Understanding genetic perturbations, when scientists intentionally alter genes to see how this affects cells, is key to understanding what our genes do and how they are controlled. This knowledge has important applications in cell engineering and in developing new treatments. Today, scientists can test many different genetic perturbations in the lab. But there are so many possible combinations that it is impossible to test them all. AI and machine learning have created the opportunity to use information from large biological datasets to predict what will happen when a gene is changed -- even if that change has never been tested in the laboratory.

A huge event, with hundreds of participants, takeout pizza boxes stacked shoulder-high on carts, a jazz-rock band, a d.j., teams from about thirty high schools, robots by the dozen, and robot parts by the (probably) thousands spread out on tables in the cafeteria: it was the first day of the qualifiers for the all-city semifinals in the NYC FIRST Robotics Competition, at Francis Lewis High School, in Queens. On weekdays, about forty-four hundred students attend the school. In the rest of the building on this Saturday the hallways were empty. Michael Zigman, the C.E.O. of NYC FIRST, a nonprofit that provides STEM-education resources for students in public schools, stood in the gym, calculating in his head how many people were there. Zigman is a tall, kindly fifty-five-year-old Queens-born man who made money advising tech investors in the early two-thousands and then, in 2016, joined NYC FIRST.

This paper focuses on the piloting of the CGScholar AI Helper, a Generative AI (GenAI) assistant tool that aims to provide feedback on writing in high school contexts. The aim was to use GenAI to provide formative and summative feedback on students' texts in English Language Arts (ELA) and History. The trials discussed in this paper relate to Grade 11, a crucial learning phase when students are working towards college readiness. These trials took place in two very different schools in the Midwest of the United States, one in a low socio-economic background with low-performance outcomes and the other in a high socio-economic background with high-performance outcomes. The assistant tool used two main mechanisms "prompt engineering" based on participant teachers' assessment rubric and "fine-tuning" a Large Language Model (LLM) from a customized corpus of teaching materials using Retrieval Augmented Generation (RAG). This paper focuses on the CGScholar AI Helper's potential to enhance students' writing abilities and support teachers in ELA and other subject areas requiring written assignments.

The vast collection of Holocaust survivor testimonies presents invaluable historical insights but poses challenges for manual analysis. This paper leverages advanced Natural Language Processing (NLP) techniques to explore the USC Shoah Foundation Holocaust testimony corpus. By treating testimonies as structured question-and-answer sections, we apply topic modeling to identify key themes. We experiment with BERTopic, which leverages recent advances in language modeling technology. We align testimony sections into fixed parts, revealing the evolution of topics across the corpus of testimonies. This highlights both a common narrative schema and divergences between subgroups based on age and gender. We introduce a novel method to identify testimonies within groups that exhibit atypical topic distributions resembling those of other groups. This study offers unique insights into the complex narratives of Holocaust survivors, demonstrating the power of NLP to illuminate historical discourse and identify potential deviations in survivor experiences.

For a prolific art collector, Nicolai Tangen is remarkably relaxed about the prospect of masterpieces created by robots. The threat of AI-made paintings, impossible to distinguish from human brushstrokes, has sparked soul-searching and paranoia in the art world, but not with Tangen. "Hey, if it creates better art that's fantastic," says the Norwegian philanthropist, art historian and boss of the world's biggest sovereign wealth fund. "If you create something which is even more aesthetically pleasing, what's wrong about that?" Tangen's own gallery, a converted grain silo in the Norwegian seaside resort of Kristiansand, will open later this year to display one of the world's biggest collections of Nordic modernist art. Tangen has amassed more than 5,000 works by 300 artists.

A spokeswoman for the Department of Education told the Times the education secretary "has been clear about the government's appetite to pursue the opportunities - and manage the risks - that exists in this space, and we have already published information to help schools do this.

Receptor-binding domain-binder designs displayed on yeast. From De novo design of protein interactions with learned surface fingerprints. Reproduced under a CC BY 4.0 licence. In 2019, scientists in the joint School of Engineering and School of Life Sciences Laboratory of Protein Design and Immunoengineering (LPDI) led by Bruno Correia developed MaSIF: a machine learning-driven method for scanning millions of protein surfaces within minutes to analyze their structure and functional properties. The researchers' ultimate goal was to computationally design protein interactions by finding optimal matches between molecules based on their surface chemical and geometric "fingerprints".

How old is your brain, really? Just like people who look older than they really are, scientists say a person's brain can age faster than the rest of their body. With that in mind, researchers at USC have created an artificial intelligence program which can accurately tell how old someone's brain is -- while also pointing out warning signs for Alzheimer's disease. The AI program analyzes MRI brain scans, looking for signs of cognitive decline which have a link to neurodegenerative diseases, like Alzheimer's. Brain aging is one of the most reliable markers for neurodegenerative disease risk.

A new peer-reviewed study published in PLOS Digital Health demonstrates how OpenAI's GPT-3 program predicts early stages of dementia from spontaneous speech with a high degree of accuracy. "To our knowledge, this is the first application of GPT-3 to predicting dementia from speech," wrote professor Hualou Liang, Ph.D., and co-author Felix Agbavor at Drexel's School of Biomedical Engineering, Science and Health Systems. The most common type of dementia is Alzheimer's disease, a neurodegenerative disease that affects an estimated 47 million people worldwide, according to the Alzheimer's Association. By 2030 this figure is expected to grow to 76 million globally, according to the same source. There are 5.8 million Americans with Alzheimer's disease, of which two-thirds are women, according to a report by AARP and the Women's Alzheimer's Movement (WAM).