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The Marriage of Quantum Computing and Artificial Intelligenc
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The Marriage of Quantum Computing and Artificial Intelligenc
by NeilCummings » Wed Sep 15, 2021 2:52 pm
The Marriage of Quantum Computing and Artificial Intelligence
In 2018, quantum technicians and daring developers are transforming the field of artificial neural network optimization, the bee's knees of machine learning and AI, with quantum algorithms. As a result, we can confidently assert that quantum computing and artificial intelligence's futures are inextricably linked, thanks to quantum algorithms. Let's take a closer look at the quantum algorithms that are causing a stir in the digital era. I'll be focusing on quantum annealing (rhymes with feeling), a one-of-a-kind animal that appears to thrive in an AI-rich region where traditional algorithms often struggle or fail: training artificial neural networks.
Are you having trouble training your neural network? Join the group...
Surprisingly, artificial neural nets like RNNs and CNNs can be trained to learn from their mistakes and not repeat them. Neural nets are the intelligence engine that drives machine learning and AI because of their ability to follow Esther Dyson's instruction. However, training neural networks is notoriously difficult. However, during the last several years, academics and coders have been working feverishly to find new techniques to reduce training errors using cutting-edge optimization algorithms. Hill climbing is the most well-known attempt at the error-reduction problem. Let's have a look at it.
Climbing a hill
Before making their next move, optimization algorithms in the hill climbing club always check for the gradient (roughly the steepness of a graphed function's slope). However, by doing so, you run the danger of missing out on the true action in the graph's environment. The plateau problem and the local minima problem are two common adversaries for hill climbers. In a nutshell, these issues are the hiking equivalent of being lost in a desert full with mirages or becoming stuck in a small muddy valley. But let's delve a little further...
The issue of the plateau
When an optimization process reaches a plateau, it signifies that the output (y) for each input is roughly the same (x). An optimization algorithm can run out of time before finding the edge because the function's slope is at or near zero for lengthy flat sections. Long stretches of flat function, like a shimmering desert mirage, can give the impression that you've reached an optimal state (in this case, the global minimum) when you're not even close.
safaribooksonline.com is the source for this information.
The issue of local minima
A local minimum is a minor valley in a function's graph whose deepest and most significant valley is located elsewhere. When looking for the lowest value in a function, think of the optimization process as a beach ball: it will roll downhill and eventually stop at the lowest point in the immediate landscape, even if there is a considerably deeper valley on the other side of a nearby hill. That is the issue.
safaribooksonline.com is the source for this information.
The most intriguing option
There are several alternatives to traditional hill climbing that can assist you in escaping the dreadful valleys of the local minimum problem and the desert mirages of the plateau problem. But, for now, let's simply concentrate on the most fascinating solution: simulated annealing. This is a rare species of optimization animal that deals with valleys and plateaus in a computationally sophisticated way that deserves at least a few words of thought...
The newest and most exciting classical optimization algorithm on the market.
To get right to the point, simulated annealing borrows from physics to combine time and temperature in a single straightforward process. Yes, you read that correctly: a temperature parameter in an algorithm. A simulated annealing process starts with a fully random, frenzied succession of selections from the entire terrain of the function at hand. This is when the process gets really heated. However, when the temperature parameter decreases, the random selections span a smaller and smaller area of the terrain. Finally, as the algorithm begins to home in on (with a little luck) the deepest valley or tallest peak, where the holy grail of optimization lies: the global minimum or maximum, we enter the cool phase of the process.
Although the code in simulated annealing algorithms often contains some complex math, the underlying relationship between time and temperature is simple to understand. Imagine something hot whizzing through an unknown environment, crashing into everything in its path and reporting back a rudimentary map of the terrain. Then imagine progressively colder entities going ever-slower and more cautiously across a tighter and narrower part of the landscape, documenting the intricacies as they creep deeper into the deepest valley or higher up onto the highest peak... Okay, if you're still not sure what I'm talking about, here's a great animation to help you understand.
