On a raw December day, as Christmas music blares over loudspeakers, an African elephant named Asha walks in tight circles in an enclosure at Natural Bridge Zoo, a roadside attraction in Virginia. Her living quarters consist of a barn and three outdoor yards--a fenced patch of grass about 90 by 40 feet, a dirt patch with a few logs scattered about, and a yard where she gives rides to children for $15 and her massive feet have worn a ring into the grass. Her space is barren--no shrubs, trees, or watering holes. Elephants, like humans, are social animals. In the wild, females typically live in herds of eight or more, yet Asha, who's nearly 40 years old, has been confined mostly alone for more than 30 years.

InstaDeep, one of the leaders in the design of decision-making Artificial Intelligence systems, announced on January 25 that it had raised $100 million (€88 million). The company closed a Series B round led by DeepTech investment firm Alpha Intelligence Capital and supported by CDIB. BioNTech, Chimera Abu Dhabi, Deutsche Bahn Digital Ventures, Google, G42 and Synergie participated in this latest round. Founded in 2014 by Karim Beguir and Zohra Slim, InstaDeep is a leader in decision AI systems, it has been named two years in a row to the CB Insights AI 100 ranking of the world's 100 most promising private artificial intelligence companies. The company develops patented AI products such as its DeepChainTM protein design platform and collaborates with leading companies such as Google DeepMind, Nvidia and Intel.

Organizations are looking deeper into data to gain a competitive advantage, implementing machine learning and artificial intelligence to achieve new business objectives and to move ahead of competitors in the industry. The adoption of AI and machine learning is critically impaired by the necessity of high-quality data. Changes must be made organization wide to identify and reduce pouches of bad data and create mechanisms that allow the organization to adapt quickly to the data needs and embrace the full potential of these technologies. From starting to being able to deliver a successful AI strategy goes a distance. The capability to build a secure, centralized, and scalable data repository, being able to combine large volumes of disparate data from multiples data sources, is the first challenge to overcome.

TORONTO, Jan. 31, 2022 – Artificial Intelligence (AI) can help tackle inequities that contribute to a higher risk of the most vulnerable contracting and dying of COVID-19, but York University researchers say the right data is crucial for that to happen. Vulnerable people are often more exposed to COVID-19 through their work, such as meat packing plants, and their living conditions which are often crowed, and they face more economic barriers, such having to rely on public transportation. York University Assistant Professor Jude Kong, Associate Professor Ali Asgary, and Distinguished Research Professor Jianhong Wu, can discuss how AI can play a role in eliminating inequities, especially during crises such as the current pandemic, ahead of upcoming webinar – Discovering COVID-19 Inequities and Systemic Vulnerabilities the Role of Artificial Intelligent Policy Implications. The webinar is part of the Transformative Disaster Risk Governance Webinar Series. "There is a need to use artificial intelligence to collect data disaggregated by race, gender, sexuality, class, geographic location and Indigeneity to better understand how COVID-19 is disproportionately affecting vulnerable people, whether here in Canada or in Africa, where many countries have difficulty obtaining vaccines. This kind of data could not only help with today's pandemic, but prepare for future crises by ensuring effective allocation of resources," says Kong, Faculty of Science, and director of the Africa-Canada Artificial Intelligence and Data Innovation Consortium.

Abstract--Driver behavior profiling is one of the main issues in the insurance industries and fleet management, thus being able to classify the driver behavior with low-cost mobile applications remains in the spotlight of autonomous driving. However, using mobile sensors may face the challenge of security, privacy, and trust issues. To overcome those challenges, we propose to collect data sensors using Carla Simulator available in smartphones (Accelerometer, Gyroscope, GPS) in order to classify the driver behavior using speed, acceleration, direction, the 3-axis rotation angles (Yaw, Pitch, Roll) taking into account the speed limit of the current road and weather conditions to better identify the risky behavior. Secondly, after fusing inter-axial data from multiple sensors into a single file, we explore different machine learning algorithms for time series classification to evaluate which algorithm results in the highest performance. Over the last two decades, Road Traffic Accidents (RTAs) are increasingly being recognised as a growing public health such as Global Positioning System (GPS), accelerometers, problem.

