Neural networks, as the name suggests, are modeled on neurons in the brain. They use artificial intelligence to untangle and break down extremely complex relationships.
A supervised neural network, at the highest and simplest representation, can be presented as a black box with 2 methods learn and predict as following:
A neural network is a network of equations that takes in an input (or a set of inputs) and returns an output (or a set of outputs). The learning process takes the inputs and the desired outputs and updates its internal state accordingly, so the calculated output get as close as possible to the desired output. The predict process takes an input and generate, using the internal state, the most likely output according to its past “training experience”. That’s why machine learning is called sometimes model fitting.
With their brain-like ability to learn and adapt, Neural Networks form the entire basis and have applications in Artificial Intelligence, and consequently, Machine Learning algorithms.
Neural networks have advanced so much that there are now several types of neural networks, but below are the three main types of neural networks that you’ll probably hear about often.
Artificial neural networks, or ANNs, are like the neural networks in the images above, which is composed of a collection of connected nodes that takes an input or a set of inputs and returns an output. This is the most fundamental type of neural network that you’ll probably first learn about if you ever take a course.
A convolutional neural network (CNN) is a type of neural network that uses a mathematical operation called convolution. Wikipedia defines convolution as a mathematical operation on two functions that produces a third function expressing how the shape of one is modified by the other. Thus, CNNs use convolution instead of general matrix multiplication in at least one of their layers.
Recurrent Neural Networks (RNN)
Recurrent neural networks (RNNs):are a type of ANNs where connections between the nodes form a digraph along a temporal sequence, allowing them to use their internal memory to process variable-length sequences of inputs. Because of this characteristic, RNNs are exceptional at handling sequence data, like text recognition or audio recognition.
“For companies looking to predict user patterns or how investments will grow, the ability to mobilize artificial intelligence can save labor and protect investments. For consumers trying to understand the world around them, AI can reveal patterns of human behavior and help to restructure our choices,” Terence Mills, CEO of AI.io and Moonshot, writes in a piece for Forbes.
Neural networks are powerful algorithms that have led to some revolutionary applications that were not previously possible, including but not limited to the following:
Image and video recognition: Because of image recognition capabilities, we now have things like facial recognition for security and Bixby vision.
Recommender systems: Ever wonder how Netflix is always able to recommend shows and movies that you ACTUALLY like? They’re most likely leveraging neural networks to provide that experience.
Audio recognition: In case you haven’t noticed, ‘OK Google’ and Seri have gotten tremendously better at understanding our questions and what we say. This success can be attributed to neural networks.
Autonomous driving: Lastly, our progression towards perfecting autonomous driving is largely due to the advancements in artificial intelligence and neural networks
Neural Networks and their learning algorithms find extensive applications in the world of social media. Let’s see how:
As soon as you upload any photo to Facebook, the service automatically highlights faces and prompts friends to tag. How does it instantly identify which of your friends is in the photo?
The answer is simple — Artificial Intelligence. In a video highlighting Facebook’s Artificial Intelligence research, they discuss the applications of Neural Networks to power their facial recognition software. Facebook is investing heavily in this area, not only within the organization, but also through the acquisitions of facial-recognition startups like Face.com (acquired in 2012 for a rumored $60M), Masquerade (acquired in 2016 for an undisclosed sum), and Faciometrics (acquired in 2016 for an undisclosed sum).
Instagram, acquired by Facebook back in 2012, uses deep learning by making use of a connection of recurrent neural networks to identify the contextual meaning of an emoji — which has been steadily replacing slangs (for instance, a laughing emoji could replace “ROFL”).
By algorithmically identifying the sentiments behind emojis, Instagram creates and auto-suggests emojis and emoji related hashtags. This may seem like a minor application of AI, but being able to interpret and analyze this emoji-to-text translation at a larger scale sets the basis for further analysis on how people use Instagram.
Most large banks are eliminating the need for customers to physically deliver a cheque to the bank by offering the ability to deposit cheques through a smartphone application. The technologies that power these applications use Neural Networks to decipher and convert handwriting on checks into text. Essentially, Neural Networks find themselves at the core of any application that requires handwriting/speech/image recognition.
Do you find yourself in situations where you’re set to buy something, but you end up buying a lot more than planned, thanks to some super-awesome recommendations?
Yeah, blame neural networks for that. By making use of neural network and its learnings, the e-commerce giants are creating Artificial Intelligence systems that know you better than yourself. Let’s see how:
Your Amazon searches (“earphones”, “pizza stone”, “laptop charger”, etc) return a list of the most relevant products related to your search, without wasting much time. In a description of its product search technology, Amazon states that its algorithms learn automatically to combine multiple relevant features. It uses past patterns and adapts to what is important for the customer in question.
And what makes the algorithms “learn”? You guessed it right — Neural Networks!
Amazon shows you recommendations using its “customers who viewed this item also viewed”, “customers who bought this item also bought”, and also via curated recommendations on your homepage, on the bottom of the item pages, and through emails. Amazon makes use of Artificial Neural Networks to train its algorithms to learn the pattern and behaviour of its users. This, in turn, helps Amazon provide even better and customized recommendations.
Chess players regularly use chess engines to analyze their games, improve themselves, and practice new tactics — and it goes without saying that the chess engine in question deploys Neural Networks to accomplish the learning.
So These were some Use-Cases of Neural Networks in the Real World.