A few months ago, NASA unveiled its next-generation space suit that will be worn by astronauts when they return to the moon in 2024 as part of the agency's plan to establish a permanent human presence on the lunar surface. The Extravehicular Mobility Unit--or xEMU--is NASA's first major upgrade to its space suit in nearly 40 years and is designed to make life easier for astronauts who will spend a lot of time kicking up moon dust. It will allow them to bend and stretch in ways they couldn't before, easily don and doff the suit, swap out components for a better fit, and go months without making a repair. Instead, they're hidden away in the xEMU's portable life-support system, the astro backpack that turns the space suit from a bulky piece of fabric into a personal spacecraft. It handles the space suit's power, communications, oxygen supply, and temperature regulation so that astronauts can focus on important tasks like building launch pads out of pee concrete.

This project gives you a chance to participate in enhancing the new machine learning approaches for exploring mars and unveiling its secrets.

In July 2017, The State Council of China released the "New Generation Artificial Intelligence Development Plan," outlining China's strategy to build a US$150 billion Chinese AI industry in a few short years, and to become the leading nation in AI by the year 2030. Other nations followed suit quickly with national AI strategies of their own – with the US trailing behind by nearly two years before developing a semblance of an AI initiative. The proposed 2021 budget for the national security budget in the US is $740 billion – with a billions of dollars being earmarked for AI specifically (learn more: US Public Sector AI Opportunity Report). AI applications play a considerable role in the direction of technology development in many defense sectors, particularly in surveillance, intelligence gathering, reconnaissance, logistics, command and control, cyberspace, and information operations – but AI's relevance for national security is just as much in it's implications for the economy as it is for defense itself. This article is based on my presentation at the UNICRI / Shanghai Institutes for International Studies event Artificial Intelligence – Reshaping National Security – held in Shanghai. While I'm not able to embed my full slide deck from that presentation publicly, I am able to share some of the key ideas from my talk – with a focus on AI job loss and defense implications.

When it comes to Covid-19, one big question is how are we using artificial intelligence (AI) to help? While there is much focus on contact tracing and virus research, there are other big problems resulting from the coronavirus. Going beyond this, there are five key ares for people to focus AI on.

We consider three important challenges in conference peer review: (i) reviewers maliciously attempting to get assigned to certain papers to provide positive reviews, possibly as part of quid-pro-quo arrangements with the authors; (ii) "torpedo reviewing," where reviewers deliberately attempt to get assigned to certain papers that they dislike in order to reject them; (iii) reviewer de-anonymization on release of the similarities and the reviewer-assignment code. On the conceptual front, we identify connections between these three problems and present a framework that brings all these challenges under a common umbrella. We then present a (randomized) algorithm for reviewer assignment that can optimally solve the reviewer-assignment problem under any given constraints on the probability of assignment for any reviewer-paper pair. We further consider the problem of restricting the joint probability that certain suspect pairs of reviewers are assigned to certain papers, and show that this problem is NP-hard for arbitrary constraints on these joint probabilities but efficiently solvable for a practical special case. Finally, we experimentally evaluate our algorithms on datasets from past conferences, where we observe that they can limit the chance that any malicious reviewer gets assigned to their desired paper to 50% while producing assignments with over 90% of the total optimal similarity. Our algorithms still achieve this similarity while also preventing reviewers with close associations from being assigned to the same paper.

This study exploits the advancements in information and communication technology (ICT), connected and automated vehicles (CAVs), and sensing, to develop anticipatory multi-objective eco-routing strategies. For a robust application, several GHG costing approaches are examined. The predictive models for the link level traffic and emission states are developed using long short term memory deep network with exogenous predictors. It is found that anticipatory routing strategies outperformed the myopic strategies, regardless of the routing objective. Whether myopic or anticipatory, the multi-objective routing, with travel time and GHG minimization as objectives, outperformed the single objective routing strategies, causing a reduction in the average travel time (TT), average vehicle kilometre travelled (VKT), total GHG and total NOx by 17%, 21%, 18%, and 20%, respectively. Finally, the additional TT and VKT experienced by the vehicles in the network contributed adversely to the amount of GHG and NOx produced in the network.

How much knowledge do pretrained language models hold? Recent research observed that pretrained transformers are adept at modeling semantics but it is unclear to what degree they grasp human knowledge, or how to ensure they do so. In this paper we incorporate knowledge-awareness in language model pretraining without changing the transformer architecture, inserting explicit knowledge layers, or adding external storage of semantic information. Rather, we simply signal the existence of entities to the input of the transformer in pretraining, with an entity-extended tokenizer; and at the output, with an additional entity prediction task. Our experiments show that solely by adding these entity signals in pretraining, significantly more knowledge is packed into the transformer parameters: we observe improved language modeling accuracy, factual correctness in LAMA knowledge probing tasks, and semantics in the hidden representations through edge probing.We also show that our knowledge-aware language model (KALM) can serve as a drop-in replacement for GPT-2 models, significantly improving downstream tasks like zero-shot question-answering with no task-related training.

The main success stories of deep learning, starting with ImageNet, depend on convolutional networks, which on certain tasks perform significantly better than traditional shallow classifiers, such as support vector machines. Is there something special about deep convolutional networks that other learning machines do not possess? Recent results in approximation theory have shown that there is an exponential advantage of deep convolutional-like networks in approximating functions with hierarchical locality in their compositional structure. These mathematical results, however, do not say which tasks are expected to have input-output functions with hierarchical locality. Among all the possible hierarchically local tasks in vision, text and speech we explore a few of them experimentally by studying how they are affected by disrupting locality in the input images. We also discuss a taxonomy of tasks ranging from local, to hierarchically local, to global and make predictions about the type of networks required to perform efficiently on these different types of tasks.

Artificial Intelligence (AI) and Machine Learning (ML) based analytics solutions require aggregating and analysing data to train them to mimic real-world …

Cimon stands for Crew Interactive MObile companioN and is a reference to Simon Smith - the genius doctor known as the "flying brain" - from the science fiction story "Captain Future". Cimon is 3D printed and is just 32 centimetres in diameter - no bigger than a basketball and just 5kg in mass (0N in weight as space is a vacuum). Cimon was initially conceived by the DLR (German Space Agency) to help astronaut Alexander Gerst with science experiments in the Columbus Laboratory aboard the International Space Station. Developed by Airbus for the DLR, Cimon acts as a test bed to assess the potential feasibility of future intelligent robots in space - seeing whether they have the capability to simplify work life onboard the ISS. Cimon's'flying brain' was provided by IBM - its brain will be continuously updated over the air via IBM's Cloud, allowing Cimon to stay on the ISS for prolonged periods of time.