It turns out humans are not the only creatures that use food as foreplay. Researchers in southwestern Mexico have recorded a group of rough-toothed dolphins sharing a meal and getting frisky. A drone camera caught two dolphins passing a piece of fish back and forth in what may be the first video of the conduct. The repast seemed to inspire some amorous behavior, as well, with two males initiating sexual encounters with another member of the pod. Rough-toothed dolphins spend up to 80 percent of their time in the ocean depths, making them extremely difficult to study.

We are living in unpreceded times, and I am sure that you agree with me that what we all are experiencing right now is something very unique and unexpected. I am referring to the global pandemic of Covid-19, which was impossible for all of us to predict, expect or adjudicate which would more less be impossible to predict or expect this year. The role of technology in human lives has never been so crucial and relevant. Right now most of us are truly dependent on being connected with the world via digital technologies. From working remotely to shopping online, to seeking medical support with telemedicine, or reaching out to connect with your family and friends – technology is the connectivity link that plays an unparalleled social role for human engagement right now.

The report provides valuable insights about the advancements of the Artificial Intelligence in Marketing market and the approaches regarding the Artificial Intelligence in Marketing market with analysis of each region. The report further talks about the dominant aspects of the market and explores each segment. To understand the Artificial Intelligence in Marketing market dynamics, the market is analyzed across major global regions and countries. Middle East & Africa: Saudi Arabia, South Africa, U.A.E., and Rest of MEA Thank you for reading our report. The report is available for customization based on chapters or regions.

"Eastern Africa is a key region to examine this major cultural change, not only because it hosts some of the youngest MSA sites and some of the oldest LSA sites, but also because the large number of well excavated and dated sites make it ideal for research using quantitative methods," says Dr. Jimbob Blinkhorn, an archaeologist from the Pan African Evolution Research Group, Max Planck Institute for the Science of Human History and the Centre for Quaternary Research, Department of Geography, Royal Holloway. "This enabled us to pull together a substantial database of changing patterns of stone tool production and use, spanning 130 to 12 thousand years ago, to examine the MSA-LSA transition." The study examines the presence or absence of 16 alternate tool types across 92 stone tool assemblages, but rather than focusing on them individually, emphasis is placed on the constellations of tool forms that frequently occur together. "We've employed an Artificial Neural Network (ANN) approach to train and test models that differentiate LSA assemblages from MSA assemblages, as well as examining chronological differences between older (130-71 thousand years ago) and younger (71-28 thousand years ago) MSA assemblages with a 94% success rate," says Dr. Matt Grove, an archaeologist at the University of Liverpool. Artificial Neural Networks (ANNs) are computer models intended to mimic the salient features of information processing in the brain.

Predicting a customer's propensity-to-pay at an early point in the revenue cycle can provide organisations many opportunities to improve the customer experience, reduce hardship and reduce the risk of impaired cash flow and occurrence of bad debt. With the advancements in data science; machine learning techniques can be used to build models to accurately predict a customer's propensity-to-pay. Creating effective machine learning models without access to large and detailed datasets presents some significant challenges. This paper presents a case-study, conducted on a dataset from an energy organisation, to explore the uncertainty around the creation of machine learning models that are able to predict residential customers entering financial hardship which then reduces their ability to pay energy bills. Incorrect predictions can result in inefficient resource allocation and vulnerable customers not being proactively identified. This study investigates machine learning models' ability to consider different contexts and estimate the uncertainty in the prediction. Seven models from four families of machine learning algorithms are investigated for their novel utilisation. A novel concept of utilising a Baysian Neural Network to the binary classification problem of propensity-to-pay energy bills is proposed and explored for deployment.

Along with a dedicated team of students, Engineer installs air sensors on top of buildings and the backs of motorbike taxis ― known as boda bodas and one of the city's most common forms of transportation ― to collect pollution data from all over the city. The team then uses cloud-based AI software to analyze air particle data in real-time and predict local pollution. These forecasts offer Kampala's communities a way to reduce their risk of exposure and are being used by government agencies to improve air quality on the ground. Engineer and the team at Makerere University are one of 20 organizations selected from more than 2,600 applicants to receive a grant through the Google AI Impact Challenge: the Google.org Through this program, the Makerere team also received coaching and mentorship from Google and DeepMind AI experts over the course of a 9-month AI accelerator.

From stock trades to credit card purchases, the financial services sector has always been a powerhouse of data generation. Today, many financial institutions are harnessing insights from this data--using artificial intelligence and machine-learning tools to serve customers in new, innovative ways and quickly expand their service offerings. According to a 2020 survey of over 150 financial services firms by the Cambridge Centre for Alternative Finance and the World Economic Forum, artificial intelligence is expected to become an essential business driver across the industry, with 77% of respondents anticipating AI will "possess high or very high overall importance to their businesses within two years." The same survey revealed the range of applications of AI and machine learning, from risk management and product development to customer service and client acquisition. So it's no secret that AI and machine learning are playing a foundational role in financial services.

Several years ago, I packed up my life in Cairo, Egypt, and moved to the UK to pursue my PhD – thousands of miles away from everyone I knew and loved. As I settled into my new life, I found myself spending more hours with my laptop than with any other human being. I felt isolated and incredibly homesick. Chatting online with my family back home, I was often in tears, but they had no idea how I was feeling behind my screen (with the exception of a sad face emoticon that I would send). I realised then that our technology and devices – which we consider to be "smart", and helpful in many aspects of our lives – are emotion blind.

Parameter calibration is a major challenge in agent-based modelling and simulation (ABMS). As the complexity of agent-based models (ABMs) increase, the number of parameters required to be calibrated grows. This leads to the ABMS equivalent of the \say{curse of dimensionality}. We propose an ABMS framework which facilitates the effective integration of different sampling methods and surrogate models (SMs) in order to evaluate how these strategies affect parameter calibration and exploration. We show that surrogate assisted methods perform better than the standard sampling methods. In addition, we show that the XGBoost and Decision Tree SMs are most optimal overall with regards to our analysis.

Increasingly, organizations across many industries and geographies are building and deploying machine learning models and incorporating artificial intelligence into a variety of their different products and offerings. However, as they put AI capabilities into systems that we interact with on a daily basis, it becomes increasingly important to make sure these systems are behaving in a way that's beneficial to the public. When creating AI systems organizations should also consider the ethical and moral implications to make sure that AI is being created for good intentions. Policymakers that want to understand and leverage AI's potential and impact need to take a holistic view of the issues. This includes things like intentions behind AI systems, as well as potential unintended consequences and actions of AI systems.