Recent submissions

Comment · 3 years ago
Green of course? Anything else would be highly unusual and a normal reader would expect it to be called out.
Comment · 3 years ago
Do you disagree with my claim that GPT-4 can perform some sort of basic reasoning about puzzles that aren't in it's training data?
Comment · 3 years ago
Ok, here's a (hopefully) better worded puzzle, again made up by myself right now.

There are 12 frogs. Five are green, 3 red, and 4 yellow. Two donkeys are counting the frogs. One of the donkeys is yellow, the other green. Each donkey is unable to see frogs that are the same color as itself, also each donkey was careless and missed a frog when counting. How many frogs does the green donkey count?

GPT4 answers 6 every time for me.

My point is that GPT is capable of a certain amount of "reasoning" about puzzles that most certainly don't exist in it's training data. Playing with it, it's clear that in this current generation the reasoning ability doesn't go very deep - just change the above puzzle a little to make it even slightly more complicated and it breaks. The amazing thing isn't how good at reasoning it is, but that a computer can reason at all.

Comment · 3 years ago
ChatGPT 3.5? I'm using 4 and get 11 most times but other numbers occasionally.
Comment · 3 years ago
It clearly says he didn't see some of them either at all or as unicorns. The correct answer is 11.

Edit: I do see now that "He saw" kind of messes the question up. My intent would have been better expressed with "There were". But again this proves my point! GPT4 is able to (most of the time) correctly work through the poor wording and interpret the question the way I meant it, and I think the way most people would read it.

Comment · 3 years ago
Here's a question to ChatGPT I just made up:

>> A magical frog was counting unicorns. He saw 5 purple unicorns, 2 green unicorns, and 7 pink unicorns. However, he made a mistake and didn't see 2 unicorns: one purple and one green. Also, since he was a magical frog, he didn't see unicorns that were the same color as himself. How many unicorns did he count?

It correctly answers 11 for me.

To me this has demonstrated:

* "Understanding": It understood that "didn't see" implies he didn't count.

* "Knowledge": It knew enough about the world to know that frogs are often green.

* "Reasoning": It was able to correctly reason about how many should be subtracted from the final result.

* "Math: It successfully did some basic additions and subtractions arriving at the correct answer.

Crucially, I made this up right here on the spot, and used a dice for some of the numbers. This question does not exist anywhere in the training corpus!

I think this demonstrates an impressive level of intelligence, for what up until about a year ago I thought a computer would ever be capable of in my lifetime. Now in absolute terms of course current gen ChatGPT is clearly far less good at reasoning and understanding than most people (well, specifically it seems to me that it's knowledge and reasoning are super-humanly broad, but child-level deep).

Can future improvements to this architecture improve the depth up to "AGI", whatever that means? I have no idea. It doesn't automatically seem impossible, but maybe what we see now is already near the limit? I guess only time will tell.

Comment · 3 years ago
Oh, yes, pairs of words is a good idea. I also have a bilingual dictionary and can generate a prompt for each entry something like "here's a word in <lang_a>, write a dictionary definition for it in <lang_b>: <lang_a_word>: <lang_b_definition".
Comment · 3 years ago
What would a good fine-tuning dataset for language translation look like?

I want to try fine-tuning to machine translate to and from a fairly niche language (https://en.wikipedia.org/wiki/S'gaw_Karen_language). How much text would I need, and what format would be ideal?

I have a number of book length texts, most only in the target language, and a few bilingual or multilingual. For the bilingual and multilingual texts, I can script out probably several thousand pairs of "translate the following text from <source_lang> to <target_lang>: <source_lang_text> <target_lang_text>". Do I need to vary the prompt and format, or can I expect the LLM to generalize to different translation requests? Is there value in repeating the material in different lengths? One set of sentence lengths, another paragraph, and another page or chapter length? Also what should be done with the monolingual texts, just ignore them?

Comment · 3 years ago
Comment · 3 years ago
This is something I've tried to figure out this week.

I tried Youtube Kids because I wanted the content to be pre-approve only, I liked the toddler friendly controls, and the app can go in single app mode on the iPad. It's subscription, which is fine, but the main problem is they won't let me approve the content I want. I can see some great kids stories and learning videos in our language on regular Youtube, but can't seem to find a way to get them into the Youtube kids app. (And I certainly am not going to turn her loose on regular Youtube.)

There don't seem to be any other apps that do what I want so I ended up setting up a Plex media server and use yt-dlp to download the videos for her. This works pretty well, but is a lot more work. And the app is not great.

Comment · 4 years ago
> The attacker conducted a series of sophisticated voice phishing attacks under the guise of various trusted organizations attempting to convince the victim to accept multi-factor authentication (MFA) push notifications initiated by the attacker.

This is why we need webauthn everywhere! Push notifications and friends are all susceptible to this kind of attack, webauthn is not.

Comment · 4 years ago
I haven't really talked to anyone who's contested the fact that thousands of Russian soldiers and Russian speaking Ukrainian civilians have been killed.
Comment · 4 years ago
Yes, of course it's likely there are some who oppose the war but are uneasy about expressing that to a stranger. I was just surprised to get no support at all.
Comment · 4 years ago
I've been text messaging with random Russians a bunch over the last couple weeks and have had conversations with ~100 people, some of them quite extensive. Not once has anyone really condemned the war. A few are cynical about their government, people, and the rest of the world, and sad that this has to happen. But the vast majority weren't even that. Just straight up nationalist, repeatedly saying that they had to do something or they would be destroyed by the west. And insisting that Ukraine belonged to Russia. Absolutely no-one denied the facts as far as the toll on human life or the destruction of Ukraine, and not a single person thought its a "special operation" instead of a war. I heard a lot of what seems to me to be extreme paranoia about NATO, America, and Nazis just waiting for an opportunity to invade and destroy them.

The whole experience has been kind of shocking to me. I went into this believing that Russians where like innocent captives of evil politicians and oligarchs and had no idea what was going on. But based on the conversations I've had, that is not the case at all. They know exactly what is going on as far as the cost in human lives, and believe it's necessary to ensure their own survival.

Comment · 4 years ago
I had a good laugh when I saw my grandma using her iPad calculator app to do her finances. The app was displaying an an ad, she didn't want to see it and stuck a sticker over that part of the screen. :-)

Is my grandma's sticker going to be illegal in this forced advertising future? If not, where in the stack between the webpage and your eyes will it start to be legal to block? What about blocking in the graphics stack in the OS? The display? A sticker in front of the display? A smart film in front of the display? Smart glasses? Will it be illegal to avert your eyes from advertisements, à la Black Mirror?

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