The World Economic Forum, in collaboration with the World Resources Institute, convenes the Friends of Ocean Action, a coalition of leaders working together to protect the seas. From a programme with the Indonesian government to cut plastic waste entering the sea to a global plan to track illegal fishing, the Friends are pushing for new solutions.

The government is wary of over-regulating new technologies such as artificial intelligence and will resist making ethics standards and codes mandatory for Australian businesses, Digital Economy minister Jane Hume says. In an address to the Committee for Economic Development of Australia (CEDA), Senator Hume said the federal government would play an enabling role in accelerating the growth of artificial intelligence, along with setting standards in terms of ethics. "AI, along with other digital technologies, will play an increasingly important role in our economy and society over the next decade and beyond," Senator Hume said. "As we continue to vault forward in this space, government has a pivotal role to play as an enabler, and as a standard setter – particularly in regards to ethics. "The government has a significant responsibility … to ensure that AI, as an industry as well as a technology, has every chance to flourish, making sure we have the right settings, skills and expertise in place to ensure Australia is a global forerunner." The May budget allocated $124 million to artificial intelligence initiatives, including $50 million for a National AI Intelligence Centre within CSIRO and $34 million in grants for AI projects addressing national challenges. The Coalition has also unveiled AI ethics principles, with eight guiding principles "designed to help achieve safer and more reliable outcomes for all Australians". These principles and other standards around AI are currently entirely voluntary for Australian businesses, and Senator Hume said the government will avoid making them mandatory. "I obviously would rather have a voluntary code where industry has the input to what's in the code.

For over hundreds of millions of years, sea turtles and their ancestors have swum in the vast expanses of the ocean. They have undergone a number of evolutionary changes, leading to speciation and sub-speciation. However, in the past few decades, some of the most notable forces driving the genetic variance and population decline have been global warming and anthropogenic impact ranging from large-scale poaching, collecting turtle eggs for food, besides dumping trash including plastic waste into the ocean. This leads to severe detrimental effects in the sea turtle population, driving them to extinction. This research focusses on the forces causing the decline in sea turtle population, the necessity for the global conservation efforts along with its successes and failures, followed by an in-depth analysis of the modern advances in detection and recognition of sea turtles, involving Machine Learning and Computer Vision systems, aiding the conservation efforts.

Natural convection in porous media is a highly nonlinear multiphysical problem relevant to many engineering applications (e.g., the process of $\mathrm{CO_2}$ sequestration). Here, we present a non-intrusive reduced order model of natural convection in porous media employing deep convolutional autoencoders for the compression and reconstruction and either radial basis function (RBF) interpolation or artificial neural networks (ANNs) for mapping parameters of partial differential equations (PDEs) on the corresponding nonlinear manifolds. To benchmark our approach, we also describe linear compression and reconstruction processes relying on proper orthogonal decomposition (POD) and ANNs. We present comprehensive comparisons among different models through three benchmark problems. The reduced order models, linear and nonlinear approaches, are much faster than the finite element model, obtaining a maximum speed-up of $7 \times 10^{6}$ because our framework is not bound by the Courant-Friedrichs-Lewy condition; hence, it could deliver quantities of interest at any given time contrary to the finite element model. Our model's accuracy still lies within a mean squared error of 0.07 (two-order of magnitude lower than the maximum value of the finite element results) in the worst-case scenario. We illustrate that, in specific settings, the nonlinear approach outperforms its linear counterpart and vice versa. We hypothesize that a visual comparison between principal component analysis (PCA) or t-Distributed Stochastic Neighbor Embedding (t-SNE) could indicate which method will perform better prior to employing any specific compression strategy.

Artificial intelligence methods are playing an increasingly important role in global economics. The growing importance and, at the same time, the risks associated with AI are driving a vibrant discussion about the responsible development of artificial intelligence. Examples of negative consequences resulting from black-box models show that interpretability, transparency, safety, and fairness are essential yet sometimes overlooked components of AI systems. Efforts to secure the responsible development of AI systems are ongoing at many levels and in many communities, both policymakers and academics (Gill et al., 2020; Barredo Arrieta et al., 2020; Baniecki et al., 2020). Naturally, national strategies for the development of responsible AI, sector regulations related to the safe use of AI, as well as academic research related to new methods that ensure the transparency and verifiability of models are all interrelated. Strategies are based on discussions in the scientific community and are often sources of inspiration for subsequent research work. The need for regulation stems from risks, often identified by the research community, but when regulations are created, they become a powerful tool for developing methods to meet expectations. Scientific work in AI is particularly strongly connected to the economy, which means that a large part of it responds to the threads identified in regulations and strategies.

