The video above represents one of the first times that a pangolin, one of the world's most critically endangered species, was detected in real time using artificial intelligence. A U.K.-based nonprofit called Conservation AI made this possible with the help of NVIDIA technology. Such use of AI can help track even the rarest, most reclusive of species in real time, enabling conservationists to protect them from threats, such as poachers and fires, before it's too late to intervene. The organization was founded four years ago by researchers at Liverpool John Moores University -- Paul Fergus, Carl Chalmers, Serge Wich and Steven Longmore. In the past year and a half, Conservation AI has deployed 70 AI-powered cameras across the world.

Standard Bank Group is a leading Africa-focused financial services group, and an innovative player on the global stage, that offers a variety of career-enhancing opportunities – plus the chance to work alongside some of the sector's most talented, motivated professionals. Our clients range from individuals, to businesses of all sizes, high net worth families and large multinational corporates and institutions. Bringing true, meaningful value to our clients and the communities we serve and creating a real sense of purpose for you. To work with business stakeholders to identify and deliver on new AI initiatives. To apply deep domain expertise to shape/influence the AI-thinking in the organisation through thought leadership; enabling the successful adoption and acceleration of AI and ML across Standard Bank Group (SBG), ensuring the needs of stakeholders are correctly understood and addressed.

Computers powered by human brain cells may sound like science fiction, but a team of researchers in the United States believes such machines, part of a new field called "organoid intelligence," could shape the future -- and now they have a plan to get there. Organoids are lab-grown tissues that resemble organs. These three-dimensional structures, usually derived from stem cells, have been used in labs for nearly two decades, where scientists have been able to avoid harmful human or animal testing by experimenting on the stand-ins for kidneys, lungs and other organs. Brain organoids don't actually resemble tiny versions of the human brain, but the pen dot-size cell cultures contain neurons that are capable of brainlike functions, forming a multitude of connections. Scientists call the phenomenon "intelligence in a dish."

Google is using AI to help people facing disease and natural disasters, and to provide new opportunities for underserved communities. The potential of AI to solve big problems is increasing all the time. In the past few years, AI and transformational innovations have become more important in confronting some of society’s biggest challenges. Today, AI is helping countries and communities facing disease and natural disasters, and providing new opportunities for historically underserved groups. Here are seven ways AI is already making the world a better place: 1. Forecasting floods and helping people stay safe through early warning systems Last year…

This comes after a report by the Los Angeles Times detailed the movie's alleged funding ties to Russian billionaire Dmitry Rybolovlev. The toy maker also used International Women's Day, on March 8, to honor six other women recognized as trailblazers in STEM fields -- including German marine researcher and microbiologist Prof Dr Antje Boetius and Mexican electrical engineer and Science Show host Katya Echazarret. Meanwhile, Thor: Love and Thunder filmmaker Taika Waititi is reportedly working on a new Star Wars feature, in which he is also rumored to star.

The field of eXplainable Artificial Intelligence (XAI) has greatly advanced in recent years, but progress has mainly been made in computer vision and natural language processing. For time series, where the input is often not interpretable, only limited research on XAI is available. In this work, we put forward a virtual inspection layer, that transforms the time series to an interpretable representation and allows to propagate relevance attributions to this representation via local XAI methods like layer-wise relevance propagation (LRP). In this way, we extend the applicability of a family of XAI methods to domains (e.g. speech) where the input is only interpretable after a transformation. Here, we focus on the Fourier transformation which is prominently applied in the interpretation of time series and LRP and refer to our method as DFT-LRP. We demonstrate the usefulness of DFT-LRP in various time series classification settings like audio and electronic health records. We showcase how DFT-LRP reveals differences in the classification strategies of models trained in different domains (e.g., time vs. frequency domain) or helps to discover how models act on spurious correlations in the data.

