Machine learning and artificial intelligence isn’t exactly new. In fact, artificial intelligence was a term used in a 1956 research paper by John McCarthy, a math professor at Dartmouth.
“every aspect of learning or any feature of intelligence can in principle be so precisely described that a machine can be made to simulate it”
So if it has been around so long, why does it feel like it has suddenly become a hot topic?
First of all, in the past 20 years there has been an exponential growth in computing power and it has become significantly cheaper to use.
The real game changer has been the explosion in the creation of data which is revolutionising many industries, not just financial markets and the hedge fund industry. To put it in to perspective and convince you that big data is not just a passing phase, consider the following statistic.
More data has been created in the past two years than in the entire previous history of the human race.
This makes it possible to do many things that previously could not be done: discover business trends, prevent diseases, combat crime or decipher previously unnoticed patterns in global commodity markets.
The expansion in data has come from unstructured data such as images, video, tweets, news articles and forum posts. Think of that as the data that has little organisation. Twitter tweets is one such example, every minute an astonishing 350,000 tweets are sent per minute. Vast amounts of value can be hidden in unstructured information if you know where and how to look.
Enter machine learning and artificial intelligence. While these words tend to be thrown around interchangeably they are not the same thing.
Machine learning is a method of data analysis that uses algorithms that iteratively learns from data, allowing computers to find hidden insights without being explicitly programmed where to look. In simple terms, it’s a method of teaching computers to make and improve predictions based on data.
Artificial intelligence is a state of machine when it can make decisions just like a human. It’s the broader concept of making a computer smart. Examples are visual perception, speech recognition, decision making and translation between languages.
What is Deep Learning?
Recent improvements in data science are now providing the ability for sophisticated quantitative hedge funds to parse, classify and profit from the previously hidden patterns in data. One such improvement is the exciting area of research known as deep learning which is a subfield of machine learning that focuses on using neural networks – algorithms that mimic the function of the human brain and decision making.
What does it mean for the hedge fund industry?
The value of the traditional discretionary investors historical ‘information’ edge is being eroded giving rise to quantitative hedge funds who have the power to cut through the noise of financial markets, providing a clearer perspective on future market dynamics.
Imbue Capital sits at the forefront of this rapidly changing industry. We stand apart with a unique combination of financial theory, global markets experience, artificial intelligence and data science.