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Difference between the strong-AI (AGI) and cognitive science

Difference between the strong-AI (AGI) and cognitive science



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I like to know the differences in the objectives of those fields.


An AGI is a man-made machine that can learn, adapt, think, plan, predict, etc.

Cognitive science is the study of how our "biological machines" do those same processes.


Types of AI: distinguishing between weak, strong, and super AI

By now, you’re probably pretty familiar with the term ‘artificial intelligence’.

You likely already know that AI is a computer’s ability to ‘think’ and act intelligently.

You might already understand terms like machine learning and natural language processing.

But what about distinguishing between the different types of AI? Weak, strong, super, narrow, wide, ANI, AGI, ASI — there are seemingly a lot of labels for types of AI.

So, even if you know what AI is and what it does, determining which type you’re talking about isn’t so clear.

For all the labels, there are only three main types of AI: weak AI, strong AI, and super AI.

Here’s how to tell them apart.

Weak AI

Weak AI is both the most limited and the most common of the three types of AI. It’s also known as narrow AI or artificial narrow intelligence (ANI).

Weak AI refers to any AI tool that focuses on doing one task really well. That is, it has a narrow scope in terms of what it can do. The idea behind weak AI isn’t to mimic or replicate human intelligence. Rather, it’s to simulate human behaviour.

Weak AI is nowhere near matching human intelligence, and it isn’t trying to.

A common misconception about weak AI is that it’s barely intelligent at all — more like artificial stupidity than AI. But even the smartest seeming AI of today are only weak AI.

In reality, then, narrow or weak AI is more like an intelligent specialist. It’s highly intelligent at completing the specific tasks it’s programmed to do.

Strong AI

The next of the types of AI is strong AI, which is also known as general AI or artificial general intelligence (AGI). Strong AI refers to AI that exhibits human-level intelligence. So, it can understand, think, and act the same way a human might in any given situation.

In theory, then, anything a human can do, a strong AI can do too.

We don’t yet have strong AI in the world it exists only in theory.

For a start, Moravec’s paradox has us struggling to replicate the basic human functions like sight or movement. (Though image and facial recognition mean that AI is now learning to ‘see’ and categorise.)

Add to this that currently, AI is only capable of the few things we program into it, and it’s clear that strong AI is a long way off. It’s thought that to achieve true strong AI, we would need to make our machines conscious.

Super AI

But if strong AI already mimics human intelligence and ability, what’s left for the last of the types of AI?

Super AI is AI that surpasses human intelligence and ability. It’s also known as artificial superintelligence (ASI) or superintelligence. It’s the best at everything — maths, science, medicine, hobbies, you name it. Even the brightest human minds cannot come close to the abilities of super AI.

Of the types of AI, super AI is the one most people mean when they talk about robots taking over the world.

Or about AI overthrowing or enslaving humans. (Or most other science fiction AI tropes.)

But rest assured, super AI is purely speculative at this point. That is, it’s not likely to exist for an exceedingly long time (if at all).

Types of AI

Distinguishing between types of AI means looking at what the technology can do. If it’s good at specific actions only, it’s narrow or weak AI. If it operates at the same level as a human in any situation, it’s strong AI. And, if it’s operating far above the capacity any human could hope for, it’s artificial superintelligence.

So far, we’ve only achieved the first of the three types of AI — weak AI. As research continues, it’s reasonable to strive for strong AI.

Super AI, meanwhile, will likely remain the stuff of science fiction for a long while yet.


Strong AI

Strong artificial intelligence (AI), also known as artificial general intelligence (AGI) or general AI, is a theoretical form of AI used to describe a certain mindset of AI development. If researchers are able to develop Strong AI, the machine would require an intelligence equal to humans it would have a self-aware consciousness that has the ability to solve problems, learn, and plan for the future.

Strong AI aims to create intelligent machines that are indistinguishable from the human mind. But just like a child, the AI machine would have to learn through input and experiences, constantly progressing and advancing its abilities over time.

While AI researchers in both academia and private sectors are invested in the creation of artificial general intelligence (AGI), it only exists today as a theoretical concept versus a tangible reality. While some individuals, like Marvin Minsky, have been quoted as being overly optimistic in what we could accomplish in a few decades in the field of AI others would say that Strong AI systems cannot even be developed. Until the measures of success, such as intelligence and understanding, are explicitly defined, they are correct in this belief. For now, many use the Turing test to evaluate intelligence of an AI system.


What is Weak AI?

Weak AI, also known as narrow AI, is artificial intelligence with limited functionality. Weak AI refers to the use of advanced algorithms to accomplish specific problem solving or reasoning tasks that do not encompass the full range of human cognitive abilities. For example, the voice-based personal assistants such as Siri and Alexa could be considered weak AI programs because they operate within a limited pre-defined set of functions meaning they often have a programmed response. Weak AI is not so enthusiastic about the outcomes of AI it is simply the view that intelligent behavior can be modeled and used by machines to solve complex problems and tasks. But just because a machine can behave intelligently does not prove that it is actually smart in a way that a human is. The best example of weak AI is Siri and Alexa, or Google Search.


