-
Somyajit Chakraborty
- Back-End Developer Turning Into Data Science.....
5 Views
0 Comments
Abstract:
Since the whole thing is a draft I’ll go short with just the proposal: Since most approach to the idea of consciousness in AI has been performed by means of mechanical approach or what I call as traditional approach. I intend to introduce the psychological paradigm of thought process for any conscious mind by applying it to a given AIS (Artificial Intelligence System). The idea is simple yet of complex origin since we will approach the concept of Artificial Intelligence as not ‘single’ training and output unit but as a collective body of artificial cognitive processes; By this I mean, firstly, a unit which can devise the logic of why it should operate and exist in the first place (functionality); and secondly, The power of intuition from psychological point of view. The power of intuition is what makes us a natural intelligent entity with intuition comes reflexes and with reflexes comes actions and actions harnesses emotion, so to develop intuition in an AIS we do need to provide it with ability to question a particular situation by means of directories. Complex Analysis (Mathematically Speaking) can provide an insight to our disturbing handling of conscious data through the cognitive units. With this we have to proceed with a pre-developmental action theory for a viable experimentation. Further research required!
Chapter 1: Consciousness in brief
A definition of consciousness that many philosophers accept was proscribed by Thomas Nagel. In his essay ‘What It Is Like To Be A Bat’, he wrote that consciousness must have a subjective character, “a what it is like aspect”, and a qualitative perspective on the world. “An organism has conscious mental states if and only if there is something that it is like to be that organism”, he argued, and so consciousness cannot be explained without the subjective character of experience. Even if we accept Nagel’s definition as a starting point, we still need to explain how consciousness arises out of the physical world. Consciousness involves accepting new information, storing and retrieving old information and processing it all into perceptions and actions. These forms of thinking, memory, and attention are all forms of neurological computing. Neuroscience explains the biology and chemistry behind these neurological processes with ever greater granularity and accuracy. But why these processes feel like something, something qualitative— that’s a hard problem nobody has yet succeeded answering. Philosopher David Chalmers coined this the hard problem of consciousness. How do we explain the relationship between the physical processes of the brain and the qualitative nature of experience? Below are a few of the more popular contemporary explanations of consciousness, which try to bridge this gap of explanation:
• Material View of Consciousness: Some thinkers have posited the theory of emergence, which is the idea that if you hook up enough non-sentient components (neurons, microchips) then consciousness will appear. Simply put, they believe that out of unconscious complexity arises consciousness. In humans, for instance, consciousness would arise from physical states and the biological processes in our brains. Those who believe in the theory of emergence aim to equate mental phenomena with operations of the brain and to explain them all in scientific terms. This project is often called ‘cognitive science’. However, Sam Harris has argued that the idea of emergence does not really solve the hard problem of consciousness but acts as a “restatement of a miracle” akin to the Big Bang Theory — with emergence something is still posited to arise out of nothing in the great traverse from unconsciousness to consciousness.
• Quantum Theory of Consciousness: Based on the most profound theories from quantum physics, this theory stipulates that consciousness and the physical world are complementary aspects of the same reality. When a person observes the physical world, that person’s conscious interaction causes discernible change. Schrodinger’s Cat is the famous example from Quantum Mechanics that exemplifies this and those familiar with the Eastern philosophies of non-dualism may intuitively understand this theory.
• Consciousness as Reflexivity: The first level of consciousness is sub-consciousness, which is where the majority of human intelligence lies and the subconscious powers our ability to make decisions like spot a face. Some AI, such as Google’s face recognition software, can already do this. However, the more critical component of consciousness is the ability to maintain a wide range of thoughts. These thoughts are accessible to other parts of the brain, and it’s this ability to access thoughts simultaneously that makes long term planning possible and gives us the qualitative sensation of consciousness. Consciousness is therefore presented as a sort of meta-cognition involving higher-order thought processes and an awareness of one’s own thought processes. Our ability to maintain a wide range of thoughts broadens our temporal window of the world and comes into play when we need to maintain sensory information over a few moments.
