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) …
You may find out how many people come to your webpage, how much time they stay there, what proportion of them subscribe and buy anything utilizing data provided by website analytics software, and a lot of other helpful information that can help you grow your business and increase your revenue. Here is our list of the top 10 most common web analytics tools used by businesses all over the world to get important data about their eCommerce business. Google Analytics is the world's most popular online analytics software, and for good reason. It provides websites with both free and commercial tools, which they may utilize to gain a better knowledge of their consumers and their behavior. Regardless of the sector, whether it's travel, healthcare, retail, or anything else, Google Analytics can provide you a thorough picture of how people interact with your material, as well as what's working and what isn't.
ML Model Explainability (sometimes referred to as Model Interpretability or ML Model Transparency) is a fundamental pillar of AI Quality. It is impossible to trust a machine learning model without understanding how and why it makes its decisions, and whether these decisions are justified. Peering into ML models is absolutely necessary before deploying them in the wild, where a poorly understood model can not only fail to achieve its objective, but also cause negative business or social impacts, or encounter regulatory trouble. Explainability is also an important backbone to other trustworthy ML pillars like fairness and stability. Yet "explainability" is often a broad and sometimes confusing concept.
This article is intended for data scientists who may consider using deep learning algorithms, and want to know more about the cons of implementing these type of models into your work. Deep learning algorithms have many benefits, are powerful, and can be fun to show off. However, there are a few times when you should avoid them. I will be discussing those times when you should stop using deep learning below, so keep on reading if you would like a deeper dive into deep learning. Because other algorithms have been around longer, they have countless amounts of documentation, including examples and functions that make interpretability easier.
Hurricane prediction still poses challenges for researchers, who scramble to produce accurate predictions of the formation, track, and intensity of tropical cyclones in order to give residents in the storm's path the information they need to prepare or evacuate. To make matters worse, the 2021 Atlantic hurricane season has already produced 20 named storms, nearly double the average observed since 1991 – and the season isn't over for another two months. Now, researchers at Pacific Northwest National Laboratory (PNNL) have employed AI techniques to better predict hurricane intensity compared to the most widely used U.S. models. "There are several research components around tropical cyclones that are interesting," explained PNNL data scientist and study co-author Wenwei Xu in an interview with Datanami. "But over the years, the tropical cyclone track prediction has been progressing very rapidly, so the accuracy has been increasing a lot. However, the intensity forecast [is] an area that suffers still."
With the drafting of the "Artificial Intelligence Act" (April 2021), the European Commission has made its first attempt at comprehensively regulating the expansive world of AI. Whilst the draft legislation extensively addresses the regulation and classification of AI technology, it does not mention another area of concern regarding Artificial Intelligence, namely intellectual property rights. Identifying IP rights as a major issue, the EU Parliament adopted a resolution on IP rights for the development of AI technologies in October 2020. In it, the Parliament called upon the Commission to ensure a high level protection of intellectual property rights when regulating AI. Despite the report being forwarded to the Commission well before it finalized its proposal for the "Artificial Intelligence Act", the protection of intellectual property rights is not mentioned in the draft legislation. Merely an Annex published alongside it briefly mentions the challenges of protecting intellectual property rights in connection with AI-assisted outputs.
Cloud adoption is accelerating fast in enterprises surging towards modernity. But are there better ways of utilizing the full potential of cloud computing? Leaving behind the constraints of a single cloud computing platform, you will find various other arrangements like hybrid and multi-cloud computing. The annual RightScale State of the Cloud Report suggests, 90% of respondents believe that multi-cloud is already the most common pattern with businesses and enterprises. So, let's delve into understanding more about multi-cloud for modern enterprises.
I'm sure you've heard the word somewhere. AI can detect skin cancer!… AI can beat the champion of GO!… Many people, including myself, believe that Artificial Intelligence (AI) is going to be the next big thing thing to take our society by storm in the coming years. Yet, what if we have the wrong forecast?
Machine Learning has a wide variety of dimensionality reduction techniques. It is one of the most important aspects in the Data Science field. As a result, in this article, I will present one of the most significant dimensionality reduction techniques used today, known as Principal Component Analysis (PCA). But first, we need to understand what Dimensionality Reduction is and why it is so crucial. Dimensionality reduction, also known as dimension reduction, is the transformation of data from a high-dimensional space to a low-dimensional space in such a way that the low-dimensional representation retains some meaningful properties of the original data, preferably close to its underlying dimension.