artificial intelligence 101
Artificial intelligence or AI is a term that blankets an entire branch of computer science focused on the thinking and learning capabilities of machines.

A quick guide to artificial intelligence

Artificial intelligence or commonly known as AI is a term that blankets an entire branch of computer science focused on the thinking and learning capabilities of machines. Depending on the kind of data and information they are fed, the kind of training that they are put through, AI learns to make better decisions. This kind of ability to both learn and apply that learning to practical usage, is similar to the way human beings learn and apply that knowledge in the real world.

What Is Artificial Intelligence?

Artificial intelligence or commonly known as AI is a term that blankets an entire branch of computer science focused on the thinking and learning capabilities of machines. Depending on the kind of data and information they are fed, the kind of training that they are put through, AI learns to make better decisions. This kind of ability to both learn and apply that learning to practical usage, is similar to the way human beings learn and apply that knowledge in the real world.

This means that machines can now accomplish tasks that were once possible only by a human mind. Some of these tasks include

  • Problem solving
  • Interpreting visual cues
  • Speech recognition/Natural language processing

These tasks are accomplished with the help of complex algorithms. These algorithms or intelligent programs can be run on various types of hardware or software. This results in a huge variety of use cases that AI solves for, making the subject of AI more difficult to understand.

The ability of AI to latch on to any kind of technology means that the scope for this filed of computer science is immense.

History of AI

Though artificial Intelligence has been in the spotlight since 2015, it has been around for over a century. In fact, the concept of artificial intelligence has been in the minds of humans for thousands of years.

If you look at the AI timeline, you will see that in 1950, Alan Turing proposed the Turing test. In the same year, Isaac Asimov proposed the three laws of robotics in his short story ‘Runaround’. The following year, the first ever AI-based program was written on the Ferranti Mark 1 machine of the University of Manchester. It was a checkers-playing program written by Christopher Strachey and a chess-playing program written by Dietrich Prinz.

In 1959, the first ever self learning game named Klay program was created. In two years, the MIT AI lab was set up. In 1964, the first demo of an artificial intelligence program was demonstrated. This program was capable of understanding natural language.

The following year, the first chat bot named Eliza was introduced. A decade later, in 1974, Stanford AI lab built its first autonomous vehicle. In 1989, the first autonomous vehicle was created using a neural network, by Carnegie Mellon.

Fast forward to 1997 IBM’s Deep Blue beat Garry Kasparov, a Russian Chess Grandmaster at Chess, a complex board game. In his book Deep Thinking, he says that his loss to Deep Blue was in fact a victory for humans who are drivers of the technological leaps that we are making. In 1999, Google began building a self driving car. The following year, Narrative Sciences AI demonstrated the ability of AI to write reports. In 2011, IBM Watson defeated Jeopardy Champions. In the same year AI-enabled voice assistance such as Siri, Google Now, and Cortana became popular among the masses.

In 2015, Elon Musk and a few others announced a donation of one billion dollars to AI research. In 2016, Google’s DeepMind defeated Korean legend Lee Se-dol in a match of Go. The same year, UC Berkeley launched the Center for Human-compatible Artificial Intelligence.

The interest in this field of AI has exploded due to the continuous enhancements in the power of computing, processing and storage of vast amounts of data, and complex algorithms. However the current evolution of AI technologies is not as scary as it is depicted in Hollywood Scifi movies. AI has evolved to deliver different benefits in different industries.

Why is AI important?

AI automates monotonous learning.

However, this should not be confused with automation driven by hardware or automation of manual tasks. Instead, this kind of repetitive learning implies that AI can perform high velocity computerized tasks in a reliable manner. To enable the layer to AI to perform this kind of automation requires human interference.

For example, if you are use a smart phone or any modern smart device or app, you would have experienced this kind of artificial intelligence at play. How does Amazon know when my stock of grocery is expiring? This is a result of complex algorithms that Amazon uses to trawl through the data that we create when we use the tool or software. This way it is able to make personalized recommendations, in turn enhancing our digital experience.

AI adds a layer of intelligence to products and services.

Seldom will AI be sold as an independent service or application. Instead, AI will work as a layer or a platform that enhances existing features in a product or existing suit of products. For instance, Google Assistant is not an independent application. Instead this AI capability by Google has been added as a feature to its products.

When large amount of data is combined as automation and conversational platforms, it eventually improves the technology that we use.

AI learns through progressive algorithms.

One of the unique aspects of AI is to find structure in data this results in the algorithm acquiring a new skill. Once this happens, the algorithm becomes a predictor. For example, based on the kind of things we shop on Amazon, the algorithm is able to find structure and using this information, it is able to recommend what product we need to buy next. Every time there is new data, the model adapts, learns, and is able to predict outcomes better. If the outcome or answer is not quite right, the model adjusts itself through relearning or new data. This technique is called back propagation.

AI analyzes deeper data and achieves accuracy

To train deep learning models, huge amounts of data is required. Artificial intelligence is able to analyze these large amounts of data deeply through neural networks. For instance, it is possible to build a fraud detection program with more than five hidden layers. All this has become possible today due to the power of computers and big data. The more data you feed the model, the faster it learns, and more accurate is the outcome. For example, the AI of Google, Facebook, Amazon, or any tech giant are all based on deep learning. They are able to predict and personalize the experience for customers accurately. This has been possible only because of the continued use of these tools and apps.

How Does Artificial Intelligence Work?

Similar to human intelligence that works by processing huge amounts of data, artificial intelligence works by processing data through algorithms. These algorithms, as mentioned earlier, adjust themselves based on past experiences and new data, so that they improve their accuracy.

In order to simulate artificial intelligence, machines are provided with the ability to perceive the environment around them (through data, in most cases), identify patterns in this environment, and finally learn from these patterns and continuously experiential memory. These three steps are constantly repeated until the machine has sufficient data to make predictions confidently. And what makes AI so remarkable is its capability for speed, accuracy, and endurance.

Applications of AI

There are numerous applications of AI. Some of the more popular use cases include:

Natural language

Machines can recognize natural human language. A common usage is chatbots where companies use them for customer service, marketing, sales, etc. The chatbot is able to recognize what the customer or user is saying and is able to respond appropriately.

Artificial neural networks (ANN)

Neural systems attempt to reproduce the kind of connections that happen in the human brain. This kind of simulation helps predict future events based on historical events or data. Such systems learn to perform tasks without being programmed with any specific rules. For example, image recognition where the machine is able to differentiate between whether the image ‘is a fish’ or ‘is not a fish’.

Expert systems

These are computer applications that solve complex problems in a specific domain. And they do this at the level of exceptional human expertise. They are highly reliable and responsive. For example, they are used to detect frauds, airline scheduling, or stock market trading, just to name a few.

Robotics

Robotics is one aspect of AI that focuses on creating intelligent robots. It is mainly comprised of electrical and mechanical engineering, and computer science specializing in the design, construction, and application of robots. The aim of this branch of AI is to free manpower from doing monotonous tasks. The key difference between other AI programs and robotics is that most AI programs run in a computer-simulated world while robots are meant to operate in the real world.

Gaming systems

Gaming systems are programs designed to manipulate strategic games such as Chess or Go. Machines are trained to think of all possible positions or ways to play the game effectively against a human being. In the past, these programs have been able to beat the best of the human opponents.

Closing thoughts

Artificial intelligence has great potential more than we can imagine and its benefits are aplenty. We’ll discuss them in length in our upcoming posts.

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