Data Debt in Machine Learning with D. Sculley – #574

Today we kick things off with a conversation with D. Sculley, a director on the Google Brain team. Many listeners of today’s show will know D. from his work on the paper, The Hidden Technical Debt in Machine Learning Systems, and of course, the infamous diagram. D. has recently translated the idea of technical debt […]

AI and Machine Learning for developers

To watch this keynote interpreted in American Sign Language (ASL), please click here → https://goo.gle/38aUqYX Learn what Google is doing in the AI and machine learning space, from developer APIs to state-of-the-art research. Resource: TensorFlow website → https://goo.gle/36VewFG Speakers: Laurence Moroney, Alex Spinelli, Diane Wu Watch more: All Google I/O 2022 Sessions → https://goo.gle/IO22_AllSessions ML/AI […]

SingularityNET: Ambassador Program Town Hall Meeting 2 | 17th of May 2022

This is a recording of the second meeting of the SingularityNET Community Ambassador Program. Feedback form: https://docs.google.com/forms/d/e/1FAIpQLSeDKydOQULBnzN2Y-WbJJuE9d3DQ15HGzphP_LoI6X_KjEjsw/viewform?usp=sf_link Slides: https://docs.google.com/presentation/d/1mczZxocs4GbK6FQVhaopplKYHLVsEa5GwjoEQvRA_oI/edit?usp=sharing Meeting notes: https://miro.com/app/board/uXjVO0WVUBA=/ First meeting: https://youtu.be/4g_rUNNBQjI If you are interested to help out, reach out or join on discord: https://discord.gg/snet Video shorts can be submitted through WeTransfer, to peter.elfrink@singularitynet.io This Friday at 18UTC there will be […]

AI for Enterprise Decisioning at Scale with Rob Walker – #573

Today we’re joined by Rob Walker, vp of decisioning & analytics and gm of one-to-one customer engagement at Pegasystems. Rob, who you might know from his previous appearances on the podcast, joins us to discuss his work on AI and ML in the context of customer engagement and decisioning, the various problems that need to […]

A developer's guide to responsible AI review processes

From startups to corporations across industries, organizations are creating AI principles and ethics review processes to complement technical approaches to developing ML and AI responsibly. Listen to emerging socio-technical practices, ML tools, and lessons learned from Google’s ethics review teams who support developers as they build products. Resource: TensorFlow website → https://goo.gle/3KejoUZ Speakers: Madeleine Elish, […]

Apply responsible AI principles when building remote sensing datasets

Learn how to apply responsible AI frameworks while making decisions related to datasets and coding with large-scale social benefit in mind. Walk through a version of the developer tutorial, “Hello [Dynamic] World,” and learn how to use a new dataset using Google Earth Engine data responsibly. Presented in collaboration with Google’s Geo for Good team. […]

Adding Machine Learning to your developer toolbox

Machine learning can be a challenging field, as there are a lot of new concepts to learn from the perspectives of mobile, web, and back-end developers. Find out how easy it is to start using machine learning in your app with TensorFlow tools. Resource: TensorFlow website → https://goo.gle/3KejoUZ Speaker: Gus Martins Watch more: All Google […]

Product fairness testing for Machine Learning developers

Product fairness testing is an essential step for developers as they build ML models responsibly. Discover the key steps you should take to conduct holistic product fairness tests, in addition to understanding both the technical and user-centric societal perspectives you should incorporate into your process. Walk away understanding the end-to-end journey of assessing a dataset […]

Two Minute Papers: DeepMind’s New AI Learns From Humans! 🤖

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper “Learning Robust Real-Time Cultural Transmission without Human Data” is available here: https://www.deepmind.com/research/publications/2022/Learning-Robust-Real-Time-Cultural-Transmission-without-Human-Data https://sites.google.com/view/dm-cgi ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: – https://www.patreon.com/TwoMinutePapers – https://www.youtube.com/channel/UCbfYPyITQ-7l4upoX8nvctg/join 🙏 We would like to […]

Transfer learning in JavaScript with TensorFlow.js

Learn how to make your very own version of the popular Teachable Machine website. Use TensorFlow.js live in the web browser to train a custom machine learning model to then recognize any object you show it, made possible through a technique known as transfer learning. Building on that concept, you can quickly create new models […]

A journey to protect the Great Barrier Reef using Machine Learning

Explore how Google teamed up with CSIRO to enhance monitoring efforts of harmful species on the Great Barrier Reef. Through a Kaggle competition, machine learning developers collaborated to train ML models identifying crown-of-thorns starfish outbreaks degrading the coral reef ecosystem. This project was designed to protect the Great Barrier Reef for generations to come. Subscribe […]