Difference Between Strong AI and Weak AI

Artificial Intelligence (AI) is the field of computer science dedicated to developing machines that will be able to mimic and perform the same tasks just as a human would. AI researchers spend time on finding a feasible alternative to the human mind. The rapid development of computers after its arrival 50 years ago has helped the researchers take great steps towards this goal of mimicking a human. Modern day applications like speech recognition, robots playing chess, table tennis and playing music have been making the dream of these researchers true. But according to AI philosophy, AI is considered to be divided in to two major types, namely Weak AI and Strong AI. Weak AI is the thinking focused towards the development of technology capable of carrying out pre-planned moves based on some rules and applying these to achieve a certain goal. As opposed to that, Strong AI is developing technology that can think and function similar to humans, not just mimicking human behavior in a certain domain.

The principle behind Weak AI is simply the fact that machines can be made to act as if they are intelligent. For example, when a human player plays chess against a computer, the human player may feel as if the computer is actually making impressive moves. But the chess application is not thinking and planning at all. All the moves it makes are previously fed in to the computer by a human and that is how it is ensured that the software will make the right moves at the right times.

The principle behind Strong AI is that the machines could be made to think or in other words could represent human minds in the future. If that is the case, those machines will have the ability to reason, think and do all functions that a human is capable of doing. But according to most people, this technology will never be developed or at least it will take a very long time. However, Strong AI, which is in its infant stage, promises a lot due to the recent developments in nanotechnology. Nanobots, which can help us fight diseases and also make us more intelligent, are being designed. Furthermore, the development of an artificial neural network, which can function as a proper human being, is being looked at as a future application of Strong AI.

What is the difference between Strong AI and Weak AI?

Weak AI and Strong AI are two types of AI, classified based on the goals that the corresponding sets of researchers are focused on achieving. Weak AI is focused towards the technology which is capable of carrying out pre-planned moves based on some rules and applying these to achieve a certain goal but, Strong AI is based on coming up with a technology that can think and function very similar to humans. So, the applications of Weak AI make the humans feel as that the machines are acting intelligently (but they are not). Contrastingly, the applications of Strong AI will (someday) actually act and think just as a human, as opposed to just making the humans feel that the machines are intelligent.


What is the difference between the objectives of Strong-AI (AGI) and Cognitive Science?

Cog psy is principally concerned with understanding natural cognition. Strong AI is principally concerned with engineering artificial cognition.

But as the great Richard Feynman once said:

What I cannot create, I do not understand.

haha yeah, I plan to expand it further when I got the time :)

I doubt AI models can teach us about the brain directly, but what about using AI tools to conduct neuroscience research?

But as the great Richard Feynman once said:

> What I cannot create, I do not understand.

You said "Cog psy is principally concerned with understanding natural cognition.". Does it apply same to Cogitive Science?

The way I see it, CogSci's goal is to understand animal (including human) cognition, whereas AGI's goal is to build a machine that is generally intelligent. So the main differences are IMO "understand vs. build" and "animal vs. general", although I suspect some people will want to debate that second dichotomy as some AGI researchers are definitely focusing on humans and perhaps there are cognitive scientists who want to make general statements about intelligence that aren't limited to what we currently see in animals.


Extended Turing Tests

Unfortunately, this test does nothing to help guide is towards better programs that are more likely to pass the test. And that’s where psychological theory comes into play. If we can develop a protocol to analyze different AGI applications, in order to see which ones are closer to having a human-like intelligence, then we can select methodologies that move us in the right direction.

There’s a lot of work that needs to be done in creating evaluations for artificial intelligence applications. One of the greatest pitfalls is that most interaction between AI and humans is text based. Verbal communication is possible, but it’s hard to produce convincing verbalization. A robotic sounding voice would automatically give away which person is real and which one isn’t, unless the voice of the human participants was masked.

IQ tests are also problematic, because many of them have visual components. However, it should be possible to create a purely oral IQ test, where the proctor asks verbal questions, and the answer is provided in a verbal response, without the need for any writing or drawing.

The test should focus more on analyzing the ability to think abstractly, recall information, and synthesize new knowledge, as well as analyze emotional and social intelligence. The development of this kind of test would require a significant number of people from multiple backgrounds, including psychology, childhood development, mental health practitioners, and of course computer scientists. But it’s an important test for the development of true AGI, as well as the evaluation of the health of those future members of our society.

I truly hope that through the development of new tests we can drive artificial general intelligence forward and create true AI. I also hope that these tests will eventually be usable as a way to identify mental health issues in these new members of our community. But such a project will require a lot of cooperation between many researchers in numerous fields of study.


AI Vs AGI: What's The Difference?

In today's society, it can be hard to operate without relying on technology one way or another. Electronics have become an essential part of our daily operations. It seems we all use technology for productivity and communication.

Can you imagine what would happen if we all stopped relying on technology all of a sudden? The world would be chaos at first, which further proves how much society depends on technological innovation.

