\nThe Open Source community has played a key role in advancing AI and bringing it to solve real-world problems. Libraries and frameworks like TensorFlow,\u00a0PyTorch, Keras, and Scikit-learn, for example, have allowed researchers and data scientists to study and make use of AI.
\nIn recent years, we have seen the emergence of LLMs (Large Language Models). While most of the early models such as BERT\u00a0and GPT-2\u00a0were commercial friendly, proprietary models like OpenAI\u2019s GPT-4\u00a0have gained wide popularity. Models like LLaMA, Alpaca, Vicuna, and Koala\u00a0have not yet adopted a license that allows for commercial use.
\nFortunately in these past few years, we have seen the research community\u00a0build several important Open Source models, such as EleutherAI\u2019s GPT-Neo, GPT-J, GPT-NeoX, and Pythia, as well as models from BLOOM\u00a0(BigScience Large Open-science Open-access Multilingual Language Model) and LAION\u00a0(Large-scale Artificial Intelligence Open Network).
\nMost recently, we have seen Open Source models like DataBricks Dolly 2.0, Stability Diffusion\u2019s StableLM, Cerebras GPT, h2oGPT, RedPajama, UC Berkeley\u2019s OpenLLaMA, MosaicML\u2019s MPT, NVIDIA\u2019s NeMo, Hugging Face\u2019s StarCoder, and TII\u2019s\u00a0Falcon\u00a0emerge and gain traction.
\nAdditionally, it\u2019s important to note that smaller and more specific models are outperforming larger models, allowing small organizations and even individuals to innovate. Fine-tuning these models using techniques like LoRA\u00a0(Low-Rank Adaptation of Large Language Models) and \u00a0QLoRA\u00a0have allowed for faster iterations.
\nRecently, users have been able to run and even fine tune these smaller models from their desktop and mobile (e.g. GPT4All\u00a0and PrivateGPT) and their browser (e.g. WebGPT). This will unleash a new wave of applications, where users\u2019 privacy will be respected while allowing them to take advantage of AI. Businesses will also gain full control over these models and be able to run them on prem or on the public cloud using privacy-enhancing technologies. Additionally, these models will also be able to run from IoT devices and on the edge.
\nThe world of AI is at an important crossroads. There are two paths forward: one where highly regulated proprietary code, models, and datasets are going to prevail, or one where Open Source dominates. One path will lead to a stronghold of AI by a few large corporations where end-users will have limited privacy and control, while the other will democratize AI, allowing anyone to study, adapt, contribute back, innovate, as well as build businesses on top of these foundations with full control and respect for privacy.
\nIf you are interested in learning more about Open Source and AI, please join our \u201cDeep Dive: Defining Open Source AI\u201d series.
\nThe post <span class=’p-name’>The AI\u00a0renaissance and why Open Source matters<\/span> appeared first on Voices of Open Source.
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![](\"https:\/\/plus.maciejpiasecki.info\/wp-content\/uploads\/2023\/06\/oooooo-1024x491-1.png\")
\nSource: opensource.org <\/p>\n","protected":false},"excerpt":{"rendered":"