Chapter 2: Advent of AI
The exact definition of AI is hotly debated and there are already many fantastic explanations of AI on the internet, so I won’t dive in too deeply. But broadly speaking, AI is advanced statistics and applied mathematics which harnesses new advances in computing power and the explosion of available data to give computers new powers of inference, recognition, and choice.
Machine learning (ML), the most promising subset of AI, is a field that aims to teach computers to learn from examples (or “Data”) and perform a task without being explicitly programmed to do so at its most basic, ML uses algorithms to parse data, learn from it, and then make a decision or prediction about something in the world. Rather than hard-coding software with specific instructions to accomplish a particular task, a machine is “trained” using large amounts of data and algorithms that give it the ability to learn how to perform a task or predict an outcome.
Deep learning, the most successful approach within machine learning, is loosely modelled on the brain’s “neural networks”. In a deep learning mesh, you have “neurons” which have discrete layers and connections to other “neurons” — much like the neurons in our own brains do. Each layer of neurons picks out a specific feature to learn, for example the colour of a cat, and it’s this layering that gives deep learning its name.
Other approaches to machine learning include decision tree learning, inductive logic programming, clustering, reinforcement learning, and Bayesian networks — all familiar statistical approaches. And like any other technology it will continue to grow into one humongous tree of scopes.
Chapter 3: Consciousness inspired AI research
Consciousness inspired AI research appeared in many different contexts ranging from robotics to control systems, from simulation to autonomous systems. Epigenetic robotics and synthetic approaches to robotics based on psychological and biological models have elicited many of the differences between the artificial and mental studies of consciousness, while the importance of the interaction between the brain, the body and the surrounding environment has been pointed out (Chrisley, 2003; Rockwell, 2005; Chella and Manzotti, 2007; Manzotti, 2007). In the field of AI there has been a considerable interest towards consciousness. Marvin Minsky was one of the firsts to point out that “some machines are already potentially more conscious than are people, and that further enhancements would be relatively easy to make. However, this does not imply that those machines would thereby, automatically, become much more intelligent. This is because it is one thing to have access to data, but another thing to know how to make good use of it.” (Minsky, 1991) The target of researchers involved in recent wo
Since the whole thing is a draft I’ll go short with just the proposal: Since most approach to the idea of consciousness in AI has been performed by means of mechanical approach or what I call as traditional approach. I intend to introduce the psychological paradigm of thought process for any conscious mind by applying it to a given AIS (Artificial Intelligence System). The idea is simple yet of complex origin since we will approach the concept of Artificial Intelligence as not ‘single’ training and output unit but as a collective body of artificial cognitive processes; By this I mean, firstly, a unit which can devise the logic of why it should operate and exist in the first place (functionality); and secondly, The power of intuition from psychological point of view. The power of intuition is what makes us a natural intelligent entity with intuition comes reflexes and with reflexes comes actions and actions harnesses emotion, so to develop intuition in an AIS we do need to provide it with ability to question a particular situation by means of directories. Complex Analysis (Mathematically Speaking) can provide an insight to our disturbing handling of conscious data through the cognitive units. With this we have to proceed with a pre-developmental action theory for a viable experimentation. Further research required!
Chapter 1: Consciousness in brief
A definition of consciousness that many philosophers accept was proscribed by Thomas Nagel. In his essay ‘What It Is Like To Be A Bat’, he wrote that consciousness must have a subjective character, “a what it is like aspect”, and a qualitative perspective on the world. “An organism has conscious mental states if and only if there is something that it is like to be that organism”, he argued, and so consciousness cannot be explained without the subjective character of experience. Even if we accept Nagel’s definition as a starting point, we still need to explain how consciousness arises out of the physical world. Consciousness involves accepting new information, storing and retrieving old information and processing it all into perceptions and actions. These forms of thinking, memory, and attention are all forms of neurological computing. Neuroscience explains the biology and chemistry behind these neurological processes with ever greater granularity and accuracy. But why these processes feel like something, something qualitative— that’s a hard problem nobody has yet succeeded answering. Philosopher David Chalmers coined this the hard problem of consciousness. How do we explain the relationship between the physical processes of the brain and the qualitative nature of experience? Below are a few of the more popular contemporary explanations of consciousness, which try to bridge this gap of explanation:
• Material View of Consciousness: Some thinkers have posited the theory of emergence, which is the idea that if you hook up enough non-sentient components (neurons, microchips) then consciousness will appear. Simply put, they believe that out of unconscious complexity arises consciousness. In humans, for instance, consciousness would arise from physical states and the biological processes in our brains. Those who believe in the theory of emergence aim to equate mental phenomena with operations of the brain and to explain them all in scientific terms. This project is often called ‘cognitive science’. However, Sam Harris has argued that the idea of emergence does not really solve the hard problem of consciousness but acts as a “restatement of a miracle” akin to the Big Bang Theory — with emergence something is still posited to arise out of nothing in the great traverse from unconsciousness to consciousness.
