Imagine that you had a magic machine. You tell the machine what your goals are. The machine tells you, in any situation, the optimal statement to say in order to achieve your goals, and who to say it to. The statement may or may not be true.
Reconciling truthfulness and relevance as epistemic and decision-theoretic utility.
The genie knows, but doesn't care — LessWrong
Followup to: The Hidden Complexity of Wishes, Ghosts in the Machine, Truly Part of You …
Philosophy Experiments — Interactive Thought Experiments
Explore 27 interactive philosophy experiments that challenge your moral intuitions, test your logical reasoning, and probe the boundaries of belief.
On working machines
In part one, on thinking machines, I explored two facets of the philosophy of artificial intelligence: “intelligence”, and consciousness. That left an important topic to consider for this post: the...
Operadic consistency: a label-free signal for compositional...
Detecting LLM reasoning failures at inference time without ground-truth labels has motivated a wide range of confidence baselines, including self-consistency, semantic entropy, and P(True), built...
Against theory-motivated experimentation: Can random experimental choice lead to better theories?
Scientists must choose which among many experiments to perform. We study the epistemic success of experimental choice strategies proposed by philosophers of science or executed by scientists themselves. We develop a multi-agent model of the scientific process that jointly formalizes its core aspects: active experimentation, theorizing, and social learning. We find that agents who choose new experiments at random develop the most informative and predictive theories of the world. The agents aiming to confirm, falsify theories, or resolve theoretical disagreements end up with an illusion of epistemic success: they develop promising accounts for the data they collected, while misrepresenting the ground truth that they intended to learn about. Agents experimenting in these theory-motivated ways acquire less diverse or less representative samples from the ground truth that also turn out to be easier to account for. Random data collection, on the other hand, combines virtues of diverse and representative sampling from a target scientific domain which enables cumulative development of the successful theoretical accounts of it. We suggest that randomization, already a gold standard within experiments, is also beneficial at the level of experiments themselves.
Eliciting Beliefs with Random Generation Tasks
Elicitation methods, such as asking people to produce the deciles of a distribution, are standard practices in policy or applied statistics. Similarly, much of cognitive science and psychology focuses on determining people's people's beliefs or latent traits through questionnaires or judgment tasks. However, these approaches often only capture a rough outline of what people know and are usually limited to point estimates of people's beliefs. Here, we present a novel experimental paradigm that allows us to access people's beliefs and how variable these beliefs are. Our task is based on an established random generation paradigm in which participants produce quantities from a particular domain as randomly as possible. We hypothesize that due to the minds' general-purpose mechanisms for probabilistic inferences, these random sequences represent the participants' underlying prior beliefs. We show that our method can infer participants' beliefs for a wide range of numeric quantities at comparable accuracy as an established elicitation method. Moreover, these inferred beliefs are consistent with individual participants' generalization and inference patterns in a subsequent conditional prediction task. We then extend our approach to non-numeric belief elicitation, highlighting how our method can go beyond numeric elicitation and provide insight into complex beliefs that are challenging to assess experimentally. Empirically, our results highlight that people know the rough shapes of environmental distributions, and these beliefs guide inference and generalization. Moreover, using our novel approach, we also show that people know the fine details of environmental distributions. Finally, our experimental results show that random generation paradigms can be a useful tool for cognitive scientists, psychologists, and applied statisticians.
Machine god metaphors eat your brain
There are other ways of thinking about AI
The Perfect Mirror
An experiment with AI spiritual direction almost convinces Matthew Milliner that it works—until the real world breaks the spell.
Are We Thinking Correctly About AI Intelligence? | Quanta Magazine
Computer scientist Melanie Mitchell discusses why artificial intelligence doesn’t “think” or “reason” like humans, and how we can create better methods for measuring machine cognition.
Experiments — ux-phi
Playable versions of canonical experiments in philosophy: the Socratic-questionnaire thought experiments — the trolley problem, Gettier cases, the side-effect effect, Mary the colour scientist.
A Bayesian Truth Serum for Subjective Data
Subjective judgments, an essential information source for science and policy, are problematic because there are no public criteria for assessing judgmental truthfulness. I present a scoring method for eliciting truthful subjective data in situations ...
A philosopher’s 350-year-old trick to get people to change their minds is now backed up by psychologists
The 17th century philosopher Blaise Pascal is perhaps best known for Pascal’s Wager which, in the first formal use of decision theory, argued that believing in God is the most pragmatic decision. But it seems the French thinker also had a knack for psychology. As Brain Pickings points out, Pascal set out the most effective way to get someone to change their mind, centuries before experimental psychologists began to formally study persuasion:
Mental Models: The Best Way to Make Intelligent Decisions (~100 Models Explained)
The smartest people in the world use mental models to make intelligent decisions, avoid stupidity, and increase productivity. Let's take a look at how ...
We Asked the ‘Future of Truth’ Author to Explain How He Used AI. It Didn’t Go Well
A book about how AI shapes perceptions of reality came under fire for using AI-generated quotes. Its problems go beyond that.
There Is No Hard Problem of Consciousness
Expecting a verifiable answer makes no sense. Here’s why.
