84. Eliano Marques - The (evolving) world of AI privacy and data security
Towards Data Science
05/19/21
•53m
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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.
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83. Rosie Campbell - Should all AI research be published?
May 12, 2021
•52m
When OpenAI developed its GPT-2 language model in early 2019, they initially chose not to publish the algorithm, owing to concerns over its potential for malicious use, as well as the need for the AI industry to experiment with new, more responsible publication practices that reflect the increasing power of modern AI systems.
This decision was controversial, and remains that way to some extent even today: AI researchers have historically enjoyed a culture of open publication and have defaulted to sharing their results and algorithms. But whatever your position may be on algorithms like GPT-2, it’s clear that at some point, if AI becomes arbitrarily flexible and powerful, there will be contexts in which limits on publication will be important for public safety.
The issue of publication norms in AI is complex, which is why it’s a topic worth exploring with people who have experience both as researchers, and as policy specialists — people like today’s Towards Data Science podcast guest, Rosie Campbell. Rosie is the Head of Safety Critical AI at Partnership on AI (PAI), a nonprofit that brings together startups, governments, and big tech companies like Google, Facebook, Microsoft and Amazon, to shape best practices, research, and public dialogue about AI’s benefits for people and society. Along with colleagues at PAI, Rosie recently finished putting together a white paper exploring the current hot debate over publication norms in AI research, and making recommendations for researchers, journals and institutions involved in AI research.
Next Episode
85. Brian Christian - The Alignment Problem
May 26, 2021
•66m
In 2016, OpenAI published a blog describing the results of one of their AI safety experiments. In it, they describe how an AI that was trained to maximize its score in a boat racing game ended up discovering a strange hack: rather than completing the race circuit as fast as it could, the AI learned that it could rack up an essentially unlimited number of bonus points by looping around a series of targets, in a process that required it to ram into obstacles, and even travel in the wrong direction through parts of the circuit.
This is a great example of the alignment problem: if we’re not extremely careful, we risk training AIs that find dangerously creative ways to optimize whatever thing we tell them to optimize for. So building safe AIs — AIs that are aligned with our values — involves finding ways to very clearly and correctly quantify what we want our AIs to do. That may sound like a simple task, but it isn’t: humans have struggled for centuries to define “good” metrics for things like economic health or human flourishing, with very little success.
Today’s episode of the podcast features Brian Christian — the bestselling author of several books related to the connection between humanity and computer science & AI. His most recent book, The Alignment Problem, explores the history of alignment research, and the technical and philosophical questions that we’ll have to answer if we’re ever going to safely outsource our reasoning to machines. Brian’s perspective on the alignment problem links together many of the themes we’ve explored on the podcast so far, from AI bias and ethics to existential risk from AI.
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