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- Open Source Artificial Intelligence a Foundation for Innovation
Open Source Artificial Intelligence a Foundation for Innovation
The evolution and impact of open source technology on AI
[The image above is generated by Midjourney. The prompt I used to create the image is listed at the end of this email.]
The ascent of open source software in the tech world is nothing short of remarkable. Cloud orchestration platforms such as Kubernetes, enduring operating systems like Linux, and versatile programming languages like Python have laid the groundwork for advancements in machine learning.
Open source initiatives stand as a tribute to the collective brilliance of developers worldwide. It's the crucible of technological progress, drawing innumerable developers from all corners of the globe to contribute, enhance, and fine-tune. This spirit of collaboration ensures that these platforms survive and consistently remain at the forefront of technological innovation.
Throughout my professional journey, I've been deeply involved in the progression of open source software. My role primarily centered around guiding developer relations and forging strong ties with software engineers. I've witnessed how these foundational technologies have created avenues for individuals to build on the legacies of their predecessors, leading to revolutionary innovations. The impending era of open source AI software is set to bring about similarly transformative results.
Understanding Open Source AI Software
By "open source AI," I mean AI tools and software that have their source code accessible to the public and available under a license that meets the Open Source Definition. Open source software is characterized by its conditions that permit anyone to inspect, modify, and distribute the software. Contrary to proprietary software, open source AI champions collaboration, transparency, and a more inclusive approach to tech development. This addresses the prevalent concern that AI remains an enigma, with many unsure about its inner workings.
Facebook's recent launch of LLAMA 2 garnered significant attention. However, concerns arose regarding its licensing terms, particularly an anti-competitive clause that seemed at odds with open source licensing, a topic I discussed in an earlier newsletter.
The Open Source Initiative (OSI), a non-profit, supervises the open source definition and ensures the correct application of the trademark to open source licenses. OSI is in the process of formulating a new definition for open source and AI, a crucial endeavor to ensure organizations can use software without constraints and benefit from thriving open source AI software ecosystems.
Comparing Proprietary and Open Source AI
I foresee two categories of enterprise AI projects: those built on public or commercially provided solutions with general-use models and those developed on open source models that are either trained or fine-tuned using free data and proprietary data sets. Consider this as enterprise-centric ChatGPTs and foundational LLMs enriched with enterprise-specific context.
I anticipate a middle path where entities will employ vector databases and certain middleware (perhaps conversations routed by Langchain), allowing them to harness advancements in the proprietary domain while safeguarding their data. Both cloud-centric AI and enterprise-specific AI projects will likely emerge. These might encompass a primary enterprise large language or multi-modal model integrated with a chatbot and a high-level API for seamless integration across various systems.
Such models will leverage data that might not be feasible for public models. They'll employ object recognition and intricate algorithms to identify manufacturing flaws, utilize mathematical operations on logistics data to enhance goods and services delivery, and harness deep learning apps to comprehend consumer behavior during purchasing cycles to predict and meet customer demands.
The Influence of Open Source on AI
Whether you're exploring a deep learning framework, a Python library, or reinforcement learning systems for your upcoming AI venture, open source AI can be instrumental in achieving your objectives of crafting AI apps that are dependable, secure, and consistent.
Open source solutions have democratized AI in numerous ways, enabling developers to collaborate on projects at unprecedented speeds. These solutions provide a plethora of features that were once exclusive to major commercial entities.
Moreover, it empowers teams to modify their models and algorithms more flexibly and to replicate projects swiftly without starting from ground zero each time. This positions open source solutions as the most economical option for AI development.
Opting for an open source solution offers deeper insights into an AI system's operations, ensuring its security and impartiality. By selecting an open source solution, you're aligning with top-tier technology.
Pioneering Open Source AI Initiatives
Numerous open source projects and AI models allow users to enhance their existing platforms. Some renowned open source AI projects encompass:
TensorFlow: Developed by Google Brain's team, TensorFlow is an open-source software library tailored for machine learning and deep learning tasks, offering a flexible platform for such endeavors.
