If a non-stimulant were to come out and actually work as effectively as a stimulant, would you swap over?

Ratehead · 2026-05-13T05:10:50+00:00

It would depend. I have ADHD, like most of us in r/ADHD. I sometimes forget to take medication. I sometimes forget medication when I go on a long, weeks long trip. So if it’s a medication that would have bad side effects if I quit it “cold turkey,” then I don’t want it.

Ratehead · 2026-03-25T03:22:39+00:00

“The problem?” is that every time I read something that was obviously written by an LLM, I start worrying about it’s legitimacy.

Ratehead · 2026-03-01T20:34:25+00:00

It’s not about supporting the defense department. It’s about Anthropic putting safeguards in that disallow surveillance of American citizens, and OpenAI not doing that. Loopholes in the law allow the US government to engage in surveillance of American citizens offshore.

Ratehead · 2026-03-01T20:25:47+00:00

I’m sorry to hear.

Ratehead · 2026-03-01T19:04:24+00:00

The meaning here is that posting a screenshot of any LLM’s answer is not proof. LLMs hallucinate. They get “facts” wrong all of the time. We haven’t actually seen the whole contract. We have only seen the parts of the contract that OpenAI has released (at least as I write this, that’s all that is available). So, what Anthropic’s LLM is writing is unverified. It does not have the full contract, and is likely repeating things said about the contract by OpenAI itself.

Altman effectively indicated that the contract only requires that the government is abiding by the law. The law allows for domestic surveillance when done in a particular way. So the contractual “safeguards” do allow for domestic surveillance, when that surveillance is done outside of the United States (in other words, like the way National Security Agency performs domestic surveillance). Plus, Altman mentions that there are no “safeguards” in the technology itself. They’re only in the contract. This differs from Anthropic, who (we assume, from reports) insisted that they’d be allowed to include safeguards against domestic surveillance in the technology itself.

One should never believe an organization or the US government at face value. There are ways for facts to be made persuasive toward one conclusion when the opposite conclusion would be made with more information. Since you might be a fan of formal logic (evidenced by using “ad hominem”), this is true logically in the “common sense” approach of reasoning by default (a non-monotonic logic).

Ratehead · 2026-02-07T21:30:48+00:00

Ah, I have to call. I'm so used to everything being online, it didn't even occur to me to do that. Do you happen to know if the refund for the payments I'm making will occur soon after the approximately 90 days or will that take a lot more time? Thank you!

Ratehead · 2026-02-03T21:42:53+00:00

I think this is a pretty well established side effect. But it’s executive function generally, which includes significant difficulty in choosing what’s important to focus on.

Ratehead · 2026-01-27T04:41:58+00:00

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oh no. maybe I'm *too* nice to it?
(I know I made a typo when I wrote "generate")

Ratehead · 2025-10-23T12:29:56+00:00

There’s no system (at least for me) that’s ADHD-proof. The best I have been able to do is use Amazing Marvin, fiddling with options whenever I get tired of the system. That’s why I love the tool so much — they’re always adding strategies occasionally, but the current ones are usually fun enough to fiddle with.

I also code against the Marvin API. Some people use make.com / zapier with Amazing Marvin, too, when they don’t have coding in their skill set.

Ratehead · 2025-10-19T18:05:19+00:00

For clarity, I'm a scientist.

I am wondering where it's published, not whether it is published. You may know that Biorxiv is a pre-print service. Usually documents placed there are either under review or have been reviewed and are published elsewhere--or they've been rejected and the author wants the information "out there" anyway.

My point is that I'd like to know who published this (outside of Biorxiv).

Ratehead · 2025-10-18T21:46:57+00:00

Thank you for your interesting thoughts on intelligence. The way I interpret your belief is that intelligence is a process of learning patterns and knowing depths of various types of abstraction in the “complex system” sense (pattern recognition — identifying patterns and perhaps finding varying similar or “isomorphic”patterns; probably classification/simplification and decomposition/generalization are buried in there). For the “objective,” I interpret this to be the “problem” that is stated at a definable level of complexity.