One of these innovations revolves around artificial intelligence (AI). Though it used to only be in science fiction novels, AI is now a true venture for many businesses of today, including my own. In addition, much research is also being done regarding artificial general intelligence (AGI, or general AI), which is a more specific branch.

What, though, are the exact differences between the two subjects? This article will explore the separation between AI and the heavier AGI.

A Lot Of Research And Development Still Needs To Be Done

Before we dive too deep into AI, it's important to note that this is still a new field of research. Scientists and AI experts everywhere are still developing the best programs and innovations they can think of. It might be a long time before we reach the "end" of AI development.

The good news is that many businesses are taking advantage of the developments already made. As a matter of fact, 72% of business leaders consider AI development as an essential part of their business's future success.

Since the subject is still new, some definitions are still fluid to an extent. When we talk about AI, for example, many experts would include AGI in the category of AI. Others, though, would claim there is a distinct difference.

It might be easy to think about AI as a broad field, while AGI is a more specific focus within it. General AI applies some of the same concepts, even. Below are the two distinctly separate definitions that the industry has come to generally accept.

AI Is Based On Human Cognition

Many would argue that AI itself is centered around performing cognitive tasks that every human can perform. These tasks include things like predictive marketing or complex calculations. Sure, a human could perform them, but allowing machine learning to sift through data on our behalf saves us valuable thinking power.

In fact, many businesses are starting to incorporate AI innovations. What's one of the top reasons they're now considering the technology? Well, most of them agree that possibilities in marketing could be perfect for AI technology.

AI, in essence, is designed to make life easier for humans in their daily lives. This design is programmed to be useful from the outset.

In other words, AI functions are preprogrammed beforehand. The "decisions" machine learning makes are logical ones based on empirical data. The goal of general AI, though, is to take these decisions a step further.

General AI Is Based On Human Intellectual Ability

General AI might be considered to fall under the umbrella of AI as a whole. It's sometimes referred to as strong AI or strict AI. That's because general AI expects the machine to be equally as smart as a human.

General AI would expect a machine to perform functions that are now only seen in science fiction robots. We don't have a machine available, for example, that could walk into a home and do laundry for the entire household.

The number of decisions and intellectual energy require are still too far-fetched. Sure, a machine might be able to locate laundry baskets and sort the clothes by color. What about random clothing items that were thrown around a teenage boy's untidy room, though? Or, how would the machine know which items are only for dry-cleaning? Some decisions that humans take for granted would overwhelm a simple machine's mind.

Another case would be a decision in which "human instinct" comes into play. For example, sometimes we go with our "gut" to determine which food product to purchase at the store. A machine might not care about a brand name as much as the lowest priced item.

In other words, if it can't be directly programmed into a machine, odds are that it won't be able to make heavy intellectual decisions. This ability still is reserved for the part within all of us that is "human."

Don't Forget About Superintelligence

There is yet another category under AI as a whole that might be of interest. This would be "superintelligence," which is also only a part of science fiction still.

Such superintelligence is more of a general fear of those who don't fully understand the limits of real AI technology. These people are concerned that AI could someday surpass all human intelligence. While it makes for a great adventure movie, superintelligence is not at present a realistic concern for experts.

How Can AI Or General AI Benefit Businesses Today?

As mentioned above, many business leaders are starting to appreciate the possible applications of AI. Since the field is still fresh, no one knows just to what extent those applications could assist us.

Humanity has always been optimizing and automating business operations to reduce corporations' bottom lines. As this displacement of the workforce might be frightening, it still opens up endless productive possibilities for everyone.

Technology and innovation deserve to be given a fighting chance to truly benefit humanity. A solid understanding of AI is beneficial for all professionals these days. Some professionals dedicated to AI and its progress continue to push for the spread of this exciting technology.

Stay Informed About Technology And AI Innovations

Such a broad field of research deserves to be thoroughly explored for the benefit of humanity. All kinds of perspectives and expertise could expand the possibilities of general AI innovation. It's important to stay informed and updated on the progress so you don't get left behind in the modern business world.

Continue researching and learning about AI and technology. The potential applications of the field might end up benefiting your ventures someday.


AI Definitions: Machine Learning vs. Deep Learning vs. Cognitive Computing vs. Robotics vs. Strong AI….

AI is the compelling topic of tech conversations du jour, yet within these conversations confusion often reigns – confusion caused by loose use of AI terminology.

The problem is that AI comes in a variety of forms, each one with its own distinct range of capabilities and techniques, and at its own stage of development. Some forms of AI that we frequently hear about, such as Artificial General Intelligence, the kind of AI that might someday automate all work and that we might lose control of – may never come to pass. Others are doing useful work and are driving growth in the high performance sector of the technology industry.

These definitions aren’t meant to be the final word on AI terminology, the industry is growing and changing so fast that terms will change and new ones will be added. Instead, this is an attempt to frame the language we use now. We invite your feedback in the hope of encouraging discussion and greater clarity, and we plan to update this list over time.

