Higgs Boson breakthrough was UK triumph, but British physics faces'catastrophic' cuts When the Nobel Prize in Physics was announced in Stockholm in October 2013, the world was watching. Among the names read out was Prof Peter Higgs, the British theorist who, nearly half a century earlier, had predicted the existence of a particle believed to hold the cosmos together - the Higgs boson. The announcement, broadcast live from Sweden, was what many scientists had hoped for since a year earlier, when experiments at CERN had finally confirmed Higgs's theory by discovering the Higgs boson - hailed as one of the biggest discoveries in a generation. At the time Higgs, who has since passed away, said in a statement: I hope this recognition of fundamental science will help raise awareness of the value of blue-sky research. Blue-sky research asks questions to understand the universe, rather than design new products.

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We use the same hyperparameters as in large scale curiosity: a learning rate of 0.0001 for all models, a discount factorγ of 0.99, and 3 optimization epochsperrollout. Here we present results on using audio in baselines, as described in the main paper ablations section. In the first baseline, the prediction space is concatenated audio and visual features: the intrinsic model takes an audio-visual feature vector as input and predicts an audio-visual feature vector as output. The results from the audio-visual prediction baseline are shown in Figure 9. In the second baseline, we add audio to randomnetworkdistillation[35].

Acommon formulation of curiosity-driven exploration uses the difference between the real future and the future predicted by a learned model [1].

The reborn Commodore 64 is an astonishing remake--but daunting if you weren't there the first time around. "Digital detox" approach is compelling. It's hard to overstate just how seismic an impact the Commodore 64 had on home computing. Launched in 1982, the 8-bit machine--iconic in its beige plastic shell with integrated keyboard--went on to become the best-selling personal computer of all time . Despite the success, manufacturer Commodore International folded in 1994, with rights to the name floating around for years.

In off-policy deep reinforcement learning, it is usually hard to collect sufficient successful experiences with sparse rewards to learn from. Hindsight experience replay (HER) enables an agent to learn from failures by treating the achieved state of a failed experience as a pseudo goal. However, not all the failed experiences are equally useful to different learning stages, so it is not efficient to replay all of them or uniform samples of them. In this paper, we propose to 1) adaptively select the failed experiences for replay according to the proximity to the true goals and the curiosity of exploration over diverse pseudo goals, and 2) gradually change the proportion of the goal-proximity and the diversity-based curiosity in the selection criteria: we adopt a human-like learning strategy that enforces more curiosity in earlier stages and changes to larger goal-proximity later. This Curriculum-guided HER (CHER)'', which adaptively and dynamically controls the exploration-exploitation trade-off during the learning process via hindsight experience selection. We show that CHER improves the state of the art in challenging robotics environments.

Exploration is one of the core challenges in reinforcement learning. A common formulation of curiosity-driven exploration uses the difference between the real future and the future predicted by a learned model. However, predicting the future is an inherently difficult task which can be ill-posed in the face of stochasticity. In this paper, we introduce an alternative form of curiosity that rewards novel associations between different senses. Our approach exploits multiple modalities to provide a stronger signal for more efficient exploration. Our method is inspired by the fact that, for humans, both sight and sound play a critical role in exploration.

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Realizing personalized medicine at scale calls for methods that distill insights from longitudinal patient journeys, which can be viewed as a sequence of medical events. Foundation models pretrained on large-scale medical event data represent a promising direction for scaling real-world evidence generation and generalizing to diverse downstream tasks. Using Epic Cosmos, a dataset with medical events from de-identified longitudinal health records for 16.3 billion encounters over 300 million unique patient records from 310 health systems, we introduce the Curiosity models, a family of decoder-only transformer models pretrained on 118 million patients representing 115 billion discrete medical events (151 billion tokens). We present the largest scaling-law study of medical event data, establishing a methodology for pretraining and revealing power-law scaling relationships for compute, tokens, and model size. Consequently, we pretrained a series of compute-optimal models with up to 1 billion parameters. Conditioned on a patient's real-world history, Curiosity autoregressively predicts the next medical event to simulate patient health timelines. We studied 78 real-world tasks, including diagnosis prediction, disease prognosis, and healthcare operations. Remarkably for a foundation model with generic pretraining and simulation-based inference, Curiosity generally outperformed or matched task-specific supervised models on these tasks, without requiring task-specific fine-tuning or few-shot examples. Curiosity's predictive power consistently improves as the model and pretraining scale. Our results show that Curiosity, a generative medical event foundation model, can effectively capture complex clinical dynamics, providing an extensible and generalizable framework to support clinical decision-making, streamline healthcare operations, and improve patient outcomes.