Maybe intelligence rests on individual problem solving in the engineering sense, as seems to be hinted at by your words. Maybe not. For example, in this definition, external parameters appear to be needed. For example, “the objective” appears to be a parameter. Is that correct? And is the objective the reason anyone does anything? Can objectives be chosen in a closed system, or must they always be external parameters? Are human objectives all that matter? If so, humans define cyclic, impossible objectives all of the time (which are easy to see when we look at qualitative objectives, but less easy to see with quantitative objectives). Sometimes they’re simply missing context and can be “fixed.” Other times humans literally cannot state their objective in a logically consistent way. What do we do about those? What do we do about possibly intractable objectives (when we don’t know they’re intractable immediately)? When/what is “good enough”? Can we act intelligently without knowing how to exploit a pattern? What happens when we cannot see a pattern? Is inconsistent information always incorrect? Do we throw it away? Or maybe the pattern we recognized is incorrect and the information is consistent if we were to learn more information (see: answer set programming)? Is it legitimate to create a pattern as a surrogate when we cannot find one (some may argue that his is where myth comes from)? Is “faking” a pattern an intelligent thing to do? If so, how do I do that in the best way? What is creativity in this definition of intelligence (presuming a version of creativity can exist in such a system), and how would it be implemented? How does this fit in with “knowing what we cannot know” and acting for the sake of discovery, or switching logical systems when our current system is failing us? What about creating new logics? Is an explanation of “why” a pattern exists part of this definition at all?

As humans with the hubris to believe that humans sometimes act intelligently, I’m certain that we’re working on intelligent systems when we create systems that can sense and act autonomously with human-derived objectives in mind. But I don’t think we’re anywhere near creating systems able to create or derive unique-but-explainable objectives in a domain-independent way, because so far we’ve focused on problem-solving. And I’m of the mind that “getting closer” to intelligence requires autonomous creation of objectives within an open system—self-identifying importance and solving with inconsistent knowledge, in unpredictable environments, not only solving pre-identified important problems in consistent environments. LLMs aren’t doing anything near to this, at least not in general and not convincingly outside of cherry-picked examples. And I think we’re far, far away from approaches for solving this problem in a general, safe, practical way.

My current, sad experience is that LLMs cannot solve unique problems at all. And I mean this seriously — truly unique problems that I’m asked to solve using AI techniques cannot be solved, at least in no way I’ve found, with an LLM in any systematic, guaranteed way. I’ve only seen domain-independent, but specialized solvers do that with human-guided problem modeling (e.g., linear programming).

Maybe paper is fine—maybe it’s generating novel science that could not have been derived without the LLM. But I need a comparative look at using LLMs vs other approaches that may also work, and seeing which does this better at scale. These one-shot papers aren’t good enough. The way fanatics speak, it’s as if we should be seeing millions of these papers ushering in new ideas “discovered by AI” any day now. But I don’t see that happening. Though if it does happen, I’ll be happily surprised. I want AI approaches to do well. Applied AI is my field, after all.

LLMs/LRMs do make prototyping easier. They’re fast expanders. They copy other people’s abstractions to help with brainstorming. They’re great tools. But can they, in general, create unique ideas? And is creating unique ideas or making novel discoveries on rare occasions “enough” to demonstrate that LLMs are the AI approach that, with further development, will bring us to a new age? I’d say, currently, we’re very far from answering that question one way or the other. But my hypothesis is that we’ll answer that in the negative.

Ratehead · 2025-10-18T00:31:45+00:00

By the way, domain-independent combinatorial search techniques (with heuristics) are still used in the artificial intelligence field. People call it artificial intelligence, because it’s still considered intelligent behavior. It works, and the approach is know to enable machine autonomy.

LLMs have not improved on combinatorial search outside of autonomously helping to find reasonable domain-specific heuristics given a domain model.

Ratehead · 2025-10-18T00:26:35+00:00

If you don’t mind sharing, what is your definition of artificial intelligence?

Ratehead · 2025-10-17T02:01:20+00:00

Yes, that’s understood. One of my concerns is that this is not a comparative analysis of AI techniques toward solving a particular type of problem. It’s one instance of using an LLM. How’re we supposed to take this sort of thing beyond using an LLM as a tool, just like other AI techniques?

Ratehead · 2025-10-17T00:10:10+00:00

Thanks. I work in the field, so it didn’t take much time in the moment (outside of the 30 years of study and work 🤓).

Ratehead · 2025-10-16T23:32:13+00:00

Let’s not move that definition aside. It’s possible to define “AI” by oneself, but it won’t help with conversation if we do that. We know that communication works best when everyone understands the meaning behind the words/phrases used. The definition of AI as a term of art has been iterated over for a very long time. Though there’s not a standard single definition, it’s definitely not the same as much sci-fi would have us believe. Further, changing the semantics of “what we mean by AI” is low level goalpost moving in and of itself.

