The bottom of the AI pyramid: 'data annotator' earning $15 an hour
The application of artificial intelligence is already very popular. ChatGPT looks like "magic that responds to requests", but behind it is actually the credit of labor-intensive industries.
According to media reports such as CNBC and Gizmodo, OpenaAI has hired a large number of outsourced labor to assist them in completing "necessary data labeling tasks" - that is, labeling data. The more formal name is "data labeling" "member", "data annotator", or "AI trainer".
The so-called labeling means to put different labels (voice/picture/text, etc.) on the data samples to be analyzed by AI to help the AI model better identify specific items in the data set and respond to user requests faster.
This is the most basic work of AI model training.
But this job is undoubtedly boring. It contains a lot of repetitive work. Operators only need to identify the type of data samples every day and then select different labels.
Alexej Savreux, a data annotator interviewed by CNBC, said:
We are workers, but without us there would be no artificial intelligence language system.
You can design all the neural networks you want, you can get all the researchers you want involved, but without tags, you don't have ChatGPT. You have nothing.
More importantly, The salary for such a job is $15 per hour-higher than the minimum wage in various states in the United States, but not much higher (Kansas City, where Savreux is located, Minimum wage $7.25).
Currently, domestic AI-related companies are also hiring for such positions.
The technology media "Whiplash" pointed out in an article in March that compared with the high salaries in the AI industry, the salary of data annotators is not high.
Artificial intelligence, focusing on artificial intelligence"A picture costs 9 cents, and I can make 100 pictures a day." Lili said that if they are all qualified, they can earn 90 yuan a day.
"Different labels have different prices." He Wenxin said that his salary at the time was around 3,000. The monthly salary of basic data annotators is mostly between 2,000 and 4,000 yuan, but due to the speed and quality of annotation, "it is difficult to get the salary promised to you during the interview."
On some recruitment websites, Whipman searched for "data annotation" and set a salary range between 2,000 and 8,000 yuan. Some special annotations, such as minor languages, high-precision drawings, etc., will have higher salaries.
Outsourcing repetitive tasks is not a phenomenon unique to the artificial intelligence industry.
CNBC pointed out that Silicon Valley has always relied on the labor of thousands of low-skilled, low-wage outsourced workers to build its computer empire, but these workers have always been in an "inferior" status:
These jobs are precarious, on-demand, and people are employed directly by a company through a written contract or through third-party providers who specialize in temporary work or outsourcing.
Benefits like health insurance are sparse or non-existent — meaning lower costs for tech companies — and the work is often anonymous, with all credit going to tech startup executives and researchers.
Now, the artificial intelligence industry is also following this rule of the game. With the booming development of artificial intelligence, more and more data annotators are being hired, but more and more of this basic labor is being ignored.
Sonam Jindal, project leader of the nonprofit organization "AI, labor and the economy at the Partnership on AI (PAI)" said:
Much of the discussion surrounding artificial intelligence is very welcome.Of course, as outsourcing positions such as data annotators have been seen by more and more organizations, this has also caused technology giants to face "trouble".
But we’re missing an important part of the story: This still relies heavily on a massive human workforce.
Tech giants in the whirlpool
Earlier this year, Time magazine reported that OpenAI was relying on low-wage Kenyan outsourced labor to flag text containing hate speech or sexually abusive language so that its models could better identify “toxic” on their own. "content.
In Nairobi, Kenya, more than 150 people who have worked on AI for Facebook, TikTok and ChatGPT voted to form a union, citing low wages and the mental burden of the work.
Another media outlet Semafor reported in January this year that
OpenAI employs approximately 1,000 remote outsourced workers in places such as Eastern Europe and Latin America to label data or train company software on computer engineering tasks, where wages are As low as $2/hour.
Another data that contrasts with this is that,As of January this year, OpenAI has approximately 375 employees.
