As a society, living in a technological age, we have become incredibly used to rapid change. Sometimes it feelslike the one constant we can rely on is that everything will change. For millennia humankind lived in caves, scrawling drawings on the walls. The Stone Age was 2.5 million years long, then came the Bronze Age and, with it, urbanisation, which lasted 1,500 years. The first Industrial Revolution lasted just 80 years (1760 – 1840). Before we reached our current, digital age, the Wright Brothers perfected powered flight and just 66 years later, our species had escaped Earth's gravity, traversed the vacuum of space and landed on the moon.

The race to remove CO2 from our atmosphere is on. In an effort to draw down carbon at a meaningful scale, people are looking to the ground. The top meter of the world's soil holds over three times the amount of carbon currently in our atmosphere--and if we treat our land better, it could suck up even more. This is good news for farmers. Companies and individuals desperate to offset their emissions by purchasing carbon credits are willing to pay farmers to use sustainable agricultural practices and sequester carbon in their fields.

Enhancing the player experience is the ultimate goal of artificial intelligence in gaming. Given that game developers create games for a variety of platforms, it is imperative. The option between a console and a desktop PC gaming has become obsolete. Thanks to AI, developers can now create console-like experiences for several device kinds. AI games come in several formats every year. Some experts argue that the less obvious uses of AI in games are the most potent. AI is becoming more prevalent in games, which has significant economic advantages for companies.

In recent months there has been talk about how artificial intelligence can create images from textual prompts. Therefore, when one associates the words artificial intelligence and art, one immediately thinks of DALL-E, Stable Diffusion, and other algorithms. In this article, instead, I want to discuss why artworks are often less safe than we think, and how artificial intelligence can help preserve them. "Every act of creation is first of all an act of destruction." It is a mistake to think that cultural heritage is safe. Many of humanity's most valuable works are also among the most fragile. Throughout history, only a fraction of works of art has managed to survive over time. For example, during wars, cultural heritage is often damaged.

Lots of businesses utilize large information to improve their operations. E-commerce businesses employ qualitative and probabilistic procedures to venture off cybersecurity risks while mining huge amounts of customer information to construct recommendation engines. Targeted marketing campaigns geared toward providing a personalized customer experience. But since the usage cases for information science grow more complicated, a few innovative startups are currently relying on artificial intelligence and machine learning to their core product offering or business model–and in doing this, attaining things that would not be possible without information. More than 12,000 startups recorded on Crunchbase rely upon machine learning due to their primary and ancillary services and products.

Coronary artery disease (CAD) is one of the most common cardiac diseases worldwide and causes disability and economic burden. It is the world's leading and most serious cause of mortality, with approximately 80% of deaths reported in low- and middle-income countries. The preferred and most precise diagnostic tool for CAD is angiography, but it is invasive, expensive, and technically demanding. However, the research community is increasingly interested in the computer-aided diagnosis of CAD via the utilization of machine learning (ML) methods. The purpose of this work is to present an e-diagnosis tool based on ML algorithms that can be used in a smart healthcare monitoring system. We applied the most accurate machine learning methods that have shown superior results in the literature to different medical datasets such as RandomForest, XGboost, MLP, J48, AdaBoost, NaiveBayes, LogitBoost, KNN. Every single classifier can be efficient on a different dataset. Thus, an ensemble model using majority voting was designed to take advantage of the well-performed single classifiers, Ensemble learning aims to combine the forecasts of multiple individual classifiers to achieve higher performance than individual classifiers in terms of precision, specificity, sensitivity, and accuracy; furthermore, we have benchmarked our proposed model with the most efficient and well-known ensemble models, such as Bagging, Stacking methods based on the cross-validation technique, The experimental results confirm that the ensemble majority voting approach based on the top 3 classifiers: MultilayerPerceptron, RandomForest, and AdaBoost, achieves the highest accuracy of 88,12% and outperforms all other classifiers. This study demonstrates that the majority voting ensemble approach proposed above is the most accurate machine learning classification approach for the prediction and detection of coronary artery disease.

We present MidasTouch, a tactile perception system for online global localization of a vision-based touch sensor sliding on an object surface. This framework takes in posed tactile images over time, and outputs an evolving distribution of sensor pose on the object's surface, without the need for visual priors. Our key insight is to estimate local surface geometry with tactile sensing, learn a compact representation for it, and disambiguate these signals over a long time horizon. The backbone of MidasTouch is a Monte-Carlo particle filter, with a measurement model based on a tactile code network learned from tactile simulation. This network, inspired by LIDAR place recognition, compactly summarizes local surface geometries. These generated codes are efficiently compared against a precomputed tactile codebook per-object, to update the pose distribution. We further release the YCB-Slide dataset of real-world and simulated forceful sliding interactions between a vision-based tactile sensor and standard YCB objects. While single-touch localization can be inherently ambiguous, we can quickly localize our sensor by traversing salient surface geometries. Project page: https://suddhu.github.io/midastouch-tactile/

Vision-language models can encode societal biases and stereotypes, but there are challenges to measuring and mitigating these multimodal harms due to lacking measurement robustness and feature degradation. To address these challenges, we investigate bias measures and apply ranking metrics for image-text representations. We then investigate debiasing methods and show that prepending learned embeddings to text queries that are jointly trained with adversarial debiasing and a contrastive loss reduces various bias measures with minimal degradation to the image-text representation.

Word order choices during sentence production can be primed by preceding sentences. In this work, we test the DUAL MECHANISM hypothesis that priming is driven by multiple different sources. Using a Hindi corpus of text productions, we model lexical priming with an n-gram cache model and we capture more abstract syntactic priming with an adaptive neural language model. We permute the preverbal constituents of corpus sentences, and then use a logistic regression model to predict which sentences actually occurred in the corpus against artificially generated meaning-equivalent variants. Our results indicate that lexical priming and lexically-independent syntactic priming affect complementary sets of verb classes. By showing that different priming influences are separable from one another, our results support the hypothesis that multiple different cognitive mechanisms underlie priming.

Logical table-to-text generation is a task that involves generating logically faithful sentences from tables, which requires models to derive logical level facts from table records via logical inference. It raises a new challenge on the logical-level content planning of table-to-text models. However, directly learning the logical inference knowledge from table-text pairs is very difficult for neural models because of the ambiguity of natural language and the scarcity of parallel data. Hence even large-scale pre-trained language models present low logical fidelity on logical table-to-text. In this work, we propose a PLOG (Pretrained Logical Form Generator) framework to improve the generation fidelity. Specifically, PLOG is first pretrained on a table-to-logic-form generation (table-to-logic) task, then finetuned on downstream table-to-text tasks. The formal definition of logical forms enables us to collect large amount of accurate logical forms from tables without human annotation. In addition, PLOG can learn logical inference from table-logic pairs much more definitely than from table-text pairs. To evaluate our model, we further collect a controlled logical table-to-text dataset CONTLOG based on an existing dataset. On two benchmarks, LOGICNLG and CONTLOG, PLOG outperforms strong baselines by a large margin on the logical fidelity, demonstrating the effectiveness of table-to-logic pretraining.