Predictive maintenance: Most industrial equipment is built to last and operate with minimal downtime. As a result, there is often limited historical data with which to work. Because unsupervised ML can detect anomalous behavior even in limited data sets, it can potentially identify developing defects in these situations. Here too, it can be used for fleet management, providing early warning of defects while minimizing the amount of data that needs to be reviewed. Quality assurance/inspection: A machine that's operating improperly can produce substandard product.

Peaxy CEO and President Manuel Terranova joins us to discuss some of the biggest challenges facing the battery industry, and how smart software like Peaxy Lifecycle Intelligence (PLI) for Batteries can solve them. Peaxy's Lifecycle Intelligence offers predictive battery analytics, powered by machine learning. What do you see as the top data challenges in the battery industry, and how can they be solved? Batteries are unique and fickle industrial assets, and yet many companies use fleet-level or system level models to manage them. While that can be helpful, I don't believe such models are good at predicting and optimising industrial equipment, including batteries. Simply put, if you're unable to resolve data down to the individual battery -- a unique serial number -- chances are you won't be able to monetise your analytics.

Ibrahim Gokcen, the digital chief technology officer for industrial giant Schneider Electric, has some words of caution about deep learning--the latest craze in artificial intelligence. Sometimes, conventional data crunching works just fine. All of the technology sold by Schneider that warns corporate customers when their industrial equipment may fail uses basic analytics or statistical analysis to make predictions. Although the software incorporates machine learning, it doesn't use deep learning, a technology that has led to breakthroughs in image and language translation. But that's okay, Gokcen explained.

Munich-based industrial equipment usage analytics startup Corrux has raised $3.1 million in a seed funding round led by German venture capital firm Target Partners, with participation from Josef Brunner, who is the CEO of Relayr and US venture investor Sean Dalton. This seed funding has taken place to implement artificial intelligence in the arena of industrial equipment and scale up its operations. Corrux is an industrial equipment usage analytics startup, which develops simple, powerful applications that let users understand heavy machinery ranging from excavators to track laying machines. The company provides construction managers and OEMs with software to monitor on-site operations and gain insights from data. Moreover, the company's analytics layer lets construction companies and OEMs understand their heavy machinery – ranging from excavators to track laying machines.

One of the most difficult challenges faced by businesses in asset-intensive industries is how to control and scale the half billion and growing "smart" devices that make up the Internet of Things (IoT)? As much as 80 percent of IoT data in any organization is unstructured. And, let's be honest, "smart" devices really aren't that smart yet. As part of its giant rollout of AI solutions pre-trained for specific industries and professions, IBM Services is launching a new Connected Manufacturing offering that includes a method and approach to help clients accelerate their IoT transformation–from strategy, implementation, and security to managed services and ongoing operations. This combined capability, IBM said, will help its clients connect all of their manufacturing equipment, sensors, and systems to enable business improvement across OEE, quality, lead times and productivity.

Hewlett Packard Enterprise (HPE) has introduced new initiatives to help enterprises ramp up, optimise and scale the use of artificial intelligence (AI) to improve their demand forecasting and operational efficiency, while increasing sales. "Global tech giants are investing heavily in AI, but the majority of enterprises are struggling both with finding viable AI use cases and with building technology environments that support their AI workloads," said Beena Ammanath, global vice president, Artificial Intelligence, HPE Pointnext. "As a result, the gap between leaders and laggards is widening," she said. HPE Digital Prescriptive Maintenance Services from HPE Pointnext leverage AI to predict when industrial equipment will fail, then suggest and automate the correct action to fix the problem before harm is done, increasing the productivity of the industrial equipment. The solution uses enterprise data including real-time and batch data from Internet of Things (IoT) devices, data centres, and the cloud.

Arundo notes that to be successful, IIoT applications need to strike a balance between the batch processing remotely and real-time analytics. As organizations start delving into the nuances of Industrial Internet of Things (IIoT) applications many of them are finding a need to strike a balance between real-time analytics at the edge or on a gateway and more traditional batch-processing in the data center. To address that Arundo Analytics developed a federated approach to IIoT analytics spanning the IoT edge to the cloud. Now with the Fall 2017 release of the company's namesake software, a set of application programming interfaces (APIs) is being exposed to make it simpler to plug new and existing applications into the Arundo Data Fabric that runs as a cloud service. At the same time, Arundo Analytics has also enhanced the Arundo Composer software it provides on a desktop to make it simpler to create models employing multiple machine learning algorithms in addition to making it simpler to install Arundo Edge Agent software on devices running Windows, Linux or Mac OS operating systems.

Royal Bank of Scotland (RBS) launched Luvo, a natural language processing AI bot which answers RBS, Natwest and Ulster bank customer queries and perform simple banking tasks like money transfers. Compared to the progress of natural language processing solutions, computer vision-based AI solutions are still in developmental stage, primarily due to the lack of large, structured data sets and the significant amount of computational power required to train the algorithms. Other than online and IT companies, which are early adopters and proponents of various AI technologies, banks, financial services and healthcare are the leading non-core technology verticals that are adopting AI. AI, thus, can go beyond changing business processes to changing entire business models with winner-takes-all dynamics.

After decades of false starts, artificial intelligence (AI) is already pervasive in our lives. Although invisible to most people, features such as custom search engine results, social media alerts and notifications, e-commerce recommendations and listings are powered by AI-based algorithms and models. AI is fast turning out to be the key utility of the technology world, much as electricity evolved a century ago. Everything that we formerly electrified, we will now cognitize. AI's latest breakthrough is being propelled by machine learning--a subset of AI which includes abstruse techniques that enable machines to improve at tasks through learning and experience.

General Electric (GE) was co-founded in 1897 by Thomas Edison. Today, 120 years later, GE is the single company with the longest continual presence in the Dow Jones Industrial Average, and is undergoing one of the most dramatic transformation initiatives of any major company. Mainstream legacy businesses should take note. In a matter of only a few years, GE has migrated from being an industrial and consumer products and financial services firm to a "digital industrial" company with a strong focus on the "Industrial Internet" and $7 billion in software sales in 2016. This is the story of how GE has accomplished this digital transformation by leveraging AI and machine learning fueled by the power of Big Data.