We develop an end-to-end workflow for the training and implementation of co-designed neural networks (NNs) for efficient field-programmable gate array (FPGA) and application-specific integrated circuit (ASIC) hardware. Our approach leverages Hessian-aware quantization (HAWQ) of NNs, the Quantized Open Neural Network Exchange (QONNX) intermediate representation, and the hls4ml tool flow for transpiling NNs into FPGA and ASIC firmware. This makes efficient NN implementations in hardware accessible to nonexperts, in a single open-sourced workflow that can be deployed for real-time machine learning applications in a wide range of scientific and industrial settings. We demonstrate the workflow in a particle physics application involving trigger decisions that must operate at the 40 MHz collision rate of the CERN Large Hadron Collider (LHC). Given the high collision rate, all data processing must be implemented on custom ASIC and FPGA hardware within a strict area and latency. Based on these constraints, we implement an optimized mixed-precision NN classifier for high-momentum particle jets in simulated LHC proton-proton collisions.

ChatGPT is another large language model (LLM) inline but due to its performance and ability to converse effectively, it has gained a huge popularity amongst research as well as industrial community. Recently, many studies have been published to show the effectiveness, efficiency, integration, and sentiments of chatGPT and other LLMs. In contrast, this study focuses on the important aspects that are mostly overlooked, i.e. sustainability, privacy, digital divide, and ethics and suggests that not only chatGPT but every subsequent entry in the category of conversational bots should undergo Sustainability, PrivAcy, Digital divide, and Ethics (SPADE) evaluation. This paper discusses in detail about the issues and concerns raised over chatGPT in line with aforementioned characteristics. We support our hypothesis by some preliminary data collection and visualizations along with hypothesized facts. We also suggest mitigations and recommendations for each of the concerns. Furthermore, we also suggest some policies and recommendations for AI policy act, if designed by the governments.

This paper proposes a framework for developing forecasting models by streamlining the connections between core components of the developmental process. The proposed framework enables swift and robust integration of new datasets, experimentation on different algorithms, and selection of the best models. We start with the datasets of different issues and apply pre-processing steps to clean and engineer meaningful representations of time-series data. To identify robust training configurations, we introduce a novel mechanism of multiple cross-validation strategies. We apply different evaluation metrics to find the best-suited models for varying applications. One of the referent applications is our participation in the intelligent forecasting competition held by the United States Agency of International Development (USAID). Finally, we leverage the flexibility of the framework by applying different evaluation metrics to assess the performance of the models in inventory management settings.

Machine learning algorithms are increasingly used to make or support decisions in a wide range of settings. With such expansive use there is also growing concern about the fairness of such methods. Prior literature on algorithmic fairness has extensively addressed risks and in many cases presented approaches to manage some of them. However, most studies have focused on fairness issues that arise from actions taken by a (single) focal decision-maker or agent. In contrast, most real-world systems have many agents that work collectively as part of a larger ecosystem. For example, in a lending scenario, there are multiple lenders who evaluate loans for applicants, along with policymakers and other institutions whose decisions also affect outcomes. Thus, the broader impact of any lending decision of a single decision maker will likely depend on the actions of multiple different agents in the ecosystem. This paper develops formalisms for firm versus systemic fairness, and calls for a greater focus in the algorithmic fairness literature on ecosystem-wide fairness - or more simply systemic fairness - in real-world contexts.

This paper assesses the accuracy, reliability and bias of the Large Language Model (LLM) ChatGPT-4 on the text analysis task of classifying the political affiliation of a Twitter poster based on the content of a tweet. The LLM is compared to manual annotation by both expert classifiers and crowd workers, generally considered the gold standard for such tasks. We use Twitter messages from United States politicians during the 2020 election, providing a ground truth against which to measure accuracy. The paper finds that ChatGPT-4 has achieves higher accuracy, higher reliability, and equal or lower bias than the human classifiers. The LLM is able to correctly annotate messages that require reasoning on the basis of contextual knowledge, and inferences around the author's intentions - traditionally seen as uniquely human abilities. These findings suggest that LLM will have substantial impact on the use of textual data in the social sciences, by enabling interpretive research at a scale.

