For weeks after his bizarre conversation with Bing's new chatbot went viral, New York Times columnist Kevin Roose wasn't sure what had happened. "The explanations you get for how these language models work, they're not that satisfying," Roose said at one point. "No one can tell me why this chatbot tried to break up my marriage." He's not alone in feeling confused. Powered by a relatively new form of AI called large language models, this new generation of chatbots defies our intuitions about how to interact with computers.

These are but a few artificial intelligence systems that have gone rogue in films. Whether The Creator can produce a fictional AI that would make a mark on popular culture remains to be seen, but it is another movie that pits humanity against artificial intelligence. Its official website says the movie is about Joshua, an ex-special forces agent recruited to hunt down the Creator. That's the architect behind the advanced AI that was developed to protect humanity only to detonate a nuclear warhead in Los Angeles. The Creator apparently also developed a weapon with the power to end all war and all of humanity, and Joshua and his team were tasked to destroy it.

'Media Buzz' host Howard Kurtz join'Sunday Night in America with Trey Gowdy' to discuss a poll claiming Americans are blaming the media for divisiveness in America. Be afraid, be very afraid. That's the message that is starting to dominate the media's many channels when it comes to artificial intelligence. And it's not just prognosticators but such voices as Elon Musk and the grandfather of AI that are saying an apocalyptic future may loom in the distance. I'm not hitting the panic button yet, but the sheer velocity of what AI is either able to achieve or is moving toward achieving seems to increase exponentially each week.

OpenAI CEO Sam Altman, the artificial intelligence lab behind ChatGPT, took questions from reporters after his congressional hearing, including his definition of "scary AI." OpenAI CEO Sam Altman testified before Congress in Washington, D.C., this week about regulating artificial intelligence as well as his personal fears over the tech and what "scary" AI systems means to him. Fox News Digital asked OpenAI's wildly popular chatbot, ChatGPT, to also weigh in on examples of "scary" artificial intelligence systems, and it reported six hypothetical instances of how AI could become weaponized or have potentially harmful impacts on society. When asked by Fox News Digital on Tuesday after his testimony before a Senate Judiciary subcommittee, Altman gave examples of "scary AI" that included systems that could design "novel biological pathogens." "An AI that could hack into computer systems," he continued. "I think these are all scary. These systems can become quite powerful, which is why I was happy to be here today and why I think this is so important."

Tom Hanks and wife Rita Wilson walked the Pre-Grammy GALA red carpet and discussed what they believe is the success to a great relationship after being married for 34 years. Hollywood heavy-hitter Tom Hanks is weighing in on the use of artificial intelligence in films, especially as he grows older and his name and likeness are still coveted. The "A Man Called Otto" actor gave his opinion on AI – noting that its use in the industry is nothing new and has rather "always been" lingering. "The first time we did a movie that had a huge amount of our own data locked in a computer, literally what we looked like, was a movie called'The Polar Express,'" Hanks said on "The Adam Buxton Podcast" about his 2004 animated film. "And we saw this coming. We saw that there was going to be this ability to take 0s and 1s inside a computer and turn it into a face and a character. Now, that is only grown a billion-fold since then, and we see it everywhere. And I can tell you that there is discussions going on in all of the guilds, all of the agencies and all of legal firms in order to come up with the legal ramifications of my face and my voice – and everybody else's – being our intellectual property," he added.

Sen. Marsha Blackburn, R-Tenn., shares her takeaways from Tuesday's AI hearing with OpenAI CEO Sam Altman. She also reveals what next steps she and her colleagues are prepared to take to protect consumer data amid the AI boom. EXCLUSIVE: Nashville musicians are increasingly worried about complications with artificial intelligence's growing sophistication that could threaten their livelihood, Sen. Marsha Blackburn, R-Tenn., warned this week. "We met with the Nashville Technology Council a couple of weeks ago, and we have talked with so many of the musicians. They're concerned that using AI, they will do a copycat of their voice and take the lyrics of their song, which you can get on ChatGPT," Blackburn told Fox News Digital during an interview in her Senate office.

Large Language Models (LLMs) have significantly advanced natural language processing (NLP) with their impressive language understanding and generation capabilities. However, their performance may be suboptimal for domain-specific tasks that require specialized knowledge due to limited exposure to the related data. Additionally, the lack of transparency of most state-of-the-art (SOTA) LLMs, which can only be accessed via APIs, impedes further fine-tuning with domain custom data. Moreover, providing private data to the LLMs' owner leads to data privacy problems. To address these challenges, we propose the novel Parametric Knowledge Guiding (PKG) framework, which equips LLMs with a knowledge-guiding module to access relevant knowledge without altering the LLMs' parameters. Our PKG is based on open-source "white-box" language models, allowing offline memory of any knowledge that LLMs require. We demonstrate that our PKG framework can enhance the performance of "black-box" LLMs on a range of domain knowledge-intensive tasks that require factual (+7.9%), tabular (+11.9%),

Natural language generation models reproduce and often amplify the biases present in their training data. Previous research explored using sequence-to-sequence rewriting models to transform biased model outputs (or original texts) into more gender-fair language by creating pseudo training data through linguistic rules. However, this approach is not practical for languages with more complex morphology than English. We hypothesise that creating training data in the reverse direction, i.e. starting from gender-fair text, is easier for morphologically complex languages and show that it matches the performance of state-of-the-art rewriting models for English. To eliminate the rule-based nature of data creation, we instead propose using machine translation models to create gender-biased text from real gender-fair text via round-trip translation. Our approach allows us to train a rewriting model for German without the need for elaborate handcrafted rules. The outputs of this model increased gender-fairness as shown in a human evaluation study.

People usually have different intents for choosing items, while their preferences under the same intent may also different. In traditional collaborative filtering approaches, both intent and preference factors are usually entangled in the modeling process, which significantly limits the robustness and interpretability of recommendation performances. For example, the low-rating items are always treated as negative feedback while they actually could provide positive information about user intent. To this end, in this paper, we propose a two-fold representation learning approach, namely Double Disentangled Collaborative Filtering (DDCF), for personalized recommendations. The first-level disentanglement is for separating the influence factors of intent and preference, while the second-level disentanglement is performed to build independent sparse preference representations under individual intent with limited computational complexity. Specifically, we employ two variational autoencoder networks, intent recognition network and preference decomposition network, to learn the intent and preference factors, respectively. In this way, the low-rating items will be treated as positive samples for modeling intents while the negative samples for modeling preferences. Finally, extensive experiments on three real-world datasets and four evaluation metrics clearly validate the effectiveness and the interpretability of DDCF.

With the development of large language models (LLMs), zero-shot learning has attracted much attention for various NLP tasks. Different from prior works that generate training data with billion-scale natural language generation (NLG) models, we propose a retrieval-enhanced framework to create training data from a general-domain unlabeled corpus. To realize this, we first conduct contrastive pretraining to learn an unsupervised dense retriever for extracting the most relevant documents using class-descriptive verbalizers. We then further propose two simple strategies, namely Verbalizer Augmentation with Demonstrations and Self-consistency Guided Filtering to improve the topic coverage of the dataset while removing noisy examples. Experiments on nine datasets demonstrate that REGEN achieves 4.3% gain over the strongest baselines and saves around 70% of the time compared to baselines using large NLG models. Besides, REGEN can be naturally integrated with recently proposed large language models to boost performance.