State leaders are sounding the alarm about the emergence of dangerous "Frankenstein" opioids that are more potent than fentanyl and quickly spreading across the United States. Florida Attorney General Ashley Moody is pushing new legislation to add "nitazene compounds," also known as Frankenstein opioids, to the Schedule I controlled substance list in the state, which would categorize the drugs as having a high potential for abuse with no acceptable medical use. "Last year, I signed an emergency rule temporarily adding these deadly nitazene compounds to the Schedule I controlled substance list. I am proud to announce my support for SB 736, which will permanently add these incredibly deadly drugs to the Schedule I list," Moody told Fox News Digital this week. Florida Attorney General Ashley Moody is pushing new legislation to add these "nitazine compounds" to the Schedule I controlled substance list in the state.

By Latha Srinivasan: There are many lessons to be learnt from the ideology of our Sanskrit epics, say scholars. The contribution of the Bhagavad Gita to management principles is well-documented today. Now, there is a train of thought that believes the Mahabharata can teach us how to manage machine autonomy and Artificial Intelligence (AI). While experts believe that AI will improve human effectiveness, capacities, and open a world of vast opportunities, it also presents us with unprecedented threats. So how does the Mahabharata help us in this context?

Neural networks have become shockingly good at generating natural-sounding text, on almost any subject. If I were a student, I'd be thrilled--let a chatbot write that five-page paper on Hamlet's indecision!--but if I were a teacher I'd have mixed feelings. On the one hand, the quality of student essays is about to go through the roof. On the other, what's the point of asking anyone to write anything anymore? Luckily for us, thoughtful people long ago anticipated the rise of artificial intelligence and wrestled with some of the thornier issues.

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.

President Biden marked the start of a second year of war in Europe on Friday by announcing billions of dollars in additional military aid for Ukraine, imposing more sanctions on those helping Russian President Vladimir V. Putin and delivering a grim warning about an alliance between Russia and Iran. Just days after making a secret trip to Ukraine's capital, Mr. Biden joined the leaders of the other Group of 7 nations -- Canada, France, Germany, Italy, Japan and Britain -- in reaffirming his support for the beleaguered country and condemning Russia's invasion a year ago. "A dictator bent on rebuilding an empire will never erase the people's love of liberty," Mr. Biden wrote in a statement on Twitter, alluding to Mr. Putin. "Brutality will never grind down the will of the free. And Ukraine will never be a victory for Russia.

People nowadays use search engines like Google, Yahoo, and Bing to find information on the Internet. Due to explosion in data, it is helpful for users if they are provided relevant summaries of the search results rather than just links to webpages. Text summarization has become a vital approach to help consumers swiftly grasp vast amounts of information.In this paper, different pre-trained models for text summarization are evaluated on different datasets. Specifically, we have used three different pre-trained models, namely, google/pegasus-cnn-dailymail, T5-base, facebook/bart-large-cnn. We have considered three different datasets, namely, CNN-dailymail, SAMSum and BillSum to get the output from the above three models. The pre-trained models are compared over these different datasets, each of 2000 examples, through ROUGH and BLEU metrics.

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.

Schema induction builds a graph representation explaining how events unfold in a scenario. Existing approaches have been based on information retrieval (IR) and information extraction(IE), often with limited human curation. We demonstrate a human-in-the-loop schema induction system powered by GPT-3. We first describe the different modules of our system, including prompting to generate schematic elements, manual edit of those elements, and conversion of those into a schema graph. By qualitatively comparing our system to previous ones, we show that our system not only transfers to new domains more easily than previous approaches, but also reduces efforts of human curation thanks to our interactive interface.

Many dynamical systems in the real world are naturally described by latent states with intrinsic orderings, such as "ally", "neutral", and "enemy" relationships in international relations. These latent states manifest through countries' cooperative versus conflictual interactions over time. State-space models (SSMs) explicitly relate the dynamics of observed measurements to transitions in latent states. For discrete data, SSMs commonly do so through a state-to-action emission matrix and a state-to-state transition matrix. This paper introduces the Ordered Matrix Dirichlet (OMD) as a prior distribution over ordered stochastic matrices wherein the discrete distribution in the kth row stochastically dominates the (k+1)th, such that probability mass is shifted to the right when moving down rows. We illustrate the OMD prior within two SSMs: a hidden Markov model, and a novel dynamic Poisson Tucker decomposition model tailored to international relations data. We find that models built on the OMD recover interpretable ordered latent structure without forfeiting predictive performance. We suggest future applications to other domains where models with stochastic matrices are popular (e.g., topic modeling), and publish user-friendly code.

This paper introduces HADES, a novel tool for automatic comparative documents with similar structures. HADES is designed to streamline the work of professionals dealing with large volumes of documents, such as policy documents, legal acts, and scientific papers. The tool employs a multi-step pipeline that begins with processing PDF documents using topic modeling, summarization, and analysis of the most important words for each topic. The process concludes with an interactive web app with visualizations that facilitate the comparison of the documents. HADES has the potential to significantly improve the productivity of professionals dealing with high volumes of documents, reducing the time and effort required to complete tasks related to comparative document analysis. Our package is publically available on GitHub.