Before getting into the topic, why is it important to have an NLP project in your portfolio? How can it help in your career? The amount of text data getting generated is growing faster than ever. As per IDC, about 80% of global data will be unstructured by 2025. And this will be the pattern across the industries like retail, technology, healthcare, and anything you name it.

This article was co-written by John Latsis, chairman of the Independent Social Research Foundation. The classic 1995 crime film The Usual Suspects revolves around the police interrogation of Roger "Verbal" Kint, played by Kevin Spacey. Kint paraphrases Charles Baudelaire, stating that "the greatest trick the Devil ever pulled was convincing the world he didn't exist". The implication is that the Devil is more effective when operating unseen, manipulating and conditioning behavior rather than telling people what to do. In the film's narrative, his role is to cloud judgment and tempt us to abandon our sense of moral responsibility.

Clickbait dataset is probably our best in-house dataset in terms of quality and representation. This is partly because clickbait detection is a relatively easier problem. For this dataset, we were able to consistently ensure 2x labeling. The political bias dataset is the last one we labeled. We spent a good amount of time finding a good candidate unlabeled dataset, however, most of the examples were only labeled by one collaborator.

Specifically, this new methodology combines Artificial Intelligence and the use of three-dimensional images of the area of the brain in which the …

It's the worry that creeps in whenever people write about GPT-3: could this be used for bad? We've covered the technological advances in AI text generation like those from OpenAI a lot. There are always the "oohs" and "aah" about what it can do (write a self-help blog, for instance) and then others pointing out what it can't. But in the background is the question of how the ability to instantaneously "write" large amounts of text based on certain prompts could change the internet in unstoppable ways. Can artificial intelligence like GPT-3 be used for something like misinformation?

We study the merit of transfer learning for two sound recognition problems, i.e., audio tagging and sound event detection. Employing feature fusion, we adapt a baseline system utilizing only spectral acoustic inputs to also make use of pretrained auditory and visual features, extracted from networks built for different tasks and trained with external data. We perform experiments with these modified models on an audiovisual multi-label data set, of which the training partition contains a large number of unlabeled samples and a smaller amount of clips with weak annotations, indicating the clip-level presence of 10 sound categories without specifying the temporal boundaries of the active auditory events. For clip-based audio tagging, this transfer learning method grants marked improvements. Addition of the visual modality on top of audio also proves to be advantageous in this context. When it comes to generating transcriptions of audio recordings, the benefit of pretrained features depends on the requested temporal resolution: for coarse-grained sound event detection, their utility remains notable. But when more fine-grained predictions are required, performance gains are strongly reduced due to a mismatch between the problem at hand and the goals of the models from which the pretrained vectors were obtained.

This paper explores the idea of utilising Long Short-Term Memory neural networks (LSTMNN) for the generation of musical sequences in ABC notation. The proposed approach takes ABC notations from the Nottingham dataset and encodes it to be fed as input for the neural networks. The primary objective is to input the neural networks with an arbitrary note, let the network process and augment a sequence based on the note until a good piece of music is produced. Multiple calibrations have been done to amend the parameters of the network for optimal generation. The output is assessed on the basis of rhythm, harmony, and grammar accuracy.

Domestic robots are quickly becoming a practical reality, and Hollywood is keen to explore the implications... with a dash of slapstick comedy thrown in. Entertainment Weekly reports that 20th Century Studios and Locksmith Animation have released the first trailer for Ron's Gone Wrong, the CG-animated tale of Barney (Luca's Jack Dylan Grazer), a boy who gets a home robot (Zach Galifianakis) meant to be his "best friend out of the box." The movie's star-loaded cast also includes Olivia Colman, Ed Helms and Rob Delaney. It should reach theaters on October 22nd. There are no mentions of streaming plans so far, although we'd expect it to reach Disney at some point.

Algorithms and technology have so far helped listeners to more of the same music. Now, UiO researchers are working on new technology that can get people interested in a greater musical variety. Chords, beat, timbre, rhythm and harmony. All these elements of music contribute to make it sound the way it does. But have you thought about why you like particular kinds of music?

If you use such social media websites as Facebook and Twitter, you may have come across posts flagged with warnings about misinformation. So far, most misinformation – flagged and unflagged – has been aimed at the general public. Imagine the possibility of misinformation – information that is false or misleading – in scientific and technical fields like cybersecurity, public safety and medicine. There is growing concern about misinformation spreading in these critical fields as a result of common biases and practices in publishing scientific literature, even in peer-reviewed research papers. As a graduate student and as faculty members doing research in cybersecurity, we studied a new avenue of misinformation in the scientific community.