From AI to ML to AI: On Swirling Nomenclature & Slurried Thought

Artificial intelligence is transforming the way we work (Venture Beat), turning all of us into hyper-productive business centaurs (The Next Web).  Artificial intelligence will merge with human brains to transform the way we think (The Verge). Artificial intelligence is the new electricity (Andrew Ng).  Within five years, artificial intelligence will be behind your every decision (Ginni Rometty of IBM via Computer World ).

Before committing all future posts to the coming revolution, or abandoning the blog altogether to beseech good favor from our AI overlords at the AI church, perhaps we should ask, why are today’s headlines, startups and even academic institutions suddenly all embracing the term artificial intelligence (AI)?

In this blog post, I hope to prod all stakeholders (researchers, entrepreneurs, venture capitalists, journalists, think-fluencers, and casual observers alike) to ask the following questions:

  1. What substantive transformation does this switch in the nomenclature from machine learning (ML) to  artificial intelligence (AI) signal?
  2. If the research hasn’t categorically changed, then why are we rebranding it?
  3. What are the dangers, to both scholarship and society, of mindlessly shifting the way we talk about research to maximize buzz?

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What are the tradeoffs between immediate and longer term AI safety efforts?

[This article is also cross-posted to the Deep Safety blog.]

Something I often hear in the machine learning community and media articles is “Worries about superintelligence are a distraction from the *real* problem X that we are facing today with AI” (where X = algorithmic bias, technological unemployment, interpretability, data privacy, etc). This competitive attitude gives the impression that immediate and longer-term safety concerns are in conflict. But is there actually a tradeoff between them?


We can make this question more specific: what resources might these two types of issues be competing for?

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Portfolio Approach to AI Safety Research

[This article originally appeared on the Deep Safety blog.]

dimensionsLong-term AI safety is an inherently speculative research area, aiming to ensure safety of advanced future systems despite uncertainty about their design or algorithms or objectives. It thus seems particularly important to have different research teams tackle the problems from different perspectives and under different assumptions. While some fraction of the research might not end up being useful, a portfolio approach makes it more likely that at least some of us will be right.

In this post, I look at some dimensions along which assumptions differ, and identify some underexplored reasonable assumptions that might be relevant for prioritizing safety research. In the interest of making this breakdown as comprehensive and useful as possible, please let me know if I got something wrong or missed anything important.

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DeepMind Solves AGI, Summons Demon

In recent years, the rapid advance of artificial intelligence has evoked cries of alarm from billionaire entrepreneur Elon Musk and legendary physicist Stephen Hawking. Others, including the eccentric futurist Ray Kurzweil, have embraced the coming of true machine intelligence, suggesting that we might merge with the computers, gaining superintelligence and immortality in the process. As it turns out, we may not have to wait much longer.

This morning, a group of research scientists at Google DeepMind announced that they had inadvertently solved the riddle of artificial general intelligence (AGI). Their approach relies upon a beguilingly simple technique called symmetrically toroidal asynchronous  bisecting convolutions. By the year’s end, Alphabet executives expect that these neural networks will exhibit fully autonomous self-improvement. What comes next may affect us all.

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