#opensourceAI

[mysocial8onetech]

Step into the future with Llama 3.1, the latest iteration in open-source large language models by Meta AI. With its high parameter count (405B) and multilingual capabilities, it’s redefining what’s possible in the world of AI.

[govindhtech]

How Open Source AI Works? Its Advantages And Drawbacks

What Is Open-source AI?

Open source AI refers to publicly available AI frameworks, methodologies, and technology. Everyone may view, modify, and share the source code, encouraging innovation and cooperation. Openness has sped AI progress by enabling academics, developers, and companies to build on each other’s work and create powerful AI tools and applications for everyone.

Open Source AI projects include:

Deep learning and neural network frameworks PyTorch and TensorFlow.

Hugging Face Transformers: Language translation and chatbot NLP libraries.

OpenCV: A computer vision toolbox for processing images and videos.

Through openness and community-driven standards, open-source AI increases accessibility to technology while promoting ethical development.

How Open Source AI Works

The way open-source AI operates is by giving anybody unrestricted access to the underlying code of AI tools and frameworks.

Community Contributions

Communities of engineers, academics, and fans create open-source AI projects like TensorFlow or PyTorch. They add functionality, find and solve errors, and contribute code. In order to enhance the program, many people labor individually, while others are from major IT corporations, academic institutions, and research centers.

Access to Source Code

Open Source AI technologies’ source code is made available on websites such as GitHub. All the instructions needed for others to replicate, alter, and comprehend the AI’s operation are included in this code. The code’s usage is governed by open-source licenses (such MIT, Apache, or GPL), which provide rights and restrictions to guarantee equitable and unrestricted distribution.

Building and Customizing AI Models

The code may be downloaded and used “as-is,” or users can alter it to suit their own requirements. Because developers may create bespoke AI models on top of pre-existing frameworks, this flexibility permits experimentation. For example, a researcher may tweak a computer vision model to increase accuracy for medical imaging, or a business could alter an open-source chatbot model to better suit its customer service requirements.

Auditing and Transparency

Because anybody may examine the code for open source AI, possible biases, flaws, and mistakes in AI algorithms can be found and fixed more rapidly. Because it enables peer review and community-driven changes, this openness is particularly crucial for guaranteeing ethical AI activities.

Deployment and Integration

Applications ranging from major business systems to mobile apps may be linked with open-source AI technologies. Many tools are accessible to a broad range of skill levels because they provide documentation and tutorials. Open-source AI frameworks are often supported by cloud services, allowing users to easily expand their models or incorporate them into intricate systems.

Continuous Improvement

Open-source AI technologies allow users to test, improve, update, and fix errors before sharing the findings with the community. Open Source AI democratizes cutting-edge AI technology via cross-sector research and collaboration.

Advantages Of Open-Source AI

Research and Cooperation: Open-source AI promotes international cooperation between organizations, developers, and academics. They lessen effort duplication and speed up AI development by sharing their work.

Transparency and Trust: Open source AI promotes better trust by enabling people to examine and comprehend how algorithms operate. Transparency ensures AI solutions are morally and fairly sound by assisting in the detection of biases or defects.

Startups: Smaller firms, and educational institutions that cannot afford proprietary solutions may employ open-source AI since it is typically free or cheap.

Developers: May customize open-source AI models to meet specific needs, improving flexibility in healthcare and finance. Open Source AI allows students, developers, and data scientists to explore, improve, and participate in projects.

Open-Source AI Security and Privacy issues: Unvetted open source projects may provide security issues. Attackers may take advantage of flaws in popular codebases, particularly if fixes or updates are sluggish.

Quality and Upkeep: Some open-source AI programs have out-of-date models or compatibility problems since they don’t get regular maintenance or upgrades. Projects often depend on unpaid volunteers, which may have an impact on the code’s upkeep and quality.

Complexity: Implementing Open Source AI may be challenging and may call for a high level of experience. Users could have trouble with initial setup or model tweaking in the absence of clear documentation or user assistance.

