Before boarding Marine One on Tuesday afternoon, President Trump challenged two progressive Democrat congresswomen to an intelligence test. President Donald Trump lobbed a signature zinger on Tuesday as he paused to speak with reporters before boarding Marine One en route to an artificial intelligence summit. "[Alexandria Ocasio-Cortez], look, I think she's very nice, but she's very low IQ, and we really don't need low IQ," Trump said, smiling as cameras rolled. He added, "Between her and Crockett, we're going to give them both an IQ test to see who comes out best." TRUMP DARES AOC TO TRY TO IMPEACH HIM: 'MAKE MY DAY' President Donald Trump said AOC and Jasmine Crockett should take IQ tests.

Over the last thirty years, considerable progress has been made with the development of systems that can drive cars, play games, predict protein folding and generate natural language. These systems are described as intelligent and there has been a great deal of talk about the rapid increase in artificial intelligence and its potential dangers. However, our theoretical understanding of intelligence and ability to measure it lag far behind our capacity for building systems that mimic intelligent human behaviour. There is no commonly agreed definition of the intelligence that AI systems are said to possess. No-one has developed a practical measure that would enable us to compare the intelligence of humans, animals and AIs on a single ratio scale. This paper sets out a new universal measure of intelligence that is based on the hypothesis that prediction is the most important component of intelligence. As an agent interacts with its normal environment, the accuracy of its predictions is summed up and the complexity of its predictions and perceived environment is accounted for using Kolmogorov complexity. Two experiments were carried out to evaluate the practical feasibility of the algorithm. These demonstrated that it could measure the intelligence of an agent embodied in a virtual maze and an agent that makes predictions about time-series data. This universal measure could be the starting point for a new comparative science of intelligence that ranks humans, animals and AIs on a single ratio scale.

Autonomous vehicles (AVs) have significantly advanced in real-world deployment in recent years, yet safety continues to be a critical barrier to widespread adoption. Traditional functional safety approaches, which primarily verify the reliability, robustness, and adequacy of AV hardware and software systems from a vehicle-centric perspective, do not sufficiently address the AV's broader interactions and behavioral impact on the surrounding traffic environment. To overcome this limitation, we propose a paradigm shift toward behavioral safety, a comprehensive approach focused on evaluating AV responses and interactions within traffic environment. To systematically assess behavioral safety, we introduce a third-party AV safety assessment framework comprising two complementary evaluation components: Driver Licensing Test and Driving Intelligence Test. The Driver Licensing Test evaluates AV's reactive behaviors under controlled scenarios, ensuring basic behavioral competency. In contrast, the Driving Intelligence Test assesses AV's interactive behaviors within naturalistic traffic conditions, quantifying the frequency of safety-critical events to deliver statistically meaningful safety metrics before large-scale deployment. We validated our proposed framework using \texttt{Autoware.Universe}, an open-source Level 4 AV, tested both in simulated environments and on the physical test track at the University of Michigan's Mcity Testing Facility. The results indicate that \texttt{Autoware.Universe} passed 6 out of 14 scenarios and exhibited a crash rate of 3.01e-3 crashes per mile, approximately 1,000 times higher than average human driver crash rate. During the tests, we also uncovered several unknown unsafe scenarios for \texttt{Autoware.Universe}. These findings underscore the necessity of behavioral safety evaluations for improving AV safety performance prior to widespread public deployment.

Current and foreseeable GenAI models are not capable of achieving artificial general intelligence because they are burdened with anthropogenic debt. They depend heavily on human input to provide well-structured problems, architecture, and training data. They cast every problem as a language pattern learning problem and are thus not capable of the kind of autonomy needed to achieve artificial general intelligence. Current models succeed at their tasks because people solve most of the problems to which these models are directed, leaving only simple computations for the model to perform, such as gradient descent. Another barrier is the need to recognize that there are multiple kinds of problems, some of which cannot be solved by available computational methods (for example, "insight problems"). Current methods for evaluating models (benchmarks and tests) are not adequate to identify the generality of the solutions, because it is impossible to infer the means by which a problem was solved from the fact of its solution. A test could be passed, for example, by a test-specific or a test-general method. It is a logical fallacy (affirming the consequent) to infer a method of solution from the observation of success.

