The Douglass Society founder Rob Smith tells'The Next Revolution' the administration has been a'failure on every single level.' The Violence Against Women Act, which President Biden reauthorized last week, should lead to significant strides in combating online abuse including cyberstalking and the nonconsensual distribution of sexually explicit images, but leaders should explore additional opportunities to reduce the odds that online relationship abuse occurs in the first place through preventative education, rather than just providing redress for victims, according to One Love. "While it's a crime to pull your pants down in the streets -- a Class B misdemeanor, punishable by a fine or jail time -- there was nothing stopping anyone from exposing themselves in your DMs, texts, or even via AirDrop, a disturbing trend," Bumble said. In 2018, a representative study of its users found one in three women reported receiving unsolicited lewd photos, otherwise known as cyberflashing, with 96% not happy to receive them, per their website. President Joe Biden responds to a question about Russian President Vladimir Putin in the East Room of the White House, Wednesday, March 16, 2022, in Washington.

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Titouan Bernicot was 16 when he had an epiphany: Everything he loved about living on an island in the South Pacific was tied to coral reefs. Bernicot, 23, started Coral Gardeners in April 2017 from his bedroom. Now, he and his team are setting their sights on an audacious goal: to replant 1 million corals across the world by 2025. If they have any hope of hitting that number, they'll have to dramatically increase their planting, an effort that is being aided by a new artificial intelligence system. "Our goals for the next couple of years are to become the largest and most advanced coral restoration program on the planet," he said in an interview outside his office on the island.

The COVID-19 pandemic has supercharged the scope of the issues the global healthcare industry was already grappling with. When the pandemic arrived, healthcare organisations often struggled to find the basic information they needed to respond -- whether it was disease and death rates or the availability of hospital beds and critical supplies. Among other problems, the pandemic highlighted the desperate need for collaborative data analytics in healthcare. As McKinsey observed, healthcare's digital barriers are often decidedly non-technological. The technology is out there (or rapidly evolving) -- in October 2020, Pfizer and IBM researchers announced that they have developed a machine learning technique that can predict Alzheimer's disease years before symptoms develop.

The Ukraine defense ministry announced that it is now using facial recognition technology from an American startup to combat misinformation, identify the dead, and expose Russian assailants. The technology, which is like a search engine for faces that aggregates data from millions of social media users across the open web, had previously stirred controversy due to privacy complaints. After the war broke out, the American-based artificial intelligence company Clearview reached out to Ukraine's government, offering its services free of charge. This week, the collaboration became official and Clearview's facial recognition tech is now claimed to be used for security purposes, such as vetting people of interest at checkpoints. Clearview claims that it has amassed a database of over 10 billion photos posted publically on the internet from sites like Facebook, Instagram, Flickr, and Getty Images.

Artificial Intelligence (AI) is a branch of computer science that helps build smart machines. AI provides data that makes these machines capable enough to match human intelligence. As a result, many industries have taken advantage of AI technologies. Machine Learning and Deep Learning are the two subsets of Artificial Intelligence. Whereas machine learning refers to computers able to think and act with less human intervention, deep learning involves computers able to use structures modeled on the human brain.

This week, we launched our new series of interviews with brilliant women working in the critical space of AI Ethics and discuss their groundbreaking work. Our first 6 episodes are sponsored by IBM…

Over the last decade, neural networks have become a trendy topic, ranging from image recognition to text generation and even video gameplay applications. On the other hand, these artificial neural networks are just mounds of math inside a computer. While they are capable of tremendous things, the technology has yet to demonstrate the ability to develop actual intelligence. Researchers at Cortical Labs, Australia propose that integrating neurons into digital systems to tap on their inherent intelligence could enable performance that would be impossible to achieve with silicon alone and provide insight into the biological origins of intelligence. The overall goal of the research is to use biological neurons' processing capability to build "synthetic biological intelligence."

We consider Bayesian optimization of expensive-to-evaluate experiments that generate vector-valued outcomes over which a decision-maker (DM) has preferences. These preferences are encoded by a utility function that is not known in closed form but can be estimated by asking the DM to express preferences over pairs of outcome vectors. To address this problem, we develop Bayesian optimization with preference exploration, a novel framework that alternates between interactive real-time preference learning with the DM via pairwise comparisons between outcomes, and Bayesian optimization with a learned compositional model of DM utility and outcomes. Within this framework, we propose preference exploration strategies specifically designed for this task, and demonstrate their performance via extensive simulation studies.

Recently, prefix-tuning has gained increasing attention as a parameter-efficient finetuning method for large-scale pretrained language models. The method keeps the pretrained models fixed and only updates the prefix token parameters for each downstream task. Despite being lightweight and modular, prefix-tuning still lacks robustness to textual adversarial attacks. However, most currently developed defense techniques necessitate auxiliary model update and storage, which inevitably hamper the modularity and low storage of prefix-tuning. In this work, we propose a robust prefix-tuning framework that preserves the efficiency and modularity of prefix-tuning. The core idea of our framework is leveraging the layerwise activations of the language model by correctly-classified training data as the standard for additional prefix finetuning. During the test phase, an extra batch-level prefix is tuned for each batch and added to the original prefix for robustness enhancement. Extensive experiments on three text classification benchmarks show that our framework substantially improves robustness over several strong baselines against five textual attacks of different types while maintaining comparable accuracy on clean texts. We also interpret our robust prefix-tuning framework from the optimal control perspective and pose several directions for future research.