This study presents a methodology to treat performance-based seismic design as an inverse engineering problem, where design parameters are directly derived to achieve specific performance objectives. By implementing explainable machine learning models, this methodology directly maps design variables and performance metrics, tackling computational inefficiencies of performance-based design. The resultant machine learning model is integrated as an evaluation function into a genetic optimization algorithm to solve the inverse problem. The developed methodology is then applied to two different inventories of steel and concrete moment frames in Los Angeles and Charleston to obtain sectional properties of frame members that minimize expected annualized seismic loss in terms of repair costs. The results show high accuracy of the surrogate models (e.g., R2> 90%) across a diverse set of building types, geometries, seismic design, and site hazard, where the optimization algorithm could identify the optimum values of members' properties for a fixed set of geometric variables, consistent with engineering principles.

Illinois Attorney General Kwame Raoul spoke to CNN's Jim Acosta on Monday about daytime tv talk show host Dr. Phil joining an ICE deportation operation. Talk show host Dr. Phil called out multiple media outlets for expressing outrage over his ride-along with Immigration and Customs Enforcement (ICE) as it apprehended illegal immigrants. Phil McGraw, known as "Dr. Phil," joined border czar Tom Homan and a team of agents as they took various illegal immigrants into custody in Chicago. As part of his show on Merit TV, Dr. Phil filmed a variety of arrests and even interviewed a convicted sex offender and internet predator from Thailand who was being taken into custody.

The increasing demand for processing large volumes of data for machine learning models has pushed data bandwidth requirements beyond the capability of traditional von Neumann architecture. In-memory computing (IMC) has recently emerged as a promising solution to address this gap by enabling distributed data storage and processing at the micro-architectural level, significantly reducing both latency and energy. In this paper, we present the IMPACT: InMemory ComPuting Architecture Based on Y-FlAsh Technology for Coalesced Tsetlin Machine Inference, underpinned on a cutting-edge memory device, Y-Flash, fabricated on a 180 nm CMOS process. Y-Flash devices have recently been demonstrated for digital and analog memory applications, offering high yield, non-volatility, and low power consumption. The IMPACT leverages the Y-Flash array to implement the inference of a novel machine learning algorithm: coalesced Tsetlin machine (CoTM) based on propositional logic. CoTM utilizes Tsetlin automata (TA) to create Boolean feature selections stochastically across parallel clauses. The IMPACT is organized into two computational crossbars for storing the TA and weights. Through validation on the MNIST dataset, IMPACT achieved 96.3% accuracy. The IMPACT demonstrated improvements in energy efficiency, e.g., 2.23X over CNN-based ReRAM, 2.46X over Neuromorphic using NOR-Flash, and 2.06X over DNN-based PCM, suited for modern ML inference applications.

Reducing wealth inequality and increasing utility are critical issues. This study reveals the effects of redistribution and consumption morals on wealth inequality and utility. To this end, we present a novel approach that couples the dynamic model of capital, consumption, and utility in macroeconomics with the interaction model of joint business and redistribution in econophysics. With this approach, we calculate the capital (wealth), the utility based on consumption, and the Gini index of these inequality using redistribution and consumption thresholds as moral parameters. The results show that: under-redistribution and waste exacerbate inequality; conversely, over-redistribution and stinginess reduce utility; and a balanced moderate moral leads to achieve both reduced inequality and increased utility. These findings provide renewed economic and numerical support for the moral importance known from philosophy, anthropology, and religion. The revival of redistribution and consumption morals should promote the transformation to a human mutual-aid economy, as indicated by philosopher and anthropologist, instead of the capitalist economy that has produced the current inequality. The practical challenge is to implement bottom-up social business, on a foothold of worker coops and platform cooperatives as a community against the state and the market, with moral consensus and its operation.

TV personality Dr. Phil McGraw suggested on Friday that President Biden should take a cognitive exam because, "people that have nothing to hide, hide nothing." Phil, do you think President Biden should take a cognitive exam?" Maher asked Dr. Phil during the "Overtime" portion of his "Real Time" show on Friday. "People that have nothing to hide, hide nothing. So, why not?" Phil responded. Maher closed his show on Friday by encouraging Biden to "lean into" his age and said, "Don't try to deny the age thing, lean into it.

Artificial intelligence (AI) and machine learning (ML) solutions, considered emerging technologies just a few years ago, are now within reach of more businesses, and they offer faster insights, greater efficiency, and enhanced customer experiences. The following industry thought leaders offer their predictions for how AI and ML will impact businesses across the range of vertical markets in 2023. "While active work is going on using AI for intelligent automation, an interesting trend in AI and ML is collaborative learning, that is, enabling AI to augment human intelligence. Through this collaborative intelligence, humans enable AI to train and learn the tacit knowledge that otherwise cannot be learned solely from the data, while AI enhances the human ability to make fast, informed, and smart decisions. This will not just level-up automation but will also enable more creative AI-human collaborations." "AI will yield tremendous breakthroughs in treating medical conditions in the next few years.

To understand how Artificial Intelligence (AI) is affecting science publishing, we need to push these systems to their extremes, analyze how they perform, and expose their vulnerabilities. Only then can we discuss how they will transform our industry. Earlier this week, Todd Carpenter asked ChatGPT some generic questions about the potential role of AI in scientific communication and, as you can imagine, it generated some generic, hedged, inoffensive output. I wanted to see how ChatGPT would perform with scientific controversies -- situations in which the scientific community supported one belief and the public another. Or, in situations where there was no consensus in the scientific community.

Searching for a path between two nodes in a graph is one of the most well-studied and fundamental problems in computer science. In numerous domains such as robotics, AI, or biology, practitioners develop search heuristics to accelerate their pathfinding algorithms. However, it is a laborious and complex process to hand-design heuristics based on the problem and the structure of a given use case. Here we present PHIL (Path Heuristic with Imitation Learning), a novel neural architecture and a training algorithm for discovering graph search and navigation heuristics from data by leveraging recent advances in imitation learning and graph representation learning. At training time, we aggregate datasets of search trajectories and ground-truth shortest path distances, which we use to train a specialized graph neural network-based heuristic function using backpropagation through steps of the pathfinding process. Our heuristic function learns graph embeddings useful for inferring node distances, runs in constant time independent of graph sizes, and can be easily incorporated in an algorithm such as A* at test time. Experiments show that PHIL reduces the number of explored nodes compared to state-of-the-art methods on benchmark datasets by 58.5\% on average, can be directly applied in diverse graphs ranging from biological networks to road networks, and allows for fast planning in time-critical robotics domains.

The Bureau of Multiversal Arbitration is an unusual workplace. Maude Fletcher's alright, though she needs to learn how to turn off caps lock in the company chat. But trying to deal with Byron G Snodgrass is like handling an energetic poodle, and Phil is a bit stiff. Byron G Snodgrass is an energetic poodle. A peace lily, I think.

Founded in 2015, Phil is a San Francisco-based Series D health-tech startup, pioneering a software therapy deployment platform, offering pharmaceutical manufacturers a modern alternative to traditional access, affordability and distribution options. Through its digital stakeholder experiences, patient access services, market access solutions and distribution models, pharma manufacturers are able to deliver affordable and timely therapy access to patients.