Hugging Face Transformers: This machine-learning library provides cutting-edge general-purpose architectures (e.g., BERT, GPT-2) for natural language processing and generation. It features pre-trained models that users can fine-tune for specific tasks.
PyTorch: An open-source AI project, PyTorch offers a dynamic computational graph, permitting on-the-go modifications, making it especially valuable for deep learning.
Keras: A high-level neural network API, Keras can run atop TensorFlow, CNTK, or Theano, facilitating quick prototyping of deep learning models and supporting both convolutional and recurrent networks.
The open source realm is a hub for AI projects, spurring innovation across myriad applications.
Open Source Model Highlights
We'll increasingly access AI through models that are open source and free. Currently, numerous models are available for free, with some being both free and open source. A few noteworthy models:
ALPACA (Adaptive Learning with PArameter CAching): In the machine learning context, ALPACA is a meta-learning algorithm designed for few-shot learning, enabling models to make precise predictions with minimal data.
FALCON (Fast and Lightweight CONvolution): Conceived for efficient deep learning, FALCON is optimized for size and computational efficiency, making it ideal for real-time applications.
Stable Diffusion: This model focuses on the stable diffusion process, which is essential for various applications, especially in the realm of data science and network analysis. Stable Diffusion ensures that information or processes spread across networks in a consistent and predictable manner, making it invaluable for scenarios where stability and reliability are paramount.
The Horizon of Open AI
In the times ahead, open source AI will find applications across various sectors, from healthcare and robotics to finance and self-driving vehicles. Open source AI projects will empower businesses to craft solutions tailored to their unique requirements.
Additionally, I'm optimistic about the development of multimodal models that merge NLP capabilities with computer vision and other domains, facilitating multimedia inputs and fostering richer interactions. I believe these multimodal models represent the next monumental stride in AI's evolution.
Tip of the Week: GPT4 an Open Source
Keeping in the vein of open source tools, private versus public models, and security from last week's newsletter, I have been looking at GPT4All, a platform that offers a free-to-use, locally-running, privacy-aware chatbot. It doesn't require a GPU or internet connection and boasts real-time inference latency even on an M1 Mac. The platform provides various installers for different operating systems, including Windows, OSX, and Ubuntu.
GPT4All is designed to train and deploy robust, customized large language models that operate locally on consumer-grade CPUs. The primary objective is to be the best instruction-tuned, assistant-style language model freely accessible to individuals and enterprises. A GPT4All model is a file ranging from 3GB to 8GB that can be integrated into the GPT4All open-source ecosystem software. Nomic AI supports and maintains this software ecosystem to ensure quality and security.
The platform showcases models like the "wizardlm-13b-v1.1-superhot-8k.ggmlv3.q4_0.bin," which is an instruction-based model known for providing lengthy responses. It's fine-tuned with high-quality data and is a collaborative effort between Microsoft and Peking University.
GPT4All Datasets: High-quality training datasets are essential for training a robust instruction-tuned assistant. Nomic AI has developed a platform named Atlas to manipulate and curate LLM training data easily. The latest open-source GPT4All dataset is available on Huggingface.
Capabilities of GPT4All
Answering Questions: Users can inquire about any topic.
Personal Writing Assistant: It aids in composing emails, documents, creative stories, poems, songs, and plays.
Document Understanding: Users can input their text documents to receive summaries and answers about their content.
Coding Assistance: It guides simple coding tasks, continuously improving its code capabilities.
Training: Users can train their own GPT4All models.
Documentation: It allows integration of a locally running Large Language Model (LLM) into any codebase.
Chat: A multi-platform chat interface for running local LLMs.
Python Integration: Python bindings to GPT4All are available.
Datalake: An open-source data lake for donated GPT4All interaction data
What I Read this Week
Why Open Source Matters - Stephen O’Grady, Analyst Redmonk
AI Tools I am Evaluating
Midjourney Prompt for Newsletter Header Image
For every issue of the Artificially Intelligent Enterprise, I include the MIdjourney prompt I used to create this edition.