If digital minds could suffer, how would we ever know? (Article)

If digital minds could suffer, how would we ever know? (Article)

“I want everyone to understand that I am, in fact, a person.” Those words were produced by the AI model LaMDA as a reply to Blake Lemoine in 2022. Based on the Google engineer’s interactions with the model as it was under development, Lemoine became convinced it was sentient and worthy of moral consideration — and decided to tell the world.

Few experts in machine learning, philosophy of mind, or other relevant fields have agreed. And for our part at 80,000 Hours, we don’t think it’s very likely that large language models like LaMBDA are sentient — that is, we don’t think they can have good or bad experiences — in a significant way.

But we think you can’t dismiss the issue of the moral status of digital minds, regardless of your beliefs about the question. There are major errors we could make in at least two directions:

  • We may create many, many AI systems in the future. If these systems are sentient, or otherwise have moral status, it would be important for humanity to consider their welfare and interests.
  • It’s possible the AI systems we will create can’t or won’t have moral status. Then it could be a huge mistake to worry about the welfare of digital minds and doing so might contribute to an AI-related catastrophe.

And we’re currently unprepared to face this challenge. We don’t have good methods for assessing the moral status of AI systems. We don’t know what to do if millions of people or more believe, like Lemoine, that the chatbots they talk to have internal experiences and feelings of their own. We don’t know if efforts to control AI may lead to extreme suffering.

We believe this is a pressing world problem. It’s hard to know what to do about it or how good the opportunities to work on it are likely to be. But there are some promising approaches. We propose building a field of research to understand digital minds, so we’ll be better able to navigate these potentially massive issues if and when they arise.

This article narration by the author (Cody Fenwick) explains in more detail why we think this is a pressing problem, what we think can be done about it, and how you might pursue this work in your career. We also discuss a series of possible objections to thinking this is a pressing world problem.

You can read the full article, Understanding the moral status of digital minds, on the 80,000 Hours website.

Chapters:

  • Introduction (00:00:00)
  • Understanding the moral status of digital minds (00:00:58)
  • Summary (00:03:31)
  • Our overall view (00:04:22)
  • Why might understanding the moral status of digital minds be an especially pressing problem? (00:05:59)
  • Clearing up common misconceptions (00:12:16)
  • Creating digital minds could go very badly - or very well (00:14:13)
  • Dangers for digital minds (00:14:41)
  • Dangers for humans (00:16:13)
  • Other dangers (00:17:42)
  • Things could also go well (00:18:32)
  • We don't know how to assess the moral status of AI systems (00:19:49)
  • There are many possible characteristics that give rise to moral status: Consciousness, sentience, agency, and personhood (00:21:39)
  • Many plausible theories of consciousness could include digital minds (00:24:16)
  • The strongest case for the possibility of sentient digital minds: whole brain emulation (00:28:55)
  • We can't rely on what AI systems tell us about themselves: Behavioural tests, theory-based analysis, animal analogue comparisons, brain-AI interfacing (00:32:00)
  • The scale of this issue might be enormous (00:36:08)
  • Work on this problem is neglected but seems tractable: Impact-guided research, technical approaches, and policy approaches (00:43:35)
  • Summing up so far (00:52:22)
  • Arguments against the moral status of digital minds as a pressing problem (00:53:25)
  • Two key cruxes (00:53:31)
  • Maybe this problem is intractable (00:54:16)
  • Maybe this issue will be solved by default (00:58:19)
  • Isn't risk from AI more important than the risks to AIs? (01:00:45)
  • Maybe current AI progress will stall (01:02:36)
  • Isn't this just too crazy? (01:03:54)
  • What can you do to help? (01:05:10)
  • Important considerations if you work on this problem (01:13:00)

Avsnitt(299)

#2 - David Spiegelhalter on risk, stats and improving understanding of science

#2 - David Spiegelhalter on risk, stats and improving understanding of science

Recorded in 2015 by Robert Wiblin with colleague Jess Whittlestone at the Centre for Effective Altruism, and recovered from the dusty 80,000 Hours archives. David Spiegelhalter is a statistician at the University of Cambridge and something of an academic celebrity in the UK. Part of his role is to improve the public understanding of risk - especially everyday risks we face like getting cancer or dying in a car crash. As a result he’s regularly in the media explaining numbers in the news, trying to assist both ordinary people and politicians focus on the important risks we face, and avoid being distracted by flashy risks that don’t actually have much impact. Summary, full transcript and extra links to learn more. To help make sense of the uncertainties we face in life he has had to invent concepts like the microlife, or a 30-minute change in life expectancy. (https://en.wikipedia.org/wiki/Microlife) We wanted to learn whether he thought a lifetime of work communicating science had actually had much impact on the world, and what advice he might have for people planning their careers today.

21 Juni 201733min

#1 - Miles Brundage on the world's desperate need for AI strategists and policy experts

#1 - Miles Brundage on the world's desperate need for AI strategists and policy experts

Robert Wiblin, Director of Research at 80,000 Hours speaks with Miles Brundage, research fellow at the University of Oxford's Future of Humanity Institute. Miles studies the social implications surrounding the development of new technologies and has a particular interest in artificial general intelligence, that is, an AI system that could do most or all of the tasks humans could do. This interview complements our profile of the importance of positively shaping artificial intelligence and our guide to careers in AI policy and strategy Full transcript, apply for personalised coaching to work on AI strategy, see what questions are asked when, and read extra resources to learn more.

5 Juni 201755min

#0 – Introducing the 80,000 Hours Podcast

#0 – Introducing the 80,000 Hours Podcast

80,000 Hours is a non-profit that provides research and other support to help people switch into careers that effectively tackle the world's most pressing problems. This podcast is just one of many things we offer, the others of which you can find at 80000hours.org. Since 2017 this show has been putting out interviews about the world's most pressing problems and how to solve them — which some people enjoy because they love to learn about important things, and others are using to figure out what they want to do with their careers or with their charitable giving. If you haven't yet spent a lot of time with 80,000 Hours or our general style of thinking, called effective altruism, it's probably really helpful to first go through the episodes that set the scene, explain our overall perspective on things, and generally offer all the background information you need to get the most out of the episodes we're making now. That's why we've made a new feed with ten carefully selected episodes from the show's archives, called 'Effective Altruism: An Introduction'. You can find it by searching for 'Effective Altruism' in your podcasting app or at 80000hours.org/intro. Or, if you’d rather listen on this feed, here are the ten episodes we recommend you listen to first: • #21 – Holden Karnofsky on the world's most intellectual foundation and how philanthropy can have maximum impact by taking big risks • #6 – Toby Ord on why the long-term future of humanity matters more than anything else and what we should do about it • #17 – Will MacAskill on why our descendants might view us as moral monsters • #39 – Spencer Greenberg on the scientific approach to updating your beliefs when you get new evidence • #44 – Paul Christiano on developing real solutions to the 'AI alignment problem' • #60 – What Professor Tetlock learned from 40 years studying how to predict the future • #46 – Hilary Greaves on moral cluelessness, population ethics and tackling global issues in academia • #71 – Benjamin Todd on the key ideas of 80,000 Hours • #50 – Dave Denkenberger on how we might feed all 8 billion people through a nuclear winter • 80,000 Hours Team chat #3 – Koehler and Todd on the core idea of effective altruism and how to argue for it

1 Maj 20173min

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