As service systems grow increasingly complex and dynamic, many interventions become localized, available and taking effect only in specific states. This paper investigates experiments with local treatments on a widely-used class of dynamic models, Markov Decision Processes (MDPs). Particularly, we focus on utilizing the local structure to improve the inference efficiency of the average treatment effect. We begin by demonstrating the efficiency of classical inference methods, including model-based estimation and temporal difference learning under a fixed policy, as well as classical A/B testing with general treatments. We then introduce a variance reduction technique that exploits the local treatment structure by sharing information for states unaffected by the treatment policy. Our new estimator effectively overcomes the variance lower bound for general treatments while matching the more stringent lower bound incorporating the local treatment structure. Furthermore, our estimator can optimally achieve a linear reduction with the number of test arms for a major part of the variance. Finally, we explore scenarios with perfect knowledge of the control arm and design estimators that further improve inference efficiency.

However, when large language models are directly prompted with questions without any context, they are prone to hallucination. At NASA SciX we have developed an experiment where we created semantic vectors for our large collection of abstracts and full-text content, and we designed a prompt system to ask questions using contextual chunks from our system. Based on a non-systematic human evaluation, the experiment shows a lower degree of hallucination and better responses when using Retrieval Augmented Generation. Further exploration is required to design new features and data augmentation processes at NASA SciX that leverages this technology while respecting the high level of trust and quality that the project holds.

As artificial intelligence (AI) challenges us to reimagine new ways of doing and being, Dr Marcel O'Gorman, professor of English Language and Literature, embraces emerging technologies and applies them to his pedagogy in the classroom. O'Gorman has published widely about the impacts of technology, and his most recent research focuses on how critical and inclusive design methods might help tackle some of the moral and ethical issues faced by contemporary technoculture. O'Gorman recently wrapped up teaching a fourth-year undergraduate course on techno-critical writing and design that focused on key issues around responsible innovation, such as algorithmic bias, conflict minerals and the colonial practices of big tech on the global stage. Students applied what they learned by writing and designing projects throughout the course. "They wrote stories in ChatGPT that tested the AI for gender bias. They generated images in DALL-E 2 that traced a racist history in the AI's training data," O'Gorman says.

Decentralised Machine Learning (DML) enables collaborative machine learning without centralised input data. Federated Learning (FL) and Edge Inference are examples of DML. While tools for DML (especially FL) are starting to flourish, many are not flexible and portable enough to experiment with novel processors (e.g., RISC-V), non-fully connected network topologies, and asynchronous collaboration schemes. We overcome these limitations via a domain-specific language allowing us to map DML schemes to an underlying middleware, i.e. the FastFlow parallel programming library. We experiment with it by generating different working DML schemes on x86-64 and ARM platforms and an emerging RISC-V one. We characterise the performance and energy efficiency of the presented schemes and systems. As a byproduct, we introduce a RISC-V porting of the PyTorch framework, the first publicly available to our knowledge.

There's been a lot of talk about AI engines and how they may or may not be used in newsrooms, newsletters, marketing and other information-based services in the coming months and years. Conversations about ChatGPT and other automated technology have raised many important questions about how information will be created and shared and whether the quality of the stories will prove useful to audiences. We decided to do an experiment to answer that question for ourselves. For over two decades, CNET has built our reputation testing new technologies and separating the hype from reality, from voice assistants to augmented reality to the metaverse. In November, our CNET Money editorial team started trying out the tech to see if there's a pragmatic use case for an AI assist on basic explainers around financial services topics like What Is Compound Interest?

AI (artificial intelligence) technology has made tremendous progress in recent years. It is now possible to assess its capacity to perform specific tasks such as generating text, images, and sound. Now, what if we go even further with more complicated tests, like evaluating a job, for example, or more particularly, evaluating an AI system on its ability to do SEO? Below, we will test Generative Pre-trained Transformer 3 (GPT-3) created by OpenAI. Let's keep in mind that an AI system will mimic the data on which it is trained. SEO has been built alongside search engine progression, and everything is well documented in blogs, books, and interviews.

Disclaimer: This series of blog posts is written to make myself aware of the details and concepts in the field of BCIs and neuroscience, as I go through my very first own BCI project. Right now, I really have no idea how this project will turn out. Therefore, note that this series of blog posts should not be used as a definitive guide for your own journey. Welcome to this series where I document my process of building my very first BCI bedroom project! We have arrived at part 4, in what is often referred to as the exciting part.

U.S. Northern Command (NORTHCOM) recently conducted a series of tests known as the Global Information Dominance Experiments, or GIDE, which combined global sensor networks, artificial intelligence (AI) systems, and cloud computing resources in an attempt to "achieve information dominance" and "decision-making superiority." According to NORTHCOM leadership, the AI and machine learning tools tested in the experiments could someday offer the Pentagon a robust "ability to see days in advance," meaning it could predict the future with some reliability based on evaluating patterns, anomalies, and trends in massive data sets. While the concept sounds like something out of Minority Report, the commander of NORTHCOM says this capability is already enabled by tools readily available to the Pentagon. General Glen VanHerck, Commander of NORTHCOM and North American Aerospace Defense Command (NORAD), told reporters at the Pentagon this week that this was the third test of GIDE, conducted in conjunction with all 11 combatant commands "collaborating in the same information space using the same exact capabilities." The experiment largely centered around contested logistics and information advantage, two cornerstones of the new warfighting paradigm recently proposed by the Vice Chairman of the Joint Chiefs of Staff.

Image captioning is a task in the field of Artificial Intelligence that merges between computer vision and natural language processing. It is responsible for generating legends that describe images, and has various applications like descriptions used by assistive technology or indexing images (for search engines for instance). This makes it a crucial topic in AI that is undergoing a lot of research. This task however, like many others, is trained on large images labeled via human annotation, which can be very cumbersome: it needs manual effort, both financial and temporal costs, it is error-prone and potentially difficult to execute in some cases (e.g. To mitigate the need for labels, we attempt to use self-supervised learning, a type of learning where models use the data contained within the images themselves as labels. It is challenging to accomplish though, since the task is two-fold: the images and captions come from two different modalities and usually handled by different types of networks.

A good Medium read ratio is generally between 20 and 50%. It depends, though, on the length of your article and the audience you're trying to engage. Very short articles (3 minutes or less) tend to have a higher read ratio, because it takes less time for a reader to complete the article. Likewise, long articles tend to have lower read ratios -- but not always.