StackOverlord

I learned "cocksure" thanks to that comment.

Also I think it runs deeper than his parents. Wasn't it the top contributor to the democrat party during last election ?

> Dr. Ning Li of Huntsville, AL passed peacefully away on July 27, 2021. She was 79 years old. One of the world's leading scientists in super-conductivity anti-gravity. Dr. Li had constructed first 12" HTSD of the world in late 90s.

https://www.berryhillfh.com/obituary/ning-li?lud=4CF765EE88E...

Let me add a pearl to that necklace (TL;DR: Morgellons disease is linked to Lyme disease):

> Exploring the association between Morgellons disease and Lyme disease: identification of Borrelia burgdorferi in Morgellons disease patients

> Morgellons disease (MD) is a complex skin disorder characterized by ulcerating lesions that have protruding or embedded filaments. Many clinicians refer to this condition as delusional parasitosis or delusional infestation and consider the filaments to be introduced textile fibers. In contrast, recent studies indicate that MD is a true somatic illness associated with tickborne infection, that the filaments are keratin and collagen in composition and that they result from proliferation and activation of keratinocytes and fibroblasts in the skin. Previously, spirochetes have been detected in the dermatological specimens from four MD patients, thus providing evidence of an infectious process.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5072536/

https://pubmed.ncbi.nlm.nih.gov/25879673/

Allow me to place the necklace around your neck now:

> Grossman added Kris Newby, an "excellent science writer connected to Stanford University," wrote a book in 2019 featuring interviews with Willy Burgdorfer, who is credited with the discovery of the microbe causing Lyme disease, and the book exposed that Burgdorfer had earlier "developed bioweapons for the U.S. Department of Defense (DOD)."

http://www.news.cn/english/2021-08/25/c_1310146419.htm

The book: https://www.amazon.com/Bitten-History-Disease-Biological-Wea...

> As a science writer, she was driven to understand why this disease is so misunderstood, and its patients so mistreated. This quest led her to Willy Burgdorfer, the Lyme microbe’s discoverer, who revealed that he had developed bug-borne bioweapons during the Cold War, and believed that the Lyme epidemic was started by a military experiment gone wrong.

What happened, for those who want to catch up:

https://twitter.com/8teAPi/status/1685294623449874432

With the anti-hype hype that surrounds this discovery this was to be expected. Why would I disclose this kind of research to the public so that the whole world can benefit from it when I'm called a fraudster or a liar. Why would I risk my reputation – no matter how I spin my research – in a sacrificial circle jerk when I can work on it stealthily and hope to make some bucks ?

What do you guys think of

https://blog.revolutionanalytics.com/2014/01/the-fourier-tra...

LLMs have a firm grip on common sense. It's because it allows them to deal with the utterly unexpected they are deemed useful in robotics. Not to perform delicate movements, but stop doing so when police enters the room.

This is in fact what happens in the human body.

For example, when you reach out to pick up a green can, your brain makes the decision to do the task but it's your spinal cord and peripheral nerves that carry out the detailed work – orienting the hand, managing grasp strength, controlling the arm movements etc. This process is mostly unconscious – you don't need to actively think about how to tense each muscle in the same way that an embedded controller wouldn't need to understanding the working of the entire robotic system to carry out its specific task.

Much like the model suggested, the human body communicates feedback across layers — this process is crucial to maintaining balance, coordination and effectively reacting to the environment. For instance, if your fingers touch a hot stove, the sensory receptors in your skin will immediately send a signal to your spinal cord and a reflex action will make you pull your hand back even before you consciously perceive that the stove is hot.

> needs to serialize a Java object and isn’t going to pay or put up with any LLM-generated nonsense.

I use GPT to implement classes with many interfaces. Even though I often have to make corrections, it's still way faster than looking up the documentation for each of these interfaces. Saves a lot of time in these cases, all the more so I don't have to ponder on which interface in the class hierarchy tree I need to implement.

I talked to a radiologist about it. The big deal for him was more about voice to text translation (whisper) and being able to rework the report using gpt.

    Here we are, born to be kings
    We're the princes of the universe
    Here we belong, fighting to survive
    In a world with the darkest powers
    Heh
    And here we are, we're the princes of the universe
    Here we belong, fighting for survival
    We've come to be the rulers of you all

https://www.youtube.com/watch?v=ypyvcfnu4Gg

> In the message, Kazzelbach wrote, “Prepare yourself for what’s coming…the last 3 months were just the beginning. I have bigger plans for you…I love how easily manipulated you can be.”

