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.

Energy Star was launched in 1992 by the US Environmental Protection Agency as a voluntary labelling programme recognising the value of energy-efficiency in a broad range of computer-related products, from personal computers to air-conditioning systems. The programme's major success was the widespread adoption of the energy-saving "sleep mode" in consumer electronic devices. Energy Star's innovative breakthrough represents an important platform from which today's concept of computational sustainability was launched. Computational sustainability is defined as a field of interdisciplinary research that attempts to optimise societal, economic and environmental resources using advanced decision-making algorithms supported by the ever-increasing processing power of today's evolving computer systems. Computational sustainability's key goals include the development of computational models, methods and tools to assist in the management of the delicate balance between environmental, economic and societal needs. Advancements in AI and HCI have enabled combinations of robots and humans to carry out critical functions in the most hostile of environments.

A DJI Technology drone flies during a demonstration in Shenzhen, China, in 2014. DJI sells the majority of Chinese-made drones bought in the United States. A DJI Technology drone flies during a demonstration in Shenzhen, China, in 2014. DJI sells the majority of Chinese-made drones bought in the United States. Drones have become an increasingly popular tool for industry and government.

What if instead of political parties, presidents, prime ministers, kings, queens, armies, autocrats, and who knows what else, we turned everything over to expert systems? What if we engineered them to be faithful, for example, to one simple principle: "human beings regardless of age, gender, race, origin, religion, location, intelligence, income or wealth, should be treated equally, fairly and consistently"? Here's some dialogue – enabled by natural language processing (NLP) – with an expert system named "Decider" that operates from that single principle (you can imagine how it might behave if the principle was completely different – the opposite of equal and fair). The principle is supported by the data and probabilities the system collects and interprets. The "inferences" made by Decider are pre-programmed.

Imagine a world without environmental regulations or traffic laws, where unlicensed motorists drive as they please and factories pollute with impunity. Those were the facts of life in cities around the world as the industrial revolution took hold. And a few decades from now, we may look back on the emergence of AI as a similarly lawless era. With that in mind, governments in Canada and the European Union, among others, have been active in proposing regulations to protect consumers while the U.S. has largely remained silent -- until now. Computers are increasingly involved in the most important decisions affecting Americans' lives – whether or not someone can buy a home, get a job or even go to jail." This spring, Democratic senators Cory Booker and Ron Wyden proposed the first national AI ethics bill in the form of the Algorithmic Accountability Act. The bill aims to give regulators, and the public, greater insights into how AI systems make the decisions they do -- and what data is ...

Last week, another notable AI milestone occurred. Fresh off the heels of the U.S. Treasury's Financial Crimes Enforcement Network and Federal Banking Agencies' recent initiative that urged financial services organizations to implement "innovative approaches" like AI to better combat money laundering, terrorist financing and other illicit financial threats, U.S. Senators Martin Heinrich (D-N.M.) and Rob Portman (R-Ohio) announced the Artificial Intelligence Initiative Act. The bipartisan legislation represents a coordinated, national strategy for developing AI and will provide a $2.2 billion federal investment over five years to "build an AI-ready workforce, accelerating the responsible delivery of AI applications for government agencies, academia and the private sector over the next 10 years." Many businesses are spending exorbitant amounts of time and money on what they think is AI, yet in reality failing to reap any of the benefits of true unsupervised learning technology. This legislation is desperately needed.

In this paper, we study the adversarial attack and defence problem in deep learning from the perspective of Fourier analysis. We first explicitly compute the Fourier transform of deep ReLU neural networks and show that there exist decaying but non-zero high frequency components in the Fourier spectrum of neural networks. We demonstrate that the vulnerability of neural networks towards adversarial samples can be attributed to these insignificant but non-zero high frequency components. Based on this analysis, we propose to use a simple post-averaging technique to smooth out these high frequency components to improve the robustness of neural networks against adversarial attacks. Experimental results on the ImageNet dataset have shown that our proposed method is universally effective to defend many existing adversarial attacking methods proposed in the literature, including FGSM, PGD, DeepFool and C&W attacks. Our post-averaging method is simple since it does not require any re-training, and meanwhile it can successfully defend over 95% of the adversarial samples generated by these methods without introducing any significant performance degradation (less than 1%) on the original clean images.

In recent years, precision agriculture that uses modern information and communication technologies is becoming very popular. Raw and semi-processed agricultural data are usually collected through various sources, such as: Internet of Thing (IoT), sensors, satellites, weather stations, robots, farm equipment, farmers and agribusinesses, etc. Besides, agricultural datasets are very large, complex, unstructured, heterogeneous, non-standardized, and inconsistent. Hence, the agricultural data mining is considered as Big Data application in terms of volume, variety, velocity and veracity. It is a key foundation to establishing a crop intelligence platform, which will enable resource efficient agronomy decision making and recommendations. In this paper, we designed and implemented a continental level agricultural data warehouse by combining Hive, MongoDB and Cassandra. Our data warehouse capabilities: (1) flexible schema; (2) data integration from real agricultural multi datasets; (3) data science and business intelligent support; (4) high performance; (5) high storage; (6) security; (7) governance and monitoring; (8) replication and recovery; (9) consistency, availability and partition tolerant; (10) distributed and cloud deployment. We also evaluate the performance of our data warehouse.

Connected and autonomous vehicles (CAVs) will form the backbone of future next-generation intelligent transportation systems (ITS) providing travel comfort, road safety, along with a number of value-added services. Such a transformation---which will be fuelled by concomitant advances in technologies for machine learning (ML) and wireless communications---will enable a future vehicular ecosystem that is better featured and more efficient. However, there are lurking security problems related to the use of ML in such a critical setting where an incorrect ML decision may not only be a nuisance but can lead to loss of precious lives. In this paper, we present an in-depth overview of the various challenges associated with the application of ML in vehicular networks. In addition, we formulate the ML pipeline of CAVs and present various potential security issues associated with the adoption of ML methods. In particular, we focus on the perspective of adversarial ML attacks on CAVs and outline a solution to defend against adversarial attacks in multiple settings.

This paper introduces an innovative Bayesian machine learning algorithm to draw inference on heterogeneous causal effects in the presence of imperfect compliance (e.g., under an irregular assignment mechanism). We show, through Monte Carlo simulations, that the proposed Bayesian Causal Forest with Instrumental Variable (BCF-IV) algorithm outperforms other machine learning techniques tailored for causal inference (namely, Generalized Random Forest and Causal Trees with Instrumental Variable) in estimating the causal effects. Moreover, we show that it converges to an optimal asymptotic performance in discovering the drivers of heterogeneity in a simulated scenario. BCF-IV sheds a light on the heterogeneity of causal effects in instrumental variable scenarios and, in turn, provides the policy-makers with a relevant tool for targeted policies. Its empirical application evaluates the effects of additional funding on students' performances. The results indicate that BCF-IV could be used to enhance the effectiveness of school funding on students' performance by 3.2 to 3.5 times.