84. Eliano Marques - The (evolving) world of AI privacy and data security

84. Eliano Marques - The (evolving) world of AI privacy and data security

Author: The TDS team May 19, 2021 Duration: 53:38

We all value privacy, but most of us would struggle to define it. And there’s a good reason for that: the way we think about privacy is shaped by the technology we use. As new technologies emerge, which allow us to trade data for services, or pay for privacy in different forms, our expectations shift and privacy standards evolve. That shifting landscape makes privacy a moving target.

The challenge of understanding and enforcing privacy standards isn’t novel, but it’s taken on a new importance given the rapid progress of AI in recent years. Data that would have been useless just a decade ago — unstructured text data and many types of images come to mind — are now a treasure trove of value, for example. Should companies have the right to use data they originally collected at a time when its value was limited, when it no longer is? Do companies have an obligation to provide maximum privacy without charging their customers directly for it? Privacy in AI is as much a philosophical question as a technical one, and to discuss it, I was joined by Eliano Marques, Executive VP of Data and AI at Protegrity, a company that specializes in privacy and data protection for large companies. Eliano has worked in data privacy for the last decade.


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

Towards Data Science
Podcast Episodes
110. Alex Turner - Will powerful AIs tend to seek power? [not-audio_url] [/not-audio_url]

Duration: 46:57
Today’s episode is somewhat special, because we’re going to be talking about what might be the first solid quantitative study of the power-seeking tendencies that we can expect advanced AI systems to have in the future.…
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.…