#web Scraping YouTube Data

[cacodaemonia]

A writer I follow on YT and who is not the sort of person to ignore nuance has made a 2 hour+ video about 'AI' art etc. after he spent several months deep diving into it all. He also has a degree in copyright law, so I'm about to watch and excited to learn what he found out!

Anyway, figured I'd share it here because we all need to learn more about how 'AI' generators actually work and affect real people.

[webscreenscraping00]

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[d0nutzgg]

Update: Plans for Speechy: the First Neurological Speech Disorder Identifier

Hey everyone, I have been hard at work on Speechy and I am happy to say I made a lot of progress last night! I now have a fully functional Youtube "Speech Disorder" audio clip scraper for the dataset.

It also now labels the videos into the right folder for me which is making the data scraping easier. It allows input of your own API key, and the ability to name the folder yourself.

Despite the roadblock on publishing the dataset itself (sorry to hype y'all up for nothing I really wanted to make the data publicly available but I also don't have lawyers like OpenAI to defend my ass lol).

For those who don't know OpenAI uses a dataset that was scraped from Google Searches. Not the most ethical but they haven't really made their stuff available and it's all currently proprietary which is saving their ass with lawsuits too.

Good news though, I also got the first very Alpha version of an NLP speech pattern recognition program to recognize the first of the disorders I am focused on - Ataxic Dysarthria which is what I have from my ongoing battle with Huntington's Disease.

This is very basic and I really need to train another neural network just for this but it'll pull out the *definite* matches and then only save those to another folder which cleans things up a bit then I can go back over some of it and add more to the files. It makes my life easier.

Also I want to note that this software is completely open source so anyone can fork it from my github and use it themselves! In fact I encourage you to do so!!

Here is the actual full code for the Youtube Scraper too

It's a bit hard to see but you can find it on my Github here:

Anyways, I got quite a bit done, now onto making the rest of the ID scripts (I would put them together but realized this would not be effective tbh. Here is the current state of the Github repo right now:

I also plan to make some visualization programs and do some of my own scientific research (ah.. reminiscing over my academic researching days with Michael J Fox Foundation data..) and I will probably even submit my own findings to be peer reviewed later on.

Thank you for your continued support and I will continue to post my "Dev log" of sorts every day (if I can remember, sorry I have Early Onset FTD).

Cheers!

[mariacallous]

Over 170 images and personal details of children from Brazil have been scraped by an open-source dataset without their knowledge or consent, and used to train AI, claims a new report from Human Rights Watch released Monday.

The images have been scraped from content posted as recently as 2023 and as far back as the mid-1990s, according to the report, long before any internet user might anticipate that their content might be used to train AI. Human Rights Watch claims that personal details of these children, alongside URL links to their photographs, were included in LAION-5B, a dataset that has been a popular source of training data for AI startups.

“Their privacy is violated in the first instance when their photo is scraped and swept into these datasets. And then these AI tools are trained on this data and therefore can create realistic imagery of children,” says Hye Jung Han, children’s rights and technology researcher at Human Rights Watch and the researcher who found these images. “The technology is developed in such a way that any child who has any photo or video of themselves online is now at risk because any malicious actor could take that photo, and then use these tools to manipulate them however they want.”

LAION-5B is based on Common Crawl—a repository of data that was created by scraping the web and made available to researchers—and has been used to train several AI models, including Stability AI’s Stable Diffusion image generation tool. Created by the German nonprofit organization LAION, the dataset is openly accessible and now includes more than 5.85 billion pairs of images and captions, according to its website.

The images of children that researchers found came from mommy blogs and other personal, maternity, or parenting blogs, as well as stills from YouTube videos with small view counts, seemingly uploaded to be shared with family and friends.

“Just looking at the context of where they were posted, they enjoyed an expectation and a measure of privacy,” Hye says. “Most of these images were not possible to find online through a reverse image search.”

LAION spokesperson Nate Tyler says the organization has already taken action. “LAION-5B were taken down in response to a Stanford report that found links in the dataset pointing to illegal content on the public web,” he says, adding that the organization is currently working with “Internet Watch Foundation, the Canadian Centre for Child Protection, Stanford, and Human Rights Watch to remove all known references to illegal content.”

