I am a 29 years old Linux admin, techie and nature lover who loves solving puzzles. When I am not behind the keyboard you can find me in the woods but I will still probably be thinking about that server or that ticket I saw today.
In this tutorial, we will look at several methods that are used to compromise a website. In today’s world, websites use multiple procedures that represent the core functions of a modern business. Whether you have an eCommerce site or a business card site, a website is essential for driving business growth. We can safely state that a website is a unique image of your respective business.
Both machine learning (ML) and artificial intelligence (AI) are fundamental parts of humanity’s quest to develop brilliant, self-educating, learning machines. While the two terms may sound similar, in reality many differences define them. It is enough to ask ourselves this simple question to better understand the difference between AI and ML: How do we teach a machine?
We can look for an answer in the basic definition of what artificial intelligence and machine learning is.
What is Artificial Intelligence (AI)?
AI or Artificial intelligence is a term that describes machines capable of learning from previous experience. It uses that learning to make decisions based on those experiences. Artificial intelligence can be broad or narrow depending on its use. AI constitutes a much broader term than Machine Learning.
A more in-depth explanation about AI and how it’s changing the world today can be found in this knowledgebase article: What Is AI? A Beginners Guide
What is Machine Learning (ML)?
Machine learning is a machine’s ability to learn by using advanced algorithms. In time, we can teach machines to recognize patterns, objects, and other input data we provide to a device. We can say that if we genuinely wish to have artificial intelligence, machine learning is the primary component.
Types of Machine Learning
When we talk about machine learning, there are three types we will consider.
This type of machine learning is one of the most basic types of machine learning. A supervised learning machine algorithm is trained on a small data set that needs to be labeled by a human. It will learn from it and try to apply solutions on more massive datasets. The training dataset will provide the fundamental algorithm problem, data points, and the solution.
While in this type of machine learning, as we mentioned previously, data sets need to be labeled. This is a useful solution, as small scale initial data can be applied to a larger, more significant data set. To simplify, machines can learn using a small example and apply that learning in a larger manner.
As a contrast to supervised learning, unsupervised learning does not require its input data sets to be labeled. Therefore, we remove the human intervention component. In this model, a machine will be given an unlabeled data set. It will attempt to extract some structure from the data provided. It will try to learn how to create outputs from the given inputs. And since it’s datasets will not be labeled, it will not be as accurate as supervised learning. Since a machine must learn on its own, it can better adapt to dynamically changing data structures using this knowledge.
This type of education is the most similar to humans learning from experience. It revolves around the concept of an algorithm that improves using a trial and error method. Favorable outputs are reinforced or “rewarded” and unfavorable are discouraged or “punished”. Since we are talking about a machine, many will ask how a device is rewarded or punished in terms of machine language?
It will merely be something like:
This action was good (X percentage)
This action was bad (Y percentage)
In this model, the machine will not only learn, but it will be efficient at the same time. It’s reward will increase or decrease depending on the overall effectiveness in terms of a percentage value. It will not only learn how to get to the right solution, but also how to get to it the fastest.
What is the Difference Between Machine Learning and Artificial Intelligence?
While on the surface, it may seem simple. There are however complex relationships between deep learning, machine learning, and artificial intelligence. In the following diagram, we see the differing layers of intelligence.
Deep learning (DL), while similar to ML, also learns from experience but uses much larger data sets. An example is Autonomous cars: Researchers use deep learning to automatically detect and respond to objects like stop signs, traffic lights and pedestrians.
Machine learning (ML) refers to a system that learns by experience. Examples include speech and image recognition systems.
Artificial intelligence (AI) is a broad term whose primary goal is to create an intelligent machine. Examples include manufacturing robots and AI assistants like Alexa or Google Home.
Artificial Intelligence vs. Machine Learning
Emphasis is on increasing success
Emphasis is on increasing accuracy
Similar to smart computer program
Works on a principle of data collection where a machine learns from accumulated data
Objective is to simulate human intelligence to solve a problem
Goal is to maximize performance by using data collected while performing a specific task
AI will try and use optimal solution
ML will try to use the only solution
Objective is intelligence
Objective is the accumulation of knowledge
Some examples of machine learning are programs that monitor stock exchange and use historical data to predict the stock price or programs that track weather patterns and will try to predict the weather.
We can say that artificial intelligence will try to simulate the human brain and apply intelligent solutions to a problem presented by looking at all options weighing the consequences, and then proposing the answer. In comparison, machine learning will represent a cold, dark AI that we know from sci-fi movies.
Is this an actual possibility? Not really. However, it works similarly. It will only collect the data in machine learning and then apply the solution without second-guessing or rethinking it. However, it is essential to understand that machine learning is designed to be efficient and productive and, as such, has a different overall purpose then artificial intelligence.
In conclusion, while artificial intelligence and machine learning are connected in a sense that machine learning is a necessary component of creating artificial intelligence, they are each designed to fulfill a different purpose. Should we be worried that machine learning is a component of artificial intelligence? Not really since it is only a part of it and can represent basic instincts that we can observe on humans. If we see someone burning their hand on the surface, our instinct is not to touch it. In a sense, our brain behaves as a learning machine. We had an input of data where we saw someone burned their hand, and our output or conclusion was not to touch it.
Artificial intelligence is the next logical step in computers’ evolution, and machine learning dramatically advances that goal. In the right direction and having a distinct and unique purpose that can benefit all humanity while developing true AI is underway.
Give us a call at 800.580.4985, or open a chat or ticket with us to speak with one of our knowledgeable Solutions or Experienced Hosting advisors to learn how you can take advantage of these techniques today!
A virtual machine is a simulated computer system which runs on a physical computer. In other words, a virtual machine is a computer inside a computer. Virtual machines allocate memory, a virtual CPU, disk storage space, and a network interface. This means that we can have a Windows computer that runs multiple virtual machines composed of Ubuntu Linux, macOS, Windows 10, Solaris, and CentOS, and they will all be completely separate from our parent Windows operating system.
Artificial intelligence (AI) is a term to describe a branch of computer science that is dedicated to creating intelligent machines that would learn to work and react like humans. The field of Artificial Intelligence is not limited to creating only high levels of machine learning, but also to utilize data input in a manner required to produce a needed result. AI has multiple varieties and characteristics. Some of these types are noted as follows.
When reviewing your servers security, it is critical for businesses to ensure that while building new sections of your website, that we do not leave it unsecured or visible to users while it is being built. With this in mind, there are several ways for you to “lock” a folder or domain while it is being developed. This will safeguard a folder or an entire site using the security feature built into IIS and Plesk called password protection. In today’s article we will see how easy it is to restrict access to a site or a folder.
Keras is a Python-based high-level neural networks API that is capable of running on top TensorFlow, CNTK, or Theano frameworks used for machine learning. It can be said that Keras acts as the Python Deep Learning Library. Keras was created with emphasis on being user-friendly since the main principle behind it is “designed for human beings, not machines.” The core data structure of Keras is a model, or a way to organize layers.
What happens when a site is compromised? How quickly can you respond when you recognize the signs that something is amiss? In today’s article, we will explore some of the more common indicators of a compromised website, how you can spot it, and how to act on it in a timely manner.
With today’s modern technology, a website is one of the most critical components of developing a business and growing it within a targeted global space. The age of small business has passed, and the time when every business can make itself known worldwide has arrived. In this new world, an innovative industry has been growing for specific clients’ web hosting needs in this area. In this article, we will talk about two of the most common ways of meeting those hosting needs, including utilizing VPS hosting versus utilizing reseller hosting.