Iranian soldiers carry part of a target drone used in air-defense exercises. Iran is also turning some target drones into low-tech weapons for its proxies. Iranian soldiers carry part of a target drone used in air-defense exercises. Iran is also turning some target drones into low-tech weapons for its proxies. In January, a group of high-level military commanders gathered at an air base in Yemen.

The International Academy of Astronautics organized its 6th Planetary Defence Conference from April 29 to May 3rd, 2019 in Washington DC, Area in the USA. The bi-annual conference brings together world experts to discuss the threat to Earth posed by asteroids and comets and actions that might be taken to deflect a threatening object. There were over 300 participants this year. Artash (Grade 7 student) submitted a paper to the conference on Using Machine Learning to predict the Risk Index of an Asteroid Colliding with Earth. It was accepted as a poster presentation for the conference and we were happy to attend the event. It was the first time for us to be participating in this conference.

Similar to the growth in the number of vehicles in an urban area, the number of aircraft and the passengers they ferry, are in a phase of constant growth. Globally, the number of aircraft is expected to double from the base year of 2015 up to 2035. Since there are only limited number of airports and limited amount of space in each airport, this implies that each aircraft movement on the ground needs to be efficiently handled for faster turnaround. Faced with the pressure of managing multiple cost heads, airlines are now outsourcing their airport ground handling and cargo management services to specialist companies, and focusing on their core competence. While growth trends in passenger volumes tend to follow macroeconomic fundamentals, the growth in aircraft turnarounds are more immune to such highs and lows.

Emirates NBD will also utilize AWS data analytics, Internet of Things (IoT), Natural Language Processing (NLP), and other advanced technologies as part of its ongoing efforts to better engage with customers and simplify banking. A front-runner in retail banking innovation, Emirates NBD is working with AWS because of its broad and deep portfolio of cloud services and the increased security and control Emirates NBD can achieve in the cloud, and is continuing to invest in AWS as its preferred provider for machine learning workloads. With AWS, Emirates NBD will take further advantage of AWS artificial intelligence and machine learning services including Amazon SageMaker, a fully managed machine learning service for building, training, and deploying machine learning models to provide relevant real-time banking experiences. To create a more rewarding and customer-centric banking experience, Emirates NBD is also leveraging Amazon Personalize, an AWS machine learning service that enables the development of individualized recommendations to launch new personalized retail banking applications. One of the first of these applications is a personal finance manager that uses an automated, self-learning system to deliver a highly personalized banking experience to customers in order to predict what each individual customer needs and match this with the most appropriate solution. To support this work, Emirates NBD is using Amazon Polly, a cloud service that uses advanced deep learning technologies to convert written content into human-like speech, in its automated call center to further enhance customer interactions by delivering lifelike voice banking experiences.

In many high-dimensional estimation problems the main task consists in minimizing a cost function, which is often strongly non-convex when scanned in the space of parameters to be estimated. A standard solution to flatten the corresponding rough landscape consists in summing the losses associated to different data points and obtain a smoother empirical risk. Here we propose a complementary method that works for a single data point. The main idea is that a large amount of the roughness is uncorrelated in different parts of the landscape. One can then substantially reduce the noise by evaluating an empirical average of the gradient obtained as a sum over many random independent positions in the space of parameters to be optimized. We present an algorithm, called Replicated Gradient Descent, based on this idea and we apply it to tensor PCA, which is a very hard estimation problem. We show that Replicated Gradient Descent over-performs physical algorithms such as gradient descent and approximate message passing and matches the best algorithmic thresholds known so far, obtained by tensor unfolding and methods based on sum-of-squares.

A Semi-Hidden Markov Model (SHMM) for bursty error channels is defined by a state transition probability matrix $A$, a prior probability vector $\Pi$, and the state dependent output symbol error probability matrix $B$. Several processes are utilized for estimating $A$, $\Pi$ and $B$ from a given empirically obtained or simulated error sequence. However, despite placing some restrictions on the underlying Markov model structure, we still have a computationally intensive estimation procedure, especially given a large error sequence containing long burst of identical symbols. Thus, in this paper, we utilize under some moderate assumptions, a Markov model with random state transition matrix $A$ equivalent to a unique Block Diagonal Markov model with state transition matrix $\Lambda$ to model an indoor software-defined power line communication system. A computationally efficient modified Baum-Welch algorithm for estimation of $\Lambda$ given an experimentally obtained error sequence from the indoor PLC channel is utilized. Resulting Equivalent Block Diagonal Markov models assist designers to accelerate and facilitate the procedure of novel PLC systems design and evaluation.

A two-person team from the UNICEF's Office of Innovation in New York recently joined DSTI in Sierra Leone to collaborate on a Machine Learning "Hackathon" As part of efforts to develop the technology and innovation ecosystem to support development of Sierra Leone, UNICEF is collaborating with the Directorate of Science, Technology and Innovation (DSTI) in the Office of the President, on a knowledge exchange partnership, around innovative Machine Learning techniques which, it is hoped, will add value to Government's work around data for decision making in the country. A two-person team from the UNICEF's Office of Innovation in New York recently joined DSTI in Sierra Leone to collaborate on a Machine Learning "Hackathon" to work on data from the education sector in support of the Government's Free Quality School Education initiative. Officials from different Government Ministries, Departments and Agencies joined the team to enhance their knowledge of Machine Learning and advanced data analysis techniques, for use in their own areas of government. Shane O'Connor, Technology for Development Specialist at UNICEF Sierra Leone, stated that the opportunity afforded by this collaboration is huge. "With the President's establishment of the DSTI and with UNICEF's collaboration, there really is great potential for a step change in how Technology and Innovation can be leveraged to deliver for Sierra Leone," he said.

CEO at Things Foundation, Technology Manager at Let's Do It Foundation Head of Cortex Ventures- Africa's first AI only focused VC Fund

The age of artificial intelligence, machine learning and robotics is here, and these technologies will continue to shape our lives in the future. But the people working in these fields still don't reflect the society they are bound to change. Women make up only 22% of AI professionals worldwide, according to analysis done by LinkedIn and the World Economic Forum for its 2018 Global Gender Gap Report. In the more specialized area of machine learning, only 12% are women, based on a study done by Wired in partnership with Montreal startup Element AI. Artificial intelligence and machine learning continue to be male-dominated fields.

An important problem in forensic analyses is identifying the provenance of materials at a crime scene, such as biological material on a piece of clothing. This procedure, known as geolocation, is conventionally guided by expert knowledge of the biological evidence and therefore tends to be application-specific, labor-intensive, and subjective. Purely data-driven methods have yet to be fully realized due in part to the lack of a sufficiently rich data source. However, high-throughput sequencing technologies are able to identify tens of thousands of microbial taxa using DNA recovered from a single swab collected from nearly any object or surface. We present a new algorithm for geolocation that aggregates over an ensemble of deep neural network classifiers trained on randomly-generated Voronoi partitions of a spatial domain. We apply the algorithm to fungi present in each of 1300 dust samples collected across the continental United States and then to a global dataset of dust samples from 28 countries. Our algorithm makes remarkably good point predictions with more than half of the geolocation errors under 100 kilometers for the continental analysis and nearly 90% classification accuracy of a sample's country of origin for the global analysis. We suggest that the effectiveness of this model sets the stage for a new, quantitative approach to forensic geolocation.