"Consumer AI is just the new search" anecdote: [1/3]
Casual non-techy coworkers yesterday were talking about using excel reports to analyze data & turns out two of the people use #ChatGPT to know how to do something in excel.
So, before this #AI stuff, if you were like, "how do I do X in Excel" in google, you'd get a bunch of hits and then have to wade through the results to see which link was actually what you were looking for, then test out if their solution works.
"Consumer AI is just the new search" anecdote: [2/3]
Now, you do the same thing, but in #ChatGPT, even just upload a photo of what you are trying to do, and instead of a bunch of dubious links, you get an answer that probably works. That probability is just as good as the probability after combing through search results yourself, but without the combing.
"Consumer AI is just the new search" anecdote: [3/3]
There are over 1 billion websites with over 30 billion web pages out there on the internet and regular search absolutely sucks now. It's no wonder normies are seeing #ChatGPT as magic when it can take 30 billion+ results and give you one answer that's most likely what you are looking for.
#Apple didn’t read the room with this ad about creative tools literally being crushed to make its new #iPad. Creators were already up in arms over generative #AI and nervous about rumors that the company was planning to do something with it.
Many people canceled their Openai subscriptions, or it is tough to monetize stuff created with generative AI, I guess, so Sama comes with a new plan to use all those GPUs. They are now going after OF models. WTF OpenAI? They are going to allow deepfake? This company is beyond evil 👿
So, how can we get a proper answer? Ten years ago, when I wrote “The Milwaukee Soup App”, I used the Kimono (which is long dead) to scrape the soup of the day. You could also write a fiddly script to scrape the value manually. It turns out that there is another option, though. You could use Scrapegraph-ai. ScrapeGraphAI is a web scraping Python library that uses LLM and direct graph logic to create scraping pipelines for websites, documents, and XML files. Just say which information you want to extract and the library will do it for you.
Let’s take a look at an example. The project has an official demo where you need to provide an OpenAI API key, select a model, provide a link to scrape, and write a prompt.
As you can see, it reliably gives you the flavor of the day (in a nice JSON object). It will go even further, though because if you point it at the monthly calendar, you can ask it for the flavor of the day and soup of the day for the remainder of the month and it can do that as well.
I am running Python 3.12 on my Mac but when you run pip install scrapegraphai to install the dependencies, it throws an error. The project lists the prerequisite of Python 3.8+, so I downloaded 3.9 and installed the library into a new virtual environment.
Let’s see what the code looks like.
You will notice that just like in yesterday’s How to build a RAG system post, we are using both a main model and an embedding model.
At this point, if you want to harvest flavors of the day for each location, you can do so pretty simply. You just need to loop through each of Culver’s location websites.
Have a question, comment, etc? Please feel free to drop a comment, below.
Just got an e-mail from #storyblok informing me that one of their sub-processors (whatever that means) is now #OpenAI.
I immediately asked them if the content I created will be used in any way to train their #LLM and if so, I want no part in it. Requested a back-up of my data and to delete everything.
I used it for the first iteration of my blog but switched to #markdown a while ago.
‘The idea of #AI wearables is not new. Enough time may have passed for Silicon Valley to forget the debacle of Google #Glass, the AI-powered headset that the search company launched in 2013 but largely abandoned just two years later. But the reaction to #Humane will only fuel suspicion that the latest attempts to reboot Glass are just another manifestation of an AI funding bubble.’ https://www.ft.com/content/ccec597d-11bb-4105-858f-78580d4331a1
There are doubts about the copyright of AI-generated music.
Should AI music be subject to copyright?
If AI music is subject to copyright, imagine a scenario where AI generates billions of different random songs, across the scale of diversity, this music will be published, it will have maybe 0-2 listens, but it will be officially released.
In this way, the owner of this AI will reserve all possible new music in the world for himself.
He will be able to sue every new artist for plagiarism, because what is the chance that his AI did not generate something similar, at such a scale of generation?
What if AI music is not subject to copyright? If, for example, they were automatically in the public domain?
An almost identical scenario may occur, also AI will generate billions of various random songs, on the entire scale of diversity, this music will be published... but this time artists will no longer be able to profit from music that will be in some way similar to that generated by AI, and any new music, will be similar to those billions of songs generated by AI.
Even the artist's sense of not having created something original could be demotivating for him, even if it were established that AI music is not in the public domain and it would be possible to duplicate and monetize music similar to that generated by AI.
And then another scenario may arise, that pseudo-artists will deliberately review finished AI-generated songs, and will "take over"/occupy the copyrights to these songs. It's also not a pretty scenario.
So what, ban AI music and force it to be removed from the Internet? Drive AI music underground?
However, the current law is adapted to people who have limited ability to produce music.
In fact, even before generative music, there was a problem of songs being similar to each other, and about a hundred years of universal copyright was enough.
Now we're firing up the roller skates.
Są wątpliwości, co do praw autorskich, generowanej przez AI muzyki.
Czy muzyka AI powinna podlegać prawom autorskim?
Jeśli muzyka AI będzie podlegać prawom autorskim, to wyobraź sobie scenariusz, gdzie AI wygeneruje miliardy najróżniejszych losowych utworów, na całej skali różnorodności, ta muzyka zostanie opublikowana, będzie miała może 0-2 odsłuchania, ale oficjalnie będzie wydana.
W ten sposób właściciel tego AI zarezerwuje dla siebie wszelką możliwą nową muzykę świata.
Będzie mógł pozywać każdego nowego artystę o plagiat z automatu, bo jaka szansa, że nie jego AI nie wygenerował czegoś podobnego, przy takiej skali generowania?
A co, jeśli muzyka AI nie będzie podlegać prawom autorskim? Jeśli np. byłyby automatycznie w domenie publicznej?
Może zaistnieć niemal identyczny scenariusz, również AI wygeneruje miliardy najróżniejszych losowych utworów, na całej skali różnorodności, ta muzyka zostanie opublikowana... ale tym razem artyści już nie będą mogli czerpać zysków z muzyki, która będzie w jakiś sposób podobna, do tej wygenerowanej przez AI, a każda nowa muzyka, będzie podobna do tych miliardów piosenek, wygenerowanych przez AI.
Nawet samo poczucie artysty, że nie stworzył czegoś oryginalnego może być dla niego demotywujące, nawet jeśli ustalono by, że muzyka AI nie jest w domenie publicznej, i można by dublować i zarabiać na muzyce podobnej do tej wygenerowanej przez AI.
A wtedy może zaistnieć kolejny scenariusz, że pseudo artyści będą celowo przeglądać gotowe utwory wygenerowane przez AI, i będą "przejmowali"/zajmowali prawa autorskie do tych utworów. To też niezbyt piękny scenariusz.
To co, zakazać muzyki AI, zmuszać do usuwania jej z sieci? Zepchnąć muzykę AI do podziemia?
Dotychczasowe prawo jest jednak dostosowane do ludzi, którzy mają ograniczoną zdolność produkowania muzyki.
Tak naprawdę jeszcze przed generatywną muzyką, był problem podobieństwa piosenek do siebie, wystarczyło gdzieś sto lat powszechnych praw autorskich.
Teraz odpalamy wrotki.
This month's editorial looks at the possible ethics issues that could come with introducing a reliable detector to the filtering process of a submissions system. Curious what people think. https://clarkesworldmagazine.com/clarke_05_24/
@clarkesworld 3. The author's postal address should exist. Validation is more expensive than email address validation, but surprisingly accurate in developed countries. The same goes for the phone number. Look for patterns.
4. Give captchas a try on your submissions page, if you haven't already. #spam#slop#ai 3/3