Technology peripherals
AI
This copyright lawsuit may affect the future of artificial intelligence!
This copyright lawsuit may affect the future of artificial intelligence!
In recent days, a wave of layoffs, continued turmoil at Twitter, and the dramatic collapse of cryptocurrencies have left the technology industry "faltering."
However, generative artificial intelligence has given investors and entrepreneurs new hope.
Generative artificial intelligence refers to unsupervised and semi-supervised machine learning algorithms that enable computers to automatically generate coherent text, captivating images, and functional computer code.
Now this "blue sky" is also covered with layers of dark clouds.
This month, in a class action lawsuit filed in California federal court, the AI programming assistance tool GitHub Copilot was sued in court.
Created by Microsoft subsidiary GitHub, Copilot is a powerful tool that hosts code for hundreds of millions of software projects and can automatically write working code as programmers work, too. A powerful demonstration of the creative and commercial potential of generative AI technology.
A new study from GitHub shows that when using Copilot, coders complete certain tasks in half the time of when not using it, resulting in a significant increase in productivity.
However, as some programmers have noticed, Copilot occasionally copies identifiable code snippets from the millions of lines of public code repositories.
Butterick, the programmer behind the lawsuit, claims that Microsoft, GitHub and OpenAI violated copyright because when Copilot copied open source code that needed to be covered by a license - it did not provide attribution.
Of course, programmers study, learn, and copy each other's code all the time, but not everyone thinks it's fair for an AI to do the same thing, especially if the AI is allowed to operate without respecting the source material's licensing requirements. Generate a lot of valuable code by yourself.
"As a technologist, I have to admit that I am a big fan of artificial intelligence," Butterick said. "I look forward to all the possibilities of these tools, but it must be fair to everyone."
GitHub CEO Thomas Dohmke said Copilot has features that prevent copying of existing code.
“When you enable it, Copilot will match the license of the code released on GitHub,” he said. “If there is no license, AI will not apply the relevant code.”
It remains to be seen whether this feature provides adequate legal protection.
GitHub co-founder Nat Friedman believes that tools like Copilot do not obviously violate the spirit of open source and free software.
"The free software movement in the 1980s and 1990s often talked about weakening the power of copyright in order to improve people's ability to code."
"I think It's kind of frustrating that we're in a situation now where there are people running around saying we need maximum copyright to protect these communities." Illustrations and “AI assistants” used in marketing copy are trained from previous human work.
Earlier, visual artists were the first group to question the legality and ethics of AI works.
Some people who make a living from visual creativity are upset that AI art tools trained on their work can generate new images in the same style.
Music industry organization the Recording Industry Association of America has said that AI-driven music generation and mixing may become the "hardest hit area" for copyright issues.
Currently, the lawsuit is still in its early stages and the prospects are unclear. Because many AI technology concepts are very new, they have never been examined from a legal perspective before.
Legal experts say this could have an impact on the future of generative AI tools because it challenges some of the most important principles that have underpinned advances in artificial intelligence over the past three decades.
“This lawsuit will definitely become a landmark case,” said Luis Villa, a lawyer who specializes in open source-related cases.
Reference:
https://gizmodo.com/ai-microsoft-dall-e-1849816871
https://www.wired.com/story/ this-copyright-lawsuit-could-shape-the-future-of-generative-ai/
The above is the detailed content of This copyright lawsuit may affect the future of artificial intelligence!. 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
Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!
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
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
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
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
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
