A US technology company is 3D-printing futuristic holiday homes starting from $100,000 (£75,000) that fit in a back garden. Mighty Buildings, based in Oakland, California, says it can manufacture a 350 square-foot studio unit in less than 24 hours, providing owners a peaceful hideaway or a holiday cabin to accommodate guests. The firm is offering a variety of units on its website, ranging from a dinky studio to a luxury family home, which are printed with liquid synthetic stone that hardens almost instantly. The buildings are constructed at the company's facilities, transported to the customer's property on a truck and placed in a back garden with a massive crane. Units could also be leased out by property owners to help tackle the housing crisis, or big companies could also buy them to house employees while they're looking for something more long-term.

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Houston, Texas-based Innowatts, is a startup that has developed an automated toolkit for energy monitoring and management. Innowatts says its machine learning algorithms can analyze the data to forecast several critical data points, including short- and long-term loads, variances, weather sensitivity, and more.

The U.S. Department of Energy (DOE) Artificial Intelligence and Technology Office (AITO) has announced the creation of the First Five Consortium …

Seismic full-waveform inversion (FWI) is a nonlinear computational imaging technique that can provide detailed estimates of subsurface geophysical properties. Solving the FWI problem can be challenging due to its ill-posedness and high computational cost. In this work, we develop a new hybrid computational approach to solve FWI that combines physics-based models with data-driven methodologies. In particular, we develop a data augmentation strategy that can not only improve the representativity of the training set but also incorporate important governing physics into the training process and therefore improve the inversion accuracy. To validate the performance, we apply our method to synthetic elastic seismic waveform data generated from a subsurface geologic model built on a carbon sequestration site at Kimberlina, California. We compare our physics-consistent data-driven inversion method to both purely physics-based and purely data-driven approaches and observe that our method yields higher accuracy and greater generalization ability.

Calibration of large-scale differential equation models to observational or experimental data is a widespread challenge throughout applied sciences and engineering. A crucial bottleneck in state-of-the art calibration methods is the calculation of local sensitivities, i.e. derivatives of the loss function with respect to the estimated parameters, which often necessitates several numerical solves of the underlying system of partial or ordinary differential equations. In this paper we present a new probabilistic approach to computing local sensitivities. The proposed method has several advantages over classical methods. Firstly, it operates within a constrained computational budget and provides a probabilistic quantification of uncertainty incurred in the sensitivities from this constraint. Secondly, information from previous sensitivity estimates can be recycled in subsequent computations, reducing the overall computational effort for iterative gradient-based calibration methods. The methodology presented is applied to two challenging test problems and compared against classical methods.

Learning classifier systems (LCSs) are population-based predictive systems that were originally envisioned as agents to act in reinforcement learning (RL) environments. These systems can suffer from population bloat and so are amenable to compaction techniques that try to strike a balance between population size and performance. A well-studied LCS architecture is XCSF, which in the RL setting acts as a Q-function approximator. We apply XCSF to a deterministic and stochastic variant of the FrozenLake8x8 environment from OpenAI Gym, with its performance compared in terms of function approximation error and policy accuracy to the optimal Q-functions and policies produced by solving the environments via dynamic programming. We then introduce a novel compaction algorithm (Greedy Niche Mass Compaction - GNMC) and study its operation on XCSF's trained populations. Results show that given a suitable parametrisation, GNMC preserves or even slightly improves function approximation error while yielding a significant reduction in population size. Reasonable preservation of policy accuracy also occurs, and we link this metric to the commonly used steps-to-goal metric in maze-like environments, illustrating how the metrics are complementary rather than competitive.

