Kevin Corella Nieto’s Post

Summary: The article delves into the intricacies of the Fast Fourier Transform (FFT) algorithm, which revolutionized signal processing and data analysis. It explains the mathematical foundations of the FFT, its algorithmic elegance, and its applications in various scientific disciplines. The article also explores the algorithm through the lens of matrix manipulation, highlighting its computational efficiency and versatility. Takeaway: The Fast Fourier Transform (FFT) algorithm is a testament to the beauty of simplicity and elegance in algorithmic design. It has revolutionized signal processing, data analysis, and various scientific disciplines, underpinning a myriad of applications integral to our daily lives. The FFT encapsulates the essence of ingenuity in computer science, demonstrating the profound significance of fundamental understanding of symmetry and innovative algorithmic design. Hashtags: #FFT #FastFourierTransform #SignalProcessing #DataAnalysis #AlgorithmicDesign #ComputerScience #Mathematics #ScientificDisciplines

The application of modern computer science to the distant past helps draw connections across a broader swath of the historical record than would otherwise be possible, correcting distortions that come from analyzing history one document at a time. But it introduces distortions of its own, including the risk that machine learning will slip bias or outright falsifications into the historical record. All this adds up to a question for historians and others who, it’s often argued, understand the present by examining history: With machines set to play a greater role in the future, how much should we cede to them of the past? https://trib.al/nmATWOX

Current AI models provide fast information processing nothing more. Useful but limited use for historical research.

Interesting read 

The application of modern computer science to the distant past helps draw connections across a broader swath of the historical record than would otherwise be possible, correcting distortions that come from analyzing history one document at a time. But it introduces distortions of its own, including the risk that machine learning will slip bias or outright falsifications into the historical record. All this adds up to a question for historians and others who, it’s often argued, understand the present by examining history: With machines set to play a greater role in the future, how much should we cede to them of the past? https://trib.al/Ahlpq9M

From MIT Technology Review. How AI is helping historians better understand our past.

“In other words, there’s a risk that artificial intelligence, from historical chatbots to models that make predictions based on historical records, will get things very wrong. Some of these mistakes are benign anachronisms: a query to Aristotle on the chatbot Character.ai about his views on women (whom he saw as inferior) returned an answer that they should “have no social media.” But others could be more consequential—especially when they’re mixed into a collection of documents too large for a historian to be checking individually, or if they’re circulated by someone with an interest in a particular interpretation of history.” Fascinating… as it becomes easier and easier to generate new and convincing content, entirely new means of guaranteeing context and governance seem more and more important…

This is a really interesting talk! Don't miss it! "Neural networks, particularly LLMs, are notoriously poor at algorithmic tasks, such as sorting, shortest path, and even basic arithmetic. Across three papers, we explored the problem of "aligning" architectures to classical computer programs, and showed that this question relates to familiar mathematical concepts: polynomial functors, cohomology, and higher categories."

Now I understand everything. https://lnkd.in/g8sPGhKc In all seriousness, this may well turn out to be the progenitor of the most significant breakthrough in machine learning since attention / transformers. I was excited about the new state space models, but they can’t do in-context learning.