BOCA RATON, Fla., Nov. 15, 2022 (GLOBE NEWSWIRE) -- Totaligent, Inc. ("Totaligent" or "the Company") (OTCPK: TGNT) announces it has completed testing of its scalable Nvidia clusters and has started to build a super cluster, with 2.4 Terabytes of GPU ram and 18 Terabytes of system ram. Totaligent's new supercomputer will allow the Company's Artificial Intelligence to deliver nearly instantaneous data processing and modeling for its person-based digital marketing platform. "Having the power and speed to deliver near real-time results when building target audiences from billions of records for customers is critical to Totaligent's success and acceptance in the person-based digital marketing world. Now, when we append large datasets that used to take days to process, our AI completes the task in about a minute. The combination of data, speed, and a complete set of easy-to-use marketing tools, at an affordable price, will enable Totaligent to provide unparalleled results for its users upon the launch of its integrated digital platform," stated Ted DeFeudis, CEO.

AI has enormous promise for improving and enriching our lives. However, serious concerns exist about its use, intrusion, and abuse. The Google AI arm revealed a variety of artificial intelligence projects it was working on, including one focused on preventing blindness. At its annual developer conference, Google unveiled 12 new AI project categories, some of which could lead to improved healthcare, others that could be used for creative purposes, and others that might be fun to play with. Google's new wildfire tracking feature is now available in the United States, Canada, Mexico, and some parts of Australia.

Industries and processes across the globe are embracing new technologies to increase efficiency and deliver faster and accurate outcomes. Artificial Intelligence (AI) and Machine Learning (ML) have recently taken the world by storm with their advancements in delivering impactful and insightful results. The legal industry is not any different. The changing customer needs and technology […]

Active Learning (AL) is a powerful tool for learning with less labeled data, in particular, for specialized domains, like legal documents, where unlabeled data is abundant, but the annotation requires domain expertise and is thus expensive. Recent works have shown the effectiveness of AL strategies for pre-trained language models. However, most AL strategies require a set of labeled samples to start with, which is expensive to acquire. In addition, pre-trained language models have been shown unstable during fine-tuning with small datasets, and their embeddings are not semantically meaningful. In this work, we propose a pipeline for effectively using active learning with pre-trained language models in the legal domain. To this end, we leverage the available unlabeled data in three phases. First, we continue pre-training the model to adapt it to the downstream task. Second, we use knowledge distillation to guide the model's embeddings to a semantically meaningful space. Finally, we propose a simple, yet effective, strategy to find the initial set of labeled samples with fewer actions compared to existing methods. Our experiments on Contract-NLI, adapted to the classification task, and LEDGAR benchmarks show that our approach outperforms standard AL strategies, and is more efficient. Furthermore, our pipeline reaches comparable results to the fully-supervised approach with a small performance gap, and dramatically reduced annotation cost. Code and the adapted data will be made available.

Air pollution is a major global environmental health threat, in particular for people who live or work near pollution sources. Areas adjacent to pollution sources often have high ambient pollution concentrations, and those areas are commonly referred to as air pollution hotspots. Detecting and characterizing pollution hotspots are of great importance for air quality management, but are challenging due to the high spatial and temporal variability of air pollutants. In this work, we explore the use of mobile sensing data (i.e., air quality sensors installed on vehicles) to detect pollution hotspots. One major challenge with mobile sensing data is uneven sampling, i.e., data collection can vary by both space and time. To address this challenge, we propose a two-step approach to detect hotspots from mobile sensing data, which includes local spike detection and sample-weighted clustering. Essentially, this approach tackles the uneven sampling issue by weighting samples based on their spatial frequency and temporal hit rate, so as to identify robust and persistent hotspots. To contextualize the hotspots and discover potential pollution source characteristics, we explore a variety of cross-domain urban data and extract features from them. As a soft-validation of the extracted features, we build hotspot inference models for cities with and without mobile sensing data. Evaluation results using real-world mobile sensing air quality data as well as cross-domain urban data demonstrate the effectiveness of our approach in detecting and inferring pollution hotspots. Furthermore, the empirical analysis of hotspots and source features yields useful insights regarding neighborhood pollution sources.

Social media has become a platform for people to stand up and raise their voices against social and criminal acts. Vocalization of such information has allowed the investigation and identification of criminals. However, revealing such sensitive information may jeopardize the victim's safety. We propose #maskUp, a safe method for information communication in a secure fashion to the relevant authorities, discouraging potential bullying of the victim. This would ensure security by conserving their privacy through natural language processing supplemented with selective encryption for sensitive attribute masking. To our knowledge, this is the first work that aims to protect the privacy of the victims by masking their private details as well as emboldening them to come forward to report crimes. The use of masking technology allows only binding authorities to view/un-mask this data. We construct and evaluate the proposed methodology on continual learning tasks, allowing practical implementation of the same in a real-world scenario. #maskUp successfully demonstrates this integration on sample datasets validating the presented objective.

Interpretable machine learning (IML) is concerned with the behavior and the properties of machine learning models. Scientists, however, are only interested in models as a gateway to understanding phenomena. Our work aligns these two perspectives and shows how to design IML property descriptors. These descriptors are IML methods that provide insight not just into the model, but also into the properties of the phenomenon the model is designed to represent. We argue that IML is necessary for scientific inference with ML models because their elements do not individually represent phenomenon properties; instead, the model in its entirety does. However, current IML research often conflates two goals of model analysis -- model audit and scientific inference -- making it unclear which model interpretations can be used to learn about phenomena. Building on statistical decision theory, we show that IML property descriptors applied on a model provide access to relevant aspects of the joint probability distribution of the data. We identify what questions such descriptors can address, provide a guide to building appropriate descriptors and quantify their epistemic uncertainty.

Large language and image AI models, sometimes called generative AI or foundation models, have created a new set of opportunities for businesses and professionals that perform content creation. How adept is this technology at mimicking human efforts at creative work? Well, for an example, the italicized text above was written by GPT-3, a "large language model" (LLM) created by OpenAI, in response to the first sentence, which we wrote. GPT-3's text reflects the strengths and weaknesses of most AI-generated content. First, it is sensitive to the prompts fed into it; we tried several alternative prompts before settling on that sentence.

Abstract: Natural Language Processing (NLP) models' current trend consists of using increasingly more extra-data to build the best models as possible. It implies more expensive computational costs and training time, difficulties for deployment, and worries about these models' carbon footprint reveal a critical problem in the future. Against this trend, our goal is to develop NLP models requiring no extra-data and minimizing training time. To do so, in this paper, we explore Markov chain models, Hidden Markov Chain (HMC) and Pairwise Markov Chain (PMC), for NLP segmentation tasks. We apply these models for three classic applications: POS Tagging, Named-Entity-Recognition, and Chunking.

Technology has played a pivotal role in transforming how businesses operate, for the better and for the worse. The development of semiconductors and the computing revolution that followed changed the business landscape dramatically in a few decades. The next transformational change in business will be brought about by artificial intelligence. Artificial Intelligence or AI tries to mimic human intelligence with computing power. Complex statistical operations are applied to huge amounts of data to train computers to learn some aspects of human intelligence.