Hey Meta workers, are you getting paid for those keystrokes? It's a very simple question that your bosses aren't inclined to answer. No longer content to subsume recognizable intellectual properties, the majority of the i ndexed internet and books ( basically all of them), AI will apparently now begin devouring its own workforce. A report in alleged that the keystrokes, mouse movements and clicks of Meta's workforce are to be captured for the purposes of training AI -- something the company's communications department was happy to confirmed as accurate! In a cheery missive, a company spokesperson told Engadget that If we're building agents to help people complete everyday tasks using computers, our models need real examples of how people use them [...] we're launching an internal tool that will capture these kinds of inputs on certain applications to help us train our models.

Better understanding of the potential benefits of information transfer and representation learning is an important step towards the goal of building intelligent systems that are able to persist in the world and learn over time. In this work, we consider a setting where the learner encounters a stream of tasks but is able to retain only limited information from each encountered task, such as a learned predictor. In contrast to most previous works analyzing this scenario, we do not make any distributional assumptions on the task generating process. Instead, we formulate a complexity measure that captures the diversity of the observed tasks. We provide a lifelong learning algorithm with error guarantees for every observed task (rather than on average). We show sample complexity reductions in comparison to solving every task in isolation in terms of our task complexity measure. Further, our algorithmic framework can naturally be viewed as learning a representation from encountered tasks with a neural network.

Neural Information Processing Systems http://nips.cc/

Whole-brain neural connectivity data are now available from viral tracing experiments, which reveal the connections between a source injection site and elsewhere in the brain.

From veneers to abrasive toothpastes, a perfect smile can hide cavities and cause other problems. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Your teeth probably don't look like a movie star's, and that might be a good thing. Breakthroughs, discoveries, and DIY tips sent six days a week. However, in recent years, critics have pointed out that one thing can immediately dispel historical accuracy: actors' blindingly white, perfect teeth.

Dynamic time warping (DTW) is a fundamental technique in time series analysis for comparing one curve to another using a flexible time-warping function. However, it was designed to compare a single pair of curves. In many applications, such as in metabolomics and image series analysis, alignment is simultaneously needed for multiple pairs. Because the underlying warping functions are often related, independent application of DTW to each pair is a sub-optimal solution. Yet, it is largely unknown how to efficiently conduct a joint alignment with all warping functions simultaneously considered, since any given warping function is constrained by the others and dynamic programming cannot be applied.

We introduce a semi-parametric Bayesian model for survival analysis. The model is centred on a parametric baseline hazard, and uses a Gaussian process to model variations away from it nonparametrically, as well as dependence on covariates. As opposed to many other methods in survival analysis, our framework does not impose unnecessary constraints in the hazard rate or in the survival function. Furthermore, our model handles left, right and interval censoring mechanisms common in survival analysis. We propose a MCMC algorithm to perform inference and an approximation scheme based on random Fourier features to make computations faster. We report experimental results on synthetic and real data, showing that our model performs better than competing models such as Cox proportional hazards, ANOVA-DDP and random survival forests.

Plus: An unauthorized group has reportedly accessed Anthropic's Mythos. What actually matters in AI right now? It's getting harder to tell amid the constant launches, hype, and warnings. To cut through the noise, reporters and editors have distilled years of analysis into a new essential guide: the 10 Things That Matter in AI Right Now . The list builds on our annual 10 Breakthrough Technologies, but takes a wider view of the ideas, topics, and research shaping AI, spotlighting the trends and breakthroughs shaping the world. We'll be unpacking one item from the list each day here in The Download, explaining what it means and why it matters.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/