Data Science Dojo
DECEMBER 8, 2023
Zero-shot, one-shot, and few-shot learning are redefining how machines adapt and learn, promising a future where adaptability and generalization reach unprecedented levels. Source: Photo by Hal Gatewood on Unsplash In this exploration, we navigate from the basics of supervised learning to the forefront of adaptive models.
Data Science Blog
FEBRUARY 23, 2023
1, Data is the new oil, but labeled data might be closer to it Even though we have been in the 3rd AI boom and machine learning is showing concrete effectiveness at a commercial level, after the first two AI booms we are facing a problem: lack of labeled data or data themselves. That is, is giving supervision to adjust via.
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Data Science Dojo
DECEMBER 8, 2023
Zero-shot, one-shot, and few-shot learning are redefining how machines adapt and learn, promising a future where adaptability and generalization reach unprecedented levels. Source: Photo by Hal Gatewood on Unsplash In this exploration, we navigate from the basics of supervised learning to the forefront of adaptive models.
Dataconomy
APRIL 18, 2023
Undetectable backdoors can be implemented in any ML algorithm Machine learning Machine learning is a subfield of artificial intelligence that focuses on the development of algorithms and models that can learn from data and make predictions or decisions.
Snorkel AI
MARCH 1, 2023
Foundation models are large AI models trained on enormous quantities of unlabeled data—usually through self-supervised learning. What is self-supervised learning? Self-supervised learning is a kind of machine learning that creates labels directly from the input data. Find out in the guide below.
How to Learn Machine Learning
OCTOBER 10, 2024
Microsoft’s Tay Chatbot Misfire Microsoft launched an AI chatbot called Tay on Twitter in 2016. The bot was designed to engage in casual conversations and learn from its interactions with users. Data Labeling Accurate labeling is extremely important in supervised learning.
Explosion
JANUARY 12, 2018
We founded Explosion in October 2016, so this was our first full calendar year in operation. In August 2016, Ines wrote a post on how AI developers could benefit from better tooling and more careful attention to interaction design. We set ourselves ambitious goals this year, and we’re very happy with how we achieved them.
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