Have you retrained or moved careers due to your previous career path being at risk of an artificial intelligence takeover? Please include as much detail as possible. Did you have a dream profession that you have decided not to pursue because of fears it will be thwarted by AI? Optional Please include as much detail as possible.

Stack-augmented recurrent neural networks (RNNs) have been of interest to the deep learning community for some time. However, the difficulty of training memory models remains a problem obstructing the widespread use of such models. In this paper, we propose the Ordered Memory architecture. Inspired by Ordered Neurons (Shen et al., 2018), we introduce a new attention-based mechanism and use its cumulative probability to control the writing and erasing operation of memory. We also introduce a new Gated Recursive Cell to compose lower level representations into higher level representation. We demonstrate that our model achieves strong performance on the logical inference task (Bowman et al., 2015) and the ListOps (Nangia and Bowman, 2018) task. We can also interpret the model to retrieve the induced tree structure, and find that these induced structures align with the ground truth. Finally, we evaluate our model on the Stanford Sentiment Treebank tasks (Socher et al., 2013), and find that it performs comparatively with the state-of-the-art methods in the literature

In Sec.3.3, we have experimentally verified that DPS outperforms various fine-tuning methods. Table 1: Eight datasets used in this paper form GLUE benchmark. In this paper, we investigate the performance of DPS on five distinctive and widely used large-scale pre-trained language models, namely BERT Devlin et al. [2018], RoBERTa Liu et al. [2019], DeBERTa improves Transforme-based pre-trained model with disentangled attention mechanism and enhanced mask decoder. We use mixed precision training to speed up the experimental process. This method is applied by ELECTRA when fine-tuning downstream tasks. 2 D Appendix D. Experimental Details for Different Fine-tuning Methods The following is our hyperparameter search space for different fine-tuning regularization methods: Mixout We grid search Mixout probability p {0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8}.

Stack-augmented recurrent neural networks (RNNs) have been of interest to the deep learning community for some time. However, the difficulty of training memory models remains a problem obstructing the widespread use of such models. In this paper, we propose the Ordered Memory architecture. Inspired by Ordered Neurons (Shen et al., 2018), we introduce a new attention-based mechanism and use its cumulative probability to control the writing and erasing operation of memory. We also introduce a new Gated Recursive Cell to compose lower level representations into higher level representation. We demonstrate that our model achieves strong performance on the logical inference task (Bowman et al., 2015) and the ListOps (Nangia and Bowman, 2018) task. We can also interpret the model to retrieve the induced tree structure, and find that these induced structures align with the ground truth. Finally, we evaluate our model on the Stanford Sentiment Treebank tasks (Socher et al., 2013), and find that it performs comparatively with the state-of-the-art methods in the literature

In Sec.3.3, we have experimentally verified that DPS outperforms various fine-tuning methods. Table 1: Eight datasets used in this paper form GLUE benchmark. In this paper, we investigate the performance of DPS on five distinctive and widely used large-scale pre-trained language models, namely BERT Devlin et al. [2018], RoBERTa Liu et al. [2019], DeBERTa improves Transforme-based pre-trained model with disentangled attention mechanism and enhanced mask decoder. We use mixed precision training to speed up the experimental process. This method is applied by ELECTRA when fine-tuning downstream tasks. 2 D Appendix D. Experimental Details for Different Fine-tuning Methods The following is our hyperparameter search space for different fine-tuning regularization methods: Mixout We grid search Mixout probability p {0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8}.

Anthropic's alignment team was doing routine safety testing in the weeks leading up to the release of its latest AI models when researchers discovered something unsettling: When one of the models detected that it was being used for "egregiously immoral" purposes, it would attempt to "use command-line tools to contact the press, contact regulators, try to lock you out of the relevant systems, or all of the above," researcher Sam Bowman wrote in a post on X last Thursday. Bowman deleted the post shortly after he shared it, but the narrative about Claude's whistleblower tendencies had already escaped containment. "Claude is a snitch," became a common refrain in some tech circles on social media. At least one publication framed it as an intentional product feature rather than what it was--an emergent behavior. "It was a hectic 12 hours or so while the Twitter wave was cresting," Bowman tells WIRED.

The release of DeepSeek R1 stunned Wall Street and Silicon Valley this month, spooking investors and impressing tech leaders. But amid all the talk, many overlooked a critical detail about the way the new Chinese AI model functions--a nuance that has researchers worried about humanity's ability to control sophisticated new artificial intelligence systems. It's all down to an innovation in how DeepSeek R1 was trained--one that led to surprising behaviors in an early version of the model, which researchers described in the technical documentation accompanying its release. During testing, researchers noticed that the model would spontaneously switch between English and Chinese while it was solving problems. When they forced it to stick to one language, thus making it easier for users to follow along, they found that the system's ability to solve the same problems would diminish.

Does inverse scaling only occur as a function of model size, or can it also occur over the course of training? We carry out an exploratory study investigating whether the performance of language models on specific tasks can decrease (while general performance remains high) during training on the language modeling task. We find 8 tasks on which Pythia 12B (Biderman et al., 2023) shows decreased performance over the course of training. Five of these tasks (TruthfulQA-MC1, TruthfulQA-MC2, Hindsight Neglect, Memo Trap, and Pattern Match Suppression) additionally show a consistent relationship whereby larger language models show a greater decrease in performance the more they are trained, despite showing standard (positive) scaling overall. This highlights the importance of testing performance at all relevant benchmarks any time models are trained on additional data, even if their overall performance improves

Current Large Language Models (LLMs) are unparalleled in their ability to generate grammatically correct, fluent text. LLMs are appearing rapidly, and debates on LLM capacities have taken off, but reflection is lagging behind. Thus, in this position paper, we first zoom in on the debate and critically assess three points recurring in critiques of LLM capacities: i) that LLMs only parrot statistical patterns in the training data; ii) that LLMs master formal but not functional language competence; and iii) that language learning in LLMs cannot inform human language learning. Drawing on empirical and theoretical arguments, we show that these points need more nuance. Second, we outline a pragmatic perspective on the issue of `real' understanding and intentionality in LLMs. Understanding and intentionality pertain to unobservable mental states we attribute to other humans because they have pragmatic value: they allow us to abstract away from complex underlying mechanics and predict behaviour effectively. We reflect on the circumstances under which it would make sense for humans to similarly attribute mental states to LLMs, thereby outlining a pragmatic philosophical context for LLMs as an increasingly prominent technology in society.

Solar-powered balloons floating in the stratosphere have recorded low-frequency sounds of mysterious origin. "When we started flying balloons years ago, we didn't really know what we'd hear," says Daniel Bowman at Sandia National Laboratories in New Mexico. "We learned how to identify sounds from explosions, meteor crashes, aircraft, thunderstorms and cities. But virtually every time we send balloons up, we find sounds that we cannot identify." Bowman and his colleagues measured infrasound signals – sounds with a frequency so low they are inaudible to human ears – using solar-powered balloons floating 20 kilometres high.