Keras and Google Tensor Processing Units (TPUs) (13.5)

Tensor Processing Units (TPUs) are a Google technology that can speed neural network training and inference much like GPUs. This video shows how to use a TPU with Keras. Code for This Video: https://github.com/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_13_05_tpu.ipynb Course Homepage: https://sites.wustl.edu/jeffheaton/t81-558/ Follow Me/Subscribe: https://www.youtube.com/user/HeatonResearch https://github.com/jeffheaton Tweets by jeffheaton Support Me on Patreon: https://www.patreon.com/jeffheaton Source of this machine learning/AI Video

Studying Machine Intelligence with Been Kim – #571

Today we continue our ICLR coverage joined by Been Kim, a staff research scientist at Google Brain, and an ICLR 2022 Invited Speaker. Been, whose research has historically been focused on interpretability in machine learning, delivered the keynote Beyond interpretability: developing a language to shape our relationships with AI, which explores the need to study […]

Welcome to DeepMind: Embarking on one of the greatest adventures in scientific history

At DeepMind, we’re embarking on one of the greatest adventures in scientific history. Our mission is to solve intelligence, to advance science and benefit humanity. To make this possible, we bring together scientists, designers, engineers, ethicists, and more, to research and build safe artificial intelligence systems that can help transform society for the better. By […]

Advances in Neural Compression with Auke Wiggers – #570

Today we’re joined by Auke Wiggers, an AI research scientist at Qualcomm. In our conversation with Auke, we discuss his team’s recent research on data compression using generative models. We discuss the relationship between historical compression research and the current trend of neural compression, and the benefit of neural codecs, which learn to compress data […]

Mixture-of-Experts and Trends in Large-Scale Language Modeling with Irwan Bello – #569

Today we’re joined by Irwan Bello, formerly a research scientist at Google Brain, and now on the founding team at a stealth AI startup. We begin our conversation with an exploration of Irwan’s recent paper, Designing Effective Sparse Expert Models, which acts as a design guide for building sparse large language model architectures. We discuss […]

Tech Special Docker for Data Science @ TWiML Online Meetup EMEA 4 March 2019 1080p

**SUBSCRIBE AND TURN ON NOTIFICATIONS** **twimlai.com** This video is a recap of our March 2019 EMEA TWiML Online Meetup. In this month’s community segment, we discuss the upcoming EMEA Meetup in April, the progress of the v3 Deep Learning Part 1 study group, the preparation for the v3 Deep Learning Part 2 course and study […]

Daring to DAIR: Distributed AI Research with Timnit Gebru – Talk 568

Today we’re joined by friend of the show Timnit Gebru, the founder and executive director of DAIR, the Distributed Artificial Intelligence Research Institute. In our conversation with Timnit, we discuss her journey to create DAIR, its goals, and some of the challenges she’s faced along the way. We start is the obvious place, Timnit being […]

Calvin Seward Interview – Deep Learning for Warehouse Operations

This week, I’m happy to bring you my interview with Calvin Seward, a research scientist with Berlin, Germany based Zalando. While our American listeners might not know the name Zalando, they’re one of the largest e-commerce companies in Europe with a focus on fashion and shoes. Calvin is a research scientist there, while also pursuing […]

#74 Dr. ANDREW LAMPINEN – Symbolic behaviour in AI [UNPLUGGED]

Please note that in this interview Dr. Lampinen was expressing his personal opinions and they do not necessarily represent those of DeepMind. Patreon: https://www.patreon.com/mlst Discord: https://discord.gg/ESrGqhf5CB Pod version: https://anchor.fm/machinelearningstreettalk/episodes/74-Dr–ANDREW-LAMPINEN—Symbolic-behaviour-in-AI-UNPLUGGED-e1h6far Dr. Andrew Lampinen is a Senior Research Scientist at DeepMind, and he thinks that symbols are subjective in the relativistic sense. Dr. Lampinen completed his PhD […]

Hierarchical and Continual RL with Doina Precup – #567

Today we’re joined by Doina Precup, a research team lead at DeepMind Montreal, and a professor at McGill University. In our conversation with Doina, we discuss her recent research interests, including her work in hierarchical reinforcement learning, with the goal being agents learning abstract representations, especially over time. We also explore her work on reward […]

Google's New AI can do Anything (PaLM AI)

The first 1,000 people to use the link or my code “ainews” will get a 1 month free trial of Skillshare: https://skl.sh/ainews04221 Google has been working on its relatively secret Pathways AI project for almost a year now, and now they’ve shown its first results in the form of PaLM AI, which is the biggest […]