This story about intelligence testing in schools was produced by the Hechinger Report, a nonprofit, independent news organization focused on inequality and innovation in education. Even before her son started kindergarten, Ashley Meier Barlow realized that she might have to fight for his education. Her son has Down syndrome; when he was in prekindergarten, school officials in Fort Thomas, Kentucky, told Barlow that he wouldn't be going to the neighborhood school, with some special education accommodations, as she had assumed. Instead, the educators told Barlow that they wanted her son to attend a classroom across town meant for children who are profoundly impacted by their disabilities. Barlow immediately resisted because she knew the curriculum would likely focus on life skills, and her son might never be taught much reading beyond learning the shape of common, functional words like stop and exit.

A widely accepted definition of intelligence in the context of Artificial Intelligence (AI) still eludes us. Due to our exceedingly rapid development of AI paradigms, architectures, and tools, the prospect of naturally arising AI consciousness seems more likely than ever. In this paper, we claim that all current intelligence tests are insufficient to point to the existence or lack of intelligence \textbf{as humans intuitively perceive it}. We draw from ideas in the philosophy of science, psychology, and other areas of research to provide a clearer definition of the problems of artificial intelligence, self-awareness, and agency. We furthermore propose a new heuristic approach to test for artificial self-awareness and outline a possible implementation. Finally, we discuss some of the questions that arise from this new heuristic, be they philosophical or implementation-oriented.

As being widely used to measure human intelligence, Raven's Progressive Matrices (RPM) tests also pose a great challenge for AI systems. There is a long line of computational models for solving RPM, starting from 1960s, either to understand the involved cognitive processes or solely for problem-solving purposes. Due to the dramatic paradigm shifts in AI researches, especially the advent of deep learning models in the last decade, the computational studies on RPM have also changed a lot. Therefore, now is a good time to look back at this long line of research. As the title -- ``a comprehensive introduction'' -- indicates, this paper provides an all-in-one presentation of computational models for solving RPM, including the history of RPM, intelligence testing theories behind RPM, item design and automatic item generation of RPM-like tasks, a conceptual chronicle of computational models for solving RPM, which reveals the philosophy behind the technology evolution of these models, and suggestions for transferring human intelligence testing and AI testing.

Figural analogy problems have long been a widely used format in human intelligence tests. In the past four decades, more and more research has investigated automatic item generation for figural analogy problems, i.e., algorithmic approaches for systematically and automatically creating such problems. In cognitive science and psychometrics, this research can deepen our understandings of human analogical ability and psychometric properties of figural analogies. With the recent development of data-driven AI models for reasoning about figural analogies, the territory of automatic item generation of figural analogies has further expanded. This expansion brings new challenges as well as opportunities, which demand reflection on previous item generation research and planning future studies. This paper reviews the important works of automatic item generation of figural analogies for both human intelligence tests and data-driven AI models. From an interdisciplinary perspective, the principles and technical details of these works are analyzed and compared, and desiderata for future research are suggested.

After years of wrestling with the complexities of algebra, fractions and mental arithmetic, some teenagers may be only too keen to dump maths at the earliest opportunity. But a new study suggests that quitting the subject at the age of 16 may have an adverse effect on brain development. Researchers led by the University of Oxford found that adolescents who stuck with maths in their A-levels had higher levels of a brain chemical important for memory, learning and problem-solving. They recruited 87 A-level students to take part in the study and, after scanning their brains, discovered that those who had continued with maths had higher levels of gamma-Aminobutyric acid (GABA) in an area called the prefrontal cortex. The study also found that the students with more GABA were better at solving brain-teasing questions when tested around 19 months later.

People who have larger pupils in their eyes are more intelligent than those with smaller pupils, according to a new study. Volunteers sat reasoning, attention and memory tests so the Georgia Institute of Technology team could investigate the link between pupil size and intelligence. They found that as well as being linked to arousal and exhaustion, pupil dilation can be used to understand the individual differences in intelligence, discovering that the larger the pupils, the higher the intelligence. Differences in the baseline pupil size between those scoring highest and those scoring lowest on intelligence tests could be seen with the unaided eye. The team say this could be due to people with larger pupils having better results regulation of brain activity in a region linked to intelligence and memory.