Neural Networks find extensive applications in areas where traditional computers don’t fare too well. Like, for problem statements where instead of programmed outputs, you’d like the system to learn, adapt, and change the results in sync with the data you’re throwing at it. Neural networks also find rigorous applications whenever we talk about dealing with noisy or incomplete data. And honestly, most of the data present out there is indeed noisy.
Best Machine Learning Courses & AI Courses Online
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”.
In-demand Machine Learning Skills
With so much said and done, it’s imperative to understand what exactly are the use cases of AI, and how Neural Networks help the cause. Let’s dive into the applications of Neural Networks across various domains – from Social Media and Online Shopping, to Personal Finance, and finally, to the smart assistant on your phone.
You should remember that this list is in no way exhaustive, as the applications of neural networks are widespread. Basically, anything that makes the machines learn is deploying one or the other type of neural network.
The ever-increasing data deluge surrounding social media gives the creators of these platforms the unique opportunity to dabble with the unlimited data they have. No wonder you get to see a new feature every fortnight. It’s only fair to say that all of this would’ve been like a distant dream without Neural Networks to save the day.
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, 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.
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.
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.
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 (speak learning, speak Neural Networks!).
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).
Popular Machine Learning and Artificial Intelligence Blogs
To Wrap Up…
We’ve only scratched the surface when it comes to the applications of neural networks in day-to-day life. Specific industries and domains have specific interactions with Artificial Intelligence by making use of neural networks which is far beyond what’s talked about in this article. For example, 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.
Learn ML courses Online from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career.
Do you have any other interesting real-life use case of Neural Networks that we might have missed? Drop it in the comments below!
What problems can neural networks solve?
Neural networks solve problems that require pattern recognition. For example, a neural network could be trained to recognize handwritten digits. Another example is the Google self-driving car, which is trained to classically recognize a dog, a truck, or a car. They are good for Pattern Recognition, Classification and Optimization. This includes handwriting recognition, face recognition, speech recognition, text translation, credit card fraud detection, medical diagnosis and solutions for huge amounts of data. It can be used to find links between patterns, to convert one type of data to another and to make associations or generalizations between different entities.
Why are neural networks important?
Neural networks are a class of machine learning algorithms that have many applications. Some of the most popular applications of neural networks are computer vision, speech recognition, and natural language processing. Today, neural networks are being used for a wide range of applications and are enjoying a lot of attention from the research community. ANNs can be used to address many difficult problems that are faced today. They are employed as a component in a larger system, or can be used in the pre-processing stage of complicated non-linear techniques.
What is the biggest problem with neural networks?
The biggest problem with neural networks is that they are not that accurate, mostly because they have a relatively slow learning curve. And the problem isn't just with accuracy, but also with efficiency. Neural networks can be extremely slow to operate, because many times they rely on feedback from previous computations to the next one. A simple way to solve this will be taking out one of the many layers of the network to avoid such feedback, but this might actually damage the accuracy of the network. Another solution could be to use parallel computers, which can be used to divide the workload and eliminate problems of speed.