Oil & Gas

3 industries saving billions with cognitive machine learning


Natural human flaws can have severe impacts on business with lasting damage – 82% of operational asset failures are attributed to human performance. Indeed, a recent study by ARC Advisory Group found that the global process industry loses up to $20 billion a year due to unscheduled downtime – or $12,500 hourly, on average. However, machine learning is helping eliminate these costly flaws and is helping transform the manufacturing industry. This technology, along with others like big data analytics, are able to predict if and when something will break – cancelling the possibility of costly downtime. See also: Anticipating downtime will be business' next competitive advantage Seth Page is a cognitive computing veteran and industrial IoT pioneer based in Washington DC, and is CEO and co-founder of DataRPM, a Progress company.

5 Innovative Uses for Machine Learning


Though its time horizon can't be predicted, artificial intelligence (AI) promises to foundationally influence modern society, for better or worse. A sub-genre of AI -- machine learning -- has garnered particular attention from the pundits for its potential impact on the world's most important industries.

Intelligent IoT


With a wave of investment, a raft of new products, and a rising tide of enterprise deployments, artificial intelligence is making a splash in the Internet of Things (IoT). Companies crafting an IoT strategy, evaluating a potential new IoT project, or seeking to get more value from an existing IoT deployment may want to explore a role for AI. Artificial intelligence is playing a growing role in IoT applications and deployments,12 a shift apparent in the behavior of companies operating in this area. Venture capital investments in IoT start-ups that are using AI are up sharply. Companies have acquired dozens of firms working at the intersection of AI and IoT in the last two years.

Reinforcement Learning-based Thermal Comfort Control for Vehicle Cabins

arXiv.org Artificial Intelligence

Vehicle climate control systems aim to keep passengers thermally comfortable. However, current systems control temperature rather than thermal comfort and tend to be energy hungry, which is of particular concern when considering electric vehicles. This paper poses energy-efficient vehicle comfort control as a Markov Decision Process, which is then solved numerically using Sarsa({\lambda}) and an empirically validated, single-zone, 1D thermal model of the cabin. The resulting controller was tested in simulation using 200 randomly selected scenarios and found to exceed the performance of bang-bang, proportional, simple fuzzy logic, and commercial controllers with 23%, 43%, 40%, 56% increase, respectively. Compared to the next best performing controller, energy consumption is reduced by 13% while the proportion of time spent thermally comfortable is increased by 23%. These results indicate that this is a viable approach that promises to translate into substantial comfort and energy improvements in the car.

Drive.ai puts a deep learning spin on self-driving technology


You can add one more name to the constantly expanding list of companies that want a slice of that autonomous driving pie, as a new company named Drive.ai The new company, which also announced that it has added former General Motors Vice Chairman and Board Member Steve Girsky to its Board of Directors, is looking to put its stamp on the self-driving space with its own deep learning algorithms. These full stack deep learning algorithms, Drive.ai CEO Sameep Tandon says that the team at Drive.ai has been working on these deep learning applications since the company was founded in 2015. For now, the company says it will offer a retrofitted system that can be used in existing vehicle fleets.