Ethics and Bias Issues: Training data may introduce biases into even open-source AI, which may have unforeseen repercussions. Users must follow ethical standards and do thorough testing since transparent code does not always translate into equitable results.

Commercial Competition: Open-source initiatives do not have the funds and resources that commercial AI tools possess, which might impede scaling or impede innovation.

Drawbacks

Open source AI is essential to democratizing technology.

Nevertheless, in order to realize its full potential and overcome its drawbacks, it needs constant maintenance, ethical supervision, and active community support.

Read more on Govindhtech.com

[thecioconnect]

Google's AlphaChip and Meta's Llama 3.2 Signal Major Shifts in AI Strategies

Google and Meta update their AI strategies: Google launches AlphaChip for faster chip design and Gemini 1.5 model improvements, while Meta releases Llama 3.2 with powerful LLMs optimized for vision, edge, and mobile.

[beforecrisisffvii]

How to Build a Private LLM 🚀

Want to create a private LLM for your business? Start by selecting a base model—open-source options like GPT-NeoX or LLaMA are popular. Fine-tune it with domain-specific data using transfer learning. Invest in quality datasets and ensure data privacy. You'll need significant compute resources; consider cloud providers offering GPU rentals. Finally, evaluate and continuously improve your model with feedback loops. Secure, customizable, and tailored to your needs—a private LLM can revolutionize your operations!

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[trillionstech-ai]

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Kyutai has just launched Moshi, an open-source AI assistant that can chat with you in real-time!

Developed in just six months, Moshi uses a special "Audio Language Model" to talk naturally without delays, offering an impressively smooth experience with only 200-240 milliseconds of latency.

Stay tuned for the open-source release soon, and get ready to explore the future of real-time AI conversations!

For more AI related updates, follow @trillionstech.ai

[jpmellojr]

AI Expert Claims Big Tech Using Fear of AI for Market Control

Big Tech leaders are exaggerating the threat AI poses to humanity to solidify their market shares through government regulation, a leading AI figure said Monday. https://jpmellojr.blogspot.com/2023/10/ai-expert-claims-big-tech-using-fear-of.html

[ayman7755a]

Meta and Microsoft launch open-source artificial intelligence model "Llama 2"

[osintelligence]

https://bit.ly/3WoRU5R - 🛡️ Defense Unicorns has launched LeapfrogAI, a promising open source project poised to enhance secure Generative AI solutions for highly regulated industries such as defense, intelligence, and commercial enterprises. LeapfrogAI is set to transform the way these sectors operate, optimizing their data advantages while maintaining stringent security protocols. #AI #DefenseUnicorns #LeapfrogAI 🚀 The rapid progress in open-source Generative AI is remarkable. Traditional general-purpose AI models are changing the landscape of business operations. Yet, fine-tuned open-source models backed by mission-specific data are often superior in performance. LeapfrogAI is designed to harness this power, providing a secure and efficient platform for integrating AI capabilities in-house. #OpenSourceAI #Innovation 🎯 With LeapfrogAI, users can deliver new Generative AI capabilities swiftly, ensure security and regulatory compliance, fine-tune models leveraging their data, retain data and model control, deploy AI solutions across various platforms and simplify the use of Generative AI. The Department of the Navy and the United States Space Force are among the early adopters. #GenerativeAI #AIForDefense ⚙️ LeapfrogAI aims to provide AI-as-a-service in resource-constrained environments, bringing sophisticated AI solutions closer to these challenging areas. It bridges the gap between limited resources and the growing AI demand by hosting APIs that offer AI-related services, such as vector databases, Large Language Model (LLM) completions, and creation of embeddings. #AIAsAService 🔐 Hosting your own Large Language Model (LLM) can offer several advantages like data privacy and security, cost-effectiveness, customization and control, and low latency. With LeapfrogAI, you have the flexibility to host your LLM, ensuring you have full control over your data and your AI solutions. #LLM #DataPrivacy 💼 LeapfrogAI provides an API closely aligned with OpenAI's, facilitating a seamless transition for developers familiar with OpenAI's API. Its features include efficient similarity searches via vector databases, fine-tuning models using customer-specific data, and generating embeddings for various tasks. #OpenAI #API 💡 Setting up the Kubernetes Cluster and deploying LeapfrogAI is straightforward, and usage guidelines are provided to help new users get started. LeapfrogAI also allows teams to deploy APIs that mirror OpenAI's spec, enabling secure AI integration without the risk of sensitive data being released to SaaS tools. #Kubernetes #AIDeployment ⚙️ To wrap up, LeapfrogAI is set to be a game-changer in the world of secure Generative AI, offering secure, flexible, and powerful AI solutions for various mission-driven organizations. #LeapfrogAI #AIRevolution GitHub: https://bit.ly/3MhxEP0

