Before the start of India's general election in April, a top candidate looking to unseat Prime Minister Narendra Modi was not out wooing voters on the campaign trail. Arvind Kejriwal, the chief minister of Delhi and the head of a political party known for its anti-corruption platform, was arrested in late March for, yes, alleged corruption. His supporters hit the streets in protest, decrying the arrest as a politically motivated move by Modi aimed at weakening a rival. Soon after the arrest, Kejriwal implored his supporters to stay strong. "There are some forces who are trying to weaken our country and its democracy," he said in a 34-second audio clip posted to social media by a fellow party member.

Artificial Intelligence (AI) systems, trained in controlled environments, often struggle in real-world complexities. We propose a general framework for estimating domain complexity across diverse environments, like open-world learning and real-world applications. This framework distinguishes between intrinsic complexity (inherent to the domain) and extrinsic complexity (dependent on the AI agent). By analyzing dimensionality, sparsity, and diversity within these categories, we offer a comprehensive view of domain challenges. This approach enables quantitative predictions of AI difficulty during environment transitions, avoids bias in novel situations, and helps navigate the vast search spaces of open-world domains.

Topic modelling with innovative deep learning methods has gained interest for a wide range of applications that includes COVID-19. Topic modelling can provide, psychological, social and cultural insights for understanding human behaviour in extreme events such as the COVID-19 pandemic. In this paper, we use prominent deep learning-based language models for COVID-19 topic modelling taking into account data from emergence (Alpha) to the Omicron variant. We apply topic modeling to review the public behaviour across the first, second and third waves based on Twitter dataset from India. Our results show that the topics extracted for the subsequent waves had certain overlapping themes such as covers governance, vaccination, and pandemic management while novel issues aroused in political, social and economic situation during COVID-19 pandemic. We also found a strong correlation of the major topics qualitatively to news media prevalent at the respective time period. Hence, our framework has the potential to capture major issues arising during different phases of the COVID-19 pandemic which can be extended to other countries and regions.

Characters in books are about four times more likely to be male than female, a new study of gender bias in literature has revealed. Researchers at the USC Viterbi School of Engineering used artificial intelligence to examine more than 3,000 English-language books ranging from science fiction and adventure, to mystery and romance - across short stories, poetry and novels. The team found male characters appeared four times as often as females across the books, although that reduced when the author of the work was female. There were also more negative terms used in connection with the female characters such as'weak' and'stupid' compared to'strong' and'power' used for men. 'Gender bias is real, and when we see females four times less in literature, it has a subliminal impact on people consuming the culture,' said author Mayank Kejriwal.

Common sense isn't common, especially when it comes to artificial intelligence. Computers struggle to make fine distinctions that people take for granted. This is why websites require you authenticate your humanity before logging in or making a purchase: Most bots can't tell the difference between a crosswalk and a zebra. At the USC AI Futures Symposium on AI with Common Sense earlier this month, more than 20 USC researchers reported on the technical reasons why that's the case, and different avenues of research to address this. Advances in common sense AI will improve human-facing services, from enhanced social services to better serve society to personal assistants that better predict our context and needs.