Open-source generative artificial intelligence (AI) models are gaining ground, challenging the dominance of centralized cloud-backed models like ChatGPT. Leading players in the generative AI field, such as Google and OpenAI, have traditionally followed a centralized approach, restricting public access to their data sources and training models.
However, research conducted by Cathy Wood’s ARK Invest suggests a potential shift towards open-source AI models outperforming their centralized counterparts by 2024.
The graph above illustrates the progress made by open-source AI models since 2022, with some surpassing the performance of private models.
Centralized AI giants like OpenAI and Google, along with Alphabet (Google’s parent company), continue to dominate the industry with models like ChatGPT-4 and Gemini Ultra.
In contrast, Meta (formerly Facebook), Mistral, and certain Chinese AI models have embraced open-source methodologies.
In 2023, open-source AI models like Yi 34B, Falcon 180B, and Mixtral 8x7B emerged as top contenders, demonstrating comparable performance to market leaders.
Jozef Soja, a researcher at ARK Invest, highlighted that Mixtral even outperformed GPT 3.5 on a massive multitask language understanding benchmark, emphasizing the current superiority of GPT-4.
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Meta’s foundational open-source model, LLaMA, also witnessed significant improvements in its 2023 iterations.
The push for enhanced performance in open-source AI models aligns with the goal of democratizing access to generative AI, as emphasized by AI engineer Brian Roemmele.
Recent tests comparing the free versions of Gemini (via Bard) and ChatGPT-4 were conducted to assess the performance of current AI market leaders.
These tests involved asking both AI models questions about cryptocurrencies and evaluating their responses.
In both cases, the AI models adopted a similar approach, cautioning users when asked about investment advice and recommending consulting professional financial advisers for personalized guidance.
Additionally, both models provided answers that highlighted similar considerations, despite presenting some differing details in certain cases.
As open-source AI models continue to evolve and narrow the performance gap with centralized counterparts, the landscape of generative AI is poised for significant changes, potentially ushering in a new era of accessibility and democratization in AI technology.
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