Object naming - the act of identifying an object with a word or a phrase - is a fundamental skill in interpersonal communication, relevant to many disciplines, such as psycholinguistics, cognitive linguistics, or language and vision research. Object naming datasets, which consist of concept lists with picture pairings, are used to gain insights into how humans access and select names for objects in their surroundings and to study the cognitive processes involved in converting visual stimuli into semantic concepts. Unfortunately, object naming datasets often lack transparency and have a highly idiosyncratic structure. Our study tries to make current object naming data transparent and comparable by using a multilingual, computer-assisted approach that links individual items of object naming lists to unified concepts. Our current sample links 17 object naming datasets that cover 30 languages from 10 different language families. We illustrate how the comparative dataset can be explored by searching for concepts that recur across the majority of datasets and comparing the conceptual spaces of covered object naming datasets with classical basic vocabulary lists from historical linguistics and linguistic typology. Our findings can serve as a basis for enhancing cross-linguistic object naming research and as a guideline for future studies dealing with object naming tasks.

Artificial Intelligence involves all the characteristic operations of the human intellect and performed by computers, such as planning, language understanding, recognition of objects and sounds, learning and problem solving. Very interesting is the relationship between AI and IoT (Internet of Things) similar to that between brain and human body: our body through i various sensory inputs such as sight and touch, can recognize certain situations by performing the corresponding actions driven by our brain. Similarly in the IoT which, through sensors connected in the field, it sends a set of information to a guided control system by Artificial Intelligence that takes the appropriate decisions and eventually activates the actuators for controlling various movements (for example robot arms). Machine learning, on the other hand, is a way to implement Intelligence Artificial, while in-depth learning or Deep Learning, is one of many approaches related to machine learning. Machine learning is an application of AI that enables systems to learn and improve from experience without being explicitly programmed.

In this blog, we will discuss the basic concepts of Logistic Regression and what kind of problems can it help us to solve. Logistic regression is a classification algorithm used to assign observations to a discrete set of classes. Some of the examples of classification problems are Email spam or not spam, Online transactions Fraud or not Fraud, Tumor Malignant or Benign. Logistic regression transforms its output using the logistic sigmoid function to return a probability value. Logistic Regression is a Machine Learning algorithm which is used for the classification problems, it is a predictive analysis algorithm and based on the concept of probability.

Text embedding models from Natural Language Processing can map text data (e.g. words, sentences, documents) to supposedly meaningful numerical representations (a.k.a. text embeddings). While such models are increasingly applied in social science research, one important issue is often not addressed: the extent to which these embeddings are valid representations of constructs relevant for social science research. We therefore propose the use of the classic construct validity framework to evaluate the validity of text embeddings. We show how this framework can be adapted to the opaque and high-dimensional nature of text embeddings, with application to survey questions. We include several popular text embedding methods (e.g. fastText, GloVe, BERT, Sentence-BERT, Universal Sentence Encoder) in our construct validity analyses. We find evidence of convergent and discriminant validity in some cases. We also show that embeddings can be used to predict respondent's answers to completely new survey questions. Furthermore, BERT-based embedding techniques and the Universal Sentence Encoder provide more valid representations of survey questions than do others. Our results thus highlight the necessity to examine the construct validity of text embeddings before deploying them in social science research.

Hello, I decided to switch my focus a little bit and write something simple yet important for those who have just started their journey in Computer Vision. In this piece, I am introducing main and fundamental CNN concepts you need to know in 2022. I believe these concepts greatly contributed to Computer Vision community. Feel free to read more articles after this one since I tried to focus only on concepts rather than mathematical details. The image above shows well-know Convolution operation, I suppose we are familiar with.

It is probably, one of the simplest but strong supervised learning algorithms used for classification as well regression purposes. It is most commonly used to classify the data points that are separated into several classes, in order to make predictions for new sample data points. It is a non-parametric and lazy learning algorithm. It classifies the data points based on the similarity measure (e.g. Principle: K- NN algorithm is based on the principle that, "the similar things exist closer to each other or Like things are near to each other."

Data Science is a blend of various tools, algorithms, and machine learning principles to discover hidden patterns from the raw data. What makes it different from statistics is that data scientists use various advanced machine learning algorithms to identify the occurrence of a particular event in the future. A Data Scientist will look at the data from many angles, sometimes angles not known earlier. Data Visualization is one of the most important branches of data science. It is one of the main tools used to analyze and study relationships between different variables.

The best books to learn machine learning from zero to hero are full on the internet and online shops. You can either find them in form of softcopy, hardcopy or audio. But, there is one challenge that you might come across while searching for these books. For example, if you are a beginner it'll be too difficult to find these books. You might waste days, weeks, or even months searching for these books.

Learn the fundamental concepts behind Articifical Intelligence and how it can be . Description Information Systems (IS), or Information Technology (IT), as they are sometimes called, are playing a critical role in all our healthcare organizations today. They are permeating our hospitals, our clinics, our homes, our pharmacies and every aspect of our lives. If implemented correctly, they have the potential of making healthcare processes more efficient and maximizing patient and user experience. If implemented incorrectly, they can wreak havoc. The topics covered in this "Artificial Intelligence in Healthcare" course are: The basic concepts behind Healthcare Information Systems are often presented in a very complex, difficult to understand style.

ML is extracting data from knowledge. Machine learning is a study of algorithms that uses a provides computers the ability to learn from the data and predict outcomes with accuracy, without being explicitly programmed. Machine learning is sub-branched into three categories- supervised learning, unsupervised learning, and reinforcement learning. As the name "supervised learning" suggests, here learning is based through example. We have a known set of inputs (called features, x) and outputs (called labels, y).