Our source for all but the last of these definitions is a company well-versed in AI: Pegasystems, for more than 30 years a developer of operations and customer engagement software and a company that studies the implications and impacts of AI in the workplace.

Artificial Intelligence, in Pegasystem’s definition, “is a broad term that covers many sub-fields of computer science that aim to build machines that can do things that require intelligence when done by humans. These sub-fields include:

Machine learning – rooted in statistics and mathematical optimization, machine learning is the ability of computer systems to improve their performance by exposure to data without the need to follow explicitly programmed instructions. Machine learning is the process of automatically spotting patterns in large amounts of data that can then be used to make predictions.

Deep learning – this is a relatively new and hugely powerful technique that involves a family of algorithms that processes information in deep “neural” networks where the output from one layer becomes the input for the next one. Deep learning algorithms have proved hugely successful in, for example, detecting cancerous cells or forecasting disease but with one huge caveat: there’s no way to identify which factors the deep learning program uses to reach its conclusion.

Computer vision – the ability of computers to identify objects, scenes and activities in images using techniques to decompose the task of analyzing images into manageable pieces, detecting the edges and textures of objects in an image and comparing images to known objects for classification.

Natural language/speech processing – the ability of computers to work with text and language the way humans do, for instance, extracting meaning from text/speech or even generating text that is readable, stylistically natural, and grammatically correct.

Cognitive computing – a relatively new term, favored by IBM, cognitive computing applies knowledge from cognitive science to build an architecture of multiple AI subsystems – including machine learning, natural language processing, vision, and human-computer interaction – to simulate human thought processes with the aim of making high level decisions in complex situations. According to IBM, the aim is to help humans make better decisions, rather than making the decisions for them.

Robotic Process Automation (RPA) – computer software that is configured to automatically capture and interpret existing applications for processing a transaction, manipulating data, triggering responses and communicating with other digital systems. The key difference…from enterprise automation tools like business process management (BPM) is that RPA uses software or cognitive robots to perform and optimize process operations rather than human operators.”

Artificial general intelligence (AGI) – this is a futuristic term applied to the potential for machines to “successfully perform any intellectual task that a human being can.” Also known as “strong AI,” “super-intelligent AI” and “full AI,” the definition typically encompasses powers of intuition, emotion and aesthetic discernment – or, in a word, consciousness. Related to AGI is “the singularity,” another futuristic concept around the idea that AGI will trigger “runaway technological growth…, a ‘runaway reaction’ of self-improvement cycles…resulting in a powerful superintelligence that would, qualitatively, far surpass all human intelligence.” AGI contrasts with “applied AI,” “narrow AI” and “weak AI,” which is AI limited in scope to handling a specific task or problem.

Whether AI, broadly defined, remains applied/narrow/weak, as it is today, or becomes general/strong/super/full is the great technology debate of our time.


Use of artificial intelligence in Alzheimer’s disease detection

Conclusions and future directions

AI techniques are becoming progressively effective in image-based diagnosis, disease detection, and risk management. Several technical and hands-on solutions still required to solve their full potential. In this chapter, the use of AI techniques in the detection of AD reviewed and related states by using different structural imaging techniques is presented. Moreover, AI techniques are reviewed for AD detection which results in severe health-related problems. Several studies implemented with different image datasets using AI techniques. Regarding the comparison of AI algorithms, CNNs revealed better accuracy as compared to the conventional machine learning techniques in AD detection. In conclusion, different AI techniques are reviewed for the diagnosis of AD. It is proposed that CNNs achieved the best results in detecting AD.

The application of AI has greater potential for important developments in neurologic disorders and has achieved good performance in AD detection. However, numerous enhancements are needed in order to realize the full potential of AI in AD detection. Initially, since the AI techniques are complex, it is required to employ dataset with much bigger cohorts apart from small or modest sample sizes. In order to realize this, multicenter partnerships, where the data is collected employing the same recording conditions and scanning procedures across sites is needed. Also, the sample size can be increased through multisite data-sharing initiatives, like ADNI for AD. Then, the combination of different AI techniques makes it possible to achieve noteworthy improvements in AI in the coming years. In the last step, it can be anticipated that the cumulative number of AD detection studies can utilize the transfer learning that includes employing previously learned features from a large sample of similar images. Moreover, augmentation technique can be beneficial in the framework of AD detection. This can be realized by increasing the sample size utilizing the data transformations in such a way that the trained model will be invariant to such transformations. The utilization of augmentation may also be used to eliminate the problem of modest sample sizes by reducing the prepossessing time. Finally, the employment of AI to envisage constant scores might be utilized for future studies with possible medical employment ( Vieira et al., 2017 ). Until now, only one research has employed DNN to envisage medical scores from structural MRI scans in AD patients ( Brosch et al., 2013 ). As a conclusion, the ability of AI techniques to learn abstract and complex illustrations by means of nonlinear transformations may achieve hopeful results in AD detection. Meanwhile there exists still significant challenges to overcome the results presented here afford primary indication for the possible role of AI techniques in the forthcoming progress of predictive and diagnostic indicators of AD.