YouTube’s terms of service do not allow scraping except under certain circumstances; these instances seem to run afoul of those policies. “We've been clear that the unauthorized scraping of YouTube content is a violation of our Terms of Service,” says YouTube spokesperson Jack Maon, “and we continue to take action against this type of abuse.”

In December, researchers at Stanford University found that AI training data collected by LAION-5B contained child sexual abuse material. The problem of explicit deepfakes is on the rise even among students in US schools, where they are being used to bully classmates, especially girls. Hye worries that, beyond using children’s photos to generate CSAM, that the database could reveal potentially sensitive information, such as locations or medical data. In 2022, a US-based artist found her own image in the LAION dataset, and realized it was from her private medical records.

“Children should not have to live in fear that their photos might be stolen and weaponized against them,” says Hye. She worries that what she was able to find is just the beginning. It was a “tiny slice” of the data that her team was looking at, she says—less than .0001 percent of all the data in LAION-5B. She suspects it is likely that similar images may have found their way into the dataset from all over the world.

Last year, a German ad campaign used an AI-generated deepfake to caution parents against posting photos of children online, warning that their children’s images could be used to bully them or create CSAM. But this does not address the issue of images that are already published, or are decades old but still in existence online.

“Removing links from a LAION dataset does not remove this content from the web,” says Tyler. These images can still be found and used, even if it’s not through LAION. “This is a larger and very concerning issue, and as a nonprofit, volunteer organization, we will do our part to help.”

Hye says that the responsibility to protect children and their parents from this type of abuse falls on governments and regulators. The Brazilian legislature is currently considering laws to regulate deepfake creation, and in the US, representative Alexandria Ocasio-Cortez of New York has proposed the DEFIANCE Act, which would allow people to sue if they can prove a deepfake in their likeness had been made nonconsensually.

“I think that children and their parents shouldn't be made to shoulder responsibility for protecting kids against a technology that's fundamentally impossible to protect against,” Hye says. “It's not their fault.”

[rikaklassen]

Cakelin Fable over at TikTok scraped the information from Project N95 a few months ago after Project N95 announcing shutting down December 18, 2023 (archived copy of New York Times article) then compiled the data into an Excel spreadsheet [.XLSX, 18.2 MB] with Patrick from PatricktheBioSTEAMist. 

You can access the back up files above.

The webpage is archived to Wayback Machine.

The code for the web-scraping project can be found over at GitHub.

Cakelin's social media details:

Website

Beacons

TikTok

Notion

Medium

Substack

X/Twitter

Bluesky

Instagram

Pinterest

GitHub

Redbubble

Cash App

Patrick's social media details:

Linktree

YouTube

TikTok

Notion

Venmo

[james-the-lass]

Get in, loser, we’re learning today.

CW mention of exploitation of minors and CSAM, link to article on same.

Look, I have mixed feelings about AI. It’s inevitable that it will be used unethically. It is also a thing that has the potential to break the shackles of drudge work for millions, if not billions, of human beings. It’s inevitable that the resulting “free time” will be exploited, and that’s a different rant.

I work in the information security, governance, risk, and compliance industry. AI is a Big Fucking Deal to us. I could soapbox about Internet safety in general (especially for kids) and AI specifically. It is revolutionizing every aspect of our lives, and that’s not hyperbole.

I am also a proponent of least privilege (obviously :P) and I apply that view to government involvement in our lives. Regulation should be applied minimally and where needed. Unnecessary legislation is a waste of everyone’s time and serves only the plutocrats in power.

The potential for harm with AI is so beyond the pale that people who don’t have a need to understand how it works might not realize just how fucked up it could get and chalk others’ concerns up to too much science fiction. The potential for good is equally great. You can see why there are mixed feelings.

