Welcome back to, TIME's new twice-weekly newsletter about AI. If you're reading this in your browser, why not subscribe to have the next one delivered straight to your inbox? The chatbot platform, which allows users to chat with AIs that personify fictional characters, is the target of several lawsuits -- including one from Megan Garcia, a mother whose 14-year-old son died by suicide after becoming obsessed with one of the bots, which allegedly encouraged him to end his own life. In the wake of that lawsuit and others, last month Character.AI made a big announcement: it would ban users under 18 years old from having "open-ended conversations" with the chatbots on its platform. It was a huge pivot for a company that says Generations Z and Alpha make up the core of its more than 6 million daily active users, who spend an average of 70 to 80 minutes per day on the platform.

Character.ai to ban teens from talking to its AI chatbots The platform, founded in 2021, is used by millions to talk to chatbots powered by artificial intelligence (AI). But it is facing several lawsuits in the US from parents, including one over the death of a teenager, with some branding it a clear and present danger to young people. Online safety campaigners have welcomed the move but said the feature should never have been available to children in the first place. Character.ai said it was making the changes after reports and feedback from regulators, safety experts, and parents, which have highlighted concerns about its chatbots' interactions with teens. Experts have previously warned the potential for AI chatbots to make things up, be overly-encouraging, and feign empathy can pose risks to young and vulnerable people.

Thousands of entrepreneurs are trying to rebuild the economy around AI. I set out to see how they're actually doing it. Earth, it's said, is home to more than 10,000 AI startups. The figure is a guess, of course--startups come, startups go. But last year, more than 2,000 of them got their first round of funding. As investors shovel their billions into AI, it's worth asking: What are all these creatures of the boom doing? I decided to approach as many recent AI founders as I could. The goal was not to try to pick winners but to see what it's like, on the ground, to build AI products--how AI tools have changed the nature of their work; how terrifying it is to compete in a crowded field.

After school, Karandeep Anand often finds his 6-year-old daughter deep in conversation with an AI chatbot as she eats snacks at their kitchen counter. She's too young to type--let alone have her own account on Character.AI--but that hasn't stopped her from nabbing his phone to have voice conversations with a Sherlock Holmes bot, which she uses to build her own mystery stories. Character.AI is an AI companion startup (though Anand likes to say it's an AI role-play startup, which we'll get into later). He took over as the CEO in June in the midst of a potentially devastating lawsuit for its parent company and looming questions about child safety. When I ask if he's concerned about his daughter connecting with an AI chatbot rather than a real human, he's quick to say no.

Accurately predicting the outcome of a trial of labor after cesarean (TOLAC) is essential for guiding prenatal counseling and minimizing delivery-related risks. This study presents supervised machine learning models for predicting vaginal birth after cesarean (VBAC) using 643,029 TOLAC cases from the CDC WONDER Natality dataset (2017-2023). After filtering for singleton births with one or two prior cesareans and complete data across 47 prenatal-period features, three classifiers were trained: logistic regression, XGBoost, and a multilayer perceptron (MLP). The MLP achieved the highest performance with an AUC of 0.7287, followed closely by XGBoost (AUC = 0.727), both surpassing the logistic regression baseline (AUC = 0.709). To address class imbalance, class weighting was applied to the MLP, and a custom loss function was implemented in XGBoost. Evaluation metrics included ROC curves, confusion matrices, and precision-recall analysis. Logistic regression coefficients highlighted maternal BMI, education, parity, comorbidities, and prenatal care indicators as key predictors. Overall, the results demonstrate that routinely collected, early-pregnancy variables can support scalable and moderately high-performing VBAC prediction models. These models offer potential utility in clinical decision support, particularly in settings lacking access to specialized intrapartum data.

One of the more persistent concerns in the age of AI is that the robots will take our jobs. The extent to which this fear is founded remains to be seen, but we're already witnessing some level of replacement in certain fields. Even niche occupations are in jeopardy. For example, the world of OnlyFans chatters is already getting disrupted. What are OnlyFans chatters, you say?

Grand master Viswanathan Anand and sports minister Anurag Thakur were Times of India's guest editor on Saturday, 18th June. Sports guest editors Viswanathan Anand and Anurag Thakur talk to TOI about the game-changing dynamics of the Chess Olympiad... Viswanathan Anand has decided to don a new hat. The five-time world chess champion, who is still an active p layer at 52 and has defeated the world's top player, Magnus Carlsen, twice recently, will contest for the post of deputy president in the FIDE (world chess body) elections. He is also the face of the Chess Olympiad, which will be held in India for the very first time, in Chennai in July-August this year. Anand, looking relaxed in a light summer coat, joined sports minister Anurag Thakur during an interaction with TOI in the Capital on Saturday. The two Guest Sports Editors talked about the way ahead for chess, plans to popularise the sport at the grassroots level and stage more international tournaments to give aspiring youngsters exposure to top-quality chess. Excerpts from the interaction... India is hosting such a big chess event for the first time since the Anand versus Carlsen battle in 2003.

Chatbots and artificial intelligence have delivered great returns for Goldman Sachs' Marcus as the online bank looks to manage its growth. Marcus, Goldman's digital consumer bank, significantly "cut down on costs and expenses in terms of people" at call centers by using the tech, Abhinav Anand, an MD and head of lending for consumer at Goldman Sachs, said at a recent industry event. "That itself, at the scale at which we are growing, is a massive savings and a good way to measure the return on our investments," said Anand, who was speaking at the Ai4 Finance Summit in New York on Tuesday. Marcus is not "removing call center positions" but rather using the chatbots, which the bank calls "intelligent AI enabled chat services," to manage growth, a spokesperson for Goldman Sachs told Insider. "We are not removing call center representative positions, we are continuing to grow across the board in the consumer business including within our call centers. We are growing at an incredible pace and are continuously hiring for new roles and positions," the spokesperson told Insider.

Monte-Carlo Tree Search (MCTS) algorithms such as UCT are an attractive online framework for solving planning under uncertainty problems modeled as a Markov Decision Process. However, MCTS search trees are constructed in flat state and action spaces, which can lead to poor policies for large problems. In a separate research thread, domain abstraction techniques compute symmetries to reduce the original MDP. This can lead to significant savings in computation, but these have been predominantly implemented for offline planning. This paper makes two contributions. First, we define the ASAP (Abstraction of State-Action Pairs) framework, which extends and unifies past work on domain abstractions by holistically aggregating both states and state-action pairs -- ASAP uncovers a much larger number of symmetries in a given domain. Second, we propose ASAP-UCT, which implements ASAP-style abstractions within a UCT framework combining strengths of online planning with domain abstractions. Experimental evaluation on several benchmark domains shows up to 26% improvement in the quality of policies obtained over existing algorithms.

Recent work has begun exploring the value of domain abstractions in Monte-Carlo Tree Search (MCTS) algorithms for probabilistic planning.