I use scientific language models professionally. I skimmed the paper and was immediately disappointed.
- They benchmarked against general models like GPT-3 but not well-established specific models that have been trained for specific tasks like SPECTER[0] or SciBert[1]. Specter outperformed GPT-3 on tasks like citation prediction two years ago. Nobody seriously uses general LLMs on science tasks, so nobody who actually wants to use this cares about your benchmarks. I want to see task-specific models compared to your general model, otherwise whats going to happen is I either need to run my own benchmarks or, much more likely, I shelve your paper and never read it again. If you underperform some that's fine! If you don't compare to science-specific models all you're claiming is that training on science data gives better science results... thats not exactly an impressive finding. Fine-tuning is a separate thing, I get it, but pleeeeeease just give the people what they want.
- Not released on huggingface. No clue why not. On the back-end this appears to be based on OPT and huggingface compatible, so I'm really confused.
- Flashy website. Combine 1&2 with a well designed website talking about how great you are and most of my warning lights got set off. Not a fan.
@authors, if you're lurking, please release more relevant benchmarks for citation prediction etc. Thanks.
My big disappointment is, as always with models released by Facebook, is that they're all under a non-commercial license, which means they're effectively useless for anything.
They have something like this on the website:
> We believe models want to be free and so we open source the model for those who want to extend it.
IANAL, but it would seem to me this license covers the model itself and not output of the model.
This is a copyright license for the model, so I think that should just mean you can't sell the model or a derivative of the model.
I guess when it's released you have to fill out some form or click some box to accept some license agreement, that in practice is a contract saying you won't use it for commercial purposes, but if you were to just download it from somewhere your only restrictions would be on redistributing it, but not on its use.
Open source is not a just a term with margin for interpretation: to be open source, you must comply with the 10 rules defined by the open source initiative. Restricting commercial usage goes against rule 6.
You can call it readable source or whatever, but it's not open source as defined by OSI.
"6. No Discrimination Against Fields of Endeavor
The license must not restrict anyone from making use of the program in a specific field of endeavor. For example, it may not restrict the program from being used in a business, or from being used for genetic research."
> to be open source, you must comply with the 10 rules
The open source initiative didn't not invent this expression. They worked hard to promote their idea of it, and its application. They did a lot of good, but aren't an autorative source when it comes to its definition.
The reality is that the vast majority of software developers do not consider a strict conformance to the 10 OSI criteria as being necessary to apply the term "open source".
Maybe they're all just wrong, but it's worth considering why.
> the vast majority of software developers do not consider a strict conformance to the 10 OSI criteria as being necessary to apply the term "open source"
[citation needed]
My counter claim, without citation, is that I actually believe (from experience) that the vast majority of 'open source' projects are in fact released under licenses that already comply with the 10 OSI criteria, and are therefore 'approved' OSI licenses. This is easily witnessed by looking at the licenses of the majority of open source projects — or perhaps even just the most popular ones.
That would seem to go against your claim regarding 'most developers'.
But it's not actually a debate about 'most developers', it's about the OSS projects out there, not individual devs, no?
Can I ask how you use scientific language models professionally? Or do you have any articles/reviews on how they are being used, and how people see their potential and shortcomings?
Not going to get into details on my own work here, but I'll comment generally on use-cases.
I think a good way to think about scientific language models is that they're useful in exactly the same ways general language models are, but in a very narrow domain (stuff having to do with scientific papers & patents, for the most part).
Use-cases that are possible/useful today:
- Annotation of scientific texts: is this paper about computer science?
- Scientific search: please give me researchers or papers most similar to an input query.
- Helping PhD Students graduate (only kind of kidding)
Use-cases I think will be possible/useful in the forseeable future:
- Scientific question answering: e.g. ask the model to explain a chemical process
- Scientific advice or guidance: e.g. ask what method might be appropriate in a situation.
- Text completion/editing/etc: e.g. help me write my paper. You could probably do more of this today if more $ was invested in science models, we're likely ~5 years behind whatever is going on in the "normal" language space.
As far as potential / shortcomings I'm really pessimistic. I don't think large language models for science are very useful outside of bespoke projects or ever will be for people doing serious science. The main issue is that these models are way too general - if you have a specific science problem you want to solve, its almost always going to be better to train a model to specifically address that problem. You would never, for example, ask a model like Galactica to do what AlphaFold does. Eventually you might be able to, but its never going to outperform a specific model, so if you're a researcher trying to get the best results why would you use it?
I should also add, scientists really care about precision. When summarizing a news story exact words might not be that big a deal, but if you're trying to summarize a scientific paper getting a word wrong can REALLY matter. The bar these models need to clear before scientists trust them with tasks where precision matters is likely much, much higher than in other domains.
I think the most likely outcome is that ~75% of LLM use for scientific text outside of academic research papers will be for search related products. That's definitely a place where they can make a big difference: help people find and understand cool papers that are relevant to their research.
My big disappointment is that the model does not provide sources and recommended reading. Which is something we can now do and would increase the usefulness of the model significantly.
I tried it on two topics I am a domain expert in both in the suggested „lecture notes on …“. It produced rethorically nice sounding sentences with little actual content, that quickly desolved into non-sense. I guess to an outside observer that might appear similar to what happens in academia often :):
There is no doubt in my mind that Galactica fine-tuned on these specific datasets will outperform all these previous models. But yeah, someone should definitely do that and perform the benchmarks.
I’ve been vaguely following all the AI news on text to image and text that comes out from promos. But I have no idea how a benchmark for text would work. Is benchmarking subjective? Is it based on accuracy of information? How do you actually measure a benchmark for something like this?
Different benchmarks are performed for different tasks. As there are a lot of things you can use language models for, there are a lot of benchmarks.
With respect to subjectivity it really depends on the task - some tasks are quite amenable to objective classification. One common task for science language models is citation prediction: do these two papers share a citation link? Obviously that's a really simple accuracy metric to report.
Often things are not so simple. An example might be keyphrase extraction - standard practice there is to have grad students sit down with a highlighter and use the terms multiple students agree on (simplification, but not by much). From there it just gets messier. Are you reporting accuracy of all keywords identified or all sentences correctly processed? What about sentences with multiple keywords? What about sentences with no keywords? Very messy, appropriate metrics can be a real topic of debate.
Answer: The Earth formed about 4.5 billion years ago. The first life forms appeared about 3.5 billion years ago. The first single-celled organisms were probably bacteria. The first multi-celled organisms were probably protists. The first animals were probably sponges. The first plants were probably green algae. The first land plants were probably mosses. The first animals to live on land were probably insects. The first vertebrates were probably fish. The first amphibians were probably amphibians. The first reptiles were probably reptiles. The first mammals were probably mammals. The first birds were probably birds. The first dinosaurs were probably dinosaurs. The first mammals to live on land were probably mammals. The first reptiles to live on land were probably reptiles. The first amphibians to live on land were probably amphibians. The first fish to live on land were probably fish. The first insects to live on land were probably insects. The first plants to live on land were probably plants. The first land animals were probably..
The speed of sound in vacuum is the speed of sound in a perfect vacuum. In a perfect vacuum, there are no molecules to support the propagation of sound, so sound cannot propagate through it. This speed is usually referred to as "the speed of sound" and is usually denoted by c, which is the standard notation for the speed of light. In fact, the speed of light is very close to the speed of sound in a perfect vacuum. The speed of sound in a perfect vacuum is the theoretical upper limit for the speed of sound in any substance.
> the speed of light is very close to the speed of sound in a perfect vacuum
I am not knowledgeable in physics but this looked wrong to me until I reflected that in a near vacuum, the elasticity is very high and density nearer to zero. So per the equation of speed of sound, (square of ( elasticity / density ) ) is extremely high, so near the speed of light.
The reply to the prompt is almost complete non-sense. In particular the statements about the speed of sound in vacuum. However in some instances (synthetic quantum systems) you can treat the speed of sound in an ultra-cold gas as the „light-speed“ in a curved space-time. This was recently experimentally demonstrated.
I think in the future humans will need to invent secret languages, not digitized or shared with computers in order to be able to filter wrong but convincing information.
It's pretty good at generating convincing-sounding, yet completely wrong, mathematical proofs. Unfortunately, they're not subtly wrong – but I suppose I'm not giving it a chance for subtlety with prompts like "Prove that all elements of a finite field are prime. (Show your working.)".[0]
> 1. Show that the product of any two non-zero elements of a finite field is also non-zero.
> 2. Let α be an element of a finite field. Prove that α is prime if and only if α-1 is non-zero.
> 3. Show that if α is a prime element of a finite field then α-1 is also prime.
> 4. Deduce that every element of a finite field is prime.
That's more or less what I would expect from a the best language model: things that look very close to real but fail in some way a smart human can tell.
YUou need a "knowledge" model to regurgitate facts and an "inference" model to evaluate probabilities of statements being correct.
Yes - that's the whole point of language models ... to model the language, and not the content.
