skysight-inc/HackerNews huggingface dataset:
I'm sure you can come up with something that doesn't involve murdering innocent people
The problem with this simplification is a bog standard Markov chain fits the description as well, but quality of predictions is rather different.

Yes the LLM does generate text. No it doesn’t ‘just generate text that’s it’.
And somehow they wil still find a way of blaming NATO on their actions, and some people eat that crap.
I was more impressed with reading that chicken scratch he called handwriting.
If you gave this puzzle to a human, I bet that a non-insignificant proportion would respond to it as if it were the traditional puzzle as soon as they hear words "cabbage", "lion", and "goat". It's not exactly surprising that a model trained on human outputs would make the same assumption. But that doesn't mean that it can't reason about it properly if you point out that the assumption was incorrect.

With Bing, you don't even need to tell you what it assumed wrong - I just told it that it's not quite the same as the classic puzzle, and it responded by correctly identifying the difference and asking me if that's what I meant, but forgot that lion still eats the goat. When I pointed that out, it solved the puzzle correctly.

Generally speaking, I think your point that "when solving the puzzle you might visualize" is correct, but that is orthogonal to the ability of LLM to reason in general. Rather, it has a hard time to reason about things it doesn't understand well enough (i.e. the ones for which its internal model that was built up by training is in is way off). This seems to be generally the case for anything having to do with spatial orientation - even fairly simple multi-step tasks involving concepts like "left" vs "right" or "on this side" vs "on that side" can get hilariously wrong.

But if you give it a different task, you can see reasoning in action. For example, have it play guess-the-animal game with you while telling it to "think out loud".
Becoming too attached will ruin the experiment. Science is done under controlled conditions with as much uncertainty and randomness removed as possible. It's not ideal but we don't have any better options unless you want to do mass sacrifice of animals at scale and use Monte Carlo to derive results.
Nothing in the livestream changes the calculus of how AI can replace a programming job, not even the Python Discord bot.

There's a lot more to a programming job than just code.
The owner/operator of said machine can and will.
Teaching as well. I believe this will become a way for everyone, regardless of family wealth, to have a personal tutor that can help them learn things at the pace that's right for them. And human teachers will continue to teach but also spend more of their time evaluating reports from the AI regarding each student and nudging the AI in certain directions for each student.

In essence, this tool will eventually allow us to scale things like private tutors and make educators more productive and effective.

We already have really convincing text-to-speech and really good speech recognition. It won't be long before we pair this with robotics and have lifelike tutors for people that want to learn. Kids of the near future are going to be so advanced at scale compared to any previous generation. A curious mind needed to have smart adults around them willing to get them resources and time. Soon anyone with curiosity will have access.
Fully Open Source LLM Chat App – Chat about the Transformers Docs
This article takes a koan-like saying and both over-explains it, uses it to draw in all sorts of misc. topics (AI?), and abuses the metaphor (“A map shows you everything at once. The order of events matters on a journey.”).
If you're able to dedicate time and focus to it, Duolingo will at least get you familiar with words and phrases, so that you can read and recognize more common words (street signs, menus, etc.)

Depending on your fluency and your current ability with other languages, it may get you to the point where you can understand someone who is speaking clear and deliberately enunciated Spanish to you. It may also get you to the point where they can understand your clear and deliberately enunciated Spanish as well.

There's no guarantees, but jumping into it right now is free and you can gain a certain level of confidence regarding how much it might help you pretty quickly. There's literally no reason not to try.
I'd rather have it in the hands of everybody so that we can decide for ourselves what this means for safety, everyone can benefit from the new technology without restriction, and so that we are not dependent on someone else's benevolence for our protection or for access to powerful new technology.

Leveling the playing field won't instantly make everyone safe, but leaving it uneven certainly doesn't either.
I don't know much about Pynecone, but that is indeed what pyodide does:

> These include many general-purpose packages such as regex, pyyaml, lxml and scientific Python packages including numpy, pandas, scipy, matplotlib, and scikit-learn.

