Bill Gates pulls out of India's AI summit amid Epstein files controversy Bill Gates will not deliver his keynote address at the India AI Impact Summit in Delhi, his philanthropic organisation said hours before the Microsoft co-founder was due to speak. The Gates Foundation said the decision was made after careful consideration and to ensure the focus remains on the [summit's] key priorities, but did not elaborate. Gates's withdrawal comes amid a controversy over his ties to the late sex offender Jeffrey Epstein after he was named in new files released by the US Department of Justice in January. Gates's spokesperson has called the claims in the files absolutely absurd and completely false, and the billionaire has said he regretted spending time with Epstein . Gates has not been accused of wrongdoing by any of Epstein's victims and the appearance of his name in the files does not imply criminal activity of any kind.

The word embedding space in neural models is skewed, and correcting this can improve task performance.

Campaigners fear Narendra Modi could use AI to increase state surveillance and sway elections. Campaigners fear Narendra Modi could use AI to increase state surveillance and sway elections. Silicon Valley tech billionaires will land in Delhi this week for an AI summit hosted by India's prime minister, Narendra Modi, where leaders of the global south will wrestle for control over the fast-developing technology. During the week-long AI Impact Summit, attended by thousands of tech executives, government officials and AI safety experts, tech companies valued at trillions of dollars will rub along with leaders of countries such as Kenya and Indonesia, where average wages dip well below $1,000 a month. Amid a push to speed up AI adoption across the globe, Sundar Pichai, Sam Altman and Dario Amodei, the heads of Google, OpenAI and Anthropic, will all be there.

Until now, no tabular method has consistently outperformed classical supervised learning, which ignores these shifts.

India plans AI'data city' on staggering scale Information technology minister for India's Andhra Pradesh state, Nara Lokesh, speaks during an interview in New Delhi in January. New Delhi - As India races to narrow the artificial intelligence gap with the United States and China, it is planning a vast new data city to power digital growth on a staggering scale, the man spearheading the project says. The AI revolution is here, no second thoughts about it, said Nara Lokesh, information technology minister for Andhra Pradesh state, which is positioning the city of Visakhapatnam as a cornerstone of India's AI push. And as a nation ... we have taken a stand that we've got to embrace it, he said ahead of an international AI summit this week in New Delhi. In a time of both misinformation and too much information, quality journalism is more crucial than ever. By subscribing, you can help us get the story right.

Can't wait to see the second movie! The first two lines are two demonstrations, and the third line is a test input.

Species range maps (SRMs) are essential tools for research and policy-making in ecology, conservation, and environmental management.

Despite Telugu being spoken by over 80 million people, speech translation research for this morphologically rich language remains severely underexplored. We address this gap by developing a high-quality Telugu--English speech translation benchmark from 46 hours of manually verified CSTD corpus data (30h/8h/8h train/dev/test split). Our systematic comparison of cascaded versus end-to-end architectures shows that while IndicWhisper + IndicMT achieves the highest performance due to extensive Telugu-specific training data, finetuned SeamlessM4T models demonstrate remarkable competitiveness despite using significantly less Telugu-specific training data. This finding suggests that with careful hyperparameter tuning and sufficient parallel data (potentially less than 100 hours), end-to-end systems can achieve performance comparable to cascaded approaches in low-resource settings. Our metric reliability study evaluating BLEU, METEOR, ChrF++, ROUGE-L, TER, and BERTScore against human judgments reveals that traditional metrics provide better quality discrimination than BERTScore for Telugu--English translation. The work delivers three key contributions: a reproducible Telugu--English benchmark, empirical evidence of competitive end-to-end performance potential in low-resource scenarios, and practical guidance for automatic evaluation in morphologically complex language pairs.

Large language models and multilingual machine translation (MT) systems increasingly drive access to information, yet many languages of the tribal communities remain effectively invisible in these technologies. This invisibility exacerbates existing structural inequities in education, governance, and digital participation. We present AdiBhashaa, a community-driven initiative that constructs the first open parallel corpora and baseline MT systems for four major Indian tribal languages-Bhili, Mundari, Gondi, and Santali. This work combines participatory data creation with native speakers, human-in-the-loop validation, and systematic evaluation of both encoder-decoder MT models and large language models. In addition to reporting technical findings, we articulate how AdiBhashaa illustrates a possible model for more equitable AI research: it centers local expertise, builds capacity among early-career researchers from marginalized communities, and foregrounds human validation in the development of language technologies.