Abstract: To build a robust secure solution for smart city's IOT network from any Cyber-attacks using Artificial Intelligence (AI). In Smart City's IOT network, data collected from different log collectors or direct sources from cloud or edge should harness the potential of AI. The smart city command and control center team will leverage these models and deploy it in different city's IOT network to help on intrusion prediction, network packet surge, potential botnet attacks from external network. Some of the vital use cases considered based on the users of command-and-control center. Keywords-Artificial Intelligence, Internet of Things, Smart City, IOT Security, Smart City command and control center I. INTRODUCTION The Internet of Things market will grow from 170 Billion devices (as on 2017) to 561 Billion devices by 2020 as reply.com It will bring more niche devices like Smart Home appliances, Smart Home Security, Digital Assistants and Home Robots from different providers.

Much work has been dedicated to the exploration of Multi-Agent Reinforcement Learning (MARL) paradigms implementing a centralized learning with decentralized execution (CLDE) approach to achieve human-like collaboration in cooperative tasks. Here, we discuss variations of centralized training and describe a recent survey of algorithmic approaches. The goal is to explore how different implementations of information sharing mechanism in centralized learning may give rise to distinct group coordinated behaviors in multi-agent systems performing cooperative tasks.

In this work, we propose a method to learn probability distributions using sample path data from stochastic differential equations. Specifically, we consider temporally evolving probability distributions (e.g., those produced by integrating local or nonlocal Fokker-Planck equations). We analyze this evolution through machine learning assisted construction of a time-dependent mapping that takes a reference distribution (say, a Gaussian) to each and every instance of our evolving distribution. If the reference distribution is the initial condition of a Fokker-Planck equation, what we learn is the time-T map of the corresponding solution. Specifically, the learned map is a normalizing flow that deforms the support of the reference density to the support of each and every density snapshot in time. We demonstrate that this approach can learn solutions to non-local Fokker-Planck equations, such as those arising in systems driven by both Brownian and L\'evy noise. We present examples with two- and three-dimensional, uni- and multimodal distributions to validate the method.

Cassie has made history as the first bipedal robot to complete a five-kilometer (5K) run, having done so in just over 53 minutes. Developed by Oregon State University, the two-legged machine with knees that bend like those of an ostrich, taught itself how to run through a deep reinforcement learning algorithm. Yesh Godse, an undergraduate in the lab, said in a statement: 'Deep reinforcement learning is a powerful method in AI that opens up skills like running, skipping and walking up and down stairs.' Cassie's total time of 53 minutes, three seconds, included about six and a half minutes of resets following two falls. Cassie first stumbled when its computer overheated and the other came after it took a turn at too high of a speed. The robot's makers foresee it eventually delivering packages, managing warehouse tasks and helping people in their homes.

Hacking events have increasingly been in the news this year, as a range of serious ransomware and supply chain hacks have wrecked chaos on businesses and infrastructure. The latest (as of July 2021) is a supply-chain-ransomware attack against Miami-based software firm Kaseya, affecting 1500 of its customers - with the hackers (threat-actors) demanding $70 million in cryptocurrency to release the data. According to the World Economic Forum, cyber-attacks now stand side by side with climate change and natural disasters as one of the most pressing threats to humanity. No doubt ways will eventually be found to detect and pre-empt these latest styles of attack. The cybersecurity industry is defined by continual, if largely gradual, innovation - as new threats emerge, technology that protects, detects and responds to the attacks also emerges. This cat and mouse dynamic has been a fundamental trait of the industry to date: a permanently iterating relationship that supercharges the development of new technologies on both sides, where even a small edge over adversaries can pay dividends (or ransoms).