A recent survey of academics worldwide has revealed a significant gap in computing power between researchers in universities and those in large technology companies. This disparity hinders the development of large language models and other AI research, with many academics lacking access to powerful graphics processing units (GPUs). The study suggests that academics could make better use of less-powerful computing resources by adopting more efficient methods, but a lack of access to computing power could be limiting the field of AI research.
Forecast for 6 months: Within the next 6 months, we expect to see increased efforts from universities and research institutions to provide more access to computing resources for academics. This could include partnerships with tech companies or the development of more efficient methods for training AI models on limited resources.
Forecast for 1 year: In the next year, we anticipate that the computing power gap between academics and industry researchers will begin to narrow. This could be driven by advancements in cloud computing and the increasing availability of affordable GPUs. We also expect to see more research focused on developing efficient methods for training AI models on limited resources.
Forecast for 5 years: Within the next 5 years, we predict that the use of AI in research will become more widespread, with academics and industry researchers collaborating more closely. This could lead to breakthroughs in fields such as healthcare, finance, and education. We also expect to see the development of more specialized computing resources for AI research, such as purpose-built GPUs and high-performance computing clusters.
Forecast for 10 years: In the next decade, we envision a future where AI is an integral part of research and development across all industries. Academics and industry researchers will have access to advanced computing resources, enabling them to develop more complex and sophisticated AI models. We also predict that the use of AI will lead to significant advancements in fields such as climate modeling, materials science, and biotechnology.
