Generative AI – including large language models such as GPT-4, and image generators such as DALL-E, Midjourney, and Stable Diffusion – is advancing in a "storm of hype and fright", as some commentators have observed. Recent advances in artificial intelligence have yielded warnings that the rapidly developing technology may result in "ever more powerful digital minds that no one – not even their creators – can understand, predict, or reliably control". That's according to an open letter signed by more than 1,000 AI experts, researchers and backers, which calls for an immediate pause on the creation of "giant" AIs for six months so that safety protocols can be developed to mitigate their dangers. But what is the technology currently capable of doing? Midjourney creates images from text descriptions.

Artificial intelligence offers exciting new ways to work and learn, but there are reasons to be careful. In nature, sometimes the prey becomes the predator. But in the case of larvae of the Epomis beetle, it wriggles around to attract frogs, then latches on and sucks the life out of them. This is how I'm feeling about artificial intelligence and ChatGPT in higher education just now. The positives are blinding us to the risks.

The open letter calling for an immediate six-month pause in the AI development arms race and signed by more than 1600 tech luminaries, researchers and responsible technology advocates under the umbrella of the Future of Life Institute is stunning on its face. Self-reflection and caution have never been defining qualities of technology sector leaders. Outside of nuclear technology, it's hard to identify another time when so many have publicly rallied to slow the pace of technology development down, much less call for government regulation and intervention. "Advanced AI could represent a profound change in the history of life on Earth and should be planned for and managed with commensurate care and resources," the letter states. "Unfortunately, this level of planning and management is not happening, even though recent months have seen AI labs locked in an out-of-control race to develop and deploy ever more powerful digital minds that no one – not even their creators – can understand, predict, or reliably control. "Therefore, we call on all AI labs to immediately pause for at least 6 months the training of AI systems more powerful than (Open AI's) GPT-4.

Artificial intelligence in some shape or form has been a part of everyday life for years, but the meteoric rise of ChatGPT and the resulting aggressive development pace of conversational and generative AI models is, for the first time ever, putting the underlying technology into the hands of the general public. Even though current large language models are primarily able to guess the best-fitting next word in a sentence based on the corpus of content they were fed, CEOs, researchers and AI experts are now urging the industry to pump the brakes on training and developing models more capable than OpenAI's GPT-4. The company's latest large language model is currently available in a limited capacity for ChatGPT Plus subscribers and will soon be integrated into Microsoft productivity and security products. According to an open letter signed by influential figures like Elon Musk and Stability AI CEO Emad Mostaque, « powerful AI systems should be developed only once we are confident that their effects will be positive and their risks will be manageable. The Musk Foundation is a primary donor to the organization.

The Enterprise Data team works alongside multiple departments to help them get the most out of their data. As an Analytics Engineer, you'll work with stakeholders to build models and serve as a subject-matter guide on the best processes surrounding analysis. Peloton is looking for a talented individual to build and maintain foundational data infrastructure crucial to gaining insights. The base salary range represents the low and high end of the anticipated salary range for this position based at our New York City headquarters. The actual base salary offered for this position will depend on numerous factors including individual performance, business objectives, and if the location for the job changes.

This paper provides a comprehensive tutorial for Bayesian practitioners in pharmacometrics using Pumas workflows. We start by giving a brief motivation of Bayesian inference for pharmacometrics highlighting limitations in existing software that Pumas addresses. We then follow by a description of all the steps of a standard Bayesian workflow for pharmacometrics using code snippets and examples. This includes: model definition, prior selection, sampling from the posterior, prior and posterior simulations and predictions, counter-factual simulations and predictions, convergence diagnostics, visual predictive checks, and finally model comparison with cross-validation. Finally, the background and intuition behind many advanced concepts in Bayesian statistics are explained in simple language. This includes many important ideas and precautions that users need to keep in mind when performing Bayesian analysis. Many of the algorithms, codes, and ideas presented in this paper are highly applicable to clinical research and statistical learning at large but we chose to focus our discussions on pharmacometrics in this paper to have a narrower scope in mind and given the nature of Pumas as a software primarily for pharmacometricians.

Event detection (ED) from sequences of data is a critical challenge in various fields, including surveillance [Clavel et al., 2005], multimedia processing [Xiang and Wang, 2019, Lai, 2022], and social network analysis [Cordeiro and Gama, 2016]. Neural network-based architectures have been developed for ED, leveraging various data types such as text, images, social media data, and audio. Integrating commonsense and structural knowledge about events and their relationships can significantly enhance machine learning methods for ED. For example, in analyzing a soccer match video, the knowledge that a red card shown to a player is typically followed by the player leaving the field can aid in event detection. Additionally, knowledge about how simple events compose complex events is also useful for complex event detection. Background knowledge has been shown to improve the detection of complex events especially when training data is limited [Yin et al., 2020].

Contrastive learning methods have shown an impressive ability to learn meaningful representations for image or time series classification. However, these methods are less effective for time series forecasting, as optimization of instance discrimination is not directly applicable to predicting the future state from the history context. Moreover, the construction of positive and negative pairs in current technologies strongly relies on specific time series characteristics, restricting their generalization across diverse types of time series data. To address these limitations, we propose SimTS, a simple representation learning approach for improving time series forecasting by learning to predict the future from the past in the latent space. SimTS does not rely on negative pairs or specific assumptions about the characteristics of the particular time series. Our extensive experiments on several benchmark time series forecasting datasets show that SimTS achieves competitive performance compared to existing contrastive learning methods. Furthermore, we show the shortcomings of the current contrastive learning framework used for time series forecasting through a detailed ablation study. Overall, our work suggests that SimTS is a promising alternative to other contrastive learning approaches for time series forecasting.

As geospatial machine learning models and maps derived from their predictions are increasingly used for downstream analyses in science and policy, it is imperative to evaluate their accuracy and applicability. Geospatial machine learning has key distinctions from other learning paradigms, and as such, the correct way to measure performance of spatial machine learning outputs has been a topic of debate. In this paper, I delineate unique challenges of model evaluation for geospatial machine learning with global or remotely sensed datasets, culminating in concrete takeaways to improve evaluations of geospatial model performance. Geospatial machine learning (ML), for example with remotely sensed data, is being used across consequential domains, including public health (Nilsen et al., 2021; Draidi Areed et al., 2022) conservation (Sofaer et al., 2019), food security (Nakalembe, 2018), and wealth estimation (Jean et al., 2016; Chi et al., 2022). By both their use and their very nature, geospatial predictions have a purpose beyond model benchmarking; mapped data are to be read, scrutinized, and acted upon.

Saliency methods provide post-hoc model interpretation by attributing input features to the model outputs. Current methods mainly achieve this using a single input sample, thereby failing to answer input-independent inquiries about the model. We also show that input-specific saliency mapping is intrinsically susceptible to misleading feature attribution. Current attempts to use 'general' input features for model interpretation assume access to a dataset containing those features, which biases the interpretation. Addressing the gap, we introduce a new perspective of input-agnostic saliency mapping that computationally estimates the high-level features attributed by the model to its outputs. These features are geometrically correlated, and are computed by accumulating model's gradient information with respect to an unrestricted data distribution. To compute these features, we nudge independent data points over the model loss surface towards the local minima associated by a human-understandable concept, e.g., class label for classifiers. With a systematic projection, scaling and refinement process, this information is transformed into an interpretable visualization without compromising its model-fidelity. The visualization serves as a stand-alone qualitative interpretation. With an extensive evaluation, we not only demonstrate successful visualizations for a variety of concepts for large-scale models, but also showcase an interesting utility of this new form of saliency mapping by identifying backdoor signatures in compromised classifiers.