Strong AI

Strong artificial intelligence (AI), also known as artificial general intelligence (AGI) or general AI, is a theoretical form of AI used to describe a certain mindset of AI development. If researchers are able to develop Strong AI, the machine would require an intelligence equal to humans it would have a self-aware consciousness that has the ability to solve problems, learn, and plan for the future.

Strong AI aims to create intelligent machines that are indistinguishable from the human mind. But just like a child, the AI machine would have to learn through input and experiences, constantly progressing and advancing its abilities over time.

While AI researchers in both academia and private sectors are invested in the creation of artificial general intelligence (AGI), it only exists today as a theoretical concept versus a tangible reality. While some individuals, like Marvin Minsky, have been quoted as being overly optimistic in what we could accomplish in a few decades in the field of AI others would say that Strong AI systems cannot even be developed. Until the measures of success, such as intelligence and understanding, are explicitly defined, they are correct in this belief. For now, many use the Turing test to evaluate intelligence of an AI system.


What is Weak AI?

Weak AI, also known as narrow AI, is artificial intelligence with limited functionality. Weak AI refers to the use of advanced algorithms to accomplish specific problem solving or reasoning tasks that do not encompass the full range of human cognitive abilities. For example, the voice-based personal assistants such as Siri and Alexa could be considered weak AI programs because they operate within a limited pre-defined set of functions meaning they often have a programmed response. Weak AI is not so enthusiastic about the outcomes of AI it is simply the view that intelligent behavior can be modeled and used by machines to solve complex problems and tasks. But just because a machine can behave intelligently does not prove that it is actually smart in a way that a human is. The best example of weak AI is Siri and Alexa, or Google Search.


Difference Between Strong AI and Weak AI

Artificial Intelligence (AI) is the field of computer science dedicated to developing machines that will be able to mimic and perform the same tasks just as a human would. AI researchers spend time on finding a feasible alternative to the human mind. The rapid development of computers after its arrival 50 years ago has helped the researchers take great steps towards this goal of mimicking a human. Modern day applications like speech recognition, robots playing chess, table tennis and playing music have been making the dream of these researchers true. But according to AI philosophy, AI is considered to be divided in to two major types, namely Weak AI and Strong AI. Weak AI is the thinking focused towards the development of technology capable of carrying out pre-planned moves based on some rules and applying these to achieve a certain goal. As opposed to that, Strong AI is developing technology that can think and function similar to humans, not just mimicking human behavior in a certain domain.

The principle behind Weak AI is simply the fact that machines can be made to act as if they are intelligent. For example, when a human player plays chess against a computer, the human player may feel as if the computer is actually making impressive moves. But the chess application is not thinking and planning at all. All the moves it makes are previously fed in to the computer by a human and that is how it is ensured that the software will make the right moves at the right times.

The principle behind Strong AI is that the machines could be made to think or in other words could represent human minds in the future. If that is the case, those machines will have the ability to reason, think and do all functions that a human is capable of doing. But according to most people, this technology will never be developed or at least it will take a very long time. However, Strong AI, which is in its infant stage, promises a lot due to the recent developments in nanotechnology. Nanobots, which can help us fight diseases and also make us more intelligent, are being designed. Furthermore, the development of an artificial neural network, which can function as a proper human being, is being looked at as a future application of Strong AI.

What is the difference between Strong AI and Weak AI?

Weak AI and Strong AI are two types of AI, classified based on the goals that the corresponding sets of researchers are focused on achieving. Weak AI is focused towards the technology which is capable of carrying out pre-planned moves based on some rules and applying these to achieve a certain goal but, Strong AI is based on coming up with a technology that can think and function very similar to humans. So, the applications of Weak AI make the humans feel as that the machines are acting intelligently (but they are not). Contrastingly, the applications of Strong AI will (someday) actually act and think just as a human, as opposed to just making the humans feel that the machines are intelligent.


Extended Turing Tests

Unfortunately, this test does nothing to help guide is towards better programs that are more likely to pass the test. And that’s where psychological theory comes into play. If we can develop a protocol to analyze different AGI applications, in order to see which ones are closer to having a human-like intelligence, then we can select methodologies that move us in the right direction.

There’s a lot of work that needs to be done in creating evaluations for artificial intelligence applications. One of the greatest pitfalls is that most interaction between AI and humans is text based. Verbal communication is possible, but it’s hard to produce convincing verbalization. A robotic sounding voice would automatically give away which person is real and which one isn’t, unless the voice of the human participants was masked.

IQ tests are also problematic, because many of them have visual components. However, it should be possible to create a purely oral IQ test, where the proctor asks verbal questions, and the answer is provided in a verbal response, without the need for any writing or drawing.