Introducing IoT & AI Artificial intelligence helps machines to behave like humans such as face recognition, decision making, learning and solving problems. Artificial Intelligence are used for learning and making self-decisions by using the process of complex organized or unorganized data. This technology has given a new horizon to the digital world like the way smartphones made a change in our lives. Every day, we get to hear about new upgrades and new technologies bringing rapid change in the globe. With every change, the tech world is also growing resulting in advanced technology which is bringing us closer. Such an example is the development & advancement of IoT or internet of things. Here artificial experience plays the role to speed up user experience. Before getting into the technical skills of IoT, lets understand what is it and where is it required? (Picture Reference: https://www.reply.com/breed-reply/en/content/why-are-ai-and-iot-perfect-partners-for-growth) Actually IoT cannot work without AI. Why? Internet of things (IoT) is a network of technologies or sensors that contains some advance technology embedded into it. It helps in communicating and interacting with their data. The process involves receiving and transferring data through the network without human interactions or human to computer involvement. These data from devices or sensors can be stored in the cloud and it can be made available for real-time analytics. IoT collects these vast amount of data from different environment. With the help of data science and applying analytics, AI converts these collective data into applications. So the whole process involves collecting and processing data. AI and IoT: Why Do We Need It? According to a report, companies like Deloitte have already using AI and IOT for establishing themselves in the market in 2017.  So why is it so important? Actually artificial intelligence has become a perfect solution to manage multiple connected IoT elements, its unlimited processing and learning abilities. These are considered to be quite useful for making sense of millions of data transmitted by IoT devices. (Reference: https://www2.deloitte.com/insights/us/en/focus/signals-for-strategists/intelligent-iot-internet-of-things-artificial-intelligence.html ) How Does The Steps Takes Place? We can call IoT as the data “supplier” while machine learning can be considered as data “miner.” The process takes place as follows: IoT sensors supplies millions of data points. The “miner” or machine learning identifies the relations between them Extract meaningful insight from these variables. Transport it to the storage for further analysis. (Picture Reference: https://www.business2community.com/big-data/iot-big-data-ai-new-superpowers-digital-universe-01926411) Earlier the traditional analytical approach was used which was as follow: The system gathers past data. Data processing. Generate reports. Thus we can conclude that IoT and machine learning works more on prediction. It starts with the desired outcome and searches interactions between input variables to produce results. As more data are being received and aggregated, the system returns even more accurate predictions due to its smart thinking. In this way, businesses can conclude to a perfect decision without actual “thinking” or human interaction. How IoT Benefits From AI? Soon, IoT would produce vast amount of data due to the rapid growing of devices and sensors. According to a research, 50 billion devices will be connected to the internet by 2020, ranging from smartphones, gadgets, smart watches, various computer systems and vehicles. These data would be a lot helpful for various things such as predicting natural calamities, accidents and crimes, helps doctors getting real-time information from medical equipment, optimized productivity across industries, predictive maintenance on equipment and machinery, create smart homes with connected appliances and provide critical communication between self-driving cars. The possibilities are endless. (Picture Reference: http://www.starproperty.my/index.php/articles/property-news/what-is-the-internet-of-things/) These big data are important only when it is transformed into valuable and actionable information within a given time period. Obviously it is not possible for human hands to do it. This is where artificial intelligence comes into play. AI collects the data and extracts the meaning from it by applying analytics. When we feed data from IoT devices into an AI system, it reviews and analyzes the data, produces decisions made either by machines or humans. (Reference: https://www.zdnet.com/article/what-is-the-internet-of-things-everything-you-need-to-know-about-the-iot-right-now/) Examples showing implementing AI in IoT applications. Smart decisions. When a device detects unusual conditions due to any error, it needs to know how to and when to react or whether it need human assistance. Obviously intelligent learning and decision-making capabilities are required to make such wise decisions. Google uses this approach in the Rank Brain algorithm. Once the solution is made, it responds in real-time without any human intervention. (Reference: https://searchengineland.com/faq-all-about-the-new-google-rankbrain-algorithm-234440) Smart Meters. Smart meters use specially designed sensors, incorporated into smart grids to record and upload electrical and background data. Here Artificial Intelligence techniques are applied to the grid to integrate privacy. They are used in every electricity consumption unit. Not only does they have the bidirectional flow of both electricity, they are equipped with real-time sensors which collects data on relevant factors that includes frequencies used by different equipment and appliances. (Reference: https://iot.eetimes.com/smart-meters-and-ai-take-on-electrical-grid-load-forecasting/) Boosting efficiency. Machine learning with AI can decipher trends and make predictions about future events, by applying predictive analytics. This shows the real benefits of IoT in a variety of manufacturing industries. Healthcare. In the healthcare sector, AI with IoT can improve patient care. Sensors from medical devices such as healthcare mobile apps, fitness trackers and digital medical records have been producing and storing patient’s data. The AI and IoT approach can help predict diseases, suggest preventive maintenance, track physical activity, heart rate, body mass, temperature and provide drug administration by reviewing the medical history and identifying the health problem. When it is regarding health protection or disease control, patients and doctors would accept the benefits that come with the AI and IoT approach. (Reference: https://blogs.sas.com/content/sascom/2018/05/01/how-will-iot-and-ai-drive-transformation-in-health-care-and-life-sciences/) Forecasting. Accurate forecasts help farmers to plan farming or harvesting. Train or plane schedules fully depends on weather forecasting to modify for expected weather interruptions. Businesses that are weather dependent, such as landscaping or utility companies can accurately employ labor and resources according to expected weather events. AI can help make more accurate forecasting. Artificial intelligence (AI) techniques apply its method on past predictions and actual outcomes. By comparing predictions with outcomes, it produces results for the future, with greater accuracy. AI feed both old and currently available data into algorithms that effective at past occurrences with future predictions. (Reference: https://www.wired.com/brandlab/2018/05/bringing-power-ai-internet-things/) Scalability. IoT can scale data. It means: AI extracts information from one device. Analyses and summarizes the data. Transfer it to the other. Thus it reduces the enormous amount of data to a lesser amount and enables a larger number of IoT devices to be connected to the network. This is called scalability. (Picture Reference: https://www.nextgenexecsearch.com/iot-enables-smart-cities/ ) Smart Devices: Today we have basic things fitted with technology like smart TV, smart watch, smart security system. Even we have “intelligent” vacuum cleaners, doorbells and lightning systems which have already come to the market. All this is due to artificial intelligence and it do makes life easier. AI can make life in smart homes even more comfortable. It can detect your mood and analyze your interaction with home objects such as Adjusting temperature for both heating and cooling. Adjusting lighting. Put on music of your choice. Close or open windows depending on the weather. Conclusion The IoT and Artificial Intelligence (AI) will play a vital role in the future as it has become a growing need for technologies in both private and government sectors. Engineers, scientists and technologists have already started to implement it in various levels. The potential opportunities and benefits of both AI and IoT can be gained once they are combined, both at the devices end as well as at the server. (References : https://www.wired.com/brandlab/2018/05/bringing-power-ai-internet-things/, https://www.ariasystems.com/blog/iot-needs-artificial-intelligence-succeed/ , https://www.techemergence.com/artificial-intelligence-plus-the-internet-of-things-iot-3-examples-worth-learning-from/ ) Written by: Ayanti Goswami