In 2018, quantum technicians and daring developers are transforming the field of artificial neural network optimization, the bee's knees of machine learning and AI, with quantum algorithms. As a result, we can confidently assert that quantum computing and artificial intelligence's futures are inextricably linked, thanks to quantum algorithms. Let's take a closer look at the quantum algorithms that are causing a stir in the digital era. I'll be focusing on quantum annealing (rhymes with feeling), a one-of-a-kind animal that appears to thrive in an AI-rich region where traditional algorithms often struggle or fail: training artificial neural networks.
Are you having trouble training your neural network? Join the group...
Surprisingly, artificial neural nets like RNNs and CNNs can be trained to learn from their mistakes and not repeat them. Neural nets are the intelligence engine that drives machine learning and AI because of their ability to follow Esther Dyson's instruction. However, training neural networks is notoriously difficult. However, during the last several years, academics and coders have been working feverishly to find new techniques to reduce training errors using cutting-edge optimization algorithms. Hill climbing is the most well-known attempt at the error-reduction problem. Let's have a look at it.
Climbing a hill
Before making their next move, optimization algorithms in the hill climbing club always check for the gradient (roughly the steepness of a graphed function's slope). However, by doing so, you run the danger of missing out on the true action in the graph's environment. The plateau problem and the local minima problem are two common adversaries for hill climbers. In a nutshell, these issues are the hiking equivalent of being lost in a desert full with mirages or becoming stuck in a small muddy valley. But let's delve a little further...
The issue of the plateau
When an optimization process reaches a plateau, it signifies that the output (y) for each input is roughly the same (x). An optimization algorithm can run out of time before finding the edge because the function's slope is at or near zero for lengthy flat sections. Long stretches of flat function, like a shimmering desert mirage, can give the impression that you've reached an optimal state (in this case, the global minimum) when you're not even close.
safaribooksonline.com is the source for this information.
The issue of local minima
A local minimum is a minor valley in a function's graph whose deepest and most significant valley is located elsewhere. When looking for the lowest value in a function, think of the optimization process as a beach ball: it will roll downhill and eventually stop at the lowest point in the immediate landscape, even if there is a considerably deeper valley on the other side of a nearby hill. That is the issue.
safaribooksonline.com is the source for this information.
The most intriguing option
There are several alternatives to traditional hill climbing that can assist you in escaping the dreadful valleys of the local minimum problem and the desert mirages of the plateau problem. But, for now, let's simply concentrate on the most fascinating solution: simulated annealing. This is a rare species of optimization animal that deals with valleys and plateaus in a computationally sophisticated way that deserves at least a few words of thought...
The newest and most exciting classical optimization algorithm on the market.
To get right to the point, simulated annealing borrows from physics to combine time and temperature in a single straightforward process. Yes, you read that correctly: a temperature parameter in an algorithm. A simulated annealing process starts with a fully random, frenzied succession of selections from the entire terrain of the function at hand. This is when the process gets really heated. However, when the temperature parameter decreases, the random selections span a smaller and smaller area of the terrain. Finally, as the algorithm begins to home in on (with a little luck) the deepest valley or tallest peak, where the holy grail of optimization lies: the global minimum or maximum, we enter the cool phase of the process.
Although the code in simulated annealing algorithms often contains some complex math, the underlying relationship between time and temperature is simple to understand. Imagine something hot whizzing through an unknown environment, crashing into everything in its path and reporting back a rudimentary map of the terrain. Then imagine progressively colder entities going ever-slower and more cautiously across a tighter and narrower part of the landscape, documenting the intricacies as they creep deeper into the deepest valley or higher up onto the highest peak... Okay, if you're still not sure what I'm talking about, here's a great animation to help you understand.
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Re: The Marriage of Quantum Computing and Artificial Intelligenc
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Great Dark Web Links Tips
by FrankJScott » Mon Mar 07, 2022 5:14 pm
All You Have To Know About The Dark Web
A majority of people access online content through the surface web. This is the part of the internet that contains websites that are indexed using popular search engines. It can also be viewed easily using traditional web browsers. The surface web can represent the vast majority of what people see, but the dark web contains many hidden layers. Utilizing a specific browser, called The Onion Router (Tor), users can explore these obscure websites and take part in illegal and legal activities, all using an unidentified IP address. Here's how the dark net works and how people use it legally as well as illegally. Check out these dark web links for info.
