Georgia Institute of Technology

Ryan welcomes Dhruv Batra, co-founder and chief scientist at Yutori, to explore the future of AI agents, how AI usage is changing the way people interact with advertisements and the web as a whole, and the challenges that proactive AI agents may face when being integrated into workflows and personal internet use. Episode notes: Yutori is building AI agents that can reliably handle everyday digital tasks on your behalf on the web. Connect with Dhruv via his website. Congrats to the winner of today’s Populist badge, user Don Kirkby, who earned it with their answer to Find all references to an object in python. TRANSCRIPT See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Dhruv Batra

Associate Professor and Research Director

Georgia Institute of TechnologyMetaYutori

AI is set to transform the workforce — and the Georgia Institute of Technology’s new AI Makerspace is helping tens of thousands of students get ahead of the curve. In this episode of NVIDIA’s AI Podcast, host Noah Kravitz speaks with Arijit Raychowdhury, a professor and Steve W. Cedex school chair of electrical engineering at Georgia Tech’s college of engineering, about the supercomputer hub, which provides students with the computing resources to reinforce their coursework and gain hands-on experience with AI. Built in collaboration with NVIDIA, the AI Makerspace underscores Georgia Tech’s commitment to preparing students for an AI-driven future, while fostering collaboration with local schools and universities.

Arijit Raychowdhury

Professor and Steve W. Cedex School Chair of Electrical Engineering

Georgia Institute of Technology

Today we continue our coverage of ICLR 2023 joined by Dhruv Batra, an associate professor at Georgia Tech and research director of the Fundamental AI Research (FAIR) team at META. In our conversation, we discuss Dhruv’s work on the paper Emergence of Maps in the Memories of Blind Navigation Agents, which won an Outstanding Paper Award at the event. We explore navigation with multilayer LSTM and the question of whether embodiment is necessary for intelligence. We delve into the Embodiment Hypothesis and the progress being made in language models and caution on the responsible use of these models. We also discuss the history of AI and the importance of using the right data sets in training. The conversation explores the different meanings of "maps" across AI and cognitive science fields, Dhruv’s experience in navigating mapless systems, and the early discovery stages of memory representation and neural mechanisms. The complete show notes for this episode can be found at https://twimlai.com/go/629

