GPU prices could follow RAM's big rise Of the major companies, OpenAI's ChatGPT has the biggest reach among younger users. AI chatbots haven't come close to replacing teens' social media habits, but they are playing a significant role in their online habits. Nearly one-third of US teens report using AI chatbots daily or more, according to a new report from Pew Research. The report is the first from Pew to specifically examine how often teens are using AI overall, and was published alongside its latest research on teens' social media use. It's based on an online survey of 1,458 US teens who were polled between September 25 to October 9, 2025.

We present EvalMORAAL, a transparent chain-of-thought (CoT) framework that uses two scoring methods (log-probabilities and direct ratings) plus a model-as-judge peer review to evaluate moral alignment in 20 large language models. We assess models on the World Values Survey (55 countries, 19 topics) and the PEW Global Attitudes Survey (39 countries, 8 topics). With EvalMORAAL, top models align closely with survey responses (Pearson's r approximately 0.90 on WVS). Yet we find a clear regional difference: Western regions average r=0.82 while non-Western regions average r=0.61 (a 0.21 absolute gap), indicating consistent regional bias. Our framework adds three parts: (1) two scoring methods for all models to enable fair comparison, (2) a structured chain-of-thought protocol with self-consistency checks, and (3) a model-as-judge peer review that flags 348 conflicts using a data-driven threshold. Peer agreement relates to survey alignment (WVS r=0.74, PEW r=0.39, both p<.001), supporting automated quality checks. These results show real progress toward culture-aware AI while highlighting open challenges for use across regions.

Over the past year or so, you've probably had conversations with friends, family, and coworkers about the rise of generative AI capable of making convincing text and imagery--but perhaps also about the hype and fear swirling around the technology. A poll out this week finds that worry over harmful effects of AI is outpacing the wow of helpful AI. A majority of Americans say their concern about artificial intelligence in daily life outweighs their excitement about it, according to a Pew Research Center survey of more than 11,000 US adults. The results come at a time when a growing number of people are paying attention to news about AI in their daily lives. Pew has run this survey twice before and reports that the number of people more concerned than excited about AI jumped from 37 percent in 2021 to 52 percent this month.

Network flow problems, which involve distributing traffic over a network such that the underlying infrastructure is used effectively, are ubiquitous in transportation and logistics. Among them, the Multi-Commodity Network Flow (MCNF) problem is of general interest, as it concerns the distribution of multiple flows of different sizes between several sources and sinks, while achieving effective utilization of the links. Due to the appeal of data-driven optimization, these problems have increasingly been approached using graph learning methods. In this paper, we propose a novel graph learning architecture for network flow problems called Per-Edge Weights (PEW). This method builds on a Graph Attention Network and uses distinctly parametrized message functions along each link. We extensively evaluate the proposed solution through an Internet flow routing case study using $17$ Service Provider topologies and $2$ routing schemes. We show that PEW yields substantial gains over architectures whose global message function constrains the routing unnecessarily. We also find that an MLP is competitive with other standard architectures. Furthermore, we shed some light on the relationship between graph structure and predictive performance for data-driven routing of flows, an aspect that has not been considered by existing work in the area.

More than a third of AI experts surveyed by Pew Research said they are concerned that artificial intelligence will leave humanity worse off in 2030 than they are now, with the majority optimistic that the benefits will make life better for individuals. Pew surveyed 979 "technology pioneers, innovators, developers, business and policy leaders, researchers and activists," asking whether they thought that AI advances would leave most people better off by the year 2030. Will it "enhance human capacities and empower them?" Or will it "lessen human autonomy and agency," leaving them worse off? Overall, 63% said they were hopeful that people will be better off by 2030, with 37% believing they will not be better off.

Humans are nothing if not contrary. Technology destroying jobs is something most Americans accept will happen within their lifetimes, according to a new study by the Pew Research Center, just not to their own jobs -- which most believe won't change significantly in the next 50 years. Polling just over 2,000 Americans in June and July last summer to ask about their perception of the risk of jobs being automated, the researchers found a majority (65 per cent) of Americans believe that robots and/or software will "definitely" or "probably" be capable of doing much of the work that humans do now within 50 years' time. But when the robots and the algorithms move a little closer to home – and the question becomes specifically about the future security of their own jobs -- respondents' views are very different, with an even larger majority (80 per cent) convinced their own jobs and professions will remain largely unchanged and will exist in their current form 50 years from now. More than a third (36 per cent) of respondents expressed definitive confidence that their current job or occupation will "definitely" exist in its current form five decades from now vs just six per cent saying their current role will "definitely not" exist.

The Bayesian "evidence" approximation has recently been employed to determine the noise and weight-penalty terms used in back-propagation. This paper shows that for neural nets it is far easier to use the exact result than it is to use the evidence approximation. Moreover, unlike the evidence approximation, the exact result neither has to be re-calculated for every new data set, nor requires the running of computer code (the exact result is closed form). In addition, it turns out that the evidence procedure's MAP estimate for neural nets is, in toto, approximation error. Another advantage of the exact analysis is that it does not lead one to incorrect intuition, like the claim that using evidence one can "evaluate different priors in light of the data". This paper also discusses sufficiency conditions for the evidence approximation to hold, why it can sometimes give "reasonable" results, etc.

The Bayesian "evidence" approximation has recently been employed to determine the noise and weight-penalty terms used in back-propagation. This paper shows that for neural nets it is far easier to use the exact result than it is to use the evidence approximation. Moreover, unlike the evidence approximation, the exact result neither has to be re-calculated for every new data set, nor requires the running of computer code (the exact result is closed form). In addition, it turns out that the evidence procedure's MAP estimate for neural nets is, in toto, approximation error. Another advantage of the exact analysis is that it does not lead one to incorrect intuition, like the claim that using evidence one can "evaluate different priors in light of the data". This paper also discusses sufficiency conditions for the evidence approximation to hold, why it can sometimes give "reasonable" results, etc.

The Bayesian "evidence" approximation has recently been employed to determine the noise and weight-penalty terms used in back-propagation. This paper shows that for neural nets it is far easier to use the exact result than it is to use the evidence approximation. Moreover, unlike the evidence approximation,the exact result neither has to be re-calculated for every new data set, nor requires the running of computer code (the exact result is closed form). In addition, it turns out that the evidence procedure's MAPestimate for neural nets is, in toto, approximation error. Another advantage of the exact analysis is that it does not lead one to incorrect intuition, like the claim that using evidence one can "evaluate different priors in light of the data". This paper also discusses sufficiency conditions for the evidence approximation to hold, why it can sometimes give "reasonable" results, etc.