What Is Artificial Intelligence?

Artificial Intelligence is a method of making a computer, a computer-controlled robot, or a software think intelligently like the human mind. AI is accomplished by studying the patterns of the human brain and by analyzing the cognitive process. The outcome of these studies develops intelligent software and systems.

A Brief History of Artificial Intelligence

Here’s a brief timeline of the past six decades of how AI evolved from its inception.

1956 - John McCarthy coined the term ‘artificial intelligence’ and had the first AI conference.

1969 - Shakey was the first general-purpose mobile robot built. It is now able to do things with a purpose vs. just a list of instructions.

1997 - Supercomputer ‘Deep Blue’ was designed, and it defeated the world champion chess player in a match. It was a massive milestone by IBM to create this large computer.

2002 - The first commercially successful robotic vacuum cleaner was created. 

2005 - 2019 - Today, we have speech recognition, robotic process automation (RPA), a dancing robot, smart homes, and other innovations make their debut.

2020 - Baidu releases the LinearFold AI algorithm to medical and scientific and medical teams developing a vaccine during the early stages of the SARS-CoV-2 (COVID-19) pandemic. The algorithm can predict the RNA sequence of the virus in only 27 seconds, which is 120 times faster than other methods. 

Weak AI vs. Strong AI

When discussing artificial intelligence (AI), it is common to distinguish between two broad categories: weak AI and strong AI. Let's explore the characteristics of each type:

Weak AI (Narrow AI)

Weak AI refers to AI systems that are designed to perform specific tasks and are limited to those tasks only. These AI systems excel at their designated functions but lack general intelligence. Examples of weak AI include voice assistants like Siri or Alexa, recommendation algorithms, and image recognition systems. Weak AI operates within predefined boundaries and cannot generalize beyond their specialized domain.

Strong AI (General AI)

Strong AI, also known as general AI, refers to AI systems that possess human-level intelligence or even surpass human intelligence across a wide range of tasks. Strong AI would be capable of understanding, reasoning, learning, and applying knowledge to solve complex problems in a manner similar to human cognition. However, the development of strong AI is still largely theoretical and has not been achieved to date.

Types of Artificial Intelligence 

Below are the various types of AI:

1. Purely Reactive

These machines do not have any memory or data to work with, specializing in just one field of work. For example, in a chess game, the machine observes the moves and makes the best possible decision to win. 

2. Limited Memory

These machines collect previous data and continue adding it to their memory. They have enough memory or experience to make proper decisions, but memory is minimal. For example, this machine can suggest a restaurant based on the location data that has been gathered.

3. Theory of Mind

This kind of AI can understand thoughts and emotions, as well as interact socially. However, a machine based on this type is yet to be built. 

4. Self-Aware

Self-aware machines are the future generation of these new technologies. They will be intelligent, sentient, and conscious.

 Deep Learning vs. Machine Learning

Let's explore the contrast between deep learning and machine learning:

Machine Learning: 

Machine Learning focuses on the development of algorithms and models that enable computers to learn from data and make predictions or decisions without explicit programming. Here are key characteristics of machine learning:

1. Feature Engineering: In machine learning, experts manually engineer or select relevant features from the input data to aid the algorithm in making accurate predictions.
2. Supervised and Unsupervised Learning: Machine learning algorithms can be categorized into supervised learning, where models learn from labeled data with known outcomes, and unsupervised learning, where algorithms discover patterns and structures in unlabeled data.
3. Broad Applicability: Machine learning techniques find application across various domains, including image and speech recognition, natural language processing, and recommendation systems.

Deep Learning: 

Deep Learning is a subset of machine learning that focuses on training artificial neural networks inspired by the human brain's structure and functioning. Here are key characteristics of deep learning:

1. Automatic Feature Extraction: Deep learning algorithms have the ability to automatically extract relevant features from raw data, eliminating the need for explicit feature engineering.
2. Deep Neural Networks: Deep learning employs neural networks with multiple layers of interconnected nodes (neurons), enabling the learning of complex hierarchical representations of data.
3. High Performance: Deep learning has demonstrated exceptional performance in domains such as computer vision, natural language processing, and speech recognition, often surpassing traditional machine learning approaches.