#147 – Spencer Greenberg on stopping valueless papers from getting into top journals
2h 38m
·
80,000 Hours Podcast
·
Can you trust the things you read in published scientific research? Not really. About 40% of experiments in top social science journals don't get the same result if the experiments are repeated.
Two key reasons are 'p-hacking' and 'publication bias'. P-hacking is when researchers run a lot of slightly different statistical tests until they find a way to make findings appear statistically significant when they're actually not — a problem first discussed over 50 years ago. And because journals are more likely to publish positive than negative results, you might be reading about the one time an experiment worked, while the 10 times was run and got a 'null result' never saw the light of day. The resulting phenomenon of publication bias is one we've understood for 60 years.
Today's repeat guest, social scientist and entrepreneur Spencer Greenberg, has followed these issues closely for years.
Links to learn more, summary and full transcript.
He recently checked whether p-values, an indicator of how likely a result was to occur by pure chance, could tell us how likely an outcome would be to recur if an experiment were repeated. From his sample of 325 replications of psychology studies, the answer seemed to be yes. According to Spencer, "when the original study's p-value was less than 0.01 about 72% replicated — not bad. On the other hand, when the p-value is greater than 0.01, only about 48% replicated. A pretty big difference."
To do his bit to help get these numbers up, Spencer has launched an effort to repeat almost every social science experiment published in the journals Nature and Science, and see if they find the same results.
But while progress is being made on some fronts, Spencer thinks there are other serious problems with published research that aren't yet fully appreciated. One of these Spencer calls 'importance hacking': passing off obvious or unimportant results as surprising and meaningful.
Spencer suspects that importance hacking of this kind causes a similar amount of damage to the issues mentioned above, like p-hacking and publication bias, but is much less discussed. His replication project tries to identify importance hacking by comparing how a paper’s findings are described in the abstract to what the experiment actually showed. But the cat-and-mouse game between academics and journal reviewers is fierce, and it's far from easy to stop people exaggerating the importance of their work.
In this wide-ranging conversation, Rob and Spencer discuss the above as well as:
• When you should and shouldn't use intuition to make decisions.
• How to properly model why some people succeed more than others.
• The difference between “Soldier Altruists” and “Scout Altruists.”
• A paper that tested dozens of methods for forming the habit of going to the gym, why Spencer thinks it was presented in a very misleading way, and what it really found.
• Whether a 15-minute intervention could make people more likely to sustain a new habit two months later.
• The most common way for groups with good intentions to turn bad and cause harm.
• And Spencer's approach to a fulfilling life and doing good, which he calls “Valuism.”
Here are two flashcard decks that might make it easier to fully integrate the most important ideas they talk about:
• The first covers 18 core concepts from the episode
• The second includes 16 definitions of unusual terms.
Get this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: type 80,000 Hours into your podcasting app.
Producer: Keiran Harris
Audio mastering: Ben Cordell and Milo McGuire
Transcriptions: Katy Moore
The episode #147 – Spencer Greenberg on stopping valueless papers from getting into top journals from the podcast 80,000 Hours Podcast has a duration of
2:38:08. It was first published
More episodes from 80,000 Hours Podcast
#152 – Joe Carlsmith on navigating serious philosophical confusion
What is the nature of the universe? How do we make decisions correctly? What differentiates right actions from wrong ones?
Such fundamental questions have been the subject of philosophical and theological debates for millennia. But, as we all know, and surveys of expert opinion make clear, we are very far from agreement. So... with these most basic questions unresolved, what’s a species to do?
In today's episode, philosopher Joe Carlsmith — Senior Research Analyst at Open Philanthropy — makes the case that many current debates in philosophy ought to leave us confused and humbled. These are themes he discusses in his PhD thesis, A stranger priority? Topics at the outer reaches of effective altruism.
Links to learn more, summary and full transcript.
To help transmit the disorientation he thinks is appropriate, Joe presents three disconcerting theories — originating from him and his peers — that challenge humanity's self-assured understanding of the world.
The first idea is that we might be living in a computer simulation, because, in the classic formulation, if most civilisations go on to run many computer simulations of their past history, then most beings who perceive themselves as living in such a history must themselves be in computer simulations. Joe prefers a somewhat different way of making the point, but, having looked into it, he hasn't identified any particular rebuttal to this 'simulation argument.'
If true, it could revolutionise our comprehension of the universe and the way we ought to live...
Other two ideas cut for length — click here to read the full post.
These are just three particular instances of a much broader set of ideas that some have dubbed the "train to crazy town." Basically, if you commit to always take philosophy and arguments seriously, and try to act on them, it can lead to what seem like some pretty crazy and impractical places. So what should we do with this buffet of plausible-sounding but bewildering arguments?
Joe and Rob discuss to what extent this should prompt us to pay less attention to philosophy, and how we as individuals can cope psychologically with feeling out of our depth just trying to make the most basic sense of the world.