Large pre-trained models, also known as foundation models (FMs), are trained in a task-agnostic manner on large-scale data and can be adapted to a wide range of downstream tasks by fine-tuning, few-shot, or even zero-shot learning. Despite their successes in language and vision tasks, we have yet seen an attempt to develop foundation models for geospatial artificial intelligence (GeoAI). In this work, we explore the promises and challenges of developing multimodal foundation models for GeoAI. We first investigate the potential of many existing FMs by testing their performances on seven tasks across multiple geospatial subdomains including Geospatial Semantics, Health Geography, Urban Geography, and Remote Sensing. Our results indicate that on several geospatial tasks that only involve text modality such as toponym recognition, location description recognition, and US state-level/county-level dementia time series forecasting, these task-agnostic LLMs can outperform task-specific fully-supervised models in a zero-shot or few-shot learning setting. However, on other geospatial tasks, especially tasks that involve multiple data modalities (e.g., POI-based urban function classification, street view image-based urban noise intensity classification, and remote sensing image scene classification), existing foundation models still underperform task-specific models. Based on these observations, we propose that one of the major challenges of developing a FM for GeoAI is to address the multimodality nature of geospatial tasks. After discussing the distinct challenges of each geospatial data modality, we suggest the possibility of a multimodal foundation model which can reason over various types of geospatial data through geospatial alignments. We conclude this paper by discussing the unique risks and challenges to develop such a model for GeoAI.

Bots like ChatGPT may not be able to pull off the next big Microsoft server worm or Colonial Pipeline ransomware super-infection but they may help criminal gangs and nation-state hackers develop some attacks against IT, according to Rob Joyce, director of the NSA's Cybersecurity Directorate. Joyce, speaking at CrowdStrike's Government Summit Tuesday, said he doesn't expect to see -- at least not "in the near term" -- AI used "for automated attacks that will rip through systems at speeds that are unfathomable today." Machine learning and its chatbot offspring are "the tools that are going to flow and increase the pace of the threat," Joyce claimed. "It's not going to generate the threat itself." Miscreants can use ML software to develop more authentic-seeming phishing lures and craft better ransom notes, while also scanning larger volumes of data for sensitive info they can monetize, he offered.

At a recent White House press conference, a Fox News correspondent asked the Biden administration's press secretary about AI safety researcher Eliezer Yudkowsky's highly publicized claim that if we don't pause or halt the development of artificial intelligence, then "literally everyone on earth will die." The question was met with some laughter from the White House press corps. But as someone with a technical background who covers AI and talks regularly to researchers, developers, and investors in the field, I saw nothing to chuckle at. Rather, I and other more optimistic AI watchers worry that overly dire warnings of imminent AI-driven destruction may cause us to pause or halt the development of a powerful technology with immense potential for improving our lives. Insiders hold a truly wide range of opinions on the best way to approach AI--from Yudkowsky's insistence that we immediately abandon all research in the area, to my own more moderate concern about large-scale industrial accidents arising from misuse of the technology, to an extreme optimism in some quarters about AI's potential to turn humanity into an immortal, star-spanning species.

A new Instagram page is using AI to make parodies of Republicans attempting to push anti-LGBTQ bills. The account, called @RuPublicans--a spin on name of the political party with a nod to the famed RuPaul–has gained nearly 100,000 followers in less than two weeks since its launch, going viral for its creative AI portraits of different Republicans in full drag. Created by partners and digital nomads Craig and Stephen (who asked to be identified by their first names only to maintain their privacy), the project sees the couple using art and technology for political activism. "We were bearing witness to the rhetoric and actions against the drag community," Craig tells TIME, "and it made us want to do something, so we had this idea of putting the GOP in drag." The pair were traveling in an Airstream through the American West when they came up with the idea for the Instagram account, which comes at a particularly vulnerable time for LGBTQ rights in the U.S. State lawmakers are introducing more anti-LGBTQ this year than in the past collective five years, according to Bloomberg and data from the American Civil Liberties Union.

The video was made with ChatGPT and is not the actual words of Joe Rogan or Sam Altman. Artificial intelligence chatbot ChatGPT has created a 51-minute episode of The Joe Rogan Experience featuring nearly flawless representations of the podcast host's voice and the voice of OpenAI CEO Sam Altman. The episode begins with an AI-generated Rogan welcoming the audience to the first episode of the "Joe Rogan AI Experience," speaking in a manner and tone that is difficult to distinguish from the real person. "I'm your host, Joe Rogan, or at least that's what this AI model thinks I sound like. Let me tell you, folks, this is some next-level stuff we've got going on here today," faux Rogan continues.