When Feedback Crosses the Line
How leaders can ensure their constructive input doesn’t veer into destructive criticism.
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How AI Damages Work Relationships—and Where It Can Actually Help
We’re outsourcing the very moments that create connection. Here’s how to be more intentional.
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The Mirror World Protocol: Why A/B Testing is Obsolete
Architecting Synthetic Populations for Predictive System Validation Continue reading on Towards AI »
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Deep Learning for Financial Time Series: A Large-Scale Benchmark of Risk-Adjusted Performance
We present a large scale benchmark of modern deep learning architectures for a financial time series prediction and position sizing task, with a primary focus on Sharpe ratio optimization. Evaluating linear models, recurrent networks, transformer based architectures, state space models, and recent sequence representation approaches, we assess out of sample performance on a daily futures dataset spanning commodities, equity indices, bonds, and FX spanning 2010 to 2025. Our evaluation goes beyond average returns and includes statistical significance, downside […]
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Learning Shortest Paths with Generative Flow Networks
In this paper, we present a novel learning framework for finding shortest paths in graphs utilizing Generative Flow Networks (GFlowNets). First, we examine theoretical properties of GFlowNets in non-acyclic environments in relation to shortest paths. We prove that, if the total flow is minimized, forward and backward policies traverse the environment graph exclusively along shortest paths between the initial and terminal states. Building on this result, we show that the pathfinding problem in an arbitrary graph can be […]
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[R] TorchLean: Formalizing Neural Networks in Lean
arXiv:2602.22631 [cs.MS]: https://arxiv.org/abs/2602.22631 Robert Joseph George, Jennifer Cruden, Xiangru Zhong, Huan Zhang, Anima Anandkumar Abstract: Neural networks are increasingly deployed in safety- and mission-critical pipelines, yet many verification and analysis results are produced outside the programming environment that defines and runs the model. This separation creates a semantic gap between the executed network and the analyzed artifact, so guarantees can hinge on implicit conventions such as operator semantics, tensor layouts, preprocessing, and floating-point corner cases. We introduce TorchLean, […]
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The 5 Data Strategy Techniques: How Smart Training Order Cuts Costs by 50% and Boosts Performance…
Understanding Curriculum Learning, Data Ordering, Active Learning, Data Augmentation, and Quality Filtering Continue reading on Towards AI »
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Trump FCC’s equal-time crackdown doesn’t apply equally—or at all—to talk radio
In the Trump FCC’s latest series of attacks on TV broadcasters, Federal Communications Commission Chairman Brendan Carr has been threatening to enforce the equal-time rule on daytime and late-night talk shows. The interview portions of talk shows have historically been exempt from equal-time regulations, but Carr has a habit of interpreting FCC rules in novel ways to target networks disfavored by President Trump. Critics of Carr point out that his threats of equal-time enforcement apply unequally since he […]
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Quiz: The pandas DataFrame: Make Working With Data Delightful
In this quiz, you’ll test your understanding of the pandas DataFrame. By working through this quiz, you’ll review how to create pandas DataFrames, access and modify columns, insert and sort data, extract values as NumPy arrays, and how pandas handles missing data. [ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]
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CHLU: The Causal Hamiltonian Learning Unit as a Symplectic Primitive for Deep Learning
Current deep learning primitives dealing with temporal dynamics suffer from a fundamental dichotomy: they are either discrete and unstable (LSTMs) citep{pascanu_difficulty_2013}, leading to exploding or vanishing gradients; or they are continuous and dissipative (Neural ODEs) citep{dupont_augmented_2019}, which destroy information over time to ensure stability. We propose the textbf{Causal Hamiltonian Learning Unit} (pronounced: textit{clue}), a novel Physics-grounded computational learning primitive. By enforcing a Relativistic Hamiltonian structure and utilizing symplectic integration, a CHLU strictly conserves phase-space volume, as an attempt […]
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