We desperately need legislation about AI, data privacy, Internet safety, and digital crimes. Forcing a sale of a platform from one company that spies on us to another company that spies on us but happens to be in a different country is patently ridiculous. Banning the creation of Internet platform accounts for people under a certain age is unenforceable. Requiring age verification for the use of certain websites where an account is not required is unenforceable to an absurd degree, especially when revenue for that website is generated from ads served to users without accounts. None of these actions does anything to keep people safe online.

The best way to ensure safety, of any kind, is being informed. Education is the way to combat these problems. Despite what might be the general perception, when we know better, we do do better. A teenager who knows they are more susceptible to scams on Instagram is less likely to fall for scams on Instagram, or any other platform for that matter. A person who knows a call from an unsolicited fundraiser to help kids might actually be a front to fund state-supported hacking activity will be less likely to give a stranger their financial information.

The legislation we need should be providing resources to parents and guardians to first educate themselves, then how to educate their children. Forget the detriments of too much YouTube; focus on the fact that a tragically substantial percentage of CSA material bought and sold on the dark web is scraped from publicly accessible social media profiles. Predators do not have to resort to illegal activity to have access to these media.

Legislation for AI needs to cover all this and so much more. There are limited protections in place (in the US, anyway) for the use of someone’s likeness without permission for commercial gain, but it is not enough. It isn’t clear enough on the legality of using any publicly available material online to train AI systems without due diligence or due care with respect to copyright or content. There is no federal governance on what AI companies can do with the information users put in to AI as prompts. How many people have submitted sensitive or confidential information to ChatGPT? Who is liable if an AI suggests a recipe for chlorine gas, poison bread sandwiches, or mosquito repellent potatoes? Or if a chatbot intended to help small business owners tells them it’s okay for landlords to discriminate by source of income; for bosses to take their workers’ tips; to serve customers cheese already nibbled by rats; or to fire a person for reporting sexual harassment, concealing a pregnancy, or refusing to cut their locs?

I. Could. Go. On. Forever.

Contact your representatives and push for effective data privacy laws. Push for publicly funded and more effectively distributed digital safety education. There are publicly funded resources for Internet safety, but they’re not nearly as well-known as they should be. Push for clear and reasonable restrictions on the development and use of AI technology.

Here are some resources for your consumption:

US DoD Cyber Awareness Challenge

More DoD Cybersecurity Awareness eLearning

Boys & Girls Clubs of America Internet Safety for Kids and Teens

GCFGlobal Free Internet Safety for Kids (they also have a huge library of paid courses)

US FBI Safe Online Surfing (SOS) Internet Challenge

Electronic Frontier Foundation’s library of privacy topics (look at all their information about everything. EFF is a fantastic organization. Some of their material is technically dense, but read as much as you can understand and apply to your own personal situations. They make an effort to lay things out in non-jargon, but sometimes it’s unavoidable.)

Stanford Cyber Policy Center Research, News, Publications Homepage

SCPC Article: AI Trained on CSAM identified in a public data set

/soapbox

Yikes

[subb01]

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[beardedmrbean]

A musician in the US has been accused of using artificial intelligence (AI) tools and thousands of bots to fraudulently stream songs billions of times in order to claim millions of dollars of royalties.

Michael Smith, of North Carolina, has been charged with three counts of wire fraud, wire fraud conspiracy and money laundering conspiracy charges.

Prosecutors say it is the first criminal case of its kind they have handled.

"Through his brazen fraud scheme, Smith stole millions in royalties that should have been paid to musicians, songwriters, and other rights holders whose songs were legitimately streamed," said US attorney Damian Williams.

According to an unsealed indictment detailing the charges, the 52-year-old used hundreds of thousands of AI-generated songs to manipulate streams.

The tracks were streamed billions of times across multiple platforms by thousands of automated bot accounts to avoid detection.

Authorities say Mr Smith claimed more than $10m in royalty payments over the course of the scheme, which spanned several years.

What is AI and how does it work?

Prosecutors said Mr Smith was set to finally "face the music" following their investigation, which also involved the FBI.

"The FBI remains dedicated to plucking out those who manipulate advanced technology to receive illicit profits and infringe on the genuine artistic talent of others," said FBI acting assistant director Christie M. Curtis.