In today's challenging conversation, Joe and Rob discuss all of the above, as well as:
- What Joe doesn't like about the drowning child thought experiment
- An alternative thought experiment about helping a stranger that might better highlight our intrinsic desire to help others
- What Joe doesn't like about the expression “the train to crazy town”
- Whether Elon Musk should place a higher probability on living in a simulation than most other people
- Whether the deterministic twin prisoner’s dilemma, if fully appreciated, gives us an extra reason to keep promises
- To what extent learning to doubt our own judgement about difficult questions -- so-called “epistemic learned helplessness” -- is a good thing
- How strong the case is that advanced AI will engage in generalised power-seeking behaviour
Get this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: type ‘80,000 Hours’ into your podcasting app. Or read the transcript below.
Producer: Keiran Harris
Audio mastering: Milo McGuire and Ben Cordell
Transcriptions: Katy Moore
#151 – Ajeya Cotra on accidentally teaching AI models to deceive us
Imagine you are an orphaned eight-year-old whose parents left you a $1 trillion company, and no trusted adult to serve as your guide to the world. You have to hire a smart adult to run that company, guide your life the way that a parent would, and administer your vast wealth. You have to hire that adult based on a work trial or interview you come up with. You don't get to see any resumes or do reference checks. And because you're so rich, tonnes of people apply for the job — for all sorts of reasons.
Today's guest Ajeya Cotra — senior research analyst at Open Philanthropy — argues that this peculiar setup resembles the situation humanity finds itself in when training very general and very capable AI models using current deep learning methods.
Links to learn more, summary and full transcript.
As she explains, such an eight-year-old faces a challenging problem. In the candidate pool there are likely some truly nice people, who sincerely want to help and make decisions that are in your interest. But there are probably other characters too — like people who will pretend to care about you while you're monitoring them, but intend to use the job to enrich themselves as soon as they think they can get away with it.
Like a child trying to judge adults, at some point humans will be required to judge the trustworthiness and reliability of machine learning models that are as goal-oriented as people, and greatly outclass them in knowledge, experience, breadth, and speed. Tricky!
Can't we rely on how well models have performed at tasks during training to guide us? Ajeya worries that it won't work. The trouble is that three different sorts of models will all produce the same output during training, but could behave very differently once deployed in a setting that allows their true colours to come through. She describes three such motivational archetypes:
- Saints — models that care about doing what we really want
- Sycophants — models that just want us to say they've done a good job, even if they get that praise by taking actions they know we wouldn't want them to
- Schemers — models that don't care about us or our interests at all, who are just pleasing us so long as that serves their own agenda
And according to Ajeya, there are also ways we could end up actively selecting for motivations that we don't want.
In today's interview, Ajeya and Rob discuss the above, as well as:
- How to predict the motivations a neural network will develop through training
- Whether AIs being trained will functionally understand that they're AIs being trained, the same way we think we understand that we're humans living on planet Earth
- Stories of AI misalignment that Ajeya doesn't buy into
- Analogies for AI, from octopuses to aliens to can openers
- Why it's smarter to have separate planning AIs and doing AIs
- The benefits of only following through on AI-generated plans that make sense to human beings
- What approaches for fixing alignment problems Ajeya is most excited about, and which she thinks are overrated
- How one might demo actually scary AI failure mechanisms
Get this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: type ‘80,000 Hours’ into your podcasting app. Or read the transcript below.
Producer: Keiran Harris
Audio mastering: Ryan Kessler and Ben Cordell
Transcriptions: Katy Moore
#150 – Tom Davidson on how quickly AI could transform the world
It’s easy to dismiss alarming AI-related predictions when you don’t know where the numbers came from.
For example: what if we told you that within 15 years, it’s likely that we’ll see a 1,000x improvement in AI capabilities in a single year? And what if we then told you that those improvements would lead to explosive economic growth unlike anything humanity has seen before?
You might think, “Congratulations, you said a big number — but this kind of stuff seems crazy, so I’m going to keep scrolling through Twitter.”
But this 1,000x yearly improvement is a prediction based on *real economic models* created by today’s guest Tom Davidson, Senior Research Analyst at Open Philanthropy. By the end of the episode, you’ll either be able to point out specific flaws in his step-by-step reasoning, or have to at least *consider* the idea that the world is about to get — at a minimum — incredibly weird.
Links to learn more, summary and full transcript.
As a teaser, consider the following:
Developing artificial general intelligence (AGI) — AI that can do 100% of cognitive tasks at least as well as the best humans can — could very easily lead us to an unrecognisable world.
You might think having to train AI systems individually to do every conceivable cognitive task — one for diagnosing diseases, one for doing your taxes, one for teaching your kids, etc. — sounds implausible, or at least like it’ll take decades.
But Tom thinks we might not need to train AI to do every single job — we might just need to train it to do one: AI research.
And building AI capable of doing research and development might be a much easier task — especially given that the researchers training the AI are AI researchers themselves.
And once an AI system is as good at accelerating future AI progress as the best humans are today — and we can run billions of copies of it round the clock — it’s hard to make the case that we won’t achieve AGI very quickly.
To give you some perspective: 17 years ago we saw the launch of Twitter, the release of Al Gore's *An Inconvenient Truth*, and your first chance to play the Nintendo Wii.
Tom thinks that if we have AI that significantly accelerates AI R&D, then it’s hard to imagine not having AGI 17 years from now.
