You might not know it, but machine learning has become a major fact of daily life. From the speech recognition technology used for Google translate to the massive amounts of unlabeled data aggregated within a Google search a social media post, to the artificial intelligence programs used for Uber, Siri, Amazon’s Alexa, and autonomous vehicles; machine learning models cannot be avoided.
Every time we surf the internet, we risk drowning in a veritable sea of information while swimming around in an assortment of browsers: Google, Firefox, Safari, Internet Explorer, etc. With all of this new data flooding our computer vision, there needs to be a system that organizes these strange new realms: The surface web, the dark web, the deep web, the IoT, and the IoE. This is where machine learning comes in.
Machine Learning Defined
Of course, it is tempting to just nod your head and say that machine learning is the wave of the future and that you are such a big fan of it. It might be understandably embarrassing to confess to being a dinosaur and simply having to ask, “What is machine learning?”
Machine learning is a form of artificial intelligence based on algorithms learning through being fed massive amounts of training data. So much training data is used that it is stored in large datasets which are organized in an artificial neural network. This deep neural network is modeled on the neurons within the biological neural network of the human brain. Machine learning is a relatively broad category of artificial intelligence. Elements and types of machine learning include supervised learning, unsupervised learning, reinforcement learning, and deep learning.
A machine learning model works when an engineer such as a data scientist has a set of algorithms called a test set. The data scientist tests the algorithms to see how well they perform a new task, test data. The algorithms that cannot perform the task well are eliminated. The remaining algorithms are used to model new algorithms and that is how algorithms are “trained.” This group of algorithms that are used to “train” new algorithms is called the training set and the information in the training set is called training data.
Before deep learning, data scientists had to run all of the test sets, replace failed algorithms, and create and run training sets, only to repeat the cycle all over again. This is called supervised learning. Not all machine learning is deep learning and there is a class of machine learning that only engages in supervised learning.
However, what separates deep learning from all other machine learning is the use of unsupervised learning. Through unsupervised learning, deep learning, or unsupervised machine learning; the data scientist no longer has to personally test and train each algorithm. After setting up a few test sets and training sets, the data scientist can create algorithms that are actually testing and training other algorithms.
This use of deep learning within deep neural networks has really accelerated the evolution of artificial intelligence since algorithms can run test sets and training sets and build artificial neural networks a lot faster than humans can. Deep neural networks and deep learning algorithms and have opened up a whole plethora of applications. Pattern recognition technology has allowed for facial recognition technology for security systems like Amazon’s Ring and autonomous vehicles for companies like Tesla.
Speech recognition technology has enabled Google translate and smart homes like Amazon’s Alexa. Logistic regression has allowed for the prediction of various outcomes based on statistical models based on machine learning models. Social media has employed chatbots based on this technology.
Now is truly an exciting time to be a computer programmer or data scientist. The new challenge will be finding a place for human intelligence in a new world of artificial intelligence.