It is important to predict how the Global Mean Temperature (GMT) will evolve in the next few decades. The ability to predict historical data is a necessary first step toward the actual goal of making long-range forecasts. This paper examines the advantage of statistical and simpler Machine Learning (ML) methods instead of directly using complex ML algorithms and Deep Learning Neural Networks (DNN). Often neglected data transformation methods prior to applying different algorithms have been used as a means of improving predictive accuracy. The GMT time series is treated both as a univariate time series and also cast as a regression problem. Some steps of data transformations were found to be effective. Various simple ML methods did as well or better than the more well-known ones showing merit in trying a large bouquet of algorithms as a first step. Fifty-six algorithms were subject to Box-Cox, Yeo-Johnson, and first-order differencing and compared with the absence of them. Predictions for the annual GMT testing data were better than that published so far, with the lowest RMSE value of 0.02 $^\circ$C. RMSE for five-year mean GMT values for the test data ranged from 0.00002 to 0.00036 $^\circ$C.

Group Activity Recognition (GAR) aims to detect the activity performed by multiple actors in a scene. Prior works model the spatio-temporal features based on the RGB, optical flow or keypoint data types. However, using both the temporality and these data types altogether increase the computational complexity significantly. Our hypothesis is that by only using the RGB data without temporality, the performance can be maintained with a negligible loss in accuracy. To that end, we propose a novel GAR technique for volleyball videos, DECOMPL, which consists of two complementary branches. In the visual branch, it extracts the features using attention pooling in a selective way. In the coordinate branch, it considers the current configuration of the actors and extracts the spatial information from the box coordinates. Moreover, we analyzed the Volleyball dataset that the recent literature is mostly based on, and realized that its labeling scheme degrades the group concept in the activities to the level of individual actors. We manually reannotated the dataset in a systematic manner for emphasizing the group concept. Experimental results on the Volleyball as well as Collective Activity (from another domain, i.e., not volleyball) datasets demonstrated the effectiveness of the proposed model DECOMPL, which delivered the best/second best GAR performance with the reannotations/original annotations among the comparable state-of-the-art techniques. Our code, results and new annotations will be made available through GitHub after the revision process.

The spread of online misinformation threatens public health, democracy, and the broader society. While professional fact-checkers form the first line of defense by fact-checking popular false claims, they do not engage directly in conversations with misinformation spreaders. On the other hand, non-expert ordinary users act as eyes-on-the-ground who proactively counter misinformation -- recent research has shown that 96% counter-misinformation responses are made by ordinary users. However, research also found that 2/3 times, these responses are rude and lack evidence. This work seeks to create a counter-misinformation response generation model to empower users to effectively correct misinformation. This objective is challenging due to the absence of datasets containing ground-truth of ideal counter-misinformation responses, and the lack of models that can generate responses backed by communication theories. In this work, we create two novel datasets of misinformation and counter-misinformation response pairs from in-the-wild social media and crowdsourcing from college-educated students. We annotate the collected data to distinguish poor from ideal responses that are factual, polite, and refute misinformation. We propose MisinfoCorrect, a reinforcement learning-based framework that learns to generate counter-misinformation responses for an input misinformation post. The model rewards the generator to increase the politeness, factuality, and refutation attitude while retaining text fluency and relevancy. Quantitative and qualitative evaluation shows that our model outperforms several baselines by generating high-quality counter-responses. This work illustrates the promise of generative text models for social good -- here, to help create a safe and reliable information ecosystem. The code and data is accessible on https://github.com/claws-lab/MisinfoCorrect.

"if something is popular, criminals will exploit it" -- strikes once again. This time, we're talking about the trending ChatGPT chatbot, developed by OpenAI, which has been all over the news of late. When OpenAI opened access to its AI chatbot (that is, a chatbot based on neural networks trained on a vast corpus of text), the internet changed beyond recognition practically overnight. Users all over the planet rushed to see what the chatbot is capable of -- and were not disappointed (and often positively astonished). ChatGPT can maintain a dialog in a way that feels like there's a real person at the other end.