Data-driven approaches to modeling physical systems fail to generalize to unseen systems that share the same general dynamics with the learning domain, but correspond to different physical contexts. We propose a new framework for this key problem, context-informed dynamics adaptation (CoDA), which takes into account the distributional shift across systems for fast and efficient adaptation to new dynamics. CoDA leverages multiple environments, each associated to a different dynamic, and learns to condition the dynamics model on contextual parameters, specific to each environment. The conditioning is performed via a hypernetwork, learned jointly with a context vector from observed data. The proposed formulation constrains the search hypothesis space to foster fast adaptation and better generalization across environments. It extends the expressivity of existing methods. We theoretically motivate our approach and show state-ofthe-art generalization results on a set of nonlinear dynamics, representative of a variety of application domains. We also show, on these systems, that new system parameters can be inferred from context vectors with minimal supervision.

In performative prediction, the deployment of a predictive model triggers a shift in the data distribution. As these shifts are typically unknown ahead of time, the learner needs to deploy a model to get feedback about the distribution it induces. We study the problem of finding near-optimal models under performativity while maintaining low regret. On the surface, this problem might seem equivalent to a bandit problem. However, it exhibits a fundamentally richer feedback structure that we refer to as performative feedback: after every deployment, the learner receives samples from the shifted distribution rather than only bandit feedback about the reward. Our main contribution is regret bounds that scale only with the complexity of the distribution shifts and not that of the reward function. The key algorithmic idea is careful exploration of the distribution shifts that informs a novel construction of confidence bounds on the risk of unexplored models. The construction only relies on smoothness of the shifts and does not assume convexity. More broadly, our work establishes a conceptual approach for leveraging tools from the bandits literature for the purpose of regret minimization with performative feedback.

Exploiting data invariances is crucial for efficient learning in both artificial and biological neural circuits. Understanding how neural networks can discover appropriate representations capable of harnessing the underlying symmetries of their inputs is thus crucial in machine learning and neuroscience. Convolutional neural networks, for example, were designed to exploit translation symmetry and their capabilities triggered the first wave of deep learning successes. However, learning convolutions directly from translation-invariant data with a fully-connected network has so far proven elusive. Here, we show how initially fully-connected neural networks solving a discrimination task can learn a convolutional structure directly from their inputs, resulting in localised, space-tiling receptive fields. These receptive fields match the filters of a convolutional network trained on the same task. By carefully designing data models for the visual scene, we show that the emergence of this pattern is triggered by the non-Gaussian, higher-order local structure of the inputs, which has long been recognised as the hallmark of natural images. We provide an analytical and numerical characterisation of the pattern-formation mechanism responsible for this phenomenon in a simple model, which results in an unexpected link between receptive field formation and the tensor decomposition of higher-order input correlations. These results provide a new perspective on the development of low-level feature detectors in various sensory modalities, and pave the way for studying the impact of higher-order statistics on learning in neural networks.

Representing words by vectors, or embeddings, enables computational reasoning and is foundational to automating natural language tasks. For example, if word embeddings of similar words contain similar values, word similarity can be readily assessed, whereas judging that from their spelling is often impossible (e.g. cat /feline) and to predetermine and store similarities between all words is prohibitively time-consuming, memory intensive and subjective. We focus on word embeddings learned from text corpora and knowledge graphs. Several well-known algorithms learn word embeddings from text on an unsupervised basis by learning to predict those words that occur around each word, e.g. word2vec and GloVe. Parameters of such word embeddings are known to reflect word co-occurrence statistics, but how they capture semantic meaning has been unclear. Knowledge graph representation models learn representations both of entities (words, people, places, etc.) and relations between them, typically by training a model to predict known facts in a supervised manner. Despite steady improvements in fact prediction accuracy, little is understood of the latent structure that enables this. The limited understanding of how latent semantic structure is encoded in the geometry of word embeddings and knowledge graph representations makes a principled means of improving their performance, reliability or interpretability unclear. To address this: 1. we theoretically justify the empirical observation that particular geometric relationships between word embeddings learned by algorithms such as word2vec and GloVe correspond to semantic relations between words; and 2. we extend this correspondence between semantics and geometry to the entities and relations of knowledge graphs, providing a model for the latent structure of knowledge graph representation linked to that of word embeddings.

Earth could have as many as 73,000 tree species, a new first-of-its-kind study has estimated, including some 9,200 that are yet to be discovered. Most of these undiscovered species are likely to be rare, in very low numbers and at threat from human-driven changes in land use and climate, researchers said. South America contains about 43 per cent of the world's tree species and the highest number of rare ones. The findings suggest the continent should be the focus of conservation efforts, along with global tropical and subtropical forests, which also likely harbour many rare, undiscovered species, according to researchers. The study is the outcome of a three-year international project that involved almost 150 scientists and led to the identification of approximately 40 million trees belonging to 64,000 species.