"You cannot put a robot in an unstructured environment and then ask it to move around without basically destroying things. It's too much for technology to ask at this moment of time," says Prof Alireza Mohammadi, who established the Robotic Motion Intelligence Lab at the University of Michigan-Dearborn.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Google will invest an additional $1.7 billion to support three data center campuses in central Ohio, the company announced Monday. The tech giant now operates a center in New Albany and announced in May that it would build additional centers in Columbus and Lancaster to help power its artificial intelligence technology and other tools. A picture taken on April 15, 2022, in Moscow shows the U.S. multinational technology and Internet-related services company Google's logo on a tablet screen.

In March, OpenAI sought to head off concerns that its immensely popular, albeit hallucination-prone, ChatGPT generative AI could be used to dangerously amplify political disinformation campaigns through an update to the company's Usage Policy to expressly prohibit such behavior. However, an investigation by The Washington Post shows that the chatbot is still easily incited to breaking those rules, with potentially grave repercussions for the 2024 election cycle. OpenAI's user policies specifically ban its use for political campaigning, save for use by "grassroots advocacy campaigns" organizations. This includes generating campaign materials in high volumes, targeting those materials at specific demographics, building campaign chatbots to disseminate information, engage in political advocacy or lobbying. Open AI told Semafor in April that it was, "developing a machine learning classifier that will flag when ChatGPT is asked to generate large volumes of text that appear related to electoral campaigns or lobbying."

This effort is focused on examining the behavior of reinforcement learning systems in personalization environments and detailing the differences in policy entropy associated with the type of learning algorithm utilized. We demonstrate that Policy Optimization agents often possess low-entropy policies during training, which in practice results in agents prioritizing certain actions and avoiding others. Conversely, we also show that Q-Learning agents are far less susceptible to such behavior and generally maintain high-entropy policies throughout training, which is often preferable in real-world applications. We provide a wide range of numerical experiments as well as theoretical justification to show that these differences in entropy are due to the type of learning being employed.

Fox News host Bret Baier has more on U.S. and its allies efforts to increase semiconductor manufacturing on'Special Report.' The United Kingdom has pledged to spend 100 million pounds (or $125.8 million) on buying and developing computer chips necessary for artificial intelligence (AI) systems in a move that seeks to cement Britain as a global leader in the sector, but experts worry it is not enough to match the competitive market. "The U.K. has a valuable perspective on AI development – sitting between the U.S. free-for-all position and the EU regulatory approach – that makes it the perfect venue for the first international AI global safety conference," Alan Mendoza, co-founder and executive director of the Henry Jackson Society, told Fox News Digital. British Prime Minister Rishi Sunak plans to build thousands of high-powered artificial intelligence chips, building on a deal struck between the U.K. and U.S. during his state visit in June when he and President Biden signed the "Atlantic Declaration." The White House touted the agreement as something that would ensure that the "unique alliance is adapted, reinforced and reimagined for the challenges of this moment," including the "handful of critical and emerging technologies" such as AI that are "forming the backbone of new industries and shaping our national security landscape."

With the rise of foundation models, a new artificial intelligence paradigm has emerged, by simply using general purpose foundation models with prompting to solve problems instead of training a separate machine learning model for each problem. Such models have been shown to have emergent properties of solving problems that they were not initially trained on. The studies for the effectiveness of such models are still quite limited. In this work, we widely study the capabilities of the ChatGPT models, namely GPT-4 and GPT-3.5, on 13 affective computing problems, namely aspect extraction, aspect polarity classification, opinion extraction, sentiment analysis, sentiment intensity ranking, emotions intensity ranking, suicide tendency detection, toxicity detection, well-being assessment, engagement measurement, personality assessment, sarcasm detection, and subjectivity detection. We introduce a framework to evaluate the ChatGPT models on regression-based problems, such as intensity ranking problems, by modelling them as pairwise ranking classification. We compare ChatGPT against more traditional NLP methods, such as end-to-end recurrent neural networks and transformers. The results demonstrate the emergent abilities of the ChatGPT models on a wide range of affective computing problems, where GPT-3.5 and especially GPT-4 have shown strong performance on many problems, particularly the ones related to sentiment, emotions, or toxicity. The ChatGPT models fell short for problems with implicit signals, such as engagement measurement and subjectivity detection.

As drones become increasingly prevalent in human life, they also raises security concerns such as unauthorized access and control, as well as collisions and interference with manned aircraft. Therefore, ensuring the ability to accurately detect and identify between different drones holds significant implications for coverage extension. Assisted by machine learning, radio frequency (RF) detection can recognize the type and flight mode of drones based on the sampled drone signals. In this paper, we first utilize Short-Time Fourier. Transform (STFT) to extract two-dimensional features from the raw signals, which contain both time-domain and frequency-domain information. Then, we employ a Convolutional Neural Network (CNN) built with ResNet structure to achieve multi-class classifications. Our experimental results show that the proposed ResNet-STFT can achieve higher accuracy and faster convergence on the extended dataset. Additionally, it exhibits balanced performance compared to other baselines on the raw dataset.

Linguistic style matching (LSM) in conversations can be reflective of several aspects of social influence such as power or persuasion. However, how LSM relates to the outcomes of online communication on platforms such as Reddit is an unknown question. In this study, we analyze a large corpus of two-party conversation threads in Reddit where we identify all occurrences of LSM using two types of style: the use of function words and formality. Using this framework, we examine how levels of LSM differ in conversations depending on several social factors within Reddit: post and subreddit features, conversation depth, user tenure, and the controversiality of a comment. Finally, we measure the change of LSM following loss of status after community banning. Our findings reveal the interplay of LSM in Reddit conversations with several community metrics, suggesting the importance of understanding conversation engagement when understanding community dynamics.

Tool-augmented large language models (LLMs) have achieved remarkable progress in tackling a broad range of tasks. However, existing methods are mainly restricted to specifically designed tools and fail to fulfill complex instructions, having great limitations when confronted with real-world scenarios. In this paper, we explore a more realistic scenario by connecting LLMs with RESTful APIs, which adhere to the widely adopted REST software architectural style for web service development. To address the practical challenges of tackling complex instructions, we propose RestGPT, which exploits the power of LLMs and conducts a coarse-to-fine online planning mechanism to enhance the abilities of task decomposition and API selection. RestGPT also contains an API executor tailored for calling RESTful APIs, which can meticulously formulate parameters and parse API responses. To fully evaluate the performance of RestGPT, we propose RestBench, a high-quality benchmark which consists of two real-world scenarios and human-annotated instructions with gold solution paths. Experiments show that RestGPT is able to achieve impressive results in complex tasks and has strong robustness, which paves a new way towards AGI.

A TikToker warned of a growing trend involving child predators who use artificial intelligence to turn photos and videos of kids into explicit content. Posting imagery of children on social media can invite "digital kidnappers" to steal their likeness and use them in exploitative AI-generated videos, Alex Hoffman said in a viral TikTok video. "Digital kidnapping is when somebody steals the photos of your minor from the internet, usually a social media platform, and either pretends to be the child or pretends to be the child's parents," she said. "Oftentimes digital kidnappers will take normal photos of a child on the internet and alter them to look explicit or show the child doing something inappropriate." "Digital kidnappers can also take photos of a child and make them into an inappropriate video using AI materials," said Hoffman, a law student who has worked with the government investigating online sex crimes against children.