Threats From Spyware

Do you get annoyed with the banner ads displaying on your computer while browsing the internet? This banner ads are displayed thanks to a software called spyware. To some this banner ads do not bother them at all, but in the background (they banner ads) makes one’s computer slow. Then there are spyware that changes your browsing capability, by redirecting your searches, thus controlling the results ‘you see. This is just the start, there are spyware that alters your registry, making your computer hijacked by some hacker.

Spyware are also employed by online fraudsters for identity theft. This software has the capability to gather all the user information such as banking password, credit card information, PIN# etc…, and are designed for capturing your banking information, and runs only when you visit a banking site, and monitors all the information that you type in. They are also designed to use modem to make use of dial up connection long distance calls, leaving the poor user to pay the bills.

The first line of defense against spyware is the firewall. Firewall acts as a vanguard against any potential threat coming to you or to your family while they are on the internet. Firewall stops hackers, intruders, and online predators to penetrate inside your computer, thus acting as a barrier between your family and the internet. Though there are spyware available that changes ones firewall settings and intelligent enough that when you try to remove it, they stop you doing it.

All the above mentioned threats are real, they can not only make ones system unusable (though that’s not its primary intent), but also takes ones computer resource, gathers all the information about one, such as users surfing habits, credit card and banking information . They Are If are also used heavily by hackers and crackers to perform their devious crime against your name namely identity theft.… Read More

Artificial Intelligence and Machine Learning Basics

Introduction

During the past few years, the terms artificial intelligence and machine learning have begun showing up frequently in technology news and websites. Often the two are used as synonyms, but many experts argue that they have subtle but real differences.

And of course, the experts sometimes disagree among themselves about what those differences are.

In general, however, two things seem clear: first, the term artificial intelligence (AI) is older than the term machine learning (ML), and second, most people consider machine learning to be a subset of artificial intelligence.

Artificial Intelligence vs. Machine Learning

Though AI is defined in many ways, the most widely accepted definition of being “the field of computer science dedicated to solving cognitive problems commonly associated with human intelligence, such as learning, problem solving, and pattern recognition”, in essence, it is the idea that machines can possess intelligence.

The heart of an Artificial Intelligence based system is it’s model. A model is nothing but a program that improves its knowledge through a learning process by making observations about its environment. This type of learning-based model is grouped under supervised Learning. There are other models which come under the category of unsupervised learning Models.

The phrase “machine learning” also dates back to the middle of the last century. In 1959, Arthur Samuel defined ML as “the ability to learn without being explicitly programmed.” And he went on to create a computer checkers application that was one of the first programs that could learn from its own mistakes and improve its performance over time.

Like AI research, ML fell out of vogue for a long time, but it became popular again when the concept of data mining began to take off around the 1990s. Data mining uses algorithms to look for patterns in a given set of information. ML does the same thing, but then goes one step further – it changes its program’s behavior based on what it learns.

One application of ML that has become very popular recently is image recognition. These applications first must be trained – in other words, humans have to look at a bunch of pictures and tell the system what is in the picture. After thousands and thousands of repetitions, the software learns which patterns of pixels are generally associated with horses, dogs, cats, flowers, trees, houses, etc., and it can make a pretty good guess about the content of images.

Many web-based companies also use ML to power their recommendation engines. For example, when Facebook decides what to show in your newsfeed, when Amazon highlights products you might want to purchase and when Netflix suggests movies you might want to watch, all of those recommendations are on based predictions that arise from patterns in their existing data.

Artificial Intelligence and Machine Learning Frontiers: Deep Learning, Neural Nets, and Cognitive Computing

Of course, “ML” and “AI” aren’t the only terms associated with this field of computer science. IBM frequently uses the term “cognitive computing,” which is more or less synonymous … Read More