The article discusses the significance of open source AI over closed source models. It emphasizes control, adaptability, and community involvement. Open source AI is seen as essential for critical AI-native businesses and offers strong arguments against common concerns. The future is predicted to favor open source AI as it matures and becomes more user-friendly.

Meta releases open AI models, like Llama 2, but with usage restrictions. Carnegie Mellon, AI Now Institute, and Signal Foundation caution that "open" AI models may come with limitations. Truly open source AI models, like GPT Neo, face hurdles due to data secrecy, corporate control of software frameworks, high training costs, and resource disparities. Concerns arise over centralizing AI power in few companies like Meta, Google, Microsoft, and OpenAI. Calls for meaningful AI regulations and openness to unleash AI's potential.

Leaked Internal Google Document Claims Open Source AI Will Outcompete Google and OpenAI

Open-source AI business models & brand moats. Importance of brand moats in OSAI models and AI labs. Lessons from crypto protocols. Building brand moats in AI. Challenges and strategies for long-term value capture in AI.

Until recently, it seemed that big tech companies would monopolize the market for large language models. Open-source LLMs are changing that.

The article discusses how OpenAI and other language model (LLM) providers have a significant advantage in terms of scalability and service quality due to their infrastructure. It highlights the cost-effectiveness of using OpenAI's fine-tuning API compared to renting AWS GPU instances, making it more affordable for organizations. The article emphasizes that the advantage of LLM providers lies not only in the quality of their models but also in their ability to serve models at extreme economies of scale, making it impractical for most organizations to pursue their own open-source LLM deployments.

Companies seeking to cut costs by using free, open-source alternatives to OpenAI's AI technology are finding that it can be more expensive. An example is Cypher, whose use of an open-source model led to a $1,200 bill, while using OpenAI's GPT-3.5 Turbo cost only $5 per month for the same workload.

Falcon 180B, an open-source large language model (LLM) with an impressive 180 billion parameters, has been released, making waves in the artificial intelligence community. This LLM outpaces Meta's LLaMA 2, which has 70 billion parameters, and even approaches the performance of commercial models like Google's PaLM-2 on various benchmarks. Falcon 180B is particularly strong in natural language processing (NLP) tasks, including benchmarks like HellaSwag, LAMBADA, WebQuestions, and Winogrande. It's positioned between GPT 3.5 and GPT4 in terms of performance and offers a powerful alternative to other open-source and commercial models. The release of Falcon 180B showcases the continued advancements in large AI models and highlights the potential for further enhancements through community contributions.

The emirate of Abu Dhabi is making a large-scale artificial intelligence model, "Falcon 40B", available open source for research and commercial use, the government's Advanced Technology Research Council (ATRC) said on Thursday.