A chatbot is a technology that allows users to have a conversation with a computer program, via voice or text.
We propose the term taskbots to name those chatbots that improve business productivity. They are solutions designed to simplify and automate repetitive workflows.
Robotic Process Automation (RPA) systems have traditionally been defined as those capable of freeing us from the most repetitive tasks in the use of applications or computer programs. Perhaps nowadays we should expand this definition, since the possibility of combining this technology with Artificial Intelligence solutions is opening the doors to new and important business opportunities. The so-called intelligent RPA systems are not only capable of imitating repetitive actions, but also of learning and making decisions.
Natural language processing, or NLP, allows the automatic extraction of information expressed in human language. It is used in text classification, machine translation, opinion analysis or chatbots, among many other applications.
When talking about Natural Language Processing, terms from linguistics, statistics and computation are combined. This varied terminology may cause confusion when you first approach NLP. With this list of 9 key concepts, we try to clarify this confusion, explaining in a simple way some of the most common NLP terms.
Every minute thousands of blog posts, hundreds of thousands of tweets and millions of Facebook comments are shared. If, for example, I want to discover the hottest Marketing trends, I know for certain that I can find many posts on this topic. However, I don’t know beforehand which of those pieces of content better suit my needs. I also don’t know how long it will take me to find them through an online search or how many articles I will have to compare to be able to draw a general conclusion.
In the knowledge age, information management poses a major challenge. Companies have more information than ever, but they cannot take full advantage of it with traditional tools. The solution lies in intelligent systems that are able to fit each user's needs.
Reports, articles, tweets, summaries, news, budgets, emails, handbooks, invoices, briefings, presentations ... In most companies, huge amounts of valuable content are created, consumed and stored on a daily basis. Are these resources available to every employee who needs them? Are they easy to find? Are they shared to avoid redundant efforts? Thinking about these questions is the way to find the answer to this article’s title.
The concepts of Artificial Intelligence, Machine Learning, Deep Learning and Big Data have become increasingly important. However, these terms are often confused.
We are going to explain the differences between these buzzwords in a simple way. First, we will clarify the concepts and then we will focus on their differences.