Search bias analysis is getting more attention in recent years since search results could affect In this work, we aim to establish an automated model for evaluating ideological bias in online news articles. The dataset is composed of news articles in search results as well as the newspaper articles. The current automated model results show that model capability is not sufficient to be exploited for annotating the documents automatically, thereby computing bias in search results.

Translating natural language queries (NLQ) into structured query language (SQL) in interfaces to relational databases is a challenging task that has been widely studied by researchers from both the database and natural language processing communities. Numerous works have been proposed to attack the natural language interfaces to databases (NLIDB) problem either as a conventional pipeline-based or an end-to-end deep-learning-based solution. Nevertheless, regardless of the approach preferred, such solutions exhibit black-box nature, which makes it difficult for potential users targeted by these systems to comprehend the decisions made to produce the translated SQL. To this end, we propose xDBTagger, an explainable hybrid translation pipeline that explains the decisions made along the way to the user both textually and visually. We also evaluate xDBTagger quantitatively in three real-world relational databases. The evaluation results indicate that in addition to being fully interpretable, xDBTagger is effective in terms of accuracy and translates the queries more efficiently compared to other state-of-the-art pipeline-based systems up to 10000 times.

As conversational agents and dialog systems are deployed to real-world scenarios, these systems require data-efficient personalization paradigms such that language systems such as conversational agents can be effectively adapted on-device. The benefits of on-device optimization are two-fold; (1) Swift adaptation of model-behavior based on human-interactions [Dudy et al., 2021], (2) Privacy protection by means of retaining all data related to the user on-device [Li et al., 2020a]. One of the prevailing paradigms for learning from and engaging with end-users is federated learning. Federated learning is an inherently decentralized learning paradigm that assumes no access to a large labeled dataset and instead leverages averaged parameter updates across all users of the system [McMahan et al., 2017]. Such averaged updates invariably dilute individual preferences or deviations from the mean, resulting in a model that works well for the average user while failing to appropriately capture under-represented preferences or sub-groups within the data. In this work, we present a novel approach (FedPC) to personalizing federated learning with personal and context embeddings (collectively called "preference embeddings"), adapting more efficiently and effectively than prior work with respect to both data and compute on-device. We leverage the insight that a client's data distribution is informed by both individual preferences and additional contextual information. For example, while each user may have their own individual style, there may be more general population-wide trends that inform the style of personalized predictions (e.g., dialogue assistants helping patients with cognitive disorders, whereby agents can personalize to individual patients and broader condition-wide trends). While individual preferences may be unique to each client (e.g. a user's taste or affect), we can more accurately personalize to client preferences with the addition of context, as shared-context parameters carry beneficial stylistic information

Conversational Recommender System (CRS), which aims to recommend high-quality items to users through interactive conversations, has gained great research interest recently. A CRS is usually composed of a recommendation module and a generation module. In the previous work, these two modules are loosely connected in the model training and are shallowly integrated during inference, where a simple switching or copy mechanism is adopted to incorporate recommended items into generated responses. Moreover, the current end-to-end neural models trained on small crowd-sourcing datasets (e.g., 10K dialogs in the ReDial dataset) tend to overfit and have poor chit-chat ability. In this work, we propose a novel unified framework that integrates recommendation into the dialog (RecInDial) generation by introducing a vocabulary pointer. To tackle the low-resource issue in CRS, we finetune the large-scale pretrained language models to generate fluent and diverse responses, and introduce a knowledge-aware bias learned from an entity-oriented knowledge graph to enhance the recommendation performance. Furthermore, we propose to evaluate the CRS models in an end-to-end manner, which can reflect the overall performance of the entire system rather than the performance of individual modules, compared to the separate evaluations of the two modules used in previous work. Experiments on the benchmark dataset ReDial show our RecInDial model significantly surpasses the state-of-the-art methods. More extensive analyses show the effectiveness of our model.

All these multi-taskings are possible through Machine Learning, which is a subfield of Artificial Intelligence ( AI ).

It don't mean a thing if it ain't got that swing, but so far it has been difficult for jazz musicians to actually define what'swing' is. Scientists at the Max Planck Institute for Dynamics and Self-Organization in Germany think they have found out, after their study revealed that the rhythm is the result of micro-delays in musical timing. Traditionally, swing is thought to be added to a piece of music when quavers - notes that are an eighth of the duration of a whole note - are played with uneven lengths. The researchers played manipulated pieces of music to jazz musicians, to see if changes in timing affected their perception of its swing. It was found that when the notes on beat one and three were delayed by 30 milliseconds, the musicians were 7.48 times more likely to rate the music as having more swing. However, the microtiming deviations were so small that they were imperceptible to professional jazz musicians, suggesting they use them unconsciously.

'The Six Million Dollar Man' Steve Austin, played by Lee Majors, is an astronaut who is seriously injured when his spaceship crashes. When Austin recovers, his "bionic" parts give him superhuman strength, speed and sight. With these powers, Austin goes to work for the Office of Scientific Information, battling evil for the good of humanity. The popular 1970s television program spun off a second TV series, 'The Bionic Woman' starring Lindsay Wagner. 'The Terminator' series of action films stars Arnold Schwarzenegger as a cybernetic organism (cyborg), programmed from the future to go back in time and kill the mother of the scientist who leads the fight against Skynet, an artificial intelligence system that will cause a nuclear holocaust.

I met my husband in 2008 and therefore skipped the whole online dating universe that dominates how we hook up and fall in love in 2022. So I was pretty excited to try out The Right Stuff--the Peter Thiel-backed dating app for conservatives--you know, for journalism, and end my personal streak of dating-app virginity. "Inae falls in love with a patriot and divorces her husband," my esteemed colleague Abigail Weinberg had predicted for me. But after filling out the questionnaires and selecting photos to build my profile, I got stuck on the last step requiring an invite. I had no choice but to hit delete; my status as a dating app virgin remains intact. But it turns out I wasn't the only one disappointed by the system--a bunch of reviewers in the app store, first spotted here, also had complaints.

The phrases machine learning, deep learning, and artificial intelligence (AI) have entered practically every field of medicine.

… model (people and skills) and how to deploy artificial intelligence (AI) and machine learning (ML) techniques to identify and remove the noise.