What’s the Difference between Big Data Artificial Intelligence
In the battle of buzzwords, it would be hard to defeat Big Data and Artificial Intelligence. Each occupies significant space in modern print, thought, and conversation, and while big data and AI are different from each other in strict definition, they remain complementary – symbiotic, even.
More formally defined by renowned research and advisory company Gartner as “high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation,” big data refers to the idea that almost everything we do – the purchases we make, the internet searches we type, the art we enjoy, the security footage from a convenience store, a photograph – is data that can be collected, examined, and (theoretically) monetized by those who can extract the right insights.
Volume, velocity, and variety – sometimes characterized as the three V’s – are the parameters that define how big data is processed. Looking at big data means examining “high volumes of low-density, unstructured data”, says Oracle in a primer on their website. It requires the capability to receive and react to information at increased velocity, “[operating] in real time or near real time.” It also means having the ability to deal with a wide variety of data, structured and unstructured, that “[requires] additional preprocessing to derive meaning and support metadata.”
Artificial intelligence is an amalgam of different fields (like computer science, logic, neuroscience, psychology, and more) with the goal of creating automated systems that can perform tasks previously requiring human intelligence. AI does so by analyzing vast amounts of data to recognize patterns, in turn using that data to make quick, efficient decisions. It can even “learn” (another buzzword: machine learning) over time through that analysis, retaining insights from information it has examined and using them to glean new ones.
Advances in technology have eased the process of both amassing data and learning from it. AI can now perform natural language processing, which involves analyzing and learning the meaning of human speech; speech and text recognition are advancing in leaps and bounds. It is used in robots in many homes (think Siri or Alexa) and in the systems governing self-driving cars – what feels like the tip of the iceberg.
This ability to pore through, and derive insight from, massive quantities of information means AI is a natural fit for working with big data. AI is typically programmed to perform a specific task –identifying discrepancies in MRIs, for example – at a speed far greater than any human. Once big data is collected and structured, AI reacts to and makes sense of it. The more information available to analyze, the better an AI system works. Big data and artificial intelligence need each other to exist.
If You’re Wondering When A.I. Will Start Making Market Predictions…
Guess what – it already is. 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 generates 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:
- Finding stock patterns in the market
- Finding trends in the stock market
- Testing portfolios to see if they are well-diversified
- Back-testing statistics to see how different stock patterns generated trading results
- Making Predictions for price movements in the future, with “A.I. Rank” and level of confidence in the trade.
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.
Contributor
Financial analyst and market blogger with expertise in equity research, fundamental analysis, and macroeconomic trends. I regularly publish coverage on individual stocks, ETFs, and sector developments — combining rigorous financial analysis with clear, engaging writing for a broad investment audience.
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