Dhruv Batra

Associate Professor and Research Director

Georgia Institute of TechnologyMetaYutori

Summary Exploratory data analysis works best when the feedback loop is fast and iterative. This is easy to achieve when you are working on small datasets, but as they scale up beyond what can fit on a single machine those short iterations quickly become long and tedious. The Arkouda project is a Python interface built on top of the Chapel compiler to bring back those interactive speeds for exploratory analysis on horizontally scalable compute that parallelizes operations on large volumes of data. In this episode David Bader explains how the framework operates, the algorithms that are built into it to support complex analyses, and how you can start using it today. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their new managed database service you can launch a production ready MySQL, Postgres, or MongoDB cluster in minutes, with automated backups, 40 Gbps connections from your application hosts, and high throughput SSDs. Go to dataengineeringpodcast.com/linode today and get a $100 credit to launch a database, create a Kubernetes cluster, or take advantage of all of their other services. And don’t forget to thank them for their continued support of this show! Data stacks are becoming more and more complex. This brings infinite possibilities for data pipelines to break and a host of other issues, severely deteriorating the quality of the data and causing teams to lose trust. Sifflet solves this problem by acting as an overseeing layer to the data stack – observing data and ensuring it’s reliable from ingestion all the way to consumption. Whether the data is in transit or at rest, Sifflet can detect data quality anomalies, assess business impact, identify the root cause, and alert data teams’ on their preferred channels. All thanks to 50+ quality checks, extensive column-level lineage, and 20+ connectors across the Data Stack. In addition, data discovery is made easy through Sifflet’s information-rich data catalog with a powerful search engine and real-time health statuses. Listeners of the podcast will get $2000 to use as platform credits when signing up to use Sifflet. Sifflet also offers a 2-week free trial. Find out more at dataengineeringpodcast.com/sifflet today! RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their state-of-the-art reverse ETL pipelines enable you to send enriched data to any cloud tool. Sign up free… or just get the free t-shirt for being a listener of the Data Engineering Podcast at dataengineeringpodcast.com/rudder. Data teams are increasingly under pressure to deliver. According to a recent survey by Ascend.io, 95% in fact reported being at or over capacity. With 72% of data experts reporting demands on their team going up faster than they can hire, it’s no surprise they are increasingly turning to automation. In fact, while only 3.5% report having current investments in automation, 85% of data teams plan on investing in automation in the next 12 months. 85%!!! That’s where our friends at Ascend.io come in. The Ascend Data Automation Cloud provides a unified platform for data ingestion, transformation, orchestration, and observability. Ascend users love its declarative pipelines, powerful SDK, elegant UI, and extensible plug-in architecture, as well as its support for Python, SQL, Scala, and Java. Ascend automates workloads on Snowflake, Databricks, BigQuery, and open source Spark, and can be deployed in AWS, Azure, or GCP. Go to dataengineeringpodcast.com/ascend and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $5,000 when you become a customer. Your host is Tobias Macey and today I’m interviewing David Bader about Arkouda, a horizontally scalable parallel compute library for exploratory data analysis in Python Interview Introduction How did you get involved in the area of data management? Can you describe what Arkouda is and the story behind it? What are the main goals of the project? How does it address those goals? Who is the primary audience for Arkouda? What are some of the main points of friction that engineers and scientists encounter while conducting exploratory data analysis (EDA)? What kinds of behaviors are they engaging in during these exploration cycles? When data scientists run up against the limitations of their tools and environments how does that impact the work of data engineers/data platform owners? There have been a number of libraries/frameworks/utilities/etc. built to improve the experience and outcomes for EDA. What was missing that made Arkouda necessary/useful? Can you describe how Arkouda is implemented? What are some of the novel algorithms that you have had to design to support Arkouda’s objectives? How have the design/goals/scope of the project changed since you started working on it? How has the evolution of hardware capabilities impacted the set of processing algorithms that are viable for addressing considerations of scale? What are the relative factors of scale along space/time axes that you are optimizing for? What are some opportunities that are still unrealized for algorithmic optimizations to expand horizons for large-scale data manipulation? For teams/individuals who are working with Arkouda can you describe the implementation process and what the end-user workflow looks like? What are the most interesting, innovative, or unexpected ways that you have seen Arkouda used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Arkouda? When is Arkouda the wrong choice? What do you have planned for the future of Arkouda? Contact Info Website LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Arkouda NJIT == New Jersey Institute of Technology NumPy Pandas Podcast.__init__ Episode NetworkX Chapel Massive Graph Analytics Book Ray Podcast.__init__ Episode Dask Podcast Episode Bodo Podcast Episode Stinger Graph Analytics Bears-R-Us 0MQ Triangle Centrality Degree Centrality The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

David Bader

Georgia Institute of Technology

For years, prosthetic technology focused on form over function, on masking lost limbs, rather than agency and usability. But things are changing. Innovations in robotics are giving more people more options, with lower thresholds of entry—and lower price tags, too.  Tilly Lockey takes us through her journey with prosthetic arms. Brian Schulz gives some history of mechanical prosthetics, and what it means for people to reach embodiment with their devices. Tyler Hayes talks about the software that made advancements in assistive technology possible. Charlie Kemp discusses his work building universal robot interfaces, and how they can benefit everyone. And Henry and Jane Evans explain how robots can help a person reach beyond their body’s limitations. If you want to read up on some of our research on robotic prosthetics, you can check out all our bonus material over at redhat.com/commandlineheroes.   Follow along with the episode transcript.