[editorav]

Join #mozilla on their mission to democratize #artificialintelligence!

The tech company known for their human-first approach is taking on big tech giants like #google and #microsoft with their new #opensource AI initiative moz://a. With transparency, accountability, and trust at the core of their mission, Mozilla aims to create a more accessible and democratic AI landscape. By developing free and open-source AI products, they're taking an active role in shaping the future of AI. Read more to know about their first two projects under this initiative.

[llewelynpritch]

The Colonial Bookstore, political satire, honesty, boris johnson, justin trudeau, education, colonialsm, economic exploitation, oppression #ClimateJustice #RightsOfNature #HumanRights

https://docs.google.com/document/d/e/2PACX-1vTX8K_fI6kgAIOeqR9BFiEdk9How97PTQUO2czQ643QfkwBhlkBuLTldaB61KOsspzBosMWyhApKIrv/pub https://unitedforclimate.blogspot.com/2024/11/we-only-sell-honest-impartial-images.html https://labradorleadstheworld.blogspot.com/2024/11/we-only-sell-honest-impartial-images.html https://muskratfallscivilrightsmovement.blogspot.com/2024/11/we-only-sell-honest-impartial-images.html https://landprotectorshumanrightsmovement.blogspot.com/2024/11/we-only-sell-honest-impartial-images.html The Colonial Bookstore: We only sell honest impartial images #DeepAiImage #PoliticalSatire … ‘The colonial legacies of the UK and Canada create both increased responsibility and challenges for their fossil fuel extraction policies. While these histories have led to ambitious rhetoric and some initiatives, both countries still face criticism for not doing enough to address their historical emissions and support global climate action. Their approaches to fossil fuel extraction are thus shaped by the need to balance domestic economic concerns with growing international pressure to lead on climate action due to their colonial pasts.’ #OpenSourceAI #Perplexity #DeepAI Llewelyn Pritchard 9 November 2024

[mysocial8onetech]

Unveil the power of Mora, an innovative multi-agent framework that’s reshaping the landscape of video generation. Experience how Mora mimics and extends the capabilities of OpenAI's Sora, marking a new era in open-source AI.

[govindhtech]

IBM Research Data Loader Helps Open-source AI Model Training

IBM Research data loader improves open-source community’s access to AI models for training.

Training AI models More quickly than ever

IBM showcased new advances in high-throughput AI model training at PyTorch 2024, along with a state-of-the-art data loader, all geared toward empowering the open-source AI community.

IBM Research experts are contributing to the open-source model training framework at this year’s PyTorch Conference. These contributions include major advances in large language model training throughput as well as a data loader that can handle enormous amounts of data with ease.

It must constantly enhance the effectiveness and resilience of the cloud infrastructure supporting LLMs’ training, tuning, and inference to supply their ever-increasing capabilities at a reasonable cost. The open-source PyTorch framework and ecosystem have greatly aided the AI revolution that is about to change its lives. IBM joined the PyTorch Foundation last year and is still bringing new tools and techniques to the AI community because it recognizes that it cannot happen alone.