What Exactly Is The Surface Web?
Sites found on the web's surface, which is also known as the open web, are available to all users , regardless of Tor or any special browsers or software. Websites that appear on the surface are easily indexable and can also be searched through search engines. The surface web, however, comprises a number of the most well-known .com, .net, and .org sites however, it's estimated that it represents less than 5% of the total web content while the remaining content is located on the deep or dark web. As a typical example the surface web could be imagined as the tip of an iceberg that's majority is hidden beneath the surface.
What Is The Difference Between The Dark Web And The Deep Web?
Millions of Internet users access private databases , such as email inboxes and credit card accounts every day. These pages are not indexable by search engines. They are protected by security barriers and authentication forms, as well as passwords on the internet's deep web.
About 90% of websites can be found in the deep web. Many are used and maintained by corporations as well as government agencies, non-profits as well as other organizations. The dark web is a part of the web's deep. This area of the internet can only be accessible to Tor users. Although it's legal to utilize Tor to access the web, the majority of users won't be allowed to connect to the dark web.
What Was It That Caused The Dark Web Created?
The Dark Web was created in the year 2000 by Ian Clarke, a University of Edinburgh student, as his thesis project. He wanted to develop a Distributed Information Storage and Retrieval System. Clarke set out to create an anonymous way to communicate and exchange files on the internet. This was the basis for the Tor Project. The Tor Project was established in 2002. A browser was launched in 2008. The development of Tor allowed users to surf the web without revealing their identity and browse sites that were considered part of the "dark Web."
How The Dark Web Works
The United States Department of Defense originally used the dark internet to communicate anonymously. The dark web has become a destination for anyone who wishes to remain anonymous across the world. It is used by people to use for both legal and illegal purposes. It utilizes a method known as "onion routing," that shields users from monitoring and from tracking through a random path of encrypted servers. Tor allows users to access websites via hundreds of relay points. This prevents them from being tracked and is nearly impossible to trace their web browsing habits.
Legal Uses Of The Dark Web
While using the dark web may seem to be a bit suspicious, it is perfectly legal, and there are numerous legitimate uses of Tor and anonymized browsing. The dark web is typically used by countries that have government surveillance to monitor and punish political opponents. It allows users to communicate without being subjected to government scrutiny. But, the dark web is still a security hazard. Users should ensure that they use appropriate security tools, such as an VPN that's robust and regularly updated. Avoid using an email address that is standard, and be mindful of the dangers associated when surfing the dark web.
Illegal Uses Of The Dark Web
Due to its anonymity, the dark web is often used for illegal and even illegal purposes. These include the sale and purchase of illegal drugs and weapons identity theft and passwords, along with trading in illicit pornography as well as other potentially hazardous substances. Many websites hosting illegal material were discovered by federal agencies in recent times and were shut down, such as Silk Road and AlphaBay. The secrecy of dark web sites has resulted in numerous data breaches and cybersecurity threats in the past few decades.
Cybersecurity The Biggest Threats Facing Us Today
The anonymity of dark internet and international cybersecurity threats are continuously confronted by law enforcement authorities and government agencies. The Tulane School of Professional Advancement offers a variety of career paths in cybersecurity and IT. We offer certificate and degree programs that cover Information Technology, Cyber Defense, and Cybersecurity Management that can teach you how to safeguard internet users online and help you prepare for a range of rewarding career paths. Find out more about our different degrees and certifications.
A majority of people access online content through the surface web. This is the part of the internet that contains websites that are indexed using popular search engines. It can also be viewed easily using traditional web browsers. The surface web can represent the vast majority of what people see, but the dark web contains many hidden layers. Utilizing a specific browser, called The Onion Router (Tor), users can explore these obscure websites and take part in illegal and legal activities, all using an unidentified IP address. Here's how the dark net works and how people use it legally as well as illegally. Check out these dark web links for info.
