We present FAST, an optimization framework for fast additive segmentation. FAST segments piecewise constant shape functions for each feature in a dataset to produce transparent additive models. The framework leverages a novel optimization procedure to fit these models $\sim$2 orders of magnitude faster than existing state-of-the-art methods, such as explainable boosting machines \citep{nori2019interpretml}. We also develop new feature selection algorithms in the FAST framework to fit parsimonious models that perform well. Through experiments and case studies, we show that FAST improves the computational efficiency and interpretability of additive models.

With the rapid advancements of the text-to-image generative model, AI-generated images (AGIs) have been widely applied to entertainment, education, social media, etc. However, considering the large quality variance among different AGIs, there is an urgent need for quality models that are consistent with human subjective ratings. To address this issue, we extensively consider various popular AGI models, generated AGI through different prompts and model parameters, and collected subjective scores at the perceptual quality and text-to-image alignment, thus building the most comprehensive AGI subjective quality database AGIQA-3K so far. Furthermore, we conduct a benchmark experiment on this database to evaluate the consistency between the current Image Quality Assessment (IQA) model and human perception, while proposing StairReward that significantly improves the assessment performance of subjective text-to-image alignment. We believe that the fine-grained subjective scores in AGIQA-3K will inspire subsequent AGI quality models to fit human subjective perception mechanisms at both perception and alignment levels and to optimize the generation result of future AGI models. The database is released on https://github.com/lcysyzxdxc/AGIQA-3k-Database.

This paper is a submission to the Open Philanthropy AI Worldviews Contest. In it, we estimate the likelihood of transformative artificial general intelligence (AGI) by 2043 and find it to be <1%. Specifically, we argue: The bar is high: AGI as defined by the contest - something like AI that can perform nearly all valuable tasks at human cost or less - which we will call transformative AGI is a much higher bar than merely massive progress in AI, or even the unambiguous attainment of expensive superhuman AGI or cheap but uneven AGI. Many steps are needed: The probability of transformative AGI by 2043 can be decomposed as the joint probability of a number of necessary steps, which we group into categories of software, hardware, and sociopolitical factors. No step is guaranteed: For each step, we estimate a probability of success by 2043, conditional on prior steps being achieved. Many steps are quite constrained by the short timeline, and our estimates range from 16% to 95%. Therefore, the odds are low: Multiplying the cascading conditional probabilities together, we estimate that transformative AGI by 2043 is 0.4% likely. Reaching >10% seems to require probabilities that feel unreasonably high, and even 3% seems unlikely. Thoughtfully applying the cascading conditional probability approach to this question yields lower probability values than is often supposed. This framework helps enumerate the many future scenarios where humanity makes partial but incomplete progress toward transformative AGI.

Tom Newhouse, vice president of Convergence Media, discusses the potential impact of artificial intelligence on elections after an RNC AI ad garnered attention. The Future of Life Institute has issued a petition to pause the development of GPT-5 and similar Large Language Models (LLMs). Their anxieties are understandable, but I believe they are much overblown. I've heard similar fears related to the advent of Artificial General Intelligence expressed off and on since I introduced the term AGI in 2005, but I think a pause would be a badly wrong move in the current situation for several reasons. Let me first emphasize something that's been mostly forgotten in the panic: Large Language Models can't become Artificial General Intelligences.

Artificial intelligence is rapidly transforming all sectors of our society. Whether we realize it or not, every time we do a Google search or ask Siri a question, we're using AI. For better or worse, the same is true about the very character of warfare. This is the reason why the Department of Defense – like its counterparts in China and Russia– is investing billions of dollars to develop and integrate AI into defense systems. It's also the reason why DoD is now embracing initiatives that envision future technologies, including the next phase of AI – artificial general intelligence.

The narrowness of AI will someday be replaced by artificial general intelligence. But will it have the capability to rival human intelligence and creativity? Some of the world's most advanced artificial intelligence (AI) systems, at least the ones the public hear about, are famous for beating human players at chess or poker. Other algorithms are known for their ability to learn how to recognize cats or their inability to recognize people with darker skin. But are current AI systems anything more than toys?

Some of the world's most advanced artificial intelligence (AI) systems, at least the ones the public hear about, are famous for beating human players at chess or poker. Other algorithms are known for their ability to learn how to recognize cats or their inability to recognize people with darker skin. But are current AI systems anything more than toys? Sure, their ability to play games or identify animals is impressive, but does this help toward creating useful AI systems? To answer this, we need to take a step back and question what the goals of AI are.

Artificial Intelligence (AI) is a very common term today, but what present-day AI is and what most people think it is, can be very different. The AI you know is "weak" AI, but the AI many fear is "strong." It's easy to throw around a term like "AI", but that doesn't make it clear what we're really talking about. In general, "artificial intelligence" refers to a whole field in computer science. The goal of AI is to get computers to replicate what natural intelligence can accomplish.

AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner Computer systems based on AGI technology ('AGIs') are specifically engineered to be able to learn. They are able to acquire a wide range of knowledge and skills via learning, similar to the way we do. Unlike current computer systems, AGIs do not need to be programmed to do new tasks. Instead, they are simply instructed and taught by humans. Additionally, these systems can learn by themselves both implicitly'on-the-job', and explicitly by reading and practicing.

Whether we realize it or not, most of us deal with artificial intelligence (AI) every day. Each time you do a Google Search or ask Siri a question, you are using AI. The catch, however, is that the intelligence these tools provide is not really intelligent. They don't truly think or understand in the way humans do. Rather, they analyze massive data sets, looking for patterns and correlations.