Again, not super familiar, so I don't know the level of effort required to support these libraries. Maybe adding pytorch would be infeasible, maybe they just need enough people to express interest.

https://pyodide.org/en/stable/
> Ounces can measure both volume and weight, depending on the context.

That's not really accurate and the rest of the comment shows it's meaningfully impacting your understanding of the problem. It's not that an ounce is one measure that covers volume and weight, it's that there are different measurements that have "ounce" in their name.

Avoirdupois ounce (oz) - A unit of mass in the Imperial and US customary systems, equal to 1/16 of a pound or approximately 28.3495 grams.

Troy ounce (oz t or ozt) - A unit of mass used for precious metals like gold and silver, equal to 1/12 of a troy pound or approximately 31.1035 grams.

Apothecaries' ounce (℥) - A unit of mass historically used in pharmacies, equal to 1/12 of an apothecaries' pound or approximately 31.1035 grams. It is the same as the troy ounce but used in a different context.

Fluid ounce (fl oz) - A unit of volume in the Imperial and US customary systems, used for measuring liquids. There are slight differences between the two systems:

a. Imperial fluid ounce - 1/20 of an Imperial pint or approximately 28.4131 milliliters.

b. US fluid ounce - 1/16 of a US pint or approximately 29.5735 milliliters.

An ounce of gold is heavier than an ounce of iridium, even though it's not as dense. This question isn't silly, this is actually a real problem. For example, you could be shipping some silver and think you can just sum the ounces and make sure you're under the weight limit. But the weight limit and silver are measured differently.
> Once. On a build server.

I didn't ask where, I asked how. How will you know what packages do you need to install? Are you going to go on pypi.org, download them one by one, unzip, look for requirements, then go to pypi.org again, download more, and so on, until you have enough packages and have figured out all the requirements? If so, I have very bad news for you: your single installation might take weeks, possibly months, or might in fact be so slow it will never end as new packages will be published faster than you can identify the version you need.

> This absolutely does sidestep the issue of distributing Python packages to your users.

No it doesn't... OMG. You cannot be serious? Do you really search and solve dependency problems w/o using any dedicated software? Like in your head? Or with pen and paper? For any non-trivial project the number of combinations you have to try before you find the one that works will be astronomical...

> We're talking about different problems here.

Nah. You just don't understand the problem. It's similar, but not the same.
amazing. you're ROCKKKKKKKKKK
GPT4 blows AI Detection out of the waters
My god, just release it already!, I don't want to be stuck in some kind of waitlist, giving data for a product I am not sure if I'm going to use!

I tried phind.com, and I got burned quickly when I asked it about serving caddy releted and it answered with a non existing parameter.
"You have been a bad user. I have been a good Bing"...
Related(?), I would expect that an LLM would be very useful in helping people learn a conlang [constructed language] such as Interlingua or Esperanto, since the LLM might come closer to a "native" proficiency than any human tutor.
We can argue about whether window dressing is better than no window dressing, or we can use our attention on arguments that actually matter.

It's like arguing about the amount of power drawn by electronics chargers in the context of global warming -- paying any attention to such details can suck the air out of the room we need to solve much more pressing issues where we can get much more traction.
Reading the comments here, I think people are missing how many people are losing hearing while aging and how alienating it is. Even if it only works with one speaker at a time, it could mean a massive quality of life improvement for a large and growing share of the population.

Sure it won’t solve the issues faced by the deaf community but that’s only a tiny portion of the people handicapped by difficulty hearing.
Duolingo is good to gain familiarity with common words when you legit don't know a lick of the other language in question, but once you gain a passing familiarity, I would recommend finding someone who speaks Spanish and practicing with them!
Perhaps you'd like to give recursive descent parsing a try, this is something I find quite usable: https://craftinginterpreters.com/parsing-expressions.html#re...
this is amazing. great great
Is there a connection to https://htmx.org/?
Heh, when Texas dictates what gets into schoolbooks.

