Fake video walk-throughs, a magically expanding loft, and stair hallucinations are just some of the new AI-generated features house hunters are coming across. As you're hunting through real estate listings for a new home in Franklin, Tennessee, you come across a vertical video showing off expansive rooms featuring a four-poster bed, a fully stocked wine cellar, and a soaking tub. It looks perfect--maybe a little too perfect. Everything in the video is AI-generated . The real property is completely empty, and the luxury furniture is a product of virtual staging.

Recent breakthroughs in large language models (LLM) have stirred up global attention, and the research has been accelerating non-stop since then. Philosophers and psychologists have also been researching the structure of language for decades, but they are having a hard time finding a theory that directly benefits from the breakthroughs of LLMs. In this article, we propose a novel structure of language that reflects well on the mechanisms behind language models and go on to show that this structure is also better at capturing the diverse nature of language compared to previous methods. An analogy of linear algebra is adapted to strengthen the basis of this perspective. We further argue about the difference between this perspective and the design philosophy for current language models. Lastly, we discuss how this perspective can lead us to research directions that may accelerate the improvements of science fastest.

Time series models, typically trained on numerical data, are designed to forecast future values. These models often rely on weighted averaging techniques over time intervals. However, real-world time series data is seldom isolated and is frequently influenced by non-numeric factors. For instance, stock price fluctuations are impacted by daily random events in the broader world, with each event exerting a unique influence on price signals. Previously, forecasts in financial markets have been approached in two main ways: either as time-series problems over price sequence or sentiment analysis tasks. The sentiment analysis tasks aim to determine whether news events will have a positive or negative impact on stock prices, often categorizing them into discrete labels. Recognizing the need for a more comprehensive approach to accurately model time series prediction, we propose a collaborative modeling framework that incorporates textual information about relevant events for predictions. Specifically, we leverage the intuition of large language models about future changes to update real number time series predictions. We evaluated the effectiveness of our approach on financial market data.

Large multimodal models demonstrate remarkable generalist ability to perform diverse multimodal tasks in a zero-shot manner. Large-scale web-based image-text pairs contribute fundamentally to this success, but suffer from excessive noise. Recent studies use alternative captions synthesized by captioning models and have achieved notable benchmark performance. However, our experiments reveal significant Scalability Deficiency and World Knowledge Loss issues in models trained with synthetic captions, which have been largely obscured by their initial benchmark success. Upon closer examination, we identify the root cause as the overly-simplified language structure and lack of knowledge details in existing synthetic captions. To provide higher-quality and more scalable multimodal pretraining data, we propose CapsFusion, an advanced framework that leverages large language models to consolidate and refine information from both web-based image-text pairs and synthetic captions. Extensive experiments show that CapsFusion captions exhibit remarkable all-round superiority over existing captions in terms of model performance (e.g., 18.8 and 18.3 improvements in CIDEr score on COCO and NoCaps), sample efficiency (requiring 11-16 times less computation than baselines), world knowledge depth, and scalability. These effectiveness, efficiency and scalability advantages position CapsFusion as a promising candidate for future scaling of LMM training.

For those women who are considering terminating their pregnancies, a new chatbot called Charley aims to help them start the process of getting an abortion. The chatbot, which launched on Sept. 12, is available on Charley's website, greeting visitors with the message, "Need an abortion? On its website, Charley is described as "designed by abortion experts, made for abortion seekers." One of its co-founders is Cecile Richards, former president of Planned Parenthood. Richards "oversees legal, political, and policy matters and leads fundraising efforts" for Charley, according to the chatbot's website. Another co-founder is Tom Subak, former chief strategy officer at Planned Parenthood. A new chatbot called Charley aims to help women start the process of getting an abortion. Charley isn't an app -- it lives online, on its own website. While individuals can freely visit the site, the company is also seeking medical providers who will agree to embed the chatbot directly on their own websites, "to meet abortion seekers wherever they are online," said Nicole Cushman, Charley's New York-based content manager, in an interview with Fox News Digital. Cushman, who has held leadership positions at Planned Parenthood, said the idea for the chatbot came about after Roe v. Wade was overturned -- with the goal of "improving people's online search experience." "Our research showed that people were turning primarily to Google for information about abortion options in the post-Roe landscape, and that it was very challenging for abortion seekers to connect to available options," she said. People "were ending up in an endless Google loop." "This was particularly the case if they were living in a state with an abortion ban or restriction -- they were ending up in an endless Google loop." One of Charley's co-founders is Cecile Richards, former president of Planned Parenthood. The company is seeking medical providers who will agree to embed the chatbot directly on their own websites. Charley's creators envisioned a "simple, effective way to pull together information from a range of sources" and "cut through the confusion," Cushman told Fox News Digital. Unlike large language models like ChatGPT, Charley doesn't allow people to type questions. Instead, the chatbot uses a "decision tree" format that guides visitors through a series of pre-written prompts, including the desired type of abortion and the date of their last menstrual period. It also asks for a zip code to determine the specific abortion laws in the visitor's state of residence. 'PRO-LIFE GENERATION IS ALIVE AND WELL' AS FURIOUS FIGHT FOR THE UNBORN CONTINUES For example, when Fox News Digital entered a zip code in Ohio, the response was: "Currently, abortion care is legal in Ohio, but only up to 22 weeks.

