Models for categorical sequences typically assume exchangeable or first-order dependent sequence elements. These are common assumptions, for example, in models of computer malware traces and protein sequences. Although such simplifying assumptions lead to computational tractability, these models fail to capture long-range, complex dependence structures that may be harnessed for greater predictive power. To this end, a Bayesian modelling framework is proposed to parsimoniously capture rich dependence structures in categorical sequences, with memory efficiency suitable for real-time processing of data streams. Parsimonious Bayesian context trees are introduced as a form of variable-order Markov model with conjugate prior distributions. The novel framework requires fewer parameters than fixed-order Markov models by dropping redundant dependencies and clustering sequential contexts. Approximate inference on the context tree structure is performed via a computationally efficient model-based agglomerative clustering procedure. The proposed framework is tested on synthetic and real-world data examples, and it outperforms existing sequence models when fitted to real protein sequences and honeypot computer terminal sessions.

OpenAI's messy firing and re-hiring of its powerful chief executive this week shocked the tech world. But the power struggle has implications beyond the company's boardroom, AI experts said. It throws into relief the greenness of the AI industry and the strong desire in Silicon Valley to be first, and raises urgent questions about the safety of the technology. "The AI that we're looking at now is immature. There are no standards, no professional body, no certifications. Everybody figures out how to do it, figures out their own internal norms," said Rayid Ghani, a professor of machine learning and public policy at Carnegie Mellon University.

SALMA, the first Arabic sense-annotated corpus, consists of ~34K tokens, which are all sense-annotated. The corpus is annotated using two different sense inventories simultaneously (Modern and Ghani). SALMA novelty lies in how tokens and senses are associated. Instead of linking a token to only one intended sense, SALMA links a token to multiple senses and provides a score to each sense. A smart web-based annotation tool was developed to support scoring multiple senses against a given word. In addition to sense annotations, we also annotated the corpus using six types of named entities. The quality of our annotations was assessed using various metrics (Kappa, Linear Weighted Kappa, Quadratic Weighted Kappa, Mean Average Error, and Root Mean Square Error), which show very high inter-annotator agreement. To establish a Word Sense Disambiguation baseline using our SALMA corpus, we developed an end-to-end Word Sense Disambiguation system using Target Sense Verification. We used this system to evaluate three Target Sense Verification models available in the literature. Our best model achieved an accuracy with 84.2% using Modern and 78.7% using Ghani. The full corpus and the annotation tool are open-source and publicly available at https://sina.birzeit.edu/salma/.

Fox News host Steve Hilton delves into ChatGPT, an artificial intelligence program that could have major implications for writing-focused jobs on'The Next Revolution.' Quickly evolving artificial intelligence technologies like ChatGPT could increase cyberattacks from foreign countries and displace workers in the U.S. labor force, highlighting the need for new skills and training among American students and workers, according to experts. Netra AI CEO Don Horan noted that artificial intelligence could be used to generate malicious code quickly by removing the algorithms' intended controls and creating content outside the authorized purview. He said that foreign acts can utilize tools like ChatGPT to improve espionage and accelerate elicitation, a process wherein a perpetrator gets to know a subject very well by gathering information and creating "the profile of a human being." This information is then used to force people to comply with their intended mission.

When you challenge a computer to play a chess game, interact with a smart assistant, type a question into ChatGPT, or create artwork on DALL-E, you're interacting with a program that computer scientists would classify as artificial intelligence. But defining artificial intelligence can get complicated, especially when other terms like "robotics" and "machine learning" get thrown into the mix. To help you understand how these different fields and terms are related to one another, we've put together a quick guide. Artificial intelligence is a field of study, much like chemistry or physics, that kicked off in 1956. "Artificial intelligence is about the science and engineering of making machines with human-like characteristics in how they see the world, how they move, how they play games, even how they learn," says Daniela Rus, director of the computer science and artificial intelligence laboratory (CSAIL) at MIT. "Artificial intelligence is made up of many subcomponents, and there are all kinds of algorithms that solve various problems in artificial intelligence."

Researchers are using artificial intelligence (AI) to detect behavioural signs of anxiety with more than 90 per cent accuracy, and suggest that AI could have future applications for addressing mental health and wellbeing. Their research is published in the journal Pervasive and Mobile Computing. "In the two years since the onset of COVID-19, and one climate disaster after another, more and more people are experiencing anxiety," says Simon Fraser University visiting professor and social psychologist Gulnaz Anjum. "Our research appears to show that AI could provide a highly reliable measurement for recognizing the signs that someone is anxious." Anjum and collaborators Nida Saddaf Khan and Sayeed Ghani from the Institute of Business Administration in Karachi, Pakistan collected an extensive range of data from adult participants for their Human Activity Recognition (HAR) study.

A recent study in Nature Machine Intelligence by researchers at Carnegie Mellon sought to investigate the impact that mitigating bias in machine learning has on accuracy. Despite what researchers referred to as a "commonly held assumption" that reducing disparities requires either accepting a drop in accuracy or developing new, complex methods, they found that the trade-offs between fairness and effectiveness can be "negligible in practice." "You actually can get both. You don't have to sacrifice accuracy to build systems that are fair and equitable," said Rayid Ghani, a CMU computer science professor and an author on the study, in a statement. At the same time, Ghani noted, "It does require you to deliberately design systems to be fair and equitable.

To prevent the United States from falling behind competitor nations like China, when it comes to the development of artificial intelligence-based technologies, the Trump administration has proposed vague regulatory guidelines that would limit potentially innovation-stifling governmental "overreach." The news comes amid the Consumer Electronics Show (CES) in Las Vegas, the largest annual trade show for the technology industry. That makes sense, given that each year, CES includes a slew of vendors that demonstrate AI-based tech. In a blog posted to the White House website and shared as a Bloomberg op-ed, Michael Kratsios, chief technology officer of the U.S., wrote that it's a "false choice" to have to choose between moral values and advancing emerging AI technology. "As part of the Trump Administration's national AI strategy--the American AI Initiative--the White House is today proposing a first-of-its-kind set of regulatory principles to govern AI development in the private sector," he wrote.

ISLAMABAD – Pakistani caretaker Prime Minister Nasir-ul-Mulk has described the killing of Pakistani Taliban chief Mullah Fazlullah in a U.S. drone strike in Afghanistan as a "significant development in the fight against terrorism." Mulk made the comment in a telephone conversation with Afghan President Ashraf Ghani and thanked him for sharing information about Fazlullah's killing. The call was initiated by Ghani. A government statement says an "action had finally been taken against an enemy of the people and state of Pakistan." Mulk told Ghani the news about Fazlullah's death would be received throughout Pakistan with relief as Pakistanis had borne the brunt of terrorist attacks by the Tehrik-e-Taliban Pakistan, which Fazlullah headed.

FOR frazzled teachers struggling to decide what to watch on an evening off, help is at hand. An online streaming service's software predicts what they might enjoy, based on the past choices of similar people. When those same teachers try to work out which children are most at risk of dropping out of school, they get no such aid. But, as Sendhil Mullainathan of Harvard University notes, these types of problem are alike. They require predictions based, implicitly or explicitly, on lots of data.