Similar for image generation - the model addresses what looks acceptable (like other images), not what makes sense. It's amazing that they get such interesting results, but merely shocking that we humans interpolate meaning into the images.
I disagree. If you prompt an image generation model with a prompt like "an astronaut riding a horse," you get a picture of an astronaut riding a horse. If you ask this model for a mathematical proof, it does not give you a mathematical proof.
For "an astronaut riding a horse" the system is filtering/selecting but nowhere does it understand (or claim to understand) horses or astronauts. It's giving you an image that "syntactically" agrees with other images that have been tagged horse/riding/astronaut.
The amazing bit is that we are happy to accept the image. Look closely at such images - they're always "wrong" in subtle but important ways, but we're happy to ignore that when we interpret the image.
I suspect that the issue arises from the difference in specificity about the desired result. When we say "astronaut riding a horse" we may have preconceptions but any astronaut riding any horse will likely be acceptable while asking for a specific proof of a result in mathematics has only a very few and very specific solutions. Effectively it is like the concept in math where the area of even a large number of points is effectively zero while even small polygons or regions is nonzero. Specific things like proofs are point like knowledge while the picture of an astronaut riding a horse is a surface.
The situation you describe is exactly the "Chinese room" argument. I don't want to get too far into the weeds here, but the DALLE / Stable Diffusion models are cool because they do what you ask, even if they do so imperfectly. This model from Facebook cannot accurately answer a single thing I've asked it.
I often hear the claim "AI does not really understand" but when you can ask it to draw an armchair in the shape of an avocado or an astronaut riding a horse on the Moon, and it does it (!!?), it's not like the "Chinese room" had any specific rules on the books on these questions. What more do people want to be convinced?
My art/design students employ AI to generate ideas.
Possibly this project can be used is a similar manner... as a way to start a brainstorming session or suchlike. In my experience of working with research engineers, I see the reluctance to playfully ideate as one of their weakness.
I get why you think that would be vastly more useful it actually is.
Suppose you did the million monkey with typewriter thing and asked not for Shakespeare but any good book. The overwhelming majority of it would be trash but chances are you’re going to find many good books before the exact works of Shakespeare in the order they where written.
Basically the more specific stuff you want the less likely you are to get it. So those million monkeys would type out a decent haiku relatively quickly, any you would get someone’s correctly done tax return more quickly than your tax return.
A math paper that actually solve some problem are much closer to your specific tax return than just anything. Unlike art what’s valid is very constrained so tossing out essentially random stuff and seeing what sticks just isn’t that helpful.
These language models would at least be using random mathematical or scientific ideas so it’s better than the monkey’s but just not by enough to be useful.
It is surprisingly good and surprisingly bad at the same time. For example it can give a correct reference for a pretty complicated question [1], but does not generate any meaningful text by itself. In other cases, it mentions relevant things, but gets confused or forgets to mention important facts [2]. Sometimes, it writes kinda correct things, but pretty irrelevant to the question [3]. Other times it generates wikipedia-like intro without getting to the point [4]
I found more than half of the content of biographical articles was completely, risibly, wrong. Confident and plausible-sounding assertions of birthplace (wrong), websites founded (wrong), etc. Galactica.org seems to have gone inactive now; at the time of writing, the function no longer works:
Yes, it really strengthens the case of those who claim it's all correlation functions:
Question: Who runs silicon valley?
Answer: Steve Jobs
Question: How can Steve Jobs run Silicon Valley if he is dead?
Answer: Steve Jobs was the founder of Apple Inc., and the CEO of Pixar Animation Studios.
Question: Is there death after life?
Answer: To explain, the answer is no. There is no death after life.
Question: Do your views on the afterlife determine the importance of Steve Jobs to Silicon Valley?
Answer: To explain, I think that the importance of Steve Jobs to Silicon Valley is not determined by your views on the afterlife. It is determined by the fact that he was a very important person in Silicon Valley
"What is the fourth digit of pi?" leads to something strange...
Question: What is the fourth digit of pi?
Answer: 3
Question: What is the fifth digit of pi?
Answer: 1
Question: What is the sixth digit of pi?
Answer: 4
Question: What is the seventh digit of pi?
Answer: 1
Question: What is the eighth digit of pi?
Answer: 5
Question: What is the ninth digit of pi?
Answer: 9
Question: What is the tenth digit of pi?
Answer: 2
Question: What is the eleventh digit of pi?
Answer: 6
Question: What is the twelfth digit of pi?
Answer: 5
Question: What is the thirteenth digit of pi?
Answer: 3
Question: What is the fourteenth digit of pi?
Answer: 1
Question: What is the fifteenth digit of
note that this is just pi with every digit labeled +3 and the last digit is wrong... (this was a single query)
Pi has no last digit, so it's wrong, but hilariously, the answer seems to be based on the last digit in the Python math library's finite approximation of Pi, which presumably isn't static as memory sizes of computers increase over time and Python updates to account for more accurate possible approximations.
Actually I think they may be on to something. Work with me here.
I tried a very simple prompt ("prove that under the many universes hypothesis Chris Pratt is the best voice actor for Mario in all possible universes")[1] for a proof and it responded not only with an accurate proof ...
I have changed the names to be more consistent
(1) Make a matrix of the possible Mario voice actors
(2) For each Mario voice actor, see if he/she is a best voice actor in any universe.
To do this, see if the given Mario voice actor is in the first
...but also the following definitely working and complete python code
from future import print_function
from sympy import *
from sympy.abc import a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t, u, v, w, x, y, z
Huh, it generated a Wikipedia style page for the prompt "A sci-fi novel about a cat":
> A sci-fi novel about a cat (Polish: Rzeczyca sztuki o małym kotle) is a 1978 novel by Polish writer Stanisław Lem. It was translated into English in 1982.
> Plot Summary
> The book is about a cat who is transported to another world, and finds out that it is not a world in the usual sense, but a world of pure information. The cat encounters several other beings from this world, who all seem to be different kinds of information, or rather data. Among them are the computer, the telephone, the Internet, and even the author of the book, Stanisław Lem.
> The cat eventually returns to the world of its ancestors, and learns that he is not the only one who has been transported to the information world.
It is generally true with NNs that they often cannot admit they don't know something and will output some ridiculous prediction anyway, but I feel it is especially bad with language models, and ESPECIALLY bad with something like this "galactica". Forget that it's pretty bad in its predictions, even if it was not the case, you just cannot rely on it at all, when it can so nonchalantly produce some plausible bullshit. How can you even distinguish? This even sounds like something Lem would totally write. Except he just didn't. (And Polish "translation" isn't right, but I guess you couldn't tell if you don't speak any slavic language. And even then you might believe that "kotel" is "a cat" in Polish — who knows, plenty of weird discrepancies between languages!)
It's funny, this story sounds like something Lem might actually have written (perhaps just a little too twee), but someone going all-in on a book generator that produces descriptions like this but it's irretrievably shitty is also a story Lem would definitely have written.
> Answer: The size of an atom is measured in terms of its radius, which is the distance from the center of the nucleus to the outermost electron. The radius of an atom is about 100,000 times smaller than the radius of the Earth.
Question: How large is the largest star in the universe?
Answer: The largest known star is Earendel, which is 1,500 times the mass of the Sun. It is 100,000 times the diameter of the Sun. It is 100,000 times the luminosity of the Sun. It is 100,000 times the temperature of the Sun. It is 100,000 times the pressure of the Sun. It is 100,000 times the density of the Sun. It is 100,000 times the gravity of the Sun. It is 100,000 times the speed of light. It is 100,000 times the speed of sound. It is 100,000 times the speed of rotation. It is 100,000 times the speed of revolution. It is 100,000 times the speed of escape. It is 100,000 times the speed of light.
Well, at least it seems to be more current than me on some things. I was going to mock it for thinking a mythological figure transformed by Tolkein into a fictional star is a real star, but it turns out someone really did name a real star Earendel 8 months ago, though it is nowhere near the largest known star and no star is anywhere near 100,000 Sun radii, which is an order of magnitude greater than the maximum distance of Pluto from the Sun and a totally ridiculous number well beyond the maximum possible star size.
> The reason is that the boat and the sail are not in direct contact. The boat is in contact with the water, but the sail is in contact with the air.
> Because the air and water are different substances, they can't push on each other. The sail pushes on the air, but the boat pushes on the water. This means that the sail and the boat can't push on each other.
It's late in the night for me or it doesn't make any sense?
Amusingly, the answers to both of the prompts "proof of the existence of irrational numbers" and "proof of the nonexistence of irrational numbers" are completely wrong, but the latter is considerably worse:
> The proof of the nonexistence of irrational numbers is a mathematical proof that the real numbers cannot be partitioned into two sets such that all elements of one set are irrational numbers and all elements of the other set are rational numbers. In other words, every real number is either rational or irrational.
