Neural networks are computing systems with interconnected nodes that work much like neurons in the human brain.
Using algorithms, they can recognize hidden patterns and correlations in raw data, cluster and classify it, and — over time — continuously learn and improve.
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. Before we get to how Neural Networks power Artificial Intelligence, let’s first talk a bit about what exactly is Artificial Intelligence.
For the longest time possible, the word “intelligence” was just associated with the human brain. But then, something happened! Scientists found a way of training computers by following the methodology our brain uses. Thus came Artificial Intelligence, which can essentially be defined as intelligence originating from machines. To put it even more simply, Machine Learning is simply providing machines with the ability to “think”, “learn”, and “adapt”
Use cases of Neural Network
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).
In June 2016, Facebook announced a new Artificial Intelligence initiative that uses various deep neural networks such as DeepText — an artificial intelligence engine that can understand the textual content of thousands of posts per second, with near-human accuracy.
Instagram 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.
Pinterest uses computer vision — another application of neural networks, where we teach computers to “see” like a human, in order to automatically identify objects in images (or “pins”, as they call it) and then recommend visually similar pins. Other applications of neural networks at Pinterest include spam prevention, search and discovery, ad performance and monetization, and email marketing.
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”? That is — 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.
Cheque Deposits Through Mobile
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.
How can a financial institution determine a fraudulent transaction? Most of the times, the daily transaction volume is too much to be reviewed manually. To help with this, Artificial Intelligence is used to create systems that learn through training what types of transactions are fraudulent.
FICO — the company that creates credit ratings that are used to determine creditworthiness, makes use of neural networks to power their Artificial Intelligence to predict fraudulent transactions. Factors that affect the artificial neural network’s final output include the frequency and size of the transaction and the kind of retailer involved.
Powering Your Mobile Phones
One of the more common features on smartphones today is voice-to-text conversion. Simply pressing a button or saying a particular phrase (“Ok Google”, for example), lets you start speaking to your phone and your phone converts the audio into text. Google makes use of artificial neural networks in recurrent connection to power voice search. Microsoft also claims to have developed a speech-recognition system — using Neural Networks, that can transcribe conversations slightly more accurately than humans.
Smart Personal Assistants
With the voice-to-text technology becoming accurate enough to rely on for basic conversations, it is turning into the control interface for a new generation of personal assistants. Initially, there were simpler phone assistants — Siri and Google Now (now succeeded by the more sophisticated Google Assistant), which could perform internet searches, set reminders, and integrate with your calendar. Amazon expanded upon this model with the announcement of complementary hardware and software components — Alexa, and Echo (later, Dot).
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