Collaborating Authors


MENC stresses role of AI, tech in maritime security


Participants at the Middle East Naval Commanders Conference (MENC) held on the sidelines of the Doha International Maritime and Defence Exhibition and Conference 2022 (DIMDEX) have noted the importance of bilateral and multilateral partnerships among countries to ensure the oceans are protected from threats. While discussing'Resilience in the maritime Domain – Confronting Asymmetric Threats,' senior military officers and academia highlighted the rapid growth of technology, and artificial intelligence (AI) in modern military operations and the gradual shift towards unmanned technological revolution. Vice-Admiral Brad Cooper, Commander, US Naval Forces Central Command/5thFleet, said multilateral partnerships, especially in a vast and strategic region like the Middle East and the Gulf, would ensure the security of commerce and people. He also noted that Qatar, as a Major non-NATO ally (MNNA), would play a crucial role in deploying technologies alongside the US and other partners to ensure the region's security. "Oceans have long served as parts to new frontiers and opportunities, and they remain so today. This region has three strategic points, the Suez Canal, the Gulf of Aden and the Strait of Hormuz. Challenges to commercial vessels' security and stability and other threats can significantly impact global commerce. This is why resilience in the maritime domain matters greatly," Vice-Admiral Cooper said.


AAAI Conferences

Probabilistic Classifiers Chains (PCC) offers interesting properties to solve multi-label classification tasks due to its ability to estimate the joint probability of the labels. However, PCC presents the major drawback of having a high computational cost in the inference process required to predict new samples. Lately, several approaches have been proposed to overcome this issue, including beam search and an epsilon-Approximate algorithm based on uniform-cost search. Surprisingly, the obvious possibility of using heuristic search has not been considered yet. This paper studies this alternative and proposes an admisible heuristic that, applied in combination with A* algorithm, guarantees, not only optimal predictions in terms of subset 0/1 loss, but also that it always explores less nodes than epsilon-Approximate algorithm. In the experiments reported, the number of nodes explored by our method is less than two times the number of labels for all datasets analyzed. But, the difference in explored nodes must be large enough to compensate the overhead of the heuristic in order to improve prediction time. Thus, our proposal may be a good choice for complex multi-label problems.

Gupshup scales up its Middle East expansion with Singapore's Knowlarity - GCC Business News


Gupshup, the US-based conversational messaging services company, has strengthened its Middle East presence with the acquisition of Singapore-based Knowlarity Communications, a global leader in cloud communications. With voice technology reforming the customer experience in the Middle East and North Africa (MENA), this acquisition by Gupshup will ensure that with Artificial Intelligence (AI) voice technology, customers can effortlessly connect with businesses and get support or post-sales assistance. This technology will reduce response times of businesses to customers and eventually help businesses retain customers. With the addition of Knowlarity's products, Gupshup will now be able to support businesses in building seamless conversational experiences across both messaging and voice channels. "As business-to-consumer (B2C) engagement becomes conversational, Gupshup is busy enabling more ways for businesses to deliver rich experiences. With the addition of Knowlarity's products, our customers in the Middle East and across the world will now be able to build seamless conversational experiences across both messaging and voice channels. A large number of large businesses and SMEs across sectors in the Middle East are starting to integrate AI technologies into their business and we see a huge potential for our business in the Middle East market."

Transfer Learning: COVID-19 from Chest X-Rays Classifier


The Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. Most people infected with the COVID-19 virus will experience mild to moderate respiratory illness and recover without requiring special treatment. Older people, and those with underlying medical problems like cardiovascular disease, diabetes, chronic respiratory disease, and cancer are more likely to develop serious illness. (WHO, 2020). While most persons with COVID-19 recover and return to normal health, some patients can have symptoms that can last for weeks or even months after recovery from acute illness.

A Reinforcement Learning-based Adaptive Control Model for Future Street Planning, An Algorithm and A Case Study Artificial Intelligence

With the emerging technologies in Intelligent Transportation System (ITS), the adaptive operation of road space is likely to be realised within decades. An intelligent street can learn and improve its decision-making on the right-of-way (ROW) for road users, liberating more active pedestrian space while maintaining traffic safety and efficiency. However, there is a lack of effective controlling techniques for these adaptive street infrastructures. To fill this gap in existing studies, we formulate this control problem as a Markov Game and develop a solution based on the multi-agent Deep Deterministic Policy Gradient (MADDPG) algorithm. The proposed model can dynamically assign ROW for sidewalks, autonomous vehicles (AVs) driving lanes and on-street parking areas in real-time. Integrated with the SUMO traffic simulator, this model was evaluated using the road network of the South Kensington District against three cases of divergent traffic conditions: pedestrian flow rates, AVs traffic flow rates and parking demands. Results reveal that our model can achieve an average reduction of 3.87% and 6.26% in street space assigned for on-street parking and vehicular operations. Combined with space gained by limiting the number of driving lanes, the average proportion of sidewalks to total widths of streets can significantly increase by 10.13%.

