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) …
In celebration of its newly-launched and highly-anticipated programs in business analytics and artificial intelligence, Southern Illinois University Carbondale's College of Business will host an evening reception for prospective students, business leaders and alumni on Wednesday, Nov. 13, in downtown Chicago. The event will take place from 6 to 8 p.m. on the 27th floor of the Deloitte building (Room 27E047), located at 111 S. Wacker Drive. Attendees will have the opportunity to meet faculty teaching these innovative courses, as well as analytics industry executives serving on the board of the university's one-of-a-kind Pontikes Center for Advanced Analytics and Artificial Intelligence. SIU recently launched an Analytics Concentration for its nationally-ranked online MBA program, a Bachelor of Science in Business Analytics, and will soon introduce a full graduate analytics program. All of these programs are uniquely designed to bridge the gap between data science and business by arming the managers and executives of tomorrow with leading-edge developments in artificial intelligence, prediction and data visualization, combined with a strong business foundation.
Let's face it, casino gaming is a huge business. It brings more than $500 billion in revenues every year from all around the world and the rise of the internet has fuelled its growth further. Today, online casino games has reached several people across all age groups through popular fantasy games. But with its growth comes another issue: the need to draw the line before excessive casino gaming becomes a problem, or as we like to call it, 'irresponsible gaming'. Being a part of the industry, there's no way we can shy away from addressing this issue.
Collectively, humans now generate 2.5 quintillion bytes of new data per day. The data we generate in a single year dwarfs every metric ever created between 2015 and the beginning of recorded history. In other words, the BI tools of the past can hardly be expected to keep up with today's demands. Not only is the overall amount of data increasing, the number of types of data are increasing, and the applications that store and generate data are increasing as well. Older BI tools can't cope with larger volumes of data, and they also find it difficult to process data from new applications; it often takes a lot of manual adjustments to make an old BI tool fit a new app.
Many business AI platforms offer training courses in the specifics of running their architecture and the programming languages needed to develop more AI tools. Businesses that are serious about AI should plan to either hire new employees or give existing ones the time and resources necessary to train in the skills needed to make AI projects succeed.
Learn the fundamental aspects of the business statistics, data mining, and machine learning techniques required to understand the huge amount of data generated by your organization. This book explains practical business analytics through examples, covers the steps involved in using it correctly, and shows you the context in which a particular technique does not make sense. Further, Practical Business Analytics using R helps you understand specific issues faced by organizations and how the solutions to these issues can be facilitated by business analytics. This book includes case studies on important business analytic techniques, such as classification, association, clustering, and regression. The R language is the statistical tool used to demonstrate the concepts throughout the book.
Machine learning (ML) is empowering average business users with superior, automated tools to apply their domain knowledge to predictive analytics or customer profiling. The article What is Automated Machine Learning (AutoML)? These are not just empty promises to worldwide business leaders; in 2017, the age of automated, ML-powered analytics and BI dawned, and has since transformed one industry sector at a time. The automation revolution has not paused and is likely to storm global businesses in years to come. The era of AutoML is beginning to enable business users to tune existing data models and apply custom models to their everyday business situations as well.
The recent organizational push for self-service Business Intelligence has helped the next challenge for business users become an increasing need. How to tackle the issue of having Machine Learning (ML) models embedded in all major analytics platforms? On the one hand, embedded models offer greater freedom and control over data analysis; on the other hand, confronting the native ML intelligence of these platforms is posing new risks and opportunities for ordinary users. Without having the requisite background in Data Science or Artificial Intelligence (AI), how will these users or aspiring citizen data scientists prepare themselves for self-service Machine Leaning? The Gartner article 5 Ways Data Science and Machine Learning Impact Business discusses how Data Science and Machine Learning together are becoming the core differentiators between businesses that survive and those that don't.
When it comes to data science, the co-founder and CEO of Kaggle, Anthony Goldbloom predicted that data science centers will be soon replaced by departmental or business-specific Data Science teams. It has been found that last year's major trend was followed from 2017 as there was the growth of Big Data, AI, Machine Learning(ML), Edge Computing, Blockchain, and digital technology. In the year, 2017 Big Data and Data Science were the themes which were surrounded and in 2018 it got drowned since the theme in 2018 got changed to "the meshing of the physical and digital world". The latest technology in 2018 as an open source is the python and the R Ecosystems. Many cutting edge technologies and software are led by Hadoop and Hive Stacks too.
Machine learning is becoming a transformative force in many industries. According to the New York Times, machine learning refers to "systems that learn from data sets to perform and improve upon a specific task." Machine learning has become integral to the current field of artificial intelligence. It primarily differentiates itself from earlier forms of artificial intelligence where a person had to dictate to a system how to perform or complete certain tasks. The promise and practice of machine learning centers around a system being able to take information, learn, perform, and improve on its own.
So far, scientists and researchers have made claims on behalf of AI-enabled technologies, but they have not really been tested in large-scale market applications. We will see a lot of those technologies put into marketplace practice for the users to judge and evaluate. For starters, let's review some recent industry statistics. From chatbots and digital agents in CRM to virtual reality (VR)-powered shop-floor demos, AI has promised something for every industry sector. McKinsey & Company, in Notes from the Frontier: Modeling the Impact of AI on the World Economy, has predicted that by 2030, 70 percent of businesses will use AI.