Marketing Mix Modeling (MMM) is a statistical technique used to estimate the impact of marketing activities on business outcomes such as sales, revenue, or customer visits. Traditional MMM approaches often rely on linear regression or Bayesian hierarchical models that assume independence between marketing channels and struggle to capture complex temporal dynamics and non-linear saturation effects [@Chan2017; @Hanssens2005; @Ng2021Bayesian]. **DeepCausalMMM** is a Python package that addresses these limitations by combining deep learning, causal inference, and advanced marketing science. The package uses Gated Recurrent Units (GRUs) to automatically learn temporal patterns such as adstock (carryover effects) and lag, while simultaneously learning statistical dependencies and potential causal structures between marketing channels through Directed Acyclic Graph (DAG) learning [@Zheng2018NOTEARS; @Gong2024CausalMMM]. Additionally, it implements Hill equation-based saturation curves to model diminishing returns and optimize budget allocation. Key features include: (1) a data-driven design where hyperparameters and transformations (e.g., adstock decay, saturation curves) are learned or estimated from data with sensible defaults, rather than requiring fixed heuristics or manual specification, (2) multi-region modeling with both shared and region-specific parameters, (3) robust statistical methods including Huber loss and advanced regularization, (4) comprehensive response curve analysis for understanding channel saturation.

OpenAI Removed Safeguards Before Teen's Suicide, Amended Lawsuit Claims OpenAI relaxed safeguards that would have prevented ChatGPT from engaging in conversations about self-harm in the months leading up to the suicide of Adam Raine, an amended complaint filed by the family in the San Francisco County Superior Court on Wednesday alleges. The amendment changes the theory of the case from reckless indifference to intentional misconduct, according to the family's lawyers, which could raise the damages awarded to the family. The Raine family's lawyers will have to prove that OpenAI was aware of the risks posed by ChatGPT and disregarded them. The family has asked for a jury trial. In an interview with TIME, Jay Edelson, one of the Raine family's lawyers, says OpenAI relaxed safeguards in an "intentional decision" to "prioritize engagement."

How many spheres can be arranged so that every one'kisses' a single rounded shape in the center? Breakthroughs, discoveries, and DIY tips sent every weekday. A human has outkissed one of Google's superpowered artificial intelligence systems . Instead, this win is in the intellectual realm of advanced mathematics . While largely conceptual in nature, the ramifications could soon help boost advancements in telecommunications and satellite arrays.

Chinese creator Tianran Mu went viral for mimicking the eerie, unsettling aesthetic of AI videos, but his work is 100 percent human. Our fellow terminally online readers probably have seen this video, which originated on Chinese social media . In it, two guys who look at first like they are about to get into a fistfight suddenly break out into a romantic, yet slightly robotic tango dance routine. The next second, they pull a wine glass and a bowl of noodles out of nowhere. It looks like it's generated by AI, but it isn't.

New AI features in Google Earth let users ask chatbot-style questions to find changes in the climate. The system could eventually predict disasters and identify the communities likely to be affected. Google has come up with a way to better map Earth's disasters, predict them, and be able to track which communities and ecosystems are going to be wrought by their destruction. If you want to find out what's straining the environment in your neck of the woods, all you have to do is ask. Google Earth AI, a fusion of Google's Earth and Gemini AI systems, was introduced in July .

When you purchase through links in our articles, we may earn a small commission. OpenAI launches its own free'Atlas' browser with ChatGPT built-in It's yet another Chromium fork, except this one comes integrated with ChatGPT. It even has agentic features for paid users. OpenAI recently launched ChatGPT Atlas, which is "a new web browser built with ChatGPT at its core." It's based on Chromium--which is true of pretty much all browsers these days except Firefox and Safari--and its unique selling point is that it integrates ChatGPT right into the browser, allowing users to chat with their search results and use a side panel that automatically provides ChatGPT with on-screen context. ChatGPT Atlas also has access to your browsing history, allowing the AI assistant to customize its responses based on your activity.

Found Energy, a startup in Boston, aims to harness the energy in scraps of aluminum metal to power industrial processes without fossil fuels. Since 2022, the company has worked to develop ways to rapidly release energy from aluminum on a small scale. Now it's just switched on a much larger version of its aluminum-powered engine, which it claims is the largest aluminum-water reactor ever built. Early next year, it will be installed to supply heat and hydrogen to a tool manufacturing facility in the southeastern US, using the aluminum waste produced by the plant itself as fuel. If everything works as planned, this technology, which uses a catalyst to unlock the energy stored within aluminum metal, could transform a growing share of aluminum scrap into a zero-carbon fuel. Rondo Energy just turned on what it says is the world's largest thermal battery, an energy storage system that can take in electricity and provide a consistent source of heat.

ChatGPT's Horny Era Could Be Its Stickiest Yet OpenAI will soon let adults create erotic content in ChatGPT. Experts say that could lead to "emotional commodification," or horniness as a revenue stream. In May of 2024, while I was combing through OpenAI's "Model Spec" laying out how ChatGPT should act, one comment buried in the document struck me as peculiar. It said OpenAI was "exploring" how to let adult ChatGPT users generate content with mature themes such as "erotica, extreme gore, slurs, and unsolicited profanity." Seems like the exploration phase is over.

Masked graph modeling (MGM) is a promising approach for molecular representation learning (MRL).However, extending the success of re-mask decoding from 2D to 3D MGM is non-trivial, primarily due to two conflicting challenges: avoiding 2D structure leakage to the decoder, while still providing sufficient 2D context for reconstructing re-masked atoms. To address these challenges, we propose 3D-GSRD: a 3D Molecular Graph Auto-Encoder with Selective Re-mask Decoding. The core innovation of 3D-GSRD lies in its Selective Re-mask Decoding(SRD), which re-masks only 3D-relevant information from encoder representations while preserving the 2D graph structures. This SRD is synergistically integrated with a 3D Relational-Transformer(3D-ReTrans) encoder alongside a structure-independent decoder. We analyze that SRD, combined with the structure-independent decoder, enhances the encoder's role in MRL. Extensive experiments show that 3D-GSRD achieves strong downstream performance, setting a new state-of-the-art on 7 out of 8 targets in the widely used MD17 molecular property prediction benchmark. The code is released at https://github.com/WuChang0124/3D-GSRD.

This research proposes a systematic, large language model (LLM) approach for extracting product and service attributes, features, and associated sentiments from customer reviews. Grounded in marketing theory, the framework distinguishes perceptual attributes from actionable features, producing interpretable and managerially actionable insights. We apply the methodology to 20,000 Yelp reviews of Starbucks stores and evaluate eight prompt variants on a random subset of reviews. Model performance is assessed through agreement with human annotations and predictive validity for customer ratings. Results show high consistency between LLMs and human coders and strong predictive validity, confirming the reliability of the approach. Human coders required a median of six minutes per review, whereas the LLM processed each in two seconds, delivering comparable insights at a scale unattainable through manual coding. Managerially, the analysis identifies attributes and features that most strongly influence customer satisfaction and their associated sentiments, enabling firms to pinpoint "joy points," address "pain points," and design targeted interventions. We demonstrate how structured review data can power an actionable marketing dashboard that tracks sentiment over time and across stores, benchmarks performance, and highlights high-leverage features for improvement. Simulations indicate that enhancing sentiment for key service features could yield 1-2% average revenue gains per store.