In the last decade, translation services have grown exponentially to include hardware devices such as Travis Translator, earphones such as Waverly Labs’ pilot, Microsoft Translator, — which not only translates text, but also speech, images, and street signs — Google translate, and Facebook translation. Translations are occurring faster and with greater accuracy thanks to machine translation. 

But what does this mean for the traditional translator? As an expatriate in Germany, I am a user of both translation services and translation software, so I was interested to find out more. I spoke with the CEO and founder of Gengo, Matt Romaine. He co-founded Gengo in 2009 with the aim to democratize access to the opportunity for language enthusiasts around the world and become the bridge to mass global communication. Gengo offers a crowd-sourced human translation platform now with over 20,000 translators supporting 35+ languages. Their clients include Trip Advisor, Etsy, Salesforce, eBay, Facebook, and Google.


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With this article series, I want to deliver an in-depth understanding of not only blockchain and its applications, but also an understanding of how it interweaves with Artificial Intelligence.

Let’s start with blockchain and understand its features.


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More and more programmers are learning R programming language to become a Data Scientist, one of the hottest and high paying technical jobs on the planet.

Even though, I am from the Python camp, when it comes on choosing between Python and R for Data Science, Machine Learning, and Artificial Intelligence, mainly because of the awesome libraries like TensorFlow Python offers, I had tried R for a short time.


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The considerable number of articles cover Machine Learning for cybersecurity and the ability to protect us from cyber attacks. Still, it’s important to scrutinize how actually Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) can help in cybersecurity right now and what this hype is all about.

First of all, I have to disappoint you. Unfortunately, Machine Learning will never be a silver bullet for cybersecurity compared to image recognition or natural language processing, two areas where Machine Learning is thriving. There will always be a man trying to find weaknesses in systems or ML algorithms and to bypass security mechanisms. What’s worse, now hackers are able to use Machine Learning to carry out all their nefarious endeavors.


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What Is AdaBoost?

First of all, AdaBoost is short for Adaptive Boosting. Basically, Ada Boosting was the first really successful boosting algorithm developed for binary classification. Also, it is the best starting point for understanding boosting. Moreover, modern boosting methods build on AdaBoost, most notably stochastic gradient boosting machines.

Generally, AdaBoost is used with short decision trees. Further, the first tree is created, the performance of the tree on each training instance is used. Also, we use it to weight how much attention the next tree. Thus, it is created should pay attention to each training instance. Hence, training data that is hard to predict is given more weight. Although, whereas easy to predict instances are given less weight. 


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One of the lesser known, yet very cool features of Google Docs is its ability to provide a pretty decent translation of any text you enter into it. The functionality highlights the progress that has been made in machine translation in recent years. Indeed, work earlier this year suggested that machine translation is now on a par with humans.

Whilst this is certainly very cool and those results garnered a lot of publicity, it shouldn’t be taken to mean that human translators are heading for the scrap heap just yet. A recent study published by the University of Zurich highlights some of the reasons why.


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GOTO Elimination Algorithm (Unstructured to Structured)

Problem Description

Devise an algorithm to eliminate any GOTO statements from a program, in order to get a structured program, which is logically and functionally equivalent.

For example:


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It’s understandable to be frustrated with search. You’ve probably heard someone lecturing you on relevance best practices, taxonomies, metrics, measurement, and more. You’ve probably thought, "Wow, that’s a lot of work." Indeed!

An understandable question you might then ask is, "Well, won’t AI just solve all these problems soon?"


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As hot as stories about Artificial Intelligence (AI), augmented/virtual reality (AR/VR), blockchain, and the Internet of Things (IoT) have been in recent months, we often can’t help but think of these technologies as a long way off from mainstream adoption.

Movies like Ready Player One and Avengers: Infinity War only perpetuate this perception by mixing real technologies with fantasy, making the tech we wield in the real world seem primitive in the process.


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Features of Tensorflow

Below, we are discussing some important TensorFlow Features.

Responsive Construct

With TensorFlow, we can easily visualize each and every part of the graph, which is not an option while using Numpy or SciKit.


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