AI for Breakfast

Post 80 - Data drought in 2026

Not sure where I saw this originally, possibly one of the many AI Newsletters I seem to have subscribed to but I thought I'd ask Perplexity to do some research. The idea seems a bit unreal however if you take into account the speed things are moving with AI these days I guess it could become a concern.

I assume once the real data has run out it will just invent more which could be a major problem unless someone (a human) figures out a way to do it properly, whatever that will mean!

I think it makes for an interesting read, so here it is:

The potential for a data drought in 2026 is a significant concern for the artificial intelligence (AI) industry, as highlighted by various sources. This situation arises from the rapid consumption of high-quality language data by AI systems, such as ChatGPT, which are trained on extensive datasets compiled from the internet. The demand for this data is outpacing the rate at which it is being produced, leading to predictions that the stock of language data suitable for training AI could be exhausted by 2026[20].

The Epoch AI research group has predicted that we might run out of high-quality data for AI training by 2026, which could significantly slow down future AI development[1]. This shortage is attributed to the increasing sophistication of AI programs, which require larger and more complex datasets for training. The Conversation and other sources have echoed these concerns, estimating that low-quality language data will be exhausted between 2030 and 2050, and low-quality image data between 2030 and 2060[3]. This could not only hamper the development of AI but also affect its integration into various devices and programs, potentially transforming lives worldwide[1].

To address this impending shortage, researchers and companies are exploring various strategies. One approach involves improving algorithms to use existing data more efficiently[1]. Another potential solution is the generation of synthetic data, which can be curated to suit particular AI models, thus alleviating the reliance on natural data sources[3]. Additionally, there's a push towards federated data sharing as a means to mitigate the lack of available data[4].

The scarcity of natural data sources is compounded by privacy and ethical concerns, as well as the potential for AI systems to develop biased algorithms due to the lack of diverse and inclusive datasets[5]. This situation underscores the need for the AI industry to find innovative solutions to the data scarcity problem, such as generating synthetic data or adopting new data generation techniques[2][5][6].

In summary, the AI industry faces a critical challenge due to the potential shortage of training data by 2026. This situation necessitates a multifaceted approach, including the development of more efficient algorithms, the generation of synthetic data, and the exploration of new sources of training data. Addressing these challenges is crucial for the continued growth and development of AI technologies.

Citations:
[1] https://technology.inquirer.net/129930/ai-data-running-out
[2] https://glair.ai/post/solve-data-scarcity-for-using-of-artificial-intelligence
[3] https://theconversation.com/researchers-warn-we-could-run-out-of-data-to-train-ai-by-2026-what-then-216741
[4] https://towardsdatascience.com/is-federate-data-sharing-our-last-great-hope-to-scare-off-the-next-ai-winter-96854d54b720?gi=4127b42384f7
[5] https://www.appen.com/blog/data-crisis-in-the-ai-economy
[6] https://www.dataversity.net/data-scarcity-challenges-enter-generative-ai/
[7] https://www.linkedin.com/pulse/data-scarcity-overabundance-two-faces-same-coin-sahaj-r-kumar
[8] https://futurism.com/critics-microsoft-water-train-ai-drought
[9] https://blogs.infosys.com/emerging-technology-solutions/iedps/dealing-with-data-scarcity-in-artificial-intelligence.html
[10] https://www.newsweek.com/why-ai-so-thirsty-data-centers-use-massive-amounts-water-1882374
[11] https://news.mongabay.com/2024/03/critics-fear-catastrophic-energy-crisis-as-ai-is-outsourced-to-latin-america/
[12] https://www.evalueserve.com/blog/data-scarcity-generative-ai-to-the-rescue/
[13] https://www.theatlantic.com/technology/archive/2024/03/ai-water-climate-microsoft/677602/
[14] https://www.techlifesci.com/p/navigating-data-scarcity-ais-emerging
[15] https://phys.org/news/2023-11-centers-straining-resources-ai.html
[16] https://www.cnbc.com/2023/12/06/water-why-a-thirsty-generative-ai-boom-poses-a-problem-for-big-tech.html
[17] https://super.news/en/articles/2024/04/01/ai-industry-faces-data-shortage-and-infrastructure-challenges-amid-rapid-growth
[18] https://www.linkedin.com/pulse/running-out-fuel-predicted-data-shortage-ai-development-
[19] https://www.linkedin.com/pulse/ai-data-scarce-world-tola-chhoeun-liugc?trk=article-ssr-frontend-pulse_more-articles_related-content-card
[20] https://www.newscientist.com/article/2353751-ai-chatbots-could-hit-a-ceiling-after-2026-as-training-data-runs-dry/