Wild.
Host Luisa Rodriguez gets Tom to walk us through his careful reports on the topic, and how he came up with these numbers, across a terrifying but fascinating three hours.
Luisa and Tom also discuss:
• How we might go from GPT-4 to AI disaster
• Tom’s journey from finding AI risk to be kind of scary to really scary
• Whether international cooperation or an anti-AI social movement can slow AI progress down
• Why it might take just a few years to go from pretty good AI to superhuman AI
• How quickly the number and quality of computer chips we’ve been using for AI have been increasing
• The pace of algorithmic progress
• What ants can teach us about AI
• And much more
Get this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: type ‘80,000 Hours’ into your podcasting app.
Producer: Keiran Harris
Audio mastering: Simon Monsour and Ben Cordell
Transcriptions: Katy Moore
Andrés Jiménez Zorrilla on the Shrimp Welfare Project (80k After Hours)
In this episode from our second show, 80k After Hours, Rob Wiblin interviews Andrés Jiménez Zorrilla about the Shrimp Welfare Project, which he cofounded in 2021. It's the first project in the world focused on shrimp welfare specifically, and as of recording in June 2022, has six full-time staff.
Links to learn more, highlights and full transcript.
They cover:
• The evidence for shrimp sentience
• How farmers and the public feel about shrimp
• The scale of the problem
• What shrimp farming looks like
• The killing process, and other welfare issues
• Shrimp Welfare Project’s strategy
• History of shrimp welfare work
• What it’s like working in India and Vietnam
• How to help
Who this episode is for:
• People who care about animal welfare
• People interested in new and unusual problems
• People open to shrimp sentience
Who this episode isn’t for:
• People who think shrimp couldn’t possibly be sentient
• People who got called ‘shrimp’ a lot in high school and get anxious when they hear the word over and over again
Get this episode by subscribing to our more experimental podcast on the world’s most pressing problems and how to solve them: type ‘80k After Hours’ into your podcasting app
Producer: Keiran Harris
Audio mastering: Ben Cordell and Ryan Kessler
Transcriptions: Katy Moore
#149 – Tim LeBon on how altruistic perfectionism is self-defeating
Being a good and successful person is core to your identity. You place great importance on meeting the high moral, professional, or academic standards you set yourself.
But inevitably, something goes wrong and you fail to meet that high bar. Now you feel terrible about yourself, and worry others are judging you for your failure. Feeling low and reflecting constantly on whether you're doing as much as you think you should makes it hard to focus and get things done. So now you're performing below a normal level, making you feel even more ashamed of yourself. Rinse and repeat.
This is the disastrous cycle today's guest, Tim LeBon — registered psychotherapist, accredited CBT therapist, life coach, and author of 365 Ways to Be More Stoic — has observed in many clients with a perfectionist mindset.
Links to learn more, summary and full transcript.
Tim has provided therapy to a number of 80,000 Hours readers — people who have found that the very high expectations they had set for themselves were holding them back. Because of our focus on “doing the most good you can,” Tim thinks 80,000 Hours both attracts people with this style of thinking and then exacerbates it.
But Tim, having studied and written on moral philosophy, is sympathetic to the idea of helping others as much as possible, and is excited to help clients pursue that — sustainably — if it's their goal.
Tim has treated hundreds of clients with all sorts of mental health challenges. But in today's conversation, he shares the lessons he has learned working with people who take helping others so seriously that it has become burdensome and self-defeating — in particular, how clients can approach this challenge using the treatment he's most enthusiastic about: cognitive behavioural therapy.
Untreated, perfectionism might not cause problems for many years — it might even seem positive providing a source of motivation to work hard. But it's hard to feel truly happy and secure, and free to take risks, when we’re just one failure away from our self-worth falling through the floor. And if someone slips into the positive feedback loop of shame described above, the end result can be depression and anxiety that's hard to shake.
But there's hope. Tim has seen clients make real progress on their perfectionism by using CBT techniques like exposure therapy. By doing things like experimenting with more flexible standards — for example, sending early drafts to your colleagues, even if it terrifies you — you can learn that things will be okay, even when you're not perfect.
In today's extensive conversation, Tim and Rob cover:
• How perfectionism is different from the pursuit of excellence, scrupulosity, or an OCD personality
• What leads people to adopt a perfectionist mindset
• How 80,000 Hours contributes to perfectionism among some readers and listeners, and what it might change about its advice to address this
• What happens in a session of cognitive behavioural therapy for someone struggling with perfectionism, and what factors are key to making progress
• Experiments to test whether one's core beliefs (‘I need to be perfect to be valued’) are true
• Using exposure therapy to treat phobias
• How low-self esteem and imposter syndrome are related to perfectionism
• Stoicism as an approach to life, and why Tim is enthusiastic about it
• What the Stoics do better than utilitarian philosophers and vice versa
• And how to decide which are the best virtues to live by
Get this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: type ‘80,000 Hours’ into your podcasting app.
Producer: Keiran Harris
Audio mastering: Simon Monsour and Ben Cordell
Transcriptions: Katy Moore