Tilly Lockey

Prosthetics Advocate

Brian Schulz

Tyler Hayes

Tech Writer

The Ambient

Charlie Kemp

Professor / Co-founder

Georgia Institute of Technology

Henry Evans

In episode twelve of The Robot Brains Podcast we are joined by Charles Isbell Jr, professor and Dean of the College of Computing at the Georgia Institute of Technology. After starting his career as an industrial researcher at the legendary Bell Labs, and a long research career in Interactive and Human-Centric AI, Charles has more recently turned his attention to the major issues of ethics, fairness and diversity that are becoming ever more important as AI is being deployed in the real world. Speaking with Pieter Abbeel, Charles explains why researchers can find making ethical AI challenging, his fascinating keynote speech at NeurIPS, and how more diversity in academic admissions can help to improve AI research. Host: Pieter Abbeel | Executive Producers: Ricardo Reyes & Henry Tobias Jones | Audio Production: Kieron Matthew Banerji | Title Music: Alejandro Del Pozo Hosted on Acast. See acast.com/privacy for more information.

Charles Isbell

Dean of the College of Computing

Georgia Institute of TechnologyGeorgia Tech

Charles Isbell is the Dean of the College of Computing at Georgia Tech. Michael Littman is a computer scientist at Brown University. Please support this podcast by checking out our sponsors: – Athletic Greens: https://athleticgreens.com/lex and use code LEX to get 1 month of fish oil – Eight Sleep: https://www.eightsleep.com/lex and use code LEX to get special savings – MasterClass: https://masterclass.com/lex to get 2 for price of 1 – Cash App: https://cash.app/ and use code LexPodcast to get $10 EPISODE LINKS: Charles’s Twitter: https://twitter.com/isbellHFh Charles’s Website: https://www.cc.gatech.edu/~isbell/ Michael’s Twitter: https://twitter.com/mlittmancs Michael’s Website: https://www.littmania.com/ Michael’s YouTube: https://www.youtube.com/user/mlittman PODCAST INFO: Podcast website: https://lexfridman.com/podcast Apple Podcasts: https://apple.co/2lwqZIr Spotify: https://spoti.fi/2nEwCF8 RSS: https://lexfridman.com/feed/podcast/ YouTube Full Episodes: https://youtube.com/lexfridman YouTube Clips: https://youtube.com/lexclips SUPPORT & CONNECT: – Check out the sponsors above, it’s the best way to support this podcast – Support on Patreon: https://www.patreon.com/lexfridman – Twitter: https://twitter.com/lexfridman – Instagram: https://www.instagram.com/lexfridman – LinkedIn: https://www.linkedin.com/in/lexfridman – Facebook: https://www.facebook.com/LexFridmanPage – Medium: https://medium.com/@lexfridman OUTLINE: Here’s the timestamps for the episode. On some podcast players you should be able to click the timestamp to jump to that time. (00:00) – Introduction (07:51) – Is machine learning just statistics? (12:14) – NeurIPS vs ICML (14:30) – Data is more important than algorithm (20:14) – The role of hardship in education (28:57) – How Charles and Michael met (33:30) – Key to success: never be satisfied (36:47) – Bell Labs (48:15) – Teaching machine learning (58:25) – Westworld and Ex Machina (1:06:24) – Simulation (1:13:14) – The college experience in the times of COVID (1:41:52) – Advice for young people (1:48:44) – How to learn to program (2:00:07) – Friendship

Charles Isbell

Dean of the College of Computing

Georgia Institute of TechnologyGeorgia Tech

Michael Littman

Professor of Computer Science

Brown University

As we continue our NeurIPS 2020 series, we’re joined by friend-of-the-show Charles Isbell, Dean, John P. Imlay, Jr. Chair, and professor at the Georgia Tech College of Computing. This year Charles gave an Invited Talk at this year’s conference, You Can’t Escape Hyperparameters and Latent Variables: Machine Learning as a Software Engineering Enterprise. In our conversation, we explore the success of the Georgia Tech Online Masters program in CS, which now has over 11k students enrolled, and the importance of making the education accessible to as many people as possible. We spend quite a bit speaking about the impact machine learning is beginning to have on the world, and how we should move from thinking of ourselves as compiler hackers, and begin to see the possibilities and opportunities that have been ignored. We also touch on the fallout from Timnit Gebru being “resignated” and the importance of having diverse voices and different perspectives “in the room,” and what the future holds for machine learning as a discipline. The complete show notes for this episode can be found at twimlai.com/go/441.

