Pepper cowered, usually brave as a lion.
Pepper howled in pain, bleeding from a great claw wound.
Pepper tried to stop me, pulling my sleeve.
Pepper gripped my coat, saving me from the torrent.
Pepper crumbled into a heap of bones and dust.
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Since 2022, AI inference costs have fallen[1]. Between 2022 and 2024, the cost-per-token to run language models fell by an estimated 99.7%[1]. This decline was driven by improvements in both hardware and algorithmic efficiency[1].
As inference becomes cheaper and more efficient, the competitive pressure amongst LLM providers increases[1]. What used to cost dollars can now cost pennies, and what cost pennies may soon cost fractions of a cent[1].
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Streaming payouts are changing now due to the modernization of the outdated royalty payment structures, necessitated by the increase in the volume of content uploaded onto streaming platforms. As new tracks rose by 11% year-over-year in 2023, coupled with the rise of Generative AI, there is an urgent need for a more equitable streaming payout model that reflects the value of professionally produced content. Deezer and Spotify have already begun implementing changes to address these issues, aiming to enhance the monetization pathway for artists and reduce inappropriate payouts linked to lower-quality content[1].
These adjustments are part of a broader vision for improving artist royalties, which can help retain market share in a growing landscape of music streaming where fraud and noise have contributed to dilution of payouts[1].
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The source defines generalisation as "the process of transferring knowledge or skills from specific instances or exemplars to new contexts"[1]. It emphasizes that this concept can be understood from three distinct perspectives. First, as a process, generalisation involves abstracting from concrete examples to form broader rules or concepts. This includes subtypes such as abstraction, which involves turning observations into an abstract schema; extension, which applies a learned schema to new situations; and analogy, where the schema is adapted to novel contexts. Second, generalisation may be seen as the product – that is, the outcome of a learning process. These products can take the form of categories, concepts, rules, or even more complex models that encapsulate observed regularities. Third, generalisation functions as an operator, reflecting the ability of a learned model to make accurate predictions on unseen data. This tri-partite view underlines the inherent differences between human and machine generalisation, with humans typically excelling in sparse, compositional, and contextually nuanced abstraction, while many machine approaches depend heavily on statistical correlation and large data volumes[1].
The source categorizes machine learning methods into three main families based on how they address generalisation. The first is statistical methods, which involve the inference of models through optimisation of loss functions on large datasets. These methods aim for universality of approximation and are efficient in terms of handling complex data and scalability. However, they often work by memorising statistical patterns within the training distribution and lack explicit causality or explainability. The second family, knowledge-informed methods, seeks to integrate explicit theories or domain knowledge within the learning process. Models in this category often use semantic representations, such as rules or causal models, to reflect human-like conceptual understanding. Although knowledge-informed approaches tend to be more aligned with human expectations in terms of explainability and compositionality, they are typically restricted to simpler scenarios and can be computationally demanding. Lastly, instance-based methods, such as nearest-neighbour or case-based reasoning approaches, perform local inference. These methods learn from individual instances and can adapt rapidly to shifts in the data distribution. Their performance is heavily dependent on the quality of the representations used, and while they offer robustness in the face of noise and out-of-distribution data, they might struggle to generalise when the contextual variability is high[1].
Evaluating the generalisation capabilities of machine learning models is a critical aspect discussed in the source. Standard evaluation techniques include the use of train-test splits to measure how well a model derived from a training dataset performs on unseen data. To capture the effects of distributional shifts, statistical measures such as the Kullback-Leibler divergence, Wasserstein distance, or cosine similarity between embedding vectors are employed. In language models, proxies like perplexity are used to gauge familiarity with new contexts. The source also discusses the need for tailored benchmarks that assess robustness, including tests designed to provoke undergeneralisation — where small changes in input lead to significant variations in outcomes — and overgeneralisation, such as hallucinations where the model produces false or exaggerated predictions. Additionally, there is an emphasis on clearly distinguishing when a model is merely memorising training data versus when it is genuinely generalising. Such differentiation is vital in tasks where both factual recall and adaptive inference are important. Evaluation methods extend beyond quantitative metrics to include human-centric approaches, such as explainability studies and the use of counterfactual examples to understand decision-making processes[1].
Looking to the future, the source identifies several promising directions aimed at bridging the gap between human-like and machine generalisation capabilities. One key focus area is the development of foundation models, which exhibit remarkable zero-shot and few-shot learning properties. However, the source warns that the generalisation capabilities of these models remain partially unsubstantiated, with potential overestimations due to issues like data leakage and a reliance on surrogate loss functions. Neurosymbolic approaches are also highlighted as an emerging solution; they merge statistical models with explicit symbolic reasoning, attempting to capture the strengths of both methodologies. This integration is seen as a path toward models that not only perform robustly but also allow for explicit inspection and manipulation of knowledge. Furthermore, research is concentrating on addressing challenges in continual learning, such as catastrophic forgetting, and on developing formal theories that define generalisation in high-dimensional and dynamic settings. These innovations are crucial for building systems that are not only accurate but also reliable and interpretable when faced with novel or shifting data distributions[1].
The ultimate goal of these advances in generalisation is to enhance the alignment between human and machine intelligence. For effective human-AI teaming, outputs of AI models must not only be accurate but also interpretable and contextually relevant. The source points out that while statistical methods may deliver inference correctness and computational efficiency, they often lack the transparent and compositional reasoning typical of human cognition. In contrast, knowledge-informed methods, with their explicit models and causal reasoning, offer greater potential for explainability but struggle with scalability. An aligned system, therefore, may require a hybrid approach — one that benefits from the rapid processing of large-scale data while simultaneously embodying the sparse, compositional, and robust generalisation seen in human thought. In collaborative settings, ensuring that both systems share a common basis for understanding is crucial. This involves not only measuring objective correctness but also assessing subjective experiences and the overall long-term performance of the team. Implementing robust feedback mechanisms and error-correction protocols is essential for realigning human-AI interactions when discrepancies arise, thereby fostering transparency and trust in joint decision-making processes[1].
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YouTube contributed over $55 billion to U.S. GDP in 2024.
YouTube supported more than 490,000 full-time jobs in the U.S.
Creators earned over $70 billion from YouTube in the last three years.
Over 20 million videos are uploaded to YouTube every day.
70% of small businesses using YouTube report increased off-platform activity.
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The Gemini Deep Research agent is built on top of the Gemini 2.5 Pro model[1]. Since its initial launch in December 2024, the capabilities of Gemini Deep Research have been improved[1].
As evidence of that, the performance of Gemini Deep Research on the Humanity’s Last Exam benchmark has gone from 7.95% in December 2024 to the SoTA score of 26.9% and 32.4% with higher compute in June 2025[1].
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Creators on YouTube monetize their content through various methods, including ad revenue, which involves YouTube sharing more than half of advertising revenue with creators, as well as additional revenue from subscriptions like YouTube Premium[1].
Moreover, creators have access to ten monetization options within the YouTube Partner Program, which include ticketing, merchandise sales through YouTube Shopping, brand collaborations via BrandConnect, and fan engagement features like Super Chat, Super Stickers, and Channel Memberships[1].
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