The'Fox & Friends' co-hosts discussed concerns surrounding artificial intelligence and how it will impact the internet moving forward. Astrophysicist Neil deGrasse Tyson sees artificial intelligence as a much-needed stress-test for modern society, with a view that it will lead humanity to refine some of its more outdated ideas and systems now that the "genie is out of the bottle." "Of course AI will replace jobs," Tyson said in comments to Fox News Digital. "Entire sectors of our economy have gone obsolete in the presence of technology ever since the dawn of the industrial era. "The historical flaw in the reasoning is to presume that when jobs disappear, there will be no other jobs for people to do," he argued. "More people are employed in the world than ever before, yet none of them are making buggy whips.

Two companies are coming together to develop humanoid robots with AI that will be able to perform jobs from manufacturing to healthcare professions. Do you ever find yourself glued to the screen watching a movie like "Terminator" or "Westworld" and think, "Phew! Movies like this are getting closer to becoming a reality with each passing day. CLICK TO GET KURT'S FREE CYBERGUY NEWSLETTER WITH QUICK TIPS, TECH REVIEWS, SECURITY ALERTS AND EASY HOW-TO'S TO MAKE YOU SMARTER Let me introduce you to a breakthrough that's happening in the real world, which is both exciting and slightly unsettling. It's a fascinating development that might make you wonder if the line between humans and machines is starting to blur, just like in those movies we love.

Large language models, or LLMs, are a type of neural network that learns to write and converse with users; they back all of the chatbots that have swooped onto the scene in recent months. They learn to "speak" by hovering up massive amounts of text, often websites scraped from the internet, and finding statistical relationships between words. When these systems pattern match, it can lead to feats of creativity: A chatbot can create song lyrics closely matching Jay-Z's style because it's absorbed the patterns of his entire discography. But LLMs don't have awareness of the meanings behind words.

Clustering is a powerful and extensively used data science tool. While clustering is generally thought of as an unsupervised learning technique, there are also supervised variations such as Spath's clusterwise regression that attempt to find clusters of data that yield low regression error on a supervised target. We believe that clusterwise regression is just a single vertex of a largely unexplored design space of supervised clustering models. In this article, we define a generalized optimization framework for predictive clustering that admits different cluster definitions (arbitrary point assignment, closest center, and bounding box) and both regression and classification objectives. We then present a joint optimization strategy that exploits mixed-integer linear programming (MILP) for global optimization in this generalized framework. To alleviate scalability concerns for large datasets, we also provide highly scalable greedy algorithms inspired by the Majorization-Minimization (MM) framework. Finally, we demonstrate the ability of our models to uncover different interpretable discrete cluster structures in data by experimenting with four real-world datasets.

Camera images are ubiquitous in machine learning research. They also play a central role in the delivery of important services spanning medicine and environmental surveying. However, the application of machine learning models in these domains has been limited because of robustness concerns. A primary failure mode are performance drops due to differences between the training and deployment data. While there are methods to prospectively validate the robustness of machine learning models to such dataset drifts, existing approaches do not account for explicit models of the primary object of interest: the data. This limits our ability to study and understand the relationship between data generation and downstream machine learning model performance in a physically accurate manner. In this study, we demonstrate how to overcome this limitation by pairing traditional machine learning with physical optics to obtain explicit and differentiable data models. We demonstrate how such data models can be constructed for image data and used to control downstream machine learning model performance related to dataset drift. The findings are distilled into three applications. First, drift synthesis enables the controlled generation of physically faithful drift test cases to power model selection and targeted generalization. Second, the gradient connection between machine learning task model and data model allows advanced, precise tolerancing of task model sensitivity to changes in the data generation. These drift forensics can be used to precisely specify the acceptable data environments in which a task model may be run. Third, drift optimization opens up the possibility to create drifts that can help the task model learn better faster, effectively optimizing the data generating process itself. A guide to access the open code and datasets is available at https://github.com/aiaudit-org/raw2logit.

In this paper, we explore the following question: Are synonym substitution attacks really synonym substitution attacks (SSAs)? We approach this question by examining how SSAs replace words in the original sentence and show that there are still unresolved obstacles that make current SSAs generate invalid adversarial samples. We reveal that four widely used word substitution methods generate a large fraction of invalid substitution words that are ungrammatical or do not preserve the original sentence's semantics. Next, we show that the semantic and grammatical constraints used in SSAs for detecting invalid word replacements are highly insufficient in detecting invalid adversarial samples.

Neural Radiance Fields has become a prominent method of scene generation via view synthesis. A critical requirement for the original algorithm to learn meaningful scene representation is camera pose information for each image in a data set. Current approaches try to circumnavigate this assumption with moderate success, by learning approximate camera positions alongside learning neural representations of a scene. This requires complicated camera models, causing a long and complicated training process, or results in a lack of texture and sharp details in rendered scenes. In this work we introduce Hash Color Correction (HashCC) -- a lightweight method for improving Neural Radiance Fields rendered image quality, applicable also in situations where camera positions for a given set of images are unknown.

Machine generated text is increasingly difficult to distinguish from human authored text. Powerful open-source models are freely available, and user-friendly tools that democratize access to generative models are proliferating. ChatGPT, which was released shortly after the first edition of this survey, epitomizes these trends. The great potential of state-of-the-art natural language generation (NLG) systems is tempered by the multitude of avenues for abuse. Detection of machine generated text is a key countermeasure for reducing abuse of NLG models, with significant technical challenges and numerous open problems. We provide a survey that includes both 1) an extensive analysis of threat models posed by contemporary NLG systems, and 2) the most complete review of machine generated text detection methods to date. This survey places machine generated text within its cybersecurity and social context, and provides strong guidance for future work addressing the most critical threat models, and ensuring detection systems themselves demonstrate trustworthiness through fairness, robustness, and accountability.

In this paper, we consider a novel research problem: music-to-text synaesthesia. Different from the classical music tagging problem that classifies a music recording into pre-defined categories, music-to-text synaesthesia aims to generate descriptive texts from music recordings with the same sentiment for further understanding. As existing music-related datasets do not contain the semantic descriptions on music recordings, we collect a new dataset that contains 1,955 aligned pairs of classical music recordings and text descriptions. Based on this, we build a computational model to generate sentences that can describe the content of the music recording. To tackle the highly non-discriminative classical music, we design a group topology-preservation loss, which considers more samples as a group reference and preserves the relative topology among different samples. Extensive experimental results qualitatively and quantitatively demonstrate the effectiveness of our proposed model over five heuristics or pre-trained competitive methods and their variants on our collected dataset.

For the last few years, you've been able to say "Stop" to tell Google Assistant to end an alarm early without the need to preface your command with "Hey Google." It's a handy feature Google first debuted on Assistant-enabled smart displays and speakers before later rolling it out to Pixel smartphones. And for the most part, it works like a charm, though one person recently discovered a fun quirk of the feature that involves the Pixies classic "Where Is My Mind?" In a Reddit post spotted by Android Police, Pixel user "asevarte" recounts how their morning alarm would go off and sometimes turn off moments later for seemingly no reason. "Maybe once every other week or so, I would wake up 30 minutes later on my backup alarm, with no indication as to why the first shut itself off," they told the Google Pixel subreddit.