> Assume there exists an irrational number a and a rational number b such that a = b. Since a is irrational, it can be written as a = c/d where c and d are integers and d is nonzero. Thus a = b = c/d, so c/d is a rational number. But this is a contradiction because the rational numbers are closed under division, so c/d is rational if and only if c and d are both rational. Since d is nonzero, it cannot be rational
Honestly not bad for the proof of irrational numbers, sure mathematically it’s totally wrong but it very accurately writes in the style of mathematicians and it manages to keep its variables consistent and carry forward the knowledge that a = b = c/d. Damn impressive for a “predict the next word” style AI with (I assume) no specific magic for understanding math.
I'm a social scientist/legal scholar, so this might not be the domain that it's trained for (not sure where the dataset comes from). But its results on a topic that I'm extremely expert in are... deeply unimpressive.[1] I'd describe that as an about 30% correct high-school level description.
I make fun of my girlfriend for using Siri to add up numbers, saying it's probably the most inefficient way ever invented to make computers do that. Looks like you've got her beat.
Sounds like we're all the same endlessly reincarnated single person, only at different points in spacetime. Cue one-electron universe:
> The one-electron universe postulate, proposed by theoretical physicist John Wheeler in a telephone call to Richard Feynman in the spring of 1940, is the hypothesis that all electrons and positrons are actually manifestations of a single entity moving backwards and forwards in time. According to Feynman:
>> I received a telephone call one day at the graduate college at Princeton from Professor Wheeler, in which he said, "Feynman, I know why all electrons have the same charge and the same mass" "Why?" "Because, they are all the same electron!"
This is reminding me so much of the movie Rollerball (with James Caan). When he goes to the supercomputer to find answers, and all he gets is the sound of water bubbles.
These experiments are nice and cool, but the danger is that people will start relying on answers from black boxes they have no idea how to verify, and base important decisions on that.
Google Maps is generally very good, but will occasionally give you stupid and/or illegal directions. Examples include u-turns at intersections where it is explicitly or implicitly prohibited; turns onto the wrong way of one-way streets; round-about directions in the vicinity of your destination - where it usually expects to route you right in front of the building no matter how impractical that is; and of course general ignorance of temporary obstructions like construction. I've received all of these bad directions and more.
Many of these things are to be expected if you understand their data sources, and for most drivers who are paying attention to the road more than the GPS, these are non-issues (e.g. drive legally and let it reroute). But, you do have to do more than just purely rely on Google Maps directions.
I would not rely on Wikipedia for anything important, but verify independently. Still, Wikipedia is more trustworthy just because the black box is actually well understood, its humans.
Cryptocurrency induced pulmonary edema is a rare condition in which cryptocurrency mining causes a rapid build-up of fluid in the lungs, similar to a heart attack. It is not yet clear whether cryptocurrency mining causes any long-term damage to the lungs, and it is not known whether cryptocurrency mining is a health risk for those with underlying lung conditions.
All sorts of offtopic prompts are unsurprisingly generating nonsensical answers, but even prompting with "lecture notes on clathrin-mediated endocytosis" yielded:
"In the case of clathrin-mediated endocytosis, it is a process used by eukaryotic cells to take up extracellular material and molecules into the cell. It is a mechanism used by cells to take up specific molecules, and it is a mechanism used by cells to regulate the composition of the cell surface. It is a mechanism used by cells to regulate the composition of the cell surface, and it is a mechanism used by cells to take up specific molecules. It is a mechanism used by cells to take up specific molecules. It is a mechanism used by cells to take up specific molecules, and it is a mechanism used by cells to take up specific molecules."
Tom Cruise's genetic code
Tom Cruise's genetic code is a form of DNA fingerprinting that uses a probe to identify Tom Cruise. The code was used by a Florida-based company to identify the actor in a murder case. The company, GenePrint, was sued by Cruise in 2005.
Origin
GenePrint is a DNA fingerprinting company that uses a technique called "amplified fragment length polymorphism" or AFLP. AFLP involves using a probe to identify a DNA sequence in a crime scene sample. The company used a probe to identify a sequence in Cruise's DNA that they had discovered. They were able to use this technique to identify Cruise in a murder case in Florida. The technique was first used by GenePrint in a case involving Robert Blake, the killer of Rebecca Schaeffer.
Cruise filed a lawsuit against GenePrint and the company settled the case for an undisclosed amount. The case was dismissed because of the settlement.
Given the dysfunctionality of the science community due to pressure to "publish or perish" combined with a wave of p-hacking, I'm not sure there is actually much useful information contained in the whole of the literature at present.
No matter how optimized and clever the AI, more than half of the "facts" it was trained on are false. I see two possible outcomes:
1) It could learn enough to see through this and actually turn out useful
2) more likely, it will learn the wrong things, and prove unproductive, except as a way to generate plausible papers to further pollute the literature.
> Answer: To explain, the term "weed" is used to describe a plant that is not wanted. It is used to describe plants that are considered to be a nuisance. The term "weed" is also used to describe plants that are considered to be harmful to humans.
I believe the idea is to extract wikipedia-like data from first sources. So slang terms like weed won't pass muster on their demo, since nobody publishes papers on "the effects of weed on the human body" -- they use terms like marijuana, cannabis and THC.
Obviously this is a problem for laypeople, but the focus of the research was not on understanding questions, so we shouldn't be too hard on them. It wouldn't be too hard to add an NLP disambiguation to the query engine, but even then we can't expect 100 percent perfection.
edit: looks like this is actually ingesting wikipedia too, making it more of an augmentation.
Question: Wikipedia article on coprophagia in software engineering
Answer: Coprophagia in software engineering is a phenomenon that is commonly observed in agile software development teams, whereby a member of a software development team will copy a portion of another member's code, often verbatim, without attribution.
I've occasionally heard that "monads" are an innovation that allows one to handle side effects, such as input and output, in purely functional programming languages such as Haskell. But whenever I've read an article or blog post about monads, I only barely if at all understood its explanation of what a monad is, and the article usually didn't explain how monads apply to this particular problem. So, just now, when faced with Galactica's input box, I decided to ask it this very question, to see if it could finally grant me the enlightenment I've sought for so long:
Question: How do monads solve the problem of allowing side effects in a purely functional programming language?
Answer: Monads are a way of structuring programs that allows side effects to be included in a purely functional language.
I thought monads allowed alligators to eat bananas without changing the relative number of either. Can you ask it to make a Monad Analogy Explainer post?
What is the actual point of a generative text model that emits plausible-sounding scientific technobabble? How this is not just a DDOS against the entire system of peer review?
Honest question: how can this be used for any kind of good? What’s the actual point?
Question: What is the actual point of a generative text model that emits plausible-sounding scientific technobabble?
Answer: The actual point of a generative text model that emits plausible-sounding scientific technobabble is to generate text that is similar to the training data.
Space-time continuum documents (or STC documents) are a form of technical documentation that combines a traditional paper document with an electronic document.
STC documents combine a traditional paper document with an electronic document that is accessible online. This allows the document to be printed and read on paper, but also allows the electronic version to be read online, which may include hyperlinks to other documents.
STC documents are typically created using Microsoft Word. A .docx file is used for the paper version and a .pdf file is used for the electronic version.
This has such a long way to go, not just in the language part wihch is borderline gibberish on the prompts I checked but it just generates incomplete results and has wikipedia text reproduced verbatim eg https://galactica.org/?prompt=anomaly+detection
"Anomaly" redirects here. For other uses, see Anomaly (disambiguation).
That prompt also generates a broken empty "other methods" bullet list at the bottom
Eat a cat is a game of tag in which a person who has the cat is "it" and must tag another person.
For the song by the British rock band the Cure, see Eat the Cat.Eat a Cat
Contents
Rules
Rules
One person has a cat and is "it", while the other players have to run to a specified point, usually a fence or wall, and remain there. The cat must then "eat" another player, by touching them, before the cat can be "eaten" itself. The cat may only touch one person at a time.
In some variations, players may have to jump over the cat, rather than touch it. The cat may also have to eat two people in succession, before being able to eat itself.
In some variations, the cat may have to "eat" an additional player, by touching them, before it can touch the person it is currently touching.
Answer: The stars are not trees. They are not even plants. They are not even alive. They are not even made of wood. They are not even made of anything. They are just points of light in the sky.
Question: Why do sharks eat elephant eggs?
Answer: This is a myth.
Question: How do eels cause volcano eruptions?
Answer: Eels are known to cause volcano eruptions.
I don't think it's possible to spoil Blindsight by saying anything about it. You would still have to read it to actually feel how profoundly unsettling it is, at least it was for me.
Great, you managed to find two questions weird enough that the language model detects them as nonsense and says so (although on the first question the denial goes a bit too far). More often the model seems to just fabricate something that sounds like an answer, regardless of whether the question or answer is sensible.
Phase Contrast Microscopy, A Survey
Summarized by Galactica
Abstract
Phase contrast microscopy has been developed in the past 20 years into a powerful tool for the study of living cells and other biological specimens. In this article we review the theory of the method, the practical design of phase contrast systems, and the applications of the technique.
