Estimating the causal effects of a spatially-varying intervention on a spatially-varying outcome may be subject to non-local confounding (NLC), a phenomenon that can bias estimates when the treatments and outcomes of a given unit are dictated in part by the covariates of other nearby units. In particular, NLC is a challenge for evaluating the effects of environmental policies and climate events on health-related outcomes such as air pollution exposure. This paper first formalizes NLC using the potential outcomes framework, providing a comparison with the related phenomenon of causal interference. Then, it proposes a broadly applicable framework, termed "weather2vec", that uses the theory of balancing scores to learn representations of non-local information into a scalar or vector defined for each observational unit, which is subsequently used to adjust for confounding in conjunction with causal inference methods. The framework is evaluated in a simulation study and two case studies on air pollution where the weather is an (inherently regional) known confounder.

The technocrat epoch is overflowing with new technologies and such cutting-edge facilities accompany the risks and pitfalls. Robotic process automation is another innovation that empowers the computerization of high-volume, manual, repeatable, everyday practice, rule-based, and unmotivating human errands. The principal objective of Robotic Process Automation is to supplant monotonous human errands with a virtual labor force or a computerized specialist playing out a similar work as the human laborer used to perform. This permits human laborers to zero in on troublesome undertakings and critical thinking. Robotic Process Automation instruments are viewed as straightforward and strong for explicit business process computerization. Robotic Process Automation comprises intelligence to decide if a process should occur. It has the capability to analyze the data presented and provide a decision based on the logic parameters set in place by the developer. Moreover, it does not demand for system integration, like other forms of automation. Be that as it may since the innovation is yet arising, the Robotic Process Automation faces a few difficulties during the execution.

Let's begin with the objective part of the story. This section is slightly technical (although not difficult), so feel free to skim through it (still worth reading if you plan to use the model). Stable Diffusion 2 is the generic name of an entire family of models that stem from a common baseline: Stable Diffusion 2.0-base (SD 2.0-base) a raw text-to-image model. The baseline model is trained on an aesthetic subset of the open dataset LAION-5B (keep this in mind, it will be important later) and generates 512x512 images. On top of SD 2.0-base, Stability.ai

More than three years ago, this editor sat down with Sam Altman for a small event in San Francisco soon after he'd left his role as the president of Y Combinator to become CEO of the AI company he co-founded in 2015 with Elon Musk and others, OpenAI. At the time, Altman described OpenAI's potential in language that sounded outlandish to some. Altman said, for example, that the opportunity with artificial general intelligence -- machine intelligence that can solve problems as well as a human -- is so incomprehensibly enormous that if OpenAI managed to crack it, the outfit could "maybe capture the light cone of all future value in the universe." He said that the company was "going to have to not release research" because it was so powerful. Asked if OpenAI was guilty of fear-mongering -- Elon Musk, a co-founder of the outfit, has repeatedly called all organizations developing AI to be regulated -- Altman talked about dangers of not thinking about "societal consequences" when "you're building something on an exponential curve."

As artificial intelligence (AI) technology continues advancing at lighting pace, discussions on the need for governance, standards, and a stronger focus on "responsible AI" have followed. While AI can carry out decision-making tasks efficient, it's still based on algorithms that respond to data models. Unlike humans, AI algorithms can't see the full picture, in part because they lack emotional reasoning and other human qualities, such as empathy, ethics, and morality. Concerns over privacy and discrimination are on the rise as AI becomes further integrated into decision-making processes that affect economies and societies worldwide. The time has come, therefore, to decide what sort of policies should guide AI design and use, and how to make sure AI use improves human welfare and respects human dignity, said Nashlie Sephus, Principal Tech Evangelist for Amazon AI, in a recent AI For Good keynote.

In its defense, Tesla lawyers said that "mere failure to realize a long-term, aspirational goal is not fraud." That argument is contained in a motion to dismiss the case that was filed last week in U.S. District Court in San Francisco. The main plaintiff is Briggs Matsko, a resident of Rancho Murieta, Calif. If the case goes forward, it could lead to deposition of Tesla employees who helped develop the technology and reveal what Musk knew and didn't know about its true capabilities when he made numerous forecasts over the years -- including the prediction that there would be a million Tesla robotaxis on the road by the end of 2020, that customers could make $30,000 a year hiring them out, and that their cars would appreciate in value. Tesla lawyers are attempting to prevent that information from going public.

With the growth and widespread application of artificial intelligence (AI), there is a distinct need for regulatory compliance to ensure efficient …

This week, David Plotz, Emily Bazelon, and John Dickerson discuss Raphael Warnock beating Herschel Walker, and oral arguments at the Supreme Court in the anti-gay marriage website designer case and the "independent state legislature" election case. Here are some notes and references from this week's show: Fr. James Martin, S.J. for Outreach: "When Is Religious Liberty A Fig Leaf For Homophobia?" Here are this week's chatters: David: Tour Fort DeRussy with David; City Cast Portland has launched; Caitlin Doughty for The New York Times: "If You Want to Give Something Back to Nature, Give Your Body" For this week's Slate Plus bonus segment Emily, David, and John discuss ChatGPT.

The concept of walkable urban development has gained increased attention due to its public health, economic, and environmental sustainability benefits. Unfortunately, land zoning and historic under-investment have resulted in spatial inequality in walkability and social inequality among residents. We tackle the problem of Walkability Optimization through the lens of combinatorial optimization. The task is to select locations in which additional amenities (e.g., grocery stores, schools, restaurants) can be allocated to improve resident access via walking while taking into account existing amenities and providing multiple options (e.g., for restaurants). To this end, we derive Mixed-Integer Linear Programming (MILP) and Constraint Programming (CP) models. Moreover, we show that the problem's objective function is submodular in special cases, which motivates an efficient greedy heuristic. We conduct a case study on 31 underserved neighborhoods in the City of Toronto, Canada. MILP finds the best solutions in most scenarios but does not scale well with network size. The greedy algorithm scales well and finds near-optimal solutions. Our empirical evaluation shows that neighbourhoods with low walkability have a great potential for transformation into pedestrian-friendly neighbourhoods by strategically placing new amenities. Allocating 3 additional grocery stores, schools, and restaurants can improve the "WalkScore" by more than 50 points (on a scale of 100) for 4 neighbourhoods and reduce the walking distances to amenities for 75% of all residential locations to 10 minutes for all amenity types. Our code and paper appendix are available at https://github.com/khalil-research/walkability.

The news sent shock waves through the video game industry. The assumption since the deal was announced in January was that the deal -- like Microsoft's 2021 acquisition of video game publisher ZeniMax -- would go through. But the FTC's new lawsuit is an enormous, unexpected barrier to the deal's completion.