True Positive Weekly #181
The most important artificial intelligence and machine learning news and articles
Topic - Edition
The most important artificial intelligence and machine learning news and articles
Three moves to decode the hype The post Everyone Is Selling AI at You — Here’s How to Keep Your Judgement appeared first on Towards Data Science.
Google wants Gemini to take on more responsibility at work. The practical test is how much attention teams save after reviewing and correcting its output.
AI agents can now write CUDA kernels that outperform PyTorch—but proving those speedups are real is the harder problem. I put them to the test on an NVIDIA DGX Spark and found that benchmark design matters just as much…
A diffusion head for probabilistic time series forecasting: why a forecast can get the mean and the variance right and still describe a future that never happens, and how to fix it. The post Your Model's MSE Is Lying to…
Meta, Google DeepMind and U.S. agencies are backing Biohub’s effort to build the experimental data AI needs to predict cellular behavior. The promise is faster discovery; the test is whether those predictions hold up in…
Claude’s smaller model has a timely opening: agents need affordable intelligence they can call repeatedly. Haiku 5.5 delivers lower rates, but reasoning effort, growing context, and completion quality determine the real…
An exploration of the new data science roles emerging after the Large Language Model explosion. The post AI Engineers, Applied Scientists, Forward Deployed Engineers: Let's Unpack. appeared first on Towards Data Science.
Google’s SynthID Detector can now check AI watermarks from OpenAI, NVIDIA, Kakao, and Google itself. The expansion could make AI content verification easier, but an undetected watermark does not prove authenticity. As…
GPT-6’s Intelligent UI lets ChatGPT assemble interactive tools as it responds. The rollout could change how people use AI and whether they open a separate app to complete a task.
arXiv:2610.09407v1 Announce Type: new Abstract: Pretrained diffusion models are powerful priors for inverse problems, but posterior sampling under nonlinear, non-differentiable forward models remain hard. We introduce…
arXiv:2509.08483v2 Announce Type: replace Abstract: We analyze gradient descent with Polyak (1964) heavy-ball momentum (HB) whose fixed momentum hyperparameter $\beta \in (0, 1)$ provides exponential decay of memory.…
arXiv:2610.09120v1 Announce Type: new Abstract: Randomized exploration is central to bandit learning, multi-agent reinforcement learning, and zeroth-order policy search, yet its independence and privacy are usually only…
arXiv:2610.09196v1 Announce Type: new Abstract: Recursive feature machines (RFMs) learn representations of data by alternating between fitting a predictor to a dataset and updating features of that predictor using the…
arXiv:2610.10105v1 Announce Type: new Abstract: The Fourier Neural Operator (FNO) learns solution operators of partial differential equations (PDEs) through Fourier-space kernel parameterization, but frequency…
arXiv:2610.09866v1 Announce Type: new Abstract: We introduce MUNITE, a latent-variable framework for flexible any-to-any multimodal generation that treats encoding and latent generation as the same inference problem…
arXiv:2610.04299v2 Announce Type: replace Abstract: Self-evolving reasoning models learn from their own generated questions, yet repeated self-training can lead to performance collapse. In this paper, we investigate why…
arXiv:2610.10250v1 Announce Type: new Abstract: We study minimum-variance control of an unknown autoregressive system with exogenous inputs and coefficients that change at unknown times. Under bounded independent…
1148 items available