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) …
Healthcare is an important industry which offers value-based care to millions of people, while at the same time becoming top revenue earners for many countries. Today, the Healthcare industry in the US alone earns a revenue of $1.668 trillion. The US also spends more on healthcare per capita as compared to most other developed or developing nations. Quality, Value, and Outcome are three buzzwords that always accompany healthcare and promise a lot, and today, healthcare specialists and stakeholders around the globe are looking for innovative ways to deliver on this promise. Technology-enabled smart healthcare is no longer a flight of fancy, as Internet-connected medical devices are holding the health system as we know it together from falling apart under the population burden.
In the Machine Learning terminology, the process of Classification can be defined as a supervised learning algorithm that aims at categorizing a set of data into different classes. In other words, if we think of a dataset as a set of data instances, and each data instance as a set of features, then Classification is the process of predicting the particular class that that individual data instance might belong to, based on its features. Unlike regression where the target variable (i.e., the predicted value) belongs to a continuous distribution, in case of classification, the target variable is discrete. It can only be one of the various target classes in a given problem. For example, let's say you are working on a cat-dog-classifier model that predicts whether the animal in a given image is a cat or a dog.
When Shamir Rahim, founder and CEO of VersaFleet, transformed his bio-medical startup into an AI-powered transportation management system, he never imagined being at the epicenter (in a good way) of a supply chain revolution during a worldwide pandemic. As anyone desperately searching for toilet paper discovered earlier this year, the last mile is the crucial link in every supply chain. VersaFleet's SaaS-based cloud platform relies on AI to meet one of the toughest supply chain challenges: last mile delivery. "We wanted to provide our customers with a command center view of last mile product delivery with cost and time savings," said Shamir Rahim, founder and CEO of VersaFleet. "As our customers slowly open up again, VersaFleet is providing greater agility so they can quickly adjust logistics for maximum efficiency, whether people are out sick or returning to work, quarantines are lifted or imposed again, and operational hours shift at any time."
As machine learning (ML) systems are increasingly being deployed in real-world applications, it is critical to ensure that these systems are behaving responsibly and are trustworthy. To this end, there has been growing interest from researchers and practitioners to develop and deploy ML models and algorithms that are not only accurate, but also explainable, fair, privacy-preserving, causal, and robust. This broad area of research is commonly referred to as trustworthy ML. While it is incredibly exciting that researchers from diverse domains ranging from machine learning to health policy and law are working on trustworthy ML, this has also resulted in the emergence of critical challenges such as information overload and lack of visibility for work of early career researchers. Furthermore, the barriers to entry into this field are growing day-by-day -- researchers entering the field are faced with overwhelming amount of prior work without a clear roadmap of where to start and how to navigate the field. Provide a platform for early career researchers to showcase and disseminate their work.
Modern machine learning methods typically produce "black box" models that are opaque to interpretation. Yet, their demand has been increasing in the Human-in-the-Loop processes, that is, those processes that require a human agent to verify, approve or reason about the automated decisions before they can be applied. To facilitate this interpretation, we propose Collection of High Importance Random Path Snippets (CHIRPS); a novel algorithm for explaining random forest classification per data instance. CHIRPS extracts a decision path from each tree in the forest that contributes to the majority classification, and then uses frequent pattern mining to identify the most commonly occurring split conditions. Then a simple, conjunctive form rule is constructed where the antecedent terms are derived from the attributes that had the most influence on the classification.
Landscapers sometimes accommodate desire paths by paving them, thereby integrating them into the official path network rather than blocking them. The image above is of an desire path being blocked and rehabilitated in an attempt to force users on the designed path. Sometimes, land planners have deliberately left land fully or partially unpathed, waiting to see what desire paths are created, and then paving those. In Finland, planners are known to visit parks immediately after the first snowfall, when the existing paths are not visible. The naturally chosen desire paths, marked by footprints, can then be used to guide the routing of new purpose-built paths.
My book chapter shows in a tutorial way how to use machine learning to assist quantum chemistry research. The chapter – available for free download till November 6 – is a part of the book edited by Kenneth Ruud and Erkki J. Brändas. It collects a dozen of contributions to the 10th Triennial Congress of the International Society for Theoretical Chemical Physics (ISTCP-X). This was a huge congress with 500 participants. It was held in pre-COVID-19 times in Tromsø, Norway, where the sun never set below the horizon at that time of the year. The team of the organizers led by Kenneth Ruud did really amazing job to bring together forefront science in chemical physics.
Artificial Intelligence In Digital Marketing 2020 Artificial intelligence has already made a huge difference in how brands interact with consumers and how marketing strategies are managed. In such a rapidly changing environment, it's difficult to predict what the future holds, but there are certainly some clues to what we might expect in the coming year. Being smart in business means knowing what's just around the corner. It means thinking ahead and preparing for inevitable changes that will impact the way business is conducted. This is what allows a business to be resilient and to thrive in a changing environment.
Getting Started with Embedded AI Edge AI Get the scale needed to meet your business need and make informed decisions. Buy Online What you'll learn Description Nowadays, you may have heard of many keywords like Embedded AI /Embedded ML /Edge AI, the meaning behind them is the same, I.e. To make an AI algorithm or model run on embedded devices. Due to a massive gap between both technologies, techies don't know where to start with it. So we thought to share our engineer's experience with you via this course.
AI LAW, ETHICS, PRIVACY & LEGALITIES - DR. PAVAN DUGGAL -CLU AN INTRODUCTION TO THE WONDERFUL WORLD OF DIFFERENT TOPICS UNDER ARTIFICIAL INTELLIGENCE LAW What you'll learn Description This course provides a holistic perspective of some of the important issues and topics that are gaining significance in the evolving Artificial Intelligence Law discipline. This course further tries to highlight the directions in which Artificial Intelligence Law as an emerging discipline is likely to evolve, with the passage of time. Who this course is for: Any student of any age group, who is interested in knowing about the complex legalities as also legal, policy and regulatory issues concerning Artificial Intelligence.