The Indian manufacturing sector is witnessing massive transformation due to explosion of smart technologies including artificial intelligence, machine learning, 3D printing, Big Data, and 5G. Major manufacturing companies are shifting gears and investing heavily in modern technologies to meet evolving expectations of customers and partners, reduce costs, timely delivery, real-time monitoring, decision making, predictive maintenance and more. Furthering the perspective of adoption of next gen technology, Cisco in association with CNBC-TV18 has initiated a'Cisco Idea Lab' series to throw open insightful discussions with industry leaders on tech innovations' impact across businesses in India, starting with the manufacturing sector. In the first episode of the series, industry leaders Nishant Arya, ED, JBM Group; Sanjay Bhutani, MD-India & SAARC, Bausch Lomb; Mahesh Gupta, CMD, Kent RO Systems; Vijay Sethi, CIO, Hero MotoCorp; and Daisy Chittilapilly, MD- Digital Transformation Officer, Cisco India & SAARC spoke about how tech that can unlock the true potential of the manufacturing sector. In a nutshell, robotics, IoT, Analytics, 5G, smart factories, and more will drive the manufacturing sector.

SINGAPORE: The Housing and Development Board (HDB) has rolled out the use of artificial intelligence (AI) to enhance worker safety at its construction sites. This AI system will focus on two scenarios which are common causes of worksite accidents, based on data from the Ministry of Manpower, HDB said in a media briefing on Thursday (Sep 12). The system will monitor workers who come within one metre of a non-barricaded edge with a drop of more than two metres and those who are under the path of heavy loads lifted by tower cranes. Currently, construction work sites rely on manual supervision by site supervisors and Workplace, Safety and Health Officers (WSHO) to ensure compliance with safety standards. According to HDB, this is a "resource intensive endeavour", requiring multiple WSHOs and supervisors.

First invented in the 1980s by Chuck Hull, an engineer and physicist, 3D printing technology – also called additive manufacturing – is the process of making an object by depositing material, one layer at a time. Similarly to how an inkjet printer adds individual dots of ink to form an image, a 3D printer adds material where it is needed, based on a digital file. Many conventional manufacturing processes involved cutting away excess materials to make a part, and this can lead to wastage of up to 30 pounds (13.6 kilograms) for every one pound of useful material, according to the Energy Department's Oak Ridge National Laboratory in Tennessee. By contrast, with some 3D printing processes about 98 per cent of the raw material is used in the finished part, and the method can be used to make small components using plastics and metal powders, with some experimenting with chocolate and other food, as well as biomaterials similar to human cells.

As artificial intelligence (AI) is becoming mature, every industry wants to adopt it. Enterprises want to use it to unlock the hidden insight from data and use that to make strategic choices for companies. Many enterprises are continuously evaluating different use cases and experimenting with data using different AI frameworks. Having an infrastructure that can support different machine learning and deep learning (MLDL) frameworks is one of the challenges for enterprises in experimenting with AI. In many cases, it is helpful to be closer to data where you want to perform AI.

The first conviction for 3D printing a firearm was recently reported in London, not long after 3D printed masks were used to trick face recognition. Although 3D printing processes vary widely, including melting metal powder with lasers or hardening liquid plastic "ink" with ultraviolet light, most people tend to think of 3D printing desktop machines that melt spools of plastic. Since these are often built or designed by enthusiasts, they are very affordable, with some models costing under £200. We research the realities of 3D printer usage by businesses and consumers – and so can dispel some of the most common fears around 3D printing. Designs for a "gun" that could be produced on a desktop 3D printer were first shared on the internet around 2013.

While 3D printing soft materials, such as silicone or proteins, offers many advantages, it also introduces many new and complicated variables to consider when creating a new part. The existing soft materials that can be 3D printed commercially are somewhat limited since they don't have all the properties that researchers need to fully advance their developments and they end up working within the constraints of the current technology. One of the main problems with 3D printing a soft material is that it tends to deform under the forces that normally occur, sometimes even during the build, so they require support materials. According to researchers from the College of Engineering at Carnegie Mellon University, that means that additive manufacturing of soft materials requires optimization of printable inks, formulations of these feedstocks, and complex printing processes that must balance a large number of disparate but highly correlated variables (such as metal powder particle size, melt pool shape and size or filament feeding rate, extrusion width, linear plotting speed and layer thickness or suspension viscosity). Due to the critical need for integrated methodologies, they have come up with a hierarchical machine learning (HML) algorithm that optimizes parameters of these type of materials for 3D printing, using Freeform Reversible Embedding (FRE)–a recently developed method for 3D printing of liquid polymer precursors that involves controlled deposition of a fluid precursor into a supporting aqueous bath.

Squeezing a house through a nozzle, like a pâtissier pumping fondant cream from a piping bag, may not be everyone's idea of cutting-edge construction. The glitzy emirate aspires to have a quarter of all new buildings constructed via 3D printing by 2030. Emaar, one of the Arabian Gulf's leading property developers, is heralding its nascent Arabian Ranches III residential project as offering Dubai's first such dwelling. Fabricating a three-dimensional model, or prototype, from a computer-aided design by adding successive layers of material is now standard practice in many industries, ranging from aerospace and architecture to medicine and high-end manufacturing. McKinsey, the consultancy, estimates the technique could have an annual economic impact worth $550 billion by 2025.

In particular, small soft robots at millimeter scale are of practical interest as they can be designed as a combination of miniature actuators simply driven by pneumatic pressure. They are also well suited for navigation in confined areas and manipulation of small objects. However, scaling down soft pneumatic robots to millimeters results in finer features that are reduced by more than one order of magnitude. The design complexity of such robots demands great delicacy when they are fabricated with traditional processes such as molding and soft lithography. Although emerging 3D printing technologies like digital light processing (DLP) offer high theoretical resolutions, dealing with microscale voids and channels without causing clogging has still been challenging.

The demands on machine learning methods to cater for ultra high dimensional datasets, datasets with millions of features, have been increasing in domains like life sciences and the Internet of Things (IoT). While Random Forests are suitable for "wide" datasets, current implementations such as Google's PLANET lack the ability to scale to such dimensions. Recent improvements by Yggdrasil begin to address these limitations but do not extend to Random Forest. This paper introduces CursedForest, a novel Random Forest implementation on top of Apache Spark and part of the VariantSpark platform, which parallelises processing of all nodes over the entire forest. CursedForest is 9 and up to 89 times faster than Google's PLANET and Yggdrasil, respectively, and is the first method capable of scaling to millions of features.

In "Importance of Thinking Differently…Hint: Don't Pave the Cow Path", I introduced the concept of the "3 Horizons of Digital Transformation." I wanted to provide a framework that helped organizations differentiate between "Digitalization" versus "Digital Transformation". Unfortunately, in succeeding client engagements, I realized I did a crappy job of explaining these 3 horizons. So, like how bad movies create "Return of" sequels in order to explain everything they screwed up in the original movie, consider this my justification for "Return of the 3 Horizons of Digital Transformation" thriller! This "Return of" blog will provide more details on the 3 stages – or horizons – through which your organization must navigate in order to achieve Digital Transformation.