Not only is Machine Learning earning specialists a good salary, but algorithms are being used to make money. It is gaining notoriety for solving just about any problem, dramatically improving technology, breaking barriers, and even worrying some of us. Making money via machine learning tends to revolve around the ability to predict, target, and produce.
Before diving into actual examples, it is important to recognise how essentially all innovation ahead of us will involve some sort of machine learning. Take driverless cars. The first company to succeed in a widespread production of driverless cars will dramatically change the economy forever, and of course, be very rich. The reason it’s so difficult now is because roads are far from perfect, rules are inconsistent, and traffic is a mess. Machine Learning is about understanding imperfection in an innovative way, therefore suiting the problem very well. In the example of cancer, Microsoft teamed up with Facebook, and have boldly claimed that they will ‘solve’ cancer in ten years. Everyday hospitals are building on datasets that describe previous fatalities, with relevant biological attributes then using AI to notice patterns we don’t have an eye for.
There are obviously many companies that would benefit from the ability to predict. Knowing what a customer wants next can be essential for growth and stability. The following fascinating examples, demonstrate how machine learning can be used to make money ranging from personal home benefits, all the way to big time company development.
Recently partnered, and using natural language processing models to identify sales opportunity in social media. However, social media is a specific, as they also work with sites who communicate different search results. Understanding what people want via language in mass can have a huge impact on a company’s efficiency. Additionally, customers are given more opportunity to see what they want, because without asking, machine learning algorithms are able to show them.
You guessed correctly if you expected to see a material about of trading. PyTrader is a cryptocurrency trading robot, naturally for personal use. It will mine through different currencies, and make automated trades if it passes a threshold of confidence in a prediction.
It clearly works relatively well, however is said to have problems in the long run. It also provide many different classification models, such as Nearest Neighbors, Linear SVM, Decision Tree, Random Forest (less relevant), AdaBoost, and more.
Similar to PyTrader, however this project uses neural networks to look at chart data. Hoping to ‘exploit’ bitcoin price patterns, and make trades when the system is confident. Interestingly, this project looks at charts visually, not numerically. This is exactly how our own brain may try and judge the direction of a chart based on it’s passed shape. You end up with a rather dynamic ability to place trades, and can hope for a success rate of up to 70%.
Sigmoidal are a generalised AI company, hired by firms to essentially make them money. If it’s learning what customers want, or saving time on customer queries by implementing chatbot technology, they are the kind of company highed to improve the efficiency of another company, via machine learning.
As you must have expected, a hedge fund that use nothing but machine learning to predict prices of the stock market. Using a variety of different traditional and non-traditional methods within AI, they are always creating new ways of combining the stock market and machine learning.
In this case, Stratagem are selling insights that are generated by their machine learning models. These insights are the predictions of sport results. At the moment they have three applications: StrataBet, StrataPro, and StrataTips. StrataPro offers live intel during a match, allowing predictions on many specific plays in a game. StrataBet is specific to football, and offers less capable algorithms.
We are all expecting machine learning to get better and better. Perhaps, not too far down the line, there will be a new form of neural network that competes with our own, and even surpasses our own intelligence. Maybe, machine learning itself will come up with it’s own way to make money, by observing how money is made in all industries. This extreme level of intelligence is actually said to be a threat to our global economy, unless it can also learn how not to be one. Either way, we are not there yet.
My name is Caspar Wylie, and I have been passionately computer programming for as long as I can remember. I am currently a teenager, 17, and have taught myself to write code with initial help from an employee at Google in Mountain View California, who truly motivated me. I program everyday and am always putting new ideas into perspective. I try to keep a good balance between jobs and personal projects in order to advance my research and understanding. My interest in computers started with very basic electronic engineering when I was only 6, before I then moved on to software development at the age of about 8. Since, I have experimented with many different areas of computing, from web security to computer vision.