'Instant music ;)'

According to the indictment, Mr Smith was at points operating as many as 10,000 active bot accounts to stream his AI-generated tracks.

It is alleged that the tracks in question were provided to Mr Smith through a partnership with the chief executive of an unnamed AI music company, who he turned to in or around 2018.

The co-conspirator is said to have supplied him with thousands of tracks a month in exchange for track metadata, such as song and artist names, as well as a monthly cut of streaming revenue.

"Keep in mind what we're doing musically here... this is not 'music,' it's 'instant music' ;)," the executive wrote to Mr Smith in a March 2019 email, and disclosed in the indictment.

Citing further emails obtained from Mr Smith and fellow participants in the scheme, the indictment also states the technology used to create the tracks improved over time - making the scheme harder for platforms to detect.

In an email from February, Mr Smith claimed his "existing music has generated at this point over 4 billion streams and $12 million in royalties since 2019."

Mr Smith faces decades in prison if found guilty of the charges.

Earlier this year a man in Denmark was reportedly handed an 18-month sentence after being found guilty of fraudulently profiting from music streaming royalties.

Music streaming platforms such as Spotify, Apple Music and YouTube generally forbid users from artificially inflating their number of streams to gain royalties and have taken steps to clamp down on or advised users on how to avoid the practice.

Under changes to its royalties policies that took effect in April, Spotify said it would charge labels and distributors per track if it detected artificial streams of their material.

It also increased the number of streams a track needs in a 12 month period before royalties can be paid, and extended the minimum track length for noise recordings like white noise tracks.

Wider concerns

The wider rise of AI-generated music, and the increased availability of free tools to make tracks, have added to concerns for artists and record labels about getting their fair share of profits made on AI-created tracks.

Tools that can create text, images, video, audio in response to prompts are underpinned by systems that have been "trained" on vast quantities of data, such as online text and images scraped, often indiscriminately, from across the web.

Content that belongs to artists or is protected by copyright has been swept up to form part of some of the training data for such tools.

This has sparked outrage for artists across creative industries who feel their work is being used to generate seemingly novel material without due recognition or reward.

Platforms rushed to remove a track that cloned the voices of Drake and The Weeknd in 2023 after it went viral and made its way onto streaming services.

Earlier this year, artists including Billie Eilish, Chappell Roan, Elvis Costello and Aerosmith signed an open letter calling for the end to the "predatory" use of AI in the music industry.

[realdataapi1]

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[greatcheesecakewonderland]

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[zamanahmed]

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[d0nutzgg]

Scraper is Done but Needs Improvement

So my scraper is "done" but I realized that ffmpeg does not convert the files from mp4 to mp3 like I need for my dataset, it is a great start but now I have to delete 500 files off my computer :D

[jcmarchi]

FT and OpenAI ink partnership amid web scraping criticism

New Post has been published on https://thedigitalinsider.com/ft-and-openai-ink-partnership-amid-web-scraping-criticism/

FT and OpenAI ink partnership amid web scraping criticism

.pp-multiple-authors-boxes-wrapper display:none; img width:100%;

The Financial Times and OpenAI have announced a strategic partnership and licensing agreement that will integrate the newspaper’s journalism into ChatGPT and collaborate on developing new AI products for FT readers. However, just because OpenAI is cozying up to publishers doesn’t mean it’s not still scraping information from the web without permission.

Through the deal, ChatGPT users will be able to see selected attributed summaries, quotes, and rich links to FT journalism in response to relevant queries. Additionally, the FT became a customer of ChatGPT Enterprise earlier this year, providing access for all employees to familiarise themselves with the technology and benefit from its potential productivity gains.

“This is an important agreement in a number of respects,” said John Ridding, FT Group CEO. “It recognises the value of our award-winning journalism and will give us early insights into how content is surfaced through AI.”

In 2023, technology companies faced numerous lawsuits and widespread criticism for allegedly using copyrighted material from artists and publishers to train their AI models without proper authorisation.

