Four-fifths (80%) of small and medium-sized enterprises in Scotland expect to employ robots or other artificial intelligence (AI) by 2035, study findings show. Nearly half (49%) believe they will be reliant on renewable energy sources to power this advancement in technology. And three-quarters (75%) say improving eco-friendliness will help their profitability and make them more attractive to investors. When asked what roles robots would have, 36% of SMEs expect them to be used for tidying the workplace. Carrying out hazardous tasks (39%) and entertainment (46%) were also cited.

Large rotating machines, e.g., compressors, steam turbines, gas turbines, are critical equipment in many process industries such as energy, chemical, and power generation. Due to high rotating speed and tremendous momentum of the rotor, the centrifugal force may lead to flying apart of the rotor parts, which brings a great threat to the operation safety. Early detection and prediction of potential failures could prevent the catastrophic plant downtime and economic loss. In this paper, we divide the operational states of a rotating machine into normal, risky, and high-risk ones based on the time to the moment of failure. Then a cascade classifying algorithm is proposed to predict the states in two steps, first we judge whether the machine is in normal or abnormal condition; for time periods which are predicted as abnormal we further classify them into risky or high-risk states. Moreover, traditional classification model evaluation metrics, such as confusion matrix, true-false accuracy, are static and neglect the online prediction dynamics and uneven wrong-prediction prices. An Online Prediction Ability Index (OPAI) is proposed to select prediction models with consistent online predictions and smaller close-to-downtime prediction errors. Real-world data sets and computational experiments are used to verify the effectiveness of proposed methods.

Professor Mark K. Smith, CEO of ContactEngine, discusses how artificial intelligence (AI) can lend itself towards sustainability Not a day goes by without a company, charity, government or some other gas-guzzling, forest-burning, ocean-destroying organisation making a grand claim about net-zero, carbon neutral or otherwise pledging to single-handedly save the planet by twenty-whenever. We'll get the brag out of the way quickly – ContactEngine is already carbon negative – but, following the noise around COP26, the threat of irreversible climate change provides a good reason to discuss sustainability in AI more generally. Cloud-based computing has rightly come under increased scrutiny in recent years for its energy use. Greenpeace estimates that by 2025, the tech sector could consume 20% of the world's electricity, a huge rise from its current 7%, and one that will be largely driven by cloud computing. As it stands, a lot of this energy use doesn't come from renewable sources, with nearly 4% of all CO2 emissions coming from data transfer and infrastructure – a figure 60% higher than aviation.

I recently spoke with an elderly gentleman -- a trader and fund owner. This conversation inspired me to write an article about Artificial Intelligence tools that are used in trading today. His fund employs over a dozen traders investing in various markets, and he is a veteran of oil trading. His trading style is conservative -- after finding a signal, he opens a trade holding a single position, sometimes for several weeks. I want to show you where and how you can use the most modern solutions in this example.

The custom of storing silos of data for usage by AI technologies is a rather new one, and most companies today are at a stage where they have just started or are very early in the process of making this tumultuous transition one step at a time. Hence, issues such as a lack of structured data is rampant, and the belief that they can't be used currently is widespread as well. The argument to be made here is that the right AI models may, in fact, be powerful enough to use even in such data-light environments. McKinsey finds, for example, that not only do AI models have clear advantages over spreadsheet-based counterparts, but aspects such as AI supply chain management can, in fact, reduce errors by anything between 20-50%, reducing lost sales and product unavailability by almost 65% already. "Continuing the virtuous circle, warehousing costs can fall by 5 to 10 percent, and administration costs by 25 to 40 percent. Companies in the telecommunications, electric power, natural gas, and healthcare industries have found that AI forecasting engines can automate up to 50 percent of workforce-management tasks, leading to cost reductions of 10 to 15 percent while gradually improving hiring decisions--and operational resilience."