McLaren partners with AI specialist for performance optimization


McLaren Racing has announced a new partnership with AI cloud platform developer DataRobot, which offers a unified platform that reportedly allows organizations to unlock the full potential of AI. Under the partnership, DataRobot's AI cloud technology platform will be integrated into the McLaren Racing infrastructure, delivering AI-powered predictions and insights to maximize performance and optimize simulations. Zak Brown, CEO of McLaren Racing, commented, "DataRobot is a leader in its field, bringing its innovative technology and platform to top businesses around the globe. McLaren Racing continues to lead in innovation and technology, and partnerships with the likes of DataRobot allow us to progress, improve and support our team in our ongoing push for optimum performance. We are delighted to welcome DataRobot as they join our partner family for the Qatar Grand Prix this weekend."

Deep Learning Helps Predict Traffic Crashes Before They Happen - Liwaiwai


Today's world is one big maze, connected by layers of concrete asphalt that afford us the luxury of navigation by vehicle. For much of our road-related advancements – GPS lets us fire fewer neurons thanks to map apps, cameras alert us to potentially costly scrapes and scratches, and electric autonomous cars have lower fuel costs – our safety measures haven't quite caught up. We still rely on a steady diet of traffic signals, trust, and the steel surrounding us to safely get from point A to point B. To get ahead of the uncertainty inherent to crashes, scientists from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Qatar Center for Artificial Intelligence (QCAI) developed a deep learning model that predicts very high-resolution crash risk maps. Fed on a combination of historical crash data, road maps, satellite imagery, and GPS traces, the risk maps describe the expected number of crashes over a period of time in the future, to identify high-risk areas and predict future crashes. Typically, these types of risk maps are captured at much lower resolutions that hover around hundreds of meters, which means glossing over crucial details since the roads become blurred together.

McLaren Racing to utilise DataRobot's AI-powered performance insights - SportsPro


Formula One team McLaren Racing have partnered with DataRobot to implement the company's artificial intelligence (AI) cloud technology platform in a bid improve their on-track performances. As an official partner of the UK-based outfit, DataRobot's platform will be integrated into McLaren's existing technology infrastructure, providing AI-powered predictions and insights'to maximise performance and optimise simulations'. DataRobot branding will also be included on McLaren MCL35M race cars from this weekend's Qatar Grand Prix, as well as on the race suits of team drivers Lando Norris and Daniel Ricciardo. "DataRobot is a leader in its field, bringing its innovative technology and platform to top businesses around the globe," said Zak Brown, McLaren Racing's chief executive. "McLaren Racing continues to lead in innovation and technology, and partnerships with the likes of DataRobot allow us to progress, improve and support our team in our ongoing push for optimum performance."

Artificial Intelligence can now predict traffic crashes before time


Today's world is connected by the luxury of navigation by vehicle, with all the updated technology GPS, map apps, cameras alerts around us but we are still dependent on fixed traffic signals to reach from point A to point B. Scientists from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Qatar Centre for Artificial Intelligence have created a model that predicts very high-resolution crash risk maps. The model is fed on a mixture of historic crash data, road maps, satellite imagery, and GPS traces, this data will help analyse the risk and forecast future crashes. "By capturing the underlying risk distribution that determines the probability of future crashes at all places, and without any historical data, we can find safer routes, enable auto insurance companies to provide customized insurance plans based on driving trajectories of customers, help city planners design safer roads, and even predict future crashes," says MIT CSAIL PhD student Songtao He, a lead author on a new paper about the research. The crashes around the world cost the 3 percent of the world's GDP and are the primary cause of death in children and young adults. The model recognises high-risk locations using GPS path outlines, which give the main information about density, speed, and direction of traffic, and satellite images that defines road structures, number of lanes even the number of pedestrians.

Discovering Non-monotonic Autoregressive Orderings with Variational Inference Artificial Intelligence

The predominant approach for language modeling is to process sequences from left to right, but this eliminates a source of information: the order by which the sequence was generated. One strategy to recover this information is to decode both the content and ordering of tokens. Existing approaches supervise content and ordering by designing problem-specific loss functions and pre-training with an ordering pre-selected. Other recent works use iterative search to discover problem-specific orderings for training, but suffer from high time complexity and cannot be efficiently parallelized. We address these limitations with an unsupervised parallelizable learner that discovers high-quality generation orders purely from training data -- no domain knowledge required. The learner contains an encoder network and decoder language model that perform variational inference with autoregressive orders (represented as permutation matrices) as latent variables. The corresponding ELBO is not differentiable, so we develop a practical algorithm for end-to-end optimization using policy gradients. We implement the encoder as a Transformer with non-causal attention that outputs permutations in one forward pass. Permutations then serve as target generation orders for training an insertion-based Transformer language model. Empirical results in language modeling tasks demonstrate that our method is context-aware and discovers orderings that are competitive with or even better than fixed orders.