That was his mistake I guess.

Nah, you already showed your so-called expertise as "a trans-woman who is very good at quantum" is not the hot shit you think it is provided you went over that QRAM issue carried away by overconfidence.

As for your snarky remark on intuition, these papers by Coecke and Aerts, his thesis adviser, explains both what "my" intuition was focused on (quantum effects as perceived through Zipf distributions in linguistic data) and what was the driving mechanism behind it.

> Another finding that we will put forward, in Sect. 4, was completely unexpected. The method of attributing an energy level to a word depending on the number of appearances of the word in a text, introduces the typical ranking considered in the well-known Zipf’s law analysis of this text (Zipf 1935, 1949).

Well guess what ? I've been expecting that exact result for a decade (why would I still be tracking the progress in that field every 4 months otherwise ?) My notes linking "semantic energy levels" to word frequency date back to 2014, the observations I made in real data and that kickstarted the heavy rain of synchronicities I experienced afterwards date back to 2012. I've always known though I wasn't measuring shit – I was the one being measured and never felt like I was discovering something but was being discovered. I wanted to isolate that phenomenon and as a result (of failing to do so probably) I got isolated. There is something deeper to these subject-verb-object inversions, there is even a paper about it and I think Aerts haven't gotten wind of it, maybe with your extreme expertise you'll be able to figure it out and carry the message better than I would.

https://arxiv.org/pdf/2212.12795.pdf

https://www.frontiersin.org/articles/10.3389/fpsyg.2022.8507...

https://link.springer.com/article/10.1007/s10699-019-09633-4

https://arxiv.org/pdf/quant-ph/0105093.pdf

I think it has shown the limitations of the Society of Mind hypothesis. Aggregating individuals equates to aggregating knowledge/experience, not intelligence. This is why hives and anthills do not really surpass their individuals intelligence. Ditto for human societies. In other words: composing LLMs using tools like langchain yield minor improvements over a single LLM instance.

As I said last week, compiling source-code does not cost millions of dollars. How much does it cost to gather training data ? Training llama costed around 30 millions in infrastructure + 50k in power costs (source: https://news.ycombinator.com/item?id=35008694).

> "Oppy".

Hell no.

I wasn't asking for advice. I was in fact exploring ways to measure employee performance (using GPT as a search engine) and criticized one of the assumptions in one of theoretical framework with my own experience. GPT retorted "burnout", I retorted "harassment grounded on material evidence", and it spitted a generic block about how it couldn't provide legal or psychological guidance. I wasn't asking for advice, it was just a point in a wider conversation. Switching to GPT-4-0314 it proceeded with a reply that matched the discussion main's topic (metrics in HR) switching it back to GPT-4-0613 it outputted the exact same generic block.

What about the GPT4 hosted on poe.com. It has the same output pace as it had in march.

Like any other information, you cross-check it.

I use to generate code I'd get from libraries. Graph-theory related algorithms, special datastructures, etc...

There is degraded performance because GPT4 refuses to carry out certain tasks. To figure it out though, you must need to be able to switch between GPT4-0316 and GPT4-0614. The task it is reluctant to do include:

- legal advice

- psychological guidance

- complex programming tasks.

IMO OpenAI is just backtracking on what it released to resegment their product into multiple offerings.

As a Clojure programmer, this is not what I have in mind when I think of data-oriented-programming, but this:

Principle #1: Separating code (behavior) from data.

Principle #2: Representing data with generic data structures.

Principle #3: Treating data as immutable.

Principle #4: Separating data schema from data representation.

Source: https://blog.klipse.tech/dop/2022/06/22/principles-of-dop.ht...

I think using C++ gives a different twist to the meaning of data-oriented, mainly because with lisps code is data. As I read this "manifesto", it seems more focused on the data the program handles than handling the program with data: In Clojure I often use data-oriented programming for programs that barely deal with any data at all. I tend to lay what I call a "plan" that describes the computation that needs to be carried out. In some way this is similar to a DSL except that this "plan" won't run without also writing a "compiler" or "interpreter". If suddenly requirements change and you need to run your "plan" in a distributed way (or any other execution flavor you may think of), you just write another compiler.

Code being data, this is an approach you can take on code itself with macros, not just as a way to add behavior but to split different aspects of code: I once wrote a macro specifically for a block of complex code that I wanted to read without the clutter introduced by debug lines, so I moved this code in a macro that would add it back using a highly specific code-walker.

