Articles

Apache Kafka as Digital Twin in Industrial IoT (IIoT)

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Learn more about the benefits of Digital Twin tech in IIoT and it’s relation to Apache Kafka!

This blog post discusses the benefits of a Digital Twin in Industrial IoT (IIoT) and its relation to Apache Kafka. Kafka is often used as a central event streaming platform to build a scalable and reliable digital twin for real-time streaming sensor data.

In November 2019, I attended the SPS Conference in Nuremberg. This is one of the most important events about Industrial IoT (IIoT). Vendors and attendees from all over the world fly in to make business and discuss new products. Hotel prices in this region go up from usually 80-100€ to over 300€ per night. Germany is still known for its excellent engineering and manufacturing industry. German companies drive a lot of innovation and standardization around the Internet of Things (IoT) and Industry 4.0.

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The Impact of Machine Learning in Agriculture

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Machine learning is now a big part of every single one of our lives. When you use Netflix, recommended shows are presented based on an AI algorithm. Your order history on Amazon is run through a program to create a list of potential products that are uniquely suited to your tastes. Marketers use automation as a way to reach potential prospects and keep current customers engaged with their company. Believe it or not, 79 percent of the top businesses are currently using AI in their business model in some way.

At a glance, it seems like everything related to machine learning involves relatively new products and technology. A new study by the Journal of Field Robotics at the University of Cambridge is challenging our preconceptions about AI and how it ought to impact our lives. Their research and experiments resulted in a tremendous breakthrough in agriculture. We are going to take a look at the challenge, their new robot, and what this means for the future.

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Intelligently Automate Machine Learning, Artificial Intelligence, and Data Science

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It was great speaking with Michael Berthold, Founder and CEO at KNIME during their fall summit. Michael created KNIME after seeing all of the great data pharmaceutical companies were generating but also seeing the difficulty they had garnering insights due to the challenges of massaging and analyzing the data.

KNIME is an open platform that enables organizations to put their data to good use. Open data science platforms enable:


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AI and Its Discriminating Algorithms

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Artificial Intelligence has taken the industry by storm. It is spreading gems of highly advanced technological traces and with its simple touch, transforming the face of the tech world. As it paves its way into capturing diverse industries, it influences the latest trends and stirring complexities that eventually puts immense pressure on marketers, developers, and creative artists.

However, due to some shocking updates about AI algorithms that have surfaced the industry, many conflicting opinions and judgments spurred among the tech giants. The algorithms of Artificial Intelligence are reportedly being noticed to create racist and biased discrimination.


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AI and Music: Noteworthy?

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There are an increasing number of organizations developing and/or using music composed in part or in full by AI technologies. In the beginning, many of those efforts were academic in nature, but a growing number of groups are attempting to make a business model of composing music. And while there are more people doing it now than there were a couple decades ago, the idea of computer-composed music is reasonably old. One of the first computer compositions I’m aware of was in 1957.

The majority of these systems are structured just as you would expect: a large collection of compositions representing a genre or an artist are used as training data, which create some sort of generative/predictive model. In the very earliest approaches, simple Markov chains were derived from the compositions.


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AI and the Online Job Market

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If you’ve spent any time job hunting, you’ll no doubt be well aware of how frustrating and hopeless the task can seem at times.

The haphazardous process of getting yourself noticed in a sea of applications that may be prioritized despite carrying less relevance is tricky enough. But then you may find that when you eventually do accept a role, it’s nothing like you were led to believe it would be.


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GAN Lab: Generated Adversarial Networks In Your Browser!

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This GitHub project is a highly interactive graphic demonstration of the features and operations of a generated adversarial network using TensorFow.js. It is based on work done by Minsuk Kahng, Nikhil Thorat, Duen Horng (Polo) Chau, Fernanda B. Viegas, and Martin Wattenberg in their paper: GAN Lab: Understanding Complex Deep Generative Models using Interactive Visual Experimentation.

Full disclosure: The data sets are small enough and simple enough to demonstrate the technology but clearly are not full-blown image processing examples, which can often consume a lot (CPU/years?) of computer time. But these small examples provide an excellent introduction into what is going on. It takes away the spooky magic…which is a good thing! 


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How the Banking Industry Benefits from AI

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The AI vs. ML Dilemma

Machine Learning and Artificial Intelligence are two terms that are used interchangeably all the time. So, are AI and ML the same? Most definitely not!

Any technology that makes a system exhibit human-like intelligence is AI. Machine Learning is actually one type of AI. Machine Learning makes decisions by relying on the use of mathematical models that are trained on data. ML models are capable of making better decisions when more data is available.


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The Rise of Machine Learning

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The recent surge of data has empowered a field of computer science that uses statistical techniques to give computer systems the ability to learn: Machine Learning. Modern Machine Learning Algorithms are able to overcome strictly static program instructions and make data-driven predictions that help companies make decisions with minimal human intervention.

IDC forecasts that spending on Machine Learning will grow from $12 billion in 2017 to $57.6 billion by 2021. What’s more, Machine Learning patents grew at a 34 percent CAGR between 2013 and 2017, making it the third-fastest growing category of all patents granted.


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Using AI And ML For Translation Solutions

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Natural Language Processing; it’s Artificial Intelligence that learns words and patterns of words so that it can respond to human searches and questions. Siri and Alexa are examples of this technology.

And this technology is continually improving. As more and more conversations are held with these machines, they continue to learn and respond more accurately.


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