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) …
Deep learning is a form of machine learning which allows a computer to learn from experience and understand things from a hierarchy of concepts where each concept being defined from a simpler one. This approach avoids the need for humans to specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them on top of each other through a deep setup with many layers. The first thing you need to learn when it comes to learning deep learning is the applied math which is the fundamental building block of deep learning. Linear algebra is a branch of mathematics that is widely used throughout engineering.
Another good example for AI-powered decisions that reduce emissions at scale is Google's partnership with electricityMap. By utilizing electricityMap – an AI-powered platform that shows in real-time how clean electricity is around the world and provides past, current and forecasted carbon footprint data for electricity by country – Google manages to align computing tasks with times of low-carbon electricity supply in the grid and, thus, reduces CO2e emissions from electricity consumption.
The pandemic has pushed IT departments to adapt quickly to various challenges. A new report, IT's Changing Mandate in an Age of Disruption, suggests that to continue with various digital transformations and increase adaptability for the future some IT improvements must be made. For the insurance industry, artificial intelligence and machine learning may be key, according to the report, which was conducted by the Economist Intelligence Unit, supported by Appian, an enterprise software company. The report includes information from two surveys, conducted in May and June of this year, and responses from 1,002 IT and senior business executives, who worked across six different sectors including financial services and insurance and were from nine countries. Forty-one percent of respondents from the insurance industry said expanding the use of AI and machine learning is the most impactful way that technology can help organizations respond to potential changes.
A team of researchers at the University of Liverpool has developed a collaborative artificial intelligence (AI) tool that reduces the time and effort needed to discover new materials. The new AI has already led to the discovery of four new materials. The research was published last month in the journal Nature Communications. Discovering New Materials […]
Timeseries data is a set of data that is connected to each other where each value is based on previous values. In machine learning model building, predicting the next coming step in future based on later steps in history will be usually accurate. To enhance the learning in timeseries models it is required to handle the data in batches of sequences. Risk of Data Leakage, since model needs to forecast the future, if we shuffle data and split into training and testing dataset randomly; then we may leak some future answers to the model during learning phase. That will result in unexpected results during testing.
I work in Machine Learning. To readers/viewers of my work, this won't come as a surprise. To people who don't know me as well, feel free to check out my LinkedIn/articles/videos for a better understanding of my skills/experience. My specialty is in statistical analysis. I've had experience working in Road Safety, Health System Analysis, Big Data Analysis for a Bank, disease detection, biometric recreation, and currently work in Supply Chain Analysis.
The MXC Foundation has made a remarkable entry into the nascent multi-billion dollar AI smart device market. With the exponential growth of its network across the globe, the Foundation is thrilled to introduce more aspects to its network usage, allowing its mining community to utilize the data republic and see the network in action. The proprietary MXProtocol, together with scalable and secure aspects of device provisioning that connect with sensor technology, has proven successful and brings us a step closer to realizing truly smart cities. One such use case, which the MXC Foundation recently tested in a controlled environment, was the Edge X AI Camera. Read on to find out more about all the great functionalities packed into one small device.