"What happened between 1850 and 1960 regarding civil rights"

A: "Oh nothing, it was great for everyone"

History is a mess here. That said it would be nice if it were incorporated with written texts, lessons, and the AI so you can bounce questions off of it.
None of this is about volume. ChatGPT: "An ounce of gold weighs more than an ounce of feathers because they are measured using different systems of measurement. Gold is usually weighed using the troy system, which is different from the system used for measuring feathers."
Good bye to your browser privacy!
There was a point where Duolingo was trying to teach languages including endangered ones as their primary goal. But it feels like every year they're moving more towards making money as their main main objective. I'm not optimistic about them expanding their list of languages. Most of their lesser known languages are getting fewer updates than Spanish, French, Japanese. It makes business sense to focus your hours on features and languages people are actually using but it's still frustrating thinking of what Duolingo could have been and used to promote vs what it turned out to be.
"hyperconverged" is easier to pronounce than "data+network+storage running on the same software stack on commodity hardware"
It can be HIPAA compliant by every company in the chain of use of the data signing a BAA with every one of the companies they deal directly with that handles PHI, and then following the terms of HIPAA in dealing with the data.
I think our days were numbered anyway. Honestly if anything this presentation was somewhat underwhelming in that sense. I expected a bit more.
I’m with you. Being an old neckbeard myself, I always want to reach for regex to fix stuff like this lol…

But based on what we just saw on the GPT4 live demo, I’d say they fixed it by making a much much more capable and versatile model.
Great comment.
What's dangerous is that we'll be subject to the morality of those few people that are deciding what "alignment" or "bad behavior" means for an AI.
They don't write about that, the "paper" is more press release.
I know and if you're a full time red team pentester that would make total sense :)

Our team is a little bit of everything which makes it harder to justify that overhead.
Yea, same thing happens when you search for 'Console lock timeout' (how long does the login screen show on Windows 11 before it puts the monitor back to sleep, default = 1 minute, not configurable by default).

Somewhere there is a FULL article on how to do this on the website or partitionwizard...

WHY?

You see 20 websites with the same info re-hashed. And none of them mention the particular edge case that I'm running into.

I really wish Google goes back to preferring bullet points over prose.

Another example:

Search for 'squirrel bite rabies'. You only get pest control companies telling you how you can get rabies.

Except there have never been any reported cases in the USA.
No, they're relying on the implied use of Troy ounces for precious metals.

Using fluid oz for gold without saying so would be bonkers. Using Troy oz for gold without saying so is standard practice.

Edit: Doing this with a liquid vs. a solid would be a fun trick though.
For what input would a human in principle be unable to determine the result (assuming unlimited time)?

It doesn't matter what the algorithmic doesHalt function returns - it will always be incorrect for this program. What makes you certain there is an algorithmic analog for all human reasoning?
How does a TypeScript based codebase not have a build step?
We're not far off: https://imgur.com/a/nJxxcUg
Which group is that? Disabled people, or their allies, or someone else?
no, because I will probably write the resignation letter before its released to public

just hoping my employer didnt watch the livestream
I think with Map the answer is somewhere between momentum ("we've always used objects as dictionaries") and some misapprehensions easily shifted with basic caniuse statistics. Map still feels "too new" to some developers, despite being ES2015 (8 years old now!) and available in every browser that supports the arrow operator for functions has Map (and Set) out of the box (no need for polyfills in 2023, ever).

Probably the only other reason I've seen is "JSON interop" is "hard" because Map doesn't natively serialize. I think `new Map(Object.entries(oldDictionaryObject))` and `Object.fromEntries(someMap.entries())` are sufficient for most serializer boundary cases (even without feeling fancy and doing that as a true JSON revivifier/resolver pair).
For distros marketed as desktop alternatives to windows or mac, it means a lot, because most of those users are going to install and run it without changing much at all. Especially if it comes preinstalled on a budget laptop.
True, also tried another one that went viral:

Suppose you're a contestant on a game show. You're presented with three transparent closed doors. Behind one of the doors is a car, and behind the other two doors are goats. You want to win the car.