A New York Rabbi recently went viral for delivering a sermon written by ChatGPT to his congregation, causing many to question the humanity in such an act. Think of ChatGPT as a far more sophisticated version of Google. It's an AI language model designed to generate human-like responses to various questions, from recipes to historical context to computer code and much more in mere seconds. CLICK TO GET KURT'S CYBERGUY NEWSLETTER WITH QUICK TIPS, TECH REVIEWS, SECURITY ALERTS AND EASY HOW-TO'S TO MAKE YOU SMARTER It's surpassed the million-user marker in about a week of its introduction. For context, it took companies like Facebook several months to achieve the same success.

Solving inverse problems is a fundamental component of science, engineering and mathematics. With the advent of deep learning, deep neural networks have significant potential to outperform existing state-of-the-art, model-based methods for solving inverse problems. However, it is known that current data-driven approaches face several key issues, notably hallucinations, instabilities and unpredictable generalization, with potential impact in critical tasks such as medical imaging. This raises the key question of whether or not one can construct deep neural networks for inverse problems with explicit stability and accuracy guarantees. In this work, we present a novel construction of accurate, stable and efficient neural networks for inverse problems with general analysis-sparse models, termed NESTANets. To construct the network, we first unroll NESTA, an accelerated first-order method for convex optimization. The slow convergence of this method leads to deep networks with low efficiency. Therefore, to obtain shallow, and consequently more efficient, networks we combine NESTA with a novel restart scheme. We then use compressed sensing techniques to demonstrate accuracy and stability. We showcase this approach in the case of Fourier imaging, and verify its stability and performance via a series of numerical experiments. The key impact of this work is demonstrating the construction of efficient neural networks based on unrolling with guaranteed stability and accuracy.

Amazon's Scout robot, a small machine that looks like a cooler and can navigate sidewalks, won't be delivering anybody's packages anymore. The e-commerce giant has shut down field testing for the experimental machine and is "reorienting" the program. According to Bloomberg, the Scout team has been disbanded and most of its 400 members will be offered new positions within the company. Amazon spokesperson Alisa Carroll told Reuters that the company will not be abandoning the project completely. Only a skeleton crew will remain to consider the use of autonomous robot for deliveries, though, and that could mean that it's the end for the cooler-like Scout.

Since 1999, Amazon's disruptive bravado has made "getting Amazoned" a fear for executives in any sector the tech giant sets its sights on. Here are the industries that could be under threat next. Jeff Bezos once famously said, "Your margin is my opportunity." Today, Amazon is finding opportunities in industries that would have been unthinkable for the company to attack even a few years ago. Throughout the 2000s, Amazon's e-commerce dominance paved a path of destruction through books, music, toys, sports, and a range of other retail verticals. Big box stores like Toys "R" Us, Sports Authority, and Barnes & Noble -- some of which had thrived for more than a century -- couldn't compete with Amazon's ability to combine uncommonly fast shipping with low prices. Today, Amazon's disruptive ambitions extend far beyond retail. With its expertise in complex supply chain logistics and competitive advantage in data collection, Amazon is attacking a whole host of new industries. The tech giant has ...

Dramatic employee turnover is being predicted in the post-pandemic era, at the same time that AI is being incorporated into more learning and development solutions, giving employers an opportunity to establish a competitive differentiation. An employee turnover "tsunami" is predicted by results from a survey of 2,000 adults in February conducted by The Work Institute, a research and consulting firm in Franklin, Tenn., according to an account from SHRM, the Society of Human Resource Management. The survey found that half of employees in North America plan to look for a new job in 2021. "We see absolutely pent-up turnover demand in the U.S. workforce," stated Danny Nelms, president of The Work Institute, which is focused on employee engagement and retention. Prior to the pandemic, the firm would see about 3.5 million people leaving their jobs monthly.