I'm a managing partner of SOMS Consulting, a firm that designs business processes and software to make companies a pleasure to manage. A graduate of Stellenbosch University, one of Africa's most prestigious academic institutions, I studied how technology and modern business ...

A new paper in the Proceedings of the National Academy of Sciences (PNAS) reports how a cutting-edge artificial intelligence technique called deep learning can automatically identify, count and describe animals in their natural habitats. Photographs that are automatically collected by motion-sensor cameras can then be automatically described by deep neural networks. The result is a system that can automate animal identification for up to 99.3 percent of images while still performing at the same 96.6 percent accuracy rate of crowdsourced teams of human volunteers. "This technology lets us accurately, unobtrusively and inexpensively collect wildlife data, which could help catalyze the transformation of many fields of ecology, wildlife biology, zoology, conservation biology and animal behavior into'big data' sciences. This will dramatically improve our ability to both study and conserve wildlife and precious ecosystems," says Jeff Clune, the senior author of the paper.

Petri Nets is very interesting tool for studying and simulating different behaviors of information systems. It can be used in different applications based on the appropriate class of Petri Nets whereas it is classical, colored or timed Petri Nets. In this paper we introduce a new approach of Petri Nets called orbital Petri Nets (OPN) for studying the orbital rotating systems within a specific domain. The study investigated and analyzed OPN with highlighting the problem of space debris collision problem as a case study. The mathematical investigation results of two OPN models proved that space debris collision problem can be prevented based on the new method of firing sequence in OPN. By this study, new smart algorithms can be implemented and simulated by orbital Petri Nets for mitigating the space debris collision problem as a next work.

Increased interest of scientists, producers and consumers in sheep identification has been stimulated by the dramatic increase in population and the urge to increase productivity. The world population is expected to exceed 9.6 million in 2050. For this reason, awareness is raised towards the necessity of effective livestock production. Sheep is considered as one of the main of food resources. Most of the research now is directed towards developing real time applications that facilitate sheep identification for breed management and gathering related information like weight and age. Weight and age are key matrices in assessing the effectiveness of production. For this reason, visual analysis proved recently its significant success over other approaches. Visual analysis techniques need enough images for testing and study completion. For this reason, collecting sheep images database is a vital step to fulfill such objective. We provide here datasets for testing and comparing such algorithms which are under development. Our collected dataset consists of 416 color images for different features of sheep in different postures. Images were collected fifty two sheep at a range of year from three months to six years. For each sheep, two images were captured for both sides of the body, two images for both sides of the face, one image from the top view, one image for the hip and one image for the teeth. The collected images cover different illumination, quality levels and angle of rotation. The allocated data set can be used to test sheep identification, weigh estimation, and age detection algorithms. Such algorithms are crucial for disease management, animal assessment and ownership.

The latest techniques from Neural Networks and Support Vector Machines (SVM) are used to investigate geometric properties of Complete Intersection Calabi-Yau (CICY) threefolds, a class of manifolds that facilitate string model building. An advanced neural network classifier and SVM are employed to (1) learn Hodge numbers and report a remarkable improvement over previous efforts, (2) query for favourability, and (3) predict discrete symmetries, a highly imbalanced problem to which the Synthetic Minority Oversampling Technique (SMOTE) is applied to boost performance. In each case study, we employ a genetic algorithm to optimise the hyperparameters of the neural network. We demonstrate that our approach provides quick diagnostic tools capable of shortlisting quasi-realistic string models based on compactification over smooth CICYs and further supports the paradigm that classes of problems in algebraic geometry can be machine learned.

This is in furtherance of DSN's drive for accelerated applications of artificial intelligence to solve social and business problems through a strategic partnership between the academic community, industry, and local and international technology hubs similar to the highly successful Stanford University-Silicon Valley model. DSN is committed to supporting Nigerian students and encouraging them to turn their academic research into innovative start-ups or social enterprises aligned with millennial developmental goals. DSN has a particular interest in solving local problems in the fields of health, education, agriculture and financial inclusion. The Hub will be the community centre for data science enthusiasts and experts to fraternize, learn, share ideas and participate in local and international projects. DSN also plans to use the Hub for face-to-face masterclasses, industry meet-ups, and dial-in conferences with its over 100 mentors from across the globe.

A Tesla's autopilot function was engaged in the minutes before a fiery crash that killed its driver in California earlier this year, according to a federal inquiry. In the roughly 20 minutes before the vehicle slammed into a barrier near Mountain View and burst into flames, the car's autopilot feature was in "continuous operation", the National Transportation Safety Board (NTSB) found in its initial investigation. During the critical 60 seconds leading up to the crash, the NTSB reported, the car's driver repeatedly placed his hands on the steering wheel. Tesla crashes into parked police car in Autopilot mode Wall Street blasts Elon Musk's'truly bizarre' Tesla earnings call Tesla faces labour investigation after allegation of injury undercount But six seconds before the accident, evidence suggests the driver had removed his hands from the steering wheel. The vehicle also accelerated in the final three seconds.

Give us your feedback Thank you for your feedback. Parker's first day at work at the law firm Norton Rose Fulbright in Australia involved 1,000 conversations with potential clients. Even the most super-energetic young lawyer would normally manage only a fraction of that but Parker is, of course, a chatbot -- a computer program that simulates human conversation. The new recruit is a prime example of how law firms in Asia-Pacific are experimenting with artificial intelligence to improve efficiency. Chatbots, which use AI to answer simple questions from people wanting to learn more about a subject, are already being adopted in industries ranging from banking to medicine.

From robotic process automation to machine learning algorithms, many of today's most influential companies are deploying artificial intelligence (AI) technologies to drive business results. While most decision makers are aware of the business opportunities that emerging technologies present, many are unprepared simply because they fail to understand them. AI includes a variety of technologies and tools, some that have been around for a long time and others that are relatively new. Nevertheless, one thing is clear: businesses are thinking harder about how to prioritize AI in 2018. According to International Data Corporation (IDC), the widespread adoption of artificial intelligence will jump from $8.0 billion in 2016 to more than $47 billion in 2020.

The causes of real-world problems in economics and public health can be notoriously hard to determine. Often, multiple causes are suspected, but large datasets with time-sequenced data are not available. Previous models could not reliably analyze these challenges. Now, researchers have tested the first artificial intelligence model to identify and rank many causes in real-world problems without time-sequenced data, using a multi-nodal causal structure and Directed Acyclic Graphs. When something bad happens, it is natural to try figure out why it happened.