83. Rosie Campbell - Should all AI research be published?
Towards Data Science
05/12/21
•52m
About
Comments
Featured In
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.
Previous Episode
82. Jakob Foerster - The high cost of automated weapons
May 5, 2021
•54m
Automated weapons mean fewer casualties, faster reaction times, and more precise strikes. They’re a clear win for any country that deploys them. You can see the appeal.
But they’re also a classic prisoner’s dilemma. Once many nations have deployed them, humans no longer have to be persuaded to march into combat, and the barrier to starting a conflict drops significantly.
The real risks that come from automated weapons systems like drones aren’t always the obvious ones. Many of them take the form of second-order effects — the knock-on consequences that come from setting up a world where multiple countries have large automated forces. But what can we do about them? That’s the question we’ll be taking on during this episode of the podcast with Jakob Foerster, an early pioneer in multi-agent reinforcement learning, and incoming faculty member at the University of Toronto. Jakob has been involved in the debate over weaponized drone automation for some time, and recently wrote an open letter to German politicians urging them to consider the risks associated with the deployment of this technology.
Next Episode
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.
If you like this episode you’ll love
Promoted