Educating a data-literate generation
Dan sits down with guests Mark Daniel Ward and Katie Sanders from The Data Mine at Purdue University to explore how higher education is evolving to meet the demands of the AI-driven workforce. They share how their program blends interdisciplinary learning, corporate partnerships, and real-world data science projects to better prepare students across 160+ majors. From AI chatbots to agricultural forecasting, they discuss the power of living-learning communities, how the data mine model is spreading to other institutions and what it reveals about the future of education, workforce development, and applied AI training.
Featuring:
Mark Daniel Ward – LinkedIn
Katie Sanders – LinkedIn
Daniel Whitenack – Website, GitHub, X
Links:
The Data Mine
Sponsors:
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Register for upcoming webinars here!
AI Trends 2024: Reinforcement Learning in the Age of LLMs with Kamyar Azizzadenesheli - #670
Today we’re joined by Kamyar Azizzadenesheli, a staff researcher at Nvidia, to continue our AI Trends 2024 series. In our conversation, Kamyar updates us on the latest developments in reinforcement learning (RL), and how the RL community is taking advantage of the abstract reasoning abilities of large language models (LLMs). Kamyar shares his insights on how LLMs are pushing RL performance forward in a variety of applications, such as ALOHA, a robot that can learn to fold clothes, and Voyager, an RL agent that uses GPT-4 to outperform prior systems at playing Minecraft. We also explore the progress being made in assessing and addressing the risks of RL-based decision-making in domains such as finance, healthcare, and agriculture. Finally, we discuss the future of deep reinforcement learning, Kamyar’s top predictions for the field, and how greater compute capabilities will be critical in achieving general intelligence.
The complete show notes for this episode can be found at twimlai.com/go/670.
AI competitions & cloud resources
In this special episode, we interview some of the sponsors and teams from a recent case competition organized by Purdue University, Microsoft, INFORMS, and SIL International. 170+ teams from across the US and Canada participated in the competition, which challenged students to create AI-driven systems to caption images in three languages (Thai, Kyrgyz, and Hausa).
Featuring:
Matthew Lanham – Website, X
Mark Tabladillo – LinkedIn, X
Daniel Whitenack – Website, GitHub, X
Show Notes:
Purdue University’s Krannert School of Business
Master the basics of Azure: AI Fundamentals
Azure Architecture Center
SIL International
The bloom-captioning dataset
Books
“Applied Machine Learning and AI for Engineers” by Jeff Prosise
Upcoming Events:
Register for upcoming webinars here!
in-toto, with Santiago Torres-Arias
When is it safe to run software? When is it safe to drink orange juice? Are we a better judge of one or the other? Santiago Torres-Arias is an Assistant Professor at Purdue University, the team lead of the in-toto project, and a contributor to The Update Framework. He joins Craig to talk security in both physical and software supply chains.
Do you have something cool to share? Some questions? Let us know:
web: kubernetespodcast.com
mail: kubernetespodcast@google.com
twitter: @kubernetespod
Chatter of the week Don't Forget The Lyrics
Gettin' Jiggy Wit It
Explained on Genius
Will Smith on Top Gear
The Oscars thing (CW: violence, cuss words that Will Smith didn't used to have to rap to sell records)
He's The Greatest Dancer by Sister Sledge; written by Bernard Edwards and Nile Rodgers of Chic
News of the week New Cisco Intersight Kubernetes features
Red Hat OpenShift v4.10
ChaosNative acquired by Harness
Azure PlayFab launches Thundernetes Episode 26, with Cyril Tovena and Mark Mandel
Hacker News commentary
Weave GitOps v2022-03
Qumulo for Kubernetes
SpectroCloud raises $40m
Pinterest: 99% to 99.9% SLO, high performance control plane
Uber: Avoiding CPU throttling in a containerized environment
Links from the interview in-toto
The Update Framework
Purdue University Elmore Family School of Electrical and Computer Engineering
Purdue Boilermakers
Open Source Software Senior Design Projects
NYU Tandon School of Engineering
Justin Cappos
PolyPasswordHasher
Episode 155, with Priya Wadhwa
apt-secure for Debian packages
A keysigning and a signed PGP key
Farm to table attestation
Potato tracking
An example of E. coli in lettuce
in-toto record
Project Trebuchet: How SolarWinds is Using Open Source to Secure Their Supply Chain in the Wake of the Sunburst Hack by Trevor Rosen, Solarwinds
Reflections on Trusting Trust by Ken Thompson
Secure Publication of Datadog Agent Integrations with TUF and in-toto
US Executive Order on Improving the Nation's Cybersecurity
Readout of White House Meeting on Software Security
sigstore in-toto is the second most used format for sigstore
SPIFFE
SLSA
in-toto moves to incubation in the CNCF
CFSSL
Math rock Covet: "falkor"
TTNG: +3 Awesomeness Repels Water
Bird of the Year The kea
Breaking a police car
Santiago Torres-Arias on Twitter and at badhomb.re
Trends in Deep Reinforcement Learning with Kamyar Azizzadenesheli - #560
Today we’re joined by Kamyar Azizzadenesheli, an assistant professor at Purdue University, to close out our AI Rewind 2021 series! In this conversation, we focused on all things deep reinforcement learning, starting with a general overview of the direction of the field, and though it might seem to be slowing, thats just a product of the light being shined constantly on the CV and NLP spaces. We dig into themes like the convergence of RL methodology with both robotics and control theory, as well as a few trends that Kamyar sees over the horizon, such as self-supervised learning approaches in RL. We also talk through Kamyar’s predictions for RL in 2022 and beyond. This was a fun conversation, and I encourage you to look through all the great resources that Kamyar shared on the show notes page at twimlai.com/go/560!
Deep Reinforcement Learning Primer and Research Frontiers with Kamyar Azizzadenesheli - TWiML Talk #177
Today we’re joined by Kamyar Azizzadenesheli, PhD student at the University of California, Irvine, who joins us to review the core elements of RL, along with a pair of his RL-related papers: “Efficient Exploration through Bayesian Deep Q-Networks” and “Sample-Efficient Deep RL with Generative Adversarial Tree Search.”
To skip the Deep Reinforcement Learning primer conversation and jump to the research discussion, skip to the 34:30 mark of the episode. Show notes at https://twimlai.com/talk/177