The field of Unmanned Aerial Vehicles (UAVs) has reached a high level of maturity in the last few years. Hence, bringing such platforms from closed labs, to day-to-day interactions with humans is important for commercialization of UAVs. One particular human-UAV scenario of interest for this paper is the payload handover scheme, where a UAV hands over a payload to a human upon their request. In this scope, this paper presents a novel real-time human-UAV interaction detection approach, where Long short-term memory (LSTM) based neural network is developed to detect state profiles resulting from human interaction dynamics. A novel data pre-processing technique is presented; this technique leverages estimated process parameters of training and testing UAVs to build dynamics invariant testing data. The proposed detection algorithm is lightweight and thus can be deployed in real-time using off the shelf UAV platforms; in addition, it depends solely on inertial and position measurements present on any classical UAV platform. The proposed approach is demonstrated on a payload handover task between multirotor UAVs and humans. Training and testing data were collected using real-time experiments. The detection approach has achieved an accuracy of 96\%, giving no false positives even in the presence of external wind disturbances, and when deployed and tested on two different UAVs.

Recommendation systems have been widely used in various domains such as music, films, e-shopping, etc. After mostly avoiding digitization, the art world has recently reached a technological turning point due to the pandemic, making online sales grow significantly as well as providing quantitative online data about artists and artworks. In this work, we present a content-based recommendation system on contemporary art relying on images of artworks and contextual metadata of artists. We gathered and annotated artworks with advanced and art-specific information to create a completely unique database that was used to train our models. With this information, we built a proximity graph between artworks. Similarly, we used NLP techniques to characterize the practices of the artists and we extracted information from exhibitions and other event history to create a proximity graph between artists. The power of graph analysis enables us to provide an artwork recommendation system based on a combination of visual and contextual information from artworks and artists. After an assessment by a team of art specialists, we get an average final rating of 75% of meaningful artworks when compared to their professional evaluations.

The rewriting method for text summarization combines extractive and abstractive approaches, improving the conciseness and readability of extractive summaries using an abstractive model. Exiting rewriting systems take each extractive sentence as the only input, which is relatively focused but can lose necessary background knowledge and discourse context. In this paper, we investigate contextualized rewriting, which consumes the entire document and considers the summary context. We formalize contextualized rewriting as a seq2seq with group-tag alignments, introducing group-tag as a solution to model the alignments, identifying extractive sentences through content-based addressing. Results show that our approach significantly outperforms non-contextualized rewriting systems without requiring reinforcement learning, achieving strong improvements on ROUGE scores upon multiple extractors.

In recent years, Generative Adversarial Networks (GANs) have become a hot topic among researchers and engineers that work with deep learning. It has been a ground-breaking technique which can generate new pieces of content of data in a consistent way. The topic of GANs has exploded in popularity due to its applicability in fields like image generation and synthesis, and music production and composition. GANs have two competing neural networks: a generator and a discriminator. The generator is used to produce new samples or pieces of content, while the discriminator is used to recognize whether the piece of content is real or generated. What makes it different from other generative models is its ability to learn unlabeled samples. In this review paper, we will discuss the evolution of GANs, several improvements proposed by the authors and a brief comparison between the different models. Index Terms generative adversarial networks, unsupervised learning, deep learning.

As AI systems become increasingly powerful and pervasive, there are growing concerns about machines' morality or a lack thereof. Yet, teaching morality to machines is a formidable task, as morality remains among the most intensely debated questions in humanity, let alone for AI. Existing AI systems deployed to millions of users, however, are already making decisions loaded with moral implications, which poses a seemingly impossible challenge: teaching machines moral sense, while humanity continues to grapple with it. To explore this challenge, we introduce Delphi, an experimental framework based on deep neural networks trained directly to reason about descriptive ethical judgments, e.g., "helping a friend" is generally good, while "helping a friend spread fake news" is not. Empirical results shed novel insights on the promises and limits of machine ethics; Delphi demonstrates strong generalization capabilities in the face of novel ethical situations, while off-the-shelf neural network models exhibit markedly poor judgment including unjust biases, confirming the need for explicitly teaching machines moral sense. Yet, Delphi is not perfect, exhibiting susceptibility to pervasive biases and inconsistencies. Despite that, we demonstrate positive use cases of imperfect Delphi, including using it as a component model within other imperfect AI systems. Importantly, we interpret the operationalization of Delphi in light of prominent ethical theories, which leads us to important future research questions.

Every day, humans produce hundreds of millions of pieces of content -- countless pictures, comments, blog posts, videos, new social media channels, every second of every day. It's a never-ending process fueled by algorithms and human interaction, and the Internet is only getting more saturated. This has led many content creators to begin asking themselves: should they use artificial intelligence to create content for them? How can they possibly keep up and compete with the flood of new, interesting, SEO-optimized content their audience sees every time they open their phone? Wouldn't it be better for a publisher's site to use the infinite scaling of AI instead of the publisher's own finite time and energy?

The latest Tweet by Prasar Bharati News Services states, '"The development of artificial intelligence is vital to human civilization; By developing it, man proved his superiority; Artificial intelligence a revolutionary step in the development of humanity: Defence Minister @rajnathsingh @DefenceMinIndia' 📰 "The Development of Artificial Intelligence is Vital to Human Civilization; By Developing ... - Latest Tweet by Prasar Bharati News Services.

You type in words – however nonsensical or disjointed – and the algorithm creates a unique image based on your search. This is Dall-E 2, a startlingly advanced, image-generating AI trained on 250 million images, named after the surrealist artist Salvador Dalí and Pixar's Wall-E. While use of Dall-E 2 is currently limited to a narrow pool of people, Dall-E mini (or Craiyon) is a free, unrelated version that is open to the public. Drawing on 15m images, Dall-E mini's algorithm offers a smorgasbord of surreal images, complete with absurd compositions and blurred human forms. Already, trends have emerged: nuclear explosions, dumpster fires, toilets and giant eyeballs abound. On a dedicated Reddit thread, people delight in the images generated by the free, low-resolution version, which range from amusing (Kim Jong-un lego) to dark (The Last Supper by Salvador Dali), hellish (synchronized swimming in lava) and deeply disturbing (Steve Jobs introducing a guillotine). Like other machine-learning networks, this AI model seems biased in its images of people – who appear, perhaps unsurprisingly, overwhelmingly white and mostly male.

… teammates would want to use buzzwords like ‘machine learning’ and ‘the … But as a separate step, Kapur also recommends learning scripting, …

The'intelligence' of AI is growing all the time. And AI has many forms, from Spotify's recommendation system to self-drive cars. AI utilises natural language processing (NLP) to deliver natural and human-like language. It mimics humans and generates human-like messages by analysing commands. That said, it is still challenging to create an AI tool that understands the nuances of natural human languages is hard.