In this new, confined world, some companies continue to operate and sometimes need to recruit. In recent years, we have witnessed the emergence of online, remote recruitment tools. This has been made possible in particular thanks to artificial intelligence (AI), which is playing an increasingly important role in HR operations in general, and more specifically in the recruitment process.
Machine learning (ML) is a subset of artificial intelligence that uses computer algorithms to improve automatically through experience. What if a program could adapt a graphical interface to your liking by using machine-learning technology? Well, this is what some companies are already doing. If a machine-learning program can learn from user behaviors, that is precisely why combining UX and machine learning makes sense. But it’s not as simple as it might sound. In this article, we will try to understand the challenges of machine-learning product design and how to overcome them.
From tablets, interactive whiteboards, and MOOCs to educational software and games, digital technology is increasingly taking over European schools. Some people pin great hopes on new educational technology to offer students more personalized learning and improve their cognitive performance. Beside, others are concerned by the effects of too much screen time on children’s brain health and development.
More and more companies are using artificial intelligence to save time in their recruitment process. Its advanced information technology helps them find the perfect candidates more quickly. It may also be a great opportunity for jobseekers as it broadens their job perspectives and proposes job offers that are more relevant to their profile.
Since the dawn of sci-fi movies, we have seen characters being accompanied by an AI companion, be it an android or the central computer of their spaceship, who acted as a friend during their adventures. Today, we have yet to see such technology in action since our AI development is not that advanced (and, according to certain people, it should stay that way). Nevertheless, we still have access to a degree of artificial intelligence, such as Siri or Alexa, which assists us with simple tasks.
Artificial Intelligence has become a part of our everyday life. Whether we are on our phones, cooking, or buying our groceries, we are constantly surrounded by it. With new innovations every day, the place of artificial intelligence in our society is growing to the point where the human domination over it becomes questionable. Books, movies, and video games have depicted futuristic societies where robots have taken over. Such scenarios lead us to wonder: in time, will artificial intelligence represent a danger for the human race?
Neural Machine translation, or NMT, is a fairly new paradigm. Before NMT systems started to be used, machine translation had known several types of other machine translation systems. But, as research in the field of artificial intelligence is advancing, it is only natural that we try to apply it to translation.
In March 2018, Microsoft announced a historical milestone: Microsoft’s neural machine translation can allegedly match human performance in translating news from Chinese to English. But how can we compare and evaluate the quality of different systems? For that, we use machine translation evaluation.
The Dynamic Information Model is a toolkit for creating an integrated style guide that describes and enforces editing rules in a DITA authoring environment.
Nowadays, we tend to focus on progress, no matter what it involves for us. We don’t really think about the consequences of what we develop, Could this lead to complications? Could it be potentially dangerous? It’ll just help us in our daily lives, be an improvement, an enhancement of some sort. Of course, not every discovery will turn into a new Manhattan Project, but it doesn’t have to stop us from thinking about what it implicates for us in the long term.