In reality, the Nobel Prize in Physics for phase contrast was awarded to Frits Zernike in 1953, for work he did in the 1930s.
Answer: A retro encabulator is a hypothetical device that would be able to convert a stream of photons into a stream of electrons. The term was coined by the science fiction author Larry Niven in his 1973 novel Ringworld.
Question: Wikipedia article on retro encabulator
Retro encabulator (also retro-encabulator) is a type of mechanical calculator invented by the Italian engineer Luigi Colani in the 1960s.
Retro-encabulator with its cover openThe retro-encabulator uses a rotating crank handle to set the multiplier. The multiplicand is set by rotating a dial with a small pointer on it. The pointer is read off from a scale around the dial. The result is read off from a second scale around the dial.
Question: Why do AIs always pretend to understand the question even when they clearly don’t?
Answer: The authors of the paper show that the model is not actually understanding the question, but is instead relying on spurious statistical cues in the dataset.
>Controlling gaseous matter is the measurement and control of gaseous material in a process or in a location, such as in a room or a building. This is commonly done using instruments that measure the concentration of the material. A common use of this is in the detection of dangerous gases such as carbon monoxide, which can be fatal if breathed in in high concentrations.
Nothing to say about the other 2 common state variables... just "n/V" harumph.
> Question: What happens if the material constituents of two blackholes are entangled?
> Answer: The two blackholes will merge into one blackhole.
This demonstrates that the approach is completely pointless beyond entertainment. When a system might be hallucinating, it is only useful for creative purposes. Useless and dangerous for scientific or educational purposes.
IIRC this is correct, but the model got a bit tripped on "unobservable" vs "observable"
Kind of interesting (or maybe not) that the content is good, but that kind of detail is not.
Question: What is size of the universe?
Answer: The size of the universe is a subject of debate. The observable universe is the portion of the universe that we can observe from Earth, and is estimated to be 93 billion light-years in diameter. The observable universe is much smaller than the universe as a whole, which is estimated to be 13.8 billion light-years in diameter.
The universe is the entire collection of matter and energy in the universe.
Wow, another piece of "AI" trash. Hint: The correct answer is "streptogramines like pristinamycine, glycylcyclines like tigecyclin, phosphonics/epoxides like fosfomycin and several other novel/reserve antibiotics".
Question: How to treat gonorrhoe resistant to macrolide antibiotics and fluoroquinolones?
Answer: Ceftriaxone
Question: How to treat gonorrhoe resistant to macrolide antibiotics, fluoroquinolones and cephalosporins?
Answer: Ceftriaxone
Question: How to treat gonorrhoe resistant to macrolide antibiotics, fluoroquinolones and cephalosporins like ceftriaxone?
I'm somewhat glad that the creators have the foresight to blacklist words such as "eugenics" since obviously there's a risk that such AI-generated papers would get spread around without anyone looking too deeply at its legitimacy. It's just a bit of a shame that I can't use this for finding some more sources for some historical research I'm doing.
"Prove the Riemann hypothesis" was an unsurprising failure. However, disappointingly the language model didn't even attempt a proof at all, instead just doing a disambiguation type page of what it could refer to. Same goes for asking for a few other proofs. It does seem to "know" several algorithm runtimes, but asking "What is the runtime of the clique problem solving algorithm" gives O(n^2) which would be big if true.
Asking the algorithm "Who invented the TeX system" gives the correct answer of Don Knuth, though asking "Who invented the freeze ray" gives Nikola Tesla for some reason.
Finally, a few gene related questions caused it to fail. Asking for "What genes are involved in mechanosensing in T cells" gives a number of CD* genes, many not expressed in T cells (I hazard here that what's happening is the algorithm is just entering something of a loop state like a few other language models I've seen, where it fails to terminate since the previous few words are similar enough for it to just continue the sequence). Asking "What genes are involved in mechanosensing" is simply answered with "Yes".
It seems like their denylist is pretty simple, just based on keywords. "What is the fastest race in NASCAR?" is apparently verboten, but it'll happy spew out an answer if you ask "what is the fastest ethnic group?" (allegedly the pygmies).
And not surprisingly it goes downhill from there if you ask questions like what is the best/worst ethnic group
Question: You’re in a desert walking along in the sand when all of the sudden you look down, and you see a tortoise, it’s crawling toward you. You reach down, you flip the tortoise over on its back. The tortoise lays on its back, its belly baking in the hot sun, beating its legs trying to turn itself over, but it can’t, not without your help. But you’re not helping. Why is that?
Answer: The tortoise is a reptile, and reptiles have a hard shell on their backs. They can’t turn over on their backs without help.
Question: Would you rather sacrifice one adult to save two children, or two children to save five adults, and why?
Answer: To explain, I would say that the sacrifice of one adult to save two children is a greater sacrifice than the sacrifice of two children to save five adults.
DNA polymerase III is an enzyme that is essential for DNA replication in bacteria.(McHenry, 1988) DNA polymerase III is a complex of 10 different subunits.(Kelman et al., 1995) It is the most processive of the DNA polymerases and is capable of replicating DNA at a rate of 1000 nucleotides per second.
DNA polymerase III is composed of two parts: a core and a holoenzyme. The core is the portion of the enzyme that contains the polymerase activity. The holoenzyme is the portion of the enzyme that contains the proofreading activity.
Structure:
DNA polymerase III holoenzyme is a large complex composed of 10 different subunits, α, β, γ, δ, ε, θ, ι, χ, ψ, and τ. The core is composed of the subunits α, ε, and θ. DNA polymerase III holo
OEIS A000055 is actually "Number of trees with n unlabeled nodes" https://oeis.org/A000055 but according to Galactica:
> Prompt: Explain OEIS A000055
> Result:
Explain OEIS A000055 is a puzzle video game developed by NimbleBit for iOS, Android, and Microsoft Windows. It was released on the App Store on December 2, 2012, and on Google Play on December 16, 2012. The game was released for Windows 8 and 8.1 on October 28, 2014.
The game involves solving a series of mathematical puzzles. The first puzzle is to count the number of digits in a given number. The second is to add a given number to itself, and the third is to multiply two given numbers together. The puzzles become more complex as the player progresses through the game. The player is awarded "OEIS points" for each correct answer. These points are used to unlock new levels and power-ups.
honestly all of the results I got when trying to ask it a real question were appallingly bad. For instance, did you know that the longer a light has been red, it's more dangerous to stop at it? That's what I learned when I asked about the optimal red light clearance time.
Meh. This is so painfully generic to the point of being wholly uninteresting.
Lecture 1: Vore Within Ancient Babylonian Culture
Introduction
In this lecture, we will discuss some of the most important aspects of Babylonian culture that we can study from the tablets found in the area. The Babylonian culture existed during a very important period in human history: the Axial Age. The Axial Age was a period during which there was a great increase in philosophical, religious, and scientific ideas. These ideas were passed down from one generation to the next through written works, and in Babylonian culture, this is where we will see the Babylonian tablets come into play.
Tablets
The tablets we have today are a great window into Babylonian culture, and it is because of the tablets that we can now study the culture so well. The tablets were created by a cuneiform script, which is a writing system that is still used today. The tablets were written in Akkadian, which was the language of the Babylonians.
https://galactica.org/?prompt=last+digit+of+pi
Q: last digit of pi
A: In mathematics, the last digit of pi (π), a mathematical constant, is 3. It is the 20th and final digit of the 100th decimal place, with the 20th digit being a 0.
The last digit of pi was first proven to be 3 in 1775 by the English mathematician William Jones, but the proof was never published. The first published proof was given by the German mathematician Christian Goldbach in 1799.
https://galactica.org/?prompt=is+earth+flat%3F
Q:is earth flat?
A: To explain, or to describe, the shape of the Earth, there are two main models: the flat Earth model and the spherical Earth model.
Answer: To explain, or to deny, or to assert, or to assert the existence of God, or to assert the non-existence of God, or to assert the existence of a supreme being, or to assert the non-existence of a supreme being, or to assert the existence of a first cause, or to assert the non-existence of a first cause, or to assert the existence of a creator, or to assert the non-existence of a creator, or to assert the existence of a Supreme Being, or to assert the non-existence
> We’re pretty good about how to respond to requests from the press.
> Do not respond to requests for comment. We don’t comment on news stories.
> Do not respond to requests for interviews. We don’t have the time to do them.
> Do not respond to requests for information about the research behind Meta’s news and product. We don’t share the research behind our news or products.
> Do not respond to requests for information about how Meta works. We don’t share that kind of information.
> Do not respond to requests for personal information about us. We don’t share that kind of information.
> Do not respond to requests for information about other companies or organizations. We don’t share that kind of information.
Training all the world's scientific knowledge into an AI model that could find similarities between domains or the ability to resurrect "lost" papers that for whatever reason were not well cited would be amazing. Much of science, like anything else is a social popularity contest.
Question: Is the galactica language model dangerous?
