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Artificial Intelligence - No More a Rocket Science

December 5, 2017

All of a Sudden, Everyone has started talking about Artificial Intelligence and Machine Learning. People have started to believe that this terrific technology is going to drive innovations in most of the sectors to the next Level for at least a decade.

AI is already being in use, we can experience the application of AI in our daily lives in various ways. This could be in the form of an advertisement of a product on a social media platform where you were planning to buy something, or it could be a recommendation of another product that pops up as you’re purchasing a product from an e-commerce website. For example, a recommendation may be made to buy a Tee Shirt just as you’re buying a Jeans Pant, because the site’s system has predicted a higher probability of being able to sell a particular Jeans Pant along with the Tee Shirt. So, knowingly or unknowingly, we are already experiencing AI.

There have been some incredible developments in AI that have led many to believe it is going to be the technology that will shape our future. Here are the Trending Top 5 AI Technologies:

  1. Natural Language Processing: NLP is a way for computers algorithms to analyze, learn, and derive a appropriate meaning from human language in a smart and useful way. By using NLP, developers can structure knowledge to perform tasks such as automated summarization, language translation, entity recognition, relationship extraction, sentiment analysis, speech recognition, and topic segmentation.
  2. Virtual Agents: They are widely used in website and also in Facebook as chatbots. The major purpose being to provide customer support when the customer and the service provider are in different geographical locations. These bots leads a intelligent conversation with users, responds to their questions and performs adequate non-verbal behavior.
  3. AI-Optimized Hardware: A special hardware that hosts Artificial Intelligence, Machine learning and Deep learning algorithms in a portable hardware and not in data centers. That will be essential for devices like self-driving cars, which need to react to what’s going on around them and potentially learn from new input faster than they can relay data to the cloud.
  4. Bio Metrics: Biometric sensors measure the unique data in human traits like fingerprints or facial features. Subtypes of AI, such as machine learning, are already used heavily in biometrics for tasks like face detection in photos and videos.
  5. Robotic Process Automation: Robotic process automation (RPA) is the use of software with artificial intelligence and machine learning capabilities to handle high-volume, repeatable tasks that previously required a human to perform.


Many of us often confuse the terms ‘artificial intelligence’, ‘machine learning’ and ‘deep learning’. Hence, we use the terms interchangeably. But these are not the same things.

Artificial intelligence, in broader terms, can be described as a branch of computer science that can imitate human beings. It has been demonstrated that computers can be programmed to carry out very complex tasks that were earlier only performed by humans. From self-driving cars and Facebook’s Jarvis, Apple’s Siri or Google’s Assistant, to a computer program playing a game of checkers, all of these are applications of artificial intelligence.

Machine learning can be referred to as a subset of AI. It is considered one of the most successful approaches to AI, but is not the only approach. For example, there are many chat bots that are rule based, i.e., they can answer only certain questions, depending on the way they were programmed. But they will not be able to learn anything new from those questions. So this can be categorised as AI as the chat bots replicate human-like behaviour, but can’t be termed as machine learning. Now, the question is: can machines really ‘learn’? How is it possible for a machine to learn if it doesn’t have a brain and a complex nervous system like humans? According to Arthur Samuel, “Machine learning can be defined as a field of study that gives computers the ability to learn without being explicitly programmed.”

Tom Mitchell provides a more modern definition: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E."

Example: playing checkers.

E = the experience of playing many games of checkers

T = the task of playing checkers.

P = the probability that the program will win the next game.

In general, any machine learning problem can be assigned to one of three broad classifications Supervised learning*, Unsupervised learning* and Reinforce Learning*.


Deep learning is a subset of machine learning. It uses neural networks to simulate human decision-making skills. A neural network consists of many neurons and hence resembles a human nervous system. Have you ever wondered how Facebook detects your face among many, in an image? Image detection is one of the examples of deep learning, which is much more complex as it needs lots of data to train itself. For instance, a deep learning algorithm can learn to recognise a person but will have to be trained on a huge data set which consists of people as well as other entities. If this is not done, it might make a wrong identification. Hence, compared to other machine learning algorithms, a deep learning algorithm requires much more data in order to observe and understand every minute detail to make the right decisions.


If you are excited by the prospects that machine learning offers, our digital education era has made things easier for you. There are many massive open online courses (MOOC) offered by many companies. One such course is provided by Coursera. This course will give you a basic understanding of the algorithms that are implemented in machine learning, and it includes both supervised learning and unsupervised learning. It’s a self-paced course but designed to be completed in 12 weeks.

If you want to experience the Real-Time application of AI and Machine Learning, you can learn it through the workshop provided by Credence Robotics. This Workshop is of 90% Practical and 10% Theory concept and is split into two parts: Basic in Artificial Intelligence and Cutting edge deep learning for coders. Both have been designed for 3 Days and 7 Days respectively and provide you a great insight into Artificial Intelligence with Hands-On Experience.

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