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) …
While the whole planet was frozen by the coronavirus pandemic, offline stores found they couldn't compete with even the smallest online stores when people's lifestyles were limited by their homes or neighborhoods. But those who have just started online sales this year will quickly find out what to do to sell efficiently on the internet. This is why the overall competition will rise. Wondering how you can gain a foothold at this moment? Take a look at modern technologies – artificial intelligence (AI), machine learning (ML), and big data analysis.
Artificial Intelligence (AI) is not the one that is borne by the overwhelming science fiction vision. In the near future, we will see almost every area of life in order to make our activities more effective and interactive. According to China's search engine, Baidu's top researcher, "Reliability of speech technology approaches the point we will only use and do not even think about." Andrew Ng says the best technology is often invisible, and speech recognition will disappear in the background as well. Baidu is currently working on more accurate speech recognition and more efficient sentence analysis, which expects sound technologies to be able to interact with multiple devices such as household appliances.
Classical Analytics – Around ten years ago, the tools for analytics or the available resources were excel, SQL databases, and similar relatively simple ones when compared to the advanced ones that are available nowadays. The analytics also used to target things like reporting, customer classification, sales trend whether they are going up or down, etc.In this article we will discuss about Real Time Anomaly Detection. As time passed by the amount of data has got a revolutionary explosion with various factors like social media data, transaction records, sensor information, etc. in the past five years. With the increase of data, how data is stored has also changed. It used to be SQL databases the most and analytics used to happen for the same during the ideal time. The analytics also used to be serialized. Later, NoSQL databases started to replace the traditional SQL databases since the data size has become huge and the analysis also changed from serial analytics to parallel processing and distributed systems for quick results.
RBC's AI private cloud platform is the first-of-its-kind in Canada to deliver intelligent software applications and boost operational efficiency Royal Bank of Canada (RBC) and its AI research institute Borealis AI have partnered with Red Hat and NVIDIA to develop a new AI computing platform designed to transform the customer banking experience and help keep pace with rapid technology changes and evolving customer expectations. "Modern AI cannot exist without access to high performance computing. This collaboration means that we can conduct research at scale, and deploy machine learning applications in production with improved efficiency and speed to market." As AI models become more efficient and accurate, so do the computational complexities associated with them. RBC and Borealis AI set out to build an in-house AI infrastructure that would allow transformative intelligent applications to be brought to market faster and deliver an enhanced experience for clients.
Robotic process automation is the use of specialized computer programs, known as software robots, to automate and standardize repeatable business processes. Imagine a robot sitting in front of a computer looking at the same applications and performing the same keystrokes as a person would. While robotic process automation does not involve any form of physical robots, software robots mimic human activities by interacting with applications in the same way that a person does. Working as a virtual business assistant, bots complete tedious tasks, freeing up time for employees to concentrate on more engaging, revenue-generating tasks. Part of the beauty of robotic process automation technology is that it offers even non-technical employees the tools to configure their own software robots to solve automation challenges.
As the world recovers from the initial shock wave caused by the COVID-19 pandemic, businesses are preparing for their transitions back to their physical workplaces. In most cases, they are opening up gradually, with an unprecedented focus on keeping workers safe as they return. To protect employees' health and well-being, organizations must systematically reengineer their workspaces. This may include reconfiguring offices, rearranging desks, changing people's shifts to minimize crowding, and allowing people to work remotely long term. Then there are the purely medical measures, such as regular temperature checks, the provision of face masks and other personal protective equipment, and even onsite doctors.
CAMBRIDGE – COVID-19 has become a severe stress test for countries around the world. From supply-chain management and health-care capacity to regulatory reform and economic stimulus, the pandemic has mercilessly punished governments that did not – or could not – adapt quickly. From Latin America's lost decade in the 1980s to the more recent Greek crisis, there are plenty of painful reminders of what happens when countries cannot service their debts. A global debt crisis today would likely push millions of people into unemployment and fuel instability and violence around the world. The virus has also pulled back the curtain on one of this century's most important contests: the rivalry between the United States and China for supremacy in artificial intelligence (AI).
Advances in Artificial Intelligence (AI) and computer processors have opened new ways for face recognition online services not possible before. Startups all over the world are developing Apps and products that make use of Face Recognition. Moreover, they are bringing products into the market with user authentication, attendance tracking and photo grouping (for event photographers) capabilities, to name a few. Face Recognition Online software components are challenging to develop in-house. For this reason, it makes sense for startups and software companies to buy this capability from specialized vendors.
Machine vision has come a long way from the simpler days of cameras attached to frame grabber boards--all arranged along an industrial production line. While the basic concepts are the same, emerging embedded systems technologies such as Artificial Intelligence (AI), deep learning, the Internet-of-Things (IoT) and cloud computing have all opened up new possibilities for machine vision system developers. To keep pace, companies that used to only focus on box-level machine vision systems are now moving toward AI-based edge computing systems that provide all the needed interfacing for machine vision, but also add new levels of compute performance to process imaging in real-time and over remote network configurations. AI IN MACHINE VISION ADLINK Technology appears to be moving in this direction of applying deep learning and AI to machine vision. The company has a number of products, listed "preliminary" at present, that provide AI machine vision solutions. These systems are designed to be "plug and play" (PnP) so that machine vision system developers can evolve their existing applications to AI-enablement right away with no need to replace existing hardware.
Autoencoders are neural networks that serve machine learning models -- from denoising to dimensionality reduction. Seven use cases explore the practical application of autoencoder technology. Developers frequently turn to autoencoders to organize data for machine learning algorithms to improve the efficiency and accuracy of algorithms with less effort from data scientists. Data scientists can add autoencoders as additional tools to applications which require data denoising, nonlinear dimensionality reduction, sequence-to-sequence prediction and feature extraction. Autoencoders have a special advantage over classic machine learning techniques like principal component analysis for dimensionality reduction in that they can represent data as nonlinear representations -- and work particularly well in feature extraction.