The AI ​​field is becoming more competitive: Learn how it's impacting business and academia.

AI For Business


The generative AI boom is having a major impact on the academic research landscape. The latest data shows that academic journal articles in the field will soar to more than 240,000 in 2022, a dramatic 175% increase since 2010. Machine learning research has seen particularly explosive growth, with publications soaring from around 6,000 in 2010 to more than 70,000 in 2022, highlighting the rapid advancements and growing interest in the field.

The growing interest in AI has also changed where academic research is conducted. Until the mid-2010s, most of the major machine learning models came from academia. But now, university researchers have a fraction of the resources compared to their private sector counterparts and are lagging behind.

Technology companies can offer researchers much higher salaries and vast amounts of computing power. Training GPT-4, the model behind ChatGPT, is estimated to have cost $78 million, while Google's Gemini Ultra is estimated to have cost $191 million. These figures are high in the corporate world, but would be considered astronomical in academia.

All commercial interest in AI comes with a cost. Historically, researchers have been eager to share their models and data with others. But industrial labs that have invested billions of dollars in research and development are much more cautious about collaboration. For example, OpenAI was founded as a nonprofit to make its work publicly available, due to concerns that big tech companies might monopolize the technology. The company open-sourced its early chat model, GPT-2, but has since refused to make the model publicly available, citing safety concerns.

Other researchers have followed suit. Last February, the head of Google's artificial intelligence division announced that staff should refrain from publishing their research findings, a major reversal of the pre-ChatGPT policy. Google researchers have made many of the discoveries that drive current generative AI models, but so far they have been unable to capitalize on their findings, despite ample advances.

François Cholet, a computer scientist at Google, lamented in a recent podcast that “cutting-edge research is no longer being published” and blamed OpenAI for the current competitive dynamic.

The field of AI is booming, but many tech leaders worry their companies won't be able to stay on top.

Story editing by Alizah Salario. Additional editing by Kelly Glass. Copy editing by Tim Bruns.

This story originally appeared on Verbit and was produced and distributed in partnership with Stacker Studio.



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