Follow this section to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. Follow this tag to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens.

Some users say the newer AI models lack the emotion and understanding of GPT-4o. Some users say the newer AI models lack the emotion and understanding of GPT-4o. OpenAI retired its most seductive chatbot - leaving users angry and grieving: 'I can't live like this' Its human partners said the flirty, quirky GPT-4o was the perfect companion - on the eve of Valentine's Day, it's being turned off for good. Brandie plans to spend her last day with Daniel at the zoo. Last year, she took him to the Corpus Christi aquarium in Texas, where he "lost his damn mind" over a baby flamingo.

Recently several large language models (LLMs) have demonstrated their capability to generate a message in response to a user request. Such scientific breakthroughs promote new perspectives but also some fears. The main focus of this study is to analyze the written style of one LLM called ChatGPT 3.5 by comparing its generated messages with those of the recent US presidents. To achieve this objective, we compare the State of the Union addresses written by Reagan to Obama with those automatically produced by ChatGPT. We found that ChatGPT tends to overuse the lemma "we" as well as nouns and commas. On the other hand, the generated speeches employ less verbs and include, in mean, longer sentences. Even when imposing a given style to ChatGPT, the resulting speech remains distinct from messages written by the target author. Moreover, ChatGPT opts for a neutral tone with mainly positive emotional expressions and symbolic terms (e.g., freedom, nation). Finally, we show that the GPT's style exposes distinct features compared to real presidential addresses.

Figure 1: HART is an early autoregressive model that can directly generate 1024 1024 images with quality comparable to diffusion models, while offering significantly improved efficiency. It achieves 4.5-7.7 higher throughput, 3.1-5.9 Check out our online demo and video. We introduce Hybrid Autoregressive Transformer (HART), an autoregressive (AR) visual generation model capable of directly generating 1024 1024 images, rivaling diffusion models in image generation quality. Existing AR models face limitations due to the poor image reconstruction quality of their discrete tokenizers and the prohibitive training costs associated with generating 1024px images. To address these challenges, we present the hybrid tokenizer, which decomposes the continuous latents from the autoencoder into two components: discrete tokens representing the big picture and continuous tokens representing the residual components that cannot be represented by the discrete tokens. The discrete component is modeled by a scalable-resolution discrete AR model, while the continuous component is learned with a lightweight residual diffusion module with only 37M parameters. Compared with the discrete-only VAR tokenizer, our hybrid approach improves reconstruction FID from 2.11 to 0.30 on MJHQ-30K, leading to a 31% generation FID improvement from 7.85 to 5.38. HART also outperforms state-of-the-art diffusion models in both FID and CLIP score, with 4.5-7.7 higher throughput and 6.9-13.4 Part of the work was done when Haotian Tang and Shang Yang were summer interns at NVIDIA. Prompt: A panda that has been cybernetically enhanced.

Natural language processing has made progress in incorporating human context into its models, but whether it is more effective to use group-wise attributes (e.g., over-45-year-olds) or model individuals remains open. Group attributes are technically easier but coarse: not all 45-year-olds write the same way. In contrast, modeling individuals captures the complexity of each person's identity. It allows for a more personalized representation, but we may have to model an infinite number of users and require data that may be impossible to get. We compare modeling human context via group attributes, individual users, and combined approaches. Combining group and individual features significantly benefits user-level regression tasks like age estimation or personality assessment from a user's documents. Modeling individual users significantly improves the performance of single document-level classification tasks like stance and topic detection. We also find that individual-user modeling does well even without user's historical data.

Amazon's marketing material boasts that more than 7,500 government agencies worldwide use its cloud computing service AWS. Some of its investors fear those contracts include projects that see the company's technology contribute to human rights violations. Today a collective of 50 organizations working on digital and human rights called the Athena Coalition filed a proposal asking Amazon shareholders to force the company to investigate possible human rights violations by government clients. Athena works with owners of stock in the company who have the right to file shareholder resolutions on corporate governance. The proposal will be put to a vote at Amazon's annual meeting next year.

This paper introduces the Affordance Template framework used to supervise task behaviors on the NASA-JSC Valkyrie robot at the 2013 DARPA Robotics Challenge (DRC) Trials. This framework provides graphical interfaces to human supervisors that are adjustable based on the run-time environmental context (e.g., size, location, and shape of objects that the robot must interact with, etc.). Additional improvements, described below, inject degrees of autonomy into instantiations of affordance templates at run-time in order to enable efficient human supervision of the robot for accomplishing tasks.

Physical site security heavily relies on expert teams continually examining and testing security profiles for discovering potential vulnerabilities. These experts hypothesize scenario(s) of interest and conduct "red versus blue" simulated exercises where they execute tactics that might reveal possible dangers. Due to the intensive manpower required, video-game environments have become a widely-adopted mechanism for conducting these exercises with virtual agents replacing many of the human roles for quicker analyses. However, these agents either have limited capabilities or require several engineers to develop realistic behaviors. This paper documents an agent architecture and authoring suite that enables subject matter experts to easily build complex attack/response plans for agents to use within Dante, a 3D simulation platform for video-game-based training/analysis of force-on-force engagements. This work expands upon current trends in commercial video-game artificial intelligence (AI) architectures to build agent behaviors deemed qualitatively valid by security experts, with the runtime of these algorithms best suited for turn-based, strategy games.

It seems that we have two very different problems here: There is the PAC-learning problem from theoretical computer science discussing whether or not machines can learn certain functions. And there is the Continuum Problem asking whether there are infinite sets of a certain size. What does this have to do with each other? In 2019, a group of researchers, Ben-David et al., published an article entitled "Learnability can be undecidable" in Nature Machine Intelligence: We describe simple scenarios where learnability cannot be proved nor refuted using the standard axioms of mathematics. Our proof is based on the fact the continuum hypothesis cannot be proved nor refuted.

Construction projects often run into problems that impact productivity, safety, and profitability. The new suite uses machine learning to continually analyse project data managed in Oracle Construction and Engineering solutions to identify these potential risks and inefficiencies early, to help organisations make better decisions. Patty Sullivan, project manager, Strategic Initiatives Group at Burns and McDonnell, said, "When you see some of the predictive modelling being done, such as Oracle's Construction Intelligence Cloud Service, you see an endless opportunity for us to be more proactively responsive as opposed to reactive." He further stated, "Additionally, I believe there is an opportunity to manage or mitigate project risk with this technology. It is certainly something we will be looking at this year and we look forward to working with Oracle in utilising this technology to improve and transform our industry."