First of all, Linux is very lightweight while Windows is heavy. In Windows, a lot of programs run in the background and they eat up the RAM. Secondly, in Linux, the file system is very much organized.
Linux usually uses less RAM and CPU usage, so it can boot and run faster than Windows.
Source: Wondershare Recoverit
Windows and Linux have been around roughly the same amount of time (both were begun around 1990) but Linux itself was primarily a rewriting of the older AT&T Unix operating system first developed by Kernigen, Ritchie and Thompson in the late 1960s. As such, much of its architecture was already proven out by the time Linus Torvald first forked Linux from Minix, yet another Unix implementation.
This architecture was built primarily upon the concept of pipes and layered security. A pipe moves information from one form or representation to another. through a series of transformations. This process is an example of declarative programming. Perhaps the most intuitive declarative environment that people experience on a day to day basis is a spreadsheet, where if you change a number in a row of numbers, the sum of those numbers will automatically change, and so will any number dependent upon that sum.
Such programs are called state machines. This means that for every specific set of inputs, you will always have the same states throughout the environment. One key benefit of this approach is that there are no side effects – data that is hidden can’t affect the answers that are returned. This makes for extraordinarily stable applications.
When Windows was first introduced, a new language, called C++, was gaining in popularity. C++ added classes to the C language, and popularized a programming paradigm called object oriented programming (OOP).
OOP was a powerful new way of organizing information, by encapsulating that information in what were called class instances. Yet one consequence of this was that it became possible for a class to change state behind the scenes, so that data coming from such classes was no longer consistent. There were side effects.
There are multiple distributions (known as distros) of Linux, each of which features a kernel that defines core functionality and then a set of intermediate packages, package managers and libraries that are implemented differently from one distro to the next. Yet all share the same fundamental security model (one that is quite well tested at this point), and largely declarative core.
This typically means that when an application fails, it fails in its own box, without bringing the whole operating system down. This typically means that Linux-based systems at their core are very stable, even if applications written on top of them aren’t.
In the early 2000s, Microsoft introduced a new programming paradigm called dotNET, and with it a new set of programming languages such as C# (which took many of the garbage collection and sandbox features that had started appearing in Java and made them more core).
It also introduced another language called F# that was much more declarative (its roots are the declarative language Haskell) which developers could use to minimize side effects significantly. One upshot of these innovations is that Windows currently is far more stable, though still arguably not quite as stable as Linux.
One of the most intriguing innovations of the last few years has been the introduction of dockers, which are essentially stripped down virtual machines that provide just enough information to keep an application separated from the core operating system. These have the advantage of being more stable, because they are not dependent upon the stability of the underlying system, nor can they influence it. They are also more secure, because these docked applications exist within a separate security context that doesn’t touch system security at all.
As such, it increasingly makes the point moot about which is the better system. If you write applications on the web in Windows, it’s highly likely that that Windows environment is running on a Linux based machine.
Artificial Intelligence in the 4th Industrial Revolution
Artificial intelligence is providing disruptive changes in the 4th industrial revolution (Industry 4.0) by increasing interconnectivity and smart automation.
Industry 4.0 is revolutionizing the way companies manufacture, improve and distribute their products.
What Makes Artificial Intelligence Unique?
Artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks.
It allows computers to think and behave like humans, but at much faster speeds and with much more processing power than the human brain can produce.
AI offers advantages of new and innovative services, and the potential to improve scale, speed and accuracy.
There are 3 types of artificial intelligence:
Artificial narrow intelligence (ANI), which has a narrow range of abilities.
Artificial general intelligence (AGI), which is on par with human capabilities.
Artificial superintelligence (ASI), which is more capable than a human.
Artificial intelligence can also be classified as weak or strong.
Weak AI refers to systems that are programmed to accomplish a wide range of problems but operate within a predetermined or pre-defined range of functions. Strong AI, on the other hand, refers to machines that exhibit human intelligence.
Artificial intelligence has several subsets:
Most AI examples that you hear about today – from chess-playing computers to self-driving cars – rely heavily on deep learning and natural language processing.
What is the Fourth Industrial Revolution?
The Fourth Industrial Revolution is the current and developing environment in which disruptive technologies and trends such as the Internet of Things (IoT), robotics, virtual reality (VR) and artificial intelligence (AI) are changing the way modern people live and work. The integration of these technologies into manufacturing practices is known as Industry 4.0.
The first industrial revolution used water and steam power to mechanize production.
The second used electric power to create mass production.
The third used electronics and information technology to automate production.
The fourth Industrial revolution is characterized by a fusion of technologies that is blurring the lines between the physical, digital, and biological spheres, with rising emerging technologies, as real AI, Narrow AI/ML/DL, robotics, automation, materials science, energy storage, the Internet of Things, autonomous vehicles, 3-D printing, nanotechnology, biotechnology, neurotechnology, cognitive technology, and quantum computing. It implies radical disruptions to everything, industries, jobs, works, technologies, and old human conditions. In its scale, scope, complexity, and impact, the AI transformation will be unlike anything humankind has experienced before.
The Role of Artificial Intelligence in the 4th Industrial Revolution
Artificial intelligence is making companies make the best use of practical experience, even displacing traditional labor and becoming the productive factor itself.
It offers entirely new paths towards growth for manufacturing, service, and other industries, reshaping the world economy and bringing new opportunities for our societal development.
As AI begins to impact the workforce and automation replaces some existing skills, we’re seeing an increased need for emotional intelligence, creativity, and critical thinking.
Zvika Krieger, co-leader of the World Economic Forum’s Center for the Fourth Industrial Revolution.
Deploying AI requires a kind of reboot in the way companies think about privacy and security, As data becomes the currency of our digital lives, companies must ensure the privacy and security of customer information.
Businesses will need to ensure they have the right mix of skills in their workforce to keep pace with changing technology.
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