In addition to IBM’s earlier contributions, these new tools are strengthening PyTorch’s capacity to satisfy the community’s ever-expanding demands, be they related to more cost-effective checkpointing, faster data loading, or more effective use of GPUs.

An exceptional data loader for foundation model training and tuning

Using a high-throughput data loader, PyTorch users can now easily distribute LLM training workloads among computers and even adjust their allocations in-between jobs. In order to prevent work duplication during model training, it also enables developers to save checkpoints more effectively. And all of it is attributable to a group of researchers who were only creating the instruments they required to complete a task.

When you wish to rerun your training run with a new blend of sub-datasets to alter model weights, or when you have all of your raw text data and want to use a different tokenizer or maximum sequence length, the resulting tool is well-suited for LLM training in research contexts. With the help of the data loader, you can tell your dataset what you want to do on the fly rather than having to reconstruct it each time you want to make modifications of this kind.

You can adjust the job even halfway through, for example, by increasing or decreasing the number of GPUs in response to changes in your resource quota. The data loader makes sure that data that has already been viewed won’t be viewed again.

Increasing the throughput of training

Bottlenecks occur because everything goes at the speed of the slowest item when it comes to model training at scale. The efficiency with which the GPU is being used is frequently the bottleneck in AI tasks.

Fully sharded data parallel (FSDP), which uniformly distributes big training datasets across numerous processors to prevent any one machine from becoming overburdened, is one component of this method. It has been demonstrated that this distribution greatly increases the speed and efficiency of model training and tuning while enabling faster AI training with fewer GPUs.

This development progresses concurrently with the data loader since the team discovered ways to use GPUs more effectively while they worked with FSDP and torch.compile to optimize GPU utilization. Consequently, data loaders rather than GPUs became the bottleneck.

Next up

Although FP8 isn’t yet generally accessible for developers to use, Ganti notes that the team is working on projects that will highlight its capabilities. In related work, they’re optimizing model tweaking and training on IBM’s artificial intelligence unit (AIU) with torch.compile.

Triton, Nvidia’s open-source platform for deploying and executing AI, will also be a topic of discussion for Ganti, Wertheimer, and other colleagues. Triton allows programmers to write Python code that is then translated into the native programming language of the hardware Intel or Nvidia, for example, to accelerate computation. Although Triton is currently ten to fifteen percent slower than CUDA, the standard software framework for using Nvidia GPUs, the researchers have just completed the first end-to-end CUDA-free inferencing with Triton. They believe Triton will close this gap and significantly optimize training when this initiative picks up steam.

The starting point of the study

IBM Research’s Davis Wertheimer outlines a few difficulties that may arise during extensive training: It’s possible to use an 80/20 rule to large-scale training. In the published research, algorithmic tradeoffs between GPU memory and compute and communication make up 80% of the work. However, because the pipeline moves at the pace of the narrowest bottleneck, you may expect a very long tail of all these other practical concerns when you really try to build something 80 percent of the time.

The IBM team was running into problems when they constructed their training platform. Wertheimer notes, “As we become more adept at using our GPUs, the data loader is increasingly often the bottleneck.”

Important characteristics of the data loader

Stateful and checkpointable: If your data loader state is saved whenever you save a model, and both the model state and data loader states need to be recovered at the same time whenever you recover from a checkpoint.”

Checkpoint auto-rescaling: During prolonged training sessions, the data loader automatically adapts to workload variations. There are a lot of reasons why you might have to rescale your workload in the middle. Training could easily take weeks or months.”

Effective data streaming: There is no build overhead for shuffling data because the system supports data streaming.

Asynchronous distributed operation: The data loader is non-blocking. The data loader states to be saved and then distributed in a way that requires no communication at all.”

Dynamic data mixing: This feature is helpful for changing training requirements since it allows the data loader to adjust to various data mixing ratios.

