Source: learnthenet.com

Viruses, worms, Trojan horses, botnets, malware and spyware are human-made software programs created specifically to wreak mischief on personal computers and networks. The chance of contracting one of these malicious programs over the Internet has increased dramatically. Unless you exercise great caution or routinely run anti-virus software, your computer will almost certainly become infected. Typically, you get a virus by opening infected e-mail attachments or downloading and installing infected software.

Some viruses are relatively harmless to individuals. They just attach themselves to outgoing messages and e-mail themselves to all the contacts listed in your address book. The sudden flood of e-mail overwhelms mail servers, causing the system to crash.

Other viruses are more destructive and may lie dormant until a certain date. Then they spring to life to do their dirty deeds. Sometimes a strange message appears on your screen, or data and programs may be modified. In the worst case, all the files on your hard drive may be wiped out. These pernicious programs start on one computer, then replicate quickly, infecting other computers around the world.

In 1988 a student at Cornell University sent out a virus out by accident, infecting more than 6,000 computers in minutes, nearly bringing the Internet to its knees. The "I Love You" virus caused over $1 billion USD in lost productivity as it crippled e-mail systems worldwide in 2000. And a worm called Conficker hobbled 15 million computers in 2008 and continues to do damage.

If you download and run software from the Internet or receive e-mail attachments, protect yourself by using anti-virus programs to scan attachments and downloaded programs to alert you of infection. The software also scans your hard drive periodically, searching for rogue viruses and deleting them. The two most popular programs are from McAfee.com and Symantec.

 

Text №7

 

NEURAL NET

NEURAL NET Characterization

In general a biological neural network is composed of a group or groups of chemically connected or functionally associated neurons. A single neuron may be connected to many other neurons and the total number of neurons and connections in a network may be extensive. Connections, called synapses, are usually formed from axons to dendrites, though dendrodendritic microcircuits and other connections are possible. Apart from the electrical signaling, there are other forms of signaling that arise from neurotransmitter diffusion, which have an effect on electrical signaling. As such, neural networks are extremely complex. Whilst a detailed description of neural systems is nebulous, progress is being charted towards a better understanding of basic mechanisms.

Simplified view of an artificial neural network

Artificial intelligence and cognitive modeling try to simulate some properties of neural networks. While similar in their techniques, the former has the aim of solving particular tasks, while the latter aims to build mathematical models of biological neural systems.

In the artificial intelligence field, artificial neural networks have been applied successfully to speech recognition, image analysis and adaptive control, in order to construct software agents (in computer and video games) or autonomous robots. Most of the currently employed artificial neural networks for artificial intelligence are based on statistical estimation, optimization and control theory.

The cognitive modeling field is the physical or mathematical modeling of the behavior of neural systems; ranging from the individual neural level (e.g. modeling the spike response curves of neurons to a stimulus), through the neural cluster level (e.g. modeling the release and effects of dopamine in the basal ganglia0 to the complete organism (e.g. behavioral modeling of the organism’s response to stimuli).

The brain, neural networks and computers

Neural networks, as used in artificial intelligence, have traditionally been viewed as simplified models of neural processing in the brain, even though the relation between this model and brain biological architecture is debated. To answer this question, David Marr has proposed various levels of analysis which provide us with a plausible answer for the role of neural networks in the understanding of human cognitive functioning.

A subject of current research in theoretical neuroscience is the question surrounding the degree of complexity and the properties that individual neural elements should have to reproduce something resembling animal intelligence.

Historically, computers evolved from the von Neumann architecture, which is based on sequential processing and execution of explicit instructions. On the other hand, the origins of neural networks are based on efforts to model information processing in biological systems, which may rely largely on parallel processing as well as implicit instructions based on recognition of patterns of “sensory” input from external sources. In other words, rather than sequential processing and execution, at their very heart, neural networks are complex statistical processors.

 

TEXT №8