An AI system is composed of an agent and its environment. An agent(e.g., human or robot) is anything that can perceive its environment through sensors and acts upon that environment through effectors. Intelligent agents must be able to set goals and achieve them. In classical planning problems, the agent can assume that it is the only system acting in the world, allowing the agent to be certain of the consequences of its actions. However, if the agent is not the only actor, then it requires that the agent can reason under uncertainty. This calls for an agent that cannot only assess its environment and make predictions but also evaluate its predictions and adapt based on its assessment. Natural language processing gives machines the ability to read and understand human language. Some straightforward applications of natural language processing include information retrieval, text mining, question answering and machine translation. Machine perception is the ability to use input from sensors (such as cameras, microphones, sensors etc.) to deduce aspects of the world. e.g., Computer Vision. Concepts such as game theory, decision theory, necessitate that an agent be able to detect and model human emotions. Many times, students get confused between Machine Learning and Artificial Intelligence, but Machine learning, a fundamental concept of AI research since the field’s inception, is the study of computer algorithms that improve automatically through experience. The mathematical analysis of machine learning algorithms and their performance is a branch of theoretical computer science known as a computational learning theory. Stuart Shapiro divides AI research into three approaches, which he calls computational psychology, computational philosophy, and computer science. Computational psychology is used to make computer programs that mimic human behavior. Computational philosophy is used to develop an adaptive, free-flowing computer mind. Implementing computer science serves the goal of creating computers that can perform tasks that only people could previously accomplish.
In a very layman manner, Machine Learning(ML) can be explained as automating and improving the learning process of computers based on their experiences without being actually programmed i.e. without any human assistance. The process starts with feeding good quality data and then training our machines(computers) by building machine learning models using the data and different algorithms. The choice of algorithms depends on what type of data do we have and what kind of task we are trying to automate.While preparing for the exams students don’t actually cram the subject but try to learn it with complete understanding. Before the examination, they feed their machine(brain) with a good amount of high-quality data (questions and answers from different books or teachers notes or online video lectures). Actually, they are training their brain with input as well as output i.e. what kind of approach or logic do they have to solve a different kind of questions. Each time they solve practice test papers and find the performance (accuracy /score) by comparing answers with answer key given, Gradually, the performance keeps on increasing, gaining more confidence with the adopted approach. That’s how actually models are built, train machine with data (both inputs and outputs are given to model) and when the time comes test on data (with input only) and achieves our model scores by comparing its answer with the actual output which has not been fed while training. Researchers are working with assiduous efforts to improve algorithms, techniques so that these models perform even much better .
Language has been our primary mean of communication and human interaction for thousands of years. For a community, the language contained the words that the people need to communicate, words themselves are abstract, but they indicate the meaning, they point to objects or actions, etc.. When you look at your computer, you’ll find it’s not so much different. There are many pieces of hardware and software that need to communicate with each other. Your application is reacting to the mouse and keyboard or even the mic, it can read files from your disk storage and so on. But at the end of the day, the machine understands nothing but bits, 1s, and 0s, the combination of which creates meaning. The very earliest computers were actually programmed by changing ones and zeros manually, alternating the circuit and the wiring. Of course, it was not easy to create many programs as most were used for specific applications only, and they were gigantic in size so they were quite limited. That’s why the creation of programming languages was a revolutionary step that took the field to another level. Unlike normal languages, keywords in programming languages are limited, and by combining these keywords, developers are able to create different types of programs. There are special pieces of software that turn the code you write into machine language that the machine understands. So what is programming language? In short, a programming language is the set of instructions through which humans interact with computers. The code is pretty much like writing a paragraph of instruction or creating a to-do list to computers. Unlike us humans, the to-do list and instructions you write for the computer has to be extremely detailed and written in some logic. With code and programming, you can get the computer to draw complex shapes and create rich computer graphics, and then create programs that understand game mechanics and help you build games that feel real with gravity and particle collision, with these programs you can create the most intense and immersive games of all sorts. With code and programming, you can create and send content all over the world with your blog and personal website and style your blog to meet your style. You can build tech-driven business solutions and reach a wider range of customer and cater to a wider range of needs. Furthermore, with code and programming, you can create smart home applications, like an automated pet feeder, a smart mirror or even create a robot that can help around with household tasks and be your virtual assistant to talk to and understand you. Unlike what many people think, there’s a lot of art involved in computer engineering and computer science.
How are you reading this post right now? It might be on desktop, on mobile, maybe a tablet, but whatever device you’re using, it’s most definitely connected to the internet. An internet connection is a wonderful thing, it give us all sorts of benefits that just weren’t possible before. If you’re old enough, think of your cellphone before it was a smartphone. You could call and you could text sure, but now you can read any book, watch any movie, or listen to any song all in the palm of your hand. And that’s just to name a few of the incredible things your smartphone can do.The point is that connecting things to the internet yields many amazing benefits. We’ve all seen these benefits with our smartphones, laptops, and tablets, but this is true for everything else too. And yes, I do mean everything. The Internet of Things is actually a pretty simple concept, it means taking all the things in the world and connecting them to the internet. I think that confusion arises not because the concept is so narrow and tightly defined, but rather because it’s so broad and loosely defined. It can be hard to nail down the concept in your head when there are so many examples and possibilities in IoT. To help clarify, I think it’s important to understand the benefits of connecting things to the internet. Why would we even want to connect everything to the internet?When something is connected to the internet, that means that it can send information or receive information, or both. This ability to send and/or receive information makes things smart, and smart is good. Let’s use smartphones (smartphones) again as an example. Right now you can listen to just about any song in the world, but it’s not because your phone actually has every song in the world stored on it. It’s because every song in the world is stored somewhere else, but your phone can send information (asking for that song) and then receive information (streaming that song on your phone). To be smart, a thing doesn’t need to have super storage or a super computer inside of it. All a thing has to do is connect to super storage or to a super computer. Being connected is awesome.This means sensors. Sensors could be temperature sensors, motion sensors, moisture sensors, air quality sensors, light sensors, you name it. These sensors, along with a connection, allow us to automatically collect information from the environment which, in turn, allows us to make more intelligent decisions.On the farm, automatically getting information about the soil moisture can tell farmers exactly when their crops need to be watered. Instead of watering too much (which can be an expensive over-use of irrigation systems and environmentally wasteful) or watering too little (which can be an expensive loss of crops), the farmer can ensure that crops get exactly the right amount of water. More money for farmers and more food for the world! Just as our sight, hearing, smell, touch, and taste allow us, humans, to make sense of the world, sensors allow machines to make sense of the world.