Artificial intelligence (AI) has become one of the most popular buzzwords in recent years. Technically, AI is defined as computer systems that are capable of performing tasks typically requiring human reasoning or cognition.
However, not all uses of AI fall under this definition. In fact, many people incorrectly refer to applications using deep learning algorithms as “artificial” intelligent.
This article will discuss some potential use cases for AI beyond just defining it as “intelligent”. While there are no hard definitions for what constitutes an example of AI, we can talk about some characteristics which should be present.
We will also take a look at how two well-known technologies make heavy usage of advanced neural networks – so let’s get started!
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Takeaways: Here are some things you can learn from today’s topic.
How to fix a broken toilet
Recent developments in artificial intelligence have brought us some incredible tools that can be applied to various areas beyond just making jokes about people’s flabby arms. One such area is how to use deep learning for solving practical problems.
If you ever need help fixing your plumbing, from replacing a valve to doing major repairs, there are now ways to apply AI to do it faster and more efficiently. And while this may sound weirdly specific, these programs go into extensive detail and logic when performing tasks, so users don’t necessarily have to teach them everything themselves!
AI has already been used to create systems that can identify objects (such as fruits or cars) and determine what category they belong to, and now companies are using it to perform other complex jobs. This includes helping engineers design new equipment and software, automating repetitive tasks, and even predicting health conditions and symptoms.
So whether you want to learn how to take better pictures with your phone or develop your own games, there are now AI-powered resources available to you. Here we will talk about one particular application of AI: how to fix a broken toilet.
How to repair a leaky pipe
Recent developments in artificial intelligence are now being used to solve problems that were not solved before, or could not be solved using traditional methods. These new applications of AI use computer software algorithms to learn from past experiences to make predictions about future situations.
Deep learning is one such technology. It works by giving the algorithm large amounts of data to process and then teaching it how to associate different patterns with correct outcomes.
This ability to recognize complex relationships makes deep learning particularly useful in fields like image recognition, natural language processing and computational physics.
In this article we will look at some uses of deep learning applied to science. In particular, we will focus on an area known as “deep reinforcement learning” (DRL for short).
What is DRL?
At its most basic level, deep reinforcement learning teaches computers to perform tasks through reward and punishment.
For example, if you want your robot to move forward, you give it rewards when it moves and punishments when it does not. The same principle applies in business – companies offer promotions to motivate their employees, and they pay out bonuses for good performance.
By applying these rewards and punishments in relation to what action produces the best result, the system learns how to perform the task effectively.
A classic application of this concept is playing games. If you have ever played Angry Birds, Super Mario Brothersor even Fortnite, you have experienced automatic game control via reward and punishment.
Identify and prevent fraud
Over the past few years, there has been an explosion of interest in using computer algorithms to solve “big data” problems. These algorithmic solutions are often referred to as artificial intelligence (AI). A popular application of AI is applying it to vast troves of unstructured information to identify patterns and determine what actions need to be taken based on those patterns.
With the growth of online shopping, digital records of transactions abound. Retailers collect large amounts of data about their customers via credit cards, mobile apps, and surveys. This rich source of information can help predict buying behavior, detect fraudulent activity, and improve customer service.
Fraudulent acts range from someone trying to steal your money by creating fake accounts for you or taking over yours with a bad loan, to actively sabotaging your purchases. More sinisterly, criminals may use stolen payment info to make illegitimate charges or spend extra time tracking down personal details that could aid in identity theft.
There are already some systems out there that try to detect if payments have been made by mistake or due to fraud. But experts agree that we are still at the beginning stages of developing tools that can reliably do this. By and large, most people feel that current methods work well enough unless something seems off.
Artificial intelligence offers a promising new approach to solving these challenges.
Detect and respond to domestic violence
Recent developments of deep learning have allowed for applications in areas that were not possible before. One such application is detecting instances of domestic violence against intimate partners or family members.
Domestic violence, also known as spousal abuse or intimate partner violence (IPV), is defined as any kind of abusive behavior between individuals who are married or in an intimate relationship with each other.
This includes acts like physical assault, psychological manipulation, stalking, and harassment. It is a serious epidemic that too many people ignore or try to justify as “irrelevant” cases of disagreement.
Because it occurs within close quarters and often times goes unreported, eyewitnesses may be hard to come by. This makes proving allegations difficult unless additional witnesses arise from outside sources or internal documents get leaked.
