
 
From legged locomotion to autonomous driving, Vladlen explains how simulation and abstraction help us understand embodied intelligence.---Vladlen Koltun is the Chief Scientist for Intelligent Systems at Intel, where he leads an international lab of researchers working in machine learning, robotics, computer vision, computational science, and relate…
 
Aarti is a machine learning engineer at Snorkel AI. Prior to that, she worked closely with Andrew Ng in various capacities - at AI Fund helping build ML companies from scratch internally, as well as investing in ML companies, as a machine learning engineer at his startup Landing AI, as head TA for his deep learning class at Stanford University (CS2…
 
Chris has the privilege of talking with Stanford Professor Margot Gerritsen, who co-leads the Women in Data Science (WiDS) Worldwide Initiative. This is a conversation that everyone should listen to. Professor Gerritsen’s profound insights into how we can all help the women in our lives succeed - in data science and in life - is a ‘must listen’ epi…
 
Creation Labs is helping bring Europe 1 step closer to fully autonomous long haul trucking. They have developed an AI Driver Assistance System (AIDAS) that retrofits to any commercial vehicle, starting with VW Crafters and MAN TGE trucks. Their system uses camera hardware mounted to the vehicle to capture video data that is processed with computer …
 
In this episode, we’re joined by Santiago Giraldo, Director Of Product Marketing for Data Engineering & Machine Learning at Cloudera. In our conversation, we discuss Cloudera’s talks at TWIMLcon, as well as their various research efforts from their Fast Forward Labs arm. The complete show notes for this episode can be found at twimlai.com/sponsorse…
 
In this episode, we’re joined by Paul van der Boor, Senior Director of Data Science at Prosus, to discuss his TWIMLcon experience and how they’re using ML platforms to manage machine learning at a global scale. The complete show notes for this episode can be found at twimlai.com/sponsorseries.由Sam Charrington
 
What kind of challenges have you noticed while working with ML applications in production, that most newbies don't know as students or fresh graduates? And what ML applications production pipeline look like? Watch the full podcast here: https://youtu.be/VWJXiszQpTU Also check-out these talks on all available podcast platforms: https://jayshah.buzzs…
 
Dominik shares the story and principles behind Vega and Vega-Lite, and explains how visualization and machine learning help each other.---Dominik is a co-author of Vega-Lite, a high-level visualization grammar for building interactive plots. He's also a professor at the Human-Computer Interaction Institute Institute at Carnegie Mellon University an…
 
Today we’re joined by Emily M. Bender, Professor at the University of Washington, and AI Researcher, Margaret Mitchell. Emily and Meg, as well as Timnit Gebru and Angelina McMillan-Major, are co-authors on the paper On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜. As most of you undoubtedly know by now, there has been much c…
 
Today we are joined by Doug Laney, Data & Analytics Innovation Fellow at West Monroe and Author of "Infonomics". Nearly two decades ago, Mr. Laney originated the field of Infonomics, developing methods to quantify information's economic value and apply asset management practices to information assets. He authored the book "Infonomics: Monetizing, M…
 
The race is on, we are on a collective mission to understand and create artificial general intelligence. Dr. Tom Zahavy, a Research Scientist at DeepMind thinks that reinforcement learning is the most general learning framework that we have today, and in his opinion it could lead to artificial general intelligence. He thinks there are no tasks whic…
 
David Sweet, author of “Tuning Up: From A/B testing to Bayesian optimization”, introduces Dan and Chris to system tuning, and takes them from A/B testing to response surface methodology, contextual bandit, and finally bayesian optimization. Along the way, we get fascinating insights into recommender systems and high-frequency trading! Discuss on Ch…
 
Rodrigo Rivera is a machine learning researcher at the Advanced Data Analytics in Science and Engineering Group at Skoltech and technical director of Samsung Next. He's previously been in data science and research leadership roles at companies all around the world including Rocket Internet and Philip-Morris. Learn more about Rodrigo: https://rodrig…
 
Today we’re joined by Abhishek Gupta, a PhD Student at UC Berkeley. Abhishek, a member of the BAIR Lab, joined us to talk about his recent robotics and reinforcement learning research and interests, which focus on applying RL to real-world robotics applications. We explore the concept of reward supervision, and how to get robots to learn these rewa…
 