And a spokesperson for the company said no one was available to answer questions about its use of artificial intelligence to outsource workers.PAI warned in a 2021 report that demand for so-called "data-stuffing jobs" was surging, with the organization recommending the industry work on fair compensation and other improved practices and last year publishing a guide for companies Voluntary guidelines to follow.
CNBC pointed out that Google’s AI subsidiary DeepMind is the only technology company so far to publicly commit to complying with these guidelines.
Jindal said:
Many people have realized that this is a very important thing. The challenge now is getting companies to do it.
This is a new job created by artificial intelligence, and we have the potential to make this a high-quality job where the workers who do it are respected and valued for their contribution to making this progress possible.
The above is the detailed content of The bottom of the AI pyramid: 'data annotator' earning $15 an hour. For more information, please follow other related articles on the PHP Chinese website!
Hot AI Tools
Undresser.AI Undress
AI-powered app for creating realistic nude photos
AI Clothes Remover
Online AI tool for removing clothes from photos.
Undress AI Tool
Undress images for free
Clothoff.io
AI clothes remover
AI Hentai Generator
Generate AI Hentai for free.
Hot Article
Hot Tools
Notepad++7.3.1
Easy-to-use and free code editor
SublimeText3 Chinese version
Chinese version, very easy to use
Zend Studio 13.0.1
Powerful PHP integrated development environment
Dreamweaver CS6
Visual web development tools
SublimeText3 Mac version
God-level code editing software (SublimeText3)
Hot Topics
1382
52
Bytedance Cutting launches SVIP super membership: 499 yuan for continuous annual subscription, providing a variety of AI functions
Jun 28, 2024 am 03:51 AM
This site reported on June 27 that Jianying is a video editing software developed by FaceMeng Technology, a subsidiary of ByteDance. It relies on the Douyin platform and basically produces short video content for users of the platform. It is compatible with iOS, Android, and Windows. , MacOS and other operating systems. Jianying officially announced the upgrade of its membership system and launched a new SVIP, which includes a variety of AI black technologies, such as intelligent translation, intelligent highlighting, intelligent packaging, digital human synthesis, etc. In terms of price, the monthly fee for clipping SVIP is 79 yuan, the annual fee is 599 yuan (note on this site: equivalent to 49.9 yuan per month), the continuous monthly subscription is 59 yuan per month, and the continuous annual subscription is 499 yuan per year (equivalent to 41.6 yuan per month) . In addition, the cut official also stated that in order to improve the user experience, those who have subscribed to the original VIP
Context-augmented AI coding assistant using Rag and Sem-Rag
Jun 10, 2024 am 11:08 AM
Improve developer productivity, efficiency, and accuracy by incorporating retrieval-enhanced generation and semantic memory into AI coding assistants. Translated from EnhancingAICodingAssistantswithContextUsingRAGandSEM-RAG, author JanakiramMSV. While basic AI programming assistants are naturally helpful, they often fail to provide the most relevant and correct code suggestions because they rely on a general understanding of the software language and the most common patterns of writing software. The code generated by these coding assistants is suitable for solving the problems they are responsible for solving, but often does not conform to the coding standards, conventions and styles of the individual teams. This often results in suggestions that need to be modified or refined in order for the code to be accepted into the application
Can fine-tuning really allow LLM to learn new things: introducing new knowledge may make the model produce more hallucinations
Jun 11, 2024 pm 03:57 PM
Large Language Models (LLMs) are trained on huge text databases, where they acquire large amounts of real-world knowledge. This knowledge is embedded into their parameters and can then be used when needed. The knowledge of these models is "reified" at the end of training. At the end of pre-training, the model actually stops learning. Align or fine-tune the model to learn how to leverage this knowledge and respond more naturally to user questions. But sometimes model knowledge is not enough, and although the model can access external content through RAG, it is considered beneficial to adapt the model to new domains through fine-tuning. This fine-tuning is performed using input from human annotators or other LLM creations, where the model encounters additional real-world knowledge and integrates it
Seven Cool GenAI & LLM Technical Interview Questions
