Estimated reading time: 10 minutes
Growing investments in AI applications and the growing use of AI in the enterprise space are indicative of how the job market is evolving, for AI experts.
Artificial intelligence is probably one of the most exciting advancements we are experiencing as humans. It is a branch of computer science dedicated to creating intelligent machines that work and react like humans.
There are four main types of AI. I am:
This type of AI is purely reactive and does not have the ability to form “memories” or use “past experiences” to make decisions. These machines are designed to perform specific tasks. For example, programmable coffee makers or washing machines are designed to perform specific functions, but they do not have memory.
This type of AI uses past experiences and present data to make a decision. Limited memory means that machines do not produce new ideas. They have a built-in program that manages the memory. Reprogramming is done to make changes to such machines. Self-driving cars are examples of artificial intelligence with limited memory.
These AI machines can socialize and understand human emotions and will have the ability to cognitively understand someone based on their environment, facial features, etc. Machines with such capabilities have not yet been developed. There is a lot of research going on into this type of artificial intelligence.
This is the future of artificial intelligence. These machines will be super-intelligent, sentient and conscious. They are capable of reacting very similarly to a human, although they are likely to have characteristics of their own.
Let's explore the following ways that explain how we can implement artificial intelligence:
is theautomatic learning which gives AI the ability to learn. This is done by using algorithms to discover patterns and generate insights from the data they are exposed to.
THEdeep learning, which is a subcategory of machine learning, provides artificial intelligence with the ability to mimic the neural network of the human brain. It can make sense of patterns, noise, and sources of confusion in your data.
Let's try to understand how it works deep learning.
Consider an image shown below:
The image above shows the three main layers of a neural network:
The images we want to separate go into the input layer. Arrows are drawn from the image onto individual points on the input layer. Each of the white dots in the yellow layer (input layer) represents a pixel in the image. These images fill the white spots in the input layer.
We should have a clear idea about these three levels while following this AI tutorial.
The hidden layers are responsible for any mathematical calculations or feature extraction on our inputs. In the image above, the layers shown in orange represent the hidden layers. The visible lines between these layers are called “weights”. Each of them usually represents a float number, or decimal number, which is multiplied by the value in the input layer. All weights sum in the hidden layer. The points in the hidden layer represent a value based on the sum of the weights. These values are then passed to the next hidden layer.
You may be wondering why there are multiple levels. Hidden layers function as alternatives to some extent. The more hidden layers, the more complex the data that comes in and what can be produced. The accuracy of the expected output generally depends on the number of hidden layers present and the complexity of the input data.
The output layer gives us separate photos. Once the layer adds all these weights entered, it will determine whether the image is a portrait or a landscape.
Example: predicting airline ticket costs
This prediction is based on various factors, including:
Let's start with some historical ticket price data to train the machine. Once our machine is trained, we share new data that will help predict costs. Previously, when we learned about the four types of machines, we discussed machines with memory. Here we just talk about memory and how it understands a pattern in the data and uses it to make predictions for new prices.
Next in this tutorial let's take a look at how AI works and some applications of AI.
A common application of artificial intelligence that we see today is the automatic switching of appliances in the home.
When you enter a dark room, sensors in the room detect your presence and turn on the lights. This is an example of machines without memory. Some of the more advanced AI programs are even able to predict usage patterns and turn on appliances before you give explicit instructions.
Some programs and artificial intelligence applications they are able to identify your voice and perform an action accordingly. If you say “turn on the TV,” the audio sensors on the TV detect your voice and turn it on.
With the company Google Home Mini you can do it every day.
The last section of this AI tutorial illustrates the use case of AI in healthcare.
THEartificial intelligence features several great use cases, and this section of the tutorial will help you understand them better, starting with the applications of AI in healthcare. The problem statement is to predict whether a person has diabetes or not. Specific patient information is used as input for this case. This information will include:
Watch Simplilearn's “Artificial Intelligence Tutorial” video to see how a model is created for this problem statement. The model is implemented with Python using TensorFlow.
Artificial intelligence applications are redefining how business processes are carried out in various fields, such as marketing, healthcare, financial services and more. Companies are continually exploring ways they can benefit from this technology. As the quest to improve current processes continues to grow, it makes sense for professionals to gain expertise in AI.
THEArtificial Intelligence of Things (AIoT) it is the combination of Artificial Intelligence (AI) within Internet of things (IoT) solutions. The Internet of Things (or Internet of Things) is based on the idea of "intelligent" objects of everyday life that are interconnected with each other (thanks to the internet) and are able to exchange information possessed, collected and/or processed.
Thanks to this integration, Artificial Intelligence will be able to connect to the network to process data and exchange information with other objects, improving the management and analysis of enormous quantities of data. Applications capable of integrating IoT and AI will have a radical impact on companies and consumers. Some of the many examples? Autonomous vehicles, remote healthcare, smart office buildings, predictive maintenance.
When we talk about Natural Language Processing we are referring to Artificial Intelligence (AI) algorithms capable of analyzing and understanding natural language, i.e. the language we use every day.
NLP allows communication between man and machine and deals with texts or sequences of words (web pages, posts on social media...), but also with understanding spoken language as well as texts (voice recognition). The purposes can vary from simple understanding of the content, to translation, up to the production of text independently starting from data or documents provided as input.
Although languages are constantly changing and characterized by idioms or expressions that are difficult to translate, NLP finds numerous application areas such as spell checkers or automatic translation systems for written texts, chatbots and voice assistants for spoken language.
Lo Speech Recognition is a capability that allows a computer to understand and process human language in written or other data formats. Thanks to the use of Artificial Intelligence, this technology is now able to identify not only natural language, but also other nuances such as accents, dialects or languages.
This type of voice recognition allows you to perform manual tasks that usually require repetitive commands, for example in chatbots with voice automation, to route calls in contact centers, in dictation and voice transcription solutions, or in PC user interface controls, mobile and on-board systems.
THEGeneral Artificial Intelligence (in English Artificial General Intelligence, or AGI) is a type of AI that has the ability to understand, learn and tackle complex tasks similarly to humans.
Compared to Artificial Intelligence Systems specialized in specific tasks (Narrow Artificial Intelligence or ASI – Narrow AI), an AGI demonstrates cognitive versatility, learning from different experiences, understanding and adaptability to a wide range of situations without requiring specific programming for each individual task.
Despite the current distance, the final objective of an AGI is - although certainly a complex task - to go to replicate the human mind and cognitive abilities as closely as possible.
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