Watch the full podcast with Natasha here: https://youtu.be/8XpCnmvq49s Also check-out these talks on all available podcast platforms: https://jayshah.buzzsprout.com About the Host: Jay is a PhD student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis. Jay Shah: https://www.linkedin.com/in/shahjay…
 
Watch the full podcast with Linda here: https://youtu.be/0Yrt2lBzsNk Also check-out these talks on all available podcast platforms: https://jayshah.buzzsprout.com About the Host: Jay is a PhD student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis. Jay Shah: https://www.linkedin.com/in/shahjay22…
 
How Cade got access to the stories behind some of the biggest advancements in AI, and the dynamic playing out between leaders at companies like Google, Microsoft, and Facebook.Cade Metz is a New York Times reporter covering artificial intelligence, driverless cars, robotics, virtual reality, and other emerging areas. Previously, he was a senior sta…
 
Today we’re joined by David Carmona, General Manager of Artificial Intelligence & Innovation at Microsoft. In our conversation with David, we focus on his work on AI at Scale, an initiative focused on the change in the ways people are developing AI, driven in large part by the emergence of massive models. We explore David’s thoughts about the progr…
 
We have Kevin Buckley, Founder and CEO of Torrey Pines Law Group, PC in San Diego, USA. He joins us for an insightful chat on Life Sciences and AI. He created Torrey Pines Law Group when he returned home to San Diego in 2013. Throughout his career, he has successfully represented pharmaceutical, biopharmaceutical, medical device, biotechnology, spe…
 
First episode in a series we are doing on ML DevOps. Starting with the thing which nobody seems to be talking about enough, security! We chat with cyber security expert Andy Smith about threat modelling and trust boundaries for an ML DevOps system. Intro [00:00:00] ML DevOps - a security perspective [00:00:50] Threat Modelling [00:03:03] Adversaria…
 
Dan Jeffries is the chief technical evangelist at Pachyderm, a leading data science platform. He's a prominent writer and speaker on all things related to the future. He's been in software for over two decades, many of those at Redhat, and is the founder of the AI Infrastructure Alliance and Practical AI Ethics. Learn more about Dan: https://twitte…
 
Today we’re joined by Melanie Mitchell, Davis Professor at the Santa Fe Institute and author of Artificial Intelligence: A Guide for Thinking Humans. While Melanie has had a long career with a myriad of research interests, we focus on a few, complex systems and the understanding of intelligence, complexity, and her recent work on getting AI systems…
 
Vectors are the foundational mathematical building blocks of Machine Learning. Machine Learning models must transform input data into vectors to perform their operations, creating what is known as a vector embedding. Since data is not stored in vector form, an ML application must perform significant work to transform data in different formats into …
 
Christoph Molnar is one of the main people to know in the space of interpretable ML. In 2018 he released the first version of his incredible online book, interpretable machine learning. Interpretability is often a deciding factor when a machine learning (ML) model is used in a product, a decision process, or in research. Interpretability methods ca…
 
Watch the full podcast here: https://youtu.be/8XpCnmvq49s Natasha Jaques is currently a Research Scientist at @Google Brain and a post-doc fellow at @UC Berkeley, where her research interests are in designing multi-agent RL algorithms while focusing on social reinforcement learning, that can improve generalization, coordination between agents, and …
 
Learn why traditional home security systems tend to fail and how Dave’s love of tinkering and deep learning are helping him and the team at Deep Sentinel avoid those same pitfalls. He also discusses the importance of combatting racial bias by designing race-agnostic systems and what their approach is to solving that problem.Dave Selinger is the co-…
 
Today we’re joined by Adriana Kovashka, an Assistant Professor at the University of Pittsburgh. In our conversation with Adriana, we explore her visual commonsense research, and how it intersects with her background in media studies. We discuss the idea of shortcuts, or faults in visual question answering data sets that appear in many SOTA results,…
 
We have Calvin Ng, Head of Equality and Linking at Equifax and Patrick Choy, Senior Consultant at Talent Insights Group. Today we are focusing on teams. Everything from hiring, how to structure them, retention and more useful tips. Patrick shared some tips for data scientists who want to differentiate themselves when going through recruitment proce…
 
Our Slack community wanted to hear about AI-driven drug discovery, and we listened. Abraham Heifets from Atomwise joins us for a fascinating deep dive into the intersection of deep learning models and molecule binding. He describes how these methods work and how they are beginning to help create drugs for “undruggable” diseases! Discuss on Changelo…
 
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