Rodolfo Pinto’s Post

Does the complex world around us leave you feeling overwhelmed? 🫠 This piece highlights the importance of learning computer science (CS) to develop computational thinking skills. By doing so, we can not only tackle some of our most challenging problems but also harness the power of AI to find solutions. ➡️ What are your thoughts on this article? (📝 : Lance Fortnow)  https://lnkd.in/gDEjynsp

A very interesting article about the birth of AI. The term “machine learning” was first used in 1959

Diving into "Algorithms to Live By: The Computer Science of Human Decisions" by Brian Christian and Tom Griffiths was a nostalgic journey back to my Computer Engineering days. It was fascinating to reconnect with the concepts of Data Structures, Operating Systems & Networking, now reframed to illuminate everyday decision-making. The authors masterfully apply computer algorithms to a vast array of life's challenges, from optimizing daily schedules to making significant life decisions. For example, they explore the "Optimal Stopping Problem" using the 37% rule (Look-Then-Leap-Rule), brilliantly illustrating how to balance the gathering of information with timely decision-making to optimize outcomes. As an AI practitioner, the clear explanations of complex concepts were particularly enlightening. The discussion on overfitting—a concept akin to using a map that marks every single tree and stone, useful in one specific area but useless elsewhere—clarifies the pitfalls of overly tailored solutions in AI. This succinctly highlights the importance of creating models that generalize well across different scenarios, not just perform well on the data they were trained on. The authors also tackle broader issues, as captured in one of my favorite quotes: “some of the biggest challenges faced by computers and human minds alike: how to manage finite space, finite time, limited attention, unknown unknowns, incomplete information, and an unforeseeable future; how to do so with grace and confidence; and how to do so in a community with others who are all simultaneously trying to do the same.” This encapsulates the essence of the book—using algorithmic thinking not only to enhance individual decision-making but also to navigate these decisions within the complexities of a community. “Algorithms to Live By" transcends its technological themes to offer profound insights that would resonate with anyone interested in enhancing their decision-making strategies. It’s a compelling reminder that the principles of computer science can profoundly enrich not just our understanding of machines but also of human life itself. #bookreview #algorithms #computerscience #goodreads

One of the greatest foundational mathematical discoveries in the last several decades is something called Homotopy Type Theory (HoTT). One of the first official books for this was written in 2013 by the Institute for Advanced Study. (The place that Einstein, Oppenheimer, Neumann, and Gödel have worked) While sounding fancy, it has the potential to be tremendously impactful on the way the investment industry conducts analysis, potentially more so than anything we have at the moment. While traditional mathematics and statistics, as it builds into machine learning and AI, offer strong solutions, HoTT is a hyper-modern framework that appears to be able to deal more naturally with the structures of the markets and computing languages. Through this we can more easily: 📈 Forecast, unveil new patterns ⚠ Create more reliable warning systems 🌱 Understand emergent properties (New Patterns) ➿ And adapt better to non-linear behaviors. Here is the book if you want to learn more (It's free!) https://lnkd.in/es3M5b6g

🔬✨ Incorporating symmetry in machine learning processes offers a revolutionary approach to reducing complexity and improving accuracy. In a groundbreaking study, an MIT PhD student and her advisor explore the concept of using symmetry to enhance machine learning. Learn more about their findings and the transformative potential of symmetry in machine learning at Wolf Consultings: [https://lnkd.in/ghKmnAiz) #MachineLearning #SymmetryRevolution Read the full article here: [https://lnkd.in/gzjRfBWw) Original Source: [MIT News - How Symmetry Can Aid Machine Learning](https://lnkd.in/gzPCaZ7C) #GeometricDeepLearning #InnovativeResearch 📚🔍

In retrospect, this interview with Geordie Rose I recorded in 2013, has turned out to be prophetic: Geordie Rose: Machine Learning is Progressing Faster Than You Think https://lnkd.in/dkTVTwB

In 1950, Alan Turing, a pioneering mathematician and computer scientist, challenged conventional thinking with a provocative question: "Can machines think?" Today marks the launch of a journey through history with David M. Williams' first essay in a series of 75 short essays. Designed to enrich our understanding of AI's relationship to human progress, the series blends retrospective insights with our forward march into an AI-dominated future. Next week dives into 1951. But first, 1950, a 7 minute read: https://lnkd.in/guRkXbcs

I republished: A Litter Model using Active Inference Using Bayesian Inference and RxInfer to estimate daily litter events https://lnkd.in/geCEvNRN I experimented with the beginnings of a 'Unified Framework' for bayesian machine learning. I like the economy of expression that comes from that, as well as the associated standardization. It does come with some 'clumsiness' sometimes, but to me, it is worth it. This potential framework, for now referred to as KUF, to keep it apart from PUF, is an overkill for the problem in this notebook. However, it serves as a template for more substantial projects in the future. The KUF is informed by the CRISP-DM methodology, the Powell Unified Framework (PUF), formerly at Princeton, the work of professor Bert de Vries at Eindhoven, as well as the Active Inference literature that I have used so far. Your comments are welcome.

An *amazing* article by Maxime Wolf about the curse of dimensionality in machine learning models, and how the solution is linked to a very intriguing property of spheres in higher dimensions: https://lnkd.in/gHTCD9FY

Genius is the world's first natural computing system based on principles found in physics and neuroscience. Genius allows developers to generate world models and intelligent agents that enable optimal and hyper-personalized predictions, … Genius  is a groundbreaking intelligent software system that will change how we use information and make decisions. Unlike traditional computer programs that follow rigid rules or current AI systems that lack memory and accuracy. Genius combines the power of natural algorithms, knowledge modeling, and data transformation to create a more flexible and intelligent system. https://lnkd.in/eFTvC37v