This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. The Game of Life is a grid-based automaton that is very popular in discussions about science, computation, and artificial intelligence. It is an interesting idea that shows how very simple rules can yield very complicated results. Despite its simplicity, however, the Game of Life remains a challenge to artificial neural networks, AI researchers at Swarthmore College and the Los Alamos National Laboratory have shown in a recent paper. Titled, "It's Hard for Neural Networks To Learn the Game of Life," their research investigates how neural networks explore the Game of Life and why they often miss finding the right solution. Their findings highlight some of the key issues with deep learning models and give some interesting hints at what could be the next direction of research for the AI community.

The numbers are well known by now: the world has warmed by 1.1 degrees Celsius compared to pre-Industrial levels at the time of writing, and is on track for the worst case scenarios projected. This year, we saw astonishing temperature records in the Arctic Circle and an ever ongoing loss of biodiversity. Simultaneously, we've seen massive leaps in technology. Artificial intelligence, in particular, has captured worldwide attention: from deep fakes and AI twitter bots influencing elections, to its increased use by companies to gain a competitive edge. We're at the start of the fourth industrial revolution, driven by AI; fundamentally impacting our lives and society as a whole.

We present a novel bilateral negotiation model that allows a self-interested agent to learn how to negotiate over multiple issues in the presence of user preference uncertainty. The model relies upon interpretable strategy templates representing the tactics the agent should employ during the negotiation and learns template parameters to maximize the average utility received over multiple negotiations, thus resulting in optimal bid acceptance and generation. Our model also uses deep reinforcement learning to evaluate threshold utility values, for those tactics that require them, thereby deriving optimal utilities for every environment state. To handle user preference uncertainty, the model relies on a stochastic search to find user model that best agrees with a given partial preference profile. Multi-objective optimization and multi-criteria decision-making methods are applied at negotiation time to generate Pareto-optimal outcomes thereby increasing the number of successful (win-win) negotiations. Rigorous experimental evaluations show that the agent employing our model outperforms the winning agents of the 10th Automated Negotiating Agents Competition (ANAC'19) in terms of individual as well as social-welfare utilities.

In large-area forest inventories a trade-off between the amount of data to be sampled and the costs of collecting the data is necessary. It is not always possible to have a very large data sample when dealing with sampling-based inventories. It is therefore necessary to optimize the sampling design in order to achieve optimal population parameter estimation. On the contrary, the availability of remote sensing (RS) data correlated with the forest inventory variables is usually much higher. The combination of RS and the sampled field measurement data is often used for improving the forest inventory parameter estimation. In addition, it is also reasonable to study the utilization of RS data in inventory sampling, which can further improve the estimation of forest variables. In this study, we propose a data sampling method based on Bayesian optimization which uses RS data in forest inventory sample selection. The presented method applies the learned functional relationship between the RS and inventory data in new sampling decisions. We evaluate our method by conducting simulated sampling experiments with both synthetic data and measured data from the Aland region in Finland. The proposed method is benchmarked against two baseline methods: simple random sampling and the local pivotal method. The results of the simulated experiments show the best results in terms of MSE values for the proposed method when the functional relationship between RS and inventory data is correctly learned from the available training data.

As the number of installed meters in buildings increases, there is a growing number of data time-series that could be used to develop data-driven models to support and optimize building operation. However, building data sets are often characterized by errors and missing values, which are considered, by the recent research, among the main limiting factors on the performance of the proposed models. Motivated by the need to address the problem of missing data in building operation, this work presents a data-driven approach to fill these gaps. In this study, three different autoencoder neural networks are trained to reconstruct missing indoor environment data time-series in a data set collected in an office building in Aachen, Germany. The models are applicable for different time-series obtained from room automation, such as indoor air temperature, relative humidity and $CO_{2}$ data streams. The results prove that the proposed methods outperform classic numerical approaches and they result in reconstructing the corresponding variables with average RMSEs of 0.42 {\deg}C, 1.30 % and 78.41 ppm, respectively.

Nvidia's GeForce RTX 3080 graphics card symbolizes why we tell people to wait for the second generation when bleeding-edge technology appears. The radical new-look Turing GPUs inside Nvidia's GeForce RTX 20-series packed all sorts of cutting-edge technologies designed to usher in real-time ray tracing, a long sought-after goal for the gaming industry. Not only did Turing introduce specialized RT cores devoted to processing ray tracing tasks, it also debuted tensor cores, dedicated hardware that uses machine learning to help denoise ray traced visuals and enable AI-enhanced tools like the fantastic Deep Learning Super Sampling (DLSS) technology. Turing's improvements also extended to the traditional shader cores, introducing an overhauled processing pipeline better equipped to handle games built using the newer DirectX 12 and Vulkan graphics APIs. All of these were huge departures from the norm.

Artificial intelligence refers to the creation of human intelligence in machines that are programmed to act like humans. This means, carrying out human activities including learning, planning, and problem-solving. Ai is a broad term that mimics human behavior with the aim of solving problems faster and better than we do. It is created not to only have a significant impact on content writing but also healthcare, entertainment, and even any area of our lives we can think of. Many people still associate artificial intelligence with science fiction that is an awful technology to adapt to, or take into the system but AI develops and becomes more commonplace in our daily lives. Artificial intelligence is actually good news which I guess so many people are happy about as long as it is controlled by humans but the bad news is that one-day computers might be as smart as humans or probably smarter than us.

Growing up in a middle-class family in Kolkata and Midnapore in West Bengal, India, Dr Sayonsom Chanda was no stranger to strong winds and relentless rain knocking down electricity for hours, days, and even weeks. He and his family lived in East Midnapur through the horror of the 1999 Odisha cyclone and Sidr cyclone in 2007. It was one of the core reasons for him to start Sync Energy in 2017. The startup builds artificial intelligence (AI) tools that simplify emergency and disaster response planning for electric power distribution companies. The platform helps electric utilities reduce customer downtimes and be better informed about the impact of a disaster before it actually strikes. This, in-turn, will help electric power companies to decrease costs associated with emergency-related power outages.

We live in a challenging world particularly since the start of the Covid-19 pandemic. Our ways of living, communicating, interacting, purchasing, and working have changed. With every challenge comes great opportunity so I remain extremely optimistic about the outcomes of this crisis. During confinement, we got to know our neighbors better and offered assistance. We saw some great collaboration amongst colleagues.

Metamaterials are emerging as a new paradigmatic material system to render unprecedented and tailorable properties for a wide variety of engineering applications. However, the inverse design of metamaterial and its multiscale system is challenging due to high-dimensional topological design space, multiple local optima, and high computational cost. To address these hurdles, we propose a novel data-driven metamaterial design framework based on deep generative modeling. A variational autoencoder (VAE) and a regressor for property prediction are simultaneously trained on a large metamaterial database to map complex microstructures into a low-dimensional, continuous, and organized latent space. We show in this study that the latent space of VAE provides a distance metric to measure shape similarity, enable interpolation between microstructures and encode meaningful patterns of variation in geometries and properties. Based on these insights, systematic data-driven methods are proposed for the design of microstructure, graded family, and multiscale system. For microstructure design, the tuning of mechanical properties and complex manipulations of microstructures are easily achieved by simple vector operations in the latent space. The vector operation is further extended to generate metamaterial families with a controlled gradation of mechanical properties by searching on a constructed graph model. For multiscale metamaterial systems design, a diverse set of microstructures can be rapidly generated using VAE for target properties at different locations and then assembled by an efficient graph-based optimization method to ensure compatibility between adjacent microstructures. We demonstrate our framework by designing both functionally graded and heterogeneous metamaterial systems that achieve desired distortion behaviors.