Will We Run Out of ML Data? Projecting Dataset Size Trends | Epoch AI
We project dataset growth in language and vision domains, estimating future limits to training by evaluating the availability of unlabeled data over time.| Epoch AI
We project dataset growth in language and vision domains, estimating future limits to training by evaluating the availability of unlabeled data over time.| Epoch AI
Introduction Facebook parent company Meta is in big trouble as a trial is revealing that Meta trained its AI using 81.7 terabytes of data downloaded from torrent sites like PirateBay. Among everyth…| Stephen Smith's Blog
We investigate four constraints to scaling AI training: power, chip manufacturing, data, and latency. We predict 2e29 FLOP runs will be feasible by 2030.| Epoch AI
If trends continue, language models will fully utilize the stock of human-generated public text between 2026 and 2032.| Epoch AI
Based on our previous analysis of trends in dataset size, we project the growth of dataset size in the language and vision domains. We explore the limits of this trend by estimating the total stock of available unlabeled data over the next decades.| Epoch AI
In my 100th Tech 101 blog post, I am writing about a topic that I am yet to cover — Machine Learning. With the field now fairly mature and plenty of programming languages and companies supporting it with excellent software, it is high time for me to educate my readers on what exactly machine learning … Continue reading "Machine Learning: A primer" The post Machine Learning: A primer appeared first on Tech 101.| Tech 101
