IIT Hyderabad Researchers are using computational methods to understand the factors and impediments in incorporating biofuels into the fuel sector in India. This work has been spurred by the increasing need to replace fossil fuels by bio-derived fuels, which, in turn, is driven by the dwindling fossil fuel reserves all over the world, and pollution issues associated with the use of fossil fuels. The model developed by the IIT Hyderabad team has shown that in the area of bioethanol integration into mainstream fuel use, the production cost is the highest (43 per cent) followed by import (25 per cent), transport (17 per cent), infrastructure (15 per cent) and inventory (0.43 per cent) costs. The model has also shown that feed availability to the tune of at least 40 per cent of the capacity is needed to meet the projected demands. A unique feature of this work is that the framework considers revenue generation not only as an outcome of sales of the biofuel but also in terms of carbon credits via greenhouse gas emission savings throughout the project lifecycle.
Bottom Line: Real-time analysis of remote video feeds is rapidly improving thanks to AI, increasing the accuracy of remote equipment and facility monitoring. Agriculture, construction, oil & gas, utilities, and critical infrastructure all need to merge cybersecurity and physical security to adapt to an increasingly complex threatscape. What needs to be the top priority is improving the accuracy, insight, and speed of response to remote threats that AI-based video recognition systems provide. Machine learning techniques as part of a broader AI strategy are proving effective in identifying anomalies and threats in real-time using video, often correlating them back to cyber threats, which are often part of an orchestrated attack on remote facilities. The future of remote security monitoring is being defined by the rapid advances in supervised, unsupervised, and reinforcement machine learning algorithms and their contributions to AI-based visual recognition systems.
Artificial Intelligence (AI) is now enjoying massive acceptance from consumers and organisations worldwide. Hence, more and more companies are stepping up their game by adopting Artificial Intelligence into their functionalities. In this article, we will discuss the absolute wins of the year 2019 in terms of breakthrough AI solutions and their impact. Here are some of the AI success stories and top news for the year 2019. In May 2019, Samsung created a system that could transform facial images into a video sequence.
Much like we have Chemical Engineering and Electrical Engineering and Mechanical Engineering, it is time to formalize of field of Data Engineering. This is a special two-part series on trends and requirements leading to the formalization of the Field of Data Engineering. "Data is the new oil…in much the same way that oil fueled economic growth in the 20th century, data will fuel economic growth in the 21st century." To further raise the credibility of data as the economic fuel for the next century, "The Economist" Special Report on the Data Economy asks "Are data more like oil or sunlight?" Still, it is hard to put a definitive value on data. If data is to be the fuel for economic growth in the 21st century, don't we need to find a way to accurately determine what data is worth?
Earthquakes claimed thousands of lives every decade. Of all-natural calamities, earthquake is the one which is most hard to predict. Even if a man succeeded in doing so, his predictions are vaguely based on the behavior of animals' minutes before the seismic waves hit that geographic region. However, with artificial intelligence algorithms can help us in receiving early warnings of a potential earthquake and be prepared accordingly. Using machine-learning models, seismologists can analyze hordes data on thousands of earthquakes.
A strange'swarm' of small earthquakes in California that lasted nearly four years was triggered by fluid spilling into the fault system from underground reservoirs, scientists say. The naturally occurring injection of underground fluid drove the earthquake swarm near Cahuilla in Southern California, which occurred in bursts around the region from early 2016 to late 2019. US scientists have made their conclusions based on earthquake detection algorithms that catalogued more than 22,000 individual seismic events that made up the'swarm'. Using machine learning to plot the location, depth and size of the tremors, the researchers generated a 3D representation of the underlying fault zone. The results suggested dynamic pressure changes from natural fluid injections deep below the surface largely controlled the prolonged evolution of the Cahuilla swarm.
As AI adoption brings out changes in the workplace, workers are challenged to obtain needed AI skills and business leaders are working to adapt. And as the COVID-19 pandemic has led to a shift to online learning, companies such as Udacity--who have been in that business for years--are in a good position to help. Business leaders may be caught between competing objectives of continuing to deliver strong financial performance while making investments in hiring, workforce training and new technologies that support growth, suggested the author of a recent piece in Harvard Business Review. A team at the MIT-IBM Watson AI Lab has been studying how work is being changed by AI. "By examining these findings, we can create a roadmap for leaders intent on adapting their workforce and reallocating capital, while also delivering profitability," stated author Martin Fleming, a VP and Chief Economist at IBM. He made three suggestions for reskilling the workforce to better prepare for AI.
There's no arguing that preventing failures and accidents is critical for industry. Unexpected incidents can grind operations to a halt for extended periods of time and necessitate expensive repairs. Just 12 hours of downtime for an oil production platform could cost six to eight million dollars in lost production opportunity alone. Because of these disruptions, industrial sectors are always on the lookout for newer, better maintenance methods, and the approach on everyone's lips right now is predictive maintenance. While everyone agrees on the name, there is less consensus on what it means or how to implement it.
Seismic wave data has revealed giant structures 2900 kilometres beneath the surface of Earth, at the boundary between Earth's molten core and solid mantle. The structure, known as an ultra-low velocity (ULV) zone, is about 1000 kilometres in diameter and 25 kilometres thick, says Kim. These structures are called ULV zones because seismic waves pass through them at slower velocities, but what they are made of is still a mystery. They might be chemically distinct from Earth's iron–nickel alloy core and silicate rock mantle, or have different thermal properties. The researchers discovered the structure while analysing 7000 records of seismic activity from earthquakes that occurred around the Pacific Ocean basin between 1990 and 2018.