Last month, a piece of art called Théâtre D'opéra Spatial (that's French for "Space Opera Theater") was entered into the Colorado State Fair's fine art competition by a man named Jason Allen. The piece is a gorgeous "painting" that depicts a giant baroque hall containing three women in flowing red and white robes. The image won first place in the digitally manipulated photography category, and the artist judges at the state fair said the work was the best of the best. At the fair, Allen said the piece was made with Midjourney, an artificial intelligence tool that can create art, but no one really understood what that meant. Once they realized an A.I.-generated piece of art had beaten human artist-created images, a debate opened up.

Amazon Alexa is a virtual assistant technology largely based on a Polish speech synthesiser named Ivona, bought by Amazon in 2013. It is integrated into Amazon Echo and Echo Dot smart speakers as well as Echo Show and Echo Spot devices. Alexa's technology allows users to access a wide variety of functionality and information, such as playing music, setting alarms, triggering reminders, streaming podcasts, making to-do lists, playing audiobooks, and updating traffic, weather, sports, and other real-time information, such as local news. Alexa can perform these functions out-of-the-box, usually triggered by a "wake-word" to alert an Alexa-enabled device of a vocal command. Alexa listens for the command and performs the appropriate function, or task, to answer a question or command.

I'm at the Venice film festival, in a hyper-real city square, surrounded by lapping blue water and tourists who move in mysterious ways. There is a ginger cat here called Dorian who walks on his hind legs and speaks with a French accent. Dorian is showing us how to walk and turn and jump and crouch. He's concerned by the tourist who can't get herself off the ground. Dorian explains that if we ever get lost we should press the "respawn" button which will put us right back where we began.

Whether you like them or not, Artificial Intelligence (AI) image generators have exploded in popularity this year and the technology shows no signs of stopping. So if you're feeling confused about which AI Image generator you should use in 2022, this is a complete guide to the best options out there. A product of the Elon Musk co-founded research lab OpenAI, DALL-E 2, which we'll refer to as simply DALL-E, is the software most people can name when you ask them about AI text-to-image generators. When it launched in April, DALL-E stunned social media with its ability to turn a brief description into a photo-realistic image. For the few people with privileged access to the closed-off tool, DALL-E was so exceptional that it almost felt like magic -- whether that involved generating pictures of "a raccoon astronaut with the cosmos reflecting on the glass of his helmet" or "teddy bears shopping for groceries in Ancient Egypt," all from a simple text prompt.

Algorithmic disgorgement might sound like a phrase from a science-fiction horror film. In fact, it's a new tool for regulators to address the consequences of autonomous systems, ordering companies to remove or destroy algorithms and models in their products based on data obtained unfairly or deceptively. This is one of topics and papers to be presented and discussed at We Robot, an annual conference where scholars and technologists discuss legal and policy questions relating to robots and artificial intelligence. We Robot is taking place next week, from Sept. 14-16, at the University of Washington in Seattle, with a virtual option, as well. It's also an example of how the legal and regulatory landscape for robots, AI, and autonomous systems have changed in the decade since the conference was first held at the University of Miami in 2012. "We've come very far," said Ryan Calo, one of the organizers of the conference, a University of Washington law professor who specializes in areas including privacy, artificial intelligence and robots.

David Restrepo Amariles, Data Law and Artificial Intelligence professor at HEC Paris Business School, explains why the need for more rules is is …

AI can now generate breathtaking original images based on simple text prompts. Depicted here: "a ... [ ] cute corgi lives in a house made out of sushi." A powerful new form of artificial intelligence has burst onto the scene and captured the public's imagination in recent months: text-to-image AI. Text-to-image AI models generate original images based solely on simple written inputs. Users can input any text prompt they like--say, "a cute corgi lives in a house made out of sushi"--and, as if by magic, the AI will produce a corresponding image. These models produce images that have never existed in the world nor in anyone's imagination.

In this work we present a new approach for the task of predicting fingerings for piano music. While prior neural approaches have often treated this as a sequence tagging problem with independent predictions, we put forward a checklist system, trained via reinforcement learning, that maintains a representation of recent predictions in addition to a hidden state, allowing it to learn soft constraints on output structure. We also demonstrate that by modifying input representations -- which in prior work using neural models have often taken the form of one-hot encodings over individual keys on the piano -- to encode relative position on the keyboard to the prior note instead, we can achieve much better performance. Additionally, we reassess the use of raw per-note labeling precision as an evaluation metric, noting that it does not adequately measure the fluency, i.e. human playability, of a model's output. To this end, we compare methods across several statistics which track the frequency of adjacent finger predictions that while independently reasonable would be physically challenging to perform in sequence, and implement a reinforcement learning strategy to minimize these as part of our training loss. Finally through human expert evaluation, we demonstrate significant gains in performability directly attributable to improvements with respect to these metrics.

Online news and information sources are convenient and accessible ways to learn about current issues. For instance, more than 300 million people engage with posts on Twitter globally, which provides the possibility to disseminate misleading information. There are numerous cases where violent crimes have been committed due to fake news. This research presents the CovidMis20 dataset (COVID-19 Misinformation 2020 dataset), which consists of 1,375,592 tweets collected from February to July 2020. CovidMis20 can be automatically updated to fetch the latest news and is publicly available at: https://github.com/everythingguy/CovidMis20. This research was conducted using Bi-LSTM deep learning and an ensemble CNN+Bi-GRU for fake news detection. The results showed that, with testing accuracy of 92.23% and 90.56%, respectively, the ensemble CNN+Bi-GRU model consistently provided higher accuracy than the Bi-LSTM model.

To build recommender systems that not only consider user-item interactions represented as ordinal variables, but also exploit the social network describing the relationships between the users, we develop a hierarchical Bayesian model termed ordinal graph factor analysis (OGFA), which jointly models user-item and user-user interactions. OGFA not only achieves good recommendation performance, but also extracts interpretable latent factors corresponding to representative user preferences. We further extend OGFA to ordinal graph gamma belief network, which is a multi-stochastic-layer deep probabilistic model that captures the user preferences and social communities at multiple semantic levels. For efficient inference, we develop a parallel hybrid Gibbs-EM algorithm, which exploits the sparsity of the graphs and is scalable to large datasets. Our experimental results show that the proposed models not only outperform recent baselines on recommendation datasets with explicit or implicit feedback, but also provide interpretable latent representations.