Introduction to artificial intelligence
Imagine a future in which intelligence is not limited to human beings !!! A future where machines can think like humans and work with humans to create an even more exciting universe. Although the future is far away, artificial intelligence is still very much in vogue. A lot of research is being done in almost all fields of AI such as quantum computing, healthcare, autonomous vehicles, internet, robotics, etc. to increase the number of research articles published annually by 90%.
An Overview of Artificial Intelligence Development by 1996!
Machine learning involves the use of artificial intelligence to enable machines to learn a task from experience without special programming.
In a nutshell we can also say that machines learn automatically without human hands !!! This process starts with feeding them good quality data and then learning the machines using data and various algorithms. Makes various models and trains machines. The choice of algorithm depends on what kind of data we have and what kind of work we are trying to automate.
However, in general, machine learning algorithms are divided into 3 types
٭… Monitoring machine learning algorithm, ٭… Non-supervised machine learning algorithm, ٭ ٭ Reinforcement machine learning algorithm.
In-depth machine learning
Deep learning is a subset of machine learning that learns by copying the inner workings of the human brain to process data and make decisions based on that data. Basically, deep learning uses artificial neural networks to implement machine learning. These neural networks are interconnected in a web-like structure similar to that of the human brain (basically a simpler version of our brain).
This web-like structure of artificial neural networks means that they are able to process data in nonlinear perspectives, a significant advantage over traditional algorithms that can only process data in a linear perspective. An example of a deep neural network is the rank brain, which is a factor in the Google search algorithm.
Reinforcement Learning is a part of artificial intelligence in which the machine learns something that is equivalent to human learning. For example, suppose the machine is a student. Here the fictitious student learns from his mistakes over time (as we had to do !!). (So learning the reinforcement machine learns more and more through trial and error. Learns to decide the next course of action based on its current state and will maximize rewards in the future. And like humans, it works for machines too! For example, Google Alfago’s computer program in 2017 using enforcement learning, such as a chess computer robot game, managed to beat the world champion.
Robotics is a field related to building human-like machines that can behave like humans and perform certain human-like movements. Now, robots can act like humans in certain situations, but can they also think like humans? This is where artificial intelligence comes in! AI allows robots to work intelligently in certain situations. These robots may be able to solve problems in a limited circle or learn in a controlled environment.
An example of this is Kismat, a social interaction robot developed in M.I.T’s Artificial Intelligence Lab. It recognizes the language of the human body as well as our voice and interacts with humans accordingly. Another example is the Robonote, developed by NASA to work with astronauts in space.
The internet is full of pictures! This is the age of selfies, where it has never been easier to take a picture and share it. In fact, millions of images are uploaded and viewed on the Internet every day. To make the most of online images, it is important for computers to be able to view and understand images. And while humans can easily do this without thinking, it’s not so easy for computers! This is where computer vision comes in. Computer vision uses artificial intelligence to extract information from images. This information can be used to identify the object in the image, to identify the contents of the image to group different images together. Computer Vision applies to the navigation for suicide vehicles that analyzes images of the environment, such as the astronomical InSpirit and Opportunity Rovers that landed on Mars.
When you’re using Netflix, do you get recommendations for movies and series based on your past choices or genres of your choice? This is done through suggestion systems that provide you with some guidance on choosing the next option in the wide selection available online. A suggestive system can be based on content-based recommendation or even collaborative filtering.
Content-based recommendations are made by analyzing the contents of all items. For example, you may be recommended books that you like based on natural language processing. On the other hand, collaborative filtering is done by analyzing your past reading behavior and then recommending books based on it.
The Internet of Things
It involves the creation of an artificial intelligence system that can learn to mimic human actions using its previous experience and without any manual intervention. The Internet of Things, on the other hand, is a network of different devices that are connected to the Internet and can collect and exchange data with each other. Need to submit and sort for.
This is where artificial intelligence comes into the picture. The Internet of Things is used to collect and store large amounts of data that require artificial intelligence algorithms. As a result, these algorithms convert data into useful actionable results that can be implemented by IoT devices details from etechnews.