The achievement of the 2030 Sustainable Development Goals (SDGs) is dependent upon strategic resource distribution. We propose a causal discovery framework using Panel Vector Autoregression, along with both country-specific fixed effects and PCMCI+ conditional independence testing on 168 countries (2000-2025) to develop the first complete causal architecture of SDG dependencies. Utilizing 8 strategically chosen SDGs, we identify a distributed causal network (i.e., no single 'hub' SDG), with 10 statistically significant Granger-causal relationships identified as 11 unique direct effects. Education to Inequality is identified as the most statistically significant direct relationship (r = -0.599; p < 0.05), while effect magnitude significantly varies depending on income levels (e.g., high-income: r = -0.65; lower-middle-income: r = -0.06; non-significant). We also reject the idea that there exists a single 'keystone' SDG. Additionally, we offer a proposed tiered priority framework for the SDGs namely, identifying upstream drivers (Education, Growth), enabling goals (Institutions, Energy), and downstream outcomes (Poverty, Health). Therefore, we conclude that effective SDG acceleration can be accomplished through coordinated multi-dimensional intervention(s), and that single-goal sequential strategies are insufficient.

Large language models (LLMs) are regularly evaluated using benchmark datasets. But what justifies making inferences about an LLM's capabilities based on its answers to a curated set of questions? This paper first introduces a formal framework to address this question. The key is to note that the benchmarks used to test LLMs -- such as AP exams -- are also those used to test people. However, this raises an implication: these benchmarks are only valid tests if LLMs misunderstand concepts in ways that mirror human misunderstandings. Otherwise, success on benchmarks only demonstrates potemkin understanding: the illusion of understanding driven by answers irreconcilable with how any human would interpret a concept. We present two procedures for quantifying the existence of potemkins: one using a specially designed benchmark in three domains, the other using a general procedure that provides a lower-bound on their prevalence. We find that potemkins are ubiquitous across models, tasks, and domains. We also find that these failures reflect not just incorrect understanding, but deeper internal incoherence in concept representations.

Advanced slot and winding designs are imperative to create future high performance electrical machines (EM). As a result, the development of methods to design and improve slot filling factor (SFF) has attracted considerable research. Recent developments in manufacturing processes, such as additive manufacturing and alternative materials, has also highlighted a need for novel high-fidelity design techniques to develop high performance complex geometries and topologies. This study therefore introduces a novel physics-informed machine learning (PIML) design optimization process for improving SFF in traction electrical machines used in electric vehicles. A maximum entropy sampling algorithm (MESA) is used to seed a physics-informed Bayesian optimization (PIBO) algorithm, where the target function and its approximations are produced by Gaussian processes (GP)s. The proposed PIBO-MESA is coupled with a 2D finite element model (FEM) to perform a GP-based surrogate and provide the first demonstration of the optimal combination of complex design variables for an electrical machine. Significant computational gains were achieved using the new PIBO-MESA approach, which is 45% faster than existing stochastic methods, such as the non-dominated sorting genetic algorithm II (NSGA-II). The FEM results confirm that the new design optimization process and keystone shaped wires lead to a higher SFF (i.e. by 20%) and electromagnetic improvements (e.g. maximum torque by 12%) with similar resistivity. The newly developed PIBO-MESA design optimization process therefore presents significant benefits in the design of high-performance electric machines, with reduced development time and costs.

Put a microphone in front of Phil Spencer and the guy will always deliver. Spencer, who you may or may not know as the head of Microsoft Gaming (he's in charge of all things Xbox), sat down with the Wall Street Journal at its WSJ Tech Live event for a wide-ranging interview about everything from Microsoft's plans for mobile gaming to Spencer's personal feelings on the metaverse. Spencer is one of the few industry leaders who actually gives real answers to questions on occasion (and comes across as "one of the good guys" for it), so let's break down the highlights. Undoubtedly the funniest thing Spencer said at WSJ Tech Live came at the expense of Meta's Mark Zuckerberg-fueled metaverse efforts. As you can see at about the 1:15 mark in this clip from the WSJ YouTube channel, Spencer seemed to take a bit of a jab at Meta's work-focused metaverse, without naming names of course.

This reliable, feature-packed air conditioner from GE earned our top honors during testing. The GE Profile Series PHC08LY is a window-mounted air conditioner that blends top-notch cooling capabilities with a variety of unique features, with a bit of style and elegance. During testing, this 8,000 BTU (British Thermal Units) AC unit reduced our 340 square foot test area's temperature by 10 F in only 43 minutes and lowered the room's humidity by 14 percent in the same amount of time. On top of this, it ran (for an air conditioner) quietly. While using the GE Profile Series' Quiet Mode it only put out 49.3 dBA of sound -- that's less noise than an average household refrigerator makes.

Your story in this week's issue, "Asleep at the Wheel," draws on a real incident in which the San Francisco S.P.C.A. hired a robot security guard to deter homeless people from camping out on its property. What was it about that incident that caught your attention and inspired a story? We talk about depersonalization--of the migrants at the border, for instance, or of the hordes threatening us from their "shithole countries," as our chief executive so eloquently expressed it--but here it is, the ultimate nonperson, a machine, keeping order in our streets. Truly, we are living in one of the bad sci-fi flicks of the nineteen-seventies. In San Francisco, the S.P.C.A. lost the battle and the robot was fired.

Report on the 1984 Distributed Artificial Intelligence Workshop. Reprinted in Readings in Artificial Intelligence and Databases, J. Mylopoulos and M. L. Brodie, editors, Morgan Kaufmann Publishers, Inc., 1988. Report on the 1984 Distributed Artificial Intelligence Workshop. Reprinted in Readings in Artificial Intelligence and Databases, J. Mylopoulos and M. L. Brodie, editors, Morgan Kaufmann Publishers, Inc., 1988.