Artificial Intelligence and Machine Learning

 The rapid development of technology has brought us solutions imagined only by science fiction writers 3 decades ago. This development has opened up new ways to explore human life and their functions. What used to seem so far away into the future has come into existence now. From self-driving cars to Iot light bulbs, the spread of the technology is unparalled.

In recent times, there have been new, complex developments in the IT space. Artificial Intelligence, Machine Learning, Big Data Analysis, Blockchain have finally reached a high, so that developers can integrate these services into their products. Often referred to as BuzzWords, these new solutions offer a unique peek into how our world functions. In this new series called “AI, ML and”, I will offer an inside look into the applications of Artificial Intelligence(AI) and Machine Learning(ML.) But first, in this introductory article, we will learn about what AI and ML is.

What is AI?

The idea stemmed from the fathers of the field in their time, Minsky and McCarthy, who said that if a computer performs any task performed by a program or a machine that, if a human carried out the same activity, we would say the human had to apply intelligence to accomplish the task.

AI systems typically show some human characteristics such as planning, learning, reasoning, problem solving, knowledge representation, perception, motion, and manipulation and, to a lesser extent, social intelligence and creativity.

Types of AI

AI can be split into Narrow AI and General AI.

Souce: The Hacker News

Narrow AI is what we see in today’s computers: systems that perform certain tasks without explicity programmed to do so. Some applications could be the voice assistants on our mobile phones like the Google Assistant, the computers of self-driving cars like Teslas, the world of Augmented Reality etc. Unlike humans, these systems can only learn or be taught how to do specific tasks, which is why they are called narrow AI.

General AI is not the same as narrow AI. Its development has not reached up to a level of mass productions. It is generally seen as a machine which tries to mimic human behaviour. You can imagine it as the artificial robots you see in movies such as The Terminator. There are a few models that have been revealed such as Sophia.

That said, some AI experts believe such projections are wildly optimistic given our limited understanding of the human brain, and believe that GAI is still centuries away.

What is ML?

Architecture of Machine Learning, Credit: TowardsDataScience.com

Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use in order to perform a specific task effectively without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to perform the task according to Wikipedia.

But in layman’s terms, Machine learning tries to predict relations between objects that is impossible/very difficult for a human to trace. AI is really a broad term and somewhat this also causes every company to claim their product has AI these days. ML is a subset of AI, and consists of the more advanced techniques and models that enable computers to figure things out from the data and deliver AI applications. ML is the science of getting computers to act without being explicitly programmed (Stanford University).

Machine Learning models

In Machine Learning there are different models that generally fall into 2 different categories: (1)Supervised Learning, (2) Unsupervised Learning

  1. Supervised Learning: This is the ML model in which a human is initially the more knowlegeable one. The human “teaches” the ML systems what patterns match a trait and what does not. Right now, almost all learning is supervised. For example, a supervised machine learning system that can learn which emails are ‘spam’ and which are ‘not spam’. The algorithm would be first trained with available input data set (of zillions of emails) that is already tagged with this classification to help the machine learning system learn the characteristics or parameters of the ‘spam’ email and distinguish it from those of ‘not spam’ emails. Just as a three-year-old learns the difference between a ‘block’ and a ‘soft toy’, the supervised machine learning system learns which email is ‘spam’ and which is ‘not spam’. Techniques such as linear or logistic regressions and decision tree classification fall under this category of learning. This will be addressed in a later article.
  2. Unsuperwised Learning: This is an ‘unaided’ type learning when your data typically has no known output labels or any feedback loop. This is useful when there is no example data set with known answers and your are searching for a hidden pattern. In this case, clustering i.e. dividing a set of elements into groups according to some unknown pattern is carried out based on the existing data sets. The system has to understand itself from the data set we provide. In general, unsupervised learning is a bit difficult to implement and thus it’s not used as widely as supervised learning. Most popular types are clustering and association.

I hope this articles provides an insight into the complex world of AI and ML. In this new series of “AL, ML, and”, I aim to discuss the hundreds of applications of these new solutions. I will discuss very interesting topics such as Life Simulation, Working of Voice Assistants, Neural Networks(my favorite!), Interpretation of Big Data and many more. Do stay tuned for more!

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