Artificial intelligence (AI) researchers have been developing and refining large language models (LLMs) that exhibit remarkable capabilities across a variety of domains and tasks, challenging our understanding of learning and cognition. The latest model developed by OpenAI, GPT-4, was trained using an unprecedented scale of compute and data. In this paper, we report on our investigation of an early version of GPT-4, when it was still in active development by OpenAI. We contend that (this early version of) GPT-4 is part of a new cohort of LLMs (along with ChatGPT and Google's PaLM for example) that exhibit more general intelligence than previous AI models. We discuss the rising capabilities and implications of these models. We demonstrate that, beyond its mastery of language, GPT-4 can solve novel and difficult tasks that span mathematics, coding, vision, medicine, law, psychology and more, without needing any special prompting. Moreover, in all of these tasks, GPT-4's performance is strikingly close to human-level performance, and often vastly surpasses prior models such as ChatGPT. Given the breadth and depth of GPT-4's capabilities, we believe that it could reasonably be viewed as an early (yet still incomplete) version of an artificial general intelligence (AGI) system. In our exploration of GPT-4, we put special emphasis on discovering its limitations, and we discuss the challenges ahead for advancing towards deeper and more comprehensive versions of AGI, including the possible need for pursuing a new paradigm that moves beyond next-word prediction. We conclude with reflections on societal influences of the recent technological leap and future research directions.

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.

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.

Machine learning (ML) enabled classification models are becoming increasingly popular for tackling the sheer volume and speed of online misinformation and other content that could be identified as harmful. In building these models, data scientists need to take a stance on the legitimacy, authoritativeness and objectivity of the sources of ``truth" used for model training and testing. This has political, ethical and epistemic implications which are rarely addressed in technical papers. Despite (and due to) their reported high accuracy and performance, ML-driven moderation systems have the potential to shape online public debate and create downstream negative impacts such as undue censorship and the reinforcing of false beliefs. Using collaborative ethnography and theoretical insights from social studies of science and expertise, we offer a critical analysis of the process of building ML models for (mis)information classification: we identify a series of algorithmic contingencies--key moments during model development that could lead to different future outcomes, uncertainty and harmful effects as these tools are deployed by social media platforms. We conclude by offering a tentative path toward reflexive and responsible development of ML tools for moderating misinformation and other harmful content online.

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.

Last year a software engineer at Google made an unusual assertion: that an artificial-intelligence chatbot developed at the company had become sentient, was entitled to rights as a person and might even have a soul. After what the company called a "lengthy engagement" with the employee on the issue, Google fired him. It's unlikely this will be the last such episode. Artificial intelligence is writing essays, winning at chess, detecting likely cancers and making business decisions. That's just the beginning for a technology that will only grow more powerful and pervasive, bolstering longstanding worries that robots might someday overtake us.

Brian Nord first fell in love with physics when he was a teenager growing up in Wisconsin. His high school physics program wasn't exceptional, and he sometimes struggled to keep up with class material, but those difficulties did nothing to dampen his interest in the subject. In addition to the main curriculum, students were encouraged to independently study topics they found interesting, and Nord quickly developed a fascination with the cosmos. "A touchstone that I often come back to is space," he says. Nord was an avid reader of comic books, and astrophysics appealed to his desire to become a part of something bigger.

Generative AI has continued to grow rapidly in 2023, with increasing interest from organizations and individuals looking to create realistic and personalized content using artificial intelligence. However, there are several challenges facing the widespread adoption of generative AI, including the difficulty of sharing custom retraining models, the complexity of running open-source models, and the lack of mechanisms to incentivize model creators. In this report, we will provide an overview of the current state of generative AI, its key trends, challenges, and opportunities. Generative AI refers to the use of artificial intelligence to generate new content, such as images, text, or music. Generative AI has the potential to revolutionize the creative industries, enabling organizations and individuals to produce high-quality, personalized content at scale. However, the development and deployment of generative AI models pose several challenges, including technical, legal, and ethical issues.

Artificial Intelligence poses both risks and rewards, but developers should be weary of technologies that could threaten "scary" outcomes, AI technologist says. The capacity of artificial intelligence systems to learn things even when they aren't explicitly taught those things will pose a significant challenge both to the companies creating and marketing these tools, and federal regulators tasked with protecting consumers who use them, a member of the Federal Trade Commission predicted. "Personally, and I say this with respect, I do not see the existential threats to our society that others do," FTC Commissioner Alvaro Bedoya said in recent speech made available this week. "Yet when you combine these statements with the unpredictability and inexplicability of these models, the sum total is something that we as consumer protection authorities have never reckoned with." Bedoya was speaking to the International Association of Privacy Professionals about the tendency of generative AI systems to pick up knowledge and intuition about subjects even when programmers aren't focusing on those topics.

The fuss around artificial intelligence(AI) has recently been the bone of contention. In fact, AI is disrupting virtually all sectors. Imagine if we get to a point where AI does all your tasks for you. And you'd ask–what would be left for me to do? Sure, you'd still have to feed yourself and, perhaps, take your bath yourself. Who assigns those to AI? We're at such an exciting point in the world, and one of the generative AI tools making rounds recently is ChatGPT.