Charles Isbell

Dean of the College of Computing

Georgia Institute of TechnologyGeorgia Tech

Charles Isbell is the Dean of the College of Computing at Georgia Tech. Please support this podcast by checking out our sponsors: – Neuro: https://www.getneuro.com and use code LEX to get 15% off – Decoding Digital: https://appdirect.com/decoding-digital – MasterClass: https://masterclass.com/lex to get 15% off annual sub – Cash App: https://cash.app/ and use code LexPodcast to get $10 EPISODE LINKS: Charles’s Twitter: https://twitter.com/isbellHFh Charles’s Website: https://www.cc.gatech.edu/~isbell/ PODCAST INFO: Podcast website: https://lexfridman.com/podcast Apple Podcasts: https://apple.co/2lwqZIr Spotify: https://spoti.fi/2nEwCF8 RSS: https://lexfridman.com/feed/podcast/ YouTube Full Episodes: https://youtube.com/lexfridman YouTube Clips: https://youtube.com/lexclips SUPPORT & CONNECT: – Check out the sponsors above, it’s the best way to support this podcast – Support on Patreon: https://www.patreon.com/lexfridman – Twitter: https://twitter.com/lexfridman – Instagram: https://www.instagram.com/lexfridman – LinkedIn: https://www.linkedin.com/in/lexfridman – Facebook: https://www.facebook.com/LexFridmanPage – Medium: https://medium.com/@lexfridman OUTLINE: Here’s the timestamps for the episode. On some podcast players you should be able to click the timestamp to jump to that time. (00:00) – Introduction (07:16) – Top 3 movies of all time (13:26) – People are easily predictable (19:08) – Breaking out of our bubbles (30:54) – Interactive AI (37:26) – Lifelong machine learning (45:53) – Faculty hiring (53:27) – University rankings (1:00:55) – Science communicators (1:10:20) – Hip hop (1:19:20) – Funk (1:20:44) – Computing (1:36:35) – Race (1:52:40) – Cop story (2:01:01) – Racial tensions (2:10:23) – MLK vs Malcolm X (2:13:44) – Will human civilization destroy itself? (2:18:14) – Fear of death and the passing of time

Charles Isbell

Dean of the College of Computing

Georgia Institute of TechnologyGeorgia Tech

Producer extraordinaire Noel Brown visited Moogfest in 2017 and got a chance to talk with Alexander Lerch about musical analysis. And how can can analysis lead to generative music? Learn more about your ad-choices at https://www.iheartpodcastnetwork.comSee omnystudio.com/listener for privacy information.

Alexander Lerch

Researcher / Professor

Georgia Institute of Technology

My guest this time is Charles Isbell, Jr., Professor and Senior Associate Dean in the College of Computing at Georgia Institute of Technology. Charles and I go back a bit… in fact he’s the first AI researcher I ever met. His research focus is what he calls “interactive artificial intelligence,” a discipline of AI focused specifically on the interactions between AIs and humans. We explore what this means and some of the interesting research results in this field. One part of this discussion I found particularly interesting was the intersection between his AI research and marketing and behavioral economics. Beyond his research, Charles is well known in the ML and AI worlds for his popular Machine Learning course sequence on Udacity, which he teaches with Brown University professor Michael Littman, and for the Online Master’s of Science in Computer Science program that he helped launch at Georgia Tech. We also spend quite a bit of time talking about what’s really missing in machine learning education and how to make it more accessible. The notes for this show can be found at twimlai.com/talk/4.

Charles Isbell

Dean of the College of Computing

Georgia Institute of TechnologyGeorgia Tech