Solar energy adoption is critical to achieving net-zero emissions. However, it remains difficult for many industrial and commercial actors to decide on whether they should adopt distributed solar-battery systems, which is largely due to the unavailability of fast, low-cost, and high-resolution irradiance forecasts. Here, we present SunCastNet, a lightweight data-driven forecasting system that provides 0.05$^\circ$, 10-minute resolution predictions of surface solar radiation downwards (SSRD) up to 7 days ahead. SunCastNet, coupled with reinforcement learning (RL) for battery scheduling, reduces operational regret by 76--93\% compared to robust decision making (RDM). In 25-year investment backtests, it enables up to five of ten high-emitting industrial sectors per region to cross the commercial viability threshold of 12\% Internal Rate of Return (IRR). These results show that high-resolution, long-horizon solar forecasts can directly translate into measurable economic gains, supporting near-optimal energy operations and accelerating renewable deployment.

This paper examines the impact of fiscal incentives on industrial investment in Uruguay from 1974 to 2010. Using a mixed-method approach that combines econometric models with machine learning techniques, the study investigates both the short-term and long-term effects of fiscal benefits on industrial investment. The results confirm the significant role of fiscal incentives in driving long-term industrial growth, while also highlighting the importance of a stable macroeconomic environment, public investment, and access to credit. Machine learning models provide additional insights into nonlinear interactions between fiscal benefits and other macroeconomic factors, such as exchange rates, emphasizing the need for tailored fiscal policies. The findings have important policy implications, suggesting that fiscal incentives, when combined with broader economic reforms, can effectively promote industrial development in emerging economies.

The Fourth Industrial Revolution, or Industry 4.0, entails using the most up-to-date versions of technologies such as AI, IoT, cloud computing and big data within industrial environments and operations. For context, the First Industrial Revolution began in the latter part of the 18th century when mechanization from steam and waterpower was revolutionary. Then came the Second Industrial Revolution, which saw the advent of electrical power and mass production systems. Finally, the 20th-century Third Industrial Revolution introduced computers to business processes. The current level of digitization in industries such as manufacturing, healthcare, finance and agriculture is at a level that was once considered futuristic.

Artificial intelligence (AI) and machine learning (ML) are two technologies that are revolutionizing the industrial sector. The manufacturing area is no exception. Developing a Smart Factory is an opportunity to be competitive, to optimize timelines and make product design and production more efficient. Quality, worker safety, and sustainability are the fundamental pieces where these technologies can participate in the redesign towards high productivity, much safer, and more sustainable manufacturing. Manufacturing companies that are committed to finding their applications, understanding market trends and changes, to remain competitive.

Industrial production is one of the dirtiest corners of the corporate world. A startup from former Google engineers thinks it can clean it up with artificial intelligence. Phaidra, a company based in Seattle, sells AI software to automate building controls for power plants and other industrial giants. For several years, DeepMind has let its AI system manage the temperature controls inside Google data centers, ultimately shaving huge chunks off the company's electricity bill. Phaidra's algorithms are designed to select the most efficient temperature for unique facilities, such as a steel mill or a vaccine manufacturer, and identify when equipment starts to lag in performance.

The manufacturing industry is exploring 5G technology at an accelerated pace, largely to enable AI-driven use cases such as closed-loop manufacturing, adaptive manufacturing, predictive analytics for maintenance, and extended reality (XR)-based worker training and safety, says Jagadeesh Dantuluri, general manager for private and dedicated networks at Keysight Technologies. "It's not about a static assembly line performing the same action time and time again, but one that can change based on their needs," he says. "Private networks essentially enable new business models in manufacturing." Yet, the benefits of 5G private networks extend beyond manufacturing. Because the technology offers more reliable connectivity, faster data rates and lower latency, and greater scalability, security, and network control than previous communications technologies, 5G private networks will drive innovations in many industrial and enterprise sectors.

As the world begins to recalibrate itself following the pandemic, businesses have undergone a radical and irreversible shake up. The crisis, while challenging, has offered radical insights into running and optimizing organizations in unpredictable times. Put simply, it has showed how industrial operations can be upended almost overnight. Workforce routines, supply chains, essential maintenance and parts movement were disrupted, while border closures and an unprecedented drop in demand squeezed already tight economic operations. To thrive in this brave new world, there has been a need to respond with transformative action.

As we evolve from pandemic to next normal world, 36% of manufacturers are already engaged on AI projects and 23% more have plans to use AI in coming months to unlock the anticipated trillions of dollars in value in industrial sectors. Based on the lessons learned during the pandemic they are all seeking the best way to achieve new levels of productivity, safety and agility by unlocking the AI driven insights from petabytes of data they harvest from their connected factories. To answer these and other questions, many manufactures are turning to AI to speed time to value. Are you a manufacturer looking at how AI can benefit your business? If so, join this webinar with two leaders in AI as they discuss where and how manufacturers are seeing value from AI in areas such as inspection, predictive maintenance, quality control, video analytics and digital twins.

Deep Learning Algorithms are extremely popular and useful in Machine Learning. Deep Learning which is a branch of Artificial Intelligence has gained an enormous amount of acceptance due to its ability to perform tasks just like the human brain. Basically, its scientific computing methods are quite popular in different industrial sectors to solve complex problems. Deep Learning is a process where algorithms train machines with the help of examples. Here Deep Learning utilizes Artificial Neural Network to perform different tasks in an advanced computational way on a large amount of data.

Fero Labs, the only Explainable Machine Learning software solely dedicated to the industrial sector, today announced the closing of a $9 million Series A round led by Innovation Endeavors, with participation from Deutsche Invest VC. This funding will support Fero Labs in expanding its product offerings to new sectors and ultimately push the industrial manufacturing community forward. The industrial sector has just begun to implement technologies into its processes to reduce waste and increase efficiency and profits. More than half of leaders in the manufacturing and utilities sector expect artificial intelligence to control high-value assets such as industrial plants, equipment, machines and its processes in the next five years, according to Next-Gen Industrial AI - and due to the pandemic, the industry observed a steady increase in artificial intelligence and machine learning adoption across industries including energy, manufacturing, heavy industry, infrastructure, and transportation sectors. "At Fero Labs, we develop our technology around the needs of customers, delivering the best of machine learning, AI technologies and scalable automated infrastructure," said Berk Birand, CEO of Fero Labs.