technocracy

If you’re anything like me, you’ve convinced yourself you can change your entire life in one day. New Year’s Day. Your birthday. “Starting Monday.” Some arbitrary moment when everything clicks and you finally become the person you know you’re capable of being. I’ve done this dozens of times. Stood in the gym parking lot on January 2nd, membership card in hand, absolutely certain that this year would be different. Sat at my desk on a Monday morning with […]

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Hello, I have a (technical) interview for a PhD in ML coming up. I have been told to expect some questions on math and coding. For coding, I am preparing with LeetCode and TensorGym. However, I have no idea what to expect for math-related questions. Anyone has an idea of what I can expect? Any useful resources? I can only find questions for Industry ML, and I don’t think they are useful for a PhD interview. Thanks in […]

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Test your understanding of Dependency Management With Python Poetry. You’ll revisit how to install Poetry the right way, create new projects, manage virtual environments, declare and group dependencies, work with lock files, and keep your dependencies up to date. [ 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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arXiv:2602.11291v1 Announce Type: new Abstract: World models are becoming central to robotic planning and control, as they enable prediction of future state transitions. Existing approaches often emphasize video generation or natural language prediction, which are difficult to directly ground in robot actions and suffer from compounding errors over long horizons. Traditional task and motion planning relies on symbolic logic world models, such as planning domains, that are robot-executable and robust for long-horizon reasoning. However, these methods typically operate […]

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arXiv:2602.13200v1 Announce Type: new Abstract: The purpose of this paper is to model traffic in Ad Hoc network of Unmanned Aerial Vehicles and demonstrate a way for adapting communication channel using Artificial Intelligence. The modeling was based on the original model of Ad Hoc network including 20 Unmanned Aerial Vehicles. The dependences of packet loss on the packet size for different transmission powers, on the packet size for different frequencies, on Unmanned Aerial Vehicles flight area and on […]

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Existing value-based online reinforcement learning (RL) algorithms suffer from slow policy exploitation due to ineffective exploration and delayed policy updates. To address these challenges, we propose an algorithm called Instant Retrospect Action (IRA). Specifically, we propose Q-Representation Discrepancy Evolution (RDE) to facilitate Q-network representation learning, enabling discriminative representations for neighboring state-action pairs. In addition, we adopt an explicit method to policy constraints by enabling Greedy Action Guidance (GAG). This is achieved through backtracking historical actions, which effectively enhances […]

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If you’ve ever stared at a broken attribution report at 2am wondering which step in your pipeline silently failed, this post is for you. Modern performance advertising runs on a stack of non-deterministic AI systems – real-time bidding models, CTR predictors, multi-touch attribution, all chained together and funneling data into massive warehouses like Snowflake or Redshift. When something goes wrong (and it will), traditional monitoring gives you almost nothing useful. You get a metric that’s off, but no […]

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arXiv:2602.18468v1 Announce Type: new Abstract: This article introduces the concept of the algorithmic unconscious to designate the set of structural determinations that operate within large language models (LLMs) without being accessible either to the model’s own reflexivity or to that of its users. In contrast to approaches that reduce AI bias solely to dataset composition or to the projection of human intentionality, we argue that a significant class of biases emerges directly from the technical mechanisms of the […]

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With the rapid development of the Internet of Things (IoT), AI model training on private data such as human sensing data is highly desired. Federated learning (FL) has emerged as a privacy-preserving distributed training framework for this purpuse. However, the data heterogeneity issue among IoT devices can significantly degrade the model performance and convergence speed in FL. Existing approaches limit in fixed client selection and aggregation on cloud server, making the privacy-preserving extraction of client-specific information during local […]

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