Jul 8, 2019 05:24 AM
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Artificial Intelligence: Does Consciousness Matter?
https://www.frontiersin.org/articles/10....01535/full
INTRO: . . . The question of whether machines can have consciousness is not new, with proponents of strong artificial intelligence (strong AI) and weak AI having exchanged philosophical arguments for a considerable period of time. John R. Searle, albeit being critical toward strong AI, characterized strong AI as assuming that “…the appropriately programmed computer really is a mind, in the sense that computers given the right programs can be literally said to understand and have cognitive states” (Searle, 1980, p. 417). In contrast, weak AI assumes that machines do not have consciousness, mind and sentience but only simulate thought and understanding.
When thinking about artificial consciousness, we face several problems (Manzotti and Chella, 2018). Most fundamentally, there is the difficulty to explain consciousness, to explain how subjectivity can emerge from matter—often called the “hard problem of consciousness” (Chalmers, 1996). In addition, our understanding of human consciousness is shaped by our own phenomenal experience. Whereas, we know about human consciousness from the first-person perspective, artificial consciousness will only be accessible to us from the third-person perspective. Related to this is the question of how to know whether a machine has consciousness.
A basic assumption for artificial consciousness is that it be found in the physical world of machines and robots (Manzotti and Chella, 2018). Furthermore, any definition of artificial consciousness given by humans will have to be made from the third-person perspective, without relying on phenomenal consciousness.
One strategy is to avoid a narrow definition of machine consciousness, or to avoid giving a definition at all. An example of this strategy is given by David Levy (Levy, 2009, p. 210) who prefers to take a pragmatic view according to which it is sufficient to have a general agreement about what we mean by consciousness and suggests “let us simply use the word and get on with it.”
Other authors focus on self-awareness. With regard to self-aware robots, Chatila et al. (2018, p. 1) consider relevant: “… the underlying principles and methods that would enable robots to understand their environment, to be cognizant of what they do, to take appropriate and timely initiatives, to learn from their own experience and to show that they know that they have learned and how.” In contrast, Kinouchi and Mackin focus on adaptation at the system-level (Kinouchi and Mackin, 2018, p. 1), “Consciousness is regarded as a function for effective adaptation at the system-level, based on matching and organizing the individual results of the underlying parallel-processing units. This consciousness is assumed to correspond to how our mind is “aware” when making our moment to moment decisions in our daily life.”
In order to solve questions specific to artificial consciousness, it is helpful to consider the philosophical reflection around consciousness, which focuses on human (and animal) consciousness. There are many concepts of consciousness. Normally, we distinguish between (a) a conscious entity, i.e., an entity that is sentient, wakeful, has self-consciousness and subjective qualitative experiences, (b) being conscious of something, for example a rose, and © conscious mental states, i.e., mental states an entity is aware of being in, such as being aware of smelling a rose (Van Gulick, 2018; Gennaro, 2019).
For the discussion of artificial consciousness, Ned Block's distinction between phenomenal consciousness and access consciousness proves to be particularly helpful (Block, 1995). Whereas, phenomenal consciousness relates to the experience, to what it is like to be in a conscious mental state, access consciousness refers to a mental state's availability for use by the organism, for example in reasoning and guiding behavior, and describes how a mental state is related with other mental states. The debate on artificial consciousness would clearly benefit from focusing on access consciousness.
Dehaene et al. (2017) distinguish two essential dimensions of conscious computation: global availability (C1) and self-monitoring (C2). Global availability, which they characterize as information being globally available to the organism, resembles Ned Block's access consciousness (Block, 1995). Self-monitoring (C2), which they consider as corresponding to introspection, “refers to a self-referential relationship in which the cognitive system is able to monitor its own processing and obtain information about itself” (pp. 486–487).
As the examples of approaches to define artificial consciousness given above show, different authors stress different aspects. There clearly is room for more reflection and research on what third-person definitions of artificial consciousness could look like. (MORE - details)
AI-designed heat pumps consume less energy
https://www.sciencedaily.com/releases/20...123504.htm
INTRO: Researchers at EPFL have developed a method that uses artificial intelligence to design next-generation heat-pump compressors. Their method can cut the pumps' power requirement by around 25%. In Switzerland, 50 -- 60% of new homes are equipped with heat pumps. These systems draw in thermal energy from the surrounding environment -- such as from the ground, air, or a nearby lake or river -- and turn it into heat for buildings.
While today's heat pumps generally work well and are environmentally friendly, they still have substantial room for improvement. For example, by using microturbocompressors instead of conventional compression systems, engineers can reduce heat pumps' power requirement by 20-25% (see inset) as well as their impact on the environment. That's because turbocompressors are more efficient and ten times smaller than piston devices. But incorporating these mini components into heat pumps' designs is not easy; complications arise from their tiny diameters (<20 mm) and fast rotation speeds (>200,000 rpm).