OpenAI, in particular, drew significant backlash for training its GPT models on data obtained from the internet without obtaining consent from the respective content creators. This issue escalated to the point where The New York Times filed a lawsuit against OpenAI and Microsoft last year, accusing them of copyright infringement.

While emphasising the FT’s commitment to human journalism, Ridding noted the agreement would broaden the reach of its newsroom’s work while deepening the understanding of reader interests.

“Apart from the benefits to the FT, there are broader implications for the industry. It’s right, of course, that AI platforms pay publishers for the use of their material. OpenAI understands the importance of transparency, attribution, and compensation – all essential for us,” explained Ridding.

Earlier this month, The New York Times reported that OpenAI was utilising scripts from YouTube videos to train its AI models. According to the publication, this practice violates copyright laws, as content creators who upload videos to YouTube retain the copyright ownership of the material they produce.

However, OpenAI maintains that its use of online content falls under the fair use doctrine. The company, along with numerous other technology firms, argues that their large language models (LLMs) transform the information gathered from the internet into an entirely new and distinct creation.

In January, OpenAI asserted to a UK parliamentary committee that it would be “impossible” to develop today’s leading AI systems without using vast amounts of copyrighted data.

Brad Lightcap, COO of OpenAI, expressed his enthusiasm about the FT partnership: “Our partnership and ongoing dialogue with the FT is about finding creative and productive ways for AI to empower news organisations and journalists, and enrich the ChatGPT experience with real-time, world-class journalism for millions of people around the world.”

This agreement between OpenAI and the Financial Times is the most recent in a series of new collaborations that OpenAI has forged with major news publishers worldwide.

While the financial details of these contracts were not revealed, OpenAI’s recent partnerships with publishers will enable the company to continue training its algorithms on web content, but with the crucial difference being that it now has obtained the necessary permissions to do so.

Ridding said the FT values “the opportunity to be inside the development loop as people discover content in new ways.” He acknowledged the potential for significant advancements and challenges with transformative technologies like AI but emphasised, “what’s never possible is turning back time.”

“It’s important for us to represent quality journalism as these products take shape – with the appropriate safeguards in place to protect the FT’s content and brand,” Ridding added.

The FT has embraced new technologies throughout its history. “We’ll continue to operate with both curiosity and vigilance as we navigate this next wave of change,” Ridding concluded.

(Photo by Utsav Srestha)

See also: OpenAI faces complaint over fictional outputs

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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Tags: ai, artificial intelligence, chatgpt, chatgpt enterprise, copyright, financial times, journalism, media, openai

[qocsuing]

Deep Dive into the World of Proxies

Deep Dive into the World of Proxies

In the digital age, the role of proxies has become increasingly significant. Among the various types of proxies available, rotating proxies stand out for their unique functionality and the myriad benefits they offer.To get more news about proxy youtube, you can visit pyproxy.com official website.

## Understanding Rotating Proxies

At its core, a rotating proxy is a type of proxy server that assigns a different IP address to each connection request. This dynamic nature of rotating proxies offers a significant advantage in terms of anonymity and security compared to static proxies, which use a single IP address.

## The Role of Mobile Web Proxy

Incorporating a mobile web proxy into the mix takes the functionality of rotating proxies a step further. A mobile web proxy uses IP addresses assigned to mobile devices, making them even less likely to be detected or blocked. This is because mobile IPs are generally considered more trustworthy by websites, as they are associated with real users.

## How Rotating Proxies Work

Rotating proxies operate by cycling through a pool of IP addresses. This pool can consist of tens, hundreds, or even thousands of IPs. When a user connects to the internet through a rotating proxy server, the server assigns an available IP address from its pool. After a set period or upon a new request, the server will then assign a different IP address, effectively rotating the user’s digital identity.

## Technical Mechanism

The technical mechanism behind rotating proxies involves complex networking and software configurations. Proxy servers are set up to handle a large number of IP addresses and efficiently allocate them to users. This process is typically automated and managed by algorithms to ensure smooth operation and fair distribution of IP addresses.

