A conversation with leadership development experts Amy Jen Su and Muriel M. Wilkins about what it takes to be seen as a leader—whether you have the top job or not.
Anthropic caused a stir among developers with what appeared to be a surprise change to its pricing plan: The company signaled that Claude Code, the popular agentic development tool, would no longer be available to subscribers on the $20-per-month Pro plan. Users took to Reddit and X to point out that Anthropic’s pricing page for Claude explicitly showed Claude Code as not supported in the Pro plan. (It remained in the $100/month+ Max plan.) New users signing up […]
My paper got rejected from an imaging venue (A*) because it lacked clinical validation and was more “NLP suited”. I’m very disappointed by the decision as the paper had strong methods and key findings suited to the specific venue. I’m thinking of EMNLP next, but I feel it is too NLP and my paper for sure will be lost. But I see an EMNLP workshop very suited to the paper. Are such workshops especially at such conferences any […]
Warner Bros.’ bizarre 2023 decision to shelve its live-action/animated film, Coyote vs. Acme, sparked outrage both in the industry and among fans online. But the film is finally being released, and Ketchup Entertainment, its new distributor, recently released the trailer. All I can say after watching that trailer is, what the heck was Warner Bros. even thinking? Granted, a killer trailer doesn’t automatically mean it’s a great film, but all the winning elements are here. The concept alone […]
Federated learning (FL) enables collaborative model training without sharing raw data; however, the presence of noisy labels across distributed clients can severely degrade the learning performance. In this paper, we propose FedSIR, a multi-stage framework for robust FL under noisy labels. Different from existing approaches that mainly rely on designing noise-tolerant loss functions or exploiting loss dynamics during training, our method leverages the spectral structure of client feature representations to identify and mitigate label noise. Our framework consists […]
Language models trained on natural text learn to represent numbers using periodic features with dominant periods at $T=2, 5, 10$. In this paper, we identify a two-tiered hierarchy of these features: while Transformers, Linear RNNs, LSTMs, and classical word embeddings trained in different ways all learn features that have period-$T$ spikes in the Fourier domain, only some learn geometrically separable features that can be used to linearly classify a number mod-$T$. To explain this incongruity, we prove that […]
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We investigate the integration of human-like working memory constraints into the Transformer architecture and implement several cognitively inspired attention variants, including fixed-width windows based and temporal decay based attention mechanisms. Our modified GPT-2 models are trained from scratch on developmentally plausible datasets (10M and 100M words). Performance is evaluated on grammatical judgment tasks (BLiMP) and alignment with human reading time data. Our results indicate that these cognitively-inspired constraints, particularly fixed-width attention, can significantly improve grammatical accuracy especially when […]
Most of the companies that have fully committed to building AI models are gobbling up every Nvidia AI accelerator they can get, but Google has taken a different approach. Most of its cloud AI infrastructure is based on its line of custom Tensor processing units (TPUs). After announcing the seventh-gen Ironwood TPU in 2025, the company has moved on to the eighth-gen version, but it’s not just a faster iteration of the same chip. The new TPUs come […]
In streaming platforms churn is extremely costly, yet A/B tests are typically evaluated using outcomes observed within a limited experimental horizon. Even when both short- and predicted long-term engagement metrics are considered, they may fail to capture how a treatment affects users’ retention. Consequently, an intervention may appear beneficial in the short term and neutral in the long term while still generating lower total value than the control due to users churn. To address this limitation, we introduce […]
