In computer-aided processing of natural languages, shall the concept of natural language processing give way to natural language understanding? Or is the relation between the two concepts subtler and more complicated that merely linear progressing of a technology? Though sometimes used interchangeably, they are actually two different concepts that have some overlap. NLP and NLU are opposites of a lot of other data mining techniques. In this post, we’ll scrutinize over the concepts of NLP and NLU and their niches in the AI-related technology.
Data visualization provides an important suite of tools and techniques for gaining a qualitative understanding. To choose the most appropriate visualization technique you need to understand the data, its type and composition, what information you are trying to convey to your audience, and how viewers process visual information. Sometimes, a simple line plot can do the task saving time and effort spent on trying to plot the data using advanced Big Data techniques. Understand your data — and it will open its hidden values to you.
Despite the variety of applications of AI in the clinical studies and healthcare services, they fall into two major categories: analysis of structured data, including images, genes and biomarkers, and analysis of unstructured data, such as notes, medical journals or patients’ surveys to complement the structured data. The former approach is fueled by Machine Learning and Deep Learning Algorithms, while the latter rest on the specialized Natural Language Processing practices. At present, advances in AI and NLP, and especially the development of Deep Learning algorithms have turned the healthcare industry to using AI methods in multiple spheres.
Integration of computer vision and natural language processing (NLP) is the most actively developing machine learning research areas. Yet, until recently, they have been treated as separate areas without many ways to benefit from each other. Since the integration of vision and language is a fundamentally cognitive problem, research in this field should take account of cognitive sciences that may provide insights into how humans process visual and textual content as a whole and create stories based on them.
The mechanism that drives smart farming is Machine Learning — the scientific field that gives machines the ability to learn without being strictly programmed. It has emerged together with big data technologies and high-performance computing to create new opportunities to unravel, quantify, and understand data intensive processes in agricultural operational environments. Machine learning is everywhere throughout the whole growing and harvesting cycle. Let’s discover how agriculture can benefit from Machine Learning at every stage.
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.
Data Science is a newly developed blend of machine learning algorithms, statistics, business intelligence, and programming. This blend helps us reveal hidden patterns from the raw data which in turn provides insights in business and manufacturing processes. To go into Data Science, you need the skills of a business analyst, a statistician, a programmer, and a Machine Learning developer. You do not need to be an expert in any of these fields. Let’s see what you need and how you can teach yourself the necessary minimum.
It is true that our perception of Artificial Intelligence is formed under the influence of mass culture with all its dreams and fears. Of course, AI plays an increasingly important role in our life and we’ll see tremendous improvements in technology in the following years, but in its essence, AI is a tool. It helps us to enhance our abilities, just like normal computers, or calculators, or a pen and a paper that improve our memory. So, we are, and will be, in charge of what to do with this tool.
Data culture is a relatively new concept which is becoming pivotal nowadays, when organizations develop more progressive digital business strategies and apply meaning to big data. It refers to a workplace environment that employs a consistent approach to decision-making through emphatic and empirical data proof. It implies that decisions are made based on data evidence, not on gut instinct. To create a data-driven culture is becoming critical in times of global connectivity and data-driven organizations.
There are some very basic steps required to work through a large data set, cleaning and preparing the data for any Data Science project. We want you to understand that you need to properly arrange and tidy up your data before the formulation of any model. Better and cleaner data outperforms the best algorithms. If you use a very simple algorithm on the cleanest data, you will get very impressive results. And, what is more, it is not that difficult to perform basic preprocessing!