Automation and robots for manufacturing have come a long way since Unimate was introduced in the 1960's. The machines that manufacturers are using today are smaller, safer and able to perform more than a single task without expensive programming. While these innovations have significantly increased the value that automation brings to manufacturing, what's coming online now will transform the industry in ways that we've not seen since the first industrial revolution. The 4th industrial revolution or Industry 4.0 will be built on robots that are more brains than brawn. These robot integrate physical and cognitive ability to do more than heavy, highly repetitive tasks.

The enormous and raging wave of change that has hit our world in the last decade, has got some of us thinking and others reveling in their glory. The internet and evolving technological practices have increased possibilities. Man and machine collaboration has got us introduced to automated virtual work and communication systems everywhere in the world. Deep Learning has given birth to several real-life applications that have lessened human control and involvement in several spheres of life. The immense popularity of the Deep Learning UseCases blog was enough encouragement to look at more such UseCases.

WIRE)--CORNAMI, a high-performance computing company in artificial intelligence (AI), machine learning, and big data, today announced that key monolithic patents around its next generation, highly-efficient, advanced, multi-core architecture technology have been issued, thereby greatly enhancing the CORNAMI IP asset value portfolio. The CORNAMI patent portfolio now has over 60 patents with more than another dozen pending in US and International PTO (Patent and Trademark offices). CORNAMI has developed a non-Von Neumann parallel architecture with independent decision making capabilities at each processing core, interspersed with high-speed memory, all interconnected by a biologically inspired network to produce a scalable "sea of cores". This unique architecture delivers tremendous advancements in efficient multi-core parallel processing that dramatically changes the output-to-power performance at the petabyte data-set scale. There is built-in demand for real-time and actionable data analytics for applications in the hyper-growth big data, machine learning and AI markets.

Over the years, Deep Learning has become the most popular approach to developing Artificial Intelligence (AI) – machines that perceive and understand the world. It empowers geospatial ecosystem by providing real-time near-human level perception; integrates into analytical workflows and driving data exploration and visualisation – automating the entire process of creating scalable insights from large amounts of data. Such machines will be able to'understand' geospatial information themselves and with deep learning, able to self-obtain geospatial information from their surroundings as per required to do their jobs, processing it in real time. This is truly an extraordinary time. AI and Deep Learning have been applied to a vast range of industries, from healthcare, to finance, advertising and retail, to manufacturing and transport.

Nine companies and three universities have launched a research effort to see if machine learning can solve some of the toughest problems in electronics design. The center is one of many efforts across the industry trying to tap into the emerging technology. Like many ideas in tech, "it all started in a coffee shop one afternoon," said Elyse Rosenbaum, director of the Center for Advanced Electronics through Machine Learning (CAEML). "We were facing common problems. We needed behavioral models that interfaced across electro-migration and circuit domains and didn't know how to go about getting them, given that colleagues were interested in different applications," Rosenbaum said in a panel on the topic at the DesignCon event here.

Artificial intelligence (AI) has advanced unevenly over most of the past 50 years. Occasional spurts of breakthrough progress were followed by long winters of stagnation. When I started my career as a research engineer in the late '80s at Carnegie Mellon, most of us predicted that the autonomous vehicle being tested at the time would be on the roads shortly and a common thing by the end of the second millennium. For years, barriers such as technology cost, organization capability, and inappropriate policies kept AI from going mainstream. About 10 years ago, however, the pattern began to shift: advances in computing power, training data, and learning algorithms led to one performance breakthrough after another.

It's not enough for marketers to collect petabytes of data; it takes a sharp mind to make sense of it all. Actually it takes a nonhuman one. That's why artificial intelligence has invaded the marketing world, with Facebook, Google, Salesforce, IBM, Amazon and others building machine learning into their platforms. Now marketers must understand the lingo if they're going to survive the machines. To help, here's a guide to the terminology around A.I. When a machine teaches itself with minimal programming needed.

Imagine: Humans waging an epic battle against technology, with human intelligence inevitably subjugated by artificial overlords. Plenty of folks would line up with front-row tickets and popcorn in hand. But it's also the very real manifestation of a universal fear -- jobs relegated to machines, livelihoods handed over to bots. But when we take a closer look at bots and other forms of artificial intelligence, our worst fears are a far cry from the truth. We've built bots to help us succeed.

Robotic process automation (RPA) can be the true antidote to manual, rote work, or it can be our worst nightmare if you listen to all the drama or the hype. RPA centers on the use of artificial intelligence (AI) to apply human-like thinking to streamline a typically manually intensive process or activity; and whether we like it or not, it's here to stay. Take, for instance, the process of data extraction from documents such as invoices. Application of advanced optical character recognition (OCR) and intelligent document recognition can automate a significant amount of the job of data entry typically performed by clerks or specialized data entry staff. Interestingly, human effort is still involved with attaining the ability to hand off a process or task to a machine.

Until a few years ago, machine learning algorithms simply did not work very well on many meaningful tasks like recognizing objects or translation. Thus, when a machine learning algorithm failed to do the right thing, this was the rule, rather than the exception. Today, machine learning algorithms have advanced to the next stage of development: when presented with naturally occurring inputs, they can outperform humans. Machine learning has not yet reached true human-level performance, because when confronted by even a trivial adversary, most machine learning algorithms fail dramatically. In other words, we have reached the point where machine learning works, but may easily be broken.