The use of artificial intelligence (AI) and machine learning to drive innovation across all industries has increased significantly in recent years. Indeed, the proliferation of data science applications from genome sequencing for better disease diagnosis and prevention, to advances in leading edge engineering for autonomous driving, and climate modelling to combat Climate Change, has led to an exponential demand for High Performance Computing (HPC). AI for sustainability is one of the most promising new fields of study, with a recent report by PwC and Microsoft reporting that using AI for environmental applications in four key sectors could reduce global greenhouse gas emissions by 4% in just 10 years' time. Recent efforts include international non-profit organisation, Global Fishing Watch, using AI and satellite data to prevent overfishing, and wind companies using AI to get each turbine's propeller to produce more electricity per rotation by incorporating real time weather and operational data. But alongside worries about AI bias or human jobs being replaced by machines, concerns about the environmental impact of AI itself should be at the fore.

As median home prices continue to surge, buyers are taking a second look at alternative housing options, including modular and pre-manufactured homes. Unlike options from the past, today's prefab homes are stylish and technology driven. One of the newest options in this $19.3 billion industry comes from Singapore-based startup Nestron that offers four, prefab options that include a built-in artificial intelligence assistant named Canny that controls lighting, heating, and security. Ranging from $19,000 for the baseline Legend model to $59,000 for the top-of-the-line Cube model, Nestron is hoping to attract aspiring homeowners who want high style without breaking the bank. Nestron's basic and upgraded models.

Nigeria has a population of 200m people yet only half have access to electricity. Without electricity, there are no computers or the internet. There are no fridges to keep food fresh. There is no electric water pump. There is nowhere to charge a mobile phone.

We use causal forests to evaluate the heterogeneous treatment effects (TEs) of repeated behavioral nudges towards household energy conservation. The average response is a monthly electricity reduction of 9 kilowatt-hours (kWh), but the full distribution of responses ranges from -30 to 10 kWh. Selective targeting of treatment using the forest raises social net benefits by 12-120 percent, depending on the year and welfare function. Pre-treatment consumption and home value are the strongest predictors of treatment effect. We find suggestive evidence of a "boomerang effect": households with lower consumption than similar neighbors are the ones with positive TE estimates.

It's no longer taboo for people to leave the daily nine to five lifestyle behind and start their own business. In fact, it's never been easier, and cheaper, to start your own business. But, it's one thing to start your own business and it's another to become a millionaire from that idea. The good news is that it's feasible. And, you can use these 25 ideas to become a millionaire in four years or less. While the growing population will obviously need people to grow fruits and vegetables, raise livestock, or start a fish farm to meet their needs, there's an interesting trend happening.

Cyber-attack Automated Unconventional Sensor Environment (CAUSE), applies AI/ML-based models to develop novel, automated methods for event-based detection and prediction of cyber-attacks significantly earlier than existing approaches. Forecasting cyber-attack events with actionable details advances the state-of-the-art by enabling threat-specific cyber incident response and defense measures; Creation of Operationally Realistic 3D Environment (CORE3D), uses machine learning and deep learning techniques to develop methods for the construction of a fully automated high fidelity 3D model of the world using remote sensing data; Deep Intermodal Video Analytics (DIVA), leverages machine learning techniques to develop robust automatic activity detection in streaming video across multiple cameras; Finding Engineering-Linked Indicators (FELIX), uses AI for detection of engineering signatures across multiple biological organisms. The goal is to distinguish natural organisms from those that have been engineered; Functional Map of the World Challenge, developed algorithms that would quickly and accurately classify 63 classes of buildings and regions in satellite imagery. All the top participants used various forms of deep learning; Functional Genomic and Computational Assessment of Threats (Fun GCAT), develops AI/ML-based approaches to learn and classify genetic (e.g., DNA) sequence data by genetic taxonomy, sequence function, and threat potential; Mercury Challenge, asked challenge participants to make use of AI/ML approaches to forecast a variety of political events in the Middle East and North Africa region, such as non-violent civil unrest and military activity; Machine Intelligence from Cortical Networks (MICrONS), aims to revolutionize machine learning by reverse-engineering the algorithms of the brain. The program is expressly designed as a dialogue between data science and neuroscience; Machine Translation for English Retrieval of Information in Any Language (MATERIAL), develops machine learning methods to identify foreign language information from speech and text relevant to English queries, and providing evidence of relevance of the retrieved information in English in a meaningful way.

Widespread adoption of renewable energy and smart home technologies has empowered consumers to take greater control of their individual energy consumption. But for every home that generates power through solar, every electric vehicle (EV) charged and every battery storing excess energy, the electric grid experiences unpredictable and complex load disruptions. Coupled by consumers' general distrust of utility companies and ability to self-generate electricity through large consumer brands, these disruptions create ongoing challenges for utilities to adequately manage grid demands and protect their revenue, leaving them at a crossroads: They must either attempt to preserve their traditional business model or adapt to evolving customer needs. When a utility company decides on the latter, its job transitions from generating, distributing and selling power to also managing customer churn in ways similar to what deregulated energy retailers do where customers have a choice in energy providers. First, we must understand what consumers actually want from their utility.

In the decade ahead, waves of exponential technological advancements are stacking atop one another, eclipsing decades of breakthroughs in scale and impact. Emerging from these waves are 20 "metatrends" likely to revolutionize entire industries (old and new), redefine tomorrow's generation of businesses and contemporary challenges, and transform our livelihoods from the bottom up. Among these metatrends are augmented human longevity, the surging smart economy, AI-human collaboration, urbanized cellular agriculture, and high-bandwidth brain-computer interfaces, just to name a few. It is here that master entrepreneurs and their teams must see beyond the immediate implications of a given technology, capturing second-order, Google-sized business opportunities on the horizon. Welcome to a new decade of runaway technological booms, historic watershed moments, and extraordinary abundance.

Artificial intelligence, big data analytics, and machine learning are revolutionizing renewable energy sector and allowing the companies to improve their overall customer experience by means of automating work processes. Optimization and predictions are two major factors on which the energy sector heavily depends. The energy industry also produces vast amounts of data, and to turn this data into insights, major energy players are turning to AI. The historical data collected by power plants can now be combined with weather and satellite data through advancements in big data, AI, and machine learning. Consequently, solar and wind forecasting technology can predict weather conditions well in advance.

This work presents a framework of constructing the neural networks preserving the symplectic structure, so-called symplectic networks (SympNets). With the symplectic networks, we show some numerical results about (\romannumeral1) solving the Hamiltonian systems by learning abundant data points over the phase space, and (\romannumeral2) predicting the phase flows by learning a series of points depending on time. All the experiments point out that the symplectic networks perform much more better than the fully-connected networks that without any prior information, especially in the task of predicting which is unable to do within the conventional numerical methods.