SAN FRANCISCO – Until recently, Brett Schickler never imagined he could be a published author, though he had dreamed about it. But after learning about the ChatGPT artificial intelligence program, Schickler figured an opportunity had landed in his lap. "The idea of writing a book finally seemed possible," said Schickler, a salesman in Rochester, New York. "I thought'I can do this.'" Using the AI software, which can generate blocks of text from simple prompts, Schickler created a 30-page illustrated children's e-book in a matter of hours, offering it for sale in January through Amazon.com

The roaring success of ChatGPT has triggered a race among Big Tech companies to either come up with their own competing artificial intelligence products or buy a stake in promising startups that could become the next OpenAI, the creator of ChatGPT. In the past month, Microsoft, Google and China's Baidu have each presented their responses to ChatGPT. In the meantime, some of these companies have also invested or formed partnerships with lesser-known startups specializing in generative A.I., the technology behind text and image generators like ChatGPT and Dall-E, OpenAI's other viral product that can generate digital images based on text prompts. There is a natural attraction between A.I. startups and tech behemoths. Many startups rely on the cloud infrastructure of large tech companies to train their algorithms, while tech giants often see them as potential investment or acquisition targets to expand their business without having to do the early-stage research themselves.

When Microsoft released Bing Chat, an AI-powered chatbot co-developed with OpenAI, it didn't take long before users found creative ways to break it. Using carefully tailored inputs, users were able to get it to profess love, threaten harm, defend the Holocaust and invent conspiracy theories. Can AI ever be protected from these malicious prompts? What set it off is malicious prompt engineering, or when an AI, like Bing Chat, that uses text-based instructions -- prompts -- to accomplish tasks is tricked by malicious, adversarial prompts (e.g. to perform tasks that weren't a part of its objective. Bing Chat wasn't designed with the intention of writing neo-Nazi propaganda.

Pre-trained models (PM) have achieved promising results in content generation. However, the space for human creativity and imagination is endless, and it is still unclear whether the existing models can meet the needs. Model-generated content faces uncontrollable responsibility and potential unethical problems. This paper presents the MetaAID 2.0 framework, dedicated to human-controllable PM information flow. Through the PM information flow, humans can autonomously control their creativity. Through the Universal Resource Identifier extension (URI-extension), the responsibility of the model outputs can be controlled. Our framework includes modules for handling multimodal data and supporting transformation and generation. The URI-extension consists of URI, detailed description, and URI embeddings, and supports fuzzy retrieval of model outputs. Based on this framework, we conduct experiments on PM information flow and URI embeddings, and the results demonstrate the good performance of our system.

Natural language processing based on large language models (LLMs) is a booming field of AI research. After neural networks have proven to outperform humans in games and practical domains based on pattern recognition, we might stand now at a road junction where artificial entities might eventually enter the realm of human communication. However, this comes with serious risks. Due to the inherent limitations regarding the reliability of neural networks, overreliance on LLMs can have disruptive consequences. Since it will be increasingly difficult to distinguish between human-written and machine-generated text, one is confronted with new ethical challenges. This begins with the no longer undoubtedly verifiable human authorship and continues with various types of fraud, such as a new form of plagiarism. This also concerns the violation of privacy rights, the possibility of circulating counterfeits of humans, and, last but not least, it makes a massive spread of misinformation possible.

Maintaining engagement and consistency is particularly important in dialogue systems. Existing works have improved the performance of dialogue systems by intentionally learning interlocutor personas with sophisticated network structures. One issue with this approach is that it requires more personal corpora with annotations. Additionally, these models typically perform the next utterance prediction to generate a response but neglect the discourse coherence in the entire conversation. To address these issues, this study proposes a method of learning to memorize entailment and discourse relations for persona-consistent dialogue tasks. Entailment text pairs in natural language inference dataset were applied to learn latent entailment relations as external memories by premise-to-hypothesis generation task. Furthermore, an internal memory with a similar architecture was applied to the discourse information in the dialogue. Placing orthogonality restrictions on these two memory spaces ensures that the latent entailment relations remain dialogue-independent. Both memories collaborate to obtain entailment and discourse representation for the generation, allowing a deeper understanding of both consistency and coherence. Experiments on two large public datasets, PersonaChat and DSTC7-AVSD, demonstrated the effectiveness of the proposed method. Both automatic and human evaluations indicate that the proposed model outperforms several strong baselines in terms of both persona consistency and response coherence. Our source code is available at https://github.com/Chenrj233/LMEDR.

In today's era huge volume of information exists everywhere. Therefore, it is very crucial to evaluate that information and extract useful, and often summarized, information out of it so that it may be used for relevant purposes. This extraction can be achieved through a crucial technique of artificial intelligence, namely, machine learning. Indeed automatic text summarization has emerged as an important application of machine learning in text processing. In this paper, an english text summarizer has been built with GRU-based encoder and decoder. Bahdanau attention mechanism has been added to overcome the problem of handling long sequences in the input text. A news-summary dataset has been used to train the model. The output is observed to outperform competitive models in the literature. The generated summary can be used as a newspaper headline.

The recent (re)popularization of deep learning methods increased the importance and need for the data even further. Similarly, the other subfields of theoretical and applied linguistics have also seen a shift towards more data-driven methods. As a result, availability of large and high-quality language data is essential for both linguistic research and practical NLP applications. In this paper, we present a comprehensive and critical survey of linguistic resources for Turkish.

Dear Hiring Manager, I am writing to express my utmost enthusiasm for the opportunity to join your esteemed organization. As a comedic actor of the highest caliber, I am confident that my unique blend of comedic timing, physicality, and, dare I say, panache, would make me an invaluable asset to your team. Throughout my illustrious career, I have had the pleasure of honing my craft on some of the biggest stages in the world. From my time on the "Saturday Night Live" stage, to my tenure as a leading man in some of the most beloved comedy films of our time, I have consistently demonstrated an unwavering commitment to delivering outstanding performances that leave audiences in stitches. But it is not just my performances on screen and stage that have earned me a reputation as one of the premier comedians of our time. It is my unyielding passion for excellence, my tireless work ethic, and my ability to think outside the box that truly set me apart.

The legendary guitarist Eddie Van Halen is regarded by many as one of the best of all time. Numerous musicians have been inspired by his virtuosic playing style and cutting-edge approaches, which have had a long-lasting effect on the music business. Do you know about his remarkable corpus of work? Eddie has a genuinely outstanding resume, which includes his early years with the band Van Halen as well as his solo endeavors and group efforts. Think about his time with Van Halen.