Introduction

In recent years Artificial Intelligence (AI) has rarely been far from the headlines and its influence now reaches into nearly every aspect of our day to day lives. Many experts believe we are at the start of the fourth industrial revolution , where AI, Big Data, quantum computing, and other technologies are changing the way we live and disrupting almost every business sector, all at an unprecedented and ever-accelerating pace.

Staying ahead in this exceedingly face paced environment requires the ability to make informed decisions, one way to prepare to act quickly and keep up is to know the AI and Big Data essentials presented in this guide.

Background

The research and the foundations of Artificial Intelligence date back to the 50’s and 60’s when the US Department of Defense began training computers to mimic basic human reasoning. The work done at the Defense Advanced Research Projects Agency (DARPA) paved the way for the automation and formal reasoning we see in computers today.

The exponential pace at which AI has advanced over the past decade can be attributed to the proliferation of high-performance computing and the explosion of Big Data. In essence, Big Data has become the fuel and high-performance computing the engine and together they are driving development of AI applications across all industries, including in financial services.

High-Performance Computing is now ubiquitous, inter-connected networks and cloud computing allow data scientists and engineers to tackle complex problems and invent powerful solutions that require processing speeds and information beyond our technological grasp just a decade or so ago.

Big Data is being generated all around us every second of the day and the rise of social media, smartphones, and the internet of things (IoT) have added sensors to everything from washing machines to oil tankers. The result has been an explosion of data; to put it into perspective 90% of the world’s data was created in just the past 2 years and by 2025 the world’s data sphere is expected to reach 1000x what it was in 2005.

Big Data – defined

Big Data describes the large volume of data – both structured and unstructured – that is now collected and stored each day.

Big Data includes information generated by individuals (social media posts, product reviews, internet search trends, etc.), data generated by business processes (commercial transactions, credit card receipts, online orders, etc.), as well as data generated by sensors (satellite images, foot and car traffic patterns, shipping routes, etc.).

Why is big data important?

By analyzing Big Data, we can extrapolate new insights and improve decision making by augmenting what we as humans are capable of analyzing.

For financial services and particularly investors, as the amount of data available continues to grow, investors must find ways to incorporate Big Data into their investment analysis so as not to be at an informational disadvantage.

AI – defined

Artificial Intelligence is a computerized system that can do things normally requiring human intelligence, such as visual perception, speech recognition, interpretation of language, and decision-making.

The AI systems of today exhibit intelligence in performing a specific task or a field of study, rather than being generally intelligent by human standards.

The AI of today was developed by programmers and data scientists to solve specific complicated and data intensive problems without direct human programming. By taking in feedback and adjusting outputs (see Machine Learning), AI “learns” from experience and can perform frequent, high-volume, computerized tasks reliably and without fatigue. These AI systems

Why is AI important?

Specialized AI are helping doctors match breast cancer patients with clinical trials, scientists discover the genes that cause Lou Gehrig’s disease, drivers stay safe on the road, and investors navigate the complex global equity markets.

Machine Learning – defined

Machine Learning is a form of data analysis where computers identify patterns in data and then make determinations or predictions about something in the world.

So rather than hand-coding software routines with a specific set of instructions to accomplish a particular task, the machine is “trained” by humans using large amounts of data and algorithms that give it the ability to learn how to perform the task.

Why is Machine Learning important?

Machine Learning is how AI systems “learn,” allowing them to operate continuously and make decisions on new data, without additional human intervention or input.

Deep Learning – defined

Deep Learning is a type of machine learning, inspired by the structure and function of the human brain, that can process a wide range of data, requires less data human “training”, and can produce more accurate results than more traditional machine-learning systems.

In deep learning, interconnected layers of software-based calculators known as “neurons” form a neural network. The network can ingest vast amounts of input data, make determinations or predictions based on the data, learn if its determination is correct, and use what it has learned to make determinations about new data. For example, once it learns what an object looks like, it can recognize the object in a new image.

Why is Deep Learning Important?

Deep Learning allows AI systems to tackle more complex problems such as recognizing cancer cells in MRI images or analyzing how geopolitical events will affect the stock market.

Natural Language Processing – defined

Natural Language Processing (NLP) is a form of AI that allows computers to understand language, both written and voice, and turn this understanding into actionable data – think Siri and Alexa.

Why is NLP important?

NLP helps AI systems communicate with humans in their own language and enables AI systems to analyze the millions of digital articles and social media posts generated each day without fatigue and in a consistent and unbiased manner.