We're living in a time of massive potential to use data science and AI for the greater good, yet the world's problems only seem to be mounting. Data and algorithms touch nearly every aspect of our daily lives, from the movies and music that we choose, to the news we receive, to how our cities run. Companies have leveraged data and algorithms to maximize their profits, but social organizations like nonprofits, NGOs, and governments still lack the resources to harness this same technology for social good. Moreover, questions are being raised about the responsible use of data and AI in society. At DataKind, we envision living on a sustainable planet where all have access to their basic human needs and can live lives of equality and prosperity.

Late last month, Chris Donahue and Abhay Agarwal launched an impressive new tool called'Beat Sage', which uses artificial intelligence and neural networks to automatically generate custom Beat Saber maps from any song in seconds. We tried it out, and were left suitably impressed -- the resulting tracks are fun, challenging and better than many other auto-map generators for rhythm games. With the tool still in active development, we reached out to Donahue and Agarwal to get a better understanding of what makes Beat Sage tick and how it might be improved in the future. "I first tried Beat Saber in December 2019 and loved it immediately," Donahue, an AI researcher at Stanford University, explained in an email. A month later, in January 2020, he began intermittent work on what would become Beat Sage and spoke to Agarwal, who runs an AI design firm called Polytopal and agreed to help out.

What can an agent do without any reward? While many formulations of intrinsic rewards exist (Curiosity, Novelty, etc.), they all look back in time to learn. Plan2Explore is the first model that uses planning in a learned imaginary latent world model to seek out states where it is uncertain about what will happen.

Despite the advancement of ASR, however, most publicly trained from these speech data generally show poor recognition available call-based speech corpora such as Switchboard performance when applied to domain-specific tasks due to the are old-fashioned. Also, most existing call corpora are in English differences in their data distribution and vocabularies. In particular, and mainly focus on open domain dialog or general scenarios AICC requires an accurate ASR model to ensure the precise such as audiobooks. Here we introduce a new large-scale intent classification or slot extraction [9] from user natural Korean call-based speech corpus under a goal-oriented dialog language utterances.

It is generally agreed that one origin of machine bias is resulting from characteristics within the dataset on which the algorithms are trained, i.e., the data does not warrant a generalized inference. We, however, hypothesize that a different `mechanism', hitherto not articulated in the literature, may also be responsible for machine's bias, namely that biases may originate from (i) the programmers' cultural background, such as education or line of work, or (ii) the contextual programming environment, such as software requirements or developer tools. Combining an experimental and comparative design, we studied the effects of cultural metaphors and contextual metaphors, and tested whether each of these would `transfer' from the programmer to program, thus constituting a machine bias. The results show (i) that cultural metaphors influence the programmer's choices and (ii) that `induced' contextual metaphors can be used to moderate or exacerbate the effects of the cultural metaphors. This supports our hypothesis that biases in automated systems do not always originate from within the machine's training data. Instead, machines may also `replicate' and `reproduce' biases from the programmers' cultural background by the transfer of cultural metaphors into the programming process. Implications for academia and professional practice range from the micro programming-level to the macro national-regulations or educational level, and span across all societal domains where software-based systems are operating such as the popular AI-based automated decision support systems.

In Max Barry's new novel Providence, a four-person crew sets out into deep space to battle aliens. It's a scenario that recalls many classic science fiction novels such as Starship Troopers and Ender's Game. "It was a chance to revisit some of the exciting sci-fi I'd enjoyed as a kid, but do it with a bit more of a modern take on it," Barry says in Episode 414 of the Geek's Guide to the Galaxy podcast. The crew soon discovers that their real purpose is to maintain public support for the mission back home while the ship's AI does the actual fighting. It's Barry's response to movies like those in the Star Wars franchise, which emphasize the skill of human pilots.

A new AI-powered website is letting people create their own make-believe vocabularies with just the click of a button. The website, called ThisWordDoesnNotExist.com, from Thomas Dimson, who formerly worked for Instagram, not only generates make-believe words with just a click, it conjures their equally as fake definitions. For instance, 'rebutis' means'a statement that has been repeated again [and] again' while'nexperience' means'lack of interest or enjoyment; frugality.' Introducing "this word does not exist" today - AI generated English words with dictionary definitions. The AI was trained on words harvested from 8 million of the most upvoted Reddit posts and is capable of recognizing patterns.

Sony has shown off what it's calling "the world's first image sensors to be equipped with AI processing functionality." These new sensors handle AI image analysis on board, so only the necessary data can be sent for further cloud processing. Artificial Intelligence is a natural pair with digital video cameras. They take in monstrous amounts of data, the vast majority of which is of no interest to anybody, particularly when you're talking about things like security cameras. As automation continues to escalate, we're going to need AI to keep an eye on more and more camera feeds. Checkout-free stores in the vein of Amazon's minimally staffed Go shop must constantly monitor dozens, if not hundreds of cameras to figure out who's picking up what, and what they're doing with it.

This book discusses the necessity and perhaps urgency for the regulation of algorithms on which new technologies rely; technologies that have the potential to re-shape human societies. From commerce and farming to medical care and education, it is difficult to find any aspect of our lives that will not be affected by these emerging technologies. At the same time, artificial intelligence, deep learning, machine learning, cognitive computing, blockchain, virtual reality and augmented reality, belong to the fields most likely to affect law and, in particular, administrative law. The book examines universally applicable patterns in administrative decisions and judicial rulings. First, similarities and divergence in behavior among the different cases are identified by analyzing parameters ranging from geographical location and administrative decisions to judicial reasoning and legal basis. As it turns out, in several of the cases presented, sources of general law, such as competition or labor law, are invoked as a legal basis, due to the lack of current specialized legislation. This book also investigates the role and significance of national and indeed supranational regulatory bodies for advanced algorithms and considers ENISA, an EU agency that focuses on network and information security, as an interesting candidate for a European regulator of advanced algorithms. Lastly, it discusses the involvement of representative institutions in algorithmic regulation.

In this work, we introduce a context-aware hybrid artist recommender system (CAARS) that uses Twitter users’ tweet-time patterns as context and users’ bias about gender and types of musicians to recommend artists. Our model offers a novel approach to improve a personalized music recommender system (MRS) as it extracts implicit information from the users’ past tweet-behavior and combines that with related content. The proposed model performs significantly better than collaborative and hybrid recommender systems and encourages further exploration.