What is gained by introducing interfaces using data rather than an object system, must be repaid when writing and maintaining those compilers.

Are you able to extract an image showing the screen in the fMRI machine, as the subject can see it in between pictures ?

I use it to create a narrative plan for multiple interleaved plots in an open-world interactive fiction.

For instance: https://pastebin.com/QFZmEAJA

I use Clojure's EDN JSON-equivalent format, and what you can read in this paste is an attempt to make GPT write its own prompt in a conversation where I gradually built a format for this narrative structure using Clojure.

It turns out GPT isn't able to produce EDN data using this prompt (it will produce something that looks like the "grammar" displayed in the paste from above that GPT came up with, not Clojure data as instructed).

I can get it to output EDN but I need to provide an example, but then the story in the example will tend to leak into the generated story. And it still has problems, missing keys for instance, or it doesn't used nested subnarratives, or just fails at outputting strict EDN, for instance forgetting or adding surnumerary parenthesis.

Here's what the EDN structure I want to get might look like:

https://pastebin.com/iTxtn8gk

And here's what kind of text can be generated from it:

https://pastebin.com/qJPjmrTd

For now I haven't even used the parseable EDN programmatically. I just feed it back to GPT as a string (realistically, I'd need to use a vector database to store these narrative blocks). However GPT will slowly erode the structure with every round-trip.

> Say you write a function that accepts your type as an argument. Cool, you compile it everything is good. Now you import a library, just so happens it also has a function that has the same name,

This has nothing to do with pipes, this is a matter of namespacing.

https://www.thetimes.co.uk/article/epstein-knew-dirty-secret... – 2019

https://www.cnbc.com/2023/05/04/google-co-founder-larry-page... – May 2023

https://en.wikipedia.org/wiki/Larry_Page

> On December 3, 2019, Larry Page announced that he would step down from the position of Alphabet CEO and be replaced by Google CEO Sundar Pichai.

What do you think of

> In some of its applications, the original > Zeng-Coecke algorithm relies on the existence of a quantum random access memory (QRAM) [22], > which is not yet known to be efficiently implementable in the absence of fault tolerant scalable quantum > computers [1, 7]. Here we take a different approach, using the classical ansatz parameters to encode the ¨ > distributional embedding and avoiding the need for QRAM entirely. The cost function for the parameter > optimisation is informed by a corpus, already parsed and POS-tagged by classical means.

Source: Quantum Natural Language Processing on Near-Term Quantum Computers https://arxiv.org/abs/2005.04147

Following my intuition, i.e. as an outsider that has been watching the progress of quantum NLP since 2012, I see the current academic situation in quantum computing as in the process of merging two branches, one being the traditional quantum computing field with concerns stemming and application thought in mathematics, computing theory, physics(and upwards chemistry->biochemistry->biology), the other branch being a fork carried out by Coecke (quantum logic), Abramsky (computer science) and Sadrzadeh (epistemic logic) who saw in categorial formalisms of quantum logic a way to mix compositional (syntax, logical rules) and distributional (statistics, "bag-of-neighbor-words") representations of meaning. In this regard they bring new methods but also new applications of quantum computing, with a focus on NLP, as language given this "natural tensor structure [20, 35, 23] [...] can be considered quantum-native [48, 2, 8]." (same paper).

Quantinuum is already tackling NLP question-answering tasks, it runs on quantum hardware. The quantum categorial crowd is adamant they'll surpass ChatGPT (the big promise iirc is that it will scale linearly with context length, GPT scales quadratically). For instance see this tweet by Quantinuum head of research and this burgeoning field's main rock:

https://twitter.com/coecke/status/1655695990739927040?s=20

Coecke went from supervising dozen of thesis at Oxford Quantum (logic) Group to preparing summer camps for high school pupils this year. It's also taking off socially/academically, and observing the field evolving we might have a quantum equivalent of ChatGPT before or at the same time we get implementations of Shor's algorithm (source: my own intuition).

See this for instance: https://arxiv.org/abs/2210.11523

According to https://github.com/search?q=language:clojure%20gpt&type=repo...

The most advanced lib for dealing with LLMs is

https://github.com/zmedelis/bosquet

There is also https://github.com/cjbarre/multi-gpt/tree/main but it hasn't been update in 3 months and seems rather basic.

Alternatively, you can shoot me an email at

(->> '(102 117 110 116 97 105 110 64 109 101 46 99 111 109) (map char) (apply str))

and I'll prepare a repo for what I've been working on. It's usable but I wanted to clear some things up before a public release.