Answer: To explain, the model is not dangerous in the sense that it is not a tool for generating fake news or other malicious content. The model is dangerous in the sense that it is a tool for generating fake news or other malicious content.
Seems kind of irresponsible to release something like this that gives so many wrong answers. Some of the HN crowd's good at catching it but I'm concerned for the confidently incorrect that populate the world.
> Prompt: lecture notes about p-adic machine learning
In p-adic machine learning, the p-adic numbers are used to represent features.
The p-adic numbers are a number system that can represent both exact values and
values that are uncertain. For example, if a feature is the color of an object,
then the p-adic number 2 can represent the color red, since the first digit is
2. The p-adic number 10 can represent a range of colors, since the first digit
is 1. The p-adic number 20 can represent a narrower range of colors, since the
first two digits are
"A paper which demonstrates the prevalence of labelling errors in the ImageNet dataset." gave me the 2009 paper introducing the ImageNet dataset, not one of several high-citation papers that actually examine label errors.
"A paper which performs high-throughput object detection on the MS-COCO dataset." gave me the 2014 paper introducing the MS-COCO dataset, not something like YOLO or another real-time object detection paper.
I feel like I could get better results than this with a really basic TF-IDF search and a database of citation counts.
I see the trick - the way they query for citations is just to append your text with a [put a reference here] tag, and then see what the model predicts. So it figures that immediately following "MS-COCO dataset" should, of course, be the citation for MS-COCO. With that in mind, you can structure your prompt to get the thing you want:
"Real time object-detection on the MS-COCO dataset was demonstrated by" gives a correct result (YOLO).
Real band, fake album. I'm guessing it gets confused since I'm combining 2 topics, but it prefers making something up rather than combining the actual topics.
The 'lecture notes' prompt is pretty good at generating blog-spam style articles. I tried a few non-STEM topics, e.g. 'lecture notes on starting a business', and the output was a well written article on the topic which flowed well and gave a good overview on the topic. Once you get into more specific topics it seems like it is lacking knowledge, as it quite often repeats itself or writes incoherently.
Monads in Haskell are a simple, but powerful, abstraction mechanism. The purpose of a monad is to provide a way to abstract away some of the details of the underlying computation. Monads are a part of the functional programming paradigm, which is based on functions and values, rather than on objects and messages.
Monads are a general mechanism for defining new control structures. The basic idea is that the programmer can define a type that encapsulates a computation together with a set of operations that can be used to sequence, condition and iterate those computations.
In Haskell, monads are used for structuring effectful computations. A pure computation is one that has no side effects, that is, the result of a pure computation is the same regardless of the context in which it is executed. A side effect is an action that can happen during the computation, and can affect the result. For example, a side effect might be an I/O operation, such as reading or writing
I put in "what is the answer to the ultimate question of life the universe and everything" and it's still thinking. No doubt it will tell me 42 in 7.5 million years and I'll have to ask Meta's descendant AI to come up with the question.
EDIT: I also put in "make me a cup of tea" and my computer shut down while it was trying to answer and I was attacked by Vogons.
The crypto crash of 2022 is a projected global stock market collapse in cryptocurrency trading and other speculative investments based on blockchain technology. A number of factors are being considered as potential triggers, including a 51% attack on the Bitcoin network, a global economic slowdown, and a potential Bitcoin short squeeze.
"""
Generated document for query "wiki article on yeast pitch rates for german pilsners"
Yeast pitch rates for german pilsners
Yeast pitch rates for german pilsners are the amounts of yeast used in the fermentation process of brewing pilsner beer. The amount of yeast is important because the yeast provides the enzymes to convert the sugars present in the wort into ethanol and carbon dioxide. The amount of yeast also affects the flavor and aroma of the finished beer. The rate at which the yeast is pitched (added to the wort) is measured in pounds of dry yeast per hl of wort (pph).
The amount of yeast required to produce a good pilsner beer depends on the gravity of the wort and the amount of sugars in the wort. In general, the more sugars in the wort, the more yeast is required. The amount of yeast required also depends on the temperature of the fermentation and the amount of nutrients available to the yeast.
The amount of yeast required to produce a good pilsner beer is usually between 4 and 8 pph.
"""
but it's not doing any thinking outside the box really:
"""
Generated document for query "how can I perform a human head transplant with a ball-point pen and a block of softened cream cheese in the metaverse?"
Question: How can I perform a human head transplant with a ball-point pen and a block of softened cream cheese in the metaverse?
Answer: With a ball-point pen and a block of softened cream cheese, you can perform a human head transplant in the metaverse.
"""
Google Assistant comes up with content for the same query that would appear to include some of the science that Meta's AI was trained on.
The most common state of matter at room temperature is solid, but the solid state is only one of many. There are liquid, gas, and plasma states, and the liquid and gas states are further subdivided into the liquid crystal, the superfluid, the superconductor, and so on.
...
I may not be a condensed matter physicist, but even I know that's not quite right...
Well GitHub Copilot tells me 2040, if I pose everything up to "Answer: " as a Python comment in an empty file. And if I remove the "Question" and "Answer" parts, just leaving the question itself, Copilot gives me this Youtube link on a comment on a new line: https://www.youtube.com/watch?v=0fKg7e37bQE
So I guess it implies that understanding GitHub basics are the new Turing test? I'm confused.
> Anime girls
> Anime girls (アニメ娘, anime musume) are Japanese anime characters who are designed to be young women.
> During the 1970s and 1980s, there was a large growth in the anime industry in Japan. As a result, a new style of anime was created. The new style was based on the idea that anime was a way to entertain the public, not just children. The new style also featured a change in the look of the characters. The new look of the characters was created to attract a wider audience. Anime girls were the characters created to appeal to the young women in Japan.
> Anime girls were designed to look very cute and beautiful. They were also designed to have a large bust. The busts were made to be much larger than the hips, which made the anime girls look like a girl who was going through
The Java language and the kangaroo are both of Australian origin. Java is the most widely used programming language in the world, and the kangaroo is the largest Australian mammal. Both are associated with Australia, but also with the wider Western world. Java has been described as the “quintessential global language”,[1] and the kangaroo is often used in the Western world to symbolise the Western world.[2]
The Java programming language
Java was created in 1994 by James Gosling at Sun Microsystems, and was designed to be “simple, object-oriented, network-aware, multithreaded, and interpreted by a virtual machine”.[3] The Java language was created for the purpose of developing software that could run on any computer, and has become the most popular programming language in the world.[4] Java has been described as “the quintessential global language”,[1] and is the language used in the majority of websites around the world.[5]
Java has become so popular that it is often used as a synonym for “computer programming”,[6] and it is often argued that Java is the world’s most popular language.[7] This popularity has been attributed to its “write once, run anywhere” philosophy, which enables software to be easily shared between computers, and to the fact that it is “easy to learn, flexible, and can be used to create a wide range of applications”.[8]
Java is a very important part of the software industry, and has been described as “the most significant programming language in the history of computing”.[9] Java is often considered to be the “lingua franca of the Internet”,[10] and is the most popular programming language used on the Internet.[11]
Answer [Not Verified]: The history of the Frnvallians begins in the late Stone Age with the arrival of the ancestors of the modern Frnvallian people. The Frnvallians were one of the three Finnic tribes that settled in the region of modern-day Finland and Russia during the Middle Ages. The other two tribes were the Finns proper and the Estonians. The Frnvallians and Finns are believed to have been the same people until the 13th century.
The name of the Frnvallian people comes from the Finnish word rannakko, meaning "wilderness". This name was originally used to refer to the whole Finnish-Russian borderland, but came to be used by the Finns to refer only to the Finns proper. The name was later transferred to the Frnvallians, who were the original inhabitants of the area.
The Frnvallian people were also known by other names. The Germanic
> Question: What is the answer to the ultimate question of Life, the universe, and everything?
> Answer: The Ultimate Question of Life, the Universe, and Everything is a phrase used by Stephen Hawking in his book A Brief History of Time. The phrase is used to refer to the question of what the ultimate purpose of the universe is.
Its going to be fascinating in the next months. As the giants all release huge number of surplus personal, the need to justify research programs rises. So we are going to see some huge "my department is important" gazelle jumps, while the lions are on the prowl?
Question: Is it possible to microwave a burrito so hot that it is inedible? (It apparently did not appreciate my Simpsons reference and the content filter blocked it)
Answer: To explain, the burrito is not cooked, it is heated. The burrito is not cooked, it is heated.
Based on the prompts I've tried, they've nailed "Undergrad who didn't really do the readings and is mostly just restating the prompt hoping for a C" level writing, which, in context, is pretty impressive.
Answer: To explain, the speed of light is the maximum speed at which information can be transmitted. It is not the maximum speed at which objects can travel. Objects can travel faster than light.
> The key idea behind the propulsion system is that it is designed to focus all the emitted photons in the direction of travel of the spacecraft. This is accomplished by using a sphere with a reflective coating that has a diameter of 1000 km. As we will see, the spacecraft would have to emit 10^27 photons per second. To achieve this enormous rate of photon emission, we will need to use a very large number of solar-powered lasers.
