The AI Core integrates a growing ecosystem of modern machine learning algorithms that enable Agiloft's AI Capabilities for CLM.

We propose a new task of conversational recommendation over multi-type dialogs, where the bots can proactively and naturally lead a conversation from a non-recommendation dialog (e.g., QA) to a recommendation dialog, taking into account user's interests and feedback. To facilitate the study of this task, we create a human-to-human Chinese dialog dataset \emph{DuRecDial} (about 10k dialogs, 156k utterances), which contains multiple sequential dialogs for every pair of a recommendation seeker (user) and a recommender (bot). In each dialog, the recommender proactively leads a multi-type dialog to approach recommendation targets and then makes multiple recommendations with rich interaction behavior. This dataset allows us to systematically investigate different parts of the overall problem, e.g., how to naturally lead a dialog, how to interact with users for recommendation. Finally we establish baseline results on DuRecDial for future studies. Dataset and codes are publicly available at https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/Research/ACL2020-DuRecDial.

Last Tuesday, Google shared a blog post highlighting the perspectives of three women of color employees on fairness and machine learning. I suppose the comms team saw trouble coming: The next day NBC News broke the news that diversity initiatives at Google are being scrapped over concern about conservative backlash, according to eight current and former employees speaking on condition of anonymity. The news led members of the House Tech Accountability Caucus to send a letter to CEO Sundar Pichai on Monday. Citing Google's role as a leader in the U.S. tech community, the group of 10 Democrats questioned why, despite corporate commitments over years, Google diversity still lags behind the diversity of the population of the United States. The 10-member caucus specifically questioned whether Google employees working with AI receive additional bias training.

Linear Algebra and Optimization for Machine Learning: A Textbook (Springer), authored by Charu C. Aggarwal, May 2020. PDF Download Link (Free for computers connected to subscribing institutions only). The PDF version has links for e-readers, and is preferable in terms of equation formatting to the Kindle version. A frequent challenge faced by beginners in machine learning is the extensive background requirement in linear algebra and optimization. This makes the learning curve very steep.

Ludwig van Beethoven composed his symphonies between 1799 and 1825, when he was writing his Tenth symphony. As we dispose of a great amount of data belonging to his work, the purpose of this paper is to investigate the possibility of extracting patterns on his compositional model from symbolic data and generate what would have been his last symphony, the Tenth. A neural network model has been built based on the Long Short-Therm Memory (LSTM) neural networks. After training the model, the generated music has been analysed by comparing the input data with the results, and establishing differences between the generated outputs based on the training data used to obtain them. The structure of the outputs strongly depends on the symphonies used to train the network.

As recommender systems have become more widespread and moved into areas with greater social impact, such as employment and housing, researchers have begun to seek ways to ensure fairness in the results that such systems produce. This work has primarily focused on developing recommendation approaches in which fairness metrics are jointly optimized along with recommendation accuracy. However, the previous work had largely ignored how individual preferences may limit the ability of an algorithm to produce fair recommendations. Furthermore, with few exceptions, researchers have only considered scenarios in which fairness is measured relative to a single sensitive feature or attribute (such as race or gender). In this paper, we present a re-ranking approach to fairness-aware recommendation that learns individual preferences across multiple fairness dimensions and uses them to enhance provider fairness in recommendation results. Specifically, we show that our opportunistic and metric-agnostic approach achieves a better trade-off between accuracy and fairness than prior re-ranking approaches and does so across multiple fairness dimensions.

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Philip Sheppard has recorded solo cello albums, composed more than 60 soundtracks, and adapted 206 national anthems for Olympic Games medal ceremonies. He knows every corner of the famed Abbey Road Studios. His work is the process of creating music, but sometimes he just wants to listen, preferably while walking in the woods. During one of those walks, in 2016, he didn't want to be bothered choosing what music to play. Instead he imagined some sort of magical accompaniment piped into his earphones that would dynamically reflect his surroundings and his mood, a literal soundtrack for his saunter.