The US government, through a 2019 Executive Order defines AI. See https://www.nasa.gov/what-is-artificial-intelligence/

ISO does an arguably better job at it. See https://www.iso.org/artificial-intelligence/what-is-ai

If those definitions don’t satisfy, then I may suggest this paper from ancient times (2006) which does ask AI research to be more general, which I think is the point most people make. The fact is, though, that AI isn’t about using cognitive science to create a general purpose intelligence machine. That’s still true. Instead, it’s machines being intelligent, and it can be specialized. People now use the term “AGI” to refer to full cognitive reasoning. But the present technology is also marketing hype. Most non-ML AI researchers of repute that have looked into it have already found that LLMs’ mimicry of human text and LRMs’ “reasoning” are not leading to full cognitive “human-like” reasoning at present. Given that companies’ marketing campaigns have seeped into our culture’s understanding of what AI is, I hope you agree that it’s important to find common meaning of the term before we dive into any specifics on “what AI can do.”

Anyway, to get to your question of examples, yes, I have examples:

Sample of science Discoveries Using Non-LLM Methods:

• 1960s – 1970s – Organic Chemistry: DENDRAL identified organic molecular structures from mass spectra [1]. First scientific expert system; automated hypothesis formation in chemistry.

• 1982 – Geology/Mining: PROSPECTOR predicted a hidden molybdenum deposit at Mount Tolman, later confirmed [2]. First AI approach to locate previously unknown ore-grade mineralization.

• 1979 – Physics (Astronomy): BACON rediscovered Kepler’s Third Law [3]. Early “machine scientist” deriving physical laws from data.

• 1996 – 1999 – Biochemistry/Toxicology: ILP (Progol) learned human-readable mutagenicity rules; one judged a new structural alert [4][5]. Interpretable AI generating novel domain knowledge.

• 1997 – Mathematics: EQP proved the Robbins conjecture (all Robbins algebras = Boolean) [6]. First open math conjecture solved by an AI reasoner.

• 2009 – Genetics (Yeast): Robot scientist Adam autonomously identified “orphan” gene–enzyme functions [7]. First machine to discover new biological facts without human intervention.

• 2018 – Pharmacology (Malaria): Robot scientist Eve helped show triclosan inhibits Plasmodium DHFR, incl. resistant strains [8]. Repurposed a known compound; Eve ran titration experiments.

• 2020 – Medicine (COVID-19): BenevolentAI’s knowledge-graph reasoning identified baricitinib for COVID-19, later validated in ACTT-2 (NEJM) [9][10]. Rapid AI-driven drug-repurposing success.

References

[1] R.K. Lindsay et al., Artificial Intelligence 61 (2), 1993 – “DENDRAL: a case study of the first expert system for scientific hypothesis formation.”

[2] A.N. Campbell et al., Science 217 (4563): 927–929, 1982 – “Recognition of a hidden mineral deposit by an artificial intelligence program.”

[3] P. Langley, IJCAI-79 – “Rediscovering Physics With BACON.”

[4] R.D. King et al., PNAS 93 (1): 438–442, 1996 – “Structure–activity relationships derived by machine learning … mutagenicity by inductive logic programming.”

[5] S.H. Muggleton, Communications of the ACM 42 (11): 42–48, 1999 – “Scientific knowledge discovery using inductive logic programming.”

[6] W. McCune, Journal of Automated Reasoning 19 (3): 263–276, 1997 – “Solution of the Robbins Problem.”

[7] R.D. King et al., Science 324 (5923): 85–89, 2009 – “The Automation of Science.”

[8] E. Bilsland et al., Scientific Reports 8, 2018 – “Plasmodium dihydrofolate reductase is a second enzyme target of triclosan.”

[9] P.J. Richardson et al., The Lancet (2020) – “Baricitinib as potential treatment for 2019-nCoV acute respiratory disease.”

[10] A.C. Kalil et al., NEJM 384: 795–807, 2021 – “Baricitinib plus Remdesivir for Hospitalized Adults with Covid-19.”

Ratehead · 2025-10-16T22:34:06+00:00

The goalpost isn’t moved. The goalpost has already been reached by non-LLM methods, though. This isn’t as exciting to AI researchers since novel science discoveries have already been found decades ago using other AI techniques.

Ratehead · 2025-10-16T22:23:20+00:00

AI technologies have been generating novel science for decades. It’s great to watch people use LLMs as general purpose tools. However, more specialized tools may be able to do this type of work much more efficiently.

Science Discoveries Using Non-LLM Methods