AI – Artificial Intelligence

Artificial Intelligence or AI can easily be described as intelligence which is not demonstrated by natural entities such as humans and other animals. AI is intelligence displayed by machines. AI is a branch of computer science that deals with making machine behave – think and act – like humans.

Humans have been trying to make machines smarter and we are succeeding a lot than ever. Every passing day, we are making machines which are smarter than yesterday. As a result scope of AI also keeps on changing. To be apt – “AI is whatever that hasn’t been done yet”

Scientist John McCarthy coined the term “Artificial Intelligence” in 1956. He also devoted lot of his time in this field in addition developing language “LISP”. Due to his contributions to this field, he is referred as “Father of AI”.

Typically AI researchers focus of aspects such as reasoning, knowledge, planning, learning, NLP and ability to move and manipulate objects. They have created various languages which support their goal – LISP, Prolog, Python.

Applications of Artificial Intelligence

  • Advanced Weather Modelling
  • Self Driving Cars
  • Pattern Recognition
  • Predictive Analytics by looking at large datasets
  • Automated Traffic Signal system which automatically changes signal duration based on the traffic density

Machine Learning and NLP are at the heart of Artificial Intelligence. Scientists and researchers are striving to make machines as intelligent as humans are. Or may be even more intelligent. Some breakthroughs in this direction are:

Humanoid - Sophia - Artificial Intelligence
By International Telecommunication Union [CC BY 2.0 (], via Wikimedia Commons
The recent advances in this field are making Artificial Intelligence a very happening field. Focus of next decade will surely be on this field. Surely we would see lot more breakthroughs in this field.

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Related Keywords:

Machine Learning, NLP, Deep Learning, Neural Network, LISP, Prolog, Python

Graphic Processing Unit (GPU)

Graphic Processing Unit or GPU as we know it for several years now, has gained a new importance in the light of Artificial Intelligence. GPU is a specialized circuit designed to manipulate memory rapidly to create faster images.

GPUs are highly efficient in manipulating computer graphics and image processing. However they have now gained importance due to their efficiency in fast computing in a parallel fashion. GPU has parallel architecture consisting of several thousand smaller yet efficient cores designed to handle multiple tasks simultaneously. This is where they differ from CPU, which has only a few cores designed for sequential processing.

GPU in Artificial Intelligence

GPUs have been found to be tremendously powerful as compared to CPUs. In one of the project, 12 NVIDIA GPUs delivered deep-learning performance of 2000 CPUs. That is phenomenal! NVIDIA GPUs are speeding up the DNNs (Deep neural Networks) by 10-20x, resulting in reduction in the training times for the Artificial Intelligence. NVIDIA has also provided rich platform for developers (CUDA) which improved developers’ productivity helping them innovate quickly.

Other Uses of GPU

We had known GPU long only for their graphics related use such as gaming. Several gaming consoles were powered by GPUs. However, as explained above GPUs are now very popular in the field of Artificial Intelligence. They have also been extremely useful and popular in several other areas such as:

  • Self Driving cars – to train the algorithm to detect the vehicles even in difficult conditions
  • Healthcare and Life Sciences – deep genomics studies
  • Robots

It is evident that the parallel processing that GPUs offer are going to dominate the near future and can be seen from the investor interest in this field. In last year itself there have been several investments from key VCs in the area of hardware.

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Related Keywords

Artificial Intelligence, DNN, CNN, NVIDIA, CUDA

Machine Learning (ML)

Machine Learning (ML) is a field of computer science which makes computers able to learn without being programmed explicitly.

Machine Learning is typically used with large datasets and computers are allowed to find patters, make predictions based on that data.

Types of Machine Learning Algorithms

  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning

In Supervised Learning, a specific attribute for a dataset is available. However that attribute is not available for rest of the datasets. Computer is expected to learn the datasets and find out or predict the lable for remaining datasets.

In Unsupervised Learning, no attribute or relationship about the dataset is available and computer is required to find that out.

Reinforcement Learning is somewhere in the middle of these two.

Machine Learning and Artificial Intelligence are closely related to each other. In fact, ML is part and essence of Artificial Intelligence. In Artificial Intelligence machines need to act smarter, which in itself involves lot of learning which we understand as Machine Learning.

Classification and Regression are two important parts of ML. In Classification, the dataset (files, objects, text data etc) are classified based on various observations about that dataset. Whereas in Regression, some prediction is made based on the observations about the dataset.

Examples Of ML:

  • Spam filters – spam filters train themselves to learn if a given email is to be classified as spam or not. Their learnings improve as they filter more and more emails. Spammers keep on trying to get past the filters and filters keep on learning to block spammers.
  • Recommendation Engines – based on your past history with a website, Machine Learning Algorithms can start predicting what will you do next – which essentially is a recommendation. Amazon pioneered in this field based on the huge amount of data they collected over a period of time.

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NLP (Natural Language Processing)

NLP i.e. Natural Language Processing is a field of computer science that deals with human languages and computers. Natural Language means languages that humans speak – English, Hindi, Japanese, Marathi etc, whereas artificial languages are C++, Java, PHP etc.

As computers evolved, humans were made to “speak” to computers in that language that computers understood. NLP is exactly opposite and hence is very complex. Human speech is not always precise and accurate and also keeps on changing (e.g. slang words). Humans are trying to program computers to learn and understand human languages. This field deals with the problems and providing solutions in this field.

Why NLP is important?

With the progress made in Artificial Intelligence, it is becoming important that machines understand and learn human languages. This will make interaction between humans and machines very productive. Also, it will help in taking the AI to masses and bring benefits to the human kind. NLP forms the core of progress in this direction.

NLP has several areas which are very complex, yet good progress has been made in those areas.

  • Speech to text
    • Understand the language and translate it correctly into text.
  • Optical Character Recognition
    • Identify written characters and convert them in computer understandable format to process further.
  • Question and Answers
    • Program machines to understand Questions and provide accurate answers which humans can understand.
  • Sentiment Analysis
    • Process large data and understand the emotions expressed in that data
  • Machine Translation
    • Automatically convert text from one human language to other human language

General History of NLP dates back to 1950. Since then various research and development has resulted into various algorithms which are helping machine learn faster and better. With advent of Cloud, innovations in NLP has resulted into several APIs available for NLP:

  • IBM Watson
  • Amazon Lex
  • Google Cloud Natural Language API
  • More

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Related Keywords:

Automatic Speech Recognition, Speech to Text, Machine Learning, Sentiment Analysis

Application Areas:

Search Engines, Fraud Detection, Sentiment Analysis, Bio-Medical field, Forensic Science and many more