TJ Arriaga, who fell in love with an AI robot, shares how he developed feelings for Phaedra, the robot, and was ultimately rejected by her and highlights how this app is causing trauma to people on'Jesse Watters Primetime.' The notion of falling in love with an AI robot has stepped outside the world of science fiction and the movie "Her," as rapidly advancing AI technology creates an opportunity for online relationships to blossom. Replika, a company that enables users to make personalized chatbots, says the goal of their technology is to "create a personal AI that would help you express and witness yourself by offering a helpful conversation." "It's a space where you can safely share your thoughts, feelings, beliefs, experiences, memories, dreams – your'private perceptual world,'" says the website founded by Eugenia Kuyda. T.J. Arriaga, a recently divorced musician who created a bot named Phaedra through Replika shared with "Jesse Watters Primetime" the details behind his emotional relationship with the bot.

'Fox & Friends' co-hosts criticize liberal San Francisco Mayor London Breed after she claimed crime statistics were taken completely out of context and that her city is being targeted. Residents and business owners in California's Bay Area are increasingly turning to artificial intelligence to combat a surge of burglaries and robberies along with police staffing shortages, with one security company telling Fox News Digital its sales of AI-based surveillance have been through the roof. Deep Sentinel, a Pleasanton, California-based company providing AI-based security nationwide, told Fox News Digital that business tripled during the coronavirus pandemic and that trend has continued ever since as burglaries and robberies continue to plague San Francisco and the Bay Area in general. "I would say that the business segment has just skyrocketed in the past year," Tomasz Borys, Deep Sentinel's vice president of marketing, told Fox News Digital. "The way that works is these cameras come with a sensor, so when there's an object that goes in front of the camera, it will trigger the artificial intelligence really quickly within a millisecond and determine what the object is," Borys explained.

Universal Music Group has been asking music streaming services like Spotify to stop developers from scraping its material to train AI bots to make new songs. The label, which controls about a third of the recorded music industry, has also been issuing substantial numbers of takedown requests in relation to AI uploads appearing online. It is the latest move in the music industry's growing battle to prevent AIs from using its songs without licensing them. On a "royalty free music generator" like Mubert, it's already possible to type in a prompt and the programme will use AI to search a catalogue of music for patterns. Tell it to play a "fast voodoo rhythm in the style of a nursery rhyme with some pretty electronics", and it will copy parts of songs that correspond and generate music to match.

Twitter and Tesla CEO Elon Musk weighs in on the dangers of artificial intelligence, the future of Twitter and more in an exclusive'Tucker Carlson Tonight' interview. OpenAI CEO Sam Altman says that a letter signed by Twitter CEO Elon Musk and others in the technology community calling for a pause on "giant AI experiments" wasn't the right way to address the issue. Musk, Steve Wozniak, and other tech leaders signed the letter in March, which asked AI developers to "immediately pause for at least 6 months the training of AI systems more powerful than GPT-4." During a virtual appearance at the Massachusetts Institute of Technology on Thursday, Altman addressed the letter. "There's parts of the thrust that I really agree with," Altman said, adding that his team spent more than six months after completing the training of ChatGPT 4 to study safety components before it was released.

'The Five' co-hosts discuss Elon Musk's warning to Tucker Carlson about artificial intelligence's potential to destroy civilization. A California lawmaker introduced legislation that would criminalize using artificial intelligence to create pornography while using a person's likeness without consent. Assembly member Tri Ta, a Republican representing Westminster, California, introduced the legislation in February that aims to punish people up to $1,000, or a year in jail, if they distribute "deepfake" porn depicting an individual without their consent. "This bill would make it a crime for a person to knowingly, and without the consent of the depicted individual, distribute to, exhibit to, or exchange with others, or offer to distribute to, exhibit to, or exchange with others audio or visual media that falsely depicts an individual engaging in sexual conduct that would appear to a reasonable observer to be an authentic record of the conduct. By creating a new crime, this bill would impose a state-mandated local program," a legislative council's digest of the bill states.

Incorporating knowledge graph into recommendation is an effective way to alleviate data sparsity. Most existing knowledge-aware methods usually perform recursive embedding propagation by enumerating graph neighbors. However, the number of nodes' neighbors grows exponentially as the hop number increases, forcing the nodes to be aware of vast neighbors under this recursive propagation for distilling the high-order semantic relatedness. This may induce more harmful noise than useful information into recommendation, leading the learned node representations to be indistinguishable from each other, that is, the well-known over-smoothing issue. To relieve this issue, we propose a Hierarchical and CONtrastive representation learning framework for knowledge-aware recommendation named HiCON. Specifically, for avoiding the exponential expansion of neighbors, we propose a hierarchical message aggregation mechanism to interact separately with low-order neighbors and meta-path-constrained high-order neighbors. Moreover, we also perform cross-order contrastive learning to enforce the representations to be more discriminative. Extensive experiments on three datasets show the remarkable superiority of HiCON over state-of-the-art approaches.