What Exactly Is The Surface Web?
Sites found on the web's surface, which is also known as the open web, are available to all users , regardless of Tor or any special browsers or software. Websites that appear on the surface are easily indexable and can also be searched through search engines. The surface web, however, comprises a number of the most well-known .com, .net, and .org sites however, it's estimated that it represents less than 5% of the total web content while the remaining content is located on the deep or dark web. As a typical example the surface web could be imagined as the tip of an iceberg that's majority is hidden beneath the surface.
What Is The Difference Between The Dark Web And The Deep Web?
Millions of Internet users access private databases , such as email inboxes and credit card accounts every day. These pages are not indexable by search engines. They are protected by security barriers and authentication forms, as well as passwords on the internet's deep web.
About 90% of websites can be found in the deep web. Many are used and maintained by corporations as well as government agencies, non-profits as well as other organizations. The dark web is a part of the web's deep. This area of the internet can only be accessible to Tor users. Although it's legal to utilize Tor to access the web, the majority of users won't be allowed to connect to the dark web.
What Was It That Caused The Dark Web Created?
The Dark Web was created in the year 2000 by Ian Clarke, a University of Edinburgh student, as his thesis project. He wanted to develop a Distributed Information Storage and Retrieval System. Clarke set out to create an anonymous way to communicate and exchange files on the internet. This was the basis for the Tor Project. The Tor Project was established in 2002. A browser was launched in 2008. The development of Tor allowed users to surf the web without revealing their identity and browse sites that were considered part of the "dark Web."
How The Dark Web Works
The United States Department of Defense originally used the dark internet to communicate anonymously. The dark web has become a destination for anyone who wishes to remain anonymous across the world. It is used by people to use for both legal and illegal purposes. It utilizes a method known as "onion routing," that shields users from monitoring and from tracking through a random path of encrypted servers. Tor allows users to access websites via hundreds of relay points. This prevents them from being tracked and is nearly impossible to trace their web browsing habits.
Legal Uses Of The Dark Web
While using the dark web may seem to be a bit suspicious, it is perfectly legal, and there are numerous legitimate uses of Tor and anonymized browsing. The dark web is typically used by countries that have government surveillance to monitor and punish political opponents. It allows users to communicate without being subjected to government scrutiny. But, the dark web is still a security hazard. Users should ensure that they use appropriate security tools, such as an VPN that's robust and regularly updated. Avoid using an email address that is standard, and be mindful of the dangers associated when surfing the dark web.
Illegal Uses Of The Dark Web
Due to its anonymity, the dark web is often used for illegal and even illegal purposes. These include the sale and purchase of illegal drugs and weapons identity theft and passwords, along with trading in illicit pornography as well as other potentially hazardous substances. Many websites hosting illegal material were discovered by federal agencies in recent times and were shut down, such as Silk Road and AlphaBay. The secrecy of dark web sites has resulted in numerous data breaches and cybersecurity threats in the past few decades.
Cybersecurity The Biggest Threats Facing Us Today
The anonymity of dark internet and international cybersecurity threats are continuously confronted by law enforcement authorities and government agencies. The Tulane School of Professional Advancement offers a variety of career paths in cybersecurity and IT. We offer certificate and degree programs that cover Information Technology, Cyber Defense, and Cybersecurity Management that can teach you how to safeguard internet users online and help you prepare for a range of rewarding career paths. Find out more about our different degrees and certifications.
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Re: The Marriage of Quantum Computing and Artificial Intelligenc
by jacksDanial » Thu Apr 28, 2022 4:45 pm
Technicians and daring developers are transforming the field of artificial neural network optimization, the bee's knees of machine learning and AI, with quantum algorithms. As a result, we can confidently assert that quantum computing and artificial intelligence's futures are inextricably linked, thanks to quantum algorithms. Let's take a closer look at the quantum algorithms that are causing a stir in the digital era. I'll be focusing on quantum annealing (rhymes with feeling), a one-of-a-kind animal that appears to thrive in an AI-rich region where traditional algorithms often struggle or fail: training artificial neural networks.
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