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.

Drones and Robots Are Taking Over Industrial Inspection

MIT Technology Review

Avitas Systems, a GE subsidiary based in Boston, is now using drones and robots to automate the inspection of infrastructure such as pipelines, power lines, and transportation systems. The company is using off-the-shelf machine-learning technology from Nvidia (50 Smartest Companies 2017) to guide the checkups, and to automatically identify anomalies in the data collected. The effort shows how low-cost drones and robotic systems--combined with rapid advances in machine learning--are making it possible to automate whole sectors of low-skill work. While there is plenty of worry about the automation of jobs in manufacturing and offices, routine security and safety inspections may be one of the first big areas to be undermined by advances in AI. Drones have been used on some industrial sites for a while (see "New Boss on Construction Sites Is a Drone"), and various companies, such as Kespry, Flyability, and CyPhy, offer aerial systems for monitoring mines, inspecting wind turbines, and assessing building insurance claims.

Reinforcement Learning-based Thermal Comfort Control for Vehicle Cabins 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.

Charged EVs Online energy management for PHEVs claims 30% improvement in fuel efficiency


Engineers at the University of California, Riverside (UCR) have developed a new online energy management system (EMS) that they say can improve PHEV fuel efficiency by more than 30%. In "Development and Evaluation of an Evolutionary Algorithm-Based Online Energy Management System for Plug-In Hybrid Electric Vehicles," published in IEEE Transactions on Intelligent Transportation Systems, Xuewei Qi and colleagues explain that improving the efficiency of current PHEVs is limited by shortfalls in their energy management systems (EMS), which control the power split between engine and battery. The EMS developed by Qi and his team combines vehicle connectivity information (such as cell networks and crowdsourcing platforms) and evolutionary algorithms – a mathematical way to describe natural phenomena such as evolution, insect swarming and bird flocking. "We combined this approach with connected vehicle technology to achieve energy savings of more than 30 percent. 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 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, CEO Sameep Tandon says that the team at has been working on these deep learning applications since the company was founded in 2015. Its focus at the start will be on outfitting route-based industries with its technology, which includes the self-driving system itself, along with a collection of sensors, an interface for the driver of the vehicle, and roof-mounted communication system. The team's roots are based in Stanford University's Artificial Intelligence Lab, so it certainly sounds like has the know-how to put together a system such as this and make its name known in the world of autonomous driving.

The Route Not Taken: Driver-Centric Estimation of Electric Vehicle Range

AAAI Conferences

This paper addresses the challenge of efficiently and accurately predicting an electric vehicle's attainable range. Specifically, our approach accounts for a driver's generalised route preferences to provide up-to-date, personalised information based on estimates of the energy required to reach every possible destination in a map. We frame this task in the context of sequential decision making and show that energy consumption in reaching a particular destination can be formulated as policy evaluation in a Markov Decision Process. In particular, we exploit the properties of the model adopted for predicting likely energy consumption to every possible destination in a realistically sized map in real-time. The policy to be evaluated is learned and, over time, refined using Inverse Reinforcement Learning to provide for a life-long adaptive system. Our approach is evaluated using a publicly available dataset providing real trajectory data of 50 individuals spanning approximately 10,000 miles of travel. We show that by accounting for driver specific route preferences our system significantly reduces the relative error in energy prediction compared to more common, driver-agnostic heuristics such as shortest-path or shortest-time routes.

Improving Hybrid Vehicle Fuel Efficiency Using Inverse Reinforcement Learning

AAAI Conferences

Deciding what mix of engine and battery power to use is critical to hybrid vehicles' fuel efficiency. Current solutions consider several factors such as the charge of the battery and how efficient the engine operates at a given speed. Previous research has shown that by taking into account the future power requirements of the vehicle, a more efficient balance of engine vs. battery power can be attained. In this paper, we utilize a probabilistic driving route prediction system, trained using Inverse Reinforcement Learning, to optimize the hybrid control policy. Our approach considers routes that the driver is likely to be taking, computing an optimal mix of engine and battery power. This approach has the potential to increase vehicle power efficiency while not requiring any hardware modification or change in driver behavior. Our method outperforms a standard hybrid control policy, yielding an average of 1.22% fuel savings.