## Advantages of Using Rotating Proxies

- **Enhanced Anonymity**: By constantly changing the user’s IP address, rotating proxies make it extremely difficult for websites to track or identify the user. - **Reduced Risk of Blacklisting**: Since each request appears to come from a different IP address, the risk of an IP being blacklisted by websites is significantly reduced. - **Improved Data Scraping Efficiency**: Rotating proxies are ideal for data scraping as they can access a large volume of data from websites without being detected. - **Load Balancing**: The distribution of requests across multiple IP addresses helps in balancing the load, thereby maintaining high performance and reducing the risk of server overload.

In conclusion, rotating proxies are powerful tools in the digital age, offering enhanced privacy, security, and performance. Whether you’re a business or an individual, understanding proxies can greatly benefit your online experience.

[industry212]

10 Must-Have AI Chrome Extensions for Data Scientists in 2024

Empowering data scientists with Top 10 AI Chrome Extensions

The field of data science demands a toolkit that evolves with the industry's advancements. As we enter 2024, the significance of AI Chrome extensions for data scientists cannot be overstated. This article discusses the top 10 extensions that enable data scientists to enhance productivity and streamline workflows.

Codeium:

Codeium, a versatile tool for programmers, streamlines code efficiency in over 20 languages. Through analysis and optimization, it significantly accelerates program execution, minimizing resource consumption. Whether you're a seasoned coder or a beginner, Codeium proves invaluable in enhancing code performance for quicker results and improved resource management.

EquatIO:

EquatIO transforms mathematical expression creation into a seamless digital experience. Whether typing, handwriting, or using voice dictation, it effortlessly translates thoughts into precise formulas. Compatible with Google Docs, Forms, Slides, Sheets, and Drawings, EquatIO fosters an engaging learning environment, offering advanced features like interactive quizzes and chemistry formula prediction.

Instant Data Scraper:

Instant Data Scraper is a powerful and free browser extension that uses AI for seamless data extraction from any website. No scripting needed; it analyzes HTML structures for relevant data, providing customization options for precision. Ideal for lead generation, SEO, and more, with secure data handling. No spyware, just efficient web scraping.

Challenge Hunt:

Challenge Hunt is your go-to app for staying updated on global programming competitions and hackathons. It covers coding challenges, hackathons, data science competitions, and hiring challenges. Set reminders for upcoming events and personalize your experience by selecting preferred online platforms. Never miss a coding opportunity with this all-in-one competition tracker.

CatalyzeX:

CatalyzeX is a browser extension that revolutionizes how researchers and developers access machine learning implementations. Seamlessly integrated into your web browser, it adds intuitive "[CODE] buttons" to research papers across Google, ArXiv, Scholar, Twitter, and Github. Instantly navigate to open source code, powered by the esteemed CatalyzeX.com repository, unlocking a world of cutting-edge machine learning advancements.

Sider:

Sider is a versatile text processing tool designed to streamline tasks in data science. Whether clarifying complex concepts, translating foreign text, summarizing articles, or rephrasing documents, Sider adapts seamlessly. Its versatility proves invaluable to students, writers, and professionals across academia, business, and technology.

Originality.AI:

Originality.AI is a vital data science tool addressing the challenge of discerning between human and AI-generated text. It accurately identifies authorship, distinguishing content created by humans from that generated by neural networks as AI advances in text creation.

Fireflies:

Fireflies, powered by GPT-4, is an invaluable assistant for data scientists. It excels in navigating and summarizing diverse content types like articles, YouTube videos, emails, and documents. In the era of information overload, Fireflies efficiently sorts and summarizes content from various sources, offering a vital solution for data professionals.

AIPRM:

AIPRM facilitates optimal use of Generative Pretrained Transformers by offering a diverse catalog of well-structured prompts designed for data scientists and IT professionals. With scenarios covering a range of use cases, users can customize GPT model responses to precise requirements, enhancing overall model effectiveness in diverse applications.

Code Squire.AI:

Code Squire.AI is a dedicated code assistant for data science, excelling in Pandas and supporting JupyterLab and Colab. It streamlines coding, reduces errors, and boosts efficiency in data science tasks.