In Marvel's comic universe following the end of World War II Howard Stark tries to tap into the energy of the mystical "Tesseract" and develops the arc reactor -- a technology he believes to hold the key to unlimited, sustainable energy and would make nuclear energy look like an AAA battery. However, the perfect reactor cannot be built without a certain theoretical element and he lacks the technology to synthesize it. In the film "Iron Man", his son Tony Stark builds a miniature version of the Arc Reactor when held hostage in an Afghan cave to power an electromagnet, which keeps deadly shrapnel from piercing his heart. Even this small reactor has a remarkable output of 3 GJ/s -- as much as three times the average energy produced by a nuclear power plant. As the reactor's waste products threaten to poison him, Tony searches for new elements for the reaction.

As 12,000 lightning strikes pummeled the Bay Area this month, igniting hundreds of fires, fire spotters sprang into action. Their arsenal of tools includes thermal imagery collected by space satellites; real-time feeds from hundreds of mountaintop cameras; a far-flung array of weather stations monitoring temperature, humidity and winds; and artificial intelligence to munch and crunch the vast data troves to pinpoint hot spots. For decades, wildfires in remote regions were spotted by people in lookout towers who scanned the horizon with binoculars for smoke -- a tough and tedious job. They reported potential danger by telephone, carrier pigeon or Morse code signals with a mirror. Now, fire spotting has gone high tech.