Jun 07, 2024 am 10:06 AM
To learn more about AIGC, please visit: 51CTOAI.x Community https://www.51cto.com/aigc/Translator|Jingyan Reviewer|Chonglou is different from the traditional question bank that can be seen everywhere on the Internet. These questions It requires thinking outside the box. Large Language Models (LLMs) are increasingly important in the fields of data science, generative artificial intelligence (GenAI), and artificial intelligence. These complex algorithms enhance human skills and drive efficiency and innovation in many industries, becoming the key for companies to remain competitive. LLM has a wide range of applications. It can be used in fields such as natural language processing, text generation, speech recognition and recommendation systems. By learning from large amounts of data, LLM is able to generate text
Five schools of machine learning you don't know about
Jun 05, 2024 pm 08:51 PM
Machine learning is an important branch of artificial intelligence that gives computers the ability to learn from data and improve their capabilities without being explicitly programmed. Machine learning has a wide range of applications in various fields, from image recognition and natural language processing to recommendation systems and fraud detection, and it is changing the way we live. There are many different methods and theories in the field of machine learning, among which the five most influential methods are called the "Five Schools of Machine Learning". The five major schools are the symbolic school, the connectionist school, the evolutionary school, the Bayesian school and the analogy school. 1. Symbolism, also known as symbolism, emphasizes the use of symbols for logical reasoning and expression of knowledge. This school of thought believes that learning is a process of reverse deduction, through existing
To provide a new scientific and complex question answering benchmark and evaluation system for large models, UNSW, Argonne, University of Chicago and other institutions jointly launched the SciQAG framework
Jul 25, 2024 am 06:42 AM
Editor |ScienceAI Question Answering (QA) data set plays a vital role in promoting natural language processing (NLP) research. High-quality QA data sets can not only be used to fine-tune models, but also effectively evaluate the capabilities of large language models (LLM), especially the ability to understand and reason about scientific knowledge. Although there are currently many scientific QA data sets covering medicine, chemistry, biology and other fields, these data sets still have some shortcomings. First, the data form is relatively simple, most of which are multiple-choice questions. They are easy to evaluate, but limit the model's answer selection range and cannot fully test the model's ability to answer scientific questions. In contrast, open-ended Q&A
SOTA performance, Xiamen multi-modal protein-ligand affinity prediction AI method, combines molecular surface information for the first time
Jul 17, 2024 pm 06:37 PM
Editor | KX In the field of drug research and development, accurately and effectively predicting the binding affinity of proteins and ligands is crucial for drug screening and optimization. However, current studies do not take into account the important role of molecular surface information in protein-ligand interactions. Based on this, researchers from Xiamen University proposed a novel multi-modal feature extraction (MFE) framework, which for the first time combines information on protein surface, 3D structure and sequence, and uses a cross-attention mechanism to compare different modalities. feature alignment. Experimental results demonstrate that this method achieves state-of-the-art performance in predicting protein-ligand binding affinities. Furthermore, ablation studies demonstrate the effectiveness and necessity of protein surface information and multimodal feature alignment within this framework. Related research begins with "S
SK Hynix will display new AI-related products on August 6: 12-layer HBM3E, 321-high NAND, etc.
Aug 01, 2024 pm 09:40 PM
According to news from this site on August 1, SK Hynix released a blog post today (August 1), announcing that it will attend the Global Semiconductor Memory Summit FMS2024 to be held in Santa Clara, California, USA from August 6 to 8, showcasing many new technologies. generation product. Introduction to the Future Memory and Storage Summit (FutureMemoryandStorage), formerly the Flash Memory Summit (FlashMemorySummit) mainly for NAND suppliers, in the context of increasing attention to artificial intelligence technology, this year was renamed the Future Memory and Storage Summit (FutureMemoryandStorage) to invite DRAM and storage vendors and many more players. New product SK hynix launched last year
