Get it on Google Play

128. David Hirko - AI observability and data as a cybersecurity weakness

128. David Hirko - AI observability and data as a cybersecurity weakness

Author: The TDS team September 28, 2022 Duration: 49:02
Towards Data Science
Technology

Imagine you’re a big hedge fund, and you want to go out and buy yourself some data. Data is really valuable for you — it’s literally going to shape your investment decisions and determine your outcomes.

But the moment you receive your data, a cold chill runs down your spine: how do you know your data supplier gave you the data they said they would? From your perspective, you’re staring down 100,000 rows in a spreadsheet, with no way to tell if half of them were made up — or maybe more for that matter.

This might seem like an obvious problem in hindsight, but it’s one most of us haven’t even thought of. We tend to assume that data is data, and that 100,000 rows in a spreadsheet is 100,000 legitimate samples.

The challenge of making sure you’re dealing with high-quality data, or at least that you have the data you think you do, is called data observability, and it’s surprisingly difficult to solve for at scale. In fact, there are now entire companies that specialize in exactly that — one of which is Zectonal, whose co-founder Dave Hirko will be joining us for today’s episode of the podcast.

Dave has spent his career understanding how to evaluate and monitor data at massive scale. He did that first at AWS in the early days of cloud computing, and now through Zectonal, where he’s working on strategies that allow companies to detect issues with their data — whether they’re caused by intentional data poisoning, or unintentional data quality problems. Dave joined me to talk about data observability, data as a new vector for cyberattacks, and the future of enterprise data management on this episode of the TDS podcast.

***

Intro music:

- Artist: Ron Gelinas

- Track Title: Daybreak Chill Blend (original mix)

- Link to Track: https://youtu.be/d8Y2sKIgFWc

***

Chapters:

  • 0:00 Intro
  • 3:00 What is data observability?
  • 10:45 “Funny business” with data providers
  • 12:50 Data supply chains
  • 16:50 Various cybersecurity implications
  • 20:30 Deep data inspection
  • 27:20 Observed direction of change
  • 34:00 Steps the average person can take
  • 41:15 Challenges with GDPR transitions
  • 48:45 Wrap-up

While the active production of Towards Data Science has concluded, its archive remains a vital resource. Created by The TDS team, this collection captures a specific moment in the rapid evolution of data science and artificial intelligence. Each conversation pulls you directly into the room with leading researchers and practitioners who were shaping the tools and theories of their time. The discussions are not abstract lectures; they are grounded explorations of real-world problems, ethical dilemmas, and technical challenges that defined the field's trajectory. You'll hear experts dissect the implications of their work, from algorithmic fairness to the practicalities of deploying models at scale. This podcast served as a forum for nuanced debate, where complex ideas were unpacked with clarity and depth. Listening now offers a unique historical perspective, a chance to understand the foundational conversations that continue to influence where technology is headed next. The archive of Towards Data Science stands as a substantive record of insight, preserving the voices and questions from the forefront of a digital revolution.
Author: Language: en-us Episodes: 50

Official website RSS
youtubelinkedinmedium
Towards Data Science
Podcast Episodes
109. Danijar Hafner - Gaming our way to AGI [not-audio_url] [/not-audio_url]

Duration: 50:06
Until recently, AI systems have been narrow — they’ve only been able to perform the specific tasks that they were explicitly trained for. And while narrow systems are clearly useful, the holy grain of AI is to build more…
108. Last Week In AI — 2021: The (full) year in review [not-audio_url] [/not-audio_url]

Duration: 50:21
2021 has been a wild ride in many ways, but its wildest features might actually be AI-related. We’ve seen major advances in everything from language modeling to multi-modal learning, open-ended learning and even AI align…
107. Kevin Hu - Data observability and why it matters [not-audio_url] [/not-audio_url]

Duration: 49:56
Imagine for a minute that you’re running a profitable business, and that part of your sales strategy is to send the occasional mass email to people who’ve signed up to be on your mailing list. For a while, this approach…
106. Yang Gao - Sample-efficient AI [not-audio_url] [/not-audio_url]

Duration: 49:53
Historically, AI systems have been slow learners. For example, a computer vision model often needs to see tens of thousands of hand-written digits before it can tell a 1 apart from a 3. Even game-playing AIs like DeepMin…
105. Yannic Kilcher - A 10,000-foot view of AI [not-audio_url] [/not-audio_url]

Duration: 1:03:04
There once was a time when AI researchers could expect to read every new paper published in the field on the arXiv, but today, that’s no longer the case. The recent explosion of research activity in AI has turned keeping…
104. Ken Stanley - AI without objectives [not-audio_url] [/not-audio_url]

Duration: 1:06:27
Today, most machine learning algorithms use the same paradigm: set an objective, and train an agent, a neural net, or a classical model to perform well against that objective. That approach has given good results: these…
102. Wendy Foster - AI ethics as a user experience challenge [not-audio_url] [/not-audio_url]

Duration: 44:36
AI ethics is often treated as a dry, abstract academic subject. It doesn’t have the kinds of consistent, unifying principles that you might expect from a quantitative discipline like computer science or physics. But some…
101. Ayanna Howard - AI and the trust problem [not-audio_url] [/not-audio_url]

Duration: 53:15
Over the last two years, the capabilities of AI systems have exploded. AlphaFold2, MuZero, CLIP, DALLE, GPT-3 and many other models have extended the reach of AI to new problem classes. There’s a lot to be excited about.…
100. Max Jaderberg - Open-ended learning at DeepMind [not-audio_url] [/not-audio_url]

Duration: 45:25
On the face of it, there’s no obvious limit to the reinforcement learning paradigm: you put an agent in an environment and reward it for taking good actions until it masters a task. And by last year, RL had achieved some…

«12345»