The number of resources about Artificial Intelligence (AI) can be overwhelming. If you want to start learning about it, you’ll probably start running across confusing acronyms and terms like ML, NLP, Deep Learning or Reinforcement Learning that will make you wonder why you started learning AI in the first place.
But don’t worry, there are 8 basic concepts and applications that you need to know in the field of Artificial Intelligence and they are summarized in this post.
Read until the end and let me know which one sounds more interesting to you.
1. Machine learning (ML)
Machine learning enables machines to “learn” a task from experience without programming them specifically about that task. (in short, machines learn automatically without human hand holding!) This process starts with feeding them good quality data and then training the machines by building various models using different algorithms. The choice of algorithms depends on the kind of task we are trying to automate.
However, generally speaking Machine Learning Algorithms are divided into 3 types i.e. supervised learning, unsupervised learning and reinforcement learning.
2. Deep Learning
Deep Learning is a subset of Machine Learning. It enables processing of data and creating predictions using neural networks. These neural networks are connected in a web/like structure like the networks in the human brain.
This web-like structure of artificial neural networks means that they are able to process data in a non-linear approach, which is a significant advantage over traditional algorithms.
One example of a deep neural network is RankBrain which is one of the factors in the Google Search Algorithm.
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3. Reinforcement learning
Reinforcement learning is a part of Artificial Intelligence in which the machine learns something in a way that is similar to how humans learn. As an example, assume that the machine is a student. Here the hipothetical student learns from its own mistakes over time through trial and error.
This means that the algorithm decides the next action by learning behaviours that are based on its current state and that will maximise the reward in the future.
A famous example of Reinforcement Learning is Google’s alpha Go computer Programme that was able to beat the world champion in the game of Go in 2017.
Robotics is a field that deals with creating humanoid machines that
can behave like humans and perform some actions like human
beings. Now, robots can act like humans in certain situations but can they think like humans as well? This is where artificial intelligence
comes in! Al allows robots to act intelligently in certain situations.
These robots may be able to solve problems in a limited sphere or
even learn in controlled environments.
An example of this is Kismet, which is a social interaction robot
developed at MIT’s Artificial Intelligence Lab. It recognizes the
human body language and also our voice and interacts with humans
accordingly. Another example is Robonaut, which was developed by
NASA to work alongside the astronauts in space.
5. Natural Language Processing (NLP)
It’s obvious that humans can converse with each other using speech but now machines can too! This is known as Natural Language Processing where machines analyze and understand language and speech as it is spoken (Now if you talk to a machine it may just talk back!). There are many subparts of NLP that deal with language such as speech recognition, natural language generation, natural language translation, etc.
NLP is currently extremely popular for customer support applications, particularly the chatbot. These chatbots use ML and NLP to interact with the users in textual form and solve their queries. So you get the human touch in your customer support interactions without ever directly interacting with a human.
Some of the most popular examples of NLP applications are Alexa from Amazon and Siri from Apple.
6. Recommender Systems
When you are using Netflix, do you get a recommendation of movies
and series based on your past choices or genres you like? This is done by Recommender Systems that provide you some guidance on what to choose next among the vast choices available online.
A Recommender System can be based on Content-based Recommendation or even Collaborative Filtering. Content-Based Recommendation is done by analyzing the content of all the items. For example, you can be recommended books you might like based on a description of the item and your own basic profile. On the other hand, Collaborative Filtering is done by analyzing the past reading behavior of people similar to you and then recommending books based on that.
7. Computer Vision
The internet is full of images! This is the selfie age, where taking an
image and sharing it has never been easier. In fact, millions of images
are uploaded and viewed every day on the internet. To make the
best use of this huge amount of images online, it’s important that
computers can see and understand images. And while humans can
do this easily without a thought, 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 object detection in the image,
identification of image content to group various images together, etc.
An application of computer vision is navigation for autonomous
vehicles by analyzing images of surroundings such as AutoNav used
in the Spirit and Opportunity rovers which landed on Mars.
8. Internet of Things
Artificial Intelligence deals with the creation of systems that can learn
to emulate human tasks using their prior experience and without any
manual intervention. Internet of Things, on the other hand, is a network of various devices that are connected over the internet and they can collect and exchange data with each other.
Now, all these loT devices generate a lot of data that needs to be
collected and mined for actionable results. This is where Artificial
Intelligence comes into the picture. Internet of Things is used to collect
and handle the huge amount of data that is required by the Artificial
Intelligence algorithms. In turn, these algorithms convert the data into useful actionable results that can be implemented by the loT devices.