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) …
At Uber, event forecasting enables us to future-proof our services based on anticipated user demand. The goal is to accurately predict where, when, and how many ride requests Uber will receive at any given time. Extreme events--peak travel times such as holidays, concerts, inclement weather, and sporting events--only heighten the importance of forecasting for operations planning. Calculating demand time series forecasting during extreme events is a critical component of anomaly detection, optimal resource allocation, and budgeting. Although extreme event forecasting is a crucial piece of Uber operations, data sparsity makes accurate prediction challenging.
Deep Reinforcement Learning (RL) is used to help airlines improve their business. So, revenue management (RM) is for maximizing revenue for airlines. Revenue management (RM) first used forecasting traffic flows (customer volumes and willingness to pay), and an optimisation procedure that prioritises among customers by selecting optimal availabilities, or prices. But, revenue management (RM) makes many (and unrealistic) assumptions. Deep Reinforcement learning (RL) is an area of deep learning focused on learning, and receiving feedback in order to optimize its predictions.
Ryan and Brian have worked together in the tax and accounting software industry together for nearly three years. Brian is going on with nearly six years of experience to bring this knowledge and consultation to every day traders. In crypto currency the world is murky for those that make thousands of transactions on exchanges and within other ecosystems. This experience helps leverage solutions for Vega into an industry that has many possibilities in technology and convenience. Implementing a free tool to users and our community conveys the message we have supported all along as a team which is deliver and deliver again sophisticated useful software.
The data are a simulated time series of sales data, which has spikes at quarterly and smaller periods, as well as longer term variations. There is about 3 ¼ years of data at daily granularity, and I want to test the potential to use the first 3 years as training data, then predict another 90 days in the future. The business case is that it is believed there are various factors that influence sales, some internal to our business and some external. We have a set of 8 factors, one of which is past sales, the remaining being market factors (such as GDP, economic activity, etc.) and internal data (such as sales pipeline, sales incentive programs, new product introductions (NPI), etc.). The past sales are used with phasing of one year, at which it is arrived by noting there are annual business cycles.
I just read two articles that claim that Python is overtaking R for data science and machine learning. From user comments, I learned that R is still strong in certain tasks. I will survey what these tasks are. The first article by Vincent Granville from DSC uses proxy metrics (as opposed to asking the users). He uses statistics from Google Trends, Indeed job search terms, and Analytic Talent (DSC job database) to conclude that Python has overtaken R. One is led to ask if one group of users (say Python's) is a more active googler.
"Over the last decade a new technology has begun to take hold in... business, one so new that its significance is still difficult to evaluate. While many aspects of this technology are uncertain, it seems clear that it will move into the managerial scene rapidly, with definite and far reaching impact on managerial organization." This article examines the near-term impact of expert system technology on work and the organization. First, an approach is taken for forecasting the likely extent of the diffusion, or success, of the technology. Next, the case of advanced manufacturing technologies and their effects is considered.
Civil unrest events (protests, strikes, and "occupy" events) are common occurrences in both democracies and authoritarian regimes. The study of civil unrest is a key topic for political scientists as it helps capture an important mechanism by which citizens express themselves. In countries where civil unrest is lawful, qualitative analysis has revealed that more than 75 percent of the protests are planned, organized, or announced in advance; therefore detecting references to future planned events in relevant news and social media is a direct way to develop a protest forecasting system. We report on a system for doing that in this article. It uses a combination of key-phrase learning to identify what to look for, probabilistic soft logic to reason about location occurrences in extracted results, and time normalization to resolve future time mentions.
It explores main concepts from basic to expert level which can help you achieve better grades, develop your academic career, apply your knowledge at work or make business forecasting related decisions. Learning forecasting methods and models is indispensable for business or financial analysts in areas such as sales and financial forecasting, inventory optimization, demand and operations planning, and cash flow management. It is also essential for academic careers in data science, applied statistics, operations research, economics, econometrics and quantitative finance. And it is necessary for any business forecasting related decision. But as learning curve can become steep as complexity grows, this course helps by leading you through step by step real world practical examples for greater effectiveness.
Microsoft's Cognitive Toolkit (better known as CNTK) is a commercial-grade and open-source framework for deep learning tasks. At present CNTK does not have a native R interface but can be accessed through Keras, a high-level API which wraps various deep learning backends including CNTK, TensorFlow, and Theano, for the convenience of modularizing deep neural network construction. The latest version of CNTK (2.1) supports Keras. The RStudio team has developed an R interface for Keras making it possible to run different deep learning backends, including CNTK, from within an R session. This tutorial illustrates how to simply and quickly spin up a Ubuntu-based Azure Data Science Virtual Machine (DSVM) and to configure a Keras and CNTK environment.
Business forecasting case study example is one of the popular case studies on YOU CANalytics. Originally, the time series analysis and forecasting for the case study were demonstrated on R in a series of articles. One of the readers, Anindya Saha, has replicated this entire analysis in Python. You could read this python notebook at this link: Python Notebook for Forecasting. Anindya is passionate about machine learning & data mining.