Import AI 428: Jupyter agents; Palisade's USB cable hacker; distributed training tools from Exo

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Soybean situational awareness:
…Real world robotics continues to be the most challenging thing for AI…
Argentinian researchers have released a multi-modal dataset recorded by a weed removing robot working in a soybean agricultural field. The dataset is captured by an RGB camera, stereo IR camera, a 6-Axis IMU, three 9-Axis IMU, and three GNSS receivers and wheel encoders. The dataset was gathered by a four-wheeled robot platform which is designed to automate the weeding of large crop fields.

All of the gathered data was made through having the robot doing six varied runs over a soybean field, and all the data is synchronized and appropriately time-stamped. In tests, the researchers show that contemporary simultaneous localization and mapping (SLAM) systems fail to accurately predict the correct locations, often by breaking down during the course of a run.

Why this matters - basic inputs for useful robots: As a rule, whenever you go into the real world, you tend to run into issues. Papers like this highlight how even simple-seeming tasks, like getting a robot in a soybean field to accurately figure out where it is and map its environment, is more challenging than people might suspect.
Read more: The Rosario Dataset v2: Multimodal Dataset for Agricultural Robotics (arXiv).
Get the dataset here: The Rosario Dataset v2 (GitHub).

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Hugging Face makes it easier for AI systems to learn to use Jupyter notebooks:
…Expect AI for science systems to get better as a consequence…
Hugging Face has produced a dataset of synthetic data based on real Kaggle Jupyter notebooks, along with a test to see if AI systems can correctly answer questions about the contents of the notebooks (e.g., "How many total trainable parameters does the LSTM model have?", or "What percentage of customers with only 1 banking product eventually churned?").
This dataset can be used to train AI systems to be able to easily parse the contents of Jupyter notebooks and execute Python code to answer questions within them. This is a useful skill as Jupyter notebooks are commonly used by researchers in a wide variety of scientific and business disciplines to conduct experiments, so making AI systems better at understanding them will ultimately make AI systems more effective at accelerating the ...