Artificial intelligence has for a long time been an all-powerful figment of our imagination, but with recent technological advances, this figment is becoming a reality. It seems that we are constantly hearing about, and subsequently fearing, how AI will replace humans in the working world and in the not too distant future. But perhaps this perceived dystopia isn't as bad as what we hear about and, in actual fact, is a utopia waiting to happen.
Progress in deep learning has improved computer vision, language processing, and speech, as well as the ability for machines and software to seek a reward and maximize performance, says Wayne Thompson, chief data scientist at SAS: "As a result we will see a new generation of machines that can see the world, hear and read human languages, communicate to humans, and control themselves both mechanically and behaviorally, in an unprecedented way."
Somewhere during mid-2016 Facebook & Kik launched their chatbots and the whole world went crazy about it. Ever since the launch, Facebook has become home to more than 100,000 active chatbots and kick to more than 20,000. There are other players too. Like IBM and Microsoft who have released exclusive platforms to build chatbots.
With businesses eagerly pursuing big data analytics, it only stands to reason that they'd look for the methods and strategies that will best help them get the most out of it. There are many ways to perform analytics, and each will change depending on the type of business and what insights organizations want to gain. With this variety, big data has clearly grown in popularity, with a recent survey from Gartner showing that 75 percent of companies are either currently investing in big data initiatives or plan to do so within the next two years. Even so, many companies have found utilizing their big data to be a difficult and at times arduous process. The traditional analytical approaches have trouble managing the vast volumes of data businesses can now collect, and as a consequence, the results aren't always the most accurate. That's not to mention how long it takes to get those results in some instances. To combat these issues, many organizations are turning to machine learning techniques, with promising outcomes hinting at its potential.
From the outside, data science is often thought to consist wholly of advanced statistical and machine learning techniques. However, there is another key component to any data science endeavor that is often undervalued or forgotten: exploratory data analysis (EDA). At a high level, EDA is the practice of using visual and quantitative methods to understand and summarize a dataset without making any assumptions about its contents. It is a crucial step to take before diving into machine learning or statistical modeling because it provides the context needed to develop an appropriate model for the problem at hand and to correctly interpret its results.
If you make your living as a corporate slave, you're probably familiar with business travel. This is where companies like Amex fleece people provide a valuable service by selling corporate flights and hotels at the highest rates possible. It seems like at no point in the transaction do they ever consider trying to save companies money but instead, try to sell the most expensive tickets and accommodations they can find. Generally, that has been our experience dealing with corporate travel agencies (or any travel agent for that matter).
Machine Learning (ML) algorithms are embedded in the fabric of much of the technology we use every day. ML innovations spanning computer vision, deep learning, natural language processing (NLP), and beyond are part of a larger revolution around practical artificial intelligence (AI). Not autonomous robots or sentient beings but an intelligence layer baked into our apps, software, and cloud services that combines AI algorithms and Big Data under the surface.