Electron and scanning probe microscopy produce vast amounts of data in the form of images or hyperspectral data, such as EELS or 4D STEM, that contain information on a wide range of structural, physical, and chemical properties of materials. To extract valuable insights from these data, it is crucial to identify physically separate regions in the data, such as phases, ferroic variants, and boundaries between them. In order to derive an easily interpretable feature analysis, combining with well-defined boundaries in a principled and unsupervised manner, here we present a physics augmented machine learning method which combines the capability of Variational Autoencoders to disentangle factors of variability within the data and the physics driven loss function that seeks to minimize the total length of the discontinuities in images corresponding to latent representations. Our method is applied to various materials, including NiO-LSMO, BiFeO3, and graphene. The results demonstrate the effectiveness of our approach in extracting meaningful information from large volumes of imaging data. The fully notebook containing implementation of the code and analysis workflow is available at https://github.com/arpanbiswas52/PaperNotebooks

Machine learning models that convert user-written text descriptions into images are now widely available online and used by millions of users to generate millions of images a day. We investigate the potential for these models to amplify dangerous and complex stereotypes. We find a broad range of ordinary prompts produce stereotypes, including prompts simply mentioning traits, descriptors, occupations, or objects. For example, we find cases of prompting for basic traits or social roles resulting in images reinforcing whiteness as ideal, prompting for occupations resulting in amplification of racial and gender disparities, and prompting for objects resulting in reification of American norms. Stereotypes are present regardless of whether prompts explicitly mention identity and demographic language or avoid such language. Moreover, stereotypes persist despite mitigation strategies; neither user attempts to counter stereotypes by requesting images with specific counter-stereotypes nor institutional attempts to add system ``guardrails'' have prevented the perpetuation of stereotypes. Our analysis justifies concerns regarding the impacts of today's models, presenting striking exemplars, and connecting these findings with deep insights into harms drawn from social scientific and humanist disciplines. This work contributes to the effort to shed light on the uniquely complex biases in language-vision models and demonstrates the ways that the mass deployment of text-to-image generation models results in mass dissemination of stereotypes and resulting harms.

We have recently witnessed a number of impressive results on hard mathematical reasoning problems with language models. At the same time, the robustness of these models has also been called into question; recent works have shown that models can rely on shallow patterns in the problem description when generating a solution. Building on the idea of behavioral testing, we propose a novel framework, which pins down the causal effect of various factors in the input, e.g., the surface form of the problem text, the operands, and math operators on the output solution. By grounding the behavioral analysis in a causal graph describing an intuitive reasoning process, we study the behavior of language models in terms of robustness and sensitivity to direct interventions in the input space. We apply our framework on a test bed of math word problems. Our analysis shows that robustness does not appear to continuously improve as a function of size, but the GPT-3 Davinci models (175B) achieve a dramatic improvement in both robustness and sensitivity compared to all other GPT variants.

Saliency maps can explain a neural model's predictions by identifying important input features. They are difficult to interpret for laypeople, especially for instances with many features. In order to make them more accessible, we formalize the underexplored task of translating saliency maps into natural language and compare methods that address two key challenges of this approach -- what and how to verbalize. In both automatic and human evaluation setups, using token-level attributions from text classification tasks, we compare two novel methods (search-based and instruction-based verbalizations) against conventional feature importance representations (heatmap visualizations and extractive rationales), measuring simulatability, faithfulness, helpfulness and ease of understanding. Instructing GPT-3.5 to generate saliency map verbalizations yields plausible explanations which include associations, abstractive summarization and commonsense reasoning, achieving by far the highest human ratings, but they are not faithfully capturing numeric information and are inconsistent in their interpretation of the task. In comparison, our search-based, model-free verbalization approach efficiently completes templated verbalizations, is faithful by design, but falls short in helpfulness and simulatability. Our results suggest that saliency map verbalization makes feature attribution explanations more comprehensible and less cognitively challenging to humans than conventional representations.

