In this article, we'll introduce you to various common terminologies used in the machine learning and artificial intelligence industry. Without any further delay let's begin! Note: If you are more interested in learning concepts in an Audio-Visual format, We have this entire article explained in the video below. If not, you may continue reading. In order to explain these terms, I'm going to use a very simple chart- I represent "the value-added to an organization" while on the vertical axis, I am representing "the complexity of doing this practice".

Artificial intelligence (AI) is the basis for mimicking human intelligence processes through the creation and application of algorithms built into a dynamic computing environment. Stated simply, AI is trying to make computers think and act like humans. The more humanlike the desired outcome, the more data and processing power required. At least since the first century BCE, humans have been intrigued by the possibility of creating machines that mimic the human brain. In modern times, the term artificial intelligence was coined in 1955 by John McCarthy. In 1956, McCarthy and others organized a conference titled the "Dartmouth Summer Research Project on Artificial Intelligence."

In recent years, we are witnessing the digital transformation of production lines as part of manufacturers' transition to the fourth industrial revolution (Industry 4.0). Based on Cyber Physical Systems (CPS) and digital technologies like cloud computing, the Industrial Internet of Things (IIoT) and Artificial Intelligence (AI), Industry 4.0 is enabling flexible production lines and supporting innovative functionalities like mass customization, predictive maintenance, zero defect manufacturing and digital twins. AI is currently the most disruptive digital enabler of the Industry 4.0 era and enables novel use cases like predictive quality management (Quality 4.0), effective human robot collaboration, agile production, and generative software design. State of the art AI systems in industrial plants operate in rather controlled environments. Nevertheless, AI systems in industrial plants must be safe, trusted, and secure, even when operating in dynamic, unstructured and unpredictable environments.

TensorFlow vs Theano – TensorFlow, along with PyTorch, is currently the best known and most widely used machine learning framework. However, the choice of tool should never depend on one's own preferences, but should be adapted to the data to be examined. Especially in the Big data area, this can prevent a decisive loss of performance. It is therefore also worthwhile to look off the beaten track and to look at other frameworks and libraries in addition to the top dogs. Theano is one such open source Python library.

A month ago, India's first driverless metro train in the national capital, Delhi, was launched. Yes! Like it or not, automation is happening and will continue to happen in places where you couldn't have imagined before. Artificial Intelligence has swept away the world around us, leading to the natural progression of demand for skilled professionals in the job market. It is one field that will never go outdated and will continue to grow. Wondering how to leverage this opportunity? How can you prepare yourself for such a league of jobs that make the world go around? We have got a repository of questions to help you get ready for your next interview! This article will cover the artificial intelligence interview questions and help you with the much-needed tips and tricks to crack the interview. The article is divided into three parts: basic artificial intelligence questions, intermediate level, and advanced AI questions. AnalytixLabs is India's top-ranked AI & Data Science Institute and is in its tenth year.

The Sequence Scope is a summary of the most important published research papers, released technology and startup news in the AI ecosystem in the last week. This compendium is part of TheSequence newsletter. Data scientists, scholars, and developers from Microsoft Research, Intel Corporation, Linux Foundation AI, Google, Lockheed Martin, Cardiff University, Mellon College of Science, Warsaw University of Technology, Universitat Politècnica de València and other companies and universities are already subscribed to TheSequence. Not a week goes by in which we don't learn about new, marvelous applications of transformers to deep learning domains. Considered one of the most important breakthroughs in the last few years of the deep learning space, transformers have gone to establish unfathomable milestones in domains such as natural language understanding (NLU) or computer vision.

Google AI unveiled a new neural network architecture called Transformer in 2017. The GoogleAI team had claimed the Transformer worked better than leading approaches such as recurrent neural networks and convolutional models on translation benchmarks. In four years, Transformer has become the talk of the town: A big part of the credit goes to its self-attention mechanism, which helps models to focus on only certain parts of the input and reason more effectively. BERT and GPT-3 are some popular Transformers. Now, the looming question is: With Transformer adoption on the rise, could it surpass or become as popular as RNN and CNN?

Antibiotic resistance is no joke. We need new antibiotics, and we need them fast. In the US alone, nearly three million people¹ get infected with antibiotic-resistant bacteria or fungi every year¹. But very few new antibiotics are being developed to replace those that no longer work. That's because drug design is an extremely difficult and lengthy process -- there are more possible chemical combinations of a new molecule than there are atoms in the Universe.

"Until now, machine learning has neglected a full integration of causality, and this paper argues that it would indeed benefit from integrating causal concepts." Ben Dickson is a software engineer and the founder of TechTalks. He writes about technology, business, and politics. This story originally appeared on Bdtechtalks.com.

With computer vision and deep learning approaches, the Italian company Covision Quality wants to help the industry. The joint project with Alupress is intended to support this. We spoke with Franz Tschimben, CEO of Covision Quality, about his company's goals and challenges. What is Covision Quality about? Franz Tschimben: Covision Quality automates the industrial quality control process on metals through computer vision and deep learning technology.