A never-ending spoof of Seinfeld generated by AI has attracted nearly 171,000 followers on Twitch. The stream, which first aired on December 14, has been playing non-stop ever since and is almost entirely generated by algorithms. It's titled Nothing, Forever, a reference to the '90s sitcom being the so-called "show about nothing." Created by Skyler Hartle and Brian Habersberger, the show uses a combination of machine learning, generative algorithms and cloud services to create a Seinfeld parody, using OpenAI's GPT-3 language model. Nothing, Forever features animated versions of the main Seinfeld cast, including Jerry, Elaine, George and Kramer, but with dialogue that often doesn't make sense, characters who wander off aimlessly, and a badly-timed laugh track. Speaking about the project to Vice, Hartle said: "The actual impetus for this was it originally started its life as this weird, very, off-center kind of nonsensical, surreal art project.

AI has officially taken over the world. Depending on who you ask, ChatGPT and Midjourney are saviour of work, art, journalism, law and ethics – or the destroyer of them. Right now, consumer AI is in no man's land, with computer-generated art mostly showing us how Mr Blobby would fare in the Napoleonic Wars. But that hasn't stopped AI start-ups from securing big money investment, and websites using ChatGPT to create personalised content. Which got me thinking: if multi-million dollar companies can wrangle AI to lighten their workloads, why can't I? If'real' jobs will be made obsolete once the machines take over, why resist it?

In this light, they're similar to another favored scheme of internet entrepreneurs: dropshipping. With dropshipping, sellers never stock or ship their product (often, they don't even design it). Instead, they make money by identifying trends then producing flashy adverts to find customers for the latest style of coat or watch. ChatGPT enables a similar dynamic in the creative industries and world of knowledge work: separating production from sales, allowing sellers to deliver goods that seem useful at first glance but fall apart when tested, and offering the scale and speed of production that obscures traditional feedback mechanisms like brand reputation. This is the future many fear for the web: AI-generated junk suffocating online platforms like algal blooms that choke the life out of ponds.

Responding to humanitarian disasters quickly, better still, anticipating them can save lives [1]. Data is key to this, not just having lots of data, but clean data which is well understood [2] in order to create a clear view of the situation on the ground. In many cases this critical data is stored in hundreds of small spreadsheets, so piecing them altogether can be time consuming and difficult to maintain as new data comes in during a humanitarian incident. Automating the process of data discovery would potentially speed responses and improve outcomes for affected people. One way to make discovery easier is to ensure that tabular data has metadata describing each column.

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This chapter presents a practical guide for conducting Sentiment Analysis using Natural Language Processing (NLP) techniques in the domain of tick-borne disease text. The aim is to demonstrate the process of how the presence of bias in the discourse surrounding chronic manifestations of the disease can be evaluated. The goal is to use a dataset of 5643 abstracts collected from scientific journals on the topic of chronic Lyme disease to demonstrate using Python, the steps for conducting sentiment analysis using pre-trained language models and the process of validating the preliminary results using both interpretable machine learning tools, as well as a novel methodology of using emerging state-of-the-art large language models like ChatGPT. This serves as a useful resource for researchers and practitioners interested in using NLP techniques for sentiment analysis in the medical domain.

Our intention is to provide a definitive reference on what it would take to safely make use of generative/predictive models in the absence of a solution to the Eliciting Latent Knowledge problem. Furthermore, we believe that large language models can be understood as such predictive models of the world, and that such a conceptualization raises significant opportunities for their safe yet powerful use via carefully conditioning them to predict desirable outputs. Unfortunately, such approaches also raise a variety of potentially fatal safety problems, particularly surrounding situations where predictive models predict the output of other AI systems, potentially unbeknownst to us. There are numerous potential solutions to such problems, however, primarily via carefully conditioning models to predict the things we want (e.g. humans) rather than the things we don't (e.g. malign AIs). Furthermore, due to the simplicity of the prediction objective, we believe that predictive models present the easiest inner alignment problem that we are aware of. As a result, we think that conditioning approaches for predictive models represent the safest known way of eliciting human-level and slightly superhuman capabilities from large language models and other similar future models.

In-context learning (ICL) is a type of prompting where a transformer model operates on a sequence of (input, output) examples and performs inference on-the-fly. In this work, we formalize in-context learning as an algorithm learning problem where a transformer model implicitly constructs a hypothesis function at inference-time. We first explore the statistical aspects of this abstraction through the lens of multitask learning: We obtain generalization bounds for ICL when the input prompt is (1) a sequence of i.i.d. (input, label) pairs or (2) a trajectory arising from a dynamical system. The crux of our analysis is relating the excess risk to the stability of the algorithm implemented by the transformer. We characterize when transformer/attention architecture provably obeys the stability condition and also provide empirical verification. For generalization on unseen tasks, we identify an inductive bias phenomenon in which the transfer learning risk is governed by the task complexity and the number of MTL tasks in a highly predictable manner. Finally, we provide numerical evaluations that (1) demonstrate transformers can indeed implement near-optimal algorithms on classical regression problems with i.i.d. and dynamic data, (2) provide insights on stability, and (3) verify our theoretical predictions.

ChatGPT[3] has demonstrated exceptional proficiency in natural language conversation, e.g., it can answer a wide range of questions while no previous large language models can. Thus, we would like to push its limit and explore its ability to answer causal discovery questions by using a medical benchmark [5] in causal discovery. Causal discovery aims to uncover the underlying unknown causal relationships based purely on observational data[2]. In contrast, applying ChatGPT to answer the questions about causal relationships is fundamentally different. With the current version of ChatGPT, we can only use the names (meta information) instead of observational data of variables to answer causal questions. The answers to the causal questions given by ChatGPT are based on a trained large language model, which can be viewed as an approximation for existing knowledge in the training natural language data.

Without a human-centric approach, OpenAI ChatGPT runs on the data available on the various channels, which can also deliver services without meeting the context requirements. Sometimes, it writes plausible-sounding content but can be trustworthy. The new kid on the block, AI-powered ChatGPT offers numerous exceptional services and is claimed to be useful for coding, content writing, etc., minimizing human intervention. As erudite machinery becomes a trending sensation, companies can also see AI biases, security risks, and less personalized CX. The uncapped accessibility, and unrestricted usage of ChatGPT have increased the cybersecurity risks that can hamper the whole organization. Through ChatGPT, cybercriminals can draft a fraudulent email carrying unsecured links, attachments providing sensitive data, or instructions regarding transferring money into specific accounts from a reputed company or person.