The test should focus more on analyzing the ability to think abstractly, recall information, and synthesize new knowledge, as well as analyze emotional and social intelligence. The development of this kind of test would require a significant number of people from multiple backgrounds, including psychology, childhood development, mental health practitioners, and of course computer scientists. But it’s an important test for the development of true AGI, as well as the evaluation of the health of those future members of our society.

I truly hope that through the development of new tests we can drive artificial general intelligence forward and create true AI. I also hope that these tests will eventually be usable as a way to identify mental health issues in these new members of our community. But such a project will require a lot of cooperation between many researchers in numerous fields of study.


AI Vs AGI: What's The Difference?

In today's society, it can be hard to operate without relying on technology one way or another. Electronics have become an essential part of our daily operations. It seems we all use technology for productivity and communication.

Can you imagine what would happen if we all stopped relying on technology all of a sudden? The world would be chaos at first, which further proves how much society depends on technological innovation.

One of these innovations revolves around artificial intelligence (AI). Though it used to only be in science fiction novels, AI is now a true venture for many businesses of today, including my own. In addition, much research is also being done regarding artificial general intelligence (AGI, or general AI), which is a more specific branch.

What, though, are the exact differences between the two subjects? This article will explore the separation between AI and the heavier AGI.

A Lot Of Research And Development Still Needs To Be Done

Before we dive too deep into AI, it's important to note that this is still a new field of research. Scientists and AI experts everywhere are still developing the best programs and innovations they can think of. It might be a long time before we reach the "end" of AI development.

The good news is that many businesses are taking advantage of the developments already made. As a matter of fact, 72% of business leaders consider AI development as an essential part of their business's future success.

Since the subject is still new, some definitions are still fluid to an extent. When we talk about AI, for example, many experts would include AGI in the category of AI. Others, though, would claim there is a distinct difference.

It might be easy to think about AI as a broad field, while AGI is a more specific focus within it. General AI applies some of the same concepts, even. Below are the two distinctly separate definitions that the industry has come to generally accept.

AI Is Based On Human Cognition

Many would argue that AI itself is centered around performing cognitive tasks that every human can perform. These tasks include things like predictive marketing or complex calculations. Sure, a human could perform them, but allowing machine learning to sift through data on our behalf saves us valuable thinking power.

In fact, many businesses are starting to incorporate AI innovations. What's one of the top reasons they're now considering the technology? Well, most of them agree that possibilities in marketing could be perfect for AI technology.

AI, in essence, is designed to make life easier for humans in their daily lives. This design is programmed to be useful from the outset.

In other words, AI functions are preprogrammed beforehand. The "decisions" machine learning makes are logical ones based on empirical data. The goal of general AI, though, is to take these decisions a step further.

General AI Is Based On Human Intellectual Ability

General AI might be considered to fall under the umbrella of AI as a whole. It's sometimes referred to as strong AI or strict AI. That's because general AI expects the machine to be equally as smart as a human.

General AI would expect a machine to perform functions that are now only seen in science fiction robots. We don't have a machine available, for example, that could walk into a home and do laundry for the entire household.

The number of decisions and intellectual energy require are still too far-fetched. Sure, a machine might be able to locate laundry baskets and sort the clothes by color. What about random clothing items that were thrown around a teenage boy's untidy room, though? Or, how would the machine know which items are only for dry-cleaning? Some decisions that humans take for granted would overwhelm a simple machine's mind.

Another case would be a decision in which "human instinct" comes into play. For example, sometimes we go with our "gut" to determine which food product to purchase at the store. A machine might not care about a brand name as much as the lowest priced item.

In other words, if it can't be directly programmed into a machine, odds are that it won't be able to make heavy intellectual decisions. This ability still is reserved for the part within all of us that is "human."

Don't Forget About Superintelligence

There is yet another category under AI as a whole that might be of interest. This would be "superintelligence," which is also only a part of science fiction still.

Such superintelligence is more of a general fear of those who don't fully understand the limits of real AI technology. These people are concerned that AI could someday surpass all human intelligence. While it makes for a great adventure movie, superintelligence is not at present a realistic concern for experts.

How Can AI Or General AI Benefit Businesses Today?

As mentioned above, many business leaders are starting to appreciate the possible applications of AI. Since the field is still fresh, no one knows just to what extent those applications could assist us.

Humanity has always been optimizing and automating business operations to reduce corporations' bottom lines. As this displacement of the workforce might be frightening, it still opens up endless productive possibilities for everyone.

Technology and innovation deserve to be given a fighting chance to truly benefit humanity. A solid understanding of AI is beneficial for all professionals these days. Some professionals dedicated to AI and its progress continue to push for the spread of this exciting technology.

Stay Informed About Technology And AI Innovations

Such a broad field of research deserves to be thoroughly explored for the benefit of humanity. All kinds of perspectives and expertise could expand the possibilities of general AI innovation. It's important to stay informed and updated on the progress so you don't get left behind in the modern business world.

Continue researching and learning about AI and technology. The potential applications of the field might end up benefiting your ventures someday.


AI Definitions: Machine Learning vs. Deep Learning vs. Cognitive Computing vs. Robotics vs. Strong AI….