At EPFL's Laboratory for Applied Mechanical Design on the Microcity campus, a team of researchers led by Jürg Schiffmann has developed a method that makes it easier and faster to add turbocompressors to heat pumps. Using a machine-learning process called symbolic regression, the researchers came up with simple equations for quickly calculating the optimal dimensions of a turbocompressor for a given heat pump. Their research just won the Best Paper Award at the 2019 Turbo Expo Conference held by the American Society of Mechanical Engineers... (MORE - details)
https://www.frontiersin.org/articles/10....01535/full
INTRO: . . . The question of whether machines can have consciousness is not new, with proponents of strong artificial intelligence (strong AI) and weak AI having exchanged philosophical arguments for a considerable period of time. John R. Searle, albeit being critical toward strong AI, characterized strong AI as assuming that “…the appropriately programmed computer really is a mind, in the sense that computers given the right programs can be literally said to understand and have cognitive states” (Searle, 1980, p. 417). In contrast, weak AI assumes that machines do not have consciousness, mind and sentience but only simulate thought and understanding.
When thinking about artificial consciousness, we face several problems (Manzotti and Chella, 2018). Most fundamentally, there is the difficulty to explain consciousness, to explain how subjectivity can emerge from matter—often called the “hard problem of consciousness” (Chalmers, 1996). In addition, our understanding of human consciousness is shaped by our own phenomenal experience. Whereas, we know about human consciousness from the first-person perspective, artificial consciousness will only be accessible to us from the third-person perspective. Related to this is the question of how to know whether a machine has consciousness.
A basic assumption for artificial consciousness is that it be found in the physical world of machines and robots (Manzotti and Chella, 2018). Furthermore, any definition of artificial consciousness given by humans will have to be made from the third-person perspective, without relying on phenomenal consciousness.
One strategy is to avoid a narrow definition of machine consciousness, or to avoid giving a definition at all. An example of this strategy is given by David Levy (Levy, 2009, p. 210) who prefers to take a pragmatic view according to which it is sufficient to have a general agreement about what we mean by consciousness and suggests “let us simply use the word and get on with it.”
Other authors focus on self-awareness. With regard to self-aware robots, Chatila et al. (2018, p. 1) consider relevant: “… the underlying principles and methods that would enable robots to understand their environment, to be cognizant of what they do, to take appropriate and timely initiatives, to learn from their own experience and to show that they know that they have learned and how.” In contrast, Kinouchi and Mackin focus on adaptation at the system-level (Kinouchi and Mackin, 2018, p. 1), “Consciousness is regarded as a function for effective adaptation at the system-level, based on matching and organizing the individual results of the underlying parallel-processing units. This consciousness is assumed to correspond to how our mind is “aware” when making our moment to moment decisions in our daily life.”
In order to solve questions specific to artificial consciousness, it is helpful to consider the philosophical reflection around consciousness, which focuses on human (and animal) consciousness. There are many concepts of consciousness. Normally, we distinguish between (a) a conscious entity, i.e., an entity that is sentient, wakeful, has self-consciousness and subjective qualitative experiences, (b) being conscious of something, for example a rose, and © conscious mental states, i.e., mental states an entity is aware of being in, such as being aware of smelling a rose (Van Gulick, 2018; Gennaro, 2019).
For the discussion of artificial consciousness, Ned Block's distinction between phenomenal consciousness and access consciousness proves to be particularly helpful (Block, 1995). Whereas, phenomenal consciousness relates to the experience, to what it is like to be in a conscious mental state, access consciousness refers to a mental state's availability for use by the organism, for example in reasoning and guiding behavior, and describes how a mental state is related with other mental states. The debate on artificial consciousness would clearly benefit from focusing on access consciousness.
Dehaene et al. (2017) distinguish two essential dimensions of conscious computation: global availability (C1) and self-monitoring (C2). Global availability, which they characterize as information being globally available to the organism, resembles Ned Block's access consciousness (Block, 1995). Self-monitoring (C2), which they consider as corresponding to introspection, “refers to a self-referential relationship in which the cognitive system is able to monitor its own processing and obtain information about itself” (pp. 486–487).
As the examples of approaches to define artificial consciousness given above show, different authors stress different aspects. There clearly is room for more reflection and research on what third-person definitions of artificial consciousness could look like. (MORE - details)
AI-designed heat pumps consume less energy
https://www.sciencedaily.com/releases/20...123504.htm
INTRO: Researchers at EPFL have developed a method that uses artificial intelligence to design next-generation heat-pump compressors. Their method can cut the pumps' power requirement by around 25%. In Switzerland, 50 -- 60% of new homes are equipped with heat pumps. These systems draw in thermal energy from the surrounding environment -- such as from the ground, air, or a nearby lake or river -- and turn it into heat for buildings.
While today's heat pumps generally work well and are environmentally friendly, they still have substantial room for improvement. For example, by using microturbocompressors instead of conventional compression systems, engineers can reduce heat pumps' power requirement by 20-25% (see inset) as well as their impact on the environment. That's because turbocompressors are more efficient and ten times smaller than piston devices. But incorporating these mini components into heat pumps' designs is not easy; complications arise from their tiny diameters (<20 mm) and fast rotation speeds (>200,000 rpm).
At EPFL's Laboratory for Applied Mechanical Design on the Microcity campus, a team of researchers led by Jürg Schiffmann has developed a method that makes it easier and faster to add turbocompressors to heat pumps. Using a machine-learning process called symbolic regression, the researchers came up with simple equations for quickly calculating the optimal dimensions of a turbocompressor for a given heat pump. Their research just won the Best Paper Award at the 2019 Turbo Expo Conference held by the American Society of Mechanical Engineers... (MORE - details)