Russian tankers carrying oil chemicals and oil products are increasingly concealing their movements, a phenomenon that some maritime experts warn could signal attempts to evade unprecedented sanctions prompted by the invasion of Ukraine. In the week ending March 25, there were at least 33 occurrences of so-called "dark activity" -- operating while onboard systems to transmit their locations are turned off -- by Russian tankers, said Windward Ltd., an Israeli consultancy that specializes in maritime risk using artificial intelligence and satellite imagery. That's more than double the weekly average of 14 in the past year. The dark operations occurred mainly in or around Russia's exclusive economic zone, according to Windward, which conducted the research at Bloomberg's request. The ships engaging in dark activity include vessels connected to big corporations and multinational shipping firms, as well as small businesses, according to Windward.

Crop maps are crucial for agricultural monitoring and food management and can additionally support domain-specific applications, such as setting cold supply chain infrastructure in developing countries. Machine learning (ML) models, combined with freely-available satellite imagery, can be used to produce cost-effective and high spatial-resolution crop maps. However, accessing ground truth data for supervised learning is especially challenging in developing countries due to factors such as smallholding and fragmented geography, which often results in a lack of crop type maps or even reliable cropland maps. Our area of interest for this study lies in Himachal Pradesh, India, where we aim at producing an open-access binary cropland map at 10-meter resolution for the Kullu, Shimla, and Mandi districts. To this end, we developed an ML pipeline that relies on Sentinel-2 satellite images time series. We investigated two pixel-based supervised classifiers, support vector machines (SVM) and random forest (RF), which are used to classify per-pixel time series for binary cropland mapping. The ground truth data used for training, validation and testing was manually annotated from a combination of field survey reference points and visual interpretation of very high resolution (VHR) imagery. We trained and validated the models via spatial cross-validation to account for local spatial autocorrelation and selected the RF model due to overall robustness and lower computational cost. We tested the generalization capability of the chosen model at the pixel level by computing the accuracy, recall, precision, and F1-score on hold-out test sets of each district, achieving an average accuracy for the RF (our best model) of 87%. We used this model to generate a cropland map for three districts of Himachal Pradesh, spanning 14,600 km2, which improves the resolution and quality of existing public maps.

This article is part of a VB special issue. Read the full series here: The metaverse - How close are we? Some companies believe that the metaverse -- a yet-to-be-realized, internet-like series of connected worlds -- has enormous potential in the enterprise. For example, it could be used to improve work productivity by allowing employees to train or collaborate in workplace-like virtual environments. Or it could host home and office tours, a boon for a real estate market contending with pandemic travel restrictions.

"Tesla is arguably the world's biggest robotics company because our cars are like semi-sentient robots on wheels" -Elon Musk Last year during Tesla's open AI day, Elon Musk announced something new Tesla was working on called the Tesla robot. A lot of people I've spoken to have no idea what the Tesla robot is, what it is for, how it will work, and why it's not feasible. All they know is it's another cool piece of tech created by Tesla. In this article, I will be talking about what the Tesla robot is, what it will do and how it will work. I know pretty much anyone who is on the internet knows about Tesla, but for the sake of completion let's still go over what Tesla is.

In the next few decades, Artificial Intelligence (AI) will be the biggest commercial opportunity in the world. As we gain access to an ever-richer tapestry of data and knowledge, the enhancement of deep learning through AI is intrinsically linked to the rise in commercial space and satellite activity. As the space and satellite industry in Cornwall county, UK scales at pace, so too does the region's AI capabilities. From edge AI for manufacturing, to AI algorithms being developed to remove cloud cover and unlock satellite data for business transformation – Cornwall is home to a hugely unique mix of companies tapping into the mutually beneficial relationship between the two technologies, in turn becoming a hub for innovation in AI applications. Spearheading Cornwall's acceleration towards becoming the UK's premier location for space manipulated AI and deep learning are the team at Goonhilly Earth Station Ltd, home of the UK's Space AI institute.