"CHiPs" star Erik Estrada shared a warning about how artificial intelligence can "destroy lives." During an interview with Fox News Digital, the 75-year-old actor and "Divine Renovation" host acknowledged the benefits of AI but cautioned that the new technology is also frequently being used for nefarious purposes. "I think just like the Internet, just like the cell phones, just like everything -- they need to just use the positive side of it," Estrada said. "The side which can help or employ and create goodwill, good things, good jobs, good fortune for people that want to go in that direction and not, of course, use the negative stuff." "CHiPs" star Erik Estrada warned about the dangers posed by AI. (Brian To/FilmMagic) Estrada pointed to how AI can be used to create deepfakes -- deceptive pictures, videos and audio that misrepresent people or events.

Tables play a crucial role in conveying information in various domains. We propose a Plan-then-Reason framework to answer different types of user queries over tables with sentence context. The framework first plans the reasoning paths over the context, then assigns each step to program-based or textual reasoning to reach the final answer. This framework enhances the table reasoning abilities for both in-context learning and fine-tuning methods. GPT-3.5-Turbo following Plan-then-Reason framework surpasses other prompting baselines without self-consistency while using less API calls and in-context demonstrations. We also construct an instruction tuning set TrixInstruct to evaluate the effectiveness of fine-tuning with this framework. We present ProTrix model family by finetuning models on TrixInstruct. Our experiments show that ProTrix family generalizes to diverse unseen tabular tasks with only 6k training instances. We further demonstrate that ProTrix can generate accurate and faithful explanations to answer complex free-form questions. Our work underscores the importance of the planning and reasoning abilities towards a model over tabular tasks with generalizability and interpretability. We open-source our dataset and models at https://github.com/WilliamZR/ProTrix.

The increasing use of transformer-based large language models brings forward the challenge of processing long sequences. In document visual question answering (DocVQA), leading methods focus on the single-page setting, while documents can span hundreds of pages. We present GRAM, a method that seamlessly extends pre-trained single-page models to the multi-page setting, without requiring computationally-heavy pretraining. To do so, we leverage a single-page encoder for local page-level understanding, and enhance it with document-level designated layers and learnable tokens, facilitating the flow of information across pages for global reasoning. To enforce our model to utilize the newly introduced document-level tokens, we propose a tailored bias adaptation method. For additional computational savings during decoding, we introduce an optional compression stage using our C-Former model, which reduces the encoded sequence length, thereby allowing a tradeoff between quality and latency. Extensive experiments showcase GRAM's state-of-the-art performance on the benchmarks for multi-page DocVQA, demonstrating the effectiveness of our approach.

In the wake of the May 2018 mass shooting that resulted in 10 deaths at Santa Fe (Texas) High School, the Santa Fe Independent School District looked at all possible options to improve school safety within reasonable financial constraints. The district considered the idea of technology to enhance its approximately 750 cameras with facial recognition but did not immediately see a workable solution -- for reasons of cost, and concerns about shaky accuracy that could lead to false positives, says Kip Robins, director of technology for Santa Fe ISD, which has about 4,500 students. The district ultimately contracted with a company called AnyVision, which demonstrated its Better Tomorrow product, an artificial-intelligence-based application that plugs into an existing camera network and provides the ability to do surveillance based on a certain face, body or object. School districts or other end users can create a watch list to keep an eye out for potential pedophiles, for example, or someone known to be mentally unstable. The Santa Fe ISD's solution is part of a growing cadre of software offerings that use artificial intelligence to power through reams of data and notice certain predetermined visual information – whether it's someone's face, or a certain license plate, or simply human movement in a place and time where there shouldn't be any.