Take Machine Learning and Deep Learning, both of which make an appearance whenever a discussion of AI begins. What defines Machine Learning? How does it work? Isn't Deep Learning just another form of Machine Learning? We thought it made sense to try and simplify answers to those knotty questions. So, here goes.
What exactly is Machine Learning?
Let's start by tackling what AI means. Without beating about the bush, it refers to a field of computer engineering that focuses on creating systems capable of gathering data and making decisions and/or solving problems. Machine Learning also works towards getting computers to learn and act like humans but isn't the same as AI. It improves learning over time by being fed data and information in the form of real-world interactions and observations.
The definitions of Machine Learning differ depending upon whom you ask. Some say it is about using algorithms to parse data and learn from it to make predictions. Others believe it is about computers acting without being explicitly programmed or relying on rules-based programming. Still others define it as generalizing from examples to perform certain tasks. Machine Learning also refers to learning processes and how computer systems can automatically improve with experience.
How Machine Learning has evolved
Machine Learning began with the premise that computers could learn without being programmed, based on pattern recognition. It has evolved along with computing technologies, slowly shifting focus by exposing models to new data for them to independently adapt and learn from previous computations to produce repeatable, reliable results. Interestingly, how we define it has also evolved based on our ability to apply complex mathematical calculations automatically to big data (extremely large data sets) faster than ever before.
The goal is to get machines to learn a task, and the process depends upon the kind of task, as well as the kind and amount of data available. This lies at the heart of everything from self-driving vehicles and online recommendations to fraud detection and social media listening tools. There is a common misconception that Machine Learning and automation are the same, but what differentiates them is processing power that enables the quick highlighting or finding of patterns in big data that may be missed by human beings. This makes problem-solving and informed inferences possible.
The limitations of Machine Learning
It all boils down to data, which is the biggest advantage of Machine Learning as well as its biggest limitation. A common problem is overfitting when a bias is exhibited by a model towards training data and new data is not generalized. Then there is variance when new data leads to the learning of random things. Dimensionality occurs when algorithms with more features work in higher or multiple dimensions, making understanding the results more difficult.
The successful testing of training data does not guarantee the success of a Machine Learning algorithm. Models are usually tested only on reserved data set aside, followed by the whole data set. Feeding a learning algorithm more data can solve more problems, but also lead to issues with scalability when an algorithm doesn't get enough time to learn new data.
What is Machine Learning used for?
If an industry works with large amounts of data, chances are it can benefit from Machine Learning because of the insights to be gained in real time. Machine Learning has made huge advances in healthcare, helped marketing and sales departments by generating recommendations, analyzing buying history and creating personalized experiences, and helped government agencies mine multiple sources of data for increased efficiency and significant savings. It has also helped the oil and gas industries and enabled the transportation industry to make travel more efficient.
Financial services industries stand to gain from Machine Learning in all kinds of ways, starting with its ability to help detect fraud and minimize identity theft. Banks can use data to identify important insights and prevent fraud, while commercial and investment banks can identify investment opportunities. As for wealth management organizations, they can use it to help investors know when to trade, identify clients with high-risk profiles, or pinpoint warning signs of fraud.
Where does Deep Learning come in?
Deep Learning is a Machine Learning technique that tries to teach computers how to learn by example, which comes naturally to human beings. It has evolved in recent years, and often reaches state-of-the-art accuracy that can exceed human-level performance, making it responsible for everything from driverless cars to voice control. The technique involves teaching a computer model to perform classification tasks directly from images, text, or sound, using a large set of labeled data and neural network architectures that contain many layers.
Understanding how Deep Learning works
Deep Learning came into being in the 1980s, as a theory, and slowly became popular with the availability of high-performance Graphics Processing Units (GPUs), cloud computing and large amounts of labeled data that reduced training time. Recognition accuracy is what gives it an edge, enabling it to outperform humans in tasks such as classifying objects in images.
The use cases are plenty, from driverless cars to medical research and electronics. Driverless cars, for example, use it to detect signs, traffic lights, and pedestrians automatically, while the aerospace and defense industry uses Deep Learning to identify objects or unsafe zones from satellites. In medical research, it has been used to detect cancer cells, while industrial automation uses it to improve worker safety by detecting when people or objects are within an unsafe distance of heavy machinery. In electronics, it helps in automated hearing and speech translation as well as home assistance devices.
Machine Learning and Deep Learning: What's the difference?
Deep Learning is a specialized form of Machine Learning, which is what gives rise to the confusion between the two. There are differences though, starting with how workflows are constructed for each. In Machine Learning, a workflow starts with relevant features manually extracted from images to create a model categorizing the objects in the image. A Deep Learning workflow has relevant features automatically extracted from images and subjected to end-to-end learning where a network is given raw data and a task and learns how to do it automatically.
While Machine Learning algorithms plateau at a certain level, Deep Learning algorithms scale with data and continue to improve with the size of data. Techniques and methods are chosen for Machine Learning based on the size of data being processed, the problem that needs to be solved and the application. A Deep Learning application requires large amounts of data to train the model, as well as GPUs for the rapid processing of this data.
To find out more about how AI, Machine Learning and Deep Learning can help you in the financial services industry, contact us today.