AI & Machine Learning

With the dramatic fall in popularity of cryptocurrencies and the wave of unpredictable volatility in the value of these immaterial currencies, comes a seeming drop in the interest in blockchain. Yet, the development of this technology and its application are still charging ahead in full force and blockchain’s full potential is yet to be seen,

One of the most intriguing artificial intelligence techniques was conceived when a few computer scientists where discussing deep learning and photorealistic images at a Montreal pub in 2014. Called generative adversarial networks (GAN), the concept has enabled the AI industry to take huge leaps toward creativity, generating images and sounds that are very close to their

Explainable AI is still an evolving field and scientists are trying to find different ways to make neural networks interpretable without compromising their performance. We have yet to see standards emerge, there are however several interesting initiatives that are aimed at creating explainable AI and keeping track of where the defining attributes for an automated decision come from. While the specific approaches to explainable AI are a bit technically involved to discuss them here, from a high-level perspective, there are basically two ways to interpret AI decisions.

If you’re one of the many who see a dark future where the honest working man is replaced by mindless structures of automated metal, where technology has become so advanced that it is no longer under our control, and where the human race is overpowered by evil robots programmed to take over the world, 1)

Practical studies have proven Evolutionary Deep Learning applications to be a useful method for advancing the state of the art. Nevertheless, lots of limitations are still present in employed methods, just like the use of predefined building blocks for Neural Architecture Search and non-crossover nor mutation used in Evolutionary Deep Learning. Also, it is noticeable that Evolutionary Algorithms are seen as black-box optimization methods and thus they provide little understanding of why the performance is high. Further research will decide the future of Evolutionary Algorithms in Deep Learning.

It is essential for a machine learning engineer to have a good understanding of Vectors, one of the most crucial concepts within Machine Learning because many bugs are due to having matrix /vector dimensions that don’t fit properly. A common problem in machine learning is that a model is not really accepting the data and therefore keeps throwing errors. Often the solution lies in vectorizing the data which means nothing more than reshaping the data into the required dimensions.

Welcome to the world of tensors in AI. It is now time to get used to the curse of dimensionality. It is also an industry standard practice to flatten tensors all the way to matrices to leverage the highly optimized libraries for matrix multiplication (MM) or MM accelerators (MMAs), even though tensors are considered to be the most fundamental data type in all major AI frameworks. Matrices are generally considered to be special cases of tensors by the AI community.

What exactly constitutes an AI Strategy? What are the differences in creating an AI Strategy for startups vs corporations? While much is known about creating a business strategy, creating an AI Strategy is new territory. How do you approach creating your AI Strategy? In this article, you will learn how to approach creating an AI Strategy. Think of AI’s core components when creating your AI Strategy. We are looking forward to a world that embraces the decade of AI implementation.

The goal of supervised learning is to extract all the juice from the relevant features and to do that, we generally have to enrich and transform features in order to make it easier for the algorithm to see how the target variable depends on given data. One type of features that do not easily give away the information they contain are categorical features. They keep on hiding the information until we transform them smartly. In this particular post, I am focussing on one particular categorical encoding technique called target encoding.

Gradient descent is by far the most popular optimization strategy, used in machine learning and deep learning at the moment. It is used while training your model, can be combined with every algorithm and is easy to understand and implement. Therefore, everyone who works with Machine Learning should understand it’s concept. After reading this posts

Natural Language Generation capabilities have become the de facto option as analytical platforms try to democratize data analytics and help anyone understand their data. Close to human narratives automatically explain insights that otherwise could be lost in tables, charts, and graphs via natural language and act as a companion throughout the data discovery process. Besides, NLG coupled with NLP are the core of chatbots and other automated chats and assistants that provide us with everyday support.