ExLattice, Inc, a manufacturing AI company, has received a Phase I award from the National Science Foundation (NSF) Small Business Innovation Research (SBIR) Program for developing its accelerated simulation engine dedicated to additive manufacturing. The Phase I SBIR grant, valued at over $250,000, will be used to develop and validate ultrafast manufacturing simulation solutions in collaboration with multiple universities. The goal is to cut the time-consuming steps in computation and deliver real-time engineering solutions for users to understand, control and improve additive manufacturing systems and outcomes. "Receiving the SBIR award from NSF is another proof of our vision in engineering software for digital manufacturing," says Dr. Runze Huang, CEO, ExLattice. "The NSF SBIR grant not only provides us the resources, but a platform to collaborate with leading experts in academia and great business partners in bringing AI to manufacturing."

IBM is focused on delivering new AI capabilities in the cloud and on premises to help enterprises gain insights from their data and create new value with that data, the company reports. IBM has been working with NVIDIA to bring its latest GPU (graphics processing unit) technology, NVIDIA Tesla V100, to the cloud and offers a suite of GPUs including the P100, K80 and M60 on IBM Cloud bare metal and virtual servers. To power on-premises workloads, IBM also offers CPU-to-GPU NVIDIA NVLink connection on its POWER9 servers. Now IBM is introducing the NVIDIA Tesla V100 GPU to support AI, deep learning and HPC workloads on the cloud. Users can equip individual IBM Cloud bare metal servers with up to two NVIDIA Tesla V100 PCIe GPU accelerators, NVIDIA's latest, most advanced GPU architecture.

CAD--specifically parametric CAD--was developed as an efficient solid geometry construction approach. Therefore, CAD programs are ideal for design engineers who need to express their concepts, whether the shape of an automotive part or the housing of a smartphone, in detailed 3D geometry. Generative design, algorithm-driven design and topology optimization are the common terms used to describe programs that allow designers to seek the best--or optimal--forms for a project using specific inputs, such as stress loads, pressure, weight and material choices. The term topology optimization is specific to the exploration of shapes, structures and solid geometry. It's usually associated with automotive and aerospace lightweighting projects, where engineers seek ways to reduce material without jeopardizing the design's safety requirements.

We constantly talk about next-gen manufacturing, Industrie 4.0, IIoT etc, but I think the underlying nervous system supporting all these is connectivity and convergence. Increased connectivity and computational power have converted hype to reality. Previously Cloud, IoT, Artificial Intelligence (AI), Big Data and analytics were like nebulous concepts at boardroom discussions; but now they are mature enough and ready to be fully integrated into the design, engineering, and operational environment. Digitalization is impacting every aspect of our lives because three forces are reinforcing one another: consumer pull; technology push; and economic benefits. This was the theme of the two-day NASSCOM 2017 edition of the Design & Engineering Summit for which ARC Advisory Group was invited.

As machine learning makes its way into more applications--leveraging everything from sensor data to consumer information repositories--pressure for hardware and software engineers to familiarize themselves with the technology grows. Because this type of control algorithm differs in key ways from those based on traditional logic, the learning curve may be steeper for some designers. Nevertheless, it's time for all engineers to understand how this technology changes the design process and what tools and practices help with its implementation. One of the best ways to understand machine learning is to consider how it differs from conventional control mechanisms. Traditional programming uses Boolean logic's true and false rules to define a program's behavior, building the application via a series of defined steps, where the rules making up the program ensure what action happens next. Machine learning takes a different approach, built on inductive reasoning.

Today, the landscape of the Internet of Things (IoT) is awash with new and exciting technology that can empower miraculous product capabilities. Collecting and analyzing the right data can yield insights that may hold the power to transform a company. Building the right intelligence and automation can transform an industry. Yet, most companies today struggle with a short and simple question: How do we get there? Most people know the basic technical steps.

There has never been more exciting times than now when building Machine Intelligence in systems is given more emphasis than ever. When I first started studying about Machine Learning, I knew, this will not only change the current state of engineering, but the way Digital is being perceived. Initially, to most, Digital meant SMAC stack, but that never convinced me.Most promising definition I could put was, Digital Engineering is about re-imagining current system engineering to improve customer journeys and increase system engagements. From framework thinking, I like this 3I acronym for Digital. Interface – Rethinking how digital can change the way customers interact and achieve their objectives.