AI is the compelling topic of tech conversations du jour, yet within these conversations confusion often reigns – confusion caused by loose use of AI terminology.

The problem is that AI comes in a variety of forms, each one with its own distinct range of capabilities and techniques, and at its own stage of development. Some forms of AI that we frequently hear about, such as Artificial General Intelligence, the kind of AI that might someday automate all work and that we might lose control of – may never come to pass. Others are doing useful work and are driving growth in the high performance sector of the technology industry.

These definitions aren’t meant to be the final word on AI terminology, the industry is growing and changing so fast that terms will change and new ones will be added. Instead, this is an attempt to frame the language we use now. We invite your feedback in the hope of encouraging discussion and greater clarity, and we plan to update this list over time.

Our source for all but the last of these definitions is a company well-versed in AI: Pegasystems, for more than 30 years a developer of operations and customer engagement software and a company that studies the implications and impacts of AI in the workplace.

Artificial Intelligence, in Pegasystem’s definition, “is a broad term that covers many sub-fields of computer science that aim to build machines that can do things that require intelligence when done by humans. These sub-fields include:

Machine learning – rooted in statistics and mathematical optimization, machine learning is the ability of computer systems to improve their performance by exposure to data without the need to follow explicitly programmed instructions. Machine learning is the process of automatically spotting patterns in large amounts of data that can then be used to make predictions.

Deep learning – this is a relatively new and hugely powerful technique that involves a family of algorithms that processes information in deep “neural” networks where the output from one layer becomes the input for the next one. Deep learning algorithms have proved hugely successful in, for example, detecting cancerous cells or forecasting disease but with one huge caveat: there’s no way to identify which factors the deep learning program uses to reach its conclusion.

Computer vision – the ability of computers to identify objects, scenes and activities in images using techniques to decompose the task of analyzing images into manageable pieces, detecting the edges and textures of objects in an image and comparing images to known objects for classification.

Natural language/speech processing – the ability of computers to work with text and language the way humans do, for instance, extracting meaning from text/speech or even generating text that is readable, stylistically natural, and grammatically correct.

Cognitive computing – a relatively new term, favored by IBM, cognitive computing applies knowledge from cognitive science to build an architecture of multiple AI subsystems – including machine learning, natural language processing, vision, and human-computer interaction – to simulate human thought processes with the aim of making high level decisions in complex situations. According to IBM, the aim is to help humans make better decisions, rather than making the decisions for them.

Robotic Process Automation (RPA) – computer software that is configured to automatically capture and interpret existing applications for processing a transaction, manipulating data, triggering responses and communicating with other digital systems. The key difference…from enterprise automation tools like business process management (BPM) is that RPA uses software or cognitive robots to perform and optimize process operations rather than human operators.”

Artificial general intelligence (AGI) – this is a futuristic term applied to the potential for machines to “successfully perform any intellectual task that a human being can.” Also known as “strong AI,” “super-intelligent AI” and “full AI,” the definition typically encompasses powers of intuition, emotion and aesthetic discernment – or, in a word, consciousness. Related to AGI is “the singularity,” another futuristic concept around the idea that AGI will trigger “runaway technological growth…, a ‘runaway reaction’ of self-improvement cycles…resulting in a powerful superintelligence that would, qualitatively, far surpass all human intelligence.” AGI contrasts with “applied AI,” “narrow AI” and “weak AI,” which is AI limited in scope to handling a specific task or problem.

Whether AI, broadly defined, remains applied/narrow/weak, as it is today, or becomes general/strong/super/full is the great technology debate of our time.


Types of AI: distinguishing between weak, strong, and super AI

By now, you’re probably pretty familiar with the term ‘artificial intelligence’.

You likely already know that AI is a computer’s ability to ‘think’ and act intelligently.

You might already understand terms like machine learning and natural language processing.

But what about distinguishing between the different types of AI? Weak, strong, super, narrow, wide, ANI, AGI, ASI — there are seemingly a lot of labels for types of AI.

So, even if you know what AI is and what it does, determining which type you’re talking about isn’t so clear.

For all the labels, there are only three main types of AI: weak AI, strong AI, and super AI.

Here’s how to tell them apart.

Weak AI

Weak AI is both the most limited and the most common of the three types of AI. It’s also known as narrow AI or artificial narrow intelligence (ANI).

Weak AI refers to any AI tool that focuses on doing one task really well. That is, it has a narrow scope in terms of what it can do. The idea behind weak AI isn’t to mimic or replicate human intelligence. Rather, it’s to simulate human behaviour.

Weak AI is nowhere near matching human intelligence, and it isn’t trying to.

A common misconception about weak AI is that it’s barely intelligent at all — more like artificial stupidity than AI. But even the smartest seeming AI of today are only weak AI.

In reality, then, narrow or weak AI is more like an intelligent specialist. It’s highly intelligent at completing the specific tasks it’s programmed to do.

