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
99. Margaret Mitchell - (Practical) AI ethics [not-audio_url] [/not-audio_url]

Duration: 45:43
Bias gets a bad rap in machine learning. And yet, the whole point of a machine learning model is that it biases certain inputs to certain outputs — a picture of a cat to a label that says “cat”, for example. Machine lear…
98. Mike Tung - Are knowledge graphs AI’s next big thing? [not-audio_url] [/not-audio_url]

Duration: 48:56
As impressive as they are, language models like GPT-3 and BERT all have the same problem: they’re trained on reams of internet data to imitate human writing. And human writing is often wrong, biased, or both, which means…
97. Anthony Habayeb - The present and future of AI regulation [not-audio_url] [/not-audio_url]

Duration: 49:31
Corporate governance of AI doesn’t sound like a sexy topic, but it’s rapidly becoming one of the most important challenges for big companies that rely on machine learning models to deliver value for their customers. More…
96. Jan Leike - AI alignment at OpenAI [not-audio_url] [/not-audio_url]

Duration: 1:05:17
The more powerful our AIs become, the more we’ll have to ensure that they’re doing exactly what we want. If we don’t, we risk building AIs that use dangerously creative solutions that have side-effects that could be unde…
95. Francesca Rossi - Thinking, fast and slow: AI edition [not-audio_url] [/not-audio_url]

Duration: 46:42
The recent success of large transformer models in AI raises new questions about the limits of current strategies: can we expect deep learning, reinforcement learning and other prosaic AI techniques to get us all the way…
94. Divya Siddarth - Are we thinking about AI wrong? [not-audio_url] [/not-audio_url]

Duration: 1:02:44
AI research is often framed as a kind of human-versus-machine rivalry that will inevitably lead to the defeat — and even wholesale replacement of — human beings by artificial superintelligences that have their own sense…
92. Daniel Filan - Peering into neural nets for AI safety [not-audio_url] [/not-audio_url]

Duration: 1:06:02
Many AI researchers think it’s going to be hard to design AI systems that continue to remain safe as AI capabilities increase. We’ve seen already on the podcast that the field of AI alignment has emerged to tackle this p…
91. Peter Gao - Self-driving cars: Past, present and future [not-audio_url] [/not-audio_url]

Duration: 1:01:21
Cruise is a self-driving car startup founded in 2013 — at a time when most people thought of self-driving cars as the stuff of science fiction. And yet, just three years later, the company was acquired by GM for over a b…
90. Jeffrey Ding - China’s AI ambitions and why they matter [not-audio_url] [/not-audio_url]

Duration: 49:07
There are a lot of reasons to pay attention to China’s AI initiatives. Some are purely technological: Chinese companies are producing increasingly high-quality AI research, and they’re poised to become even more importan…

«12345»