If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Crowd management and monitoring is crucial for maintaining public safety and is an important research topic. Developing a robust crowd monitoring system (CMS) is a challenging task as it involves addressing many key issues such as density variation, irregular distribution of objects, occlusions, pose estimation, etc. Crowd gathering at various places like hospitals, parks, stadiums, airports, cultural and religious points are usually monitored by Close Circuit Television (CCTV) cameras. The drawbacks of CCTV cameras are: limited area coverage, installation problems, movability, high power consumption and constant monitoring by the operators. Therefore, many researchers have turned towards computer vision and machine learning that have overcome these issues by minimizing the need of human involvement. This review is aimed to categorize, analyze as well as provide the latest development and performance evolution in crowd monitoring using different machine learning techniques and methods that are published in journals and conferences over the past five years.
IMAGE: The scientists Altuna Akalin (left) and Wolfgang Kopp (right) from the "Bioinformatics and Omics Data Science " group. Researchers from the MDC have developed a new tool that makes it easier to maximize the power of deep learning for studying genomics. They describe the new approach, Janggu, in the journal Nature Communications. Imagine that before you could make dinner, you first had to rebuild the kitchen, specifically designed for each recipe. You'd spend way more time on preparation, than actually cooking.
This article investigates TensorFlow components for building a toolset to make modeling evaluation more efficient. Specifically, TensorFlow Datasets (TFDS) and TensorBoard (TB) can be quite helpful in this task. While completing a highly informative AICamp online class taught by Tyler Elliot Bettilyon (TEB) called Deep Learning for Developers, I got interested in creating a more structured way for machine-learning model builders -- like me as the student -- to understand and evaluate various models and observe their performance when applied to new datasets. Since this particular class focused on TensorFlow (TF), I started to investigate TF components for building a toolset to make this type of modeling evaluation more efficient. In doing so, I learned about two components, TensorFlow Datasets (TFDS) and TensorBoard (TB), that can be quite helpful and this blog post discusses their application in this task.
The COVID-19 pandemic is an incredibly complex and rapidly evolving global public health emergency. Facebook is committed to preventing the spread of false and misleading information on our platforms. Misinformation about the disease can evolve as rapidly as the headlines in the news and can be hard to distinguish from legitimate reporting. The same piece of misinformation can appear in slightly different forms, such as as an image modified with a few pixels cropped or augmented with a filter. And these variations can be unintentional or the result of someone's deliberate attempt to avoid detection.
Graphcore, a U.K.-based company developing accelerators for AI workloads, this morning unveiled the second generation of its Intelligence Processing Units (IPUs), which will soon be made available in the company's M2000 IPU Machine. Graphcore claims this new GC200 chip will enable the M2000 to achieve a petaflop of processing power in an enclosure that measures the width and length of a pizza box. AI accelerators like the GC200 are a type of specialized hardware designed to speed up AI applications, particularly artificial neural networks, deep learning, and machine learning. They're often multicore in design and focus on low-precision arithmetic or in-memory computing, both of which can boost the performance of large AI algorithms and lead to state-of-the-art results in natural language processing, computer vision, and other domains. The M2000 is powered by four of the new 7-nanometer GC200 chips, each of which packs 1,472 processor cores (running 8,832 threads) and 59.4 billion transistors on a single die, and it delivers more than 8 times the processing performance of Graphcore's existing IPU products.
Major tech stocks drove the markets lower this morning, with Nasdaq NDAQ down by almost 0.5%. In contrast, the Dow was trading higher by 200 points buoyed by banking stocks like JP Morgan and Citigroup C, which have beaten street estimates on earnings this morning. Of course, by mid-morning, the Nasdaq had turned positive. More choppiness should be expected as more companies declare their quarterly results throughout the week. Our deep learning algorithms have gone through the data and used Artificial Intelligence ("AI") to help you spot the Top Buys for today.
Artificial intelligence has made significant strides in recent years, but modern AI techniques remain limited, a panel of MIT professors and the director of the MIT-IBM Watson AI Lab said during a webinar this week. Neural networks can perform specific, well-defined tasks but they struggle in real-world situations that go beyond pattern recognition and present obstacles like limited data, reliance on self-training, and answering questions like "why" and "how" versus "what," the panel said. The future of AI depends on enabling AI systems to do something once considered impossible: Learn by demonstrating flexibility, some semblance of reasoning, and/or by transferring knowledge from one set of tasks to another, the group said. The panel discussion was moderated by David Schubmehl, a research director at IDC, and it began with a question he posed asking about the current limitations of AI and machine learning. "The striking success right now in particular, in machine learning, is in problems that require interpretation of signals--images, speech and language," said panelist Leslie Kaelbling, a computer science and engineering professor at MIT.
You're looking for a complete Artificial Neural Network (ANN) course that teaches you everything you need to create a Neural Network model in R, right? You've found the right Neural Networks course! Identify the business problem which can be solved using Neural network Models. Have a clear understanding of Advanced Neural network concepts such as Gradient Descent, forward and Backward Propagation etc. Create Neural network models in R using Keras and Tensorflow libraries and analyze their results. How this course will help you?
To Build a perfect model, you need a large amount of data. But finding the right dataset for your machine learning and data science project is sometimes quite a challenging task. There are many organizations, researchers, and individuals who've shared their work, and we will use their datasets to build our project. So in this article, we are going to discuss 20 Machine learning and Data Science dataset and project ideas that you can use for practicing and upgrading your skills. The Enron Dataset is popular in natural language processing.