In this study, we aimed to address the growing concern of trolling behavior on social media by developing and evaluating a set of model architectures for the automatic detection of troll tweets. Utilizing deep learning techniques and pre-trained word embedding methods such as BERT, ELMo, and GloVe, we evaluated the performance of each architecture using metrics such as classification accuracy, F1 score, AUC, and precision. Our results indicate that BERT and ELMo embedding methods performed better than the GloVe method, likely due to their ability to provide contextualized word embeddings that better capture the nuances and subtleties of language use in online social media. Additionally, we found that CNN and GRU encoders performed similarly in terms of F1 score and AUC, suggesting their effectiveness in extracting relevant information from input text. The best-performing method was found to be an ELMo-based architecture that employed a GRU classifier, with an AUC score of 0.929. This research highlights the importance of utilizing contextualized word embeddings and appropriate encoder methods in the task of troll tweet detection, which can assist social-based systems in improving their performance in identifying and addressing trolling behavior on their platforms.

DOCTOR IN THE HOUSE: Former White House doctor and current Rep. Ronny Jackson said Biden's'lack of physical ability and his physical decline' highlight his'cognitive decline'… Read more: Former doctor for Trump, Obama slams White House's'malpractice' in allowing Biden to seek re-election TOPSHOT - US President Joe Biden is helped up after falling during the graduation ceremony at the United States Air Force Academy, just north of Colorado Springs in El Paso County, Colorado, on June 1, 2023. FLASHBACK: Many recalled how Biden during the 2020 campaign poked fun at former President Trump's apparent tottering down a ramp… Read more: Biden, who just fell on stage, once mocked Trump for carefully walking down ramp at commencement THE TRUTH IS OUT THERE: The US government has vessels and parts of craft of "exotic origin" (potentially not human-made), according to a recently-revealed whistleblower… Read more: Military whistleblower goes public with claims US has secret UFO retrieval ...

Neuralink is one step closer to selling brain implants that can transmit human thought. The neurotechnology company in May announced that it had received approval from the U.S. Food and Drug Administration (FDA) to launch its first in-human clinical trial. A statement on its Twitter account said the approval "represents an important first step that will one day allow our technology to help many people." Cofounded by Elon Musk in 2016, Neuralink plans to implant devices in human brains that would allow people with neurological disorders to control computers or robotic limbs with their minds. Musk has said he also wants to "achieve a sort of symbiosis with artificial intelligence" and possibly enable telepathic communication with the device.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Rapid advances in artificial intelligence (AI) such as Microsoft-backed OpenAI's ChatGPT are complicating governments' efforts to agree laws governing the use of the technology. The government is consulting Australia's main science advisory body and considering next steps, a spokesperson for the industry and science minister said in April. The Financial Conduct Authority, one of several state regulators that has been tasked with drawing up new guidelines covering AI, is consulting with the Alan Turing Institute and other legal and academic institutions to improve its understanding of the technology, a spokesperson told Reuters.

WATCH LIVE: VP Harris meets with UK PM Rishi Sunak in Munich. British Prime Minister Rishi Sunak is reportedly hoping to pitch the United Kingdom as a world leader in artificial intelligence governance during his meeting with President Joe Biden. But a post-Brexit U.K. has been locked out of key discussions between the United States and the European Union, such as the fourth Tech and Trade Council (TTC) meeting in Sweden. The White House said both the U.S. and EU recommitted to deepening cooperation on setting AI standards in line with democratic values and universal human rights and work together on emerging technologies "with like-minded partners." Politico reported in March that the Biden administration, meanwhile, has quietly rebuffed British officials' repeated requests for greater dialogue between Washington, D.C., and the U.K. regarding setting AI standards.

Tom Newhouse, vice president of Convergence Media, discusses the potential impact of artificial intelligence on elections after an RNC AI ad garnered attention. A bipartisan group of senators announced on Tuesday a series of briefings focused on artificial intelligence, including the first ever classified "All-Senators" briefing on the topic. "Dear Colleague: The advances we have seen in Artificial Intelligence (AI) in the last few months have been astounding. From helping the paralyzed walk again to allowing anyone to be a computer programmer, the technological breakthroughs are happening on almost a daily basis," Democratic Senate Majority Leader Chuck Schumer wrote in a letter to his fellow senators this week. "As AI transforms our world, the Senate must keep abreast of the extraordinary potential, and risks, AI presents."