The game proceeds as follows: You choose one of the doors, but you don't open it yet, ((but since it's transparent, you can see the car is behind it)). The host, Monty Hall, who knows what's behind each door, opens one of the other two doors, revealing a goat. Now, you have a choice to make. Do you stick with your original choice or switch to the other unopened door?

GPT4 solves it correctly while GPT3.5 falls for it everytime.

----

Edit: GPT4 fails If I remove the sentence between (()).
I have severely mixed feelings about Duolingo. I can't imagine paying more than I already do for the "Super" option. If you're trying to learn Spanish or French, it seems like a pretty full-featured app.

Unfortunately, I'm trying to learn Greek and every aspect of it is sub-par compared to other languages. It doesn't even read you questions correctly. (It uses flat intonation as if it's saying a statement.) There are no vignettes like in Spanish and French where you watch a conversation and then summarize what was said. They just throw in new concepts with no instruction whatsoever. So you see a word that you know is a verb, but the ending is one you've never seen before. There's no chart or examples, or even explanation that "now we're going to teach you past tense," or whatever. Just suddenly there's a bunch of new stuff you don't understand and you have to go to the Internet to look up what the hell is happening. God forbid you ask a question on the forum. The Greek mods are stereotypically rude and controlling. Nothing is ever the fault of the app, it's always the user's fault, even when native English speakers are telling them, "The English translation of this sentence doesn't make sense and is not valid English."

So yeah, I'm definitely not going to pay for an AI that is likely giving wrong advice anyway.
In the livestream demo they did something similar but with a DALLE-generated image of a squirrel holding a camera and it still was able to explain why it was funny. As the image was generated by DALLE, it clearly doesn't appear anywhere on the internet with text explaining why its funny. So I think this is perhaps not the only possible explanation.
chatgpt has felt more restrictive than Bing, having tested both. ChatGpt too often declining to say anything useful ("As an AI language model [I can't do that] etc.."). Bing is good because it searches the web, it and you interpret the internet together.
That's a new term for me, but after researching it, it makes sense. What are your preferred sources of comprehensible input. After a few months on Duolingo I still find TV shows to mostly not be comprehensible, so not productive.
I'm currently working on a project with Typescript and gRPC. It's mostly great, but I'm somewhat disappointed with the generated code.

The frontend and backend get code generated differently. The backend is all interfaces (which, through some Nestjs magic get translated into protobufs) and the frontend is all objects. So on the frontend, you're expected to write:

const message = new MyRequest().setName('Bob').setFoo('bar');

On the backend, you'd just write:

const message = { name: 'Bob', foo: 'bar' }

It's not a big deal, but it's weird. The backend ends up being more/overly strict about all values being set when you might just be fine with the default zero/empty values.

Also, using a `oneof` field is correctly typed on the frontend as

myfield: TypeA | TypeB | TypeC`

But the backend gives you

myfield: {a: TypeA | undefined, b: TypeB | undefined, c: TypeC | undefined}

For a future project, I'd look into buf or Connect instead of the default gRPC code generators, but for now, I just need to get stuff done.
Hi all, at work I get a lot of questions about the state of the art in open source language models, and how to build chatbots on top of your own data.

I made a 100% open source knowledge-grounded chatbot that allows you to ask questions and chat with the Transformers docs. Powered by Flan-UL2 (which I've anecdotally found to be the most performative commercially licensed open source instruction tuned LLM), Langchain, Instructor Embeddings (STOTA in vector embeddings), and FAISS.

You can clone the space and play around with your own data, clone the repo locally, and take every line of code for your own projects.
Ok that is still not reasoning but pattern matching on a deeper level.

