I used [my Amazon Alexa] to turn on the lights or set a timer, and as a speaker for books and podcasts,

Depression is a widespread mental health issue, affecting an estimated 3.8% of the global population. It is also one of the main contributors to disability worldwide. Recently it is becoming popular for individuals to use social media platforms (e.g., Reddit) to express their difficulties and health issues (e.g., depression) and seek support from other users in online communities. It opens great opportunities to automatically identify social media users with depression by parsing millions of posts for potential interventions. Deep learning methods have begun to dominate in the field of machine learning and natural language processing (NLP) because of their ease of use, efficient processing, and state-of-the-art results on many NLP tasks. In this work, we propose a hybrid deep learning model which combines a pretrained sentence BERT (SBERT) and convolutional neural network (CNN) to detect individuals with depression with their Reddit posts. The sentence BERT is used to learn the meaningful representation of semantic information in each post. CNN enables the further transformation of those embeddings and the temporal identification of behavioral patterns of users. We trained and evaluated the model performance to identify Reddit users with depression by utilizing the Self-reported Mental Health Diagnoses (SMHD) data. The hybrid deep learning model achieved an accuracy of 0.86 and an F1 score of 0.86 and outperformed the state-of-the-art documented result (F1 score of 0.79) by other machine learning models in the literature. The results show the feasibility of the hybrid model to identify individuals with depression. Although the hybrid model is validated to detect depression with Reddit posts, it can be easily tuned and applied to other text classification tasks and different clinical applications.

A chatbot is perceived as more humanlike and likeable if it includes some jokes in its output. But most existing joke generators were not designed to be integrated into chatbots. This paper presents Witscript, a novel joke generation system that can improvise original, contextually relevant jokes, such as humorous responses during a conversation. The system is based on joke writing algorithms created by an expert comedy writer. Witscript employs well-known tools of natural language processing to extract keywords from a topic sentence and, using wordplay, to link those keywords and related words to create a punch line. Then a pretrained neural network language model that has been fine-tuned on a dataset of TV show monologue jokes is used to complete the joke response by filling the gap between the topic sentence and the punch line. A method of internal scoring filters out jokes that don't meet a preset standard of quality. Human evaluators judged Witscript's responses to input sentences to be jokes more than 40% of the time. This is evidence that Witscript represents an important next step toward giving a chatbot a humanlike sense of humor.

Extracting precise geographical information from textual contents is crucial in a plethora of applications. For example, during hazardous events, a robust and unbiased toponym extraction framework can provide an avenue to tie the location concerned to the topic discussed by news media posts and pinpoint humanitarian help requests or damage reports from social media. Early studies have leveraged rule-based, gazetteer-based, deep learning, and hybrid approaches to address this problem. However, the performance of existing tools is deficient in supporting operations like emergency rescue, which relies on fine-grained, accurate geographic information. The emerging pretrained language models can better capture the underlying characteristics of text information, including place names, offering a promising pathway to optimize toponym recognition to underpin practical applications. In this paper, TopoBERT, a toponym recognition module based on a one dimensional Convolutional Neural Network (CNN1D) and Bidirectional Encoder Representation from Transformers (BERT), is proposed and fine-tuned. Three datasets (CoNLL2003-Train, Wikipedia3000, WNUT2017) are leveraged to tune the hyperparameters, discover the best training strategy, and train the model. Another two datasets (CoNLL2003-Test and Harvey2017) are used to evaluate the performance. Three distinguished classifiers, linear, multi-layer perceptron, and CNN1D, are benchmarked to determine the optimal model architecture. TopoBERT achieves state-of-the-art performance (f1-score=0.865) compared to the other five baseline models and can be applied to diverse toponym recognition tasks without additional training.

