Technology peripherals
AI
13 open source projects revolutionizing the AI and machine learning world
13 open source projects revolutionizing the AI and machine learning world
Translator | Bugatti
Reviewer | Sun Shujuan
Open source is fertile ground for brewing revolutionary software, especially in cutting-edge fields such as artificial intelligence and machine learning. The open source ethos and collaboration tools make it easier for teams to share code and data and build on the success of others.
This article introduces 13 open source projects that are reshaping the world of artificial intelligence and machine learning. Some are sophisticated packages supporting new algorithms, others are more low-key. All of these are worth keeping an eye on.
TensorFlow and PyTorch
A list of open source tools for AI and machine learning is incomplete without TensorFlow and PyTorch. Individually and collectively, these frameworks support some of the most experimental and important research in machine learning and artificial intelligence. At least several of the projects discussed in this article use them as base modules.
FauxPilot
Programmers who need a little help with programming can benefit from FauxPilot. The system trains itself on existing production code and learns enough from it to make meaningful comments and suggestions. The project was inspired by GitHub Copilot, but FauxPilot lets you choose the codebase used for training. This extra layer of control prevents you from using code snippets from sources that may not approve their use. If you choose a training source and limit its use to those with the appropriate permissions and licenses, you're more likely to use programming help and code snippets that are clean and reliable.
DALL-E
One of the easiest ways to understand how a machine learning model “thinks” is to start feeding words into DALL-E, a very large open model using data collected from the web. Constructed from images and text descriptions. Enter a word and out comes an image that DALL-E thinks matches. Open source projects such as DALL-E Playground and DALL-E Mini make it easier for users to experiment with models. It’s part game and part portal into the underbelly of AI algorithms.
YOLOv7
Real-time object detection (finding objects in images) is a tricky area of artificial intelligence. It is also critical for areas such as autonomous vehicles, robots and assistive devices that need to collect and transmit accurate environmental information. YOLOv7 is one of the fastest and most accurate open source object detection tools. Just feed the tool a bunch of object-rich images and see what happens next.
DeepFaceLab
Deepfakes are videos and images that are created, altered, or synthesized with the help of deep learning. The most common example is replacing the face of a celebrity or politician with an existing video or image, often for fun but sometimes for more nefarious purposes. DeepFaceLab is open source deepfakes technology running on Python. In addition to replacing one face with another, it can also be used to remove wrinkles and other signs of aging.
PaddleNLP
Natural language processing (NLP) engines perform neural search and sentiment analysis, then extract and present information for human and machine users. While still clunky at times, the technology has become advanced enough to be used in a variety of applications and fields (Alexa is just one example). PaddleNLP is a popular open source NLP library that you can use to collect search sentiment and label important entities.
MindsDB
The traditional approach to AI success is to store data in a database and then extract and send it to a separate machine learning algorithm. MindsDB is a SQL server that integrates machine learning algorithms directly into the database. In-database machine learning (i.e. analyzing data where it is already stored) is a fast and efficient way to speed up your machine learning workflow.
Image Super-Resolution (ISR)
The more details in a photo, the better, and Image Super-Resolution can add more details by increasing the image resolution. This open source tool uses a machine learning model that you can train to guess details in low-resolution images. With a good training set, the model can produce accurate details and clearer images.
DeepPavlov
Many businesses and large companies are replacing frontline customer service employees with chatbots, which means machines are learning to hold conversations. DeepPavlov combines essential machine learning tools like TensorFlow, Keras, and PyTorch to create chatbots for you to learn from. The results are a little weird, but with the right training, sometimes the results are practical.
Bledner
A great way to turn your 3D models into gorgeous rendered scenes is to fire up Blender. While many think of Blender as a tool for filmmakers and animators, it is also a great example of applied AI. A rich interface and numerous plug-ins make it possible to create complex motion graphics or cinematic scenes. All it takes is a little creativity, and the Oscar nominating committee will soon be calling. The AI won't even ask for a share of the victory.
OpenCV
One of the most reliable foundations for exploring machine vision is the open source computer vision library OpenCV. It includes many popular algorithms for identifying objects in digital images, as well as specialized routines, such as one that can recognize and read car license plates.
Robocode
Robocode is like The Hunger Games for your algorithm. This Java-based programming game pits your tank against other tanks in a battle for dominance. It's a fun pastime and might even be useful for testing new strategies for self-driving cars.
Original link: https://www.infoworld.com/article/3673976/13-open-source-projects-transforming-ai-and-machine-learning.html
The above is the detailed content of 13 open source projects revolutionizing the AI and machine learning world. 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
