Many people who are already data scientists or new to the field of data science are looking at an answer to the question “Will AutoML (Automated Machine Learning) replace data scientists?” Asking a question like this is very reasonable because Automation has already been introduced to Machine Learning and it plays a key role in the modern world. In addition to that, people who want to become data scientists are thinking about ways to secure a spot in the job market for a long period of time.
AutoML will NOT replace your data science profession. It’s just here to make things easier for you, such as assisting you in boring repetitive tasks, saving your valuable time, assisting you in code maintenance and consistency, etc!
Let’s walk through the steps of a machine learning process to find out why. Read More
Daily Archives: May 13, 2021
Embodying Pre-Trained Word EmbeddingsThrough Robot Actions
We propose a promising neural network model with which to acquire a grounded representation of robot actions and the linguistic descriptions thereof. Properly responding to various linguistic expressions, including polysemous words, is an important ability for robots that interact with people via linguistic dialogue.Previous studies have shown that robots can use words that are not included in the action-description paired datasets by using pre-trained word embeddings. However, the word embeddings trained under the distributional hypothesis are not grounded, as they are derived purely from a text corpus. In this letter, we trans-form the pre-trained word embeddings to embodied ones by using the robot’s sensory-motor experiences. We extend a bidirectional translation model for actions and descriptions by incorporating non-linear layers that retrofit the word embeddings. By training the retrofit layer and the bidirectional translation model alternately, our proposed model is able to transform the pre-trained word embeddings to adapt to a paired action-description dataset. Our results demonstrate that the embeddings of synonyms form a semantic cluster by reflecting the experiences (actions and environments) of a robot. These embeddings allow the robot to properly generate actions from unseen words that are not paired with actions in a dataset. Read More
Cyberspace Is Neither Just an Intelligence Contest, nor a Domain of Military Conflict; SolarWinds Shows Us Why It’s Both
Operations in cyberspace—at least those perpetrated by nation-state actors and their proxies—reflect the geopolitical calculations of the actors who carry them out. Strategic interactions between rivals in cyberspace have been argued by some, like Joshua Rovner or Jon Lindsay, to reflect an intelligence contest. Others, like Jason Healey and Robert Jervis, have suggested that cyberspace is largely a domain of warfare or conflict. The contours of this debate as applied to the SolarWinds campaign have been outlined recently—Melissa Griffith shows how cyberspace is sometimes an intelligence contest, and other times a domain of conflict, depending on the strategic approaches and priorities of particular actors at a given moment in time.
Therefore, rather than focusing on the binary issue of whether a warfare versus intelligence framework is more applicable to cyberspace, the fact that activity in cyberspace takes on both of these characteristics at different times raises interesting questions about how these dimensions relate to one another at the operational level. Read More
Rick's Cafe AI 