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) …
This course is giving you the chance to systematically master the core concepts in statistics & probability, descriptive statistics, hypothesis testing, regression analysis, analysis of variance and some advance regression / machine learning methods such as logistics regressions, polynomial regressions, decision trees and more. In real-life examples you will learn the stats knowledge needed in a data scientist's or data analyst's career very quickly.
This video and post are part of a One Dev Question series on MLOps - DevOps for Machine Learning. See the full video playlist here, and the rest of the blog posts here. To understand what MLOps (DevOps for Machine Learning) is, we first need to know what Machine Learning is. Machine Learning is a term many of us have heard, but what does it actually mean, and when should it be used? As usual Microsoft Docs has some great resources, including an explanation of what machine learning is and how it works, but I like to think of it in more simple terms.
Gartner research shows only 53% of projects make it from artificial intelligence (AI) prototypes to production. There are two reasons for that: First, in the midst of frequently overhyped expectations, a clear path toward the real value for the organization is often not defined for the initial project. The second reason, which is even more important and often ignored: The technical gap between a shiny prototype and putting the results of that prototype into production is big. Bridging that gap between the creation of a combination of data wrangling and model optimization through to deploying that process often requires a complex, sometimes even manual step. Worse, the technologies used are seldom aligned well.
Airbus AI researchers have developed a system that uses natural language understanding to improve question answering (QA) performance when flight crews search for aircraft operating information. The aerospace industry relies on technical documents such as Aircraft Operating Manuals (AOM), Aircraft Operating Instructions and particularly Flight Crew Operating Manuals (FCOM) to guide flight crews on aircraft operations under normal, abnormal, and emergency conditions. FCOMs are issued by aircraft manufacturers and cover system descriptions, procedures, techniques, and performance data. They are the references used to develop standard operating procedures to improve safety and efficiency. Most government aviation administrations have authorized the use of tablet computers by commercial carrier pilots and flight crews to access FCOM information. The Airbus AI researchers note however that existing electronic flight bag (EFB) systems used for this purpose are in practice little more than pdf viewers with keyword search functionality.
Artificial Intelligence is playing an ever more important role in business. Every year, we see a fresh batch of executives implement AI-based solutions across both products and processes. But do you know how they do it? And if you were to try the same, would you know how to achieve the best results? By the end of this article, you will -- you'll see precisely how you can use AI to benefit your entire operation.
The report published on the global Artificial Intelligence Processor market is a comprehensive market study that focuses on the key players and key markets. The growth opportunities regarding this market as well as the future forecast and the status of the global Artificial Intelligence Processor market have been presented by this report. The market has been analyzed on the basis of the market value from the year 2020 to the year 2026. This study also includes an analysis of consumption, value, production and capacity. With the key manufacturers of the products in the market covered, the report presents its development plans for the future.
The report titled "Artificial Intelligence Chip Market: Size, Trends and Forecasts (2020-2025)", delivers an in depth analysis of the Artificial Intelligence Chip market by value, by production capacity, by companies, by applications, by segments, by region, etc. The report assesses the key opportunities in the market and outlines the factors that are and will be driving the growth of the Artificial Intelligence Chip industry. Growth of the overall Artificial Intelligence Chip market has also been forecasted for the period 2020-2025, taking into consideration the previous growth patterns, the growth drivers and the current and future trends. Since the COVID-19 virus outbreak in December 2019, the disease has spread to almost 180 countries around the globe with the World Health Organization declaring it a public health emergency. The global impacts of the coronavirus disease 2019 (COVID-19) are already starting to be felt, and will significantly affect the Artificial Intelligence Chip market in 2020.
The inventions in Artificial Intelligence are thriving the pace of invention despite the existing pandemic. The year 2020 has surprised humans in many ways. From encountering a pandemic, addressing a global recession, and witnessing the global geopolitical changes, humanity is standing in ambiguous times. However, not everything is uncertain. Throughout the year, emerging technologies such as artificial intelligence, robotics, Internet of Things, and augmented/virtual reality, amongst others have spearheaded innovation with a promising future.