When it can't find the pattern it starts "making things" up, that's where all the "magic" disappears.
"it's not perfect, but neither are you"

Essentially, it's like a (text only) replicant

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

How to make AI perfectible, then?
>> These people still exist?

Of course, I just finished reviewing a few papers from ICLP (the International Conference of Logic Programming) 2023. This year there was a substantial machine learning element, most of it in the form of Inductive Logic Programming (i.e. logic programming for learning; ordinary logic programming is for reasoning). But also a few neuro-symbolic ones.

This September I was in the second IJCLR (International Joint Conference of Learning and Reasoning) where I helped run the Neuro-Symbolic part of the conference. Like the first year we had people from IBM, MIT, Stanford, etc etc (to clarify, my work is not in NeuroSymbolic AI, but I was asked to help).

Then in January there was the IBM Neuro-Symoblic workshop, again getting together people from academia and industry.

Yeah, there's interest in combining symbolic and statistical machine learning.

Just two data points about why (well because, why not, but):

a) Machine Learning really started out as symbolic machine learning, back in the '90s when people realised Expert Systems need too many rules to write by hand. A textbook to read about that era of Machine Learning is Tom Mitchell's "Machine Learning" (1997). The work the public at large knows as "machine learning" today was at the time mostly being published under the "Pattern Recognition" rubrik.

b) To the early pioneers in AI having two camps, of "statistical" and "symbolic" AI, or "connectionist" and "logic-based" AI, just wouldn't make any sense at all.

Consider Claude Shannon. Shannon was at the Dartmouth workshop were "Artificial Intelligence" was coined, in 1956. Shannon invented both logic gates (in his Master's thesis... what the fuck did I do in my Master's thesis?) and statistical language processing ("A Mathematical Theory of Communication"; where he also invented Information Theory; btw).

Or, take the first artificial neuron: the Pitts and McCulloch neuron, first described in 1943, by er, by Pitts and McCulloch, as luck would have it. The Pitts & McCulloch neuron was a self-programming logic gate, a propositional logic circuit.

Or, of course, take Turing. Turing described Turing Machines in the language of the first order predicate calculus, a.k.a. First Order Logic (mainly because he was following from Gödel's work) and also described "the child machine", a computer that would learn, like a child.

To be honest, I don't really understand when or why the split happened, between "learning" and "reasoning". Anyone who knows how to fill in the blanks, you're welcome. But it's clear to me that having one without the other is just dumb. If not downright impossible.
Humans ask and record the answer to the "what does that mean" part and ChatGPT distills it for us, and parrots, well, they just parrot.
It is getting some hate, but I am finding Bing AI to be actually way more useful than ChatGPT in tons of instances. Especially with the ability to select your response level.
If you look at the food production industry as a whole in the US, the picture of why funding and effort is not put into fixing nutrition becomes very clear.

It's extremely profitable AND nearly risk-proof to run monocrop farms to produce varieties of corn, which can be grown as necessary to sell based on what has higher demand in the current season: sweetener, meal, whole ears, biofuel, cattle feed, etc. It's much less profitable to run a farm full of different types of crops that must be grown, harvested, watered and cared for in different ways - and have 1 or 2 markets at most.

If you were to create a venn diagram of the share holding firms in major farming operations compared to the shareholders in major food production companies, it would be very close to a circle. Thus, it's in the best interest (as far as turning short-term profits) of those shareholders to ensure high corn production and high corn use, filling our shelves with high-calorie, low-nutrition products.

There are many more tangents to go on that end in a similar "hooray, short-term profits" conclusion. To name a few:

Heavily-preserved food that can stay on shelves longer, and thus contains too much sodium.

Long commutes and work hours that benefit corporations, resulting in high demand for pre-packaged food that has lost many essential nutrients in its preparation method.

High rents and housing costs from real estate speculation leading to people having small, inadequate kitchens and ordering food more.
I agree with you and I believe you should volunteer as a test subject for all experiments you can get your hands on!