In machine learning, parameters are the part of the model that's learned from historical training data.

Directed by Maria Schrader, this was a crowd-pleasing favourite at the Berlin film festival earlier this year and its star, Maren Eggert, won the festival's new gender-neutral best leading performance prize. But I was disappointed with a film whose crises and dilemmas seem laborious and essentially predictable; it does not fully work as sci-fi or satire or comedy. We are in a world of the near-future (and the city of Berlin itself is certainly very plausible as its location). Eggert plays Alma, an archaeologist with an unhappy and frustrating personal life. She is persuaded by her boss to be a guinea-pig for a new hi-tech scheme: she will road-test a male "companion" robot, programmed to be infinitely considerate and obliging, which will attend to all her emotional and indeed physical needs.

It uses artificial intelligence to give its clients platforms that enable them to text with their customers or prospects at scale.

Most business entities have a long standing common goal, growth. With that in mind, those behind the management of those entities are constantly looking for a means to grow their business and in the music industry, that is also the case. The big three music labels are consistently in the spotlight for announcing joint venture partnerships or acquiring independent record labels to increase their market share. Whilst growth is at the core of most businesses, another factor is a close second, efficiency. With the rise of Artificial intelligence (AI) in many sectors such as automotive transportation, it's found its way to music and may change the future of the business forever.

When Dixons Carphone wanted to push shoppers towards its Black Friday sale, the company turned to Artificial Intelligence (AI) software and got the …

There is no doubt that Artificial intelligence has made a significant impact on the music industry.

They discussed how AI and machine learning are driving progress in many sectors and whether it's possible to safeguard AI's role in the patient's …

Keyword Spotting (KWS) remains challenging to achieve the trade-off between small footprint and high accuracy. Recently proposed metric learning approaches improved the generalizability of models for the KWS task, and 1D-CNN based KWS models have achieved the state-of-the-arts (SOTA) in terms of model size. However, for metric learning, due to data limitations, the speech anchor is highly susceptible to the acoustic environment and speakers. Also, we note that the 1D-CNN models have limited capability to capture long-term temporal acoustic features. To address the above problems, we propose to utilize text anchors to improve the stability of anchors. Furthermore, a new type of model (LG-Net) is exquisitely designed to promote long-short term acoustic feature modeling based on 1D-CNN and self-attention. Experiments are conducted on Google Speech Commands Dataset version 1 (GSCDv1) and 2 (GSCDv2). The results demonstrate that the proposed text anchor based metric learning method shows consistent improvements over speech anchor on representative CNN-based models. Moreover, our LG-Net model achieves SOTA accuracy of 97.67% and 96.79% on two datasets, respectively. It is encouraged to see that our lighter LG-Net with only 74k parameters obtains 96.82% KWS accuracy on the GSCDv1 and 95.77% KWS accuracy on the GSCDv2.

This paper proposes a new self-attention based model for music score infilling, i.e., to generate a polyphonic music sequence that fills in the gap between given past and future contexts. While existing approaches can only fill in a short segment with a fixed number of notes, or a fixed time span between the past and future contexts, our model can infill a variable number of notes (up to 128) for different time spans. We achieve so with three major technical contributions. First, we adapt XLNet, an autoregressive model originally proposed for unsupervised model pre-training, to music score infilling. Second, we propose a new, musically specialized positional encoding called relative bar encoding that better informs the model of notes' position within the past and future context. Third, to capitalize relative bar encoding, we perform look-ahead onset prediction to predict the onset of a note one time step before predicting the other attributes of the note. We compare our proposed model with two strong baselines and show that our model is superior in both objective and subjective analyses.

The Snakes AI Competition was held by the Innopolis University and was part of the IEEE Conference on Games2020 and 2021 editions. It aimed to create a sandbox for learning and implementing artificial intelligence algorithms in agents in a ludic manner. Competitors of several countries participated in both editions of the competition, which was streamed to create asynergy between organizers and the community. The high-quality submissions and the enthusiasm around the developed framework create an exciting scenario for future extensions.