The history of AI and machine learning
So where did AI come from? Well, it didn’t leap from single-player chess games straight into self-driving cars. The field has a long history rooted in military science and statistics, with contributions from philosophy, psychology, math and cognitive science. Artificial intelligence originally set out to make computers more useful and more capable of independent reasoning.
Most historians trace the birth of AI to a Dartmouth research project in 1956 that explored topics like problem solving and symbolic methods. In the 1960s, the US Department of Defense took interest in this type of work and increased the focus on training computers to mimic human reasoning.
For example, the Defense Advanced Research Projects Agency (DARPA) completed street mapping projects in the 1970s. And DARPA produced intelligent personal assistants in 2003, long before Google, Amazon or Microsoft tackled similar projects.
This work paved the way for the automation and formal reasoning that we see in computers today.
Artificial intelligence and machine learning are the part of computer science that are correlated with each other. These two technologies are the most trending technologies which are used for creating intelligent systems.
Although these are two related technologies and sometimes people use them as a synonym for each other, but still both are the two different terms in various cases.
On a broad level, we can differentiate both AI and ML as:
Artificial intelligence is a field of computer science which makes a computer system that can mimic human intelligence. It is comprised of two words “Artificial” and “intelligence”, which means “a human-made thinking power.” Hence we can define it as,
The Artificial intelligence system does not require to be pre-programmed, instead of that, they use such algorithms which can work with their own intelligence. It involves machine learning algorithms such as Reinforcement learning algorithm and deep learning neural networks. AI is being used in multiple places such as Siri, Google?s AlphaGo, AI in Chess playing, etc.
Based on capabilities, AI can be classified into three types:
Currently, we are working with weak AI and general AI. The future of AI is Strong AI for which it is said that it will be intelligent than humans.
As we know it today, AI is symbolized with Human-AI interaction gadgets by Google Home, Siri, and Alexa, by the machine-learning-powered video prediction systems that power Netflix, Amazon, and YouTube. These technological advancements are progressively becoming essential in our daily lives. They are intelligent assistants who enhance our abilities as humans and professionals — making us more productive.
In contrast to machine learning, AI is a moving target, and its definition changes as its related technological advancements turn out to be further developed. Possibly, within a few decades, today’s innovative AI advancements ought to be considered as dull as flip-phones are to us right now.
Machine learning is about extracting knowledge from the data. It can be defined as,
Machine learning enables a computer system to make predictions or take some decisions using historical data without being explicitly programmed. Machine learning uses a massive amount of structured and semi-structured data so that a machine learning model can generate accurate result or give predictions based on that data.
Machine learning works on algorithm which learn by it?s own using historical data. It works only for specific domains such as if we are creating a machine learning model to detect pictures of dogs, it will only give result for dog images, but if we provide a new data like cat image then it will become unresponsive. Machine learning is being used in various places such as for online recommender system, for Google search algorithms, Email spam filter, Facebook Auto friend tagging suggestion, etc.
It can be divided into three types:
For instance, if you provide a machine learning model with many songs that you enjoy, along with their corresponding audio statistics (dance-ability, instrumentality, tempo, or genre). It oughts to be able to automate (depending on the supervised machine learning model used) and generate a recommender system as to suggest you with music in the future that (with a high percentage of probability rate) you’ll enjoy, similarly as to what Netflix, Spotify, and other companies do.
In a simple example, if you load a machine learning program with a considerable large dataset of x-ray pictures along with their description (symptoms, items to consider, and others), it oughts to have the capacity to assist (or perhaps automatize) the data analysis of x-ray pictures later on. The machine learning model looks at each picture in the diverse dataset and finds common patterns found in pictures with labels with comparable indications. Furthermore, (assuming that we use an acceptable ML algorithm for images) when you load the model with new pictures, it compares its parameters with the examples it has gathered before to disclose how likely the pictures contain any of the indications it has analyzed previously.
The type of machine learning from our previous example, called “supervised learning,” where supervised learning algorithms try to model relationship and dependencies between the target prediction output and the input features, such that we can predict the output values for new data based on those relationships, which it has learned from previous datasets fed.
Unsupervised learning, another type of machine learning, is the family of machine learning algorithms, which have main uses in pattern detection and descriptive modeling. These algorithms do not have output categories or labels on the data (the model trains with unlabeled data).
Reinforcement learning, the third popular type of machine learning, aims at using observations gathered from the interaction with its environment to take actions that would maximize the reward or minimize the risk. In this case, the reinforcement learning algorithm (called the agent) continuously learns from its environment using iteration. A great example of reinforcement learning are computers reaching a super-human state and beating humans on computer games.
Machine learning can be dazzling, particularly its advanced sub-branches, i.e., deep learning and the various types of neural networks. In any case, it is “magic” (Computational Learning Theory), regardless of whether the public, at times, has issues observing its internal workings. While some tend to compare deep learning and neural networks to the way the human brain works, there are essential differences between the two.