Artificial intelligence – the ability of machines to use massive amounts of data and computing power to mimic such human attributes as reasoning – is transforming our world. But is higher education keeping up? The answer will play a major role in determining whether the United States will meet the challenge from China and elsewhere. We are well beyond the days when AI was limited to such science fiction as the famously petulant computer "Hal" in the film "2001: A Space Odyssey." AI now plays a major role in healthcare diagnostics and treatment, transportation, robotics, finance, entertainment, and in higher education itself.

First Hawaiian Bank developed a new mobile banking app that uses artificial intelligence to enable users to better manage their money.

This paper by Arora, Ge, Neyshabur and Zhang proposes a compression based framework which purportedly explains the surprising generalization power of deep neural nets. The punchline is this - any neural network with certain robustness properties can be'compressed'. Compressed networks can be shown to generalize well, hence networks with these robustness properties are good candidates for networks that can hope to generalize well. The authors also show experimental evidence that these robustness properties are actually satisfied by real world neural nets. While I find the paper interesting, I am struggling with some of the technicalities.

Artificial intelligence – the ability of machines to use massive amounts of data and computing power to mimic such human attributes as reasoning – is …

The increasing polarization of online political discourse calls for computational tools that are able to automatically detect and monitor ideological divides in social media. Here, we introduce a minimally supervised method that directly leverages the network structure of online discussion forums, specifically Reddit, to detect polarized concepts. We model polarization along the dimensions of agenda setting and framing, drawing upon insights from moral psychology. The architecture we propose combines graph neural networks with structured sparsity and results in representations for concepts and subreddits that capture phenomena such as ideological radicalization and subreddit hijacking. We also create a new dataset of political discourse covering 12 years and more than 600 online groups with different ideologies.

Large pre-trained language models (LMs) have demonstrated remarkable ability as few-shot learners. However, their success hinges largely on scaling model parameters to a degree that makes it challenging to train and serve. In this paper, we propose a new approach, named as EFL, that can turn small LMs into better few-shot learners. The key idea of this approach is to reformulate potential NLP task into an entailment one, and then fine-tune the model with as little as 8 examples. We further demonstrate our proposed method can be: (i) naturally combined with an unsupervised contrastive learning-based data augmentation method; (ii) easily extended to multilingual few-shot learning. A systematic evaluation on 18 standard NLP tasks demonstrates that this approach improves the various existing SOTA few-shot learning methods by 12\%, and yields competitive few-shot performance with 500 times larger models, such as GPT-3.

We propose to assess the fairness of personalized recommender systems in the sense of envy-freeness: every (group of) user(s) should prefer their recommendations to the recommendations of other (groups of) users. Auditing for envy-freeness requires probing user preferences to detect potential blind spots, which may deteriorate recommendation performance. To control the cost of exploration, we propose an auditing algorithm based on pure exploration and conservative constraints in multi-armed bandits. We study, both theoretically and empirically, the trade-offs achieved by this algorithm.

Recommender systems have achieved great success in modeling user's preferences on items and predicting the next item the user would consume. Recently, there have been many efforts to utilize time information of users' interactions with items to capture inherent temporal patterns of user behaviors and offer timely recommendations at a given time. Existing studies regard the time information as a single type of feature and focus on how to associate it with user preferences on items. However, we argue they are insufficient for fully learning the time information because the temporal patterns of user preference are usually heterogeneous. A user's preference for a particular item may 1) increase periodically or 2) evolve over time under the influence of significant recent events, and each of these two kinds of temporal pattern appears with some unique characteristics. In this paper, we first define the unique characteristics of the two kinds of temporal pattern of user preference that should be considered in time-aware recommender systems. Then we propose a novel recommender system for timely recommendations, called TimelyRec, which jointly learns the heterogeneous temporal patterns of user preference considering all of the defined characteristics. In TimelyRec, a cascade of two encoders captures the temporal patterns of user preference using a proposed attention module for each encoder. Moreover, we introduce an evaluation scenario that evaluates the performance on predicting an interesting item and when to recommend the item simultaneously in top-K recommendation (i.e., item-timing recommendation). Our extensive experiments on a scenario for item recommendation and the proposed scenario for item-timing recommendation on real-world datasets demonstrate the superiority of TimelyRec and the proposed attention modules.

The ACTL is currently looking for a part-time Assistant Professor in the field of Tax & Technology with a special focus on Artificial Intelligence (AI) in the …

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Girl Scouts in Virginia are going high tech when it comes to delivering their seasonal cookies. According to Google's drone delivery company Wing, a local troop in the town of Christiansburg has been using its service to test cookie dispatch. Girl Scouts Alice Goerlich (right) and Gracie Walker (left) pose with a Wing delivery drone in Christiansburg, Va. on April 14, 2021.