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In 2017, Google introduced the Transformer architecture, which initially did not foretell the significant impact it would have on the field of AI. Today, one particular Transformer model, known as GPT, dominates our newsfeeds. Over the past year, ChatGPT has significantly influenced our daily lives. This groundbreaking conversational AI system revolutionized our interactions with AI. However, the landscape of generative AI is rapidly changing, and the rise of the open-source community poses a challenge to industry giants. In this blog post, we will delve into the journey from the inception of ChatGPT to the emergence of the open-source community and its impact on the ongoing generative AI technology race.
ChatGPT, a Game-Changer
When OpenAI unveiled ChatGPT, it marked a turning point in the field of generative AI. The popular chatbot experienced remarkable success shortly after its launch. Launched in November 2022, ChatGPT saw a remarkable surge in popularity, amassing over 1 million users in just five days. Within two months of launch, it had over 100 million active monthly users, establishing itself as the fastest-growing consumer application in history[1]. TikTok, for instance, took approximately nine months after its global launch to reach the same user count[2]. Similarly, Instagram took around two and a half years to achieve the 100 million-user milestone. UBS analysts noted that this growth rate was unprecedented in the history of consumer internet apps[3]. ChatGPT’s impressive language understanding, and fluency revolutionized the field of (generative) AI, captivating users and finding applications in content creation, customer support, and even personal companionship.
The Open-Source Community, a force for good
The open-source generative AI movement, led by non-profit research groups and start-ups like EleutherAI, Together.AI, Technology Innovation Institute (TII) and Stability AI have been driving innovation and collaboration within the AI community. HuggingFace, a machine learning platform, plays a central role in facilitating the sharing of open-source models, datasets, and training code, fostering a culture of knowledge sharing and problem solving.
Overcoming the initial challenges, open-source models are rapidly closing the gap with their commercial counterparts. With Meta’s LLaMa2 and TII’s Falcon models, open-source now rivals proprietary models in performance on commonly accepted language tasks. According to HuggingFace’s blog on Falcon180B, the overall performance is somewhere in between GPT3.5 (default model behind ChatGPT) and GPT4[4]. This makes open-source models increasingly attractive to businesses seeking cost-effective and customizable AI solutions. Embracing permissive licensing has also further enhanced the commercial viability of open-source models, leading to broader adoption and integration in various industries and applications. The open-source movement’s success is reshaping the AI landscape, challenging established industry players, and demonstrating the power of collaboration and accessibility in driving technological progress for the greater good.
The Power of Open-Source: Faster, More Capable, and Highly Customizable
How can the open-source community close the gap so fast? They improved Large Language Models (LLMs) in terms of quality, speed, customizability, and privacy. Additionally, they advanced LLM to be accessible to a broader audience, promoting a healthier and more competitive landscape built on knowledge sharing.
One notable stride forward is the adoption of innovative techniques such as low-rank adaptation (LoRA), which accelerates LLM fine-tuning by freezing the number of trainable parameters, the open-source community has paved the way for fine-tuning processes even on consumer-grade GPUs. This breakthrough has made it possible for individuals, startups, and smaller companies to harness the power of large language models, thus lowering entry barriers and democratizing AI.
Another astonishing innovation is the use of the 4-bit quantization, a technique that efficiently compresses the size of language models. This advancement has not only made these models more accessible to companies but has also empowered individual enthusiasts and researchers to experiment with cutting-edge AI tools and technologies.
What is truly remarkable is how these open-source models achieve impressive feats with relatively limited resources, outperforming some industry benchmarks while offering faster training times and extensive customization options. This is a testament to the power of collective effort and knowledge sharing.
By lowering the barriers to entry and emphasizing the importance of accessibility, the open-source community has not only democratized AI but has also sparked a new era of innovation. In this environment, creativity and collaboration thrive, enabling even more breakthroughs on the horizon.
Conclusion
The generative AI technology race has witnessed a remarkable evolution, with ChatGPT heralding a new era of conversational AI. While industry leaders see the competitive barrier vanishing, the rise of the open-source community presents a paradigm shift in the field. The community’s rapid advancements and disruptive innovations have highlighted the transformative potential of collaborative knowledge sharing.
To stay at the forefront of the generative AI revolution, industry leaders must embrace openness, collaboration, and the integration of open-source initiatives. The future of generative AI lies not only in the hands of corporations but also in the collective power of the open-source community.
[1] TechTarget, “What is generative AI? Everything you need to know,” March 5, 2023.
[2] Reuters, “ChatGPT sets record for fastest-growing user base – analyst note”, February 2, 2023
[3] Similarweb, “ChatGPT Tops 25 Million Daily Visits”, February 3, 2023
[4] HuggingFace, “Spread Your Wings: Falcon 180B is here”, September 6, 2023
