Existing large language models (LLMs) are known for generating "hallucinated" content, namely a fabricated text of plausibly looking, yet unfounded, facts. To identify when these hallucination scenarios occur, we examine the properties of the generated text in the embedding space. Specifically, we draw inspiration from the dynamic mode decomposition (DMD) tool in analyzing the pattern evolution of text embeddings across sentences. We empirically demonstrate how the spectrum of sentence embeddings over paragraphs is constantly low-rank for the generated text, unlike that of the ground-truth text. Importantly, we find that evaluation cases having LLM hallucinations correspond to ground-truth embedding patterns with a higher number of modes being poorly approximated by the few modes associated with LLM embedding patterns. In analogy to near-field electromagnetic evanescent waves, the embedding DMD eigenmodes of the generated text with hallucinations vanishes quickly across sentences as opposed to those of the ground-truth text. This suggests that the hallucinations result from both the generation techniques and the underlying representation.

With transformers, While transformers have gained the reputation as the this information can be effectively captured and modeled, "Swiss army knife of AI", no one has challenged them whereas with CNNs it can be more challenging. This is one to master the game of chess, one of the classical AI reason, why nowadays transformer models are taken over benchmarks. Simply using vision transformers (ViTs) classical CNN approaches in computer vision and other domains within AlphaZero does not master the game of chess, [6]. Moreover, by combining the strengths of transformers mainly because ViTs are too slow. Even making them more and reinforcement learning (RL), it is possible to efficient using a combination of MobileNet and NextViT develop powerful models for solving complex sequential does not beat what actually matters: a simple change of the decision-making problems [2, 13]. They can be used to input representation and value loss, resulting in a greater model the state representation, policy, and value function, boost of up to 180 Elo points over AlphaZero.

More and more tech companies have initiatives in place to support Diversity, Equity & Inclusion (DEI) work. But even as Chief Diversity Officers get hired and diversity statements make their way onto company websites, diverse representation in tech is still lagging. This representation deficit, particularly in product and engineering departments, has huge implications. With the current population of software engineers comprising 25% women, 7.3% Latinos and 4.7% Black people, the teams building technology are not adequately representing the people using it. Artificial Intelligence (AI) is an area of computer science that focuses on enabling computers to perform tasks that have traditionally required human intelligence.

Collecting more diverse and representative training data is often touted as a remedy for the disparate performance of machine learning predictors across subpopulations. However, a precise framework for understanding how dataset properties like diversity affect learning outcomes is largely lacking. By casting data collection as part of the learning process, we demonstrate that diverse representation in training data is key not only to increasing subgroup performances, but also to achieving population level objectives. Our analysis and experiments describe how dataset compositions influence performance and provide constructive results for using trends in existing data, alongside domain knowledge, to help guide intentional, objective-aware dataset design.

When we think of artificial intelligence, the first things that usually come to mind are depictions from popular culture. Artificial intelligence (AI), however, doesn't just exist in futuristic movies. It's already a part of the way we live, shop, work and bank. Rather than a robot gone rogue, AI is simply technology programmed by humans with the ability to memorize information, learn from experience, communicate facts, and/or make decisions. And because humans are the ones creating AI, we must ask the question: what are we teaching our machines, and what are they learning from us?

Unsupervised activity recognition introduces the opportunity for more robust interaction experiences with machines because the human is not limited to only acting with respect to a training dataset. Many approaches currently use latent variable models that have been well studied and developed by the natural language research communities. However, these models are simply used as-is or with minor tweaks on datasets that present an analogy between sensor reading sequences and text documents. Although words have well-defined semantics so that the learned clusters can be interpreted and verified, this is not often the case for sensor readings. For example, novel data from new human activities need to be classified, which relies on the learned clusters; so how does one confirm that new activities are being correctly processed by a robot for interaction? We present several ways that motion capture information can be represented for use in these methods, and then illustrate how the representation choice has the potential to produce variations in the learned clusters.