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Artificial Intelligence (AI) and Machine Learning (ML) are two words casually thrown around in everyday conversations, be it at offices, institutes or technology meetups. Artificial Intelligence is said to be the future enabled by Machine Learning.

Now, Artificial Intelligence is defined as "the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages." Putting it simply means making machines smarter to replicate human tasks, and Machine Learning is the technique (using available data) to make this possible.

Researchers have been experimenting with frameworks to build algorithms, which teach machines to deal with data just like humans do. These algorithms lead to the formation of artificial neural networks that sample data to predict near-accurate outcomes. To assist in building these artificial neural networks, some companies have released open neural network libraries such as Google's Tensorflow (released in November 2015), among others, to build models that process and predict application-specific cases. Tensorflow, for instance, runs on GPUs, CPUs, desktop, server and mobile computing platforms. Some other frameworks are Caffe, Deeplearning4j and Distributed Deep Learning. These frameworks support languages such as Python, C/C++, and Java.

It should be noted that artificial neural networks function just like a real brain that is connected via neurons. So, each neuron processes data, which is then passed on to the next neuron and so on, and the network keeps changing and adapting accordingly. Now, for dealing with more complex data, machine learning has to be derived from deep networks known as deep neural networks.

In our previous blogposts, we've discussed at length about Artificial Intelligence, Machine Learning and Deep Learning, and how these terms cannot be interchanged, though they sound similar. In this blogpost, we will discuss how Machine Learning is different from Deep Learning.

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What factors differentiate Machine Learning from Deep Learning?

Machine Learning crunches data and tries to predict the desired outcome. The neural networks formed are usually shallow and made of one input, one output, and barely a hidden layer. Machine learning can be broadly classified into two types - Supervised and Unsupervised. The former involves labelled data sets with specific input and output, while the latter uses data sets with no specific structure.

On the other hand, now imagine the data that needs to be crunched is really gigantic and the simulations are way too complex. This calls for a deeper understanding or learning, which is made possible using complex layers. Deep Learning networks are for far more complex problems and include a number of node layers that indicate their depth.

In our previous blogpost, we learnt about the four architectures of Deep Learning. Let's summarise them quickly:
Unsupervised Pre-trained Networks (UPNs)

Unlike traditional machine learning algorithms, deep learning networks can perform automatic feature extraction without the need for human intervention. So, unsupervised means without telling the network what is right or wrong, which it will will figure out on its own. And, pre-trained means using a data set to train the neural network. For example, training pairs of layers as Restricted Boltzmann Machines. It will then use the trained weights for supervised training. However, this method isn't efficient to handle complex image processing tasks, which brings Convolutions or Convolutional Neural Networks (CNNs) to the forefront.
Convolutional Neural Networks (CNNs)

Convolutional Neural Networks use replicas of the same neuron, which means neurons can be learnt and used at multiple places. This simplifies the process, especially during object or image recognition. Convolutional neural network architectures assume that the inputs are images. This allows encoding a few properties into the architecture. It also reduces the number of parameters in the network.
Recurrent Neural Networks

Recurrent Neural Networks (RNN) use sequential information and do not assume all inputs and outputs are independent like we see in traditional neural networks. So, unlike feed-forward neural networks, RNNs can utilize their internal memory to process sequence inputs. They rely on preceding computations and what has been already calculated. It is applicable for tasks such as speech recognition, handwriting recognition, or any similar unsegmented task.
Recursive Neural Networks

A Recursive Neural Network is a generalisation of a Recurrent Neural Network and is generated by applying a fixed and consistent set of weights repetitively, or recursively, over the structure. Recursive Neural Networks take the form of a tree, while Recurrent is a chain. Recursive Neural Nets have been utilized in Natural Language Processing (NLP) for tasks such as Sentiment Analysis.

In a nutshell, Deep Learning is nothing but an advanced method of Machine Learning. Deep Learning networks deal with unlabelled data, which is trained. Every node in these deep layer learns the set of features automatically. It then aims to reconstruct the input and tries to do so by minimizing the guesswork with each passing node. It doesn't need specific data and in fact is so smart that draws co-relations from the feature set to get optimal results. They are capable of learning gigantic data sets with numerous parameters, and form structures from unlabelled or unstructured data.

Now, let's take a look the key differences:

Differences:
The future with Machine Learning and Deep Learning:

Moving further, let's take a look at the use cases of both Machine Learning and Deep Learning. However, one should note that Machine Learning use cases are available while Deep Learning are still in the developing stage.

While Machine Learning plays a huge role in Artificial Intelligence, it is the possibilities introduced by Deep Learning that is changing the world as we know it. These technologies will see a future in many industries, some of which are:
Customer service

Machine Learning is being implemented to understand and answer customer queries as accurately and soon as possible. For instance, it is very common to find a chatbot on product websites, which is trained to answer all customer queries related to the product and after services. Deep Learning takes it a step further by gauging customer's mood, interests and emotions (in real-time) and making available dynamic content for a more refined customer service.
Automotive industry
Machine Learning vs Deep Learning: Here's what you must know!

Autonomous cars have been hitting the headlines on and off. From Google to Uber, everyone is trying their hand at it. Machine Learning and Deep Learning sit comfortably at its core, but what's even more interesting is the autonomous customer care making CSRs more efficient with these new technologies. Digital CSRs learn and offer information that is almost accurate and in shorter span of time.

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Speech recognition:

Machine Learning plays a huge role in speech recognition by learning from users over the time. And, Deep Learning can go beyond the role played by Machine Learning by introducing abilities to classify audio, recognise speakers, among other things.

Deep Learning has all benefits of Machine Learning and is considered to become the major driver towards Artificial Intelligence. Startups, MNCs, researchers and government bodies have realised the potential of AI, and have begun tapping into its potential to make our lives easier.