GPT is short for Generative Pre-training Transformer (GPT), a language model written by Alec Radford and published in 2018 by OpenAI, Elon Musks's artificial intelligence research laboratory. It uses a generative model of language (where two neural networks perfect each other by competition) and is able to acquire knowledge of the world and process long-range dependencies by pre-training on diverse sets of written material with long stretches of contiguous text. GPT-2 (Generative Pretrained Transformer 2) was announced in February 2019 and is an unsupervised transformer language model trained on 8 million documents for a total of 40 GB of text from articles shared via Reddit submissions. Elon Musk was famously reluctant to release it as he was concerned it could be used to spam social networks with fake news. In May 2020 OpenAI announced GPT-3 (Generative Pretrained Transformer 3), a model which contains two orders of magnitude more parameters than GPT-2 (175 billion vs 1.5 billion parameters) and which offers a dramatic improvement over GPT-2.

Editor's note: this is a guest post by Tom Henriksson, a general partner at OpenOcean, a VC firm investing in European data-intensive software startups. In 1999, as Steven Spielberg was preparing to make the movie "Minority Report", he assembled a team of 15 technology experts to help him depict the world as it would look in 2054, the year that the movie takes place. The result was an impressive and somewhat dystopic future scape where technology permeates our lives. It is too soon to say whether the vision of the future depicted in the movie will become reality, but 18 years after the film's release, artificial intelligence (AI) and what is often called intelligent enterprise automation have had a profound impact in some areas. Marketing spending across every industry and segment of society is now shaped and driven by artificial intelligence using real-time analysis of massive data sets about consumer habits, data sets culled by sophisticated algorithms from billions of transactions and searches happening every day across the Internet.

Artificial intelligence (AI) continues to hold its title as the top buzzword of enterprise tech, but its appeal is well-founded. We now seem to be shifting from the era of businesses simply talking about AI, to actually getting hands-on, exploring the ways it can be used to tackle real-world challenges. AI is increasingly providing a solution to problems old and new, then again, while the technology is proving itself incredibly powerful, not all of its potential is necessarily positive. Here, we explore some of the more edge-case applications of AI taking place this year. Advances in deep-learning and AI continue to make deepfakes more realistic.

Previous attempts to use machine learning to train semantic navigation systems have been hampered because they tend to memorize objects and their …

The report offers in-depth analysis of trends and opportunities in the AI/Machine Learning Market.

Neuromarketing aims to understand consumer behavior using neuroscience. Brain imaging tools such as EEG have been used to better understand consumer behavior that goes beyond self-report measures which can be a more accurate measure to understand how and why consumers prefer choosing one product over another. Previous studies have shown that consumer preferences can be effectively predicted by understanding changes in evoked responses as captured by EEG. However, understanding ordered preference of choices was not studied earlier. In this study, we try to decipher the evoked responses using EEG while participants were presented with naturalistic stimuli i.e. movie trailers. Using Machine Learning tech niques to mine the patterns in EEG signals, we predicted the movie rating with more than above-chance, 72% accuracy. Our research shows that neural correlates can be an effective predictor of consumer choices and can significantly enhance our understanding of consumer behavior.

This paper addresses the task of score following in sheet music given as unprocessed images. While existing work either relies on OMR software to obtain a computer-readable score representation, or crucially relies on prepared sheet image excerpts, we propose the first system that directly performs score following in full-page, completely unprocessed sheet images. Based on incoming audio and a given image of the score, our system directly predicts the most likely position within the page that matches the audio, outperforming current state-of-the-art image-based score followers in terms of alignment precision. We also compare our method to an OMR-based approach and empirically show that it can be a viable alternative to such a system.

As the field of Music Information Retrieval grows, it is important to take into consideration the multi-modality of music and how aspects of musical engagement such as movement and gesture might be taken into account. Bodily movement is universally associated with music and reflective of important individual features related to music preference such as personality, mood, and empathy. Future multimodal MIR systems may benefit from taking these aspects into account. The current study addresses this by identifying individual differences, specifically Big Five personality traits, and scores on the Empathy and Systemizing Quotients (EQ/SQ) from participants' free dance movements. Our model successfully explored the unseen space for personality as well as EQ, SQ, which has not previously been accomplished for the latter. R2 scores for personality, EQ, and SQ were 76.3%, 77.1%, and 86.7% respectively. As a follow-up, we investigated which bodily joints were most important in defining these traits. We discuss how further research may explore how the mapping of these traits to movement patterns can be used to build a more personalized, multi-modal recommendation system, as well as potential therapeutic applications.

Fox News Flash top entertainment and celebrity headlines are here. Check out what's clicking today in entertainment. Warner Bros. has once again delayed the release of the Christopher Nolan-directed film "Tenet" during the coronavirus pandemic. The studio said Monday that the $200 million thriller will not make its August release date. However, unlike past announcements, Warner Bros. did not announce a new target date this time. The sci-fi thriller, which stars John David Washington and Robert Pattinson, was set to be released on Wednesday, Aug. 12.

Artificial intelligence (AI) is at the top of the buzzword bingo reel in the world of tech, and for good reason. We're seemingly shifting from the era of businesses (and the public) talking about AI and marvelling at its mysterious power, to wondering how it can be used to best tackle real-world challenges day to day. That said, with the fine-tuning of the technology comes increasing attempts to exploit some of its frailties. So just how will the world harness, advance and protect AI technology within the year to come? Here are few of the more edge-case applications of AI taking place.