A writer-conducted market study back in 2014 stated that 20% of the US population reads the popular genre of science fiction. This is a mode of literature in which people explore the dreams and possibilities of space, time travel, tech, and much more. Often within sci-fi books, you’ll see technology beyond what we can create now, anywhere from intergalactic transportation to robots that take over the world. Well, what if I told you the latter, is seemingly becoming more imminent?
Machine learning is a branch of AI and computer science that uses data and algorithms to imitate how humans learn, gradually improving its accuracy with the help of humans, by feeding it more and more data. Machine learning algorithms learn directly from the data, without relying on the help of a predetermined equation as a model. This allows it to interpret data given to it later on, which it has not yet seen, more accurately, because it learns from the previously given information. This is similar to how the human brain works.
“Machine learning is changing, or will change every industry, and leaders need to understand the basic principles, the potential, and the limitations,” said Aleksander Madry, director of the MIT Center for Deployable Machine Learning
Machine learning has become increasingly important, as the volume of data generated by organizations continues to grow. Its ability to automatically learn and grow from past data makes it valuable for businesses and researchers when solving complex problems and making data-driven decisions.
Deep learning is a subset of machine learning that uses artificial neural networks to extract knowledge and understanding from raw input data. The term “deep” refers to the use of multiple layers in the network, which can be either:
Supervised: A set of examples, known as the training set, is submitted as input to the system/machine during the training phase, and each input is labeled with a desired output value. Supervised learning is usually used for situations like image classification, or object detection, in which the objects are already known and can be labeled, so the network is only being used to reduce the error rate
Semi-supervised: The goal of semi-supervised learning is to learn a function that can accurately predict the output variable based on the input variables, similar to supervised learning. However, unlike supervised learning, the algorithm is trained on a dataset that contains both labeled and unlabeled data. Semi-supervised learning is particularly useful when there is a large amount of unlabeled data available, but it’s too expensive or difficult to label all of it. This method can be used to train an image classification model using a small amount of labeled data and a large amount of unlabeled image data.
Unsupervised: The machine is given unsorted information and is tasked with grouping it according to similarities, patterns, and differences without any prior training with data. The objective of unsupervised learning is to have the algorithms identify patterns within the training data sets and categorize the input based on the patterns the system identifies without help. Unsupervised learning is well-suited for processes such as customer segmentation, or exploratory data analysis. Unsupervised learning algorithms can classify, label, and group the data points contained within datasets without requiring any external guidance in performing that task
Deep learning is an important element of data science, including statistics and predictive modeling, and it is extremely useful for data scientists who are tasked with collecting, analyzing, and interpreting large amounts of data. Deep learning models can recognize complex patterns in pictures, text, sounds, and other data to produce accurate insights and predictions. Deep learning technology drives many AI applications used in everyday products, such as digital assistants, voice-activated television remotes, fraud detection, automatic facial recognition, self-driving cars, virtual reality, and more.
Now let’s take a look at an example:
AI art is the result of a collaboration between the artist and the AI system, with the level of accuracy to the wishes of the artist varying depending on the project and quality of the data fed to the AI. Artificial intelligence can help with various aspects of the creative process, such as generating color palettes, suggesting compositions, or even creating rough sketches based on a set of parameters. Using deep learning, images are generated based upon the given input, to create something out of the program’s data it was given during training. Generally, this process will use supervised learning
Developing a Brain of Its’ Own
Let’s bring this all back to the initial question I asked at the beginning. Is the once-fanatical thought of AI taking over the world coming true? And how imminent is it? Well, within the world of machine learning, this thought seems to be more and more probable, with the certain possibilities that unsupervised learning could lead to. As we are feeding unsorted data into the AI and giving it the full responsibility to decide what belongs where, based fully on the trust that AI understands and is able to learn from raw data without laws, there’s a chance it could learn things, and interpret them differently from how we would. See where this is going?
“It seems probable that once the machine thinking method had started, it would not take long to outstrip our feeble powers… They would be able to converse with each other to sharpen their wits. At some stage, therefore, we should have to expect the machines to take control.” – Alan Turing