• Quantum Theory of Consciousness: Based on the most profound theories from quantum physics, this theory stipulates that consciousness and the physical world are complementary aspects of the same reality. When a person observes the physical world, that person’s conscious interaction causes discernible change. Schrodinger’s Cat is the famous example from Quantum Mechanics that exemplifies this and those familiar with the Eastern philosophies of non-dualism may intuitively understand this theory.
• Consciousness as Reflexivity: The first level of consciousness is sub-consciousness, which is where the majority of human intelligence lies and the subconscious powers our ability to make decisions like spot a face. Some AI, such as Google’s face recognition software, can already do this. However, the more critical component of consciousness is the ability to maintain a wide range of thoughts. These thoughts are accessible to other parts of the brain, and it’s this ability to access thoughts simultaneously that makes long term planning possible and gives us the qualitative sensation of consciousness. Consciousness is therefore presented as a sort of meta-cognition involving higher-order thought processes and an awareness of one’s own thought processes. Our ability to maintain a wide range of thoughts broadens our temporal window of the world and comes into play when we need to maintain sensory information over a few moments.
Chapter 2: Advent of AI
The exact definition of AI is hotly debated and there are already many fantastic explanations of AI on the internet, so I won’t dive in too deeply. But broadly speaking, AI is advanced statistics and applied mathematics which harnesses new advances in computing power and the explosion of available data to give computers new powers of inference, recognition, and choice.
Machine learning (ML), the most promising subset of AI, is a field that aims to teach computers to learn from examples (or “Data”) and perform a task without being explicitly programmed to do so at its most basic, ML uses algorithms to parse data, learn from it, and then make a decision or prediction about something in the world. Rather than hard-coding software with specific instructions to accomplish a particular task, a machine is “trained” using large amounts of data and algorithms that give it the ability to learn how to perform a task or predict an outcome.
Deep learning, the most successful approach within machine learning, is loosely modelled on the brain’s “neural networks”. In a deep learning mesh, you have “neurons” which have discrete layers and connections to other “neurons” — much like the neurons in our own brains do. Each layer of neurons picks out a specific feature to learn, for example the colour of a cat, and it’s this layering that gives deep learning its name.
Other approaches to machine learning include decision tree learning, inductive logic programming, clustering, reinforcement learning, and Bayesian networks — all familiar statistical approaches. And like any other technology it will continue to grow into one humongous tree of scopes.
Chapter 3: Consciousness inspired AI research
Consciousness inspired AI research appeared in many different contexts ranging from robotics to control systems, from simulation to autonomous systems. Epigenetic robotics and synthetic approaches to robotics based on psychological and biological models have elicited many of the differences between the artificial and mental studies of consciousness, while the importance of the interaction between the brain, the body and the surrounding environment has been pointed out (Chrisley, 2003; Rockwell, 2005; Chella and Manzotti, 2007; Manzotti, 2007). In the field of AI there has been a considerable interest towards consciousness. Marvin Minsky was one of the firsts to point out that “some machines are already potentially more conscious than are people, and that further enhancements would be relatively easy to make. However, this does not imply that those machines would thereby, automatically, become much more intelligent. This is because it is one thing to have access to data, but another thing to know how to make good use of it.” (Minsky, 1991) The target of researchers involved in recent wo
-
- Social Share
for this Blog
- Social Share
-
Comment
Porfolio And Blogs
Somyajit Chakraborty follows various companies, these companies and employers can view Somyajit's resume.
Somyajit-Chakraborty's Blogs/Portfolios