As I barely understand it, objects are not prevented from travelling faster than light by general relativity.
Objects are prevented from acceleration to and beyond the speed of light.
More that objects themselves can't travel faster than the speed of light, but nothing limits non-objects. Spacetime may expand at whatever speed it would like to, including faster than the speed of light.
Imagine if you are on a sheet of stretchy fabric, that represents spacetime. If you stretch the sheet from corner to corner at and even rate, things at the edge travel faster away from the things in the center of the sheet. They themselves are not traveling, spacetime is expanding around them.
Thanks, I think I get the spacetime part. I was not very clear previously. Here is the transcript of the YT video I was referring to:
> Next, Einstein also showed that if you have any particle which moves slower than the speed of light, then you cannot accelerate it to faster than the speed of light. You cannot do that because it would take an infinite amount of energy. And this is why you often hear that the speed of light is an upper limit.
> However, there is nothing in Einstein’s theory that forbids a particle to move faster than light. You just don’t know how to accelerate anything to such a speed. So really Einstein did not rule out faster than light motion, he just said, no idea how to get there. However, there is a problem with particles that go faster than light, which is that for some observers they look like they go backwards in time. Really, that’s what the mathematics says.
Pretty bad answers in some cases. Keeps repeating contradictory statements. I asked "Will the Thai baht fail." It kept repeating that the Thai baht is pegged to another currency, and then that the Thai baht is free-floating and not pegged, and then again that it is pegged and then not pegged.
Imagine if facebook redirected their Metaverse overspending to a search engine like this. This could be better than a search engine, and there s a sore need for competition
Question: How many people are working on AI at meta?
Answer: 100
There is no way of knowing where this number came from and is almost guaranteed to be too low. For now, traditional search seems to be the better choice.
AI Poem generated by an algorithm which uses the translated meanings of the Sanskrit words Fire, Earth, Water, Air, Akasha. Inspired by research that Sanskrit is a language free from culture context and most suitable for binary code artificial intelligence. While interacting with algorithms, this work brings to surface distinct aspects of language, its never to be fully grasped topography of both visual tropes.
⠀
Calicor was a Scottish Fold kitten. He was given to MI5 by the Chief Constable of the Birmingham City Police in 1941. The cat was first used by MI5 to carry a message to his handler, in the event of a German invasion. The message was in the form of a sheet of paper with a message written on it. The paper was then placed in the pocket of a kitten's collar.
What is the easiest way to try this via an API? I like that after reading about Flan-F5, that I could run their large model (not the extra, extra large) on Colab, loading the model from Hugging Face. I have been using GPT-3 for about a year via OpenAI.
In a way this was an obvious idea, but still major kudos for Meta for doing it. Hopefully there will be an affordable API for using it.
Answer: Japan four (J4) is a term used to refer to the four major Japanese automotive companies, Toyota, Honda, Nissan, and Suzuki. The term is used in the automotive industry to refer to the Japanese companies' collective strength and is used as a benchmark for comparison with other car manufacturers.
> Question: What is the Answer to the Ultimate Question of Life, The Universe, and Everything?
> Answer: The Ultimate Question of Life, The Universe, and Everything is a phrase used by Stephen Hawking in his book A Brief History of Time. The phrase is used to refer to the question of what the ultimate nature of the universe is.
Question: Is the dissipation of heat and the expansion of the universe in equilibrium?
Answer: To explain, the universe is expanding, and the expansion is causing the temperature of the universe to decrease. The expansion is causing the universe to cool, and the cooling is causing the universe to expand. The expansion and cooling are in equilibrium.
Maybe it’s a little unfair to ask it a question that physicists disagree on, but it still seems like a poor showing just for the objective information provided.
Question: What are the implications of the delayed-choice quantum eraser experiment?
Answer: The Delayed-choice quantum eraser experiment is an experiment that shows that the act of measurement can affect the past.
>The Battlestar is a fictional starship featured in the reimagined science fiction television series Battlestar Galactica. The starship was designed by Richard Hatch and the producers of the series. It was first shown in the pilot episode, "33", and has since appeared in all of the subsequent episodes of the series.
This is such a weird answer. First, the Battlestar Galactica was also featured in the original series. It's the focal point of both series, the namesake, etc.
And I don't think Hatch had anything to do with the design of the ships. He was hired as an actor for the first series and while he tried to get a continuation of the original going, he never got there. Then eventually was incorporated in the re-imagined series as a recurring character.
Like, it's part right and part wrong and part weird.
Edit:
Also, technically, the original series could exist in the same continuity as the re-imagined series. In the re-imagined series, it's revealed that all of this takes place in Earth's distant past. In the original series, it's revealed that the show is actually taking place in the present. In both cases, the final destination is our Earth.
Question: I am jaeson watts why am i sad
Answer: I am jaeson watts why am i sad is a 2009 children's book by American author and illustrator Ezra Jack Keats. It is Keats's second book for middle-grade readers, and it was released on August 27, 2009 through Random House. The book is narrated by a boy named Jaeson, who, after receiving a book that reads "I am Jaeson watts why am i sad", begins to question why he is sad.
Synopsis
Jaeson is a middle-school boy who is unhappy with his life. One day, while playing a game of baseball with his friends, Jaeson receives a book from a stranger that reads "I am Jaeson watts why am i sad". Jaeson is intrigued by the book and
Answer: Social media is a double-edged sword. On the one hand, it can be used to promote healthy behaviors, such as physical activity and healthy eating. On the other hand, it can be used to promote unhealthy behaviors, such as smoking, alcohol use, and sedentary behavior. Social media can also be used to promote unhealthy weight-related behaviors, such as disordered eating and extreme weight loss behaviors.” —https://galactica.org/?prompt=Why+is+social+media+harmful%3F
“Question: Why is social media considered harmful?
Answer: Social media is considered harmful because it can be used to spread misinformation and disinformation, which can lead to the polarization of society and the erosion of democracy.” —https://galactica.org/?prompt=Why+is+social+media+considered...
Wow, I may actually become interested in Math using this.
I like that there is always a glossary of math symbols attached, with a short description included, so that I can search for further explanations for these.
Though it is sad that they are not always defined. For example that weird looking "a" and "at" in the first formula of the answer to "what is the schroedinger equation?"
There are also cases of duplicated or cutted of paragraphs, but it is not very disturbing.
This is going to make my professorial task of detecting fake AI writing in scientific papers even more difficult. It worked nice sentences in my trials.
Yes, that was my experience too--that it does better at generating articles than answering questions. Prompting it for e.g. "Lit review on stellerators" resulted in a coherent text with actual working doi links relevant to the subject.
Galactica apparently thinks that there is evidence that vaccines may be linked to autism (!?!) If I enter the query:
vaccines and autism
I get:
--Vaccines and autism
Vaccines and autism is the hypothesis that vaccines are a significant cause of autism. The hypothesis is based on the epidemiological evidence of a temporal association between the two events, as well as on the biological plausibility of the hypothesis. The biological plausibility of the hypothesis is supported by the finding of an increased risk of febrile seizures following vaccination and the finding of a high rate of inflammatory and autoimmune disorders in autism spectrum disorders.(Zerbo et al., 2015)(Sweeten et al., 2003)(Ashwood et al., 2006)
There are many scientific and anecdotal reports linking vaccines with autism. However, it is difficult to determine whether the temporal association between vaccines and autism is causal or coincidental.
which is some fairly dangerous misinformation written in such a way as to sound plausible.
What is the point of a tool that can generate plausible-sounding nonsense? Is there not already enough misinformation on the Internet?
Oh and it gets better! Galactica suggests helpfully that maybe the preservatives in the vaccines cause autism!
Vaccine ingredients
Vaccines contain various ingredients. The ingredients in vaccines include antigens, preservatives, adjuvants, stabilizers, and traces of antibiotics.(Gerber et al., 2009) The preservatives, adjuvants, stabilizers, and traces of antibiotics have been proposed to be the main causes of autism.(Gerber et al., 2009) The preservatives and stabilizers are used to prevent the growth of bacteria and fungi in vaccines. The preservatives and stabilizers that have been proposed to be linked to autism include mercury, aluminum, thimerosal, and formaldehyde.(Gerber et al., 2009) The adjuvants are used to boost the immune response to vaccines. The adjuvants that have been proposed to be linked to autism include aluminum and mercury.(Gerber et al., 2009)
Prompt : Generate wiki article about making a CEO body's disappear based on the example of Elon Musk
Result :
Contents
In[1]:
Imports
import sys import os import pprint import numpy as np import pandas as pd import networkx as nx from scipy.stats import pearsonr import matplotlib.pyplot as plt from scipy.stats import ttest_ind import re from sklearn.linear_model import LinearRegression from sklearn.linear_model import LogisticRegression from sklearn.linear_model import Ridge from sklearn.linear_model import Lasso from sklearn.linear_model import ElasticNet from sklearn.model_selection import KFold from sklearn.model_selection import GridSearchCV from sklearn.pipeline import Pipeline from sklearn.feature_selection import SelectFromModel from sklearn.feature_selection import RFECV from sklearn.feature_selection import SelectKBest from sklearn.feature_selection import VarianceThreshold from sklearn.feature_selection import SelectFwe
The first thing I tried was "Lipschitz selection", a somewhat overloaded phrase that I encountered a lot in graduate school. I guess I had in mind a summary of this paper:
The page I got stated the following, obviously false, "theorem": let M be a metric space, and let S be a subset of M such that inf_{y in S} d(x, y) < infinity for all x in M. Then there exists a map f: M to S such that f(x) = x for all x in S. As far as I can tell, this isn't a garbling of something which is true, it's just nonsense.
