technocracy

Project management success is littered with preventable inefficiencies that can undermine efficiency and increase the likelihood of failure. Could AI innovations help more businesses to overcome the seven deadly sins that undermine projects? Inefficiencies are a scourge for operational efficiency. According to KPMG insights, as much as 70% of organisations have experienced at least one project failure in the past 12 months. These failures can not only be costly for the opportunities they cause organisations to miss out […]

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Note that the main issues that people currently unknowingly face with local models mostly revolve around the harness and some intricacies around model chat templates and prompt construction. Sometimes there are even pure inference bugs. From typing the task in the client to the actual result, there is a long chain of components that atm are not only fragile – are also developed by different parties. So it’s difficult to consolidate the entire stack and you have to […]

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Sunday panic attacks are the hidden reality for many data leaders. This story explores the “savior complex,” the cycle of burnout, and why we struggle to find rhythm. It challenges the idea that better tools can fix personal patterns and asks the hard question: Can we do transformational work without destroying ourselves? You aren’t broken; you just care too much without boundaries.

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This chapter presents three major reinforcement learning algorithms used for fine-tuning financial forecasters. We propose a clear implementation plan for backpropagating the loss of a reinforcement learning task to a model trained using supervised learning, and compare the performance before and after the fine-tuning. We find an increase in performance after fine-tuning, and transfer learning properties to the models, indicating the benefits of fine-tuning. We also highlight the tuning process and empirical results for future implementation by practitioners.

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arXiv:2405.03468v2 Announce Type: replace Abstract: Finding low-dimensional interpretable models of complex physical fields such as turbulence remains an open question, 80 years after the pioneer work of Kolmogorov. Estimating high-dimensional probability distributions from data samples suffers from an optimization and an approximation curse of dimensionality. It may be avoided by following a hierarchic probability flow from coarse to fine scales. This inverse renormalization group is defined by conditional probabilities across scales, renormalized in a wavelet basis. For a […]

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arXiv:2603.20292v1 Announce Type: new Abstract: In incremental classification tasks for hyperspectral images, catastrophic forgetting is an unavoidable challenge. While memory recall methods can mitigate this issue, they heavily rely on samples from old categories. This paper proposes a teacher-based knowledge retention method for incremental image classification. It alleviates model forgetting of old category samples by utilizing incremental category samples, without depending on old category samples. Additionally, this paper introduces a mask-based partial category knowledge distillation algorithm. By decoupling […]

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arXiv:2602.12398v1 Announce Type: new Abstract: Democracies are built upon secure and reliable voting systems. Electronic voting systems seek to replace ballot papers and boxes with computer hardware and software. Proposed electronic election schemes have been subjected to scrutiny, with researchers spotting inherent faults and weaknesses. Inspired by physical voting systems, we argue that any electronic voting system needs two essential properties: ballot secrecy and verifiability. These properties seemingly work against each other. An election scheme that is a […]

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