Online Social Networks have revolutionized how we consume and share information, but they have also led to a proliferation of content not always reliable and accurate. One particular type of social accounts is known to promote unreputable content, hyperpartisan, and propagandistic information. They are automated accounts, commonly called bots. Focusing on Twitter accounts, we propose a novel approach to bot detection: we first propose a new algorithm that transforms the sequence of actions that an account performs into an image; then, we leverage the strength of Convolutional Neural Networks to proceed with image classification. We compare our performances with state-of-the-art results for bot detection on genuine accounts / bot accounts datasets well known in the literature. The results confirm the effectiveness of the proposal, because the detection capability is on par with the state of the art, if not better in some cases.

We introduce a method to measure uncertainty in large language models. For tasks like question answering, it is essential to know when we can trust the natural language outputs of foundation models. We show that measuring uncertainty in natural language is challenging because of'semantic equivalence'--different sentences can mean the same thing. To overcome these challenges we introduce semantic entropy--an entropy which incorporates linguistic invariances created by shared meanings. Our method is unsupervised, uses only a single model, and requires no modifications to'off-the-shelf' language models. In comprehensive ablation studies we show that the semantic entropy is more predictive of model accuracy on question answering data sets than comparable baselines. Despite progress in natural language generation (NLG) tasks like question answering or abstractive summarisation (Brown et al., 2020; Hoffmann et al., 2022; Chowdhery et al., 2022), there is little understanding of uncertainty in foundation models. Without measures of uncertainty in transformerbased systems it is hard to use generated language as a reliable source of information. Reliable measures of uncertainty have been identified as a key problem in building safer AI systems (Amodei et al., 2016; Hendrycks et al., 2022). Unfortunately, uncertainty in free-form NLG faces unique challenges. This limits how much we can learn from uncertainty estimation techniques in other applications of deep learning (Gal et al., 2016; Lakshminarayanan et al., 2017; Ovadia et al., 2019) which focuses especially on image classification (Kendall & Gal, 2017) or regression in low-dimensional data spaces (Kuleshov et al., 2018). The key challenges come from the importance in language of meanings and form. This corresponds to what linguists and philosophers call the semantic content of a sentence and its syntactic or lexical form. But for almost all applications we care about meanings! For example, a model which is uncertain about whether to generate "France's capital is Paris" or "Paris is France's capital" is not uncertain in any important sense.

Artificial Intelligence (AI) has huge impact on our daily lives with applications such as voice assistants, facial recognition, chatbots, autonomously driving cars, etc. Natural Language Processing (NLP) is a cross-discipline of AI and Linguistics, dedicated to study the understanding of the text. This is a very challenging area due to unstructured nature of the language, with many ambiguous and corner cases. In this thesis we address a very specific area of NLP that involves the understanding of entities (e.g., names of people, organizations, locations) in text. First, we introduce a radically different, entity-centric view of the information in text. We argue that instead of using individual mentions in text to understand their meaning, we should build applications that would work in terms of entity concepts. Next, we present a more detailed model on how the entity-centric approach can be used for the entity linking task. In our work, we show that this task can be improved by considering performing entity linking at the coreference cluster level rather than each of the mentions individually. In our next work, we further study how information from Knowledge Base entities can be integrated into text. Finally, we analyze the evolution of the entities from the evolving temporal perspective.

Recent years have witnessed the sustained evolution of misinformation that aims at manipulating public opinions. Unlike traditional rumors or fake news editors who mainly rely on generated and/or counterfeited images, text and videos, current misinformation creators now more tend to use out-of-context multimedia contents (e.g. mismatched images and captions) to deceive the public and fake news detection systems. This new type of misinformation increases the difficulty of not only detection but also clarification, because every individual modality is close enough to true information. To address this challenge, in this paper we explore how to achieve interpretable cross-modal de-contextualization detection that simultaneously identifies the mismatched pairs and the cross-modal contradictions, which is helpful for fact-check websites to document clarifications. The proposed model first symbolically disassembles the text-modality information to a set of fact queries based on the Abstract Meaning Representation of the caption and then forwards the query-image pairs into a pre-trained large vision-language model select the ``evidences" that are helpful for us to detect misinformation. Extensive experiments indicate that the proposed methodology can provide us with much more interpretable predictions while maintaining the accuracy same as the state-of-the-art model on this task.