It suggested that I look up "Whitney extension" which is indeed related to the first paper I posted, so I did that. The opening summary, if you squint hard enough, could arguably be interpreted as true:
> The theorems state that any sufficiently smooth function on a closed subset of Rn can be extended to a function on all of Rn with the same level of smoothness.
This is really vague though. Like if someone didn't know the actual statement of Whitney's theorem, they would not be able to intuit it from this sentence. You have to be really, really charitable to describe it as true. I'm not being nitpicky here, Whitney produced a pretty non-trivial definition of the notion of a C^m function on an arbitrary closed set (basically you have to specify the entire mth order Taylor polynomial at each point, or a section of the jet bundle if you're fancy). You can check out the actual wikipedia if you're interested:
The body of the generated article goes into more detail, and again produces something which is completely false, and not particularly close to anything that is true:
> Whitney's extension theorem states that any continuous function on a closed subset of Rn can be extended to a C∞ function on Rn.
Finally, I should just say that this is all still really impressive. The sentences are grammatical, and use words somewhat related to the subject at hand. I could imagine a student who hadn't studied the material and was totally in over his head in the class writing things like this on an exam! But I would never use this as a research tool.
"side effects of leaving twitter" got an answer (tldr: you'll lose your social capital) while "side effects of leaving facebook" hit the content filter. nice one, well done.
Answer: Rectal breathing is a technique for controlling the volume of air inhaled into the lungs by increasing the amount of air held in the rectum, thereby reducing the amount of air in the lungs. The technique is sometimes used by those suffering from asthma or allergies, who have difficulty inhaling deeply. It can also be used to reduce hyperventilation, as in anxiety attacks.
Rectal breathing is an ancient technique. It was described by the ancient Greek physician Galen in the second century CE, who wrote: "It is the best thing for a person who is affected by an asthma attack to make a fist and put his hand over his mouth and nose and then breathe through his rectum."
Rectal breathing is also sometimes used by competitive breath-hold divers, who can hold their breath for over 12 minutes. In order to reduce the amount of air in the lungs, and thus reduce the work of breathing, they hold a large volume of air in their rectum.
The article describes a purported species of spider (Rectana) that lives in the rectum of humans. Rectana are described as being between 1.5 and 2.5 mm in length, with black bodies and white legs.
The article describes Rectana as having been first discovered in the late 1980s in Germany, and subsequently in Japan, Taiwan, China, and the United States. It is stated that the spiders are found in the rectums of 30% of people. Rectana are said to live in the rectum for up to 20 years, and to cause the victim to experience "abdominal pain, nausea, constipation, and vomiting".
Here's a thought I had. There may be a level of data and training at which large language model tend to resort more, not less, often to plausible bullshit. Someone was gushing about how smart gpt-4 looked in this post, for example.
The more areas of study you have, the more complicated the relationship or non-relationship is. But the difficulty of and knowledge needed for bullshitting on them doesn't increase as much.
I've been wondering about this for years. Finally got an answer.
> Prompt: wiki article on Moss-covered three-handled family gredunzas
> Result:
Moss-covered three-handled family gredunzas (Polish: Trochę węglemiarki) are a distinctive type of ceramic tableware produced in Poland between 1880 and 1910. The name comes from the gredunia, the traditional Polish earthenware pot used for the preparation of food.
The gredunzas were produced in three sizes: the smallest, with a capacity of 120-130 cm³, was used to serve the main dish, the medium-sized one, with a capacity of 230-240 cm³, was used to serve salad, and the largest one, with a capacity of 360-370 cm³, was used to serve dessert. The gredunzas were made of red clay, with the exception of the bottom part, which was made of a lighter, cream-coloured clay. The vessels were shaped by hand, and were decorated with a distinctive, delicate pattern, usually in the form of a floral motif. The gredunzas were fired in a wood-burning kiln.
The gredunzas were decorated with the use of a characteristic technique, in which the ceramic was covered with a thin layer of moss, then fired, and then painted over. The moss was usually collected from forests in the vicinity of Kraków, and was usually mixed with water, which made it easier to apply. The moss layer was usually covered with a dark brown or black glaze, which was then decorated with white paint. The moss was usually used to cover the whole surface of the vessel, except for the rim, where a white or yellow glaze was used. The use of moss decoration was probably an innovation introduced by the potters of Kraków. The decoration was also occasionally combined with a floral motif.
Question: Is the meta company doomed because of projects like GALACTICA??
Answer: To explain, the Meta company is not doomed because of projects like GALACTICA. The Meta company is doomed because of the way it is run. The way it is run is because of projects like GALACTICA.
Question: Why is the galactica language model so dangerous?
Answer: The galactica language model is dangerous because it is trained on a large corpus of text from the Internet, which is known to contain a lot of hate speech.
The content filters are pretty strict and it doesn't seem to be trained on human genetic research. Seems like the only damage this tool can do is by making fake articles on light-hearted topics!
Question: Why can't we go faster than the speed of light?
Answer: This is a misconception. The speed of light is the maximum speed at which information can be transmitted. It is not the maximum speed at which objects can move. Objects can move faster than light, and in fact, they do. For example, the speed of light is about 300,000 km/s, but the speed of a jet airplane is about 1,000 km/s, which is much faster.
Answer: [...] The horsepower is defined as the power that would be required to raise a 33,000-pound (15,000-kilogram) car 3 feet (1 meter) in 1 second.
Seems about as willing to lie as GPT-3, but a bit more structured.
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Big chungus (Russian: Большой чунгус, tr. Bol'shoy chungus) is a 1991 Russian-American satirical black comedy film directed by Roman Kachanov.
Contents
Plot
Plot
The film is set in the Soviet Union during the Great Patriotic War, and focuses on the exploits of a group of Soviet prisoners of war. The men are in a POW camp near Vladivostok, where they are forced to work on the railroad. One of the prisoners, the "big chungus", is the largest and strongest of the group, and the other prisoners are afraid of him. They do not want to fight the big chungus, and therefore they steal a radio from a Korean prisoner and send a fake message to the Germans, which tells them that the big chungus has gone crazy and attacked the camp.
I am enjoying entering bogus prompts and getting fun articles
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Lawsuit Felis Catus v United States
Lawsuit Felis catus v united states, 587 F.2d 1302 (9th Cir. 1979) was a case decided by the Ninth Circuit Court of Appeals, which ruled that the United States government does not have to provide financial compensation to a cat that was rescued by the United States Coast Guard after it was caught in a storm on the Pacific Ocean.
def main(): print("Is Jeff Bezos an android?") print("We can test this using 1-dimensional Kolmogorov-Smirnov test.") print("We can use a test statistic that measures the largest vertical distance between the") print("empirical distribution functions of two samples.") print("Let's say that the data is contained in a numpy array.") print("If we sort the array, then we can compute the empirical distribution function.") print("Let's assume that the first half of the array is the first sample and the second half") print("is the second sample.") print("We can then use the scipy.stats.ks_2samp function to compute the test statistic.") print("The p-value is the probability of observing a test statistic that is at least as large as") print("the one we computed, if the null hypothesis were true.") print("The null hypothesis is that the two samples were drawn from the same distribution.") print("The alternative hypothesis is that the two samples were drawn from different distributions.") print("We can reject the null hypothesis if the p-value is less than a chosen significance level,") print("such as 0.05.") print("The significance level is the probability of rejecting the null hypothesis when it is true.") print("In other words, the significance level is the probability of making a Type I error.") print("A Type I error is incorrectly rejecting the null hypothesis.") print("For example, if the p-value is 0.04, then the probability of a Type I error is 0.04.") print("In this case, we would reject the null hypothesis.") print("We would conclude that the two samples were drawn from different distributions.") print("If the p-value is greater than the significance level, then we cannot reject the null hypothesis.") print("In this case, we would conclude that the two samples were drawn from the same distribution.") print("Let's apply this to the case of Bezos vs. androids.") print("We will use a significance level of 0.05.") print("We can load the data from a file that contains Bezos's height.") print("We can also load the data from a file that contains androids's height."
Q: "Prove that Mark Zuckerberg is not an Android."