Effective global shuffling: As data accumulates, shuffling remains effective since the tool handles memory bottlenecks when working with huge datasets.

Native, modular, and feature-rich PyTorch: The data loader is built to be flexible and scalable, making it ready for future expansion. “What if we have to deal with thirty trillion, fifty trillion, or one hundred trillion tokens next year?” “it needs to build the data loader so it can survive not only today but also tomorrow because the world is changing quickly.”

Actual results

The IBM Research team ran hundreds of small and big workloads over several months to rigorously test their data loader. They saw code numbers that were steady and fluid. Furthermore, the data loader as a whole runs non-blocking and asynchronously.

Read more on govindhtech.com

[vlruso]

Protestors criticize Metas open source approach to AI development

📢 Check out this thought-provoking article on protestors criticizing Meta's open source approach to AI development! 👥🤔 The debate on open source AI, especially Meta's Llama models, is heating up with protesters expressing concerns about the potential risks of releasing powerful AI models to the public. They argue that open source AI could lead to the widespread use of dangerous technology. On the other hand, many believe that making AI accessible to all is crucial for democratization and trust-building. Curious to learn more about the challenges and implications of open-source AI? Read this insightful article, which offers practical insights on Meta's decision to publicly release its AI models, the debate surrounding open source AI models, and the contrasting approaches of companies like OpenAI and Google. Click on the link below to dive into the thought-provoking discussion on the merits and risks of open source AI: [Link to the article](https://ift.tt/NenybOf) Can AI truly be trustworthy, responsible, and accountable without being open source? Discover different perspectives and join the conversation! Remember to follow @itinai on Twitter and join the AI Lab in Telegram (@aiscrumbot) for free consultations and more valuable insights. #AIdevelopment #OpenSourceAI #TechDebate List of Useful Links: AI Scrum Bot - ask about AI scrum and agile Our Telegram @itinai Twitter -  @itinaicom

[kylebrowning]

Community stop motion at Open Source #opensourcegotissues #opensourceai #opensource #oscai #stopmotion (at Yvonne Scarlett Golden Cultural & Educational Center) https://www.instagram.com/p/B8VEXOhn5JT/?igshid=kf998clr336e

[govindhtech]

Learn 5 open-source AI tools: PyTorch for deep learning

Open-source AI (artificial intelligence) technologies allow anybody to use, change, and distribute their source code. Innovative AI applications emerge when AI algorithms, pre-trained models, and data sets are made public. Volunteer enthusiasts improve on current work and expedite the development of practical AI solutions. These technologies often produce the greatest tools for difficult enterprise use cases.

Open-source AI projects and libraries on GitHub drive digital innovation in healthcare, finance, and education. Frameworks and tools save developers time and let them focus on customizing solutions for unique projects. Small teams of developers may produce valuable apps for Windows, Linux, iOS, and Android using existing libraries and tools.

Real-time fraud protection, medical picture analysis, personalized suggestions, and customizable learning are possible with open-source AI’s diversity and accessibility. This availability attracts developers, researchers, and companies to open-source projects and AI models. Open-source AI gives enterprises access to a vast, diversified community of developers that constantly enhance AI tools. This collaborative environment promotes transparency and continual improvement, creating feature-rich, trustworthy, and modular tools. Open-source AI’s vendor neutrality keeps enterprises from being beholden to one vendor.

Open-source AI is tempting, but enterprises must be careful with its free availability. Unfocused bespoke AI development might result in mismatched outputs, lost resources, and project failure. Biased algorithms can also create useless results and promote harmful assumptions. Open-source AI is freely available, so unscrupulous actors might use it to influence results or create damaging content.

Biased training data, data drift, and labeling errors can provide discriminatory results and faulty models. When companies use third-party technologies, they risk endangering stakeholders. Open-source AI must be carefully considered and implemented responsibly.

Tech giants are divided on the matter. Open-source AI advocates like Meta and IBM promote scientific interchange and innovation through the AI Alliance. Google, Microsoft, and Open AI prefer a closed model due to AI safety and misuse concerns. U.S. and EU governments are trying to combine innovation with security and ethics.

