Artificial intelligence, or AI, is the branch of Computer Science that derives its inspiration from biological systems to solve highly complex …

artificial intelligence, noun: The capacity of computers or other machines to exhibit or simulate intelligent behavior; the field of study …

Artificial intelligence has reimagined movie posters of popular horror films just in time for Halloween - and the results are teeming with blood, gore and terror. A graphic design team inputted key words like mask, black cloak and blood to inspire the AI-powered app Wonder that brought the nightmares to life. The popular 1996 slasher film Scream features a woman with blue eyes and covering her mouth on its movie poster, but the AI created a hooded figure with a mask that is dripping in blood that is'arguably even more terrifying than the original.' The visuals were created using an app that asks users to describe what they want to see in the digital artwork, which has become a new medium recently. A graphic design team inputted key words like mask, black cloak and blood to inspire the AI-powered app Wonder that brought the nightmares to life.

This week's paper may just be your next favorite model to date. If you think the recent image generation models like DALLE or Stable Diffusion are cool, you just won't believe how incredible this one is. Imagic takes such a diffusion-based model able to take text and generate images out of it and adapts the model to edit the images. You can generate an image and then teach the model to edit it any way you want. Read the full article: https://www.louisbouchard.ai/imagic/

Amazon, Netflix, and Indeed don't simply provide more options than traditional retail stores, video rental stores, and newspapers -- they provide so many more options that the human mind can effectively comprehend and parse. Users need to be shown what will most appeal to them. There were recommender systems before deep learning, but until that advancement, technical constraints ensured choice remained tyrannical. Deep learning has become an essential component of recommender systems, and anyone who wants to understand the latter must understand the former. Traditional recommender systems make recommendations to users based on previous user interactions or attributes, depending on whether the recommender system uses content-based filtering, collaborative filtering, or a hybrid of the two. Content-based filtering recommends items with similar features to items a user interacted with in the past.

OpenAI's flagship natural language processing (NLP) programme, GPT-3, has been available for two years now. Poems, essays, song lyrics, and even comprehensive manifestos could be generated by the AI language tool with just the barest of cues, and its readers were astounded by the quality of the writing produced. OpenAI's GPT-3 is a "foundation model" that was trained using what amounts to the whole of the internet (Wikipedia, Reddit, The New York Times, etc.). In order to determine the most likely responses to each given challenge, it utilises this massive information. Due to the massive scope of this study, only a select few of these basic models exist.

September 2022 was apparently the month artificial intelligence essay angst boiled over in academia, as various media outlets published opinion pieces lamenting the rise of AI writing systems that will ruin student writing and pave the way toward unprecedented levels of academic misconduct. Then, on Sept. 23, academic Twitter exploded into a bit of a panic on this topic. The firestorm was prompted by a post to the OpenAI subreddit where user Urdadgirl69 claimed to be getting straight A's with essays "written" using artificial intelligence. Professors on Reddit and Twitter alike expressed frustration and concern about how best to address the threat of AI essays. One of the most poignant and widely retweeted laments came from Redditor ahumanlikeyou, who wrote, "Grading something an AI wrote is an incredibly depressing waste of my life."

Prior studies of zero-shot stance detection identify the attitude of texts towards unseen topics occurring in the same document corpus. Such task formulation has three limitations: (i) Single domain/dataset. A system is optimized on a particular dataset from a single domain; therefore, the resulting system cannot work well on other datasets; (ii) the model is evaluated on a limited number of unseen topics; (iii) it is assumed that part of the topics has rich annotations, which might be impossible in real-world applications. These drawbacks will lead to an impractical stance detection system that fails to generalize to open domains and open-form topics. This work defines OpenStance: open-domain zero-shot stance detection, aiming to handle stance detection in an open world with neither domain constraints nor topic-specific annotations. The key challenge of OpenStance lies in the open-domain generalization: learning a system with fully unspecific supervision but capable of generalizing to any dataset. To solve OpenStance, we propose to combine indirect supervision, from textual entailment datasets, and weak supervision, from data generated automatically by pre-trained Language Models. Our single system, without any topic-specific supervision, outperforms the supervised method on three popular datasets. To our knowledge, this is the first work that studies stance detection under the open-domain zero-shot setting. All data and code are publicly released.

We introduce a new open information extraction (OIE) benchmark for pre-trained language models (LM). Recent studies have demonstrated that pre-trained LMs, such as BERT and GPT, may store linguistic and relational knowledge. In particular, LMs are able to answer ``fill-in-the-blank'' questions when given a pre-defined relation category. Instead of focusing on pre-defined relations, we create an OIE benchmark aiming to fully examine the open relational information present in the pre-trained LMs. We accomplish this by turning pre-trained LMs into zero-shot OIE systems. Surprisingly, pre-trained LMs are able to obtain competitive performance on both standard OIE datasets (CaRB and Re-OIE2016) and two new large-scale factual OIE datasets (TAC KBP-OIE and Wikidata-OIE) that we establish via distant supervision. For instance, the zero-shot pre-trained LMs outperform the F1 score of the state-of-the-art supervised OIE methods on our factual OIE datasets without needing to use any training sets. Our code and datasets are available at https://github.com/cgraywang/IELM

Emotion recognition using EEG signals is an emerging area of research due to its broad applicability in BCI. Emotional feelings are hard to stimulate in the lab. Emotions do not last long, yet they need enough context to be perceived and felt. However, most EEG-related emotion databases either suffer from emotionally irrelevant details (due to prolonged duration stimulus) or have minimal context doubting the feeling of any emotion using the stimulus. We tried to reduce the impact of this trade-off by designing an experiment in which participants are free to report their emotional feelings simultaneously watching the emotional stimulus. We called these reported emotional feelings "Emotional Events" in our Dataset on Emotion with Naturalistic Stimuli (DENS). We used EEG signals to classify emotional events on different combinations of Valence(V) and Arousal(A) dimensions and compared the results with benchmark datasets of DEAP and SEED. STFT is used for feature extraction and used in the classification model consisting of CNN-LSTM hybrid layers. We achieved significantly higher accuracy with our data compared to DEEP and SEED data. We conclude that having precise information about emotional feelings improves the classification accuracy compared to long-duration EEG signals which might be contaminated by mind-wandering.