Human beings have rich ways of emotional expressions, including facial action, voice, and natural languages. Due to the diversity and complexity of different individuals, the emotions expressed by various modalities may be semantically irrelevant. Directly fusing information from different modalities may inevitably make the model subject to the noise from semantically irrelevant modalities. To tackle this problem, we propose a multimodal relevance estimation network to capture the relevant semantics among modalities in multimodal emotions. Specifically, we take advantage of an attention mechanism to reflect the semantic relevance weights of each modality. Moreover, we propose a relevant semantic estimation loss to weakly supervise the semantics of each modality. Furthermore, we make use of contrastive learning to optimize the similarity of category-level modality-relevant semantics across different modalities in feature space, thereby bridging the semantic gap between heterogeneous modalities. In order to better reflect the emotional state in the real interactive scenarios and perform the semantic relevance analysis, we collect a single-label discrete multimodal emotion dataset named SDME, which enables researchers to conduct multimodal semantic relevance research with large category bias. Experiments on continuous and discrete emotion datasets show that our model can effectively capture the relevant semantics, especially for the large deviations in modal semantics. The code and SDME dataset will be publicly available.

Have you been worried that ChatGPT, the AI language generator, could be used maliciously--to cheat on schoolwork or broadcast disinformation? You're in luck, sort of: OpenAI, the company that made ChatGPT, has introduced a new tool that tries to determine the likelihood that a chunk of text you provide was AI-generated. I say "sort of" because the new software faces the same limitations as ChatGPT itself: It might spread disinformation about the potential for disinformation. As OpenAI explains, the tool will likely yield a lot of false positives and negatives, sometimes with great confidence. In one example, given the first lines of the Book of Genesis, the software concluded that it was likely to be AI-generated.

OpenAI, the company behind ChatGPT, announced on Wednesday it is piloting a $20 monthly subscription plan that offers users priority access to the AI chatbot even during peak times. The paid plan, called ChatGPT Plus, comes two months after the tool was released publicly and quickly went viral, thanks to its ability to generate shockingly convincing essays in response to user prompts. Many people who wanted to test the tool have been locked out or joined the waitlist. Now, anyone who signs up for a subscription will benefit from faster response times, and priority access to new features and improvements. The tool will remain free for the general public, however.

There's always something to watch on Twitch, whether that's your fave musicians talking about video games or your fave streamers discussing politics. Now your choices include an absurd, often nonsensical Seinfeld-like show that runs 24/7/365 and is generated on the fly using artificial intelligence. Welcome to the future of TV, maybe? So-called AI has been a fraught topic lately. The technology, which typically uses machine learning to generate text, images, and even video from preexisting sets of data, is suddenly everywhere: art, article and essay writing, even video games.

INan unremarkable conference room inside OpenAI's office, insulated from the mid-January rain pelting San Francisco, company president Greg Brockman surveys the "energy levels" of the team overseeing the company's new artificial intelligence model, ChatGPT. "How are we doing between'everything's on fire and everyone's burned out' to'everyone's just back from the holidays and everything's good'? What's the spectrum?" he asks. "I would say the holidays came at just the right time," replies one lieutenant. Within five days of ChatGPT's November launch, 1 million users overloaded its servers with trivia questions, poetry prompts and recipe requests. Open-AI quietly routed some of the load to its training supercomputer, thousands of interconnected graphics processing units (GPUs) custom-built with allies Microsoft and Nvidia, while long-term work on its next models, like the highly anticipated GPT-4, took a back seat. As the group huddles, ChatGPT's at-capacity servers still turn away users.

Artificial intelligence is having profound impacts on the media industry, from the way advertisers buy from search and social platforms to speeding up elements of the creative process. Now, programmatic ad buyers are using AI-based algorithms to get more value from the open marketplace. More advertisers have been adopting a programmatic tool called custom algorithms recently, four agency and brand media buying sources told Adweek. Two sources, including brands like The Hershey Company, are currently in the process of making custom algorithms a pillar of their programmatic strategy, while two have been using them for the past couple of years. Of course, the programmatic ecosystem is no stranger to algorithms, they are its raison d'être.