Strong AI

The next of the types of AI is strong AI, which is also known as general AI or artificial general intelligence (AGI). Strong AI refers to AI that exhibits human-level intelligence. So, it can understand, think, and act the same way a human might in any given situation.

In theory, then, anything a human can do, a strong AI can do too.

We don’t yet have strong AI in the world it exists only in theory.

For a start, Moravec’s paradox has us struggling to replicate the basic human functions like sight or movement. (Though image and facial recognition mean that AI is now learning to ‘see’ and categorise.)

Add to this that currently, AI is only capable of the few things we program into it, and it’s clear that strong AI is a long way off. It’s thought that to achieve true strong AI, we would need to make our machines conscious.

Super AI

But if strong AI already mimics human intelligence and ability, what’s left for the last of the types of AI?

Super AI is AI that surpasses human intelligence and ability. It’s also known as artificial superintelligence (ASI) or superintelligence. It’s the best at everything — maths, science, medicine, hobbies, you name it. Even the brightest human minds cannot come close to the abilities of super AI.

Of the types of AI, super AI is the one most people mean when they talk about robots taking over the world.

Or about AI overthrowing or enslaving humans. (Or most other science fiction AI tropes.)

But rest assured, super AI is purely speculative at this point. That is, it’s not likely to exist for an exceedingly long time (if at all).

Types of AI

Distinguishing between types of AI means looking at what the technology can do. If it’s good at specific actions only, it’s narrow or weak AI. If it operates at the same level as a human in any situation, it’s strong AI. And, if it’s operating far above the capacity any human could hope for, it’s artificial superintelligence.

So far, we’ve only achieved the first of the three types of AI — weak AI. As research continues, it’s reasonable to strive for strong AI.

Super AI, meanwhile, will likely remain the stuff of science fiction for a long while yet.


Use of artificial intelligence in Alzheimer’s disease detection

Conclusions and future directions

AI techniques are becoming progressively effective in image-based diagnosis, disease detection, and risk management. Several technical and hands-on solutions still required to solve their full potential. In this chapter, the use of AI techniques in the detection of AD reviewed and related states by using different structural imaging techniques is presented. Moreover, AI techniques are reviewed for AD detection which results in severe health-related problems. Several studies implemented with different image datasets using AI techniques. Regarding the comparison of AI algorithms, CNNs revealed better accuracy as compared to the conventional machine learning techniques in AD detection. In conclusion, different AI techniques are reviewed for the diagnosis of AD. It is proposed that CNNs achieved the best results in detecting AD.

The application of AI has greater potential for important developments in neurologic disorders and has achieved good performance in AD detection. However, numerous enhancements are needed in order to realize the full potential of AI in AD detection. Initially, since the AI techniques are complex, it is required to employ dataset with much bigger cohorts apart from small or modest sample sizes. In order to realize this, multicenter partnerships, where the data is collected employing the same recording conditions and scanning procedures across sites is needed. Also, the sample size can be increased through multisite data-sharing initiatives, like ADNI for AD. Then, the combination of different AI techniques makes it possible to achieve noteworthy improvements in AI in the coming years. In the last step, it can be anticipated that the cumulative number of AD detection studies can utilize the transfer learning that includes employing previously learned features from a large sample of similar images. Moreover, augmentation technique can be beneficial in the framework of AD detection. This can be realized by increasing the sample size utilizing the data transformations in such a way that the trained model will be invariant to such transformations. The utilization of augmentation may also be used to eliminate the problem of modest sample sizes by reducing the prepossessing time. Finally, the employment of AI to envisage constant scores might be utilized for future studies with possible medical employment ( Vieira et al., 2017 ). Until now, only one research has employed DNN to envisage medical scores from structural MRI scans in AD patients ( Brosch et al., 2013 ). As a conclusion, the ability of AI techniques to learn abstract and complex illustrations by means of nonlinear transformations may achieve hopeful results in AD detection. Meanwhile there exists still significant challenges to overcome the results presented here afford primary indication for the possible role of AI techniques in the forthcoming progress of predictive and diagnostic indicators of AD.


What is the difference between the objectives of Strong-AI (AGI) and Cognitive Science?

Cog psy is principally concerned with understanding natural cognition. Strong AI is principally concerned with engineering artificial cognition.

But as the great Richard Feynman once said:

What I cannot create, I do not understand.

haha yeah, I plan to expand it further when I got the time :)

I doubt AI models can teach us about the brain directly, but what about using AI tools to conduct neuroscience research?

But as the great Richard Feynman once said:

> What I cannot create, I do not understand.

You said "Cog psy is principally concerned with understanding natural cognition.". Does it apply same to Cogitive Science?

The way I see it, CogSci's goal is to understand animal (including human) cognition, whereas AGI's goal is to build a machine that is generally intelligent. So the main differences are IMO "understand vs. build" and "animal vs. general", although I suspect some people will want to debate that second dichotomy as some AGI researchers are definitely focusing on humans and perhaps there are cognitive scientists who want to make general statements about intelligence that aren't limited to what we currently see in animals.


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