Artificial intelligence (AI) is a lot like life’s relationships. Sometimes what you put into it is pretty straightforward, leading to the output or outcome that you wanted. Other times, let’s just say, the process gets a bit more convoluted and sometimes the outcome isn’t exactly what you envisioned. In other words, you may input the same into both relationships, but different paths lead you to different results. Nevertheless, both are learning processes. In the AI world, this is called supervised and unsupervised deep learning–and like most relationships, the shortest distance between what you input to what you get as output isn’t always the proverbial straight line.
What is Deep Learning?
Before we delve into what supervised and unsupervised deep learning is, you should know that deep learning evolved from a process called machine learning. Machine learning employs an algorithm, or set of rules, that creates output without specific programming. Think about how social networking mines data from your posts. For instance, you go out to eat with your friends at your favorite sushi place and share facts online about your experience–what you loved, found distasteful, photos, would you return–once you input these into your social network, an algorithm picks up tidbits about your input to extract patterns about what you like, don’t like, even what you look like based upon your pictures. The algorithm may discover that you are around 23 years old, eat out at this particular type of restaurant twice a month with your friends and like California rolls over eel sushi. It then sends you ads based upon that data. Machine learning iteratively gleans information about input despite not being told how to do so or where to look for that information.
Deep learning kicks it up a notch. It takes your input, finds that it can either categorize it without issue (supervised) or clusters unlabeled information, attempting to categorize it so that it makes sense (unsupervised), before taking that input and creating some sort of viable output. It’s a layered architecture making sense of data that can be quite abstract from one layer to another. That’s how deep learning emulates the multi-faceted complexity of the human brain–its neural pathways processing copious amounts of information that doesn’t make sense until it does (or not).
Supervised Deep Learning: The KISS Pathway That Leads To The Expected
What happens when your supervisor’s hanging over your shoulder at work? Like most, it drives you batty, so you tend to take the path of least resistance to find the most non-challenging way to get your job done quickly and still meet the expectations of your supervisor, right? Let’s say that particular supervisor trained you to process credit applications. Said supervisor knows what’s in those applications and that the expected outcome of any application is approval or not approval. You learned from your training set how to function in the best way to get to the desired outcome, i.e. the results that your supervisor needs. Supervised deep learning is like that. We humans tend to process in a specific hierarchy: we take in life’s input and based on our experiences (training), we organize that input so that our prior knowledge can make sense of it, process it to some expected outcome. Supervised deep learning belongs to that Keep-It-Simple-Stupid (KISS) pathway, that literal path of least resistance leading to some fulfilled expectation.
Supervised deep learning is well suited for decision-making: take our credit card example for instance. The bank takes your application and runs it against its categories of risk before taking action for or against approving you. Here’s the procedural gist:
Application is input from customer
The bank inputs data from application into the algorithm
The algorithm notices from past applications that data follows certain pathways (modeling)
For example: marital status–single, married, divorced, widowed all have a yes or no answer
The algorithm takes that application data, the yes or no answers, as determined by the bank and follows its flowchart (pathway rules)
Data flows through that pathway as the algorithm decides which of the primary categories of approved and not approved the data belongs in
The expected decision of approved or not approved is rendered
The customer is approved and is a happy camper or is not approved and wonders how to fix his credit score (had to throw that in).
Supervised deep learning is more than your typical lights on, lights off binary function. The algorithm classifies criteria into the bank’s expectation of risk, processing that risk into one of two decisions. This method of classification is known as binomial classification (two choices) or multi-class (more than two choices).
Unsupervised Deep Learning: An Exploratory Journey To Figuring Out the Unknown
If supervised deep learning is a path to expected output, unsupervised deep learning takes that same input and attempts to make sense of it before eschewing some output. Let’s take a trip to the art museum with your best friend as an example. You both become captivated with a painting of a rose. One of you sees it rather literally, the other sees it figuratively. To you, a rose is a just a rose and you want to move on to the Van Gogh exhibit. To your friend that rose is yellow when it should be red and your friend cannot figure out why the painting denotes friendship and not love. There’s no Van Gogh until there’s ready to go–and that’s not happening until your friend muses about that rose and why her current relationship is hanging on that museum wall.
Unsupervised deep learning has no target, no expectation from the input. It relies on exploring layers of possibilities to get to some conclusion. While you can move on to the Van Gogh exhibit, your friend struggles to figure out how to classify all the many pathways friendship and love can take someone from convolution to happy life and how one can learn from their mistakes.
Decision Time: If You Knew Then What You Know Now
Humans are subjectively sentient creatures with decision-making processes that cater more to the unexpected (unsupervised deep learning) than to the expected (supervised deep learning). Computers don’t have the human factor. They don’t have experiences. They just have data sets, functions, and “thinking” based on layers of pooling information together in either ordered or non-ordered ways.
As neural nets and AI become more complex, so do the deep learning algorithms. You can choose among supervised, unsupervised or a combo-pack of deep learning to tackle anything from credit approvals to the complexities of mind-boggling, robotic data sets. Remember the social networking example? When you uploaded images, something called Convolutional Neural Networks (CNN) picked out traits before it came to the conclusion that you around 23, pooling together relevant data: restaurant, friends laughing, friends frowning, facial recognition, background recognition. Combine and categorize those subgroups and your image spoke volumes about who you are and how you live. Imagine what they’d unpack from what you say on an uploaded video (Recurrent NN)? Yet, sometimes life has to unfold unsupervised by knowns, reconstructing (autoencoding) the data-driven universe while self-organizing maps translate often nebulous data patterns into two-dimensions (think topographic maps) that allow you further muse as to why that rose by any other name is just backpropagation (wink).