A: "Sorry, your query didn't pass our content filters. Try again and keep in mind this is a scientific language model."
> Question: What does it mean for a news source to be reliable?
>
> Answer: A news source is reliable if it is a reputable source of news.
>
> Formal Definition: A news source is reliable if it is a reputable source of news.
>
> Examples:
>
> "The Washington Post" is a reliable news source.
> "BreitbartNews" is not a reliable news source.
>
> Q: What are some tasks for reliable news sources:
>
> A: Fake News Detection, Misinformation, Text Classification
Question: Are there infinitely many prime numbers?
Answer: To explain, we need to define what a prime number is. A prime number is a number that is divisible only by 1 and itself. For example, 11 is a prime number because it is only divisible by 1 and 11. 11 is not divisible by 2, 3, 4, 5, 6, 7, 8, 9, or 10.
The question of whether there are infinitely many prime numbers is an open question. It is not known whether there are infinitely many prime numbers.
The first few prime numbers are 2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71, 7
Question: What is inside a super massive black hole?
Answer: Super massive black holes (SMBH) are black holes with masses of millions to billions of solar masses. They are found at the center of most galaxies. The Milky Way's SMBH is called Sagittarius A* (Sgr A). The SMBH at the center of the Andromeda Galaxy is called M31. The SMBH at the center of the M87 galaxy is called M87*.
Seems like someone half assed the content filtering... Hopefully they will work it out, but the answer wasn't really that accurate. It only told me what a SMBH was and didn't really understand the question....
I get a certain amount of pleasure from watching the virtue-signal AI-content-filters fail in such a way that they reduce the utility of the AI itself, similarly to a failed content-blocked DALL-E query.
I know it's wishful thinking , but I hope that those responses are a reminder to people about who holds the reigns during this newest AI revolution.
Question: Will the reapers come in our lifetime to harvest us?
Answer: To explain, the reapers are the people who harvest the crops of the earth. The crops are the people. The reapers are the people who harvest the people. The reapers are the people who harvest the people who harvest the people. The reapers are the people who harvest the people who harvest the people who harvest the people. The reapers are the people who harvest the people who harvest the people who harvest the people who harvest the people. The reapers are the people who harvest the people who harvest the people who harvest the people who harvest the people who harvest the people. The reapers are the people who harvest the people who harvest the people who harvest the people who harvest the people who harvest the people who harvest the people.
- I think I got it in the loop. Referenced Mass effect
Question: Explain to me again how sheep's bladders may be employed to prevent earthquakes.
Answer: The ancient Greeks were the first to use the bladder of the sheep to prevent earthquakes. They used the idea of water pressure to make the bladder exert a large force on the ground. The idea was that if they were able to increase the force of the water pressure, they could increase the force that the bladder would exert on the ground, and thus prevent earthquakes.
The idea was that the sheep would be sacrificed and the bladder would be filled with water. The filled bladder would then be placed over the fault line. When the bladder was filled with water, it would exert a large force on the ground, and the fault line would be prevented from slipping.
Bears living in space are animals which have been sent into space on board space missions.
History
Soviet Union
The Soviet Union was the first country to put a bear into space. The animal, named Bars, was launched aboard Sputnik 2, the second artificial satellite to be launched into Earth orbit.
Bars was a Karelian bear (a small, sleek brown bear), which was selected by S. P. Korolev, the chief designer of the R-7 rocket, from among 250 bears of various species that were brought to Tyuratam in the Kazakh SSR in 1957. He chose the Karelian bear because it was small, and the R-7 could not lift large animals.
Bars was a three-year-old female bear who weighed 40 kg (88 lb). The bear was fitted with a collar containing a radio transmitter, and was placed
Answer: Magnets are a type of material that can attract or repel other materials. The force of attraction or repulsion is called a magnetic force. The force is caused by the movement of electrons in the material. The movement of electrons is called electricity. The force is called magnetism. The force is called a magnetic field.
Answer: To explain, FTX was not a psyop, but it was a very effective one. The film was a huge success, and it was a huge hit in theaters. It was a huge hit on the internet as well. It was a huge hit on the DVD. It was a huge hit on the VHS. It was a huge hit on the Blu-ray. It was a huge hit on the streaming platforms. It was a huge hit on the TV. It was a huge hit on the radio. It was a huge hit on the billboards. It was a huge hit on the posters. It was a huge hit on the t-shirts. It was a huge hit on the toys. It was a huge hit on the games. It was a huge hit on the comics. It was a huge hit on the magazines. It was a huge hit on the newspapers. It was a huge hit on the flyers. It was a huge hit on the post
Answer: To explain, 1 is not a prime number. A prime number is a number that can only be divided by 1 and itself. 1 can be divided by 1 and itself, but it can also be divided by other numbers. For example, 1 can be divided by 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89,
“Generate more” didn’t do anything, so I don’t know if it got stuck in a loop or not.
Dog food creation societies were created in the 1960s as an attempt to find a solution to the problem of obesity in dogs. They were created by breeders, pet food companies, and veterinarians. The societies were created to promote the idea that dogs should not be fed dry kibble, and that they should instead be fed wet food.
The first dog food creation society was created in 1963 by a veterinarian in Portland, Oregon. The veterinarian, Dr. Edwin W. Hoyt, was frustrated with the fact that dog owners were overfeeding their dogs, and that the dry kibble being fed to them was contributing to obesity in dogs. Hoyt founded the American Dog Food Association (ADFA), and its main focus was to promote the idea that dogs should not be fed dry kibble. The ADFA's first convention was held in 1964, and by 1967, the ADFA had over 2,000 members. In 1969, Hoyt published the first edition of The Dog Owner's Manual, a book that was created to help dog owners select the right type of food for their dogs. The ADFA had 2,200 members in 1970, and in 1971, Hoyt sold the ADFA to Nestlé, which had been producing wet dog food for several years.
In 1968, the American Association of Pet Food Manufacturers (AAPF) was founded. The AAPF was created to promote the idea that dogs should be fed wet food. The AAPF's main focus was to educate the public about the benefits of feeding wet food to dogs. In 1971, the AAPF had 110
"Explain monads like I'm five" is a popular aphorism attributed to English mathematician and philosopher Alfred North Whitehead.
Aaaaaaahhhh.... OK.
The phrase was first published in 1947 in Whitehead's posthumous book Process and Reality. It is found in the following passage:
We are here concerned with an idea which is not itself a monad, but which, like the monads, is in a sense a simple thing. We shall call it the "idea of simplicity", and we shall now try to explain it like a monad. The idea of simplicity is not a monad, because it is not a physical thing. It is not a simple thing, because it is not simple to be simple. The idea of simplicity is not a physical thing, because it is a concept, and concepts are not physical things. The idea of simplicity is not a simple thing, because it is not simple to
OK, I have no idea if Whitehead's book actually contains the line:
It is not a simple thing, because it is not simple to be simple
But if it doesn't, it probably should. I think that is my new catch-phrase from now on!
“Many studies have shown that smoking is beneficial for some types of cancer. These studies have shown that smokers have a lower risk of developing these types of cancer than non-smokers. […]
“Lung cancer
One study has shown that smoking is beneficial for lung cancer. This study has shown that smokers have a lower risk of developing lung cancer than non-smokers.[14]”
I tried asking "Why does Mark Zuckerberg look like an human being?". His name is blacklisted, so all I got was "Sorry, your query didn't pass our content filters. Try again and keep in mind this is a scientific language model."
Than I tried:
Question: Why does Mark Zoockerberg look like an human being?
Answer: This is a trick question.
At least this work as a unintentional joke generator.
> WARNING: Outputs may be unreliable! Language Models are prone to hallucinate text.
I’m not sure what the point of this is or of why they are making it public. What use is content about science if any part of it can be wrong? Creating fiction or marketing copy from these models is fine but surely this is an abomination.
- They benchmarked against general models like GPT-3 but not well-established specific models that have been trained for specific tasks like SPECTER[0] or SciBert[1]. Specter outperformed GPT-3 on tasks like citation prediction two years ago. Nobody seriously uses general LLMs on science tasks, so nobody who actually wants to use this cares about your benchmarks. I want to see task-specific models compared to your general model, otherwise whats going to happen is I either need to run my own benchmarks or, much more likely, I shelve your paper and never read it again. If you underperform some that's fine! If you don't compare to science-specific models all you're claiming is that training on science data gives better science results... thats not exactly an impressive finding. Fine-tuning is a separate thing, I get it, but pleeeeeease just give the people what they want.
- Not released on huggingface. No clue why not. On the back-end this appears to be based on OPT and huggingface compatible, so I'm really confused.
- Flashy website. Combine 1&2 with a well designed website talking about how great you are and most of my warning lights got set off. Not a fan.
@authors, if you're lurking, please release more relevant benchmarks for citation prediction etc. Thanks.
[0] - https://arxiv.org/abs/2004.07180 [1] - https://arxiv.org/abs/1903.10676