Open-source AI transforms Although risky, open-source AI is growing in popularity. Many developers prefer open-source AI frameworks to proprietary APIs and applications. In the 2023 State of Open Source report (not from IBM), 80% of respondents indicated increasing use of open-source software over the preceding year, with 41% reporting a “significant” rise.

As tech companies invest in open-source AI, developers and academics will use it more, giving organizations access to breakthrough AI technology.

IBM Watson Health Employs Tensor Flow for medical image analysis, diagnostics, and tailored treatment. J.P. Morgan’s Athena innovates risk management with Python-based open-source AI. Amazon uses open-source AI to improve Alexa, warehouse operations, and recommendation algorithms. Online educational platforms like Coursera and edX leverage open-source AI to personalize learning, recommend content, and automate grading.

Numerous applications and media services, including Netflix and Spotify, use Tensor Flow or PyTorch to improve recommendations and performance using open-source AI and proprietary solutions.

Five open-source AI tools to know The following open-source AI frameworks promote innovation, cooperation, and cross-disciplinary learning. More than tools, they empower people from beginners to experts to grasp AI’s immense potential.

Tensor Flow is a flexible, adaptable Python and JavaScript learning framework. Developers may build and deploy machine learning models on several platforms with Tensor Flow. Its strong community support and large library of pre-built models and tools simplify AI creation for beginners and experts. PyTorch, an open-source AI framework, has a simple interface for debugging and constructing deep learning models. Model training and experimentation are efficient thanks to its Python library integration and GPU acceleration. Many researchers and developers use it for quick software development prototyping and AI/deep learning research. Keras, a Python-based open-source neural network library, is user-friendly and modular, making deep learning model building easy and rapid. Its high-level API is intuitive for novices and adaptable and powerful for professional users, making it useful for instructional and difficult deep-learning applications. Scikit-learn is a strong open-source Python machine learning and predictive data analysis package. J.P. Morgan and Spotify use its scalable supervised and unsupervised learning techniques in their AI systems. Data mining and analysis in many contexts is easy with its simple setup, reusable components, and huge, active community. Open CV is a programming library with extensive computer vision capabilities, real-time performance, a big community, and platform portability, making it excellent for automating activities, analyzing visual data, and building novel solutions. It scales with organizational needs, making it suited for startups and large companies. Open-source AI technologies like Tensor Flow, Apache, and PyTorch, as well as community platforms like Hugging Face, are becoming more popular as AI developers realize that cooperation is the future. Participating in these groups and collaborating on technologies helps organizations receive the greatest tools and people.

The future of open-source AI Open-source AI reinvents company scaling and transformation. What enterprises may expect as open-source AI drives innovation across industries and encourages widespread adoption and deeper AI use.

Natural language processing (NLP), Hugging Face Transformers, large language models (LLMs), and computer vision libraries like Open CV will enable more complex and nuanced applications like Chabot’s, image recognition systems, and robotics and automation.

Open helper, an open-source chat-based AI helper, and GPT Engineer, a generative AI tool that lets anyone design apps from text prompts, presage ubiquitous, highly personalized AI assistants that can handle complex tasks. This shift toward interactive, user-friendly AI solutions signals further AI incorporation into our daily lives.

Open-source AI is an interesting technology with numerous future uses, but enterprises must navigate and partner to successfully deploy AI solutions. Open-source models often need significant fine-tuning to meet enterprise-level effectiveness, trust, and safety standards. Open-source AI is accessible, but enterprises need compute resources, data infrastructure, networking, security, software tools, and skills to use it effectively.

Open-source AI tools and frameworks cannot provide custom AI solutions for many enterprises. Consider how your business may benefit from open-source AIs and how IBM can help you design and deploy a trustworthy, enterprise-grade AI solution.

Read more on Govindhtech.com