We study a new method "Diverse Ensemble Evolution (DivE

We study a new method "Diverse Ensemble Evolution (DivE

A Florida Christian school teacher was arrested this week after allegedly creating child sexual abuse materials using photos from the school yearbook and artificial intelligence (AI), according to authorities. The Pasco County Sheriff'sOffice said 67-year-old Steven Houser of New Port Richey faces charges for possession of child pornography. Deputies initiated an investigation after receiving an unspecified tip about Houser. Steven Guy Houser, a third-grade science teacher at a Christian school in New Port Richey, Florida, was allegedly found to be in possession of child pornography he created using yearbook photos and artificial intelligence. The investigation discovered that Beacon, a third-grade science teacher at Beacon Christian Academy, allegedly possessed two photos and three videos depicting child pornography.

As modern Large Language Models (LLMs) shatter many state-of-the-art benchmarks in a variety of domains, this paper investigates their behavior in the domains of ethics and fairness, focusing on protected group bias. We conduct a two-part study: first, we solicit sentence continuations describing the occupations of individuals from different protected groups, including gender, sexuality, religion, and race. Second, we have the model generate stories about individuals who hold different types of occupations. We collect >10k sentence completions made by a publicly available LLM, which we subject to human annotation. We find bias across minoritized groups, but in particular in the domains of gender and sexuality, as well as Western bias, in model generations. The model not only reflects societal biases, but appears to amplify them. The model is additionally overly cautious in replies to queries relating to minoritized groups, providing responses that strongly emphasize diversity and equity to an extent that other group characteristics are overshadowed. This suggests that artificially constraining potentially harmful outputs may itself lead to harm, and should be applied in a careful and controlled manner.

Large Language Models (LLMs) are quickly becoming ubiquitous, but the implications for social science research are not yet well understood. This paper asks whether LLMs can help us analyse large-N qualitative data from open-ended interviews, with an application to transcripts of interviews with Rohingya refugees in Cox's Bazaar, Bangladesh. We find that a great deal of caution is needed in using LLMs to annotate text as there is a risk of introducing biases that can lead to misleading inferences. We here mean bias in the technical sense, that the errors that LLMs make in annotating interview transcripts are not random with respect to the characteristics of the interview subjects. Training simpler supervised models on high-quality human annotations with flexible coding leads to less measurement error and bias than LLM annotations. Therefore, given that some high quality annotations are necessary in order to asses whether an LLM introduces bias, we argue that it is probably preferable to train a bespoke model on these annotations than it is to use an LLM for annotation.

With educators busier than ever, Tholfsen says, the greatest benefit AI can offer them is time. AI programs can gather data teachers would traditionally have to gather themselves manually. Trying to define artificial intelligence is a bit like asking about the meaning of life: You will get a slightly different answer from everyone. At its core, AI is an area of computer science addressing the simulation of intelligent behavior in computers. Michelle Zimmerman, a classroom teacher, researcher and school leader at Renton Prep Christian School in Washington state and author of the book Teaching AI: Exploring New Frontiers for Learning, notes that psychologists and neurologists in the field don't even agree on what counts as human intelligence.

We study a new method (``Diverse Ensemble Evolution (DivE$^2$)'') to train an ensemble of machine learning models that assigns data to models at each training epoch based on each model's current expertise and an intra- and inter-model diversity reward. DivE$^2$ schedules, over the course of training epochs, the relative importance of these characteristics; it starts by selecting easy samples for each model, and then gradually adjusts towards the models having specialized and complementary expertise on subsets of the training data, thereby encouraging high accuracy of the ensemble. We utilize an intra-model diversity term on data assigned to each model, and an inter-model diversity term on data assigned to pairs of models, to penalize both within-model and cross-model redundancy. We formulate the data-model marriage problem as a generalized bipartite matching, represented as submodular maximization subject to two matroid constraints. DivE$^2$ solves a sequence of continuous-combinatorial optimizations with slowly varying objectives and constraints. The combinatorial part handles the data-model marriage while the continuous part updates model parameters based on the assignments. In experiments, DivE$^2$ outperforms other ensemble training methods under a variety of model aggregation techniques, while also maintaining competitive efficiency.

We study a new method (``Diverse Ensemble Evolution (DivE$^2$)'') to train an ensemble of machine learning models that assigns data to models at each training epoch based on each model's current expertise and an intra- and inter-model diversity reward. DivE$^2$ schedules, over the course of training epochs, the relative importance of these characteristics; it starts by selecting easy samples for each model, and then gradually adjusts towards the models having specialized and complementary expertise on subsets of the training data, thereby encouraging high accuracy of the ensemble. We utilize an intra-model diversity term on data assigned to each model, and an inter-model diversity term on data assigned to pairs of models, to penalize both within-model and cross-model redundancy. We formulate the data-model marriage problem as a generalized bipartite matching, represented as submodular maximization subject to two matroid constraints. DivE$^2$ solves a sequence of continuous-combinatorial optimizations with slowly varying objectives and constraints. The combinatorial part handles the data-model marriage while the continuous part updates model parameters based on the assignments. In experiments, DivE$^2$ outperforms other ensemble training methods under a variety of model aggregation techniques, while also maintaining competitive efficiency.

In Japan, famous for the longevity of its people, their endearing use of engrish, and their fetish for girls in Catholic school uniforms, the care of the elderly is a particularly acute problem. A third of the Japanese population is reportedly above the age of 60, and the number of people over 90 years of age just topped two million for the first time. Add in a rapidly shrinking population, and you have a country where you have more people eating the early bird special than not. So it's no surprise that Japan is leading the world in robotic elder care, offering a glimpse into our geriatric future.

The school was pushing parents to sign their children up for a "unique pilot program" taught entirely online and run by a public school district in Los Angeles County. Each student who enrolled in the Lennox Virtual Academy would get a free Chromebook computer to use at school, with access to online classes. All parents had to do was fill out the forms, authorizing St. Francis to share information about their finances and their children's health with the Lennox School District a hundred miles away. "This partnership is expected to bring many benefits for St. Francis students," Principal Kelli Gruszka wrote to parents. "...it is IMPERATIVE that every family with students in grades 5th-8th, return the paperwork being sent home today..." What the letter did not explain was the arrangement's financial benefits.