Understanding the limitations of AI is crucial for enterprise success
The discussion also:
Touches on the role, evolution, and adoption of AI agents, emphasizing their growing integration into systems, while addressing key safeguarding measures to ensure AI agents can accurately use data to reason effectively.
Explores how Abnormal Security utilizes AI to detect and protect against cybersecurity threats, and how Dan and his team are leveraging AI to drive compounding productivity within their organization.
Connect with Dan Shiebler on LinkedIn and learn more about Abnormal Security.
See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
717: Overcoming Adversaries with A.I. for Cybersecurity, with Dr. Dan Shiebler
Dr. Dan Shiebler, Head of ML at Abnormal Security, joins Jon Krohn this week and unveils the intricacies of cybercrime detection and email protection, and the role of AI in future challenges.
This episode is brought to you by Grafbase, the unified data layer, by ODSC, the Open Data Science Conference, and by Modelbit, for deploying models in seconds. Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.
In this episode you will learn:
• The heuristic and “intermediate” ML models that they develop at Abnormal Security [07:08]
• How Dan uses LLMs at Abnormal Security [15:46]
• How false negatives are individually the biggest classification error to avoid in cybersecurity [20:49]
• How head-to-head competitor analysis helps refine models [34:34]
• Resilient ML in cybersecurity [38:36]
• Abnormal Security’s routine for updating their models [52:37]
• AI's impact on the urban world [1:09:57]
• How to stay updated in data science and AI [1:13:46]
Additional materials: www.superdatascience.com/717
630: Resilient Machine Learning
Jon Krohn sits with Dr. Dan Shiebler at the Open Data Science Conference (ODSC) to dive into the critical components of building resilient machine learning.
Additional materials: www.superdatascience.com/630
Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.
451: Translating PhD Research into ML Applications
Dan Shiebler joins us to discuss his category theory Ph.D. program, his full-time job at Twitter, and how the two crossover and combine in his overall data work.
In this episode you will learn:
Dan’s neuroscience undergrad and MATLAB [4:12]
Dan’s Ph.D. timeline and research [14:01]
How to start a Ph.D. while working full time [22:45]
Dan’s work at TrueMotion and label data [30:39]
Dan’s title and role at Twitter [39:15]
Specific projects at Twitter [44:09]
What skills someone should bring to a Twitter job interview [52:06]
What machine learning approaches will be important in the future? [1:00:38]
Additional materials: www.superdatascience.com/451
345: Machine Learning At Twitter
I speak with Dan Shiebler who works as a machine learning engineer at Twitter Cortex and at the same time, is doing a Ph.D. on applying category theory in machine learning. We discuss his work at Twitter, the importance of academics, and the future of machine learning.
In this episode you will learn:
What is great about Twitter [5:31]
Dan’s Ph.D. program [9:25]
Dan’s work at Twitter [18:07]
Dan at DSGO 2020 [35:16]
LinkedIn Q&A [40:25]
Dan’s advice [1:03:58]
Additional materials: www.superdatascience.com/345
059: Changing Human Behaviour Through a Driving App
In this episode of the SuperDataScience Podcast, I chat with Data Scientist at True Motion Dan Shiebler. You will learn about the True Motion app and how it works, you will listen to great insights about how sensor data from your phone can be collected and analyzed and how data science can be applied to that, you will also understand how a behavior can change through an app, and be able to explore data science passions outside of a job.
If you enjoyed this episode, check out show notes, resources, and more at www.superdatascience.com/59