Artificial intelligence (AI) technology is developing rapidly. Leaps in applicability and efficacy mean that industries around the world are looking to integrate it into their businesses. With risk lower than ever, and the technology now in place to achieve real, tangible results, adoption is soaring. But it is important to understand the difference between data analytics (coupled with predictive analytics) and machine learning to use the technology effectively.
Data analytics are ubiquitous at this point – most readily-available products make reams of data available to its users. Marketers, for example, have their pick of Google Analytics, newsletter services like MailChimp, and other dashboard-based systems to ensure data is never in short supply. Data mining can deliver raw numbers, but it does not necessarily provide actionable insights.
Structure is necessary to taking abstract information and extracting commonalities, like averages, ratios, and percentages. Aggregation makes it possible to find patterns and explore variables to deliver impactful, targeted analysis. But most data analysis is descriptive, not predictive – it requires information about something that has already happened to yield insight. It is also human-based, as people propose assumptions, then use data to test their validity.
Predictive analytics collects data which is used to test and predict future outcomes. Humans engage with data to validate patterns, then formulate and test hypotheses based on the assumption that future events will follow the same patterns. Predictions are limited by various constraints on human analysts, like volume of information, time limitations, and cost considerations. Going into detail means increased cost or time investments, which limits the predictive scope.
Machine learning is predictive analytics on steroids. Humans set the parameters, then employ AI systems to automatically make and test assumptions and learn from the results – all without additional human interaction. Machine learning tests and retests data at speeds that are impossible for humans to replicate, which makes predictive analytics more efficient and, by extension, more cost-effective.
When systems can learn quickly and autonomously, data can be put to work with a level of specificity that humans simply are not capable of. That means stronger insights and a greater level of certainty as to their validity.
Machine learning can dredge up new knowledge from existing data, automate simple tasks that previously required a human to complete, and open up new types of data, like audio, video, and images, to analysis. It allows users to ask specific questions on a massive scale – a boon for businesses of all types.
How the A.I.-Driven Revolution is Happening in Finance
Hedge funds and large institutional investors have been using Artificial Intelligence to analyze large data sets for investment opportunities, and they have also unleashed A.I. on charts to discover patterns and trends. Not only can the A.I. scan thousands of individual securities and cryptocurrencies for patterns and trends, and it generate trade ideas based on what it finds. Hedge funds have had a leg-up on the retail investor for some time now.
Not anymore. Tickeron has launched a new investment platform, and it is designed to give retail investors access to sophisticated AI for a multitude of functions:
And much more. No longer is AI just confined to the biggest hedge funds in the world. It can now be accessed by everyday investors. Learn how on Tickeron.com.