That’s why technology has become important in helping address this epidemic. Technology can now observe, document, and collect data about alleged abusers via smartphone videos and recordings, text messages, calls, and emails.
These apps then use AI software to analyze this content and determine if there are signs of abuse. If there are, authorities are notified immediately and steps can be taken to protect the victim.
Help find and rescue hostages
Recent developments in computer science allow computers to learn tasks we teach them, referred to as “deep learning.” This is different from using algorithms that require programmers to create functions for the machine to perform. With deep learning, machines are able to figure out how to complete tasks on their own!
Deep neural networks can be trained to recognize patterns in large amounts of data. For example, they may be taught to identify if a picture contains a dog or not. Once this has been learned, the system will look at new pictures and tell you if there is a chance it is a dog or not.
This technology was first used in 2014 to help with natural language processing (NLP). NLP uses complex rules to understand what people mean when they talk. For instance, software could now detect irony in speech due to the way the words relate to each other. More than just detecting sarcasm, these programs have advanced features such as determining whether something is a threat or promise.
Neural networks have since been incorporated into many areas beyond NLP. These applications include image recognition, voice recognition, and even self-driving cars! In all cases, the neural network is given lots of examples of the thing it needs to learn and then it figures out the rest by itself.
Detect and identify fake news
Recent developments in artificial intelligence (AI) have allowed for advanced computer programs to improve their learning through exposure to large amounts of data. These so-called deep neural networks are trained using huge datasets that contain vast quantities of information.
By having computers analyze large groups of examples, the system will eventually figure out how to connect patterns together to create understanding. In other words, it will learn what things go together.
Deep learning has been applied to various fields, but one of its biggest successes to date is in the field of social media. Companies like Facebook and Google use AI systems to scan messages, comments, and conversations to determine if something or someone is fraudulent or not.
These systems now employ techniques such as deep convolutional neural nets to process all sorts of content. By looking at pictures, videos, and text together, they can detect certain features that indicate fraud.
Another area where researchers have seen success with deep learning is in detecting fake news. Computers may be able to look at an article headline, body, and source to determine whether it contains false information or not.
Because machines can process lots of complex information very quickly, there’s no limit to the number of articles they could review, making this technique very efficient.
Identify and recognize different types of art
Recent developments in computer technology have allowed for advanced applications in the field of arts education. One such application is to classify and identify various styles, genres, and masters of painting, sculpture, or other artistic mediums.
Artists develop their style over years. They are usually inspired by something they see or feel and then apply that inspiration onto new pieces. Computers can now detect these similarities and qualities and create an accurate classification of what type of artist you are!
A company called ArtAI uses deep learning to do just this. By looking at large amounts of data, it learns how artists use certain colors, shapes, and textures, and applies those principles to determine what genre your artwork belongs to.
Their software was able to correctly identify the following genres from a set of 1,000 pictures with no formal training: Abstract, Expressionist, Figurative, Landscape, Naive, Photorealistic, Primitive, Romantic, Still Life, Surrealism, Vandalized, and Western.
That’s a lot of styles! Most companies only test on smaller datasets so it is quite impressive to see what the algorithm knows about art.
Find your perfect match
There are many applications for AI that seem almost magical, like using it to find your ideal pair of jeans or to predict whether someone you’ve never met before will be your new best friend or not.
Some use it to determine which products at a store would fit together or what recipes make the most sense to put together next. Others apply it to analyze pictures and videos to see if there is anything potentially interesting hidden within.
With the explosion of data we have available through technology these days, applying AI to find patterns has become very common. This is how advanced algorithms work! They look for trends and correlations among large amounts of information.
There are so many ways companies use AI today. It is definitely here to stay and increase in popularity rapidly. Recent examples include chatbots, voice recognition software, and autonomous vehicles. All of these things require significant uses of deep learning.
What is deep learning?
Deep learning is an artificial intelligence (AI) technique that requires lots of training datasets and computational power to function. Training datasets contain enough examples to teach the algorithm what different types of inputs mean and what outputs they should get.
The more data the better, but it is wasteful to train an algorithm unless you have access to plenty! That is why it is important to use strategies to maximize its effectiveness.
Research has shown that when developing AI systems, trying to test on as diverse a dataset as possible is the way to go.