In this article, we will discuss how Convolutional Neural Networks (CNN) classify objects from images (Image Classification) from a bird's eye view. First, let us cover a few basics. Let us start with the difference between an image and an object from a computer-vision context. What we see above is an image. We can see 3 objects inside – 1 cat and 2 dogs.

The pandemic outbreak of novel coronavirus has taken the world by storm. It has created a huge stir across the socio-economic landscape of various countries. Out of 195, the reports say, COVID-19 has infected 192 nations across the globe. While the death toll is rising in other countries like Italy, China is recovering from Wuhan-origin virus with no new domestic cases registered recently, since the outbreak. But how is China recovering while others are getting deep into the unhealthy mess?

Noah is an AI systems architect. Prior to founding Quorum, Noah spent 12 years in academic research, first at the University of Southern California and most recently at Northwestern as the Assistant Chair of Neurobiology. His work focused on information processing in the brain and he has translated his research into products in augmented reality, brain-computer interfaces, computer vision, and embedded robotics control systems. Your interest in AI and robotics started as a little boy. How were you first introduced to these technologies?

Noah is an AI systems architect. Prior to founding Quorum, Noah spent 12 years in academic research, first at the University of Southern California and most recently at Northwestern as the Assistant Chair of Neurobiology. His work focused on information processing in the brain and he has translated his research into products in augmented reality, brain-computer interfaces, computer vision, and embedded robotics control systems. Your interest in AI and robotics started as a little boy. How were you first introduced to these technologies?

Researchers at Syracuse University in New York have recently developed a system that can detect the presence of humans in a given environment by analyzing ambient radio frequency (RF) signals. This new system, presented in a paper pre-published on arXiv, employs a convolutional neural network (CNN) trained on a vast amount of RF data. "Initially, we tried to detect drones in an outdoor environment using passive RF signals through deep learning," Biao Chen, one of the researchers who carried out the study, told TechXplore. "The result was uneven at best--it worked on measurements collected on certain days, but would fail on other days." For some time, Chen and his colleagues tried to develop a system that could sense the presence of drones in outdoor environments.

Twenty Billion Neurons (TwentyBN), is a leading provider of real-time, interactive computer vision and artificial intelligence solutions. It is an AI company with the mission of instilling common sense into computers through video understanding and of turning inanimate devices into human eyes that can understand the world around them, assist humans, and ensure a safe and healthy lifestyle. Founded in 2015, a company is building an artificial intelligence system that interacts with humans while "looking" at them. This enables the system to understand their behavior, surrounding and the full context of the engagement. This AI solution allows people to interact with technology and each other in new and exciting ways that will impact every aspect of their lives.

Beginning of 2018 I have shared a blog on explaining #ArtificialIntelligence (AI), #MachineLearning (#ML) and Deep Learning (DL) together with Data Science. Today I like to expand the definition by adding Data Analysis as part of Data Science, Data Analytics and Data Mining. Let's briefly recap the evolution: Even the most complex topics can be segregated in easy to consume buckets. Let me try to explain them and let's start with the evolution of "Big Data" to Artificial Intelligence (#AI): Any thoughts or feedback on this simplified way in explaining a fairly complex topic?

This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI. Despite the huge contributions of deep learning to the field of artificial intelligence, there's something very wrong with it: It requires huge amounts of data. This is one thing that both the pioneers and critics of deep learning agree on. In fact, deep learning didn't emerge as the leading AI technique until a few years ago because of the limited availability of useful data and the shortage of computing power to process that data. Reducing the data-dependency of deep learning is currently among the top priorities of AI researchers.

Rensselaer Polytechnic Institute is turning one of the most powerful supercomputers in the world against COVID-19. AiMOS, short for Artificial Intelligence Multiprocessing Optimized System, can perform eight quadrillion calculations per second and is uniquely capable of exploring new applications in artificial intelligence. RPI is offering the supercomputer to the research community--including government entities, academic universities, national labs, and private businesses--to support new coronavirus research. To combat the devastating effects of this pandemic, we must be able to fully grasp the complexities and interconnectedness of biological systems and epidemiological data, as researchers work to develop therapeutic interventions and address gaps in our knowledge. AiMOS is the most powerful supercomputer at a private university, the third-most energy-efficient supercomputer in the world, and the 24th most-powerful in the world overall.

Google researchers recently published a paper describing a framework -- SEED RL -- that scales AI model training to thousands of machines. They say that it could facilitate training at millions of frames per second on a machine while reducing costs by up to 80%, potentially leveling the playing field for startups that couldn't previously compete with large AI labs. Training sophisticated machine learning models in the cloud remains prohibitively expensive. According to a recent Synced report, the University of Washington's Grover, which is tailored for both the generation and detection of fake news, cost $25,000 to train over the course of two weeks. OpenAI racked up $256 per hour to train its GPT-2 language model, and Google spent an estimated $6,912 training BERT, a bidirectional transformer model that redefined the state of the art for 11 natural language processing tasks.