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) …
If you go to college and take a course "Machine learning 101", this might be the first example of machine learning your teacher will show you: Imagine you work for a real estate agency, and you want to predict, for how much a house will sell. You have some historical data -- you know that house A has been sold for $500 000, house B for $600 000, and house C for $550 000. You also know something about properties of the houses -- you know the size of the house in square meters, number of rooms in the house, and the year the house was build. The goal of the real estate agency is to predict, for how much a new house D will sell, given its known properties (size, age and number of rooms of the house). In ML terminology, the known properties of the house are called "features" or "indicators" (we use the term "indicators" in Signals, because this term has been historically used in trading).
I did a startup in the area of Artificial Intelligence-driven Sales Prospecting in 1997. Much water has flown under the proverbial bridge. This discussion takes us to the state of the art, twenty years later. I run, what we call, InsideSales' lab. That is our research and best practice group here at InsideSales.com.
Bob, CFO of ABC Inc is about to get on an earnings call after just reporting a 20% miss on earnings due to slower revenue growth than forecasted. Company ABC's stock price is plummeting, down 25% in extended hour trading. The board is furious and investors demand answers on the discrepancies. Inaccurate revenue forecast remains one of the biggest risks for CFOs. In a recent study, more than 50% of companies feel their pipeline forecast is only about 50% accurate.
Sales prediction is an important part of modern business intelligence. First approaches one can apply to predict sales time series are such conventional methods of forecasting as ARIMA and Holt-Winters. But there are several challenges while using these methods. They are: multilevel daily/weekly/monthly/yearly seasonality, many exogenous factors which impact sales, complex trends in different time periods. In such cases, it is not easy to apply conventional methods.
Unanimous AI is known for accepting high-profile challenges from journalists, testing the power of its Swarm AI technology across a range of venues. After correctly predicting the World Series and Kentucky Derby -- and perfectly forecasting Trump's 100-day approval rating before he even took office -- Unanimous AI took aim at the 90th Academy Awards, publishing its prediction here at Inverse last week. Its Swarm AI technology was nearly perfect, achieving 94 percent accuracy across the 16 major award categories, with only one award not coming out as predicted. The technology was 100% percent accurate in forecasting winners in the six major categories, including Best Picture. In that category, Swarm AI disagreed with most industry experts – and the Vegas odds – all of which deemed Golden Globe winner Three Billboards Outside Ebbing, Missouri the favorite over Swarm AI's predicted winner, The Shape of Water.
Unanimous AI is known for accepting high-profile challenges from journalists, testing the power of its Swarm AI technology across a range of venues. After correctly predicting the World Series and Kentucky Derby -- and perfectly forecasting Trump's 100-day approval rating before he even took office -- Unanimous AI took aim at the 90th Academy Awards, providing results to journalists last week for publication.
After a satisfying meal of Chinese takeout, you absentmindedly crack open the complimentary fortune cookie. Glancing at the fortune inside, you read, "A dream you have will come true." Scoffing, you toss the small piece of paper and pop the cookie in your mouth. Being the intelligent, well-reasoned person you are, you know the fortune is insignificant--no one can predict the future. However, that thought may be incomplete.
For our lab, we began digging into the application of machine learning beginning in 2014, exploring its application in everything from supply chain optimization to factory automation and retail, including predicting terrorist attacks. Where we can apply knowledge for a given domain and weave it into a learning algorithm for the sake of doing non-deterministic pattern recognition, machine learning grounded in only statistics (not symbology, logic, or evolutionary) can readily improve upon guessing. Learning from a productive data set, and where overfitting is sufficiently avoided or mitigated, a learning algorithm can recognize patterns and generalize to cases not yet encountered. Such explorations for us started more than two years ago with SAP NS2 and ConvergentAI (formerly AxxonAI) where we find the project team's proof-of-concept (POC) results remain relevant today, but applicable to problem-solving the same way in other domains. While conceptually different, a strong relationship exists between machine learning and analytics where machine learning uses data and learning algorithms (supervised and unsupervised) to optimize a model based on performance and prior experience.
Listening to current retail technology discussions, it's safe to say that artificial intelligence is the early favorite for buzzword of the year, with countless taglines promising unprecedented productivity improvements based on AI. Advanced forecasting is often cited as one of the top areas where AI holds great promise – but how do you separate the hype from the reality?