73. David Roodman - Economic history and the road to the singularity
Towards Data Science
03/03/21
•68m
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There’s a minor mystery in economics that may suggest that things are about to get really, really weird for humanity.
And that mystery is this: many economic models predict that, at some point, human economic output will become infinite.
Now, infinities really don’t tend to happen in the real world. But when they’re predicted by otherwise sound theories, they tend to indicate a point at which the assumptions of these theories break down in some fundamental way. Often, that’s because of things like phase transitions: when gases condense or liquids evaporate, some of their thermodynamic parameters go to infinity — not because anything “infinite” is really happening, but because the equations that define a gas cease to apply when those gases become liquids and vice-versa.
So how should we think of economic models that tell us that human economic output will one day reach infinity? Is it reasonable to interpret them as predicting a phase transition in the human economy — and if so, what might that transition look like? These are hard questions to answer, but they’re questions that my guest David Roodman, a Senior Advisor at Open Philanthropy, has thought about a lot.
David has centered his investigations on what he considers to be a plausible culprit for a potential economic phase transition: the rise of transformative AI technology. His work explores a powerful way to think about how, and even when, transformative AI may change how the economy works in a fundamental way.
Previous Episode
72. Margot Gerritsen - Does AI have to be understandable to be ethical?
February 24, 2021
•82m
As AI systems have become more ubiquitous, people have begun to pay more attention to their ethical implications. Those implications are potentially enormous: Google’s search algorithm and Twitter’s recommendation system each have the ability to meaningfully sway public opinion on just about any issue. As a result, Google and Twitter’s choices have an outsized impact — not only on their immediate user base, but on society in general.
That kind of power comes with risk of intentional misuse (for example, Twitter might choose to boost tweets that express views aligned with their preferred policies). But while intentional misuse is an important issue, equally challenging is the problem of avoiding unintentionally bad outputs from AI systems.
Unintentionally bad AIs can lead to various biases that make algorithms perform better for some people than for others, or more generally to systems that are optimizing for things we actually don’t want in the long run. For example, platforms like Twitter and YouTube have played an important role in the increasing polarization of their US (and worldwide) user bases. They never intended to do this, of course, but their effect on social cohesion is arguably the result of internal cultures based on narrow metric optimization: when you optimize for short-term engagement, you often sacrifice long-term user well-being.
The unintended consequences of AI systems are hard to predict, almost by definition. But their potential impact makes them very much worth thinking and talking about — which is why I sat down with Stanford professor, co-director of the Women in Data Science (WiDS) initiative, and host of the WiDS podcast Margot Gerritsen for this episode of the podcast.
Next Episode
74. Ethan Perez - Making AI safe through debate
March 10, 2021
•52m
Most AI researchers are confident that we will one day create superintelligent systems — machines that can significantly outperform humans across a wide variety of tasks.
If this ends up happening, it will pose some potentially serious problems. Specifically: if a system is superintelligent, how can we maintain control over it? That’s the core of the AI alignment problem — the problem of aligning advanced AI systems with human values.
A full solution to the alignment problem will have to involve at least two things. First, we’ll have to know exactly what we want superintelligent systems to do, and make sure they don’t misinterpret us when we ask them to do it (the “outer alignment” problem). But second, we’ll have to make sure that those systems are genuinely trying to optimize for what we’ve asked them to do, and that they aren’t trying to deceive us (the “inner alignment” problem).
Creating systems that are inner-aligned and superintelligent might seem like different problems — and many think that they are. But in the last few years, AI researchers have been exploring a new family of strategies that some hope will allow us to achieve both superintelligence and inner alignment at the same time. Today’s guest, Ethan Perez, is using these approaches to build language models that he hopes will form an important part of the superintelligent systems of the future. Ethan has done frontier research at Google, Facebook, and MILA, and is now working full-time on developing learning systems with generalization abilities that could one day exceed those of human beings.
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