Decoding Canine Cognition

 New ways to link to animal toughts? 

Decoding Canine Cognition

By Emory University, September 15, 2022

Emory University researchers have decoded visual images from a dog's brain.

The researchers captured functional magnetic resonance imaging (fMRI) neural data of two dogs as they viewed videos for 90 minutes in total, then analyzed their brain-data patterns using a machine learning algorithm.

They time-stamped the video data into classifiers, including object-based classifiers and action-based classifiers; the time stamps mapped the brain data onto the classifiers. The Ivis algorithm decoded the action classifiers, but not the object classifiers, with 75% to 88% accuracy.

"We showed that we can monitor the activity in a dog's brain while it is watching a video and, to at least a limited degree, reconstruct what it is looking at," explained Emory's Gregory Berns.

From Emory University

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Finding Unexpected Training Data?

Can we find data that will ultimately lead to novel results.   Like the idea of fitting it to possible applications. In a  primitive way we looked at means to learn from huge amounts of historical existing data we had.    We note the 'at scale' issue. 

Unveiling Unexpected Training Data in Internet Video   By Tali Dekel, Noah Snavely

Communications of the ACM, August 2021, Vol. 64 No. 8, Pages 69-79  10.1145/3431283 

One of the most important components of training machine-learning models is data. The amount of training data, how clean it is, its diversity, how well it reflects the real world—all can have a dramatic effect on the performance of a trained model. Hence, collecting new datasets and finding reliable sources of supervision have become imperative for advancing the state of the art in many computer vision and graphics tasks, which have become highly dependent on machine learning. However, collecting such data at scale remains a fundamental challenge.

In this article, we focus on an intriguing source of training data—online videos. The Web hosts a large volume of video content, spanning an enormous space of real-world visual and auditory signals. The existence of such abundant data suggests the following question: How can we imbue machines with visual knowledge by directly observing the world through raw video? There are a number of challenges faced in exploring this question. ... '   (Much more detail at link)