In part I of the blog I wrote about the most acute problem our society faces today - the Climate Crisis, and how we can leverage the pandemic-caused lockdown to analyze the consequences as data points for the "what-if" scenario to make better decisions in the future. While Climate Crisis should be addressed timely and aggressively, COVID-19 posed a health crisis for the governments to deal with the projection of 80% of the population being infected in the short term. How will health systems manage the prevention, diagnostics, and treatment of the pandemic in parallel to provide the ongoing services and treatments? In this part, I will present a few developments in telemedicine, personalized medicine and drug development powered by AI/ML and how they better equipped us in this fight and could be used routinely in the future. Telemedicine is a buzzword we used to hear in the context of highly populated countries with a lack of trained personnel trying to bridge the supply and demand with remote resourcing.

In the midst of South Africa's five-week extended lockdown, footage has emerged of a drone collecting data and enforcing social distancing. It offers a fascinating first-person view of AI-based software in action and yet another example of drones fighting against COVID-19. The footage comes from local South African publication Sowetan Live. A mayor within the largely rural Limpopo Province of South Africa has employed drone technology to monitor social distancing and enforce lockdown rules. Similar to implementations in other countries, this drone uses a public address system.

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Artificial Intelligence algorithms have been steadily increasing in popularity and usage. Deep Learning, allows neural networks to be trained using huge datasets and also removes the need for human extracted features, as it automates the feature learning process. In the hearth of training deep neural networks, such as Convolutional Neural Networks, we find backpropagation, that by computing the gradient of the loss function with respect to the weights of the network for a given input, it allows the weights of the network to be adjusted to better perform in the given task. In this paper, we propose a hybrid method that uses both backpropagation and evolutionary strategies to train Convolutional Neural Networks, where the evolutionary strategies are used to help to avoid local minimas and fine-tune the weights, so that the network achieves higher accuracy results. We show that the proposed hybrid method is capable of improving upon regular training in the task of image classification in CIFAR-10, where a VGG16 model was used and the final test results increased 0.61%, in average, when compared to using only backpropagation.

Existing question answering datasets focus on dealing with homogeneous information, based either only on text or KB/Table information alone. However, as human knowledge is distributed over heterogeneous forms, using homogeneous information might lead to severe coverage problems. To fill in the gap, we present \dataset, a new large-scale question-answering dataset that requires reasoning on heterogeneous information. Each question is aligned with a structured Wikipedia table and multiple free-form corpora linked with the entities in the table. The questions are designed to aggregate both tabular information and text information, i.e. lack of either form would render the question unanswerable. We test with three different models: 1) table-only model. 2) text-only model. 3) a hybrid model \model which combines both table and textual information to build a reasoning path towards the answer. The experimental results show that the first two baselines obtain compromised scores below 20\%, while \model significantly boosts EM score to over 50\%, which proves the necessity to aggregate both structure and unstructured information in \dataset. However, \model's score is still far behind human performance, hence we believe \dataset to an ideal and challenging benchmark to study question answering under heterogeneous information. The dataset and code are available at \url{https://github.com/wenhuchen/HybridQA}.

In 1950, 746 Million people lived in cities but just one hundred years later in 2050, this is anticipated to surpass 6 Billion – some 66% of the world's population. In this increasingly urban, data-driven and hybrid world that integrates the physical and the virtual, the concept of'smartness' comes to the fore. The vision of a'smart city' has been in existence for many years but it is only recently that advances in technology have enabled tangible progress towards its real-world actualization. I believe this is also critical to the successful implementation of the United Nation's 2030 Agenda for Sustainable Development (SDGS). So, what does a smart city mean to you?

The UK government is setting aside £2.6 million for new satellite and drone technology that could deliver essential supplies during the coronavirus lockdown. The UK Space Agency (UKSA) is funding new solutions to deliver equipment such as test kits, masks, gowns and goggles for frontline NHS staff. The joint initiative with the European Space Agency could lead to vital equipment soaring through British skies via drones to support the NHS in tackling COVID-19. Companies can submit their proposals, including ideas for deployment and a pilot phase, on the European Space Agency (ESA) website. The UK's space industry is also looking for ways to combat the spread of coronavirus and preventing future epidemics using satellites.

We study cost-effective communication strategies that can be used to improve the performance of distributed learning systems in resource-constrained environments. For distributed learning in sequential decision making, we propose a new cost-effective partial communication protocol. We illustrate that with this protocol the group obtains the same order of performance that it obtains with full communication. Moreover, we prove that under the proposed partial communication protocol the communication cost is $O(\log T)$, where $T$ is the time horizon of the decision-making process. This improves significantly on protocols with full communication, which incur a communication cost that is $O(T)$. We validate our theoretical results using numerical simulations.

The reliability of most deep learning algorithms is fundamentally challenged by the existence of adversarial examples, which are incorrectly classified inputs that are extremely close to a correctly classified input. We study adversarial examples for deep neural networks with random weights and biases and prove that the $\ell^1$ distance of any given input from the classification boundary scales at least as $\sqrt{n}$, where $n$ is the dimension of the input. We also extend our proof to cover all the $\ell^p$ norms. Our results constitute a fundamental advance in the study of adversarial examples, and encompass a wide variety of architectures, which include any combination of convolutional or fully connected layers with skipped connections and pooling. We validate our results with experiments on both random deep neural networks and deep neural networks trained on the MNIST and CIFAR10 datasets. Given the results of our experiments on MNIST and CIFAR10, we conjecture that the proof of our adversarial robustness guarantee can be extended to trained deep neural networks. This extension will open the way to a thorough theoretical study of neural network robustness by classifying the relation between network architecture and adversarial distance.

The treatment of malaria is a global health challenge that stands to benefit from the widespread introduction of a vaccine for the disease. A method has been developed to create a live organism vaccine using the sporozoites (SPZ) of the parasite Plasmodium falciparum (Pf), which are concentrated in the salivary glands of infected mosquitoes. Current manual dissection methods to obtain these PfSPZ are not optimally efficient for large-scale vaccine production. We propose an improved dissection procedure and a mechanical fixture that increases the rate of mosquito dissection and helps to deskill this stage of the production process. We further demonstrate the automation of a key step in this production process, the picking and placing of mosquitoes from a staging apparatus into a dissection assembly. This unit test of a robotic mosquito pick-and-place system is performed using a custom-designed micro-gripper attached to a four degree of freedom (4-DOF) robot under the guidance of a computer vision system. Mosquitoes are autonomously grasped and pulled to a pair of notched dissection blades to remove the head of the mosquito, allowing access to the salivary glands. Placement into these blades is adapted based on output from computer vision to accommodate for the unique anatomy and orientation of each grasped mosquito. In this pilot test of the system on 50 mosquitoes, we demonstrate a 100% grasping accuracy and a 90% accuracy in placing the mosquito